From 1a779ee2b11f48d3ce51ac61f80a6453c8c4e8bc Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 11 Aug 2023 00:12:11 +0200 Subject: [PATCH 01/79] Bump `torch>=1.8.0` and `torchvision>=0.9.0` (#11970) --- .github/workflows/ci-testing.yml | 8 ++++---- .github/workflows/greetings.yml | 2 +- README.md | 4 ++-- README.zh-CN.md | 2 +- models/experimental.py | 4 ++-- requirements.txt | 4 ++-- 6 files changed, 12 insertions(+), 12 deletions(-) diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index 13f9787ed8c9..80ae42955605 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -63,9 +63,9 @@ jobs: python-version: '3.9' model: yolov5n - os: ubuntu-latest - python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8 + python-version: '3.8' # torch 1.8.0 requires python >=3.6, <=3.8 model: yolov5n - torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/ + torch: '1.8.0' # min torch version CI https://pypi.org/project/torchvision/ steps: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 @@ -75,8 +75,8 @@ jobs: - name: Install requirements run: | python -m pip install --upgrade pip wheel - if [ "${{ matrix.torch }}" == "1.7.0" ]; then - pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu + if [ "${{ matrix.torch }}" == "1.8.0" ]; then + pip install -r requirements.txt torch==1.8.0 torchvision==0.9.0 --extra-index-url https://download.pytorch.org/whl/cpu else pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu fi diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 8aca12d3c370..2203b2c60a94 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -31,7 +31,7 @@ jobs: ## Requirements - [**Python>=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started: + [**Python>=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). To get started: ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 diff --git a/README.md b/README.md index a27fe2474fd8..b9941b74ef34 100644 --- a/README.md +++ b/README.md @@ -78,8 +78,8 @@ See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) for full documentatio Install Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a -[**Python>=3.7.0**](https://www.python.org/) environment, including -[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). +[**Python>=3.8.0**](https://www.python.org/) environment, including +[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). ```bash git clone https://github.com/ultralytics/yolov5 # clone diff --git a/README.zh-CN.md b/README.zh-CN.md index fe382af359a0..49503c3c8b68 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -72,7 +72,7 @@ pip install ultralytics
安装 -克隆 repo，并要求在 [**Python>=3.7.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/) 。 +克隆 repo，并要求在 [**Python>=3.7.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。 ```bash git clone https://github.com/ultralytics/yolov5 # clone diff --git a/models/experimental.py b/models/experimental.py index d60d1808da11..11f75e2254b3 100644 --- a/models/experimental.py +++ b/models/experimental.py @@ -87,11 +87,11 @@ def attempt_load(weights, device=None, inplace=True, fuse=True): model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode - # Module compatibility updates + # Module updates for m in model.modules(): t = type(m) if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): - m.inplace = inplace # torch 1.7.0 compatibility + m.inplace = inplace if t is Detect and not isinstance(m.anchor_grid, list): delattr(m, 'anchor_grid') setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) diff --git a/requirements.txt b/requirements.txt index ee9e7dbcfb80..33bb7dba2611 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,8 +12,8 @@ PyYAML>=5.3.1 requests>=2.23.0 scipy>=1.4.1 thop>=0.1.1 # FLOPs computation -torch>=1.7.0 # see https://pytorch.org/get-started/locally (recommended) -torchvision>=0.8.1 +torch>=1.8.0 # see https://pytorch.org/get-started/locally (recommended) +torchvision>=0.9.0 tqdm>=4.64.0 ultralytics>=8.0.147 # protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 From 493981cd3b87112fe0e32fe20478b83b3ef33659 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 11 Aug 2023 00:53:13 +0200 Subject: [PATCH 02/79] Bump `Python>=3.8.0` (#11971) --- .github/workflows/greetings.yml | 2 +- README.zh-CN.md | 2 +- utils/general.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 2203b2c60a94..3058d78b0a66 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -31,7 +31,7 @@ jobs: ## Requirements - [**Python>=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). To get started: + [**Python>=3.8.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). To get started: ```bash git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 diff --git a/README.zh-CN.md b/README.zh-CN.md index 49503c3c8b68..d8b2a900bf96 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -72,7 +72,7 @@ pip install ultralytics
安装 -克隆 repo，并要求在 [**Python>=3.7.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。 +克隆 repo，并要求在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。 ```bash git clone https://github.com/ultralytics/yolov5 # clone diff --git a/utils/general.py b/utils/general.py index 017103752ef7..135141e21436 100644 --- a/utils/general.py +++ b/utils/general.py @@ -381,7 +381,7 @@ def check_git_info(path='.'): return {'remote': None, 'branch': None, 'commit': None} -def check_python(minimum='3.7.0'): +def check_python(minimum='3.8.0'): # Check current python version vs. required python version check_version(platform.python_version(), minimum, name='Python ', hard=True) From dd104811c2e0419529aa7177ce160321e9694837 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 12 Aug 2023 11:40:24 +0200 Subject: [PATCH 03/79] Update links.yml (#11978) Signed-off-by: Glenn Jocher --- .github/workflows/links.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index cd65b961f7f9..98803c7e1a43 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -28,7 +28,7 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' - name: Test Markdown, HTML, YAML, Python and Notebook links with retry if: github.event_name == 'workflow_dispatch' @@ -37,4 +37,4 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' From 94e943e609f296fc2b0eddf32f3f9b28ad1da106 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 23 Aug 2023 20:55:36 +0200 Subject: [PATCH 04/79] Update links.yml to exclude Kaggle (#12022) --- .github/workflows/links.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index 98803c7e1a43..4bee94534b06 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -28,7 +28,7 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' - name: Test Markdown, HTML, YAML, Python and Notebook links with retry if: github.event_name == 'workflow_dispatch' @@ -37,4 +37,4 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' From 9974d51f1b155b7145cceec8cd6b51691668ca96 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 27 Aug 2023 23:04:07 +0200 Subject: [PATCH 05/79] Update README.md (#12040) * Update README.md Signed-off-by: Glenn Jocher * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Glenn Jocher Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> From 378ed7496799e0c12c1957900561258fbab44db3 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 31 Aug 2023 15:45:22 +0200 Subject: [PATCH 06/79] Docker COPY with checkout `fetch-depth: 0` (#12066) Docker COPY with checkout fetch-depth: 0 --- .github/workflows/docker.yml | 2 ++ utils/docker/Dockerfile | 3 +-- utils/docker/Dockerfile-arm64 | 3 +-- utils/docker/Dockerfile-cpu | 3 +-- 4 files changed, 5 insertions(+), 6 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 13e79216fc20..cbb1371b8ee9 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -16,6 +16,8 @@ jobs: steps: - name: Checkout repo uses: actions/checkout@v3 + with: + fetch-depth: 0 # copy full .git directory to access full git history in Docker images - name: Set up QEMU uses: docker/setup-qemu-action@v2 diff --git a/utils/docker/Dockerfile b/utils/docker/Dockerfile index 4346fc823c16..d1fc87f7c84c 100644 --- a/utils/docker/Dockerfile +++ b/utils/docker/Dockerfile @@ -24,8 +24,7 @@ RUN rm -rf /usr/src/app && mkdir -p /usr/src/app WORKDIR /usr/src/app # Copy contents -# COPY . /usr/src/app (issues as not a .git directory) -RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app +COPY . /usr/src/app # Install pip packages COPY requirements.txt . diff --git a/utils/docker/Dockerfile-arm64 b/utils/docker/Dockerfile-arm64 index 7b5c610e5071..802703e8d3f7 100644 --- a/utils/docker/Dockerfile-arm64 +++ b/utils/docker/Dockerfile-arm64 @@ -27,8 +27,7 @@ RUN mkdir -p /usr/src/app WORKDIR /usr/src/app # Copy contents -# COPY . /usr/src/app (issues as not a .git directory) -RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app +COPY . /usr/src/app ENV DEBIAN_FRONTEND teletype diff --git a/utils/docker/Dockerfile-cpu b/utils/docker/Dockerfile-cpu index 17316986c9d5..2d1363a91ee5 100644 --- a/utils/docker/Dockerfile-cpu +++ b/utils/docker/Dockerfile-cpu @@ -30,8 +30,7 @@ RUN mkdir -p /usr/src/app WORKDIR /usr/src/app # Copy contents -# COPY . /usr/src/app (issues as not a .git directory) -RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app +COPY . /usr/src/app # Usage Examples ------------------------------------------------------------------------------------------------------- From 8c30c583b0ab400ae497f488863c32654a3cf5c0 Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Mon, 4 Sep 2023 16:22:33 +0530 Subject: [PATCH 07/79] Add new `--save-csv` argument to detect.py (#12042) * Update detect.py added support for saving result in csv,used for testing Signed-off-by: Akash A Desai <62583018+akashAD98@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update detect.py Signed-off-by: Akash A Desai <62583018+akashAD98@users.noreply.github.com> * Update detect.py changed save_in_csv to save_csv Signed-off-by: Akash A Desai <62583018+akashAD98@users.noreply.github.com> * Update detect.py Signed-off-by: Akash A Desai <62583018+akashAD98@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- detect.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/detect.py b/detect.py index 8934a093f45e..e18a9883d627 100644 --- a/detect.py +++ b/detect.py @@ -29,6 +29,7 @@ """ import argparse +import csv import os import platform import sys @@ -63,6 +64,7 @@ def run( device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results save_txt=False, # save results to *.txt + save_csv=False, # save results in CSV format save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes nosave=False, # do not save images/videos @@ -135,6 +137,18 @@ def run( # Second-stage classifier (optional) # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + # Define the path for the CSV file + csv_path = save_dir / 'predictions.csv' + + # Create or append to the CSV file + def write_to_csv(image_name, prediction, confidence): + data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence} + with open(csv_path, mode='a', newline='') as f: + writer = csv.DictWriter(f, fieldnames=data.keys()) + if not csv_path.is_file(): + writer.writeheader() + writer.writerow(data) + # Process predictions for i, det in enumerate(pred): # per image seen += 1 @@ -162,6 +176,14 @@ def run( # Write results for *xyxy, conf, cls in reversed(det): + c = int(cls) # integer class + label = names[c] if hide_conf else f'{names[c]}' + confidence = float(conf) + confidence_str = f'{confidence:.2f}' + + if save_csv: + write_to_csv(p.name, label, confidence_str) + if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format @@ -229,6 +251,7 @@ def parse_opt(): parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='show results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-csv', action='store_true', help='save results in CSV format') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') parser.add_argument('--nosave', action='store_true', help='do not save images/videos') From a6659d05051e01c8feca7ecb348c1cce7d67aaaa Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 5 Sep 2023 21:31:00 +0200 Subject: [PATCH 08/79] [pre-commit.ci] pre-commit suggestions (#12079) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/executablebooks/mdformat: 0.7.16 → 0.7.17](https://github.com/executablebooks/mdformat/compare/0.7.16...0.7.17) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b8099b97890e..ff2190614378 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -40,7 +40,7 @@ repos: name: YAPF formatting - repo: https://github.com/executablebooks/mdformat - rev: 0.7.16 + rev: 0.7.17 hooks: - id: mdformat name: MD formatting From ec690e42af3745c41c16b60f264a8be275dab69d Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 10 Sep 2023 04:59:27 +0200 Subject: [PATCH 09/79] Update YouTube URL https://youtu.be/LNwODJXcvt4 (#12106) Update example YouTube URL --- README.md | 2 +- README.zh-CN.md | 2 +- classify/predict.py | 2 +- classify/tutorial.ipynb | 2 +- detect.py | 2 +- segment/predict.py | 2 +- segment/tutorial.ipynb | 2 +- tutorial.ipynb | 2 +- utils/dataloaders.py | 2 +- 9 files changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index b9941b74ef34..8903b95c0024 100644 --- a/README.md +++ b/README.md @@ -128,7 +128,7 @@ python detect.py --weights yolov5s.pt --source 0 # list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` diff --git a/README.zh-CN.md b/README.zh-CN.md index d8b2a900bf96..d5fe368f287b 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -121,7 +121,7 @@ python detect.py --weights yolov5s.pt --source 0 # list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` diff --git a/classify/predict.py b/classify/predict.py index 1cbbc094a41e..653c374f768f 100644 --- a/classify/predict.py +++ b/classify/predict.py @@ -11,7 +11,7 @@ list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: diff --git a/classify/tutorial.ipynb b/classify/tutorial.ipynb index 844da0c418f5..f85715ca844e 100644 --- a/classify/tutorial.ipynb +++ b/classify/tutorial.ipynb @@ -87,7 +87,7 @@ " screen # screenshot\n", " path/ # directory\n", " 'path/*.jpg' # glob\n", - " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", "```" ] diff --git a/detect.py b/detect.py index e18a9883d627..fd9637138dd6 100644 --- a/detect.py +++ b/detect.py @@ -11,7 +11,7 @@ list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: diff --git a/segment/predict.py b/segment/predict.py index 77e8df79066a..113bc472e637 100644 --- a/segment/predict.py +++ b/segment/predict.py @@ -11,7 +11,7 @@ list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'https://youtu.be/LNwODJXcvt4' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: diff --git a/segment/tutorial.ipynb b/segment/tutorial.ipynb index cd215e7f3467..0e6091869b7c 100644 --- a/segment/tutorial.ipynb +++ b/segment/tutorial.ipynb @@ -87,7 +87,7 @@ " screen # screenshot\n", " path/ # directory\n", " 'path/*.jpg' # glob\n", - " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", "```" ] diff --git a/tutorial.ipynb b/tutorial.ipynb index f666dbde7e8c..d63117dbe0ed 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -100,7 +100,7 @@ " screen # screenshot\n", " path/ # directory\n", " 'path/*.jpg' # glob\n", - " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", "```" ] diff --git a/utils/dataloaders.py b/utils/dataloaders.py index 26201c3c78fc..1fbd0361ded4 100644 --- a/utils/dataloaders.py +++ b/utils/dataloaders.py @@ -355,7 +355,7 @@ def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, t # Start thread to read frames from video stream st = f'{i + 1}/{n}: {s}... ' if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video - # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' + # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4' check_requirements(('pafy', 'youtube_dl==2020.12.2')) import pafy s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL From 8c45e51c74aa66c51300ceb58ef4f94f6f46e699 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Sep 2023 20:48:02 +0200 Subject: [PATCH 10/79] Bump actions/checkout from 3 to 4 (#12109) Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 4. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ci-testing.yml | 4 ++-- .github/workflows/codeql-analysis.yml | 2 +- .github/workflows/docker.yml | 2 +- .github/workflows/links.yml | 2 +- .github/workflows/translate-readme.yml | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index 80ae42955605..b0e974aa2a2b 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -21,7 +21,7 @@ jobs: python-version: [ '3.10' ] # requires python<=3.10 model: [ yolov5n ] steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} @@ -67,7 +67,7 @@ jobs: model: yolov5n torch: '1.8.0' # min torch version CI https://pypi.org/project/torchvision/ steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index 05db12dabd1a..53af304ee992 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -23,7 +23,7 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index cbb1371b8ee9..77a7c9720286 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -15,7 +15,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Checkout repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 # copy full .git directory to access full git history in Docker images diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index 4bee94534b06..c2665fd3f237 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -13,7 +13,7 @@ jobs: Links: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Download and install lychee run: | diff --git a/.github/workflows/translate-readme.yml b/.github/workflows/translate-readme.yml index d5e2be26f523..7a4dd3f32265 100644 --- a/.github/workflows/translate-readme.yml +++ b/.github/workflows/translate-readme.yml @@ -14,7 +14,7 @@ jobs: Translate: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup Node.js uses: actions/setup-node@v3 with: From 9e97ac37f5e76ee535d06a919fd0e9209fae1d5a Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 14 Sep 2023 01:32:48 +0200 Subject: [PATCH 11/79] [Snyk] Security upgrade numpy from 1.21.3 to 1.22.2 (#12118) fix: requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321964 - https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321966 - https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321970 Co-authored-by: snyk-bot From 8a94907db168d47b33e51753ab6a20ee1ccd0b60 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Sep 2023 14:55:29 +0200 Subject: [PATCH 12/79] Bump docker/build-push-action from 4 to 5 (#12135) Bumps [docker/build-push-action](https://github.com/docker/build-push-action) from 4 to 5. - [Release notes](https://github.com/docker/build-push-action/releases) - [Commits](https://github.com/docker/build-push-action/compare/v4...v5) --- updated-dependencies: - dependency-name: docker/build-push-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/docker.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 77a7c9720286..3f6d9ed3978a 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -32,7 +32,7 @@ jobs: password: ${{ secrets.DOCKERHUB_TOKEN }} - name: Build and push arm64 image - uses: docker/build-push-action@v4 + uses: docker/build-push-action@v5 continue-on-error: true with: context: . @@ -42,7 +42,7 @@ jobs: tags: ultralytics/yolov5:latest-arm64 - name: Build and push CPU image - uses: docker/build-push-action@v4 + uses: docker/build-push-action@v5 continue-on-error: true with: context: . @@ -51,7 +51,7 @@ jobs: tags: ultralytics/yolov5:latest-cpu - name: Build and push GPU image - uses: docker/build-push-action@v4 + uses: docker/build-push-action@v5 continue-on-error: true with: context: . From e421dcf5ea5baa85273e3f80d537144806b4502a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Sep 2023 14:56:59 +0200 Subject: [PATCH 13/79] Bump docker/setup-buildx-action from 2 to 3 (#12136) Bumps [docker/setup-buildx-action](https://github.com/docker/setup-buildx-action) from 2 to 3. - [Release notes](https://github.com/docker/setup-buildx-action/releases) - [Commits](https://github.com/docker/setup-buildx-action/compare/v2...v3) --- updated-dependencies: - dependency-name: docker/setup-buildx-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 3f6d9ed3978a..bd44fa844fe7 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -23,7 +23,7 @@ jobs: uses: docker/setup-qemu-action@v2 - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v2 + uses: docker/setup-buildx-action@v3 - name: Login to Docker Hub uses: docker/login-action@v2 From 831c8e90c12750843e4c94b109e20379b97d5b37 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Sep 2023 14:58:39 +0200 Subject: [PATCH 14/79] Bump docker/login-action from 2 to 3 (#12137) Bumps [docker/login-action](https://github.com/docker/login-action) from 2 to 3. - [Release notes](https://github.com/docker/login-action/releases) - [Commits](https://github.com/docker/login-action/compare/v2...v3) --- updated-dependencies: - dependency-name: docker/login-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index bd44fa844fe7..6cf0a29b0b08 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -26,7 +26,7 @@ jobs: uses: docker/setup-buildx-action@v3 - name: Login to Docker Hub - uses: docker/login-action@v2 + uses: docker/login-action@v3 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_TOKEN }} From 9b6357c88647f54b0e8c77ce276ffb2e926e3f74 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Sep 2023 15:13:34 +0200 Subject: [PATCH 15/79] Bump docker/setup-qemu-action from 2 to 3 (#12138) Bumps [docker/setup-qemu-action](https://github.com/docker/setup-qemu-action) from 2 to 3. - [Release notes](https://github.com/docker/setup-qemu-action/releases) - [Commits](https://github.com/docker/setup-qemu-action/compare/v2...v3) --- updated-dependencies: - dependency-name: docker/setup-qemu-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 6cf0a29b0b08..27672b407ef4 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -20,7 +20,7 @@ jobs: fetch-depth: 0 # copy full .git directory to access full git history in Docker images - name: Set up QEMU - uses: docker/setup-qemu-action@v2 + uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 From 005dcfa956fc5602b1364f452ade2d91037839a3 Mon Sep 17 00:00:00 2001 From: Lucid1ty <70336891+Lucid1ty@users.noreply.github.com> Date: Mon, 18 Sep 2023 21:15:56 +0800 Subject: [PATCH 16/79] Update YOLOv5 docs URL https://docs.ultralytics.com/yolov5 (#12132) Update YOLOv5 docs URL https://docs.ultralytics.com/yolov5/ Signed-off-by: Lucid1ty <70336891+Lucid1ty@users.noreply.github.com> Co-authored-by: Glenn Jocher --- README.zh-CN.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.zh-CN.md b/README.zh-CN.md index d5fe368f287b..0f1d4ee1ef7f 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -19,7 +19,7 @@ YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表 Ultralytics 对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。 -我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 文档 了解详细信息，在 GitHub 上提交问题以获得支持，并加入我们的 Discord 社区进行问题和讨论！ +我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 文档 了解详细信息，在 GitHub 上提交问题以获得支持，并加入我们的 Discord 社区进行问题和讨论！ 如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格 @@ -67,7 +67,7 @@ pip install ultralytics ##
文档
-有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com)。请参阅下面的快速入门示例。 +有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com/yolov5/)。请参阅下面的快速入门示例。
安装 From 6262c7feb42cd181f165681b9aff428785c0ff7e Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 21 Sep 2023 00:32:04 +0200 Subject: [PATCH 17/79] Update for https://yolovision.ultralytics.com #YV23 (#12155) * Update README.md Signed-off-by: Glenn Jocher * Update README.zh-CN.md Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher --- README.md | 4 ++++ README.zh-CN.md | 4 ++++ 2 files changed, 8 insertions(+) diff --git a/README.md b/README.md index 8903b95c0024..a32acb3f3629 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,11 @@

+ + +

[English](README.md) | [简体中文](README.zh-CN.md) diff --git a/README.zh-CN.md b/README.zh-CN.md index 0f1d4ee1ef7f..f83f722aa626 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -1,7 +1,11 @@

+ + +

[英文](README.md)|[简体中文](README.zh-CN.md)
From bb9706e7d179fbf30a5eedd0b0008d1ec470f768 Mon Sep 17 00:00:00 2001 From: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com> Date: Fri, 29 Sep 2023 14:23:05 -0300 Subject: [PATCH 18/79] ONNX export Path to str() (#12177) * Update export.py Signed-off-by: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com> * Update export.py Signed-off-by: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com> * Update export.py Transformed the f variable into a string on the export onnx. This bug was making it impossible to export any models in .onnx, since it was making the typehint not accept the users input as it is specified in the functions documentation Signed-off-by: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Luis Filipe Araujo de Souza <58831491+Doquey@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- export.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/export.py b/export.py index 92d42472dfc4..71e4eb94d1c4 100644 --- a/export.py +++ b/export.py @@ -155,7 +155,7 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX import onnx LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...') - f = file.with_suffix('.onnx') + f = str(file.with_suffix('.onnx')) output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0'] if dynamic: From dd9e3382c9af9697fb071d26f1fd1698e9be3e04 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 3 Oct 2023 15:46:59 +0200 Subject: [PATCH 19/79] [pre-commit.ci] pre-commit suggestions (#12189) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/asottile/pyupgrade: v3.10.1 → v3.14.0](https://github.com/asottile/pyupgrade/compare/v3.10.1...v3.14.0) - [github.com/google/yapf: v0.40.0 → v0.40.2](https://github.com/google/yapf/compare/v0.40.0...v0.40.2) - [github.com/codespell-project/codespell: v2.2.5 → v2.2.6](https://github.com/codespell-project/codespell/compare/v2.2.5...v2.2.6) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ff2190614378..39ab266f70f3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,7 +22,7 @@ repos: - id: detect-private-key - repo: https://github.com/asottile/pyupgrade - rev: v3.10.1 + rev: v3.14.0 hooks: - id: pyupgrade name: Upgrade code @@ -34,7 +34,7 @@ repos: name: Sort imports - repo: https://github.com/google/yapf - rev: v0.40.0 + rev: v0.40.2 hooks: - id: yapf name: YAPF formatting @@ -56,7 +56,7 @@ repos: name: PEP8 - repo: https://github.com/codespell-project/codespell - rev: v2.2.5 + rev: v2.2.6 hooks: - id: codespell args: From e4df1ec5bab52601d5de6d62d428dfd03ab53be1 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 14 Oct 2023 19:13:57 +0200 Subject: [PATCH 20/79] [Snyk] Security upgrade ubuntu from lunar-20230615 to 23.04 (#12232) * fix: utils/docker/Dockerfile-cpu to reduce vulnerabilities The following vulnerabilities are fixed with an upgrade: - https://snyk.io/vuln/SNYK-UBUNTU2304-GLIBC-5901400 - https://snyk.io/vuln/SNYK-UBUNTU2304-GLIBC-5901400 - https://snyk.io/vuln/SNYK-UBUNTU2304-GLIBC-5927364 - https://snyk.io/vuln/SNYK-UBUNTU2304-GLIBC-5927364 - https://snyk.io/vuln/SNYK-UBUNTU2304-XZUTILS-5854648 * Update Dockerfile-cpu Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher Co-authored-by: snyk-bot --- utils/docker/Dockerfile-cpu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/docker/Dockerfile-cpu b/utils/docker/Dockerfile-cpu index 2d1363a91ee5..e1696d0aebd8 100644 --- a/utils/docker/Dockerfile-cpu +++ b/utils/docker/Dockerfile-cpu @@ -3,7 +3,7 @@ # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu -FROM ubuntu:lunar-20230615 +FROM ubuntu:23.04 # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ From 4d687c8c56e3ee4e6851e48c1c5089c731ef0fcd Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 21 Oct 2023 18:07:56 +0200 Subject: [PATCH 21/79] Exclude url.com and gstatic.com from links.yml (#12263) * Exclude url.com and gstatic.com from links.yml Signed-off-by: Glenn Jocher * Update links.yml Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher --- .github/workflows/links.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index c2665fd3f237..5d897da3c144 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -28,7 +28,7 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' - name: Test Markdown, HTML, YAML, Python and Notebook links with retry if: github.event_name == 'workflow_dispatch' @@ -37,4 +37,4 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|url\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' From 9236fdd5c272aee394b7172a6138e635585e01dd Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 26 Oct 2023 18:57:48 +0200 Subject: [PATCH 22/79] Fix discord social image URL (#12286) --- README.md | 4 ++-- README.zh-CN.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index a32acb3f3629..95b5e65c1ac3 100644 --- a/README.md +++ b/README.md @@ -48,7 +48,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens - +
@@ -495,7 +495,7 @@ For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https:/ - + [tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/README.zh-CN.md b/README.zh-CN.md index f83f722aa626..9b7c065b9745 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -47,7 +47,7 @@ YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表 - + @@ -488,7 +488,7 @@ Ultralytics 提供两种许可证选项以适应各种使用场景： - + [tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation From 53efd07fef2bd0138871b94ed20ad8923a20e5cd Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 26 Oct 2023 19:28:21 +0200 Subject: [PATCH 23/79] Update social media links (#12287) * Update social media links * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- README.md | 83 +++++++++++++++++------------------------------ README.zh-CN.md | 85 +++++++++++++++++++------------------------------ 2 files changed, 62 insertions(+), 106 deletions(-) diff --git a/README.md b/README.md index 95b5e65c1ac3..dc64fcd89f65 100644 --- a/README.md +++ b/README.md @@ -29,26 +29,19 @@ We hope that the resources here will help you get the most out of YOLOv5. Please To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
- - - - - - - - - - - - - - - - - - - - + Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics Instagram + + Ultralytics Discord
@@ -56,10 +49,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens ##
YOLOv8 🚀 NEW
-We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model -released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. -YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of -object detection, image segmentation and image classification tasks. +We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks. See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with: @@ -96,8 +86,7 @@ pip install -r requirements.txt # install
Inference -YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest -YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). +YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). ```python import torch @@ -120,8 +109,7 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
Inference with detect.py -`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from -the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. +`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash python detect.py --weights yolov5s.pt --source 0 # webcam @@ -143,11 +131,7 @@ python detect.py --weights yolov5s.pt --source 0 # The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) -and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest -YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are -1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the -largest `--batch-size` possible, or pass `--batch-size -1` for -YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. +and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. ```bash python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 @@ -476,26 +460,19 @@ For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https:/
- - - - - - - - - - - - - - - - - - - - + Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics Instagram + + Ultralytics Discord
[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/README.zh-CN.md b/README.zh-CN.md index 9b7c065b9745..d9816c2d98ee 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -28,33 +28,25 @@ YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表 - - - - - - - - - - - - - - - - - - - - + Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics Instagram + + Ultralytics Discord ##
YOLOv8 🚀 新品
-我们很高兴宣布 Ultralytics YOLOv8 🚀 的发布，这是我们新推出的领先水平、最先进的（SOTA）模型，发布于 **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**。 -YOLOv8 旨在快速、准确且易于使用，使其成为广泛的物体检测、图像分割和图像分类任务的极佳选择。 +我们很高兴宣布 Ultralytics YOLOv8 🚀 的发布，这是我们新推出的领先水平、最先进的（SOTA）模型，发布于 **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**。 YOLOv8 旨在快速、准确且易于使用，使其成为广泛的物体检测、图像分割和图像分类任务的极佳选择。 请查看 [YOLOv8 文档](https://docs.ultralytics.com)了解详细信息，并开始使用： @@ -89,8 +81,7 @@ pip install -r requirements.txt # install
推理 -使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 -YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 +使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 ```python import torch @@ -113,8 +104,7 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
使用 detect.py 推理 -`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 -最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。 +`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。 ```bash python detect.py --weights yolov5s.pt --source 0 # webcam @@ -134,12 +124,8 @@ python detect.py --weights yolov5s.pt --source 0 #
训练 -下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 -最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) -将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 -YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快）。 -尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 -YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 +下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) +将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快）。 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 ```bash python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 @@ -254,7 +240,7 @@ YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结
-##
实例分割模型 ⭐ 新
+##
实例分割模型 ⭐ 新
我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。 @@ -469,26 +455,19 @@ Ultralytics 提供两种许可证选项以适应各种使用场景：
- - - - - - - - - - - - - - - - - - - - + Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics Instagram + + Ultralytics Discord
[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation From c2f131abbeed0f5f852f30d0ac18aa00e1bde1c5 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 29 Oct 2023 20:08:19 +0100 Subject: [PATCH 24/79] [Snyk] Security upgrade werkzeug from 2.2.3 to 3.0.1 (#12288) fix: utils/google_app_engine/additional_requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-WERKZEUG-6035177 Co-authored-by: snyk-bot --- utils/google_app_engine/additional_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/google_app_engine/additional_requirements.txt b/utils/google_app_engine/additional_requirements.txt index fce1511588e3..a29141a633fd 100644 --- a/utils/google_app_engine/additional_requirements.txt +++ b/utils/google_app_engine/additional_requirements.txt @@ -2,4 +2,4 @@ pip==21.1 Flask==2.3.2 gunicorn==19.10.0 -werkzeug>=2.2.3 # not directly required, pinned by Snyk to avoid a vulnerability +werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability From 1c60c5353652ee7f08b8f80636aa690ab226176d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 2 Nov 2023 11:00:35 +0100 Subject: [PATCH 25/79] Bump actions/setup-node from 3 to 4 (#12295) Bumps [actions/setup-node](https://github.com/actions/setup-node) from 3 to 4. - [Release notes](https://github.com/actions/setup-node/releases) - [Commits](https://github.com/actions/setup-node/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/setup-node dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/translate-readme.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/translate-readme.yml b/.github/workflows/translate-readme.yml index 7a4dd3f32265..bd12954c7fb5 100644 --- a/.github/workflows/translate-readme.yml +++ b/.github/workflows/translate-readme.yml @@ -16,7 +16,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Setup Node.js - uses: actions/setup-node@v3 + uses: actions/setup-node@v4 with: node-version: 16 # ISO Language Codes: https://cloud.google.com/translate/docs/languages From 61473c345eaf1a12308a8d8a567d9dc9eefed5f3 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 3 Nov 2023 16:02:55 +0100 Subject: [PATCH 26/79] Update links.yml (#12319) * Update links.yml Signed-off-by: Glenn Jocher * Update links.yml Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher --- .github/workflows/links.yml | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index 5d897da3c144..05408fd6227b 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -1,6 +1,11 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -# YOLO Continuous Integration (CI) GitHub Actions tests broken link checker -# Accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter) +# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee +# Ignores the following status codes to reduce false positives: +# - 403(OpenVINO, 'forbidden') +# - 429(Instagram, 'too many requests') +# - 500(Zenodo, 'cached') +# - 502(Zenodo, 'bad gateway') +# - 999(LinkedIn, 'unknown status code') name: Check Broken links @@ -28,7 +33,7 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' + command: lychee --accept 403,429,500,502,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' - name: Test Markdown, HTML, YAML, Python and Notebook links with retry if: github.event_name == 'workflow_dispatch' @@ -37,4 +42,4 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|url\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' + command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' From fd39c2b1d89ed9bb3cdc9660f70179047ea46e2e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 3 Nov 2023 16:03:52 +0100 Subject: [PATCH 27/79] Bump pip from 21.1 to 23.3 in /utils/google_app_engine (#12316) Bumps [pip](https://github.com/pypa/pip) from 21.1 to 23.3. - [Changelog](https://github.com/pypa/pip/blob/main/NEWS.rst) - [Commits](https://github.com/pypa/pip/compare/21.1...23.3) --- updated-dependencies: - dependency-name: pip dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- utils/google_app_engine/additional_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/google_app_engine/additional_requirements.txt b/utils/google_app_engine/additional_requirements.txt index a29141a633fd..c1a2af2c1145 100644 --- a/utils/google_app_engine/additional_requirements.txt +++ b/utils/google_app_engine/additional_requirements.txt @@ -1,5 +1,5 @@ # add these requirements in your app on top of the existing ones -pip==21.1 +pip==23.3 Flask==2.3.2 gunicorn==19.10.0 werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability From 6ca8f33796332f1b03018074412ca3cfe5cc9ba5 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 3 Nov 2023 18:59:36 +0100 Subject: [PATCH 28/79] [Snyk] Security upgrade ubuntu from 23.04 to mantic-20231011 (#12308) * fix: utils/docker/Dockerfile-cpu to reduce vulnerabilities The following vulnerabilities are fixed with an upgrade: - https://snyk.io/vuln/SNYK-UBUNTU2304-COREUTILS-5484945 - https://snyk.io/vuln/SNYK-UBUNTU2304-GLIBC-5919743 - https://snyk.io/vuln/SNYK-UBUNTU2304-GLIBC-5919743 - https://snyk.io/vuln/SNYK-UBUNTU2304-PROCPS-5816666 - https://snyk.io/vuln/SNYK-UBUNTU2304-XZUTILS-5854648 * Update apt install libgl1-mesa-glx to libgl1 --------- Co-authored-by: snyk-bot --- utils/docker/Dockerfile | 2 +- utils/docker/Dockerfile-arm64 | 2 +- utils/docker/Dockerfile-cpu | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/utils/docker/Dockerfile b/utils/docker/Dockerfile index d1fc87f7c84c..f4727162065a 100644 --- a/utils/docker/Dockerfile +++ b/utils/docker/Dockerfile @@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria ENV DEBIAN_FRONTEND noninteractive RUN apt update RUN TZ=Etc/UTC apt install -y tzdata -RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg +RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg # RUN alias python=python3 # Security updates diff --git a/utils/docker/Dockerfile-arm64 b/utils/docker/Dockerfile-arm64 index 802703e8d3f7..0de85bf8d609 100644 --- a/utils/docker/Dockerfile-arm64 +++ b/utils/docker/Dockerfile-arm64 @@ -12,7 +12,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria ENV DEBIAN_FRONTEND noninteractive RUN apt update RUN TZ=Etc/UTC apt install -y tzdata -RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev +RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev # RUN alias python=python3 # Install pip packages diff --git a/utils/docker/Dockerfile-cpu b/utils/docker/Dockerfile-cpu index e1696d0aebd8..c7ba1e321c89 100644 --- a/utils/docker/Dockerfile-cpu +++ b/utils/docker/Dockerfile-cpu @@ -3,7 +3,7 @@ # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu -FROM ubuntu:23.04 +FROM ubuntu:mantic-20231011 # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ @@ -11,7 +11,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria # Install linux packages # g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package RUN apt update \ - && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 + && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 # RUN alias python=python3 # Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error From 8566421831dfa55768b9102fad0a8e286b2ad5e1 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 4 Nov 2023 19:38:03 +0100 Subject: [PATCH 29/79] [Snyk] Security upgrade pillow from 9.5.0 to 10.0.1 (#12169) fix: requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878 Co-authored-by: snyk-bot --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 33bb7dba2611..63f4a9125415 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,7 +6,7 @@ gitpython>=3.1.30 matplotlib>=3.3 numpy>=1.22.2 opencv-python>=4.1.1 -Pillow>=7.1.2 +Pillow>=10.0.1 psutil # system resources PyYAML>=5.3.1 requests>=2.23.0 From 5fbddafef32adfd2c3aa6a264ffdc77221c5c254 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 4 Nov 2023 19:55:43 +0100 Subject: [PATCH 30/79] [Snyk] Security upgrade pillow from 9.5.0 to 10.0.0 (#12323) fix: requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6043904 Signed-off-by: Glenn Jocher Co-authored-by: snyk-bot --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 63f4a9125415..a61e8e26fdfe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,7 +6,7 @@ gitpython>=3.1.30 matplotlib>=3.3 numpy>=1.22.2 opencv-python>=4.1.1 -Pillow>=10.0.1 +Pillow>=10.0.0 psutil # system resources PyYAML>=5.3.1 requests>=2.23.0 From d5d514e1f98a957dbdf62780c941d6b8b954d8a6 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 4 Nov 2023 20:33:33 +0100 Subject: [PATCH 31/79] [Snyk] Fix for 5 vulnerabilities (#12325) fix: requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321964 - https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321966 - https://snyk.io/vuln/SNYK-PYTHON-NUMPY-2321970 - https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878 - https://snyk.io/vuln/SNYK-PYTHON-PILLOW-6043904 Co-authored-by: snyk-bot --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a61e8e26fdfe..63f4a9125415 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,7 +6,7 @@ gitpython>=3.1.30 matplotlib>=3.3 numpy>=1.22.2 opencv-python>=4.1.1 -Pillow>=10.0.0 +Pillow>=10.0.1 psutil # system resources PyYAML>=5.3.1 requests>=2.23.0 From b56b7245d2c25cda3c6d2bf2c5eeb25005443397 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 6 Nov 2023 13:56:24 +0100 Subject: [PATCH 32/79] [Snyk] Security upgrade pillow from 9.5.0 to 10.0.1 (#12327) fix: requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878 Co-authored-by: snyk-bot From 84ec8b586bd1c696f4bbf139e844a11d219f2711 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 6 Nov 2023 23:56:27 +0100 Subject: [PATCH 33/79] [pre-commit.ci] pre-commit suggestions (#12335) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/pre-commit/pre-commit-hooks: v4.4.0 → v4.5.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.4.0...v4.5.0) - [github.com/asottile/pyupgrade: v3.14.0 → v3.15.0](https://github.com/asottile/pyupgrade/compare/v3.14.0...v3.15.0) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 39ab266f70f3..ab272b2a1477 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,7 +11,7 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: end-of-file-fixer - id: trailing-whitespace @@ -22,7 +22,7 @@ repos: - id: detect-private-key - repo: https://github.com/asottile/pyupgrade - rev: v3.14.0 + rev: v3.15.0 hooks: - id: pyupgrade name: Upgrade code From b6a65e1552b5e15614812e48a5614882fdd313ac Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 16 Nov 2023 21:13:59 +0100 Subject: [PATCH 34/79] [Snyk] Security upgrade pillow from 9.5.0 to 10.0.1 (#12334) fix: requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878 Co-authored-by: snyk-bot From b378d10f0c4efe247b73c21ba26d53b9b0d9797c Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 19 Nov 2023 00:53:01 +0100 Subject: [PATCH 35/79] =?UTF-8?q?Add=20Hindi=20=E0=A4=B9=E0=A4=BF=E0=A4=A8?= =?UTF-8?q?=E0=A5=8D=E0=A4=A6=E0=A5=80=20and=20Arabic=20=D8=A7=D9=84=D8=B9?= =?UTF-8?q?=D8=B1=D8=A8=D9=8A=D8=A9=20Docs=20translations=20(#12394)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add Hindi हिन्दी and Arabic العربية Docs translations Signed-off-by: Glenn Jocher * Update README.md Signed-off-by: Glenn Jocher * Update README.zh-CN.md Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher --- README.md | 3 +-- README.zh-CN.md | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index dc64fcd89f65..c778a17258e9 100644 --- a/README.md +++ b/README.md @@ -8,8 +8,7 @@ -->

-[English](README.md) | [简体中文](README.zh-CN.md) -
+[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
YOLOv5 CI diff --git a/README.zh-CN.md b/README.zh-CN.md index d9816c2d98ee..1bd91d7ecddc 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -8,7 +8,7 @@ -->

-[英文](README.md)|[简体中文](README.zh-CN.md)
+[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
YOLOv5 CI From 7c54e5d23bf91ab544e2cc102bda273590baa109 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 20 Nov 2023 01:58:21 +0100 Subject: [PATCH 36/79] Update dependabot.yml (#12396) Signed-off-by: Glenn Jocher --- .github/dependabot.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index c1b3d5d514c3..2d4ae31873b8 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,3 +1,7 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Dependabot for package version updates +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + version: 2 updates: - package-ecosystem: pip @@ -12,7 +16,7 @@ updates: - dependencies - package-ecosystem: github-actions - directory: "/" + directory: "/.github/workflows" schedule: interval: weekly time: "04:00" From 45147c0a70b7a32cbdf68f4bd07eefbd9eabe63c Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 20 Nov 2023 20:50:46 +0100 Subject: [PATCH 37/79] Update ci-testing.yml (#12404) * Update ci-testing.yml Signed-off-by: Glenn Jocher * Delete .github/workflows/translate-readme.yml Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher --- .github/workflows/ci-testing.yml | 17 ++++------------- .github/workflows/translate-readme.yml | 26 -------------------------- 2 files changed, 4 insertions(+), 39 deletions(-) delete mode 100644 .github/workflows/translate-readme.yml diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index b0e974aa2a2b..b80a3a31c0ae 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -18,7 +18,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-latest ] - python-version: [ '3.10' ] # requires python<=3.10 + python-version: [ '3.11' ] # requires python<=3.10 model: [ yolov5n ] steps: - uses: actions/checkout@v4 @@ -30,8 +30,7 @@ jobs: run: | python -m pip install --upgrade pip wheel pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu - python --version - pip --version + yolo checks pip list - name: Benchmark DetectionModel run: | @@ -53,7 +52,7 @@ jobs: fail-fast: false matrix: os: [ ubuntu-latest, windows-latest ] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049 - python-version: [ '3.10' ] + python-version: [ '3.11' ] model: [ yolov5n ] include: - os: ubuntu-latest @@ -83,15 +82,7 @@ jobs: shell: bash # for Windows compatibility - name: Check environment run: | - python -c "import utils; utils.notebook_init()" - echo "RUNNER_OS is ${{ runner.os }}" - echo "GITHUB_EVENT_NAME is ${{ github.event_name }}" - echo "GITHUB_WORKFLOW is ${{ github.workflow }}" - echo "GITHUB_ACTOR is ${{ github.actor }}" - echo "GITHUB_REPOSITORY is ${{ github.repository }}" - echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}" - python --version - pip --version + yolo checks pip list - name: Test detection shell: bash # for Windows compatibility diff --git a/.github/workflows/translate-readme.yml b/.github/workflows/translate-readme.yml deleted file mode 100644 index bd12954c7fb5..000000000000 --- a/.github/workflows/translate-readme.yml +++ /dev/null @@ -1,26 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -# README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md - -name: Translate README - -on: - push: - branches: - - translate_readme # replace with 'master' to enable action - paths: - - README.md - -jobs: - Translate: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Setup Node.js - uses: actions/setup-node@v4 - with: - node-version: 16 - # ISO Language Codes: https://cloud.google.com/translate/docs/languages - - name: Adding README - Chinese Simplified - uses: dephraiim/translate-readme@main - with: - LANG: zh-CN From 3d8f00455955d5759b8facb85930cecfc70f03b9 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 20 Nov 2023 20:51:50 +0100 Subject: [PATCH 38/79] Created using Colaboratory (#12405) * Created using Colaboratory * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- tutorial.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index d63117dbe0ed..1657c67965b0 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -24,12 +24,11 @@ " \n", " \n", "\n", + "[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", "\n", - "
\n", " \"Run\n", " \"Open\n", " \"Open\n", - "
\n", "\n", "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", "\n", From cc232e3e3557009bfaa0eb60fdda3787178e886d Mon Sep 17 00:00:00 2001 From: Troy Date: Thu, 23 Nov 2023 20:12:05 +0800 Subject: [PATCH 39/79] Increase NCCL timeout to 3 hours (#12345) * Increase NCCL timeout to 3 hours When training on a large dataset using DDP, the scanning process will be very long, and it will raise NCCL timeout error. Change the default timeout 30min to 3 hours, same as ultralytics yolov8 (https://github.com/ultralytics/ultralytics/pull/3343) Signed-off-by: Troy * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Troy Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- train.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/train.py b/train.py index 004c8eeda121..4c3bec34835f 100644 --- a/train.py +++ b/train.py @@ -23,7 +23,7 @@ import sys import time from copy import deepcopy -from datetime import datetime +from datetime import datetime, timedelta from pathlib import Path try: @@ -529,7 +529,8 @@ def main(opt, callbacks=Callbacks()): assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' torch.cuda.set_device(LOCAL_RANK) device = torch.device('cuda', LOCAL_RANK) - dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo', + timeout=timedelta(seconds=10800)) # Train if not opt.evolve: From 3f02fdee1d8f1a6cf18a24be3438096466367d9f Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 23 Nov 2023 13:26:13 +0100 Subject: [PATCH 40/79] [Snyk] Security upgrade pillow from 9.5.0 to 10.0.1 (#12390) fix: requirements.txt to reduce vulnerabilities The following vulnerabilities are fixed by pinning transitive dependencies: - https://snyk.io/vuln/SNYK-PYTHON-PILLOW-5918878 Co-authored-by: snyk-bot From 0873e5fa33ace2c2332061dbe8de8fcbd2af4a9f Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 11 Dec 2023 13:45:00 +0100 Subject: [PATCH 41/79] [Snyk] Security upgrade ubuntu from mantic-20231011 to 23.10 (#12460) fix: utils/docker/Dockerfile-cpu to reduce vulnerabilities The following vulnerabilities are fixed with an upgrade: - https://snyk.io/vuln/SNYK-UBUNTU2310-GNUTLS28-6069047 - https://snyk.io/vuln/SNYK-UBUNTU2310-PERL-6085371 - https://snyk.io/vuln/SNYK-UBUNTU2310-PROCPS-5972730 - https://snyk.io/vuln/SNYK-UBUNTU2310-PROCPS-5972730 - https://snyk.io/vuln/SNYK-UBUNTU2310-TAR-6096092 Co-authored-by: snyk-bot --- utils/docker/Dockerfile-cpu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/docker/Dockerfile-cpu b/utils/docker/Dockerfile-cpu index c7ba1e321c89..573ad3276e73 100644 --- a/utils/docker/Dockerfile-cpu +++ b/utils/docker/Dockerfile-cpu @@ -3,7 +3,7 @@ # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu -FROM ubuntu:mantic-20231011 +FROM ubuntu:23.10 # Downloads to user config dir ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ From f400bba7836c1dbc2771db251984f20009b5fa81 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Dec 2023 13:47:08 +0100 Subject: [PATCH 42/79] Bump actions/setup-python from 4 to 5 in /.github/workflows (#12493) * Bump actions/setup-python from 4 to 5 in /.github/workflows Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] * Bump actions/stale from 8 to 9 in /.github/workflows (#12492) Bumps [actions/stale](https://github.com/actions/stale) from 8 to 9. - [Release notes](https://github.com/actions/stale/releases) - [Changelog](https://github.com/actions/stale/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/stale/compare/v8...v9) --- updated-dependencies: - dependency-name: actions/stale dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- .github/workflows/ci-testing.yml | 4 ++-- .github/workflows/stale.yml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index b80a3a31c0ae..aed7530e36d4 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -22,7 +22,7 @@ jobs: model: [ yolov5n ] steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies @@ -67,7 +67,7 @@ jobs: torch: '1.8.0' # min torch version CI https://pypi.org/project/torchvision/ steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index 65c8f70798f1..e78f82b31dce 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -9,7 +9,7 @@ jobs: stale: runs-on: ubuntu-latest steps: - - uses: actions/stale@v8 + - uses: actions/stale@v9 with: repo-token: ${{ secrets.GITHUB_TOKEN }} From a04b49f1aa2aa25b246b6bd1d9e909924ee8019a Mon Sep 17 00:00:00 2001 From: Angelo Delli Santi Date: Sat, 16 Dec 2023 23:31:22 +0100 Subject: [PATCH 43/79] Limit tensorflow version and add checks (#12494) * Limit tensorflow version and add checks * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Moving check in export script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- export.py | 5 +++++ requirements.txt | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/export.py b/export.py index 71e4eb94d1c4..5611ab95b1dc 100644 --- a/export.py +++ b/export.py @@ -397,6 +397,11 @@ def export_saved_model(model, from models.tf import TFModel LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + if tf.__version__ > '2.13.1': + helper_url = 'https://github.com/ultralytics/yolov5/issues/12489' + LOGGER.info( + f'WARNING ⚠️ using Tensorflow {tf.__version__} > 2.13.1 might cause issue when exporting the model to tflite {helper_url}' + ) # handling issue https://github.com/ultralytics/yolov5/issues/12489 f = str(file).replace('.pt', '_saved_model') batch_size, ch, *imgsz = list(im.shape) # BCHW diff --git a/requirements.txt b/requirements.txt index 63f4a9125415..115055b1629c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -34,7 +34,7 @@ seaborn>=0.11.0 # nvidia-pyindex # TensorRT export # nvidia-tensorrt # TensorRT export # scikit-learn<=1.1.2 # CoreML quantization -# tensorflow>=2.4.0 # TF exports (-cpu, -aarch64, -macos) +# tensorflow>=2.4.0,<=2.13.1 # TF exports (-cpu, -aarch64, -macos) # tensorflowjs>=3.9.0 # TF.js export # openvino-dev>=2023.0 # OpenVINO export From dafe39b07520d0ebb23b8335f1d8c23383792288 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Dec 2023 16:30:47 +0100 Subject: [PATCH 44/79] Bump github/codeql-action from 2 to 3 in /.github/workflows (#12521) Bumps [github/codeql-action](https://github.com/github/codeql-action) from 2 to 3. - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/github/codeql-action/compare/v2...v3) --- updated-dependencies: - dependency-name: github/codeql-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/codeql-analysis.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index 53af304ee992..11db0dceca3b 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -27,7 +27,7 @@ jobs: # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL - uses: github/codeql-action/init@v2 + uses: github/codeql-action/init@v3 with: languages: ${{ matrix.language }} # If you wish to specify custom queries, you can do so here or in a config file. @@ -38,7 +38,7 @@ jobs: # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). # If this step fails, then you should remove it and run the build manually (see below) - name: Autobuild - uses: github/codeql-action/autobuild@v2 + uses: github/codeql-action/autobuild@v3 # ℹ️ Command-line programs to run using the OS shell. # 📚 https://git.io/JvXDl @@ -52,4 +52,4 @@ jobs: # make release - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v2 + uses: github/codeql-action/analyze@v3 From f33d42df74cda293015975d3686cb8e62d15a957 Mon Sep 17 00:00:00 2001 From: Angelo Delli Santi Date: Mon, 18 Dec 2023 17:14:05 +0100 Subject: [PATCH 45/79] Parametrize multiple of number of channels in Conv (#12508) * Parametrize multiple of number of channels in Conv * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix issue when exporting Signed-off-by: Angelo Delli Santi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Angelo Delli Santi Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- models/tf.py | 9 ++++++--- models/yolo.py | 9 ++++++--- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/models/tf.py b/models/tf.py index 62ba3ebf0782..17cca1e54fcf 100644 --- a/models/tf.py +++ b/models/tf.py @@ -380,9 +380,12 @@ def call(self, inputs): def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] + anchors, nc, gd, gw, ch_mul = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get( + 'channel_multiple') na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + if not ch_mul: + ch_mul = 8 layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args @@ -399,7 +402,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3x]: c1, c2 = ch[f], args[0] - c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 + c2 = make_divisible(c2 * gw, ch_mul) if c2 != no else c2 args = [c1, c2, *args[1:]] if m in [BottleneckCSP, C3, C3x]: @@ -414,7 +417,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) if m is Segment: - args[3] = make_divisible(args[3] * gw, 8) + args[3] = make_divisible(args[3] * gw, ch_mul) args.append(imgsz) else: c2 = ch[f] diff --git a/models/yolo.py b/models/yolo.py index 4f4d567bec73..9cd3cddc25c3 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -299,10 +299,13 @@ def _from_yaml(self, cfg): def parse_model(d, ch): # model_dict, input_channels(3) # Parse a YOLOv5 model.yaml dictionary LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') + anchors, nc, gd, gw, act, ch_mul = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get( + 'activation'), d.get('channel_multiple') if act: Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() LOGGER.info(f"{colorstr('activation:')} {act}") # print + if not ch_mul: + ch_mul = 8 na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) @@ -319,7 +322,7 @@ def parse_model(d, ch): # model_dict, input_channels(3) BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: c1, c2 = ch[f], args[0] if c2 != no: # if not output - c2 = make_divisible(c2 * gw, 8) + c2 = make_divisible(c2 * gw, ch_mul) args = [c1, c2, *args[1:]] if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: @@ -335,7 +338,7 @@ def parse_model(d, ch): # model_dict, input_channels(3) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) if m is Segment: - args[3] = make_divisible(args[3] * gw, 8) + args[3] = make_divisible(args[3] * gw, ch_mul) elif m is Contract: c2 = ch[f] * args[0] ** 2 elif m is Expand: From 63555c8e2230328585d09fdc50a6601822a70ded Mon Sep 17 00:00:00 2001 From: Angelo Delli Santi Date: Mon, 18 Dec 2023 17:14:59 +0100 Subject: [PATCH 46/79] Add option to quantize per-tensor (#12516) * Add option to quantize per-tensor * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- export.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/export.py b/export.py index 5611ab95b1dc..d550a85fd99f 100644 --- a/export.py +++ b/export.py @@ -448,7 +448,8 @@ def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')): @try_export -def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')): +def export_tflite(keras_model, im, file, int8, per_tensor, data, nms, agnostic_nms, + prefix=colorstr('TensorFlow Lite:')): # YOLOv5 TensorFlow Lite export import tensorflow as tf @@ -469,6 +470,8 @@ def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=c converter.inference_input_type = tf.uint8 # or tf.int8 converter.inference_output_type = tf.uint8 # or tf.int8 converter.experimental_new_quantizer = True + if per_tensor: + converter._experimental_disable_per_channel = True f = str(file).replace('.pt', '-int8.tflite') if nms or agnostic_nms: converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS) @@ -713,6 +716,7 @@ def run( keras=False, # use Keras optimize=False, # TorchScript: optimize for mobile int8=False, # CoreML/TF INT8 quantization + per_tensor=False, # TF per tensor quantization dynamic=False, # ONNX/TF/TensorRT: dynamic axes simplify=False, # ONNX: simplify model opset=12, # ONNX: opset version @@ -798,7 +802,14 @@ def run( if pb or tfjs: # pb prerequisite to tfjs f[6], _ = export_pb(s_model, file) if tflite or edgetpu: - f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms) + f[7], _ = export_tflite(s_model, + im, + file, + int8 or edgetpu, + per_tensor, + data=data, + nms=nms, + agnostic_nms=agnostic_nms) if edgetpu: f[8], _ = export_edgetpu(file) add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) @@ -837,6 +848,7 @@ def parse_opt(known=False): parser.add_argument('--keras', action='store_true', help='TF: use Keras') parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile') parser.add_argument('--int8', action='store_true', help='CoreML/TF/OpenVINO INT8 quantization') + parser.add_argument('--per-tensor', action='store_true', help='TF per-tensor quantization') parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes') parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model') parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version') From ba63208025fb27df31f4f02265631f72bbbba6a5 Mon Sep 17 00:00:00 2001 From: Ryan <35791309+Gary55555@users.noreply.github.com> Date: Wed, 27 Dec 2023 06:58:32 +0800 Subject: [PATCH 47/79] Create `labels` dir on labels save (#12551) * Update val.py When saving predicted labels, create a folder named labels. Signed-off-by: Ryan <35791309+Gary55555@users.noreply.github.com> * Update val.py Signed-off-by: Glenn Jocher * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Ryan <35791309+Gary55555@users.noreply.github.com> Signed-off-by: Glenn Jocher Co-authored-by: Glenn Jocher Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- val.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/val.py b/val.py index 8da3ef7667aa..b3d05f4305ce 100644 --- a/val.py +++ b/val.py @@ -72,7 +72,8 @@ def save_one_json(predn, jdict, path, class_map): def process_batch(detections, labels, iouv): """ - Return correct prediction matrix + Return correct prediction matrix. + Arguments: detections (array[N, 6]), x1, y1, x2, y2, conf, class labels (array[M, 5]), class, x1, y1, x2, y2 @@ -258,6 +259,7 @@ def run( # Save/log if save_txt: + (save_dir / 'labels').mkdir(parents=True, exist_ok=True) save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') if save_json: save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary From 6970d88b8ace427de4ca1ab3a841097ccec6d234 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 1 Jan 2024 15:50:40 +0100 Subject: [PATCH 48/79] Update requirements.txt to `ultralytics 8.0.232` (#12565) Signed-off-by: Glenn Jocher --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 115055b1629c..eb5038f981c2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,7 +15,7 @@ thop>=0.1.1 # FLOPs computation torch>=1.8.0 # see https://pytorch.org/get-started/locally (recommended) torchvision>=0.9.0 tqdm>=4.64.0 -ultralytics>=8.0.147 +ultralytics>=8.0.232 # protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 # Logging --------------------------------------------------------------------- From 43c43d824486fa72d643400d43c960dc2e35b616 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 1 Jan 2024 18:54:18 +0100 Subject: [PATCH 49/79] Delete .github/PULL_REQUEST_TEMPLATE.md (#12564) Signed-off-by: Glenn Jocher --- .github/PULL_REQUEST_TEMPLATE.md | 13 ------------- 1 file changed, 13 deletions(-) delete mode 100644 .github/PULL_REQUEST_TEMPLATE.md diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index d96d5afd2836..000000000000 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,13 +0,0 @@ - - -copilot:all From 52db52b927d8050f25eeaec162ac715c42a6da9a Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Wed, 3 Jan 2024 09:06:26 +0200 Subject: [PATCH 50/79] Add ndjson logging for training (#10970) * Add ndjson logging for training This adds support for NDJSON (newline-delimited JSON) metrics logging, for both console (stdout) output and a file (like the current CSV file). NDJSON can be easily grepped from the output and/or parsed with e.g. `jq`. The feature is enabled with the `--ndjson-console` and `--ndjson-file` switches to `train.py`. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- train.py | 21 +++++++++++++++++++-- utils/loggers/__init__.py | 24 +++++++++++++++++++++++- 2 files changed, 42 insertions(+), 3 deletions(-) diff --git a/train.py b/train.py index 4c3bec34835f..ad37f2ca858d 100644 --- a/train.py +++ b/train.py @@ -58,7 +58,7 @@ get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save) -from utils.loggers import Loggers +from utils.loggers import LOGGERS, Loggers from utils.loggers.comet.comet_utils import check_comet_resume from utils.loss import ComputeLoss from utils.metrics import fitness @@ -98,7 +98,20 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Loggers data_dict = None if RANK in {-1, 0}: - loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance + include_loggers = list(LOGGERS) + if getattr(opt, 'ndjson_console', False): + include_loggers.append('ndjson_console') + if getattr(opt, 'ndjson_file', False): + include_loggers.append('ndjson_file') + + loggers = Loggers( + save_dir=save_dir, + weights=weights, + opt=opt, + hyp=hyp, + logger=LOGGER, + include=tuple(include_loggers), + ) # Register actions for k in methods(loggers): @@ -482,6 +495,10 @@ def parse_opt(known=False): parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') + # NDJSON logging + parser.add_argument('--ndjson-console', action='store_true', help='Log ndjson to console') + parser.add_argument('--ndjson-file', action='store_true', help='Log ndjson to file') + return parser.parse_known_args()[0] if known else parser.parse_args() diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index ba7d2790e613..aa6eae4c6f8c 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -2,7 +2,7 @@ """ Logging utils """ - +import json import os import warnings from pathlib import Path @@ -58,6 +58,18 @@ comet_ml = None +def _json_default(value): + """Format `value` for JSON serialization (e.g. unwrap tensors). Fall back to strings.""" + if isinstance(value, torch.Tensor): + try: + value = value.item() + except ValueError: # "only one element tensors can be converted to Python scalars" + pass + if isinstance(value, float): + return value + return str(value) + + class Loggers(): # YOLOv5 Loggers class def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): @@ -86,6 +98,8 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, for k in LOGGERS: setattr(self, k, None) # init empty logger dictionary self.csv = True # always log to csv + self.ndjson_console = ('ndjson_console' in self.include) # log ndjson to console + self.ndjson_file = ('ndjson_file' in self.include) # log ndjson to file # Messages if not comet_ml: @@ -228,6 +242,14 @@ def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header with open(file, 'a') as f: f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') + if self.ndjson_console or self.ndjson_file: + json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default) + if self.ndjson_console: + print(json_data) + if self.ndjson_file: + file = self.save_dir / 'results.ndjson' + with open(file, 'a') as f: + print(json_data, file=f) if self.tb: for k, v in x.items(): From bd1a8295fb197082f82fdd41a353ad82c71505ef Mon Sep 17 00:00:00 2001 From: tiancailin Date: Wed, 3 Jan 2024 15:07:19 +0800 Subject: [PATCH 51/79] Fix bug in issue #12457. (#12459) * Fix bug in #12457. When run 'python.exe segment/predict.py --visualize' will throw AttributeError: 'tuple' object has no attribute 'shape' * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- utils/plots.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/utils/plots.py b/utils/plots.py index db6f94a6674d..5901ca2dbfaa 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -59,7 +59,9 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec n: Maximum number of feature maps to plot save_dir: Directory to save results """ - if 'Detect' not in module_type: + if ('Detect' + not in module_type) and ('Segment' + not in module_type): # 'Detect' for Object Detect task,'Segment' for Segment task batch, channels, height, width = x.shape # batch, channels, height, width if height > 1 and width > 1: f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename From 69b0faf2f7d43b32833ec4b46b2faf50b5b7ceed Mon Sep 17 00:00:00 2001 From: Yakuho Date: Wed, 3 Jan 2024 15:09:23 +0800 Subject: [PATCH 52/79] Update selectable device Profile (#12353) * Update selectable device Profile * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- classify/predict.py | 2 +- classify/val.py | 2 +- detect.py | 2 +- segment/predict.py | 2 +- segment/val.py | 2 +- utils/general.py | 7 ++++--- val.py | 2 +- 7 files changed, 10 insertions(+), 9 deletions(-) diff --git a/classify/predict.py b/classify/predict.py index 653c374f768f..b056a0cd707b 100644 --- a/classify/predict.py +++ b/classify/predict.py @@ -106,7 +106,7 @@ def run( # Run inference model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup - seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.Tensor(im).to(model.device) diff --git a/classify/val.py b/classify/val.py index 4b92e9f105db..6814c4d780e1 100644 --- a/classify/val.py +++ b/classify/val.py @@ -97,7 +97,7 @@ def run( workers=workers) model.eval() - pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile()) + pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device)) n = len(dataloader) # number of batches action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing' desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}' diff --git a/detect.py b/detect.py index fd9637138dd6..1ea4e0b60dd7 100644 --- a/detect.py +++ b/detect.py @@ -116,7 +116,7 @@ def run( # Run inference model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup - seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device) diff --git a/segment/predict.py b/segment/predict.py index 113bc472e637..8e3d97dfeb92 100644 --- a/segment/predict.py +++ b/segment/predict.py @@ -117,7 +117,7 @@ def run( # Run inference model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup - seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device) diff --git a/segment/val.py b/segment/val.py index dc8081840e37..304d0c751314 100644 --- a/segment/val.py +++ b/segment/val.py @@ -233,7 +233,7 @@ def run( class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R', 'mAP50', 'mAP50-95)') - dt = Profile(), Profile(), Profile() + dt = Profile(device=device), Profile(device=device), Profile(device=device) metrics = Metrics() loss = torch.zeros(4, device=device) jdict, stats = [], [] diff --git a/utils/general.py b/utils/general.py index 135141e21436..73925ce5fb95 100644 --- a/utils/general.py +++ b/utils/general.py @@ -182,9 +182,10 @@ def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): class Profile(contextlib.ContextDecorator): # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager - def __init__(self, t=0.0): + def __init__(self, t=0.0, device: torch.device = None): self.t = t - self.cuda = torch.cuda.is_available() + self.device = device + self.cuda = True if (device and str(device)[:4] == 'cuda') else False def __enter__(self): self.start = self.time() @@ -196,7 +197,7 @@ def __exit__(self, type, value, traceback): def time(self): if self.cuda: - torch.cuda.synchronize() + torch.cuda.synchronize(self.device) return time.time() diff --git a/val.py b/val.py index b3d05f4305ce..1a4219c38962 100644 --- a/val.py +++ b/val.py @@ -191,7 +191,7 @@ def run( class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95') tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 - dt = Profile(), Profile(), Profile() # profiling times + dt = Profile(device=device), Profile(device=device), Profile(device=device) # profiling times loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] callbacks.run('on_val_start') From c0b0729386b9c6fca072b507599499570e13177a Mon Sep 17 00:00:00 2001 From: chaoqin <33347064+XevenQC@users.noreply.github.com> Date: Wed, 3 Jan 2024 15:10:10 +0800 Subject: [PATCH 53/79] Fix the bug that multi webcam detection failed with OpenVINO (#11935) * Fix the bug that multi webcam detection failed with OpenVINO It would failed with the following error when detect multi webcam. "Input blob size is not equal network input size (2457600!=1228800)" Signed-off-by: Chao Qin * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Chao Qin Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- detect.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/detect.py b/detect.py index 1ea4e0b60dd7..03bc29de999e 100644 --- a/detect.py +++ b/detect.py @@ -124,12 +124,22 @@ def run( im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim + if model.xml and im.shape[0] > 1: + ims = torch.chunk(im, im.shape[0], 0) # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False - pred = model(im, augment=augment, visualize=visualize) - + if model.xml and im.shape[0] > 1: + pred = None + for image in ims: + if pred is None: + pred = model(image, augment=augment, visualize=visualize).unsqueeze(0) + else: + pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0) + pred = [pred, None] + else: + pred = model(im, augment=augment, visualize=visualize) # NMS with dt[2]: pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) From b61143c7e55c6968288e6b034e14051fed871534 Mon Sep 17 00:00:00 2001 From: Nouran Ali Date: Wed, 3 Jan 2024 09:10:41 +0200 Subject: [PATCH 54/79] solves #11028 : added imagenet small versions 10,100 and 1000 (#12141) * added imagenet small versions 10,100 and 1000 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- data/ImageNet10.yaml | 32 + data/ImageNet100.yaml | 120 ++++ data/ImageNet1000.yaml | 1022 ++++++++++++++++++++++++++++++ data/scripts/get_imagenet10.sh | 29 + data/scripts/get_imagenet100.sh | 29 + data/scripts/get_imagenet1000.sh | 29 + 6 files changed, 1261 insertions(+) create mode 100644 data/ImageNet10.yaml create mode 100644 data/ImageNet100.yaml create mode 100644 data/ImageNet1000.yaml create mode 100644 data/scripts/get_imagenet10.sh create mode 100644 data/scripts/get_imagenet100.sh create mode 100644 data/scripts/get_imagenet1000.sh diff --git a/data/ImageNet10.yaml b/data/ImageNet10.yaml new file mode 100644 index 000000000000..a498d43968ef --- /dev/null +++ b/data/ImageNet10.yaml @@ -0,0 +1,32 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet10 ← downloads here + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet10 # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + + +# Download script/URL (optional) +download: data/scripts/get_imagenet10.sh diff --git a/data/ImageNet100.yaml b/data/ImageNet100.yaml new file mode 100644 index 000000000000..2e6e44ec3e65 --- /dev/null +++ b/data/ImageNet100.yaml @@ -0,0 +1,120 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet100 ← downloads here + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet100 # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + 10: brambling + 11: goldfinch + 12: house finch + 13: junco + 14: indigo bunting + 15: American robin + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: American dipper + 21: kite + 22: bald eagle + 23: vulture + 24: great grey owl + 25: fire salamander + 26: smooth newt + 27: newt + 28: spotted salamander + 29: axolotl + 30: American bullfrog + 31: tree frog + 32: tailed frog + 33: loggerhead sea turtle + 34: leatherback sea turtle + 35: mud turtle + 36: terrapin + 37: box turtle + 38: banded gecko + 39: green iguana + 40: Carolina anole + 41: desert grassland whiptail lizard + 42: agama + 43: frilled-necked lizard + 44: alligator lizard + 45: Gila monster + 46: European green lizard + 47: chameleon + 48: Komodo dragon + 49: Nile crocodile + 50: American alligator + 51: triceratops + 52: worm snake + 53: ring-necked snake + 54: eastern hog-nosed snake + 55: smooth green snake + 56: kingsnake + 57: garter snake + 58: water snake + 59: vine snake + 60: night snake + 61: boa constrictor + 62: African rock python + 63: Indian cobra + 64: green mamba + 65: sea snake + 66: Saharan horned viper + 67: eastern diamondback rattlesnake + 68: sidewinder + 69: trilobite + 70: harvestman + 71: scorpion + 72: yellow garden spider + 73: barn spider + 74: European garden spider + 75: southern black widow + 76: tarantula + 77: wolf spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse + 83: prairie grouse + 84: peacock + 85: quail + 86: partridge + 87: grey parrot + 88: macaw + 89: sulphur-crested cockatoo + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: duck + 98: red-breasted merganser + 99: goose +# Download script/URL (optional) +download: data/scripts/get_imagenet100.sh diff --git a/data/ImageNet1000.yaml b/data/ImageNet1000.yaml new file mode 100644 index 000000000000..410bdbcafc83 --- /dev/null +++ b/data/ImageNet1000.yaml @@ -0,0 +1,1022 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet100 ← downloads here + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet1000 # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + 10: brambling + 11: goldfinch + 12: house finch + 13: junco + 14: indigo bunting + 15: American robin + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: American dipper + 21: kite + 22: bald eagle + 23: vulture + 24: great grey owl + 25: fire salamander + 26: smooth newt + 27: newt + 28: spotted salamander + 29: axolotl + 30: American bullfrog + 31: tree frog + 32: tailed frog + 33: loggerhead sea turtle + 34: leatherback sea turtle + 35: mud turtle + 36: terrapin + 37: box turtle + 38: banded gecko + 39: green iguana + 40: Carolina anole + 41: desert grassland whiptail lizard + 42: agama + 43: frilled-necked lizard + 44: alligator lizard + 45: Gila monster + 46: European green lizard + 47: chameleon + 48: Komodo dragon + 49: Nile crocodile + 50: American alligator + 51: triceratops + 52: worm snake + 53: ring-necked snake + 54: eastern hog-nosed snake + 55: smooth green snake + 56: kingsnake + 57: garter snake + 58: water snake + 59: vine snake + 60: night snake + 61: boa constrictor + 62: African rock python + 63: Indian cobra + 64: green mamba + 65: sea snake + 66: Saharan horned viper + 67: eastern diamondback rattlesnake + 68: sidewinder + 69: trilobite + 70: harvestman + 71: scorpion + 72: yellow garden spider + 73: barn spider + 74: European garden spider + 75: southern black widow + 76: tarantula + 77: wolf spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse + 83: prairie grouse + 84: peacock + 85: quail + 86: partridge + 87: grey parrot + 88: macaw + 89: sulphur-crested cockatoo + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: duck + 98: red-breasted merganser + 99: goose + 100: black swan + 101: tusker + 102: echidna + 103: platypus + 104: wallaby + 105: koala + 106: wombat + 107: jellyfish + 108: sea anemone + 109: brain coral + 110: flatworm + 111: nematode + 112: conch + 113: snail + 114: slug + 115: sea slug + 116: chiton + 117: chambered nautilus + 118: Dungeness crab + 119: rock crab + 120: fiddler crab + 121: red king crab + 122: American lobster + 123: spiny lobster + 124: crayfish + 125: hermit crab + 126: isopod + 127: white stork + 128: black stork + 129: spoonbill + 130: flamingo + 131: little blue heron + 132: great egret + 133: bittern + 134: crane (bird) + 135: limpkin + 136: common gallinule + 137: American coot + 138: bustard + 139: ruddy turnstone + 140: dunlin + 141: common redshank + 142: dowitcher + 143: oystercatcher + 144: pelican + 145: king penguin + 146: albatross + 147: grey whale + 148: killer whale + 149: dugong + 150: sea lion + 151: Chihuahua + 152: Japanese Chin + 153: Maltese + 154: Pekingese + 155: Shih Tzu + 156: King Charles Spaniel + 157: Papillon + 158: toy terrier + 159: Rhodesian Ridgeback + 160: Afghan Hound + 161: Basset Hound + 162: Beagle + 163: Bloodhound + 164: Bluetick Coonhound + 165: Black and Tan Coonhound + 166: Treeing Walker Coonhound + 167: English foxhound + 168: Redbone Coonhound + 169: borzoi + 170: Irish Wolfhound + 171: Italian Greyhound + 172: Whippet + 173: Ibizan Hound + 174: Norwegian Elkhound + 175: Otterhound + 176: Saluki + 177: Scottish Deerhound + 178: Weimaraner + 179: Staffordshire Bull Terrier + 180: American Staffordshire Terrier + 181: Bedlington Terrier + 182: Border Terrier + 183: Kerry Blue Terrier + 184: Irish Terrier + 185: Norfolk Terrier + 186: Norwich Terrier + 187: Yorkshire Terrier + 188: Wire Fox Terrier + 189: Lakeland Terrier + 190: Sealyham Terrier + 191: Airedale Terrier + 192: Cairn Terrier + 193: Australian Terrier + 194: Dandie Dinmont Terrier + 195: Boston Terrier + 196: Miniature Schnauzer + 197: Giant Schnauzer + 198: Standard Schnauzer + 199: Scottish Terrier + 200: Tibetan Terrier + 201: Australian Silky Terrier + 202: Soft-coated Wheaten Terrier + 203: West Highland White Terrier + 204: Lhasa Apso + 205: Flat-Coated Retriever + 206: Curly-coated Retriever + 207: Golden Retriever + 208: Labrador Retriever + 209: Chesapeake Bay Retriever + 210: German Shorthaired Pointer + 211: Vizsla + 212: English Setter + 213: Irish Setter + 214: Gordon Setter + 215: Brittany + 216: Clumber Spaniel + 217: English Springer Spaniel + 218: Welsh Springer Spaniel + 219: Cocker Spaniels + 220: Sussex Spaniel + 221: Irish Water Spaniel + 222: Kuvasz + 223: Schipperke + 224: Groenendael + 225: Malinois + 226: Briard + 227: Australian Kelpie + 228: Komondor + 229: Old English Sheepdog + 230: Shetland Sheepdog + 231: collie + 232: Border Collie + 233: Bouvier des Flandres + 234: Rottweiler + 235: German Shepherd Dog + 236: Dobermann + 237: Miniature Pinscher + 238: Greater Swiss Mountain Dog + 239: Bernese Mountain Dog + 240: Appenzeller Sennenhund + 241: Entlebucher Sennenhund + 242: Boxer + 243: Bullmastiff + 244: Tibetan Mastiff + 245: French Bulldog + 246: Great Dane + 247: St. Bernard + 248: husky + 249: Alaskan Malamute + 250: Siberian Husky + 251: Dalmatian + 252: Affenpinscher + 253: Basenji + 254: pug + 255: Leonberger + 256: Newfoundland + 257: Pyrenean Mountain Dog + 258: Samoyed + 259: Pomeranian + 260: Chow Chow + 261: Keeshond + 262: Griffon Bruxellois + 263: Pembroke Welsh Corgi + 264: Cardigan Welsh Corgi + 265: Toy Poodle + 266: Miniature Poodle + 267: Standard Poodle + 268: Mexican hairless dog + 269: grey wolf + 270: Alaskan tundra wolf + 271: red wolf + 272: coyote + 273: dingo + 274: dhole + 275: African wild dog + 276: hyena + 277: red fox + 278: kit fox + 279: Arctic fox + 280: grey fox + 281: tabby cat + 282: tiger cat + 283: Persian cat + 284: Siamese cat + 285: Egyptian Mau + 286: cougar + 287: lynx + 288: leopard + 289: snow leopard + 290: jaguar + 291: lion + 292: tiger + 293: cheetah + 294: brown bear + 295: American black bear + 296: polar bear + 297: sloth bear + 298: mongoose + 299: meerkat + 300: tiger beetle + 301: ladybug + 302: ground beetle + 303: longhorn beetle + 304: leaf beetle + 305: dung beetle + 306: rhinoceros beetle + 307: weevil + 308: fly + 309: bee + 310: ant + 311: grasshopper + 312: cricket + 313: stick insect + 314: cockroach + 315: mantis + 316: cicada + 317: leafhopper + 318: lacewing + 319: dragonfly + 320: damselfly + 321: red admiral + 322: ringlet + 323: monarch butterfly + 324: small white + 325: sulphur butterfly + 326: gossamer-winged butterfly + 327: starfish + 328: sea urchin + 329: sea cucumber + 330: cottontail rabbit + 331: hare + 332: Angora rabbit + 333: hamster + 334: porcupine + 335: fox squirrel + 336: marmot + 337: beaver + 338: guinea pig + 339: common sorrel + 340: zebra + 341: pig + 342: wild boar + 343: warthog + 344: hippopotamus + 345: ox + 346: water buffalo + 347: bison + 348: ram + 349: bighorn sheep + 350: Alpine ibex + 351: hartebeest + 352: impala + 353: gazelle + 354: dromedary + 355: llama + 356: weasel + 357: mink + 358: European polecat + 359: black-footed ferret + 360: otter + 361: skunk + 362: badger + 363: armadillo + 364: three-toed sloth + 365: orangutan + 366: gorilla + 367: chimpanzee + 368: gibbon + 369: siamang + 370: guenon + 371: patas monkey + 372: baboon + 373: macaque + 374: langur + 375: black-and-white colobus + 376: proboscis monkey + 377: marmoset + 378: white-headed capuchin + 379: howler monkey + 380: titi + 381: Geoffroy's spider monkey + 382: common squirrel monkey + 383: ring-tailed lemur + 384: indri + 385: Asian elephant + 386: African bush elephant + 387: red panda + 388: giant panda + 389: snoek + 390: eel + 391: coho salmon + 392: rock beauty + 393: clownfish + 394: sturgeon + 395: garfish + 396: lionfish + 397: pufferfish + 398: abacus + 399: abaya + 400: academic gown + 401: accordion + 402: acoustic guitar + 403: aircraft carrier + 404: airliner + 405: airship + 406: altar + 407: ambulance + 408: amphibious vehicle + 409: analog clock + 410: apiary + 411: apron + 412: waste container + 413: assault rifle + 414: backpack + 415: bakery + 416: balance beam + 417: balloon + 418: ballpoint pen + 419: Band-Aid + 420: banjo + 421: baluster + 422: barbell + 423: barber chair + 424: barbershop + 425: barn + 426: barometer + 427: barrel + 428: wheelbarrow + 429: baseball + 430: basketball + 431: bassinet + 432: bassoon + 433: swimming cap + 434: bath towel + 435: bathtub + 436: station wagon + 437: lighthouse + 438: beaker + 439: military cap + 440: beer bottle + 441: beer glass + 442: bell-cot + 443: bib + 444: tandem bicycle + 445: bikini + 446: ring binder + 447: binoculars + 448: birdhouse + 449: boathouse + 450: bobsleigh + 451: bolo tie + 452: poke bonnet + 453: bookcase + 454: bookstore + 455: bottle cap + 456: bow + 457: bow tie + 458: brass + 459: bra + 460: breakwater + 461: breastplate + 462: broom + 463: bucket + 464: buckle + 465: bulletproof vest + 466: high-speed train + 467: butcher shop + 468: taxicab + 469: cauldron + 470: candle + 471: cannon + 472: canoe + 473: can opener + 474: cardigan + 475: car mirror + 476: carousel + 477: tool kit + 478: carton + 479: car wheel + 480: automated teller machine + 481: cassette + 482: cassette player + 483: castle + 484: catamaran + 485: CD player + 486: cello + 487: mobile phone + 488: chain + 489: chain-link fence + 490: chain mail + 491: chainsaw + 492: chest + 493: chiffonier + 494: chime + 495: china cabinet + 496: Christmas stocking + 497: church + 498: movie theater + 499: cleaver + 500: cliff dwelling + 501: cloak + 502: clogs + 503: cocktail shaker + 504: coffee mug + 505: coffeemaker + 506: coil + 507: combination lock + 508: computer keyboard + 509: confectionery store + 510: container ship + 511: convertible + 512: corkscrew + 513: cornet + 514: cowboy boot + 515: cowboy hat + 516: cradle + 517: crane (machine) + 518: crash helmet + 519: crate + 520: infant bed + 521: Crock Pot + 522: croquet ball + 523: crutch + 524: cuirass + 525: dam + 526: desk + 527: desktop computer + 528: rotary dial telephone + 529: diaper + 530: digital clock + 531: digital watch + 532: dining table + 533: dishcloth + 534: dishwasher + 535: disc brake + 536: dock + 537: dog sled + 538: dome + 539: doormat + 540: drilling rig + 541: drum + 542: drumstick + 543: dumbbell + 544: Dutch oven + 545: electric fan + 546: electric guitar + 547: electric locomotive + 548: entertainment center + 549: envelope + 550: espresso machine + 551: face powder + 552: feather boa + 553: filing cabinet + 554: fireboat + 555: fire engine + 556: fire screen sheet + 557: flagpole + 558: flute + 559: folding chair + 560: football helmet + 561: forklift + 562: fountain + 563: fountain pen + 564: four-poster bed + 565: freight car + 566: French horn + 567: frying pan + 568: fur coat + 569: garbage truck + 570: gas mask + 571: gas pump + 572: goblet + 573: go-kart + 574: golf ball + 575: golf cart + 576: gondola + 577: gong + 578: gown + 579: grand piano + 580: greenhouse + 581: grille + 582: grocery store + 583: guillotine + 584: barrette + 585: hair spray + 586: half-track + 587: hammer + 588: hamper + 589: hair dryer + 590: hand-held computer + 591: handkerchief + 592: hard disk drive + 593: harmonica + 594: harp + 595: harvester + 596: hatchet + 597: holster + 598: home theater + 599: honeycomb + 600: hook + 601: hoop skirt + 602: horizontal bar + 603: horse-drawn vehicle + 604: hourglass + 605: iPod + 606: clothes iron + 607: jack-o'-lantern + 608: jeans + 609: jeep + 610: T-shirt + 611: jigsaw puzzle + 612: pulled rickshaw + 613: joystick + 614: kimono + 615: knee pad + 616: knot + 617: lab coat + 618: ladle + 619: lampshade + 620: laptop computer + 621: lawn mower + 622: lens cap + 623: paper knife + 624: library + 625: lifeboat + 626: lighter + 627: limousine + 628: ocean liner + 629: lipstick + 630: slip-on shoe + 631: lotion + 632: speaker + 633: loupe + 634: sawmill + 635: magnetic compass + 636: mail bag + 637: mailbox + 638: tights + 639: tank suit + 640: manhole cover + 641: maraca + 642: marimba + 643: mask + 644: match + 645: maypole + 646: maze + 647: measuring cup + 648: medicine chest + 649: megalith + 650: microphone + 651: microwave oven + 652: military uniform + 653: milk can + 654: minibus + 655: miniskirt + 656: minivan + 657: missile + 658: mitten + 659: mixing bowl + 660: mobile home + 661: Model T + 662: modem + 663: monastery + 664: monitor + 665: moped + 666: mortar + 667: square academic cap + 668: mosque + 669: mosquito net + 670: scooter + 671: mountain bike + 672: tent + 673: computer mouse + 674: mousetrap + 675: moving van + 676: muzzle + 677: nail + 678: neck brace + 679: necklace + 680: nipple + 681: notebook computer + 682: obelisk + 683: oboe + 684: ocarina + 685: odometer + 686: oil filter + 687: organ + 688: oscilloscope + 689: overskirt + 690: bullock cart + 691: oxygen mask + 692: packet + 693: paddle + 694: paddle wheel + 695: padlock + 696: paintbrush + 697: pajamas + 698: palace + 699: pan flute + 700: paper towel + 701: parachute + 702: parallel bars + 703: park bench + 704: parking meter + 705: passenger car + 706: patio + 707: payphone + 708: pedestal + 709: pencil case + 710: pencil sharpener + 711: perfume + 712: Petri dish + 713: photocopier + 714: plectrum + 715: Pickelhaube + 716: picket fence + 717: pickup truck + 718: pier + 719: piggy bank + 720: pill bottle + 721: pillow + 722: ping-pong ball + 723: pinwheel + 724: pirate ship + 725: pitcher + 726: hand plane + 727: planetarium + 728: plastic bag + 729: plate rack + 730: plow + 731: plunger + 732: Polaroid camera + 733: pole + 734: police van + 735: poncho + 736: billiard table + 737: soda bottle + 738: pot + 739: potter's wheel + 740: power drill + 741: prayer rug + 742: printer + 743: prison + 744: projectile + 745: projector + 746: hockey puck + 747: punching bag + 748: purse + 749: quill + 750: quilt + 751: race car + 752: racket + 753: radiator + 754: radio + 755: radio telescope + 756: rain barrel + 757: recreational vehicle + 758: reel + 759: reflex camera + 760: refrigerator + 761: remote control + 762: restaurant + 763: revolver + 764: rifle + 765: rocking chair + 766: rotisserie + 767: eraser + 768: rugby ball + 769: ruler + 770: running shoe + 771: safe + 772: safety pin + 773: salt shaker + 774: sandal + 775: sarong + 776: saxophone + 777: scabbard + 778: weighing scale + 779: school bus + 780: schooner + 781: scoreboard + 782: CRT screen + 783: screw + 784: screwdriver + 785: seat belt + 786: sewing machine + 787: shield + 788: shoe store + 789: shoji + 790: shopping basket + 791: shopping cart + 792: shovel + 793: shower cap + 794: shower curtain + 795: ski + 796: ski mask + 797: sleeping bag + 798: slide rule + 799: sliding door + 800: slot machine + 801: snorkel + 802: snowmobile + 803: snowplow + 804: soap dispenser + 805: soccer ball + 806: sock + 807: solar thermal collector + 808: sombrero + 809: soup bowl + 810: space bar + 811: space heater + 812: space shuttle + 813: spatula + 814: motorboat + 815: spider web + 816: spindle + 817: sports car + 818: spotlight + 819: stage + 820: steam locomotive + 821: through arch bridge + 822: steel drum + 823: stethoscope + 824: scarf + 825: stone wall + 826: stopwatch + 827: stove + 828: strainer + 829: tram + 830: stretcher + 831: couch + 832: stupa + 833: submarine + 834: suit + 835: sundial + 836: sunglass + 837: sunglasses + 838: sunscreen + 839: suspension bridge + 840: mop + 841: sweatshirt + 842: swimsuit + 843: swing + 844: switch + 845: syringe + 846: table lamp + 847: tank + 848: tape player + 849: teapot + 850: teddy bear + 851: television + 852: tennis ball + 853: thatched roof + 854: front curtain + 855: thimble + 856: threshing machine + 857: throne + 858: tile roof + 859: toaster + 860: tobacco shop + 861: toilet seat + 862: torch + 863: totem pole + 864: tow truck + 865: toy store + 866: tractor + 867: semi-trailer truck + 868: tray + 869: trench coat + 870: tricycle + 871: trimaran + 872: tripod + 873: triumphal arch + 874: trolleybus + 875: trombone + 876: tub + 877: turnstile + 878: typewriter keyboard + 879: umbrella + 880: unicycle + 881: upright piano + 882: vacuum cleaner + 883: vase + 884: vault + 885: velvet + 886: vending machine + 887: vestment + 888: viaduct + 889: violin + 890: volleyball + 891: waffle iron + 892: wall clock + 893: wallet + 894: wardrobe + 895: military aircraft + 896: sink + 897: washing machine + 898: water bottle + 899: water jug + 900: water tower + 901: whiskey jug + 902: whistle + 903: wig + 904: window screen + 905: window shade + 906: Windsor tie + 907: wine bottle + 908: wing + 909: wok + 910: wooden spoon + 911: wool + 912: split-rail fence + 913: shipwreck + 914: yawl + 915: yurt + 916: website + 917: comic book + 918: crossword + 919: traffic sign + 920: traffic light + 921: dust jacket + 922: menu + 923: plate + 924: guacamole + 925: consomme + 926: hot pot + 927: trifle + 928: ice cream + 929: ice pop + 930: baguette + 931: bagel + 932: pretzel + 933: cheeseburger + 934: hot dog + 935: mashed potato + 936: cabbage + 937: broccoli + 938: cauliflower + 939: zucchini + 940: spaghetti squash + 941: acorn squash + 942: butternut squash + 943: cucumber + 944: artichoke + 945: bell pepper + 946: cardoon + 947: mushroom + 948: Granny Smith + 949: strawberry + 950: orange + 951: lemon + 952: fig + 953: pineapple + 954: banana + 955: jackfruit + 956: custard apple + 957: pomegranate + 958: hay + 959: carbonara + 960: chocolate syrup + 961: dough + 962: meatloaf + 963: pizza + 964: pot pie + 965: burrito + 966: red wine + 967: espresso + 968: cup + 969: eggnog + 970: alp + 971: bubble + 972: cliff + 973: coral reef + 974: geyser + 975: lakeshore + 976: promontory + 977: shoal + 978: seashore + 979: valley + 980: volcano + 981: baseball player + 982: bridegroom + 983: scuba diver + 984: rapeseed + 985: daisy + 986: yellow lady's slipper + 987: corn + 988: acorn + 989: rose hip + 990: horse chestnut seed + 991: coral fungus + 992: agaric + 993: gyromitra + 994: stinkhorn mushroom + 995: earth star + 996: hen-of-the-woods + 997: bolete + 998: ear + 999: toilet paper + + +# Download script/URL (optional) +download: data/scripts/get_imagenet1000.sh diff --git a/data/scripts/get_imagenet10.sh b/data/scripts/get_imagenet10.sh new file mode 100644 index 000000000000..71e17c5d3fb7 --- /dev/null +++ b/data/scripts/get_imagenet10.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Download ILSVRC2012 ImageNet dataset https://image-net.org +# Example usage: bash data/scripts/get_imagenet.sh +# parent +# ├── yolov5 +# └── datasets +# └── imagenet ← downloads here + +# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val +if [ "$#" -gt 0 ]; then + for opt in "$@"; do + case "${opt}" in + --train) train=true ;; + --val) val=true ;; + esac + done +else + train=true + val=true +fi + +# Make dir +d='../datasets/imagenet10' # unzip directory +mkdir -p $d && cd $d + +# Download/unzip train +wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip +unzip imagenet10.zip && rm imagenet10.zip diff --git a/data/scripts/get_imagenet100.sh b/data/scripts/get_imagenet100.sh new file mode 100644 index 000000000000..c57106b485db --- /dev/null +++ b/data/scripts/get_imagenet100.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Download ILSVRC2012 ImageNet dataset https://image-net.org +# Example usage: bash data/scripts/get_imagenet.sh +# parent +# ├── yolov5 +# └── datasets +# └── imagenet ← downloads here + +# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val +if [ "$#" -gt 0 ]; then + for opt in "$@"; do + case "${opt}" in + --train) train=true ;; + --val) val=true ;; + esac + done +else + train=true + val=true +fi + +# Make dir +d='../datasets/imagenet100' # unzip directory +mkdir -p $d && cd $d + +# Download/unzip train +wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet100.zip +unzip imagenet100.zip && rm imagenet100.zip diff --git a/data/scripts/get_imagenet1000.sh b/data/scripts/get_imagenet1000.sh new file mode 100644 index 000000000000..451dd0f6b585 --- /dev/null +++ b/data/scripts/get_imagenet1000.sh @@ -0,0 +1,29 @@ +#!/bin/bash +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Download ILSVRC2012 ImageNet dataset https://image-net.org +# Example usage: bash data/scripts/get_imagenet.sh +# parent +# ├── yolov5 +# └── datasets +# └── imagenet ← downloads here + +# Arguments (optional) Usage: bash data/scripts/get_imagenet.sh --train --val +if [ "$#" -gt 0 ]; then + for opt in "$@"; do + case "${opt}" in + --train) train=true ;; + --val) val=true ;; + esac + done +else + train=true + val=true +fi + +# Make dir +d='../datasets/imagenet1000' # unzip directory +mkdir -p $d && cd $d + +# Download/unzip train +wget https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet1000.zip +unzip imagenet1000.zip && rm imagenet1000.zip From 66edf38933208d157700a9ebd65835e97504d34c Mon Sep 17 00:00:00 2001 From: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> Date: Wed, 3 Jan 2024 08:12:46 +0100 Subject: [PATCH 55/79] improving evolve (#11348) * improving evole in train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix gen_ranges value in mutation part. Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * fix invalid syntax in line 532 remove on tab from "else" Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * fix range index Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py fix population size add crossover min and max rate Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update comments Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * save population for last generation The latest version incorporates a significant update whereby all hyper parameters are now stored in the population section of "evolve_population.yaml," located in "yolov5\data\hyps," following the transition to the new generation. This development allows for the continuation of a previously abandoned evolution process by utilizing the former population. Additionally, a new argument, "--evolve_population," has been introduced to enable the relocation of the manual "evolve_population.yaml" to any project directory to load for the aforementioned purpose. This enhancement offers greater flexibility and convenience to the users, making it easier for them to resume their evolutionary process. Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove try - except Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Add resume resume_evolve arg for **resume evolve from last generation**. Population will load from data/hyp by default and load all yaml file form them. Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update README.zh-CN.md Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> * Update train.py update pop_size Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> --------- Signed-off-by: Shayan Mousavinia <45814390+ShAmoNiA@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- train.py | 246 +++++++++++++++++++++++++++++++++++++++---------------- 1 file changed, 176 insertions(+), 70 deletions(-) diff --git a/train.py b/train.py index ad37f2ca858d..378ff4bd30ea 100644 --- a/train.py +++ b/train.py @@ -468,6 +468,11 @@ def parse_opt(known=False): parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') parser.add_argument('--noplots', action='store_true', help='save no plot files') parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') + parser.add_argument('--evolve_population', + type=str, + default=ROOT / 'data/hyps', + help='location for loading population') + parser.add_argument('--resume_evolve', type=str, default=None, help='resume evolve from last generation') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') @@ -555,37 +560,48 @@ def main(opt, callbacks=Callbacks()): # Evolve hyperparameters (optional) else: - # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + # Hyperparameter evolution metadata (including this hyperparameter True-False, lower_limit, upper_limit) meta = { - 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) - 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) - 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 - 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay - 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) - 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum - 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr - 'box': (1, 0.02, 0.2), # box loss gain - 'cls': (1, 0.2, 4.0), # cls loss gain - 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight - 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) - 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight - 'iou_t': (0, 0.1, 0.7), # IoU training threshold - 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold - 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) - 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) - 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) - 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) - 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) - 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) - 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) - 'scale': (1, 0.0, 0.9), # image scale (+/- gain) - 'shear': (1, 0.0, 10.0), # image shear (+/- deg) - 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 - 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) - 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) - 'mosaic': (1, 0.0, 1.0), # image mixup (probability) - 'mixup': (1, 0.0, 1.0), # image mixup (probability) - 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) + 'lr0': (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (False, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (False, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (False, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (False, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (False, 0.0, 0.2), # warmup initial bias lr + 'box': (False, 0.02, 0.2), # box loss gain + 'cls': (False, 0.2, 4.0), # cls loss gain + 'cls_pw': (False, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (False, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (False, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (False, 0.1, 0.7), # IoU training threshold + 'anchor_t': (False, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (False, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (True, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (True, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (True, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (True, 0.0, 0.9), # image scale (+/- gain) + 'shear': (True, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (True, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (True, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (True, 0.0, 1.0), # image mixup (probability) + 'mixup': (True, 0.0, 1.0), # image mixup (probability) + 'copy_paste': (True, 0.0, 1.0)} # segment copy-paste (probability) + + # GA configs + pop_size = 50 + mutation_rate_min = 0.01 + mutation_rate_max = 0.5 + crossover_rate_min = 0.5 + crossover_rate_max = 1 + min_elite_size = 2 + max_elite_size = 5 + tournament_size_min = 2 + tournament_size_max = 10 with open(opt.hyp, errors='ignore') as f: hyp = yaml.safe_load(f) # load hyps dict @@ -604,46 +620,128 @@ def main(opt, callbacks=Callbacks()): f'gs://{opt.bucket}/evolve.csv', str(evolve_csv), ]) - for _ in range(opt.evolve): # generations to evolve - if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate - # Select parent(s) - parent = 'single' # parent selection method: 'single' or 'weighted' - x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) - n = min(5, len(x)) # number of previous results to consider - x = x[np.argsort(-fitness(x))][:n] # top n mutations - w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) - if parent == 'single' or len(x) == 1: - # x = x[random.randint(0, n - 1)] # random selection - x = x[random.choices(range(n), weights=w)[0]] # weighted selection - elif parent == 'weighted': - x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination - - # Mutate - mp, s = 0.8, 0.2 # mutation probability, sigma - npr = np.random - npr.seed(int(time.time())) - g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 - ng = len(meta) - v = np.ones(ng) - while all(v == 1): # mutate until a change occurs (prevent duplicates) - v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) - for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) - hyp[k] = float(x[i + 7] * v[i]) # mutate - - # Constrain to limits - for k, v in meta.items(): - hyp[k] = max(hyp[k], v[1]) # lower limit - hyp[k] = min(hyp[k], v[2]) # upper limit - hyp[k] = round(hyp[k], 5) # significant digits - - # Train mutation - results = train(hyp.copy(), opt, device, callbacks) - callbacks = Callbacks() - # Write mutation results - keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss', - 'val/obj_loss', 'val/cls_loss') - print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) - + # Delete the items in meta dictionary whose first value is False + del_ = [] + for item in meta.keys(): + if meta[item][0] is False: + del_.append(item) + hyp_GA = hyp.copy() # Make a copy of hyp dictionary + for item in del_: + del meta[item] # Remove the item from meta dictionary + del hyp_GA[item] # Remove the item from hyp_GA dictionary + + # Set lower_limit and upper_limit arrays to hold the search space boundaries + lower_limit = np.array([meta[k][1] for k in hyp_GA.keys()]) + upper_limit = np.array([meta[k][2] for k in hyp_GA.keys()]) + + # Create gene_ranges list to hold the range of values for each gene in the population + gene_ranges = [] + for i in range(len(upper_limit)): + gene_ranges.append((lower_limit[i], upper_limit[i])) + + # Initialize the population with initial_values or random values + initial_values = [] + + # If resuming evolution from a previous checkpoint + if opt.resume_evolve is not None: + assert os.path.isfile(ROOT / opt.resume_evolve), 'evolve population path is wrong!' + with open(ROOT / opt.resume_evolve, errors='ignore') as f: + evolve_population = yaml.safe_load(f) + for value in evolve_population.values(): + value = np.array([value[k] for k in hyp_GA.keys()]) + initial_values.append(list(value)) + + # If not resuming from a previous checkpoint, generate initial values from .yaml files in opt.evolve_population + else: + yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith('.yaml')] + for file_name in yaml_files: + with open(os.path.join(opt.evolve_population, file_name)) as yaml_file: + value = yaml.safe_load(yaml_file) + value = np.array([value[k] for k in hyp_GA.keys()]) + initial_values.append(list(value)) + + # Generate random values within the search space for the rest of the population + if (initial_values is None): + population = [generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size)] + else: + if (pop_size > 1): + population = [ + generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size - len(initial_values))] + for initial_value in initial_values: + population = [initial_value] + population + + # Run the genetic algorithm for a fixed number of generations + list_keys = list(hyp_GA.keys()) + for generation in range(opt.evolve): + if (generation >= 1): + save_dict = {} + for i in range(len(population)): + little_dict = {} + for j in range(len(population[i])): + little_dict[list_keys[j]] = float(population[i][j]) + save_dict['gen' + str(generation) + 'number' + str(i)] = little_dict + + with open(save_dir / 'evolve_population.yaml', 'w') as outfile: + yaml.dump(save_dict, outfile, default_flow_style=False) + + # Adaptive elite size + elite_size = min_elite_size + int((max_elite_size - min_elite_size) * (generation / opt.evolve)) + # Evaluate the fitness of each individual in the population + fitness_scores = [] + for individual in population: + for key, value in zip(hyp_GA.keys(), individual): + hyp_GA[key] = value + hyp.update(hyp_GA) + results = train(hyp.copy(), opt, device, callbacks) + callbacks = Callbacks() + # Write mutation results + keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', + 'val/box_loss', 'val/obj_loss', 'val/cls_loss') + print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) + fitness_scores.append(results[2]) + + # Select the fittest individuals for reproduction using adaptive tournament selection + selected_indices = [] + for i in range(pop_size - elite_size): + # Adaptive tournament size + tournament_size = max(max(2, tournament_size_min), + int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10)))) + # Perform tournament selection to choose the best individual + tournament_indices = random.sample(range(pop_size), tournament_size) + tournament_fitness = [fitness_scores[j] for j in tournament_indices] + winner_index = tournament_indices[tournament_fitness.index(max(tournament_fitness))] + selected_indices.append(winner_index) + + # Add the elite individuals to the selected indices + elite_indices = [i for i in range(pop_size) if fitness_scores[i] in sorted(fitness_scores)[-elite_size:]] + selected_indices.extend(elite_indices) + # Create the next generation through crossover and mutation + next_generation = [] + for i in range(pop_size): + parent1_index = selected_indices[random.randint(0, pop_size - 1)] + parent2_index = selected_indices[random.randint(0, pop_size - 1)] + # Adaptive crossover rate + crossover_rate = max(crossover_rate_min, + min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve))) + if random.uniform(0, 1) < crossover_rate: + crossover_point = random.randint(1, len(hyp_GA) - 1) + child = population[parent1_index][:crossover_point] + population[parent2_index][crossover_point:] + else: + child = population[parent1_index] + # Adaptive mutation rate + mutation_rate = max(mutation_rate_min, + min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve))) + for j in range(len(hyp_GA)): + if random.uniform(0, 1) < mutation_rate: + child[j] += random.uniform(-0.1, 0.1) + child[j] = min(max(child[j], gene_ranges[j][0]), gene_ranges[j][1]) + next_generation.append(child) + # Replace the old population with the new generation + population = next_generation + # Print the best solution found + best_index = fitness_scores.index(max(fitness_scores)) + best_individual = population[best_index] + print('Best solution found:', best_individual) # Plot results plot_evolve(evolve_csv) LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' @@ -651,6 +749,14 @@ def main(opt, callbacks=Callbacks()): f'Usage example: $ python train.py --hyp {evolve_yaml}') +def generate_individual(input_ranges, individual_length): + individual = [] + for i in range(individual_length): + lower_bound, upper_bound = input_ranges[i] + individual.append(random.uniform(lower_bound, upper_bound)) + return individual + + def run(**kwargs): # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') opt = parse_opt(True) From 151c953d5e5a744ed90e1ce59bda4cd7ad7f38b0 Mon Sep 17 00:00:00 2001 From: Victor Sonck Date: Wed, 3 Jan 2024 08:14:39 +0100 Subject: [PATCH 56/79] Add segmentation and classification support for ClearML (#10752) * Added ClearML instance segmentation and classification support * Cleaned up ClearML plot output * typos * Log results as plots instead of debug samples * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Glenn Jocher Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- utils/loggers/__init__.py | 49 +++++++++++++++---- utils/loggers/clearml/clearml_utils.py | 65 ++++++++++++++++++++++++-- 2 files changed, 101 insertions(+), 13 deletions(-) diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index aa6eae4c6f8c..381d477d127c 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -173,10 +173,11 @@ def on_pretrain_routine_end(self, labels, names): paths = self.save_dir.glob('*labels*.jpg') # training labels if self.wandb: self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]}) - # if self.clearml: - # pass # ClearML saves these images automatically using hooks if self.comet_logger: self.comet_logger.on_pretrain_routine_end(paths) + if self.clearml: + for path in paths: + self.clearml.log_plot(title=path.stem, plot_path=path) def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): log_dict = dict(zip(self.keys[:3], vals)) @@ -255,9 +256,7 @@ def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): for k, v in x.items(): self.tb.add_scalar(k, v, epoch) elif self.clearml: # log to ClearML if TensorBoard not used - for k, v in x.items(): - title, series = k.split('/') - self.clearml.task.get_logger().report_scalar(title, series, v, epoch) + self.clearml.log_scalars(x, epoch) if self.wandb: if best_fitness == fi: @@ -311,9 +310,10 @@ def on_train_end(self, last, best, epoch, results): self.wandb.finish_run() if self.clearml and not self.opt.evolve: - self.clearml.task.update_output_model(model_path=str(best if best.exists() else last), - name='Best Model', - auto_delete_file=False) + self.clearml.log_summary(dict(zip(self.keys[3:10], results))) + [self.clearml.log_plot(title=f.stem, plot_path=f) for f in files] + self.clearml.log_model(str(best if best.exists() else last), + "Best Model" if best.exists() else "Last Model", epoch) if self.comet_logger: final_results = dict(zip(self.keys[3:10], results)) @@ -325,6 +325,8 @@ def on_params_update(self, params: dict): self.wandb.wandb_run.config.update(params, allow_val_change=True) if self.comet_logger: self.comet_logger.on_params_update(params) + if self.clearml: + self.clearml.task.connect(params) class GenericLogger: @@ -337,7 +339,7 @@ class GenericLogger: include: loggers to include """ - def __init__(self, opt, console_logger, include=('tb', 'wandb')): + def __init__(self, opt, console_logger, include=('tb', 'wandb', 'clearml')): # init default loggers self.save_dir = Path(opt.save_dir) self.include = include @@ -356,6 +358,22 @@ def __init__(self, opt, console_logger, include=('tb', 'wandb')): else: self.wandb = None + if clearml and 'clearml' in self.include: + try: + # Hyp is not available in classification mode + if 'hyp' not in opt: + hyp = {} + else: + hyp = opt.hyp + self.clearml = ClearmlLogger(opt, hyp) + except Exception: + self.clearml = None + prefix = colorstr('ClearML: ') + LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' + f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme') + else: + self.clearml = None + def log_metrics(self, metrics, epoch): # Log metrics dictionary to all loggers if self.csv: @@ -372,6 +390,9 @@ def log_metrics(self, metrics, epoch): if self.wandb: self.wandb.log(metrics, step=epoch) + if self.clearml: + self.clearml.log_scalars(metrics, epoch) + def log_images(self, files, name='Images', epoch=0): # Log images to all loggers files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path @@ -384,6 +405,12 @@ def log_images(self, files, name='Images', epoch=0): if self.wandb: self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) + if self.clearml: + if name == 'Results': + [self.clearml.log_plot(f.stem, f) for f in files] + else: + self.clearml.log_debug_samples(files, title=name) + def log_graph(self, model, imgsz=(640, 640)): # Log model graph to all loggers if self.tb: @@ -395,11 +422,15 @@ def log_model(self, model_path, epoch=0, metadata={}): art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata) art.add_file(str(model_path)) wandb.log_artifact(art) + if self.clearml: + self.clearml.log_model(model_path=model_path, model_name=model_path.stem) def update_params(self, params): # Update the parameters logged if self.wandb: wandb.run.config.update(params, allow_val_change=True) + if self.clearml: + self.clearml.task.connect(params) def log_tensorboard_graph(tb, model, imgsz=(640, 640)): diff --git a/utils/loggers/clearml/clearml_utils.py b/utils/loggers/clearml/clearml_utils.py index 4e999bfee5db..c7627a261186 100644 --- a/utils/loggers/clearml/clearml_utils.py +++ b/utils/loggers/clearml/clearml_utils.py @@ -3,6 +3,8 @@ import re from pathlib import Path +import matplotlib.image as mpimg +import matplotlib.pyplot as plt import numpy as np import yaml from ultralytics.utils.plotting import Annotator, colors @@ -78,18 +80,22 @@ def __init__(self, opt, hyp): # Maximum number of images to log to clearML per epoch self.max_imgs_to_log_per_epoch = 16 # Get the interval of epochs when bounding box images should be logged - self.bbox_interval = opt.bbox_interval + # Only for detection task though! + if 'bbox_interval' in opt: + self.bbox_interval = opt.bbox_interval self.clearml = clearml self.task = None self.data_dict = None if self.clearml: self.task = Task.init( - project_name=opt.project if opt.project != 'runs/train' else 'YOLOv5', + project_name=opt.project if not str(opt.project).startswith('runs/') else 'YOLOv5', task_name=opt.name if opt.name != 'exp' else 'Training', tags=['YOLOv5'], output_uri=True, reuse_last_task_id=opt.exist_ok, - auto_connect_frameworks={'pytorch': False} + auto_connect_frameworks={ + 'pytorch': False, + 'matplotlib': False} # We disconnect pytorch auto-detection, because we added manual model save points in the code ) # ClearML's hooks will already grab all general parameters @@ -112,6 +118,57 @@ def __init__(self, opt, hyp): # to give it to them opt.data = self.data_dict + def log_scalars(self, metrics, epoch): + """ + Log scalars/metrics to ClearML + + arguments: + metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} + epoch (int) iteration number for the current set of metrics + """ + for k, v in metrics.items(): + title, series = k.split('/') + self.task.get_logger().report_scalar(title, series, v, epoch) + + def log_model(self, model_path, model_name, epoch=0): + """ + Log model weights to ClearML + + arguments: + model_path (PosixPath or str) Path to the model weights + model_name (str) Name of the model visible in ClearML + epoch (int) Iteration / epoch of the model weights + """ + self.task.update_output_model(model_path=str(model_path), + name=model_name, + iteration=epoch, + auto_delete_file=False) + + def log_summary(self, metrics): + """ + Log final metrics to a summary table + + arguments: + metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} + """ + for k, v in metrics.items(): + self.task.get_logger().report_single_value(k, v) + + def log_plot(self, title, plot_path): + """ + Log image as plot in the plot section of ClearML + + arguments: + title (str) Title of the plot + plot_path (PosixPath or str) Path to the saved image file + """ + img = mpimg.imread(plot_path) + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[]) # no ticks + ax.imshow(img) + + self.task.get_logger().report_matplotlib_figure(title, "", figure=fig, report_interactive=False) + def log_debug_samples(self, files, title='Debug Samples'): """ Log files (images) as debug samples in the ClearML task. @@ -125,7 +182,7 @@ def log_debug_samples(self, files, title='Debug Samples'): it = re.search(r'_batch(\d+)', f.name) iteration = int(it.groups()[0]) if it else 0 self.task.get_logger().report_image(title=title, - series=f.name.replace(it.group(), ''), + series=f.name.replace(f"_batch{iteration}", ''), local_path=str(f), iteration=iteration) From 46ae996cb174a3e07cee80367fddc07783fd02ff Mon Sep 17 00:00:00 2001 From: davidsvaughn Date: Wed, 3 Jan 2024 02:15:07 -0500 Subject: [PATCH 57/79] Bugfix: update dataloaders.py to fix Multi-GPU DDP RAM multiple-cache issue (#10383) * Update dataloaders.py This is to address (and hopefully fix) this issue: Multi-GPU DDP RAM multiple-cache bug #3818 (https://github.com/ultralytics/yolov5/issues/3818). This was a very serious and "blocking" issue until I could figure out what was going on. The problem was especially bad when running Multi-GPU jobs with 8 GPUs, RAM usage was 8x higher than expected (!), causing repeated OOM failures. Hopefully this fix will help others. DDP causes each RANK to launch it's own process (one for each GPU) with it's own trainloader, and its own RAM image cache. The DistributedSampler used by DDP (https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py) will feed only a subset of images (1/WORLD_SIZE) to each available GPU on each epoch, but since the images are shuffled between epochs, each GPU process must still cache all images. So I created a subclass of DistributedSampler called SmartDistributedSampler that forces each GPU process to always sample the same subset (using modulo arithmetic with RANK and WORLD_SIZE) while still allowing random shuffling between epochs. I don't believe this disrupts the overall "randomness" of the sampling, and I haven't noticed any performance degradation. Signed-off-by: davidsvaughn * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update dataloaders.py move extra parameter (rank) to end so won't mess up pre-existing positional args * Update dataloaders.py removing extra '#' * Update dataloaders.py sample from DDP index array (self.idx) in mixup mosaic * Merging self.indices and self.idx (DDP indices) into single attribute (self.indices). Also adding SmartDistributedSampler to segmentation dataloader * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Multiply GB displayed by WORLD_SIZE --------- Signed-off-by: davidsvaughn Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- utils/dataloaders.py | 51 ++++++++++++++++++++++++++++++------ utils/segment/dataloaders.py | 11 +++++--- 2 files changed, 50 insertions(+), 12 deletions(-) diff --git a/utils/dataloaders.py b/utils/dataloaders.py index 1fbd0361ded4..d422ef0711cb 100644 --- a/utils/dataloaders.py +++ b/utils/dataloaders.py @@ -41,6 +41,7 @@ VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders # Get orientation exif tag @@ -100,6 +101,34 @@ def seed_worker(worker_id): random.seed(worker_seed) +# Inherit from DistributedSampler and override iterator +# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py +class SmartDistributedSampler(distributed.DistributedSampler): + + def __iter__(self): + # deterministically shuffle based on epoch and seed + g = torch.Generator() + g.manual_seed(self.seed + self.epoch) + + # determine the the eventual size (n) of self.indices (DDP indices) + n = int((len(self.dataset) - self.rank - 1) / self.num_replicas) + 1 # num_replicas == WORLD_SIZE + idx = torch.randperm(n, generator=g) + if not self.shuffle: + idx = idx.sort()[0] + + idx = idx.tolist() + if self.drop_last: + idx = idx[:self.num_samples] + else: + padding_size = self.num_samples - len(idx) + if padding_size <= len(idx): + idx += idx[:padding_size] + else: + idx += (idx * math.ceil(padding_size / len(idx)))[:padding_size] + + return iter(idx) + + def create_dataloader(path, imgsz, batch_size, @@ -133,12 +162,13 @@ def create_dataloader(path, stride=int(stride), pad=pad, image_weights=image_weights, - prefix=prefix) + prefix=prefix, + rank=rank) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers - sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates generator = torch.Generator() generator.manual_seed(6148914691236517205 + seed + RANK) @@ -449,7 +479,9 @@ def __init__(self, stride=32, pad=0.0, min_items=0, - prefix=''): + prefix='', + rank=-1, + seed=0): self.img_size = img_size self.augment = augment self.hyp = hyp @@ -527,7 +559,10 @@ def __init__(self, nb = bi[-1] + 1 # number of batches self.batch = bi # batch index of image self.n = n - self.indices = range(n) + self.indices = np.arange(n) + if rank > -1: # DDP indices (see: SmartDistributedSampler) + # force each rank (i.e. GPU process) to sample the same subset of data on every epoch + self.indices = self.indices[np.random.RandomState(seed=seed).permutation(n) % WORLD_SIZE == RANK] # Update labels include_class = [] # filter labels to include only these classes (optional) @@ -576,14 +611,14 @@ def __init__(self, b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes self.im_hw0, self.im_hw = [None] * n, [None] * n fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image - results = ThreadPool(NUM_THREADS).imap(fcn, range(n)) - pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) + results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices) + pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) for i, x in pbar: if cache_images == 'disk': b += self.npy_files[i].stat().st_size else: # 'ram' self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) - b += self.ims[i].nbytes + b += self.ims[i].nbytes * WORLD_SIZE pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})' pbar.close() @@ -663,7 +698,7 @@ def __getitem__(self, index): # MixUp augmentation if random.random() < hyp['mixup']: - img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1))) + img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices))) else: # Load image diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py index 3ee826dba69c..5398617eef68 100644 --- a/utils/segment/dataloaders.py +++ b/utils/segment/dataloaders.py @@ -12,7 +12,7 @@ from torch.utils.data import DataLoader, distributed from ..augmentations import augment_hsv, copy_paste, letterbox -from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker +from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, SmartDistributedSampler, seed_worker from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn from ..torch_utils import torch_distributed_zero_first from .augmentations import mixup, random_perspective @@ -57,12 +57,13 @@ def create_dataloader(path, image_weights=image_weights, prefix=prefix, downsample_ratio=mask_downsample_ratio, - overlap=overlap_mask) + overlap=overlap_mask, + rank=rank) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers - sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates generator = torch.Generator() generator.manual_seed(6148914691236517205 + seed + RANK) @@ -98,9 +99,11 @@ def __init__( prefix='', downsample_ratio=1, overlap=False, + rank=-1, + seed=0, ): super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls, - stride, pad, min_items, prefix) + stride, pad, min_items, prefix, rank, seed) self.downsample_ratio = downsample_ratio self.overlap = overlap From f6a781ac76d00840521d51ed7366733938c2cd7e Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Wed, 3 Jan 2024 12:34:34 +0200 Subject: [PATCH 58/79] Use named imports in models.yolo (#12576) --- models/yolo.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/models/yolo.py b/models/yolo.py index 9cd3cddc25c3..f6cdbcb5d2d8 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -8,12 +8,16 @@ import argparse import contextlib +import math import os import platform import sys from copy import deepcopy from pathlib import Path +import torch +import torch.nn as nn + FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv5 root directory if str(ROOT) not in sys.path: @@ -21,10 +25,12 @@ if platform.system() != 'Windows': ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative -from models.common import * # noqa -from models.experimental import * # noqa +from models.common import (C3, C3SPP, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C3Ghost, C3x, Classify, Concat, + Contract, Conv, CrossConv, DetectMultiBackend, DWConv, DWConvTranspose2d, Expand, Focus, + GhostBottleneck, GhostConv, Proto) +from models.experimental import MixConv2d from utils.autoanchor import check_anchor_order -from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args +from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args from utils.plots import feature_visualization from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device, time_sync) From dfa884f9db1cabbc8787a5862214827bfe3c9ed4 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 3 Jan 2024 11:35:32 +0100 Subject: [PATCH 59/79] Create pyproject.toml (#12577) Signed-off-by: Glenn Jocher --- pyproject.toml | 148 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 148 insertions(+) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000000..0ef1dc43dee4 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,148 @@ +# Ultralyticsv5 YOLO 🚀, AGPL-3.0 license + +# Overview: +# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library. +# It defines essential project metadata, dependencies, and settings used to develop and deploy the library. + +# Key Sections: +# - [build-system]: Specifies the build requirements and backend (e.g., setuptools, wheel). +# - [project]: Includes details like name, version, description, authors, dependencies and more. +# - [project.optional-dependencies]: Provides additional, optional packages for extended features. +# - [tool.*]: Configures settings for various tools (pytest, yapf, etc.) used in the project. + +# Installation: +# The Ultralytics library can be installed using the command: 'pip install ultralytics' +# For development purposes, you can install the package in editable mode with: 'pip install -e .' +# This approach allows for real-time code modifications without the need for re-installation. + +# Documentation: +# For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com + +[build-system] +requires = ["setuptools>=43.0.0", "wheel"] +build-backend = "setuptools.build_meta" + +# Project settings ----------------------------------------------------------------------------------------------------- +[project] +name = "YOLOv5" +description = "Ultralytics YOLOv5 for SOTA object detection, instance segmentation and image classification." +readme = "README.md" +requires-python = ">=3.8" +license = { "text" = "AGPL-3.0" } +keywords = ["machine-learning", "deep-learning", "computer-vision", "ML", "DL", "AI", "YOLO", "YOLOv3", "YOLOv5", "YOLOv8", "HUB", "Ultralytics"] +authors = [ + { name = "Glenn Jocher" }, + { name = "Ayush Chaurasia" }, + { name = "Jing Qiu" } +] +maintainers = [ + { name = "Glenn Jocher" }, + { name = "Ayush Chaurasia" }, + { name = "Jing Qiu" } +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Software Development", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: Image Recognition", + "Operating System :: POSIX :: Linux", + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", +] + +# Required dependencies ------------------------------------------------------------------------------------------------ +dependencies = [ + "matplotlib>=3.3.0", + "numpy>=1.22.2", + "opencv-python>=4.6.0", + "pillow>=7.1.2", + "pyyaml>=5.3.1", + "requests>=2.23.0", + "scipy>=1.4.1", + "torch>=1.8.0", + "torchvision>=0.9.0", + "tqdm>=4.64.0", # progress bars + "psutil", # system utilization + "py-cpuinfo", # display CPU info + "thop>=0.1.1", # FLOPs computation + "pandas>=1.1.4", + "seaborn>=0.11.0", # plotting + "ultralytics>=8.0.232" +] + +# Optional dependencies ------------------------------------------------------------------------------------------------ +[project.optional-dependencies] +dev = [ + "ipython", + "check-manifest", + "pre-commit", + "pytest", + "pytest-cov", + "coverage[toml]", + "mkdocs-material", + "mkdocstrings[python]", + "mkdocs-redirects", # for 301 redirects + "mkdocs-ultralytics-plugin>=0.0.34", # for meta descriptions and images, dates and authors +] +export = [ + "onnx>=1.12.0", # ONNX export + "coremltools>=7.0", # CoreML export + "openvino-dev>=2023.0", # OpenVINO export + "tensorflow<=2.13.1", # TF bug https://github.com/ultralytics/ultralytics/issues/5161 + "jax<=0.4.21", # tensorflowjs bug https://github.com/google/jax/issues/18978 + "jaxlib<=0.4.21", # tensorflowjs bug https://github.com/google/jax/issues/18978 + "tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow +] +# tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos) +# tflite-support # for TFLite model metadata +# scikit-learn==0.19.2 # CoreML quantization +# nvidia-pyindex # TensorRT export +# nvidia-tensorrt # TensorRT export +logging = [ + "comet", # https://docs.ultralytics.com/integrations/comet/ + "tensorboard>=2.13.0", + "dvclive>=2.12.0", +] +extra = [ + "ipython", # interactive notebook + "albumentations>=1.0.3", # training augmentations + "pycocotools>=2.0.6", # COCO mAP +] + +[project.urls] +"Bug Reports" = "https://github.com/ultralytics/yolov5/issues" +"Funding" = "https://ultralytics.com" +"Source" = "https://github.com/ultralytics/yolov5/" + +# Tools settings ------------------------------------------------------------------------------------------------------- +[tool.pytest] +norecursedirs = [".git", "dist", "build"] +addopts = "--doctest-modules --durations=30 --color=yes" + +[tool.isort] +line_length = 120 +multi_line_output = 0 + +[tool.ruff] +line-length = 120 + +[tool.docformatter] +wrap-summaries = 120 +wrap-descriptions = 120 +in-place = true +pre-summary-newline = true +close-quotes-on-newline = true + +[tool.codespell] +ignore-words-list = "crate,nd,strack,dota,ane,segway,fo,gool,winn,commend" +skip = '*.csv,*venv*,docs/??/,docs/mkdocs_??.yml' From c42aba5b2f0a3e8a0004739ff0d5d0f83f288012 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 3 Jan 2024 11:36:55 +0100 Subject: [PATCH 60/79] Delete .pre-commit-config.yaml (#12578) * Delete .pre-commit-config.yaml Signed-off-by: Glenn Jocher * Delete setup.cfg Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher --- .pre-commit-config.yaml | 73 ----------------------------------------- setup.cfg | 56 ------------------------------- 2 files changed, 129 deletions(-) delete mode 100644 .pre-commit-config.yaml delete mode 100644 setup.cfg diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index ab272b2a1477..000000000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,73 +0,0 @@ -# Ultralytics YOLO 🚀, AGPL-3.0 license -# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md - -exclude: 'docs/' -# Define bot property if installed via https://github.com/marketplace/pre-commit-ci -ci: - autofix_prs: true - autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' - autoupdate_schedule: monthly - # submodules: true - -repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.5.0 - hooks: - - id: end-of-file-fixer - - id: trailing-whitespace - - id: check-case-conflict - # - id: check-yaml - - id: check-docstring-first - - id: double-quote-string-fixer - - id: detect-private-key - - - repo: https://github.com/asottile/pyupgrade - rev: v3.15.0 - hooks: - - id: pyupgrade - name: Upgrade code - - - repo: https://github.com/PyCQA/isort - rev: 5.12.0 - hooks: - - id: isort - name: Sort imports - - - repo: https://github.com/google/yapf - rev: v0.40.2 - hooks: - - id: yapf - name: YAPF formatting - - - repo: https://github.com/executablebooks/mdformat - rev: 0.7.17 - hooks: - - id: mdformat - name: MD formatting - additional_dependencies: - - mdformat-gfm - - mdformat-black - # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md" - - - repo: https://github.com/PyCQA/flake8 - rev: 6.1.0 - hooks: - - id: flake8 - name: PEP8 - - - repo: https://github.com/codespell-project/codespell - rev: v2.2.6 - hooks: - - id: codespell - args: - - --ignore-words-list=crate,nd,strack,dota - -# - repo: https://github.com/asottile/yesqa -# rev: v1.4.0 -# hooks: -# - id: yesqa - -# - repo: https://github.com/asottile/dead -# rev: v1.5.0 -# hooks: -# - id: dead diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 2cde6a494836..000000000000 --- a/setup.cfg +++ /dev/null @@ -1,56 +0,0 @@ -# Project-wide configuration file, can be used for package metadata and other toll configurations -# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments -# Local usage: pip install pre-commit, pre-commit run --all-files - -[metadata] -license_files = LICENSE -description_file = README.md - -[tool:pytest] -norecursedirs = - .git - dist - build -addopts = - --doctest-modules - --durations=25 - --color=yes - -[flake8] -max-line-length = 120 -exclude = .tox,*.egg,build,temp -select = E,W,F -doctests = True -verbose = 2 -# https://pep8.readthedocs.io/en/latest/intro.html#error-codes -format = pylint -# see: https://www.flake8rules.com/ -ignore = E731,F405,E402,W504,E501 - # E731: Do not assign a lambda expression, use a def - # F405: name may be undefined, or defined from star imports: module - # E402: module level import not at top of file - # W504: line break after binary operator - # E501: line too long - # removed: - # F401: module imported but unused - # E231: missing whitespace after ‘,’, ‘;’, or ‘:’ - # E127: continuation line over-indented for visual indent - # F403: ‘from module import *’ used; unable to detect undefined names - - -[isort] -# https://pycqa.github.io/isort/docs/configuration/options.html -line_length = 120 -# see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html -multi_line_output = 0 - -[yapf] -based_on_style = pep8 -spaces_before_comment = 2 -COLUMN_LIMIT = 120 -COALESCE_BRACKETS = True -SPACES_AROUND_POWER_OPERATOR = True -SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = True -SPLIT_BEFORE_CLOSING_BRACKET = False -SPLIT_BEFORE_FIRST_ARGUMENT = False -# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False From f56355d3210f3b6448834e4d48fba458b9fcb150 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 7 Jan 2024 01:43:03 +0100 Subject: [PATCH 61/79] Update links.yml (#12588) * Update links.yml Signed-off-by: Glenn Jocher * Update links.yml Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher --- .github/workflows/links.yml | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index 05408fd6227b..1c7b28257d80 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -33,7 +33,15 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 403,429,500,502,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' + command: | + lychee + --accept 403,429,500,502,999 + --exclude-loopback + --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' + --exclude-path '**/ci.yaml' + --github-token ${{ secrets.GITHUB_TOKEN }} + './**/*.md' + './**/*.html' - name: Test Markdown, HTML, YAML, Python and Notebook links with retry if: github.event_name == 'workflow_dispatch' @@ -42,4 +50,16 @@ jobs: timeout_minutes: 5 retry_wait_seconds: 60 max_attempts: 3 - command: lychee --accept 429,999 --exclude-loopback --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' --exclude-path '**/ci.yaml' --exclude-mail --github-token ${{ secrets.GITHUB_TOKEN }} './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb' + command: | + lychee + --accept 429,999 + --exclude-loopback + --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' + --exclude-path '**/ci.yaml' + --github-token ${{ secrets.GITHUB_TOKEN }} + './**/*.md' + './**/*.html' + './**/*.yml' + './**/*.yaml' + './**/*.py' + './**/*.ipynb' From ad97dc6a6d8b4384eb7224e98c8584ecd8bcfd2a Mon Sep 17 00:00:00 2001 From: Paula Derrenger <107626595+pderrenger@users.noreply.github.com> Date: Mon, 8 Jan 2024 01:29:14 +0100 Subject: [PATCH 62/79] Update Actions with Lychee and GitHub Token (#12592) * Add Ultralytics Actions * Auto-format by Ultralytics actions * Update format.yml Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher Co-authored-by: UltralyticsAssistant Co-authored-by: Glenn Jocher --- .github/workflows/format.yml | 25 + benchmarks.py | 116 ++--- classify/predict.py | 127 ++--- classify/train.py | 255 +++++----- classify/val.py | 81 ++-- detect.py | 184 +++---- export.py | 616 +++++++++++++----------- hubconf.py | 72 +-- models/common.py | 316 ++++++------ models/experimental.py | 35 +- models/tf.py | 252 ++++++---- models/yolo.py | 150 ++++-- segment/predict.py | 143 +++--- segment/train.py | 522 +++++++++++--------- segment/val.py | 297 +++++++----- train.py | 595 +++++++++++++---------- utils/__init__.py | 29 +- utils/activations.py | 9 +- utils/augmentations.py | 76 ++- utils/autoanchor.py | 72 +-- utils/autobatch.py | 24 +- utils/aws/resume.py | 16 +- utils/callbacks.py | 63 ++- utils/dataloaders.py | 602 ++++++++++++----------- utils/downloads.py | 91 ++-- utils/flask_rest_api/example_request.py | 12 +- utils/flask_rest_api/restapi.py | 28 +- utils/general.py | 574 +++++++++++++--------- utils/loggers/__init__.py | 195 ++++---- utils/loggers/clearml/clearml_utils.py | 123 ++--- utils/loggers/clearml/hpo.py | 78 +-- utils/loggers/comet/__init__.py | 243 +++++----- utils/loggers/comet/comet_utils.py | 54 +-- utils/loggers/comet/hpo.py | 114 ++--- utils/loggers/wandb/wandb_utils.py | 109 +++-- utils/loss.py | 58 +-- utils/metrics.py | 118 ++--- utils/plots.py | 254 +++++----- utils/segment/augmentations.py | 22 +- utils/segment/dataloaders.py | 149 +++--- utils/segment/general.py | 19 +- utils/segment/loss.py | 46 +- utils/segment/metrics.py | 156 +++--- utils/segment/plots.py | 39 +- utils/torch_utils.py | 230 +++++---- utils/triton.py | 43 +- val.py | 269 ++++++----- 47 files changed, 4228 insertions(+), 3473 deletions(-) create mode 100644 .github/workflows/format.yml diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml new file mode 100644 index 000000000000..8d623f4f6e7e --- /dev/null +++ b/.github/workflows/format.yml @@ -0,0 +1,25 @@ +# Ultralytics 🚀 - AGPL-3.0 license +# Ultralytics Actions https://github.com/ultralytics/actions +# This workflow automatically formats code and documentation in PRs to official Ultralytics standards + +name: Ultralytics Actions + +on: + push: + branches: [main,master] + pull_request_target: + branches: [main,master] + +jobs: + format: + runs-on: ubuntu-latest + steps: + - name: Run Ultralytics Formatting + uses: ultralytics/actions@main + with: + token: ${{ secrets.GITHUB_TOKEN }} # automatically generated + python: true + docstrings: true + markdown: true + spelling: true + links: true diff --git a/benchmarks.py b/benchmarks.py index b590ff63cb01..09e82e588a2a 100644 --- a/benchmarks.py +++ b/benchmarks.py @@ -1,6 +1,6 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -Run YOLOv5 benchmarks on all supported export formats +Run YOLOv5 benchmarks on all supported export formats. Format | `export.py --include` | Model --- | --- | --- @@ -50,115 +50,115 @@ def run( - weights=ROOT / 'yolov5s.pt', # weights path - imgsz=640, # inference size (pixels) - batch_size=1, # batch size - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - half=False, # use FP16 half-precision inference - test=False, # test exports only - pt_only=False, # test PyTorch only - hard_fail=False, # throw error on benchmark failure + weights=ROOT / "yolov5s.pt", # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / "data/coco128.yaml", # dataset.yaml path + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure ): y, t = [], time.time() device = select_device(device) model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc. for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU) try: - assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported - assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML - if 'cpu' in device.type: - assert cpu, 'inference not supported on CPU' - if 'cuda' in device.type: - assert gpu, 'inference not supported on GPU' + assert i not in (9, 10), "inference not supported" # Edge TPU and TF.js are unsupported + assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13" # CoreML + if "cpu" in device.type: + assert cpu, "inference not supported on CPU" + if "cuda" in device.type: + assert gpu, "inference not supported on GPU" # Export - if f == '-': + if f == "-": w = weights # PyTorch format else: - w = export.run(weights=weights, - imgsz=[imgsz], - include=[f], - batch_size=batch_size, - device=device, - half=half)[-1] # all others - assert suffix in str(w), 'export failed' + w = export.run( + weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half + )[-1] # all others + assert suffix in str(w), "export failed" # Validate if model_type == SegmentationModel: - result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half) metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls)) else: # DetectionModel: - result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half) metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls)) speed = result[2][1] # times (preprocess, inference, postprocess) y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference except Exception as e: if hard_fail: - assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}' - LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}') + assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}" + LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}") y.append([name, None, None, None]) # mAP, t_inference if pt_only and i == 0: break # break after PyTorch # Print results - LOGGER.info('\n') + LOGGER.info("\n") parse_opt() notebook_init() # print system info - c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', ''] + c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""] py = pd.DataFrame(y, columns=c) - LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') + LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)") LOGGER.info(str(py if map else py.iloc[:, :2])) if hard_fail and isinstance(hard_fail, str): - metrics = py['mAP50-95'].array # values to compare to floor + metrics = py["mAP50-95"].array # values to compare to floor floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n - assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}' + assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}" return py def test( - weights=ROOT / 'yolov5s.pt', # weights path - imgsz=640, # inference size (pixels) - batch_size=1, # batch size - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - half=False, # use FP16 half-precision inference - test=False, # test exports only - pt_only=False, # test PyTorch only - hard_fail=False, # throw error on benchmark failure + weights=ROOT / "yolov5s.pt", # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / "data/coco128.yaml", # dataset.yaml path + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure ): y, t = [], time.time() device = select_device(device) for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable) try: - w = weights if f == '-' else \ - export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights - assert suffix in str(w), 'export failed' + w = ( + weights + if f == "-" + else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] + ) # weights + assert suffix in str(w), "export failed" y.append([name, True]) except Exception: y.append([name, False]) # mAP, t_inference # Print results - LOGGER.info('\n') + LOGGER.info("\n") parse_opt() notebook_init() # print system info - py = pd.DataFrame(y, columns=['Format', 'Export']) - LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)') + py = pd.DataFrame(y, columns=["Format", "Export"]) + LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)") LOGGER.info(str(py)) return py def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--test', action='store_true', help='test exports only') - parser.add_argument('--pt-only', action='store_true', help='test PyTorch only') - parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric') + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") + parser.add_argument("--batch-size", type=int, default=1, help="batch size") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--test", action="store_true", help="test exports only") + parser.add_argument("--pt-only", action="store_true", help="test PyTorch only") + parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric") opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML print_args(vars(opt)) @@ -169,6 +169,6 @@ def main(opt): test(**vars(opt)) if opt.test else run(**vars(opt)) -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/classify/predict.py b/classify/predict.py index b056a0cd707b..b7d2f05d7bce 100644 --- a/classify/predict.py +++ b/classify/predict.py @@ -48,43 +48,54 @@ from models.common import DetectMultiBackend from utils.augmentations import classify_transforms from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams -from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, - increment_path, print_args, strip_optimizer) +from utils.general import ( + LOGGER, + Profile, + check_file, + check_img_size, + check_imshow, + check_requirements, + colorstr, + cv2, + increment_path, + print_args, + strip_optimizer, +) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( - weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) - source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - imgsz=(224, 224), # inference size (height, width) - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - view_img=False, # show results - save_txt=False, # save results to *.txt - nosave=False, # do not save images/videos - augment=False, # augmented inference - visualize=False, # visualize features - update=False, # update all models - project=ROOT / 'runs/predict-cls', # save results to project/name - name='exp', # save results to project/name - exist_ok=False, # existing project/name ok, do not increment - half=False, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - vid_stride=1, # video frame-rate stride + weights=ROOT / "yolov5s-cls.pt", # model.pt path(s) + source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) + data=ROOT / "data/coco128.yaml", # dataset.yaml path + imgsz=(224, 224), # inference size (height, width) + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + nosave=False, # do not save images/videos + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / "runs/predict-cls", # save results to project/name + name="exp", # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride ): source = str(source) - save_img = not nosave and not source.endswith('.txt') # save inference images + save_img = not nosave and not source.endswith(".txt") # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) - is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) - webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) - screenshot = source.lower().startswith('screen') + is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) + webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) + screenshot = source.lower().startswith("screen") if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) @@ -127,15 +138,15 @@ def run( seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count - s += f'{i}: ' + s += f"{i}: " else: - p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt - s += '%gx%g ' % im.shape[2:] # print string + s += "%gx%g " % im.shape[2:] # print string annotator = Annotator(im0, example=str(names), pil=True) # Print results @@ -143,17 +154,17 @@ def run( s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, " # Write results - text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i) + text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i) if save_img or view_img: # Add bbox to image annotator.text([32, 32], text, txt_color=(255, 255, 255)) if save_txt: # Write to file - with open(f'{txt_path}.txt', 'a') as f: - f.write(text + '\n') + with open(f"{txt_path}.txt", "a") as f: + f.write(text + "\n") # Stream results im0 = annotator.result() if view_img: - if platform.system() == 'Linux' and p not in windows: + if platform.system() == "Linux" and p not in windows: windows.append(p) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) @@ -162,7 +173,7 @@ def run( # Save results (image with detections) if save_img: - if dataset.mode == 'image': + if dataset.mode == "image": cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video @@ -175,18 +186,18 @@ def run( h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos - vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) vid_writer[i].write(im0) # Print time (inference-only) - LOGGER.info(f'{s}{dt[1].dt * 1E3:.1f}ms') + LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms") # Print results - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) @@ -194,23 +205,23 @@ def run( def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='show results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--visualize', action='store_true', help='visualize features') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model path(s)") + parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[224], help="inference size h,w") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--view-img", action="store_true", help="show results") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--nosave", action="store_true", help="do not save images/videos") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--visualize", action="store_true", help="visualize features") + parser.add_argument("--update", action="store_true", help="update all models") + parser.add_argument("--project", default=ROOT / "runs/predict-cls", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save results to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) @@ -218,10 +229,10 @@ def parse_opt(): def main(opt): - check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/classify/train.py b/classify/train.py index ecbea1d8c0de..63befed0f780 100644 --- a/classify/train.py +++ b/classify/train.py @@ -1,6 +1,6 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -Train a YOLOv5 classifier model on a classification dataset +Train a YOLOv5 classifier model on a classification dataset. Usage - Single-GPU training: $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 @@ -40,33 +40,61 @@ from models.experimental import attempt_load from models.yolo import ClassificationModel, DetectionModel from utils.dataloaders import create_classification_dataloader -from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_info, check_git_status, - check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save) +from utils.general import ( + DATASETS_DIR, + LOGGER, + TQDM_BAR_FORMAT, + WorkingDirectory, + check_git_info, + check_git_status, + check_requirements, + colorstr, + download, + increment_path, + init_seeds, + print_args, + yaml_save, +) from utils.loggers import GenericLogger from utils.plots import imshow_cls -from utils.torch_utils import (ModelEMA, de_parallel, model_info, reshape_classifier_output, select_device, smart_DDP, - smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first) - -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +from utils.torch_utils import ( + ModelEMA, + de_parallel, + model_info, + reshape_classifier_output, + select_device, + smart_DDP, + smart_optimizer, + smartCrossEntropyLoss, + torch_distributed_zero_first, +) + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) GIT_INFO = check_git_info() def train(opt, device): init_seeds(opt.seed + 1 + RANK, deterministic=True) - save_dir, data, bs, epochs, nw, imgsz, pretrained = \ - opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \ - opt.imgsz, str(opt.pretrained).lower() == 'true' - cuda = device.type != 'cpu' + save_dir, data, bs, epochs, nw, imgsz, pretrained = ( + opt.save_dir, + Path(opt.data), + opt.batch_size, + opt.epochs, + min(os.cpu_count() - 1, opt.workers), + opt.imgsz, + str(opt.pretrained).lower() == "true", + ) + cuda = device.type != "cpu" # Directories - wdir = save_dir / 'weights' + wdir = save_dir / "weights" wdir.mkdir(parents=True, exist_ok=True) # make dir - last, best = wdir / 'last.pt', wdir / 'best.pt' + last, best = wdir / "last.pt", wdir / "best.pt" # Save run settings - yaml_save(save_dir / 'opt.yaml', vars(opt)) + yaml_save(save_dir / "opt.yaml", vars(opt)) # Logger logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None @@ -75,51 +103,55 @@ def train(opt, device): with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): data_dir = data if data.is_dir() else (DATASETS_DIR / data) if not data_dir.is_dir(): - LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...') + LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...") t = time.time() - if str(data) == 'imagenet': - subprocess.run(['bash', str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True) + if str(data) == "imagenet": + subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True) else: - url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip' + url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip" download(url, dir=data_dir.parent) s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n" LOGGER.info(s) # Dataloaders - nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes - trainloader = create_classification_dataloader(path=data_dir / 'train', - imgsz=imgsz, - batch_size=bs // WORLD_SIZE, - augment=True, - cache=opt.cache, - rank=LOCAL_RANK, - workers=nw) - - test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val + nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()]) # number of classes + trainloader = create_classification_dataloader( + path=data_dir / "train", + imgsz=imgsz, + batch_size=bs // WORLD_SIZE, + augment=True, + cache=opt.cache, + rank=LOCAL_RANK, + workers=nw, + ) + + test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val" # data/test or data/val if RANK in {-1, 0}: - testloader = create_classification_dataloader(path=test_dir, - imgsz=imgsz, - batch_size=bs // WORLD_SIZE * 2, - augment=False, - cache=opt.cache, - rank=-1, - workers=nw) + testloader = create_classification_dataloader( + path=test_dir, + imgsz=imgsz, + batch_size=bs // WORLD_SIZE * 2, + augment=False, + cache=opt.cache, + rank=-1, + workers=nw, + ) # Model with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): - if Path(opt.model).is_file() or opt.model.endswith('.pt'): - model = attempt_load(opt.model, device='cpu', fuse=False) + if Path(opt.model).is_file() or opt.model.endswith(".pt"): + model = attempt_load(opt.model, device="cpu", fuse=False) elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0 - model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None) + model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None) else: - m = hub.list('ultralytics/yolov5') # + hub.list('pytorch/vision') # models - raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m)) + m = hub.list("ultralytics/yolov5") # + hub.list('pytorch/vision') # models + raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m)) if isinstance(model, DetectionModel): LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'") model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model reshape_classifier_output(model, nc) # update class count for m in model.modules(): - if not pretrained and hasattr(m, 'reset_parameters'): + if not pretrained and hasattr(m, "reset_parameters"): m.reset_parameters() if isinstance(m, torch.nn.Dropout) and opt.dropout is not None: m.p = opt.dropout # set dropout @@ -135,8 +167,8 @@ def train(opt, device): if opt.verbose: LOGGER.info(model) images, labels = next(iter(trainloader)) - file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg') - logger.log_images(file, name='Train Examples') + file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg") + logger.log_images(file, name="Train Examples") logger.log_graph(model, imgsz) # log model # Optimizer @@ -163,11 +195,13 @@ def train(opt, device): best_fitness = 0.0 scaler = amp.GradScaler(enabled=cuda) val = test_dir.stem # 'val' or 'test' - LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n' - f'Using {nw * WORLD_SIZE} dataloader workers\n' - f"Logging results to {colorstr('bold', save_dir)}\n" - f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n' - f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}") + LOGGER.info( + f'Image sizes {imgsz} train, {imgsz} test\n' + f'Using {nw * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n' + f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}" + ) for epoch in range(epochs): # loop over the dataset multiple times tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness model.train() @@ -198,15 +232,14 @@ def train(opt, device): if RANK in {-1, 0}: # Print tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses - mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) - pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36 + mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0) # (GB) + pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36 # Test if i == len(pbar) - 1: # last batch - top1, top5, vloss = validate.run(model=ema.ema, - dataloader=testloader, - criterion=criterion, - pbar=pbar) # test accuracy, loss + top1, top5, vloss = validate.run( + model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar + ) # test accuracy, loss fitness = top1 # define fitness as top1 accuracy # Scheduler @@ -220,26 +253,28 @@ def train(opt, device): # Log metrics = { - 'train/loss': tloss, - f'{val}/loss': vloss, - 'metrics/accuracy_top1': top1, - 'metrics/accuracy_top5': top5, - 'lr/0': optimizer.param_groups[0]['lr']} # learning rate + "train/loss": tloss, + f"{val}/loss": vloss, + "metrics/accuracy_top1": top1, + "metrics/accuracy_top5": top5, + "lr/0": optimizer.param_groups[0]["lr"], + } # learning rate logger.log_metrics(metrics, epoch) # Save model final_epoch = epoch + 1 == epochs if (not opt.nosave) or final_epoch: ckpt = { - 'epoch': epoch, - 'best_fitness': best_fitness, - 'model': deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(), - 'ema': None, # deepcopy(ema.ema).half(), - 'updates': ema.updates, - 'optimizer': None, # optimizer.state_dict(), - 'opt': vars(opt), - 'git': GIT_INFO, # {remote, branch, commit} if a git repo - 'date': datetime.now().isoformat()} + "epoch": epoch, + "best_fitness": best_fitness, + "model": deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(), + "ema": None, # deepcopy(ema.ema).half(), + "updates": ema.updates, + "optimizer": None, # optimizer.state_dict(), + "opt": vars(opt), + "git": GIT_INFO, # {remote, branch, commit} if a git repo + "date": datetime.now().isoformat(), + } # Save last, best and delete torch.save(ckpt, last) @@ -249,49 +284,51 @@ def train(opt, device): # Train complete if RANK in {-1, 0} and final_epoch: - LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)' - f"\nResults saved to {colorstr('bold', save_dir)}" - f'\nPredict: python classify/predict.py --weights {best} --source im.jpg' - f'\nValidate: python classify/val.py --weights {best} --data {data_dir}' - f'\nExport: python export.py --weights {best} --include onnx' - f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')" - f'\nVisualize: https://netron.app\n') + LOGGER.info( + f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)' + f"\nResults saved to {colorstr('bold', save_dir)}" + f'\nPredict: python classify/predict.py --weights {best} --source im.jpg' + f'\nValidate: python classify/val.py --weights {best} --data {data_dir}' + f'\nExport: python export.py --weights {best} --include onnx' + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')" + f'\nVisualize: https://netron.app\n' + ) # Plot examples images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels pred = torch.max(ema.ema(images.to(device)), 1)[1] - file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / 'test_images.jpg') + file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg") # Log results - meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()} - logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch) + meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()} + logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch) logger.log_model(best, epochs, metadata=meta) def parse_opt(known=False): parser = argparse.ArgumentParser() - parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path') - parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...') - parser.add_argument('--epochs', type=int, default=10, help='total training epochs') - parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)') - parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--project', default=ROOT / 'runs/train-cls', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False') - parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer') - parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate') - parser.add_argument('--decay', type=float, default=5e-5, help='weight decay') - parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon') - parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head') - parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)') - parser.add_argument('--verbose', action='store_true', help='Verbose mode') - parser.add_argument('--seed', type=int, default=0, help='Global training seed') - parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path") + parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...") + parser.add_argument("--epochs", type=int, default=10, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"') + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False") + parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer") + parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate") + parser.add_argument("--decay", type=float, default=5e-5, help="weight decay") + parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon") + parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head") + parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)") + parser.add_argument("--verbose", action="store_true", help="Verbose mode") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") return parser.parse_known_args()[0] if known else parser.parse_args() @@ -300,17 +337,17 @@ def main(opt): if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() - check_requirements(ROOT / 'requirements.txt') + check_requirements(ROOT / "requirements.txt") # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: - assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size' - assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' - assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size" + assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" + assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" torch.cuda.set_device(LOCAL_RANK) - device = torch.device('cuda', LOCAL_RANK) - dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + device = torch.device("cuda", LOCAL_RANK) + dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Parameters opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run @@ -328,6 +365,6 @@ def run(**kwargs): return opt -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/classify/val.py b/classify/val.py index 6814c4d780e1..b170253d6e0c 100644 --- a/classify/val.py +++ b/classify/val.py @@ -1,6 +1,6 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -Validate a trained YOLOv5 classification model on a classification dataset +Validate a trained YOLOv5 classification model on a classification dataset. Usage: $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) @@ -36,22 +36,30 @@ from models.common import DetectMultiBackend from utils.dataloaders import create_classification_dataloader -from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr, - increment_path, print_args) +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + Profile, + check_img_size, + check_requirements, + colorstr, + increment_path, + print_args, +) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( - data=ROOT / '../datasets/mnist', # dataset dir - weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) + data=ROOT / "../datasets/mnist", # dataset dir + weights=ROOT / "yolov5s-cls.pt", # model.pt path(s) batch_size=128, # batch size imgsz=224, # inference size (pixels) - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu workers=8, # max dataloader workers (per RANK in DDP mode) verbose=False, # verbose output - project=ROOT / 'runs/val-cls', # save to project/name - name='exp', # save to project/name + project=ROOT / "runs/val-cls", # save to project/name + name="exp", # save to project/name exist_ok=False, # existing project/name ok, do not increment half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference @@ -64,7 +72,7 @@ def run( training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model - half &= device.type != 'cpu' # half precision only supported on CUDA + half &= device.type != "cpu" # half precision only supported on CUDA model.half() if half else model.float() else: # called directly device = select_device(device, batch_size=batch_size) @@ -84,25 +92,22 @@ def run( device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 - LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") # Dataloader data = Path(data) - test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val - dataloader = create_classification_dataloader(path=test_dir, - imgsz=imgsz, - batch_size=batch_size, - augment=False, - rank=-1, - workers=workers) + test_dir = data / "test" if (data / "test").exists() else data / "val" # data/test or data/val + dataloader = create_classification_dataloader( + path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers + ) model.eval() pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device)) n = len(dataloader) # number of batches - action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing' - desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}' + action = "validating" if dataloader.dataset.root.stem == "val" else "testing" + desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}" bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0) - with torch.cuda.amp.autocast(enabled=device.type != 'cpu'): + with torch.cuda.amp.autocast(enabled=device.type != "cpu"): for images, labels in bar: with dt[0]: images, labels = images.to(device, non_blocking=True), labels.to(device) @@ -123,19 +128,19 @@ def run( top1, top5 = acc.mean(0).tolist() if pbar: - pbar.desc = f'{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}' + pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}" if verbose: # all classes LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}") LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}") for i, c in model.names.items(): acc_i = acc[targets == i] top1i, top5i = acc_i.mean(0).tolist() - LOGGER.info(f'{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}') + LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}") # Print results - t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image + t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt) # speeds per image shape = (1, 3, imgsz, imgsz) - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t) + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t) LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") return top1, top5, loss @@ -143,28 +148,28 @@ def run( def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path') - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)') - parser.add_argument('--batch-size', type=int, default=128, help='batch size') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output') - parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)") + parser.add_argument("--batch-size", type=int, default=128, help="batch size") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output") + parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") opt = parser.parse_args() print_args(vars(opt)) return opt def main(opt): - check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/detect.py b/detect.py index 03bc29de999e..b7d77ef431d4 100644 --- a/detect.py +++ b/detect.py @@ -47,54 +47,68 @@ from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams -from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, - increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) +from utils.general import ( + LOGGER, + Profile, + check_file, + check_img_size, + check_imshow, + check_requirements, + colorstr, + cv2, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + strip_optimizer, + xyxy2xywh, +) from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( - weights=ROOT / 'yolov5s.pt', # model path or triton URL - source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - imgsz=(640, 640), # inference size (height, width) - conf_thres=0.25, # confidence threshold - iou_thres=0.45, # NMS IOU threshold - max_det=1000, # maximum detections per image - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - view_img=False, # show results - save_txt=False, # save results to *.txt - save_csv=False, # save results in CSV format - save_conf=False, # save confidences in --save-txt labels - save_crop=False, # save cropped prediction boxes - nosave=False, # do not save images/videos - classes=None, # filter by class: --class 0, or --class 0 2 3 - agnostic_nms=False, # class-agnostic NMS - augment=False, # augmented inference - visualize=False, # visualize features - update=False, # update all models - project=ROOT / 'runs/detect', # save results to project/name - name='exp', # save results to project/name - exist_ok=False, # existing project/name ok, do not increment - line_thickness=3, # bounding box thickness (pixels) - hide_labels=False, # hide labels - hide_conf=False, # hide confidences - half=False, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - vid_stride=1, # video frame-rate stride + weights=ROOT / "yolov5s.pt", # model path or triton URL + source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) + data=ROOT / "data/coco128.yaml", # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_csv=False, # save results in CSV format + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / "runs/detect", # save results to project/name + name="exp", # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride ): source = str(source) - save_img = not nosave and not source.endswith('.txt') # save inference images + save_img = not nosave and not source.endswith(".txt") # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) - is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) - webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) - screenshot = source.lower().startswith('screen') + is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) + webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) + screenshot = source.lower().startswith("screen") if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) @@ -148,12 +162,12 @@ def run( # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Define the path for the CSV file - csv_path = save_dir / 'predictions.csv' + csv_path = save_dir / "predictions.csv" # Create or append to the CSV file def write_to_csv(image_name, prediction, confidence): - data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence} - with open(csv_path, mode='a', newline='') as f: + data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence} + with open(csv_path, mode="a", newline="") as f: writer = csv.DictWriter(f, fieldnames=data.keys()) if not csv_path.is_file(): writer.writeheader() @@ -164,14 +178,14 @@ def write_to_csv(image_name, prediction, confidence): seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count - s += f'{i}: ' + s += f"{i}: " else: - p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt - s += '%gx%g ' % im.shape[2:] # print string + txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt + s += "%gx%g " % im.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) @@ -187,9 +201,9 @@ def write_to_csv(image_name, prediction, confidence): # Write results for *xyxy, conf, cls in reversed(det): c = int(cls) # integer class - label = names[c] if hide_conf else f'{names[c]}' + label = names[c] if hide_conf else f"{names[c]}" confidence = float(conf) - confidence_str = f'{confidence:.2f}' + confidence_str = f"{confidence:.2f}" if save_csv: write_to_csv(p.name, label, confidence_str) @@ -197,20 +211,20 @@ def write_to_csv(image_name, prediction, confidence): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format - with open(f'{txt_path}.txt', 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') + with open(f"{txt_path}.txt", "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") if save_img or save_crop or view_img: # Add bbox to image c = int(cls) # integer class - label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}") annotator.box_label(xyxy, label, color=colors(c, True)) if save_crop: - save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) # Stream results im0 = annotator.result() if view_img: - if platform.system() == 'Linux' and p not in windows: + if platform.system() == "Linux" and p not in windows: windows.append(p) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) @@ -219,7 +233,7 @@ def write_to_csv(image_name, prediction, confidence): # Save results (image with detections) if save_img: - if dataset.mode == 'image': + if dataset.mode == "image": cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video @@ -232,18 +246,18 @@ def write_to_csv(image_name, prediction, confidence): h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos - vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) vid_writer[i].write(im0) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") # Print results - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) @@ -251,34 +265,34 @@ def write_to_csv(image_name, prediction, confidence): def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='show results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-csv', action='store_true', help='save results in CSV format') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') - parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--visualize', action='store_true', help='visualize features') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') - parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') - parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL") + parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") + parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--view-img", action="store_true", help="show results") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-csv", action="store_true", help="save results in CSV format") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes") + parser.add_argument("--nosave", action="store_true", help="do not save images/videos") + parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3") + parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--visualize", action="store_true", help="visualize features") + parser.add_argument("--update", action="store_true", help="update all models") + parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save results to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)") + parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels") + parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) @@ -286,10 +300,10 @@ def parse_opt(): def main(opt): - check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/export.py b/export.py index d550a85fd99f..74701c37a947 100644 --- a/export.py +++ b/export.py @@ -64,30 +64,42 @@ ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH -if platform.system() != 'Windows': +if platform.system() != "Windows": ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.experimental import attempt_load from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel from utils.dataloaders import LoadImages -from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version, - check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save) +from utils.general import ( + LOGGER, + Profile, + check_dataset, + check_img_size, + check_requirements, + check_version, + check_yaml, + colorstr, + file_size, + get_default_args, + print_args, + url2file, + yaml_save, +) from utils.torch_utils import select_device, smart_inference_mode -MACOS = platform.system() == 'Darwin' # macOS environment +MACOS = platform.system() == "Darwin" # macOS environment class iOSModel(torch.nn.Module): - def __init__(self, model, im): super().__init__() b, c, h, w = im.shape # batch, channel, height, width self.model = model self.nc = model.nc # number of classes if w == h: - self.normalize = 1. / w + self.normalize = 1.0 / w else: - self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]) # broadcast (slower, smaller) + self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller) # np = model(im)[0].shape[1] # number of points # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4) # explicit (faster, larger) @@ -99,19 +111,20 @@ def forward(self, x): def export_formats(): # YOLOv5 export formats x = [ - ['PyTorch', '-', '.pt', True, True], - ['TorchScript', 'torchscript', '.torchscript', True, True], - ['ONNX', 'onnx', '.onnx', True, True], - ['OpenVINO', 'openvino', '_openvino_model', True, False], - ['TensorRT', 'engine', '.engine', False, True], - ['CoreML', 'coreml', '.mlmodel', True, False], - ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True], - ['TensorFlow GraphDef', 'pb', '.pb', True, True], - ['TensorFlow Lite', 'tflite', '.tflite', True, False], - ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False], - ['TensorFlow.js', 'tfjs', '_web_model', False, False], - ['PaddlePaddle', 'paddle', '_paddle_model', True, True], ] - return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU']) + ["PyTorch", "-", ".pt", True, True], + ["TorchScript", "torchscript", ".torchscript", True, True], + ["ONNX", "onnx", ".onnx", True, True], + ["OpenVINO", "openvino", "_openvino_model", True, False], + ["TensorRT", "engine", ".engine", False, True], + ["CoreML", "coreml", ".mlmodel", True, False], + ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True], + ["TensorFlow GraphDef", "pb", ".pb", True, True], + ["TensorFlow Lite", "tflite", ".tflite", True, False], + ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False], + ["TensorFlow.js", "tfjs", "_web_model", False, False], + ["PaddlePaddle", "paddle", "_paddle_model", True, True], + ] + return pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"]) def try_export(inner_func): @@ -119,28 +132,28 @@ def try_export(inner_func): inner_args = get_default_args(inner_func) def outer_func(*args, **kwargs): - prefix = inner_args['prefix'] + prefix = inner_args["prefix"] try: with Profile() as dt: f, model = inner_func(*args, **kwargs) - LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)') + LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)") return f, model except Exception as e: - LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}') + LOGGER.info(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}") return None, None return outer_func @try_export -def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')): +def export_torchscript(model, im, file, optimize, prefix=colorstr("TorchScript:")): # YOLOv5 TorchScript model export - LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...') - f = file.with_suffix('.torchscript') + LOGGER.info(f"\n{prefix} starting export with torch {torch.__version__}...") + f = file.with_suffix(".torchscript") ts = torch.jit.trace(model, im, strict=False) - d = {'shape': im.shape, 'stride': int(max(model.stride)), 'names': model.names} - extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap() + d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names} + extra_files = {"config.txt": json.dumps(d)} # torch._C.ExtraFilesMap() if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) else: @@ -149,22 +162,22 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:' @try_export -def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')): +def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")): # YOLOv5 ONNX export - check_requirements('onnx>=1.12.0') + check_requirements("onnx>=1.12.0") import onnx - LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...') - f = str(file.with_suffix('.onnx')) + LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...") + f = str(file.with_suffix(".onnx")) - output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0'] + output_names = ["output0", "output1"] if isinstance(model, SegmentationModel) else ["output0"] if dynamic: - dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640) + dynamic = {"images": {0: "batch", 2: "height", 3: "width"}} # shape(1,3,640,640) if isinstance(model, SegmentationModel): - dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85) - dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160) + dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) + dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"} # shape(1,32,160,160) elif isinstance(model, DetectionModel): - dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85) + dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) torch.onnx.export( model.cpu() if dynamic else model, # --dynamic only compatible with cpu @@ -173,16 +186,17 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX verbose=False, opset_version=opset, do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False - input_names=['images'], + input_names=["images"], output_names=output_names, - dynamic_axes=dynamic or None) + dynamic_axes=dynamic or None, + ) # Checks model_onnx = onnx.load(f) # load onnx model onnx.checker.check_model(model_onnx) # check onnx model # Metadata - d = {'stride': int(max(model.stride)), 'names': model.names} + d = {"stride": int(max(model.stride)), "names": model.names} for k, v in d.items(): meta = model_onnx.metadata_props.add() meta.key, meta.value = k, str(v) @@ -192,36 +206,37 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX if simplify: try: cuda = torch.cuda.is_available() - check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1')) + check_requirements(("onnxruntime-gpu" if cuda else "onnxruntime", "onnx-simplifier>=0.4.1")) import onnxsim - LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') + LOGGER.info(f"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...") model_onnx, check = onnxsim.simplify(model_onnx) - assert check, 'assert check failed' + assert check, "assert check failed" onnx.save(model_onnx, f) except Exception as e: - LOGGER.info(f'{prefix} simplifier failure: {e}') + LOGGER.info(f"{prefix} simplifier failure: {e}") return f, model_onnx @try_export -def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:')): +def export_openvino(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:")): # YOLOv5 OpenVINO export - check_requirements('openvino-dev>=2023.0') # requires openvino-dev: https://pypi.org/project/openvino-dev/ + check_requirements("openvino-dev>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ import openvino.runtime as ov # noqa from openvino.tools import mo # noqa - LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...') - f = str(file).replace(file.suffix, f'_openvino_model{os.sep}') - f_onnx = file.with_suffix('.onnx') - f_ov = str(Path(f) / file.with_suffix('.xml').name) + LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...") + f = str(file).replace(file.suffix, f"_openvino_model{os.sep}") + f_onnx = file.with_suffix(".onnx") + f_ov = str(Path(f) / file.with_suffix(".xml").name) if int8: - check_requirements('nncf>=2.4.0') # requires at least version 2.4.0 to use the post-training quantization + check_requirements("nncf>=2.4.0") # requires at least version 2.4.0 to use the post-training quantization import nncf import numpy as np from openvino.runtime import Core from utils.dataloaders import create_dataloader + core = Core() onnx_model = core.read_model(f_onnx) # export @@ -233,24 +248,21 @@ def prepare_input_tensor(image: np.ndarray): input_tensor = np.expand_dims(input_tensor, 0) return input_tensor - def gen_dataloader(yaml_path, task='train', imgsz=640, workers=4): + def gen_dataloader(yaml_path, task="train", imgsz=640, workers=4): data_yaml = check_yaml(yaml_path) data = check_dataset(data_yaml) - dataloader = create_dataloader(data[task], - imgsz=imgsz, - batch_size=1, - stride=32, - pad=0.5, - single_cls=False, - rect=False, - workers=workers)[0] + dataloader = create_dataloader( + data[task], imgsz=imgsz, batch_size=1, stride=32, pad=0.5, single_cls=False, rect=False, workers=workers + )[0] return dataloader # noqa: F811 def transform_fn(data_item): """ - Quantization transform function. Extracts and preprocess input data from dataloader item for quantization. + Quantization transform function. + + Extracts and preprocess input data from dataloader item for quantization. Parameters: data_item: Tuple with data item produced by DataLoader during iteration Returns: @@ -264,77 +276,77 @@ def transform_fn(data_item): quantization_dataset = nncf.Dataset(ds, transform_fn) ov_model = nncf.quantize(onnx_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED) else: - ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework='onnx', compress_to_fp16=half) # export + ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half) # export ov.serialize(ov_model, f_ov) # save - yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml + yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml return f, None @try_export -def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')): +def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")): # YOLOv5 Paddle export - check_requirements(('paddlepaddle', 'x2paddle')) + check_requirements(("paddlepaddle", "x2paddle")) import x2paddle from x2paddle.convert import pytorch2paddle - LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...') - f = str(file).replace('.pt', f'_paddle_model{os.sep}') + LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...") + f = str(file).replace(".pt", f"_paddle_model{os.sep}") - pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im]) # export - yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml + pytorch2paddle(module=model, save_dir=f, jit_type="trace", input_examples=[im]) # export + yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml return f, None @try_export -def export_coreml(model, im, file, int8, half, nms, prefix=colorstr('CoreML:')): +def export_coreml(model, im, file, int8, half, nms, prefix=colorstr("CoreML:")): # YOLOv5 CoreML export - check_requirements('coremltools') + check_requirements("coremltools") import coremltools as ct - LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...') - f = file.with_suffix('.mlmodel') + LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...") + f = file.with_suffix(".mlmodel") if nms: model = iOSModel(model, im) ts = torch.jit.trace(model, im, strict=False) # TorchScript model - ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) - bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None) + ct_model = ct.convert(ts, inputs=[ct.ImageType("image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) + bits, mode = (8, "kmeans_lut") if int8 else (16, "linear") if half else (32, None) if bits < 32: if MACOS: # quantization only supported on macOS with warnings.catch_warnings(): - warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress numpy==1.20 float warning + warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) else: - print(f'{prefix} quantization only supported on macOS, skipping...') + print(f"{prefix} quantization only supported on macOS, skipping...") ct_model.save(f) return f, ct_model @try_export -def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')): +def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr("TensorRT:")): # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt - assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`' + assert im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. `python export.py --device 0`" try: import tensorrt as trt except Exception: - if platform.system() == 'Linux': - check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com') + if platform.system() == "Linux": + check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com") import tensorrt as trt - if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 + if trt.__version__[0] == "7": # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 grid = model.model[-1].anchor_grid model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 model.model[-1].anchor_grid = grid else: # TensorRT >= 8 - check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0 + check_version(trt.__version__, "8.0.0", hard=True) # require tensorrt>=8.0.0 export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 - onnx = file.with_suffix('.onnx') + onnx = file.with_suffix(".onnx") - LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...') - assert onnx.exists(), f'failed to export ONNX file: {onnx}' - f = file.with_suffix('.engine') # TensorRT engine file + LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...") + assert onnx.exists(), f"failed to export ONNX file: {onnx}" + f = file.with_suffix(".engine") # TensorRT engine file logger = trt.Logger(trt.Logger.INFO) if verbose: logger.min_severity = trt.Logger.Severity.VERBOSE @@ -344,11 +356,11 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose config.max_workspace_size = workspace * 1 << 30 # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice - flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) network = builder.create_network(flag) parser = trt.OnnxParser(network, logger) if not parser.parse_from_file(str(onnx)): - raise RuntimeError(f'failed to load ONNX file: {onnx}') + raise RuntimeError(f"failed to load ONNX file: {onnx}") inputs = [network.get_input(i) for i in range(network.num_inputs)] outputs = [network.get_output(i) for i in range(network.num_outputs)] @@ -359,33 +371,35 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose if dynamic: if im.shape[0] <= 1: - LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument') + LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument") profile = builder.create_optimization_profile() for inp in inputs: profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape) config.add_optimization_profile(profile) - LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}') + LOGGER.info(f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}") if builder.platform_has_fast_fp16 and half: config.set_flag(trt.BuilderFlag.FP16) - with builder.build_engine(network, config) as engine, open(f, 'wb') as t: + with builder.build_engine(network, config) as engine, open(f, "wb") as t: t.write(engine.serialize()) return f, None @try_export -def export_saved_model(model, - im, - file, - dynamic, - tf_nms=False, - agnostic_nms=False, - topk_per_class=100, - topk_all=100, - iou_thres=0.45, - conf_thres=0.25, - keras=False, - prefix=colorstr('TensorFlow SavedModel:')): +def export_saved_model( + model, + im, + file, + dynamic, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25, + keras=False, + prefix=colorstr("TensorFlow SavedModel:"), +): # YOLOv5 TensorFlow SavedModel export try: import tensorflow as tf @@ -396,13 +410,13 @@ def export_saved_model(model, from models.tf import TFModel - LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') - if tf.__version__ > '2.13.1': - helper_url = 'https://github.com/ultralytics/yolov5/issues/12489' + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + if tf.__version__ > "2.13.1": + helper_url = "https://github.com/ultralytics/yolov5/issues/12489" LOGGER.info( - f'WARNING ⚠️ using Tensorflow {tf.__version__} > 2.13.1 might cause issue when exporting the model to tflite {helper_url}' + f"WARNING ⚠️ using Tensorflow {tf.__version__} > 2.13.1 might cause issue when exporting the model to tflite {helper_url}" ) # handling issue https://github.com/ultralytics/yolov5/issues/12489 - f = str(file).replace('.pt', '_saved_model') + f = str(file).replace(".pt", "_saved_model") batch_size, ch, *imgsz = list(im.shape) # BCHW tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) @@ -414,7 +428,7 @@ def export_saved_model(model, keras_model.trainable = False keras_model.summary() if keras: - keras_model.save(f, save_format='tf') + keras_model.save(f, save_format="tf") else: spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) m = tf.function(lambda x: keras_model(x)) # full model @@ -423,21 +437,24 @@ def export_saved_model(model, tfm = tf.Module() tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]) tfm.__call__(im) - tf.saved_model.save(tfm, - f, - options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version( - tf.__version__, '2.6') else tf.saved_model.SaveOptions()) + tf.saved_model.save( + tfm, + f, + options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) + if check_version(tf.__version__, "2.6") + else tf.saved_model.SaveOptions(), + ) return f, keras_model @try_export -def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')): +def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")): # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow import tensorflow as tf from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 - LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') - f = file.with_suffix('.pb') + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + f = file.with_suffix(".pb") m = tf.function(lambda x: keras_model(x)) # full model m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) @@ -448,14 +465,15 @@ def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')): @try_export -def export_tflite(keras_model, im, file, int8, per_tensor, data, nms, agnostic_nms, - prefix=colorstr('TensorFlow Lite:')): +def export_tflite( + keras_model, im, file, int8, per_tensor, data, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:") +): # YOLOv5 TensorFlow Lite export import tensorflow as tf - LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") batch_size, ch, *imgsz = list(im.shape) # BCHW - f = str(file).replace('.pt', '-fp16.tflite') + f = str(file).replace(".pt", "-fp16.tflite") converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] @@ -463,7 +481,8 @@ def export_tflite(keras_model, im, file, int8, per_tensor, data, nms, agnostic_n converter.optimizations = [tf.lite.Optimize.DEFAULT] if int8: from models.tf import representative_dataset_gen - dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False) + + dataset = LoadImages(check_dataset(check_yaml(data))["train"], img_size=imgsz, auto=False) converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100) converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.target_spec.supported_types = [] @@ -472,77 +491,87 @@ def export_tflite(keras_model, im, file, int8, per_tensor, data, nms, agnostic_n converter.experimental_new_quantizer = True if per_tensor: converter._experimental_disable_per_channel = True - f = str(file).replace('.pt', '-int8.tflite') + f = str(file).replace(".pt", "-int8.tflite") if nms or agnostic_nms: converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS) tflite_model = converter.convert() - open(f, 'wb').write(tflite_model) + open(f, "wb").write(tflite_model) return f, None @try_export -def export_edgetpu(file, prefix=colorstr('Edge TPU:')): +def export_edgetpu(file, prefix=colorstr("Edge TPU:")): # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/ - cmd = 'edgetpu_compiler --version' - help_url = 'https://coral.ai/docs/edgetpu/compiler/' - assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}' - if subprocess.run(f'{cmd} > /dev/null 2>&1', shell=True).returncode != 0: - LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}') - sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system + cmd = "edgetpu_compiler --version" + help_url = "https://coral.ai/docs/edgetpu/compiler/" + assert platform.system() == "Linux", f"export only supported on Linux. See {help_url}" + if subprocess.run(f"{cmd} > /dev/null 2>&1", shell=True).returncode != 0: + LOGGER.info(f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}") + sudo = subprocess.run("sudo --version >/dev/null", shell=True).returncode == 0 # sudo installed on system for c in ( - 'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -', - 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', - 'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'): - subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True) + "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -", + 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', + "sudo apt-get update", + "sudo apt-get install edgetpu-compiler", + ): + subprocess.run(c if sudo else c.replace("sudo ", ""), shell=True, check=True) ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] - LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...') - f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model - f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model - - subprocess.run([ - 'edgetpu_compiler', - '-s', - '-d', - '-k', - '10', - '--out_dir', - str(file.parent), - f_tfl, ], check=True) + LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...") + f = str(file).replace(".pt", "-int8_edgetpu.tflite") # Edge TPU model + f_tfl = str(file).replace(".pt", "-int8.tflite") # TFLite model + + subprocess.run( + [ + "edgetpu_compiler", + "-s", + "-d", + "-k", + "10", + "--out_dir", + str(file.parent), + f_tfl, + ], + check=True, + ) return f, None @try_export -def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')): +def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")): # YOLOv5 TensorFlow.js export - check_requirements('tensorflowjs') + check_requirements("tensorflowjs") import tensorflowjs as tfjs - LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...') - f = str(file).replace('.pt', '_web_model') # js dir - f_pb = file.with_suffix('.pb') # *.pb path - f_json = f'{f}/model.json' # *.json path + LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...") + f = str(file).replace(".pt", "_web_model") # js dir + f_pb = file.with_suffix(".pb") # *.pb path + f_json = f"{f}/model.json" # *.json path args = [ - 'tensorflowjs_converter', - '--input_format=tf_frozen_model', - '--quantize_uint8' if int8 else '', - '--output_node_names=Identity,Identity_1,Identity_2,Identity_3', + "tensorflowjs_converter", + "--input_format=tf_frozen_model", + "--quantize_uint8" if int8 else "", + "--output_node_names=Identity,Identity_1,Identity_2,Identity_3", str(f_pb), - str(f), ] + str(f), + ] subprocess.run([arg for arg in args if arg], check=True) json = Path(f_json).read_text() - with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order + with open(f_json, "w") as j: # sort JSON Identity_* in ascending order subst = re.sub( r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' r'"Identity.?.?": {"name": "Identity.?.?"}, ' r'"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}}}', + r'{"outputs": {"Identity": {"name": "Identity"}, ' r'"Identity_1": {"name": "Identity_1"}, ' r'"Identity_2": {"name": "Identity_2"}, ' - r'"Identity_3": {"name": "Identity_3"}}}', json) + r'"Identity_3": {"name": "Identity_3"}}}', + json, + ) j.write(subst) return f, None @@ -555,8 +584,8 @@ def add_tflite_metadata(file, metadata, num_outputs): from tflite_support import metadata as _metadata from tflite_support import metadata_schema_py_generated as _metadata_fb - tmp_file = Path('/tmp/meta.txt') - with open(tmp_file, 'w') as meta_f: + tmp_file = Path("/tmp/meta.txt") + with open(tmp_file, "w") as meta_f: meta_f.write(str(metadata)) model_meta = _metadata_fb.ModelMetadataT() @@ -580,22 +609,22 @@ def add_tflite_metadata(file, metadata, num_outputs): tmp_file.unlink() -def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline:')): +def pipeline_coreml(model, im, file, names, y, prefix=colorstr("CoreML Pipeline:")): # YOLOv5 CoreML pipeline import coremltools as ct from PIL import Image - print(f'{prefix} starting pipeline with coremltools {ct.__version__}...') + print(f"{prefix} starting pipeline with coremltools {ct.__version__}...") batch_size, ch, h, w = list(im.shape) # BCHW t = time.time() # YOLOv5 Output shapes spec = model.get_spec() out0, out1 = iter(spec.description.output) - if platform.system() == 'Darwin': - img = Image.new('RGB', (w, h)) # img(192 width, 320 height) + if platform.system() == "Darwin": + img = Image.new("RGB", (w, h)) # img(192 width, 320 height) # img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection - out = model.predict({'image': img}) + out = model.predict({"image": img}) out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape else: # linux and windows can not run model.predict(), get sizes from pytorch output y s = tuple(y[0].shape) @@ -605,7 +634,7 @@ def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline: nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height na, nc = out0_shape # na, nc = out0.type.multiArrayType.shape # number anchors, classes - assert len(names) == nc, f'{len(names)} names found for nc={nc}' # check + assert len(names) == nc, f"{len(names)} names found for nc={nc}" # check # Define output shapes (missing) out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80) @@ -639,8 +668,8 @@ def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline: nms_spec.description.output.add() nms_spec.description.output[i].ParseFromString(decoder_output) - nms_spec.description.output[0].name = 'confidence' - nms_spec.description.output[1].name = 'coordinates' + nms_spec.description.output[0].name = "confidence" + nms_spec.description.output[1].name = "coordinates" output_sizes = [nc, 4] for i in range(2): @@ -656,10 +685,10 @@ def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline: nms = nms_spec.nonMaximumSuppression nms.confidenceInputFeatureName = out0.name # 1x507x80 nms.coordinatesInputFeatureName = out1.name # 1x507x4 - nms.confidenceOutputFeatureName = 'confidence' - nms.coordinatesOutputFeatureName = 'coordinates' - nms.iouThresholdInputFeatureName = 'iouThreshold' - nms.confidenceThresholdInputFeatureName = 'confidenceThreshold' + nms.confidenceOutputFeatureName = "confidence" + nms.coordinatesOutputFeatureName = "coordinates" + nms.iouThresholdInputFeatureName = "iouThreshold" + nms.confidenceThresholdInputFeatureName = "confidenceThreshold" nms.iouThreshold = 0.45 nms.confidenceThreshold = 0.25 nms.pickTop.perClass = True @@ -667,10 +696,14 @@ def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline: nms_model = ct.models.MLModel(nms_spec) # 4. Pipeline models together - pipeline = ct.models.pipeline.Pipeline(input_features=[('image', ct.models.datatypes.Array(3, ny, nx)), - ('iouThreshold', ct.models.datatypes.Double()), - ('confidenceThreshold', ct.models.datatypes.Double())], - output_features=['confidence', 'coordinates']) + pipeline = ct.models.pipeline.Pipeline( + input_features=[ + ("image", ct.models.datatypes.Array(3, ny, nx)), + ("iouThreshold", ct.models.datatypes.Double()), + ("confidenceThreshold", ct.models.datatypes.Double()), + ], + output_features=["confidence", "coordinates"], + ) pipeline.add_model(model) pipeline.add_model(nms_model) @@ -681,73 +714,77 @@ def pipeline_coreml(model, im, file, names, y, prefix=colorstr('CoreML Pipeline: # Update metadata pipeline.spec.specificationVersion = 5 - pipeline.spec.description.metadata.versionString = 'https://github.com/ultralytics/yolov5' - pipeline.spec.description.metadata.shortDescription = 'https://github.com/ultralytics/yolov5' - pipeline.spec.description.metadata.author = 'glenn.jocher@ultralytics.com' - pipeline.spec.description.metadata.license = 'https://github.com/ultralytics/yolov5/blob/master/LICENSE' - pipeline.spec.description.metadata.userDefined.update({ - 'classes': ','.join(names.values()), - 'iou_threshold': str(nms.iouThreshold), - 'confidence_threshold': str(nms.confidenceThreshold)}) + pipeline.spec.description.metadata.versionString = "https://github.com/ultralytics/yolov5" + pipeline.spec.description.metadata.shortDescription = "https://github.com/ultralytics/yolov5" + pipeline.spec.description.metadata.author = "glenn.jocher@ultralytics.com" + pipeline.spec.description.metadata.license = "https://github.com/ultralytics/yolov5/blob/master/LICENSE" + pipeline.spec.description.metadata.userDefined.update( + { + "classes": ",".join(names.values()), + "iou_threshold": str(nms.iouThreshold), + "confidence_threshold": str(nms.confidenceThreshold), + } + ) # Save the model - f = file.with_suffix('.mlmodel') # filename + f = file.with_suffix(".mlmodel") # filename model = ct.models.MLModel(pipeline.spec) - model.input_description['image'] = 'Input image' - model.input_description['iouThreshold'] = f'(optional) IOU Threshold override (default: {nms.iouThreshold})' - model.input_description['confidenceThreshold'] = \ - f'(optional) Confidence Threshold override (default: {nms.confidenceThreshold})' - model.output_description['confidence'] = 'Boxes × Class confidence (see user-defined metadata "classes")' - model.output_description['coordinates'] = 'Boxes × [x, y, width, height] (relative to image size)' + model.input_description["image"] = "Input image" + model.input_description["iouThreshold"] = f"(optional) IOU Threshold override (default: {nms.iouThreshold})" + model.input_description[ + "confidenceThreshold" + ] = f"(optional) Confidence Threshold override (default: {nms.confidenceThreshold})" + model.output_description["confidence"] = 'Boxes × Class confidence (see user-defined metadata "classes")' + model.output_description["coordinates"] = "Boxes × [x, y, width, height] (relative to image size)" model.save(f) # pipelined - print(f'{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)') + print(f"{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)") @smart_inference_mode() def run( - data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' - weights=ROOT / 'yolov5s.pt', # weights path - imgsz=(640, 640), # image (height, width) - batch_size=1, # batch size - device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu - include=('torchscript', 'onnx'), # include formats - half=False, # FP16 half-precision export - inplace=False, # set YOLOv5 Detect() inplace=True - keras=False, # use Keras - optimize=False, # TorchScript: optimize for mobile - int8=False, # CoreML/TF INT8 quantization - per_tensor=False, # TF per tensor quantization - dynamic=False, # ONNX/TF/TensorRT: dynamic axes - simplify=False, # ONNX: simplify model - opset=12, # ONNX: opset version - verbose=False, # TensorRT: verbose log - workspace=4, # TensorRT: workspace size (GB) - nms=False, # TF: add NMS to model - agnostic_nms=False, # TF: add agnostic NMS to model - topk_per_class=100, # TF.js NMS: topk per class to keep - topk_all=100, # TF.js NMS: topk for all classes to keep - iou_thres=0.45, # TF.js NMS: IoU threshold - conf_thres=0.25, # TF.js NMS: confidence threshold + data=ROOT / "data/coco128.yaml", # 'dataset.yaml path' + weights=ROOT / "yolov5s.pt", # weights path + imgsz=(640, 640), # image (height, width) + batch_size=1, # batch size + device="cpu", # cuda device, i.e. 0 or 0,1,2,3 or cpu + include=("torchscript", "onnx"), # include formats + half=False, # FP16 half-precision export + inplace=False, # set YOLOv5 Detect() inplace=True + keras=False, # use Keras + optimize=False, # TorchScript: optimize for mobile + int8=False, # CoreML/TF INT8 quantization + per_tensor=False, # TF per tensor quantization + dynamic=False, # ONNX/TF/TensorRT: dynamic axes + simplify=False, # ONNX: simplify model + opset=12, # ONNX: opset version + verbose=False, # TensorRT: verbose log + workspace=4, # TensorRT: workspace size (GB) + nms=False, # TF: add NMS to model + agnostic_nms=False, # TF: add agnostic NMS to model + topk_per_class=100, # TF.js NMS: topk per class to keep + topk_all=100, # TF.js NMS: topk for all classes to keep + iou_thres=0.45, # TF.js NMS: IoU threshold + conf_thres=0.25, # TF.js NMS: confidence threshold ): t = time.time() include = [x.lower() for x in include] # to lowercase - fmts = tuple(export_formats()['Argument'][1:]) # --include arguments + fmts = tuple(export_formats()["Argument"][1:]) # --include arguments flags = [x in include for x in fmts] - assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}' + assert sum(flags) == len(include), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}" jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans - file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights + file = Path(url2file(weights) if str(weights).startswith(("http:/", "https:/")) else weights) # PyTorch weights # Load PyTorch model device = select_device(device) if half: - assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0' - assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both' + assert device.type != "cpu" or coreml, "--half only compatible with GPU export, i.e. use --device 0" + assert not dynamic, "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both" model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model # Checks imgsz *= 2 if len(imgsz) == 1 else 1 # expand if optimize: - assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu' + assert device.type == "cpu", "--optimize not compatible with cuda devices, i.e. use --device cpu" # Input gs = int(max(model.stride)) # grid size (max stride) @@ -767,12 +804,12 @@ def run( if half and not coreml: im, model = im.half(), model.half() # to FP16 shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape - metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata + metadata = {"stride": int(max(model.stride)), "names": model.names} # model metadata LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)") # Exports - f = [''] * len(fmts) # exported filenames - warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning + f = [""] * len(fmts) # exported filenames + warnings.filterwarnings(action="ignore", category=torch.jit.TracerWarning) # suppress TracerWarning if jit: # TorchScript f[0], _ = export_torchscript(model, im, file, optimize) if engine: # TensorRT required before ONNX @@ -786,30 +823,27 @@ def run( if nms: pipeline_coreml(ct_model, im, file, model.names, y) if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats - assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.' - assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.' - f[5], s_model = export_saved_model(model.cpu(), - im, - file, - dynamic, - tf_nms=nms or agnostic_nms or tfjs, - agnostic_nms=agnostic_nms or tfjs, - topk_per_class=topk_per_class, - topk_all=topk_all, - iou_thres=iou_thres, - conf_thres=conf_thres, - keras=keras) + assert not tflite or not tfjs, "TFLite and TF.js models must be exported separately, please pass only one type." + assert not isinstance(model, ClassificationModel), "ClassificationModel export to TF formats not yet supported." + f[5], s_model = export_saved_model( + model.cpu(), + im, + file, + dynamic, + tf_nms=nms or agnostic_nms or tfjs, + agnostic_nms=agnostic_nms or tfjs, + topk_per_class=topk_per_class, + topk_all=topk_all, + iou_thres=iou_thres, + conf_thres=conf_thres, + keras=keras, + ) if pb or tfjs: # pb prerequisite to tfjs f[6], _ = export_pb(s_model, file) if tflite or edgetpu: - f[7], _ = export_tflite(s_model, - im, - file, - int8 or edgetpu, - per_tensor, - data=data, - nms=nms, - agnostic_nms=agnostic_nms) + f[7], _ = export_tflite( + s_model, im, file, int8 or edgetpu, per_tensor, data=data, nms=nms, agnostic_nms=agnostic_nms + ) if edgetpu: f[8], _ = export_edgetpu(file) add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) @@ -823,58 +857,66 @@ def run( if any(f): cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel) - dir = Path('segment' if seg else 'classify' if cls else '') - h = '--half' if half else '' # --half FP16 inference arg - s = '# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference' if cls else \ - '# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference' if seg else '' - LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)' - f"\nResults saved to {colorstr('bold', file.parent.resolve())}" - f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" - f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" - f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" - f'\nVisualize: https://netron.app') + dir = Path("segment" if seg else "classify" if cls else "") + h = "--half" if half else "" # --half FP16 inference arg + s = ( + "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" + if cls + else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" + if seg + else "" + ) + LOGGER.info( + f'\nExport complete ({time.time() - t:.1f}s)' + f"\nResults saved to {colorstr('bold', file.parent.resolve())}" + f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" + f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" + f'\nVisualize: https://netron.app' + ) return f # return list of exported files/dirs def parse_opt(known=False): parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)') - parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--half', action='store_true', help='FP16 half-precision export') - parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True') - parser.add_argument('--keras', action='store_true', help='TF: use Keras') - parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile') - parser.add_argument('--int8', action='store_true', help='CoreML/TF/OpenVINO INT8 quantization') - parser.add_argument('--per-tensor', action='store_true', help='TF per-tensor quantization') - parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes') - parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model') - parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version') - parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log') - parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)') - parser.add_argument('--nms', action='store_true', help='TF: add NMS to model') - parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model') - parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep') - parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep') - parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold') - parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold') + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model.pt path(s)") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640, 640], help="image (h, w)") + parser.add_argument("--batch-size", type=int, default=1, help="batch size") + parser.add_argument("--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--half", action="store_true", help="FP16 half-precision export") + parser.add_argument("--inplace", action="store_true", help="set YOLOv5 Detect() inplace=True") + parser.add_argument("--keras", action="store_true", help="TF: use Keras") + parser.add_argument("--optimize", action="store_true", help="TorchScript: optimize for mobile") + parser.add_argument("--int8", action="store_true", help="CoreML/TF/OpenVINO INT8 quantization") + parser.add_argument("--per-tensor", action="store_true", help="TF per-tensor quantization") + parser.add_argument("--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes") + parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model") + parser.add_argument("--opset", type=int, default=17, help="ONNX: opset version") + parser.add_argument("--verbose", action="store_true", help="TensorRT: verbose log") + parser.add_argument("--workspace", type=int, default=4, help="TensorRT: workspace size (GB)") + parser.add_argument("--nms", action="store_true", help="TF: add NMS to model") + parser.add_argument("--agnostic-nms", action="store_true", help="TF: add agnostic NMS to model") + parser.add_argument("--topk-per-class", type=int, default=100, help="TF.js NMS: topk per class to keep") + parser.add_argument("--topk-all", type=int, default=100, help="TF.js NMS: topk for all classes to keep") + parser.add_argument("--iou-thres", type=float, default=0.45, help="TF.js NMS: IoU threshold") + parser.add_argument("--conf-thres", type=float, default=0.25, help="TF.js NMS: confidence threshold") parser.add_argument( - '--include', - nargs='+', - default=['torchscript'], - help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle') + "--include", + nargs="+", + default=["torchscript"], + help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle", + ) opt = parser.parse_known_args()[0] if known else parser.parse_args() print_args(vars(opt)) return opt def main(opt): - for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]): + for opt.weights in opt.weights if isinstance(opt.weights, list) else [opt.weights]: run(**vars(opt)) -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/hubconf.py b/hubconf.py index f0192698fbe3..691d8eb64749 100644 --- a/hubconf.py +++ b/hubconf.py @@ -14,7 +14,8 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - """Creates or loads a YOLOv5 model + """ + Creates or loads a YOLOv5 model. Arguments: name (str): model name 'yolov5s' or path 'path/to/best.pt' @@ -39,9 +40,9 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo if not verbose: LOGGER.setLevel(logging.WARNING) - check_requirements(ROOT / 'requirements.txt', exclude=('opencv-python', 'tensorboard', 'thop')) + check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop")) name = Path(name) - path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path + path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path try: device = select_device(device) if pretrained and channels == 3 and classes == 80: @@ -49,91 +50,95 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model if autoshape: if model.pt and isinstance(model.model, ClassificationModel): - LOGGER.warning('WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. ' - 'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).') + LOGGER.warning( + "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. " + "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)." + ) elif model.pt and isinstance(model.model, SegmentationModel): - LOGGER.warning('WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. ' - 'You will not be able to run inference with this model.') + LOGGER.warning( + "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. " + "You will not be able to run inference with this model." + ) else: model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS except Exception: model = attempt_load(path, device=device, fuse=False) # arbitrary model else: - cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path + cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0] # model.yaml path model = DetectionModel(cfg, channels, classes) # create model if pretrained: ckpt = torch.load(attempt_download(path), map_location=device) # load - csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 - csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect + csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect model.load_state_dict(csd, strict=False) # load - if len(ckpt['model'].names) == classes: - model.names = ckpt['model'].names # set class names attribute + if len(ckpt["model"].names) == classes: + model.names = ckpt["model"].names # set class names attribute if not verbose: LOGGER.setLevel(logging.INFO) # reset to default return model.to(device) except Exception as e: - help_url = 'https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading' - s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.' + help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading" + s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help." raise Exception(s) from e -def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None): +def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None): # YOLOv5 custom or local model return _create(path, autoshape=autoshape, verbose=_verbose, device=device) def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-nano model https://github.com/ultralytics/yolov5 - return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device) def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-small model https://github.com/ultralytics/yolov5 - return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device) def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-medium model https://github.com/ultralytics/yolov5 - return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device) def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-large model https://github.com/ultralytics/yolov5 - return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device) def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 - return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device) def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device) def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device) + return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device) -if __name__ == '__main__': +if __name__ == "__main__": import argparse from pathlib import Path @@ -144,7 +149,7 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=T # Argparser parser = argparse.ArgumentParser() - parser.add_argument('--model', type=str, default='yolov5s', help='model name') + parser.add_argument("--model", type=str, default="yolov5s", help="model name") opt = parser.parse_args() print_args(vars(opt)) @@ -154,12 +159,13 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=T # Images imgs = [ - 'data/images/zidane.jpg', # filename - Path('data/images/zidane.jpg'), # Path - 'https://ultralytics.com/images/zidane.jpg', # URI - cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV - Image.open('data/images/bus.jpg'), # PIL - np.zeros((320, 640, 3))] # numpy + "data/images/zidane.jpg", # filename + Path("data/images/zidane.jpg"), # Path + "https://ultralytics.com/images/zidane.jpg", # URI + cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV + Image.open("data/images/bus.jpg"), # PIL + np.zeros((320, 640, 3)), + ] # numpy # Inference results = model(imgs, size=320) # batched inference diff --git a/models/common.py b/models/common.py index 75cc4e97bbc7..09e7560f4d84 100644 --- a/models/common.py +++ b/models/common.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Common modules -""" +"""Common modules.""" import ast import contextlib @@ -28,20 +26,34 @@ try: import ultralytics - assert hasattr(ultralytics, '__version__') # verify package is not directory + assert hasattr(ultralytics, "__version__") # verify package is not directory except (ImportError, AssertionError): import os - os.system('pip install -U ultralytics') + os.system("pip install -U ultralytics") import ultralytics from ultralytics.utils.plotting import Annotator, colors, save_one_box from utils import TryExcept from utils.dataloaders import exif_transpose, letterbox -from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr, - increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, - xyxy2xywh, yaml_load) +from utils.general import ( + LOGGER, + ROOT, + Profile, + check_requirements, + check_suffix, + check_version, + colorstr, + increment_path, + is_jupyter, + make_divisible, + non_max_suppression, + scale_boxes, + xywh2xyxy, + xyxy2xywh, + yaml_load, +) from utils.torch_utils import copy_attr, smart_inference_mode @@ -223,7 +235,7 @@ def __init__(self, c1, c2, k=(5, 9, 13)): def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) @@ -239,7 +251,7 @@ def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) @@ -278,9 +290,11 @@ def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride self.conv = nn.Sequential( GhostConv(c1, c_, 1, 1), # pw DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw - GhostConv(c_, c2, 1, 1, act=False)) # pw-linear - self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, - act=False)) if s == 2 else nn.Identity() + GhostConv(c_, c2, 1, 1, act=False), + ) # pw-linear + self.shortcut = ( + nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() + ) def forward(self, x): return self.conv(x) + self.shortcut(x) @@ -309,9 +323,9 @@ def __init__(self, gain=2): def forward(self, x): b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' s = self.gain - x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) + x = x.view(b, s, s, c // s**2, h, w) # x(1,2,2,16,80,80) x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) - return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) + return x.view(b, c // s**2, h * s, w * s) # x(1,16,160,160) class Concat(nn.Module): @@ -326,7 +340,7 @@ def forward(self, x): class DetectMultiBackend(nn.Module): # YOLOv5 MultiBackend class for python inference on various backends - def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True): + def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True): # Usage: # PyTorch: weights = *.pt # TorchScript: *.torchscript @@ -348,65 +362,68 @@ def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, fp16 &= pt or jit or onnx or engine or triton # FP16 nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) stride = 32 # default stride - cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA + cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA if not (pt or triton): w = attempt_download(w) # download if not local if pt: # PyTorch model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) stride = max(int(model.stride.max()), 32) # model stride - names = model.module.names if hasattr(model, 'module') else model.names # get class names + names = model.module.names if hasattr(model, "module") else model.names # get class names model.half() if fp16 else model.float() self.model = model # explicitly assign for to(), cpu(), cuda(), half() elif jit: # TorchScript - LOGGER.info(f'Loading {w} for TorchScript inference...') - extra_files = {'config.txt': ''} # model metadata + LOGGER.info(f"Loading {w} for TorchScript inference...") + extra_files = {"config.txt": ""} # model metadata model = torch.jit.load(w, _extra_files=extra_files, map_location=device) model.half() if fp16 else model.float() - if extra_files['config.txt']: # load metadata dict - d = json.loads(extra_files['config.txt'], - object_hook=lambda d: { - int(k) if k.isdigit() else k: v - for k, v in d.items()}) - stride, names = int(d['stride']), d['names'] + if extra_files["config.txt"]: # load metadata dict + d = json.loads( + extra_files["config.txt"], + object_hook=lambda d: {int(k) if k.isdigit() else k: v for k, v in d.items()}, + ) + stride, names = int(d["stride"]), d["names"] elif dnn: # ONNX OpenCV DNN - LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') - check_requirements('opencv-python>=4.5.4') + LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...") + check_requirements("opencv-python>=4.5.4") net = cv2.dnn.readNetFromONNX(w) elif onnx: # ONNX Runtime - LOGGER.info(f'Loading {w} for ONNX Runtime inference...') - check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) + LOGGER.info(f"Loading {w} for ONNX Runtime inference...") + check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime")) import onnxruntime - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] + + providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] if cuda else ["CPUExecutionProvider"] session = onnxruntime.InferenceSession(w, providers=providers) output_names = [x.name for x in session.get_outputs()] meta = session.get_modelmeta().custom_metadata_map # metadata - if 'stride' in meta: - stride, names = int(meta['stride']), eval(meta['names']) + if "stride" in meta: + stride, names = int(meta["stride"]), eval(meta["names"]) elif xml: # OpenVINO - LOGGER.info(f'Loading {w} for OpenVINO inference...') - check_requirements('openvino>=2023.0') # requires openvino-dev: https://pypi.org/project/openvino-dev/ + LOGGER.info(f"Loading {w} for OpenVINO inference...") + check_requirements("openvino>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ from openvino.runtime import Core, Layout, get_batch + core = Core() if not Path(w).is_file(): # if not *.xml - w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir - ov_model = core.read_model(model=w, weights=Path(w).with_suffix('.bin')) + w = next(Path(w).glob("*.xml")) # get *.xml file from *_openvino_model dir + ov_model = core.read_model(model=w, weights=Path(w).with_suffix(".bin")) if ov_model.get_parameters()[0].get_layout().empty: - ov_model.get_parameters()[0].set_layout(Layout('NCHW')) + ov_model.get_parameters()[0].set_layout(Layout("NCHW")) batch_dim = get_batch(ov_model) if batch_dim.is_static: batch_size = batch_dim.get_length() - ov_compiled_model = core.compile_model(ov_model, device_name='AUTO') # AUTO selects best available device - stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata + ov_compiled_model = core.compile_model(ov_model, device_name="AUTO") # AUTO selects best available device + stride, names = self._load_metadata(Path(w).with_suffix(".yaml")) # load metadata elif engine: # TensorRT - LOGGER.info(f'Loading {w} for TensorRT inference...') + LOGGER.info(f"Loading {w} for TensorRT inference...") import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download - check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 - if device.type == 'cpu': - device = torch.device('cuda:0') - Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) + + check_version(trt.__version__, "7.0.0", hard=True) # require tensorrt>=7.0.0 + if device.type == "cpu": + device = torch.device("cuda:0") + Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) logger = trt.Logger(trt.Logger.INFO) - with open(w, 'rb') as f, trt.Runtime(logger) as runtime: + with open(w, "rb") as f, trt.Runtime(logger) as runtime: model = runtime.deserialize_cuda_engine(f.read()) context = model.create_execution_context() bindings = OrderedDict() @@ -428,22 +445,24 @@ def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) - batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size + batch_size = bindings["images"].shape[0] # if dynamic, this is instead max batch size elif coreml: # CoreML - LOGGER.info(f'Loading {w} for CoreML inference...') + LOGGER.info(f"Loading {w} for CoreML inference...") import coremltools as ct + model = ct.models.MLModel(w) elif saved_model: # TF SavedModel - LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') + LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...") import tensorflow as tf + keras = False # assume TF1 saved_model model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt - LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') + LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...") import tensorflow as tf def wrap_frozen_graph(gd, inputs, outputs): - x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped ge = x.graph.as_graph_element return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) @@ -452,46 +471,50 @@ def gd_outputs(gd): for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef name_list.append(node.name) input_list.extend(node.input) - return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp')) + return sorted(f"{x}:0" for x in list(set(name_list) - set(input_list)) if not x.startswith("NoOp")) gd = tf.Graph().as_graph_def() # TF GraphDef - with open(w, 'rb') as f: + with open(w, "rb") as f: gd.ParseFromString(f.read()) - frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd)) + frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu from tflite_runtime.interpreter import Interpreter, load_delegate except ImportError: import tensorflow as tf - Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, + + Interpreter, load_delegate = ( + tf.lite.Interpreter, + tf.lite.experimental.load_delegate, + ) if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime - LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') - delegate = { - 'Linux': 'libedgetpu.so.1', - 'Darwin': 'libedgetpu.1.dylib', - 'Windows': 'edgetpu.dll'}[platform.system()] + LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...") + delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[ + platform.system() + ] interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) else: # TFLite - LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') + LOGGER.info(f"Loading {w} for TensorFlow Lite inference...") interpreter = Interpreter(model_path=w) # load TFLite model interpreter.allocate_tensors() # allocate input_details = interpreter.get_input_details() # inputs output_details = interpreter.get_output_details() # outputs # load metadata with contextlib.suppress(zipfile.BadZipFile): - with zipfile.ZipFile(w, 'r') as model: + with zipfile.ZipFile(w, "r") as model: meta_file = model.namelist()[0] - meta = ast.literal_eval(model.read(meta_file).decode('utf-8')) - stride, names = int(meta['stride']), meta['names'] + meta = ast.literal_eval(model.read(meta_file).decode("utf-8")) + stride, names = int(meta["stride"]), meta["names"] elif tfjs: # TF.js - raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported') + raise NotImplementedError("ERROR: YOLOv5 TF.js inference is not supported") elif paddle: # PaddlePaddle - LOGGER.info(f'Loading {w} for PaddlePaddle inference...') - check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle') + LOGGER.info(f"Loading {w} for PaddlePaddle inference...") + check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle") import paddle.inference as pdi + if not Path(w).is_file(): # if not *.pdmodel - w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir - weights = Path(w).with_suffix('.pdiparams') + w = next(Path(w).rglob("*.pdmodel")) # get *.pdmodel file from *_paddle_model dir + weights = Path(w).with_suffix(".pdiparams") config = pdi.Config(str(w), str(weights)) if cuda: config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) @@ -499,19 +522,20 @@ def gd_outputs(gd): input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) output_names = predictor.get_output_names() elif triton: # NVIDIA Triton Inference Server - LOGGER.info(f'Using {w} as Triton Inference Server...') - check_requirements('tritonclient[all]') + LOGGER.info(f"Using {w} as Triton Inference Server...") + check_requirements("tritonclient[all]") from utils.triton import TritonRemoteModel + model = TritonRemoteModel(url=w) - nhwc = model.runtime.startswith('tensorflow') + nhwc = model.runtime.startswith("tensorflow") else: - raise NotImplementedError(f'ERROR: {w} is not a supported format') + raise NotImplementedError(f"ERROR: {w} is not a supported format") # class names - if 'names' not in locals(): - names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)} - if names[0] == 'n01440764' and len(names) == 1000: # ImageNet - names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names + if "names" not in locals(): + names = yaml_load(data)["names"] if data else {i: f"class{i}" for i in range(999)} + if names[0] == "n01440764" and len(names) == 1000: # ImageNet + names = yaml_load(ROOT / "data/ImageNet.yaml")["names"] # human-readable names self.__dict__.update(locals()) # assign all variables to self @@ -538,26 +562,26 @@ def forward(self, im, augment=False, visualize=False): im = im.cpu().numpy() # FP32 y = list(self.ov_compiled_model(im).values()) elif self.engine: # TensorRT - if self.dynamic and im.shape != self.bindings['images'].shape: - i = self.model.get_binding_index('images') + if self.dynamic and im.shape != self.bindings["images"].shape: + i = self.model.get_binding_index("images") self.context.set_binding_shape(i, im.shape) # reshape if dynamic - self.bindings['images'] = self.bindings['images']._replace(shape=im.shape) + self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) for name in self.output_names: i = self.model.get_binding_index(name) self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) - s = self.bindings['images'].shape + s = self.bindings["images"].shape assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" - self.binding_addrs['images'] = int(im.data_ptr()) + self.binding_addrs["images"] = int(im.data_ptr()) self.context.execute_v2(list(self.binding_addrs.values())) y = [self.bindings[x].data for x in sorted(self.output_names)] elif self.coreml: # CoreML im = im.cpu().numpy() - im = Image.fromarray((im[0] * 255).astype('uint8')) + im = Image.fromarray((im[0] * 255).astype("uint8")) # im = im.resize((192, 320), Image.BILINEAR) - y = self.model.predict({'image': im}) # coordinates are xywh normalized - if 'confidence' in y: - box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels - conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) + y = self.model.predict({"image": im}) # coordinates are xywh normalized + if "confidence" in y: + box = xywh2xyxy(y["coordinates"] * [[w, h, w, h]]) # xyxy pixels + conf, cls = y["confidence"].max(1), y["confidence"].argmax(1).astype(np.float) y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) else: y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) @@ -576,17 +600,17 @@ def forward(self, im, augment=False, visualize=False): y = self.frozen_func(x=self.tf.constant(im)) else: # Lite or Edge TPU input = self.input_details[0] - int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model + int8 = input["dtype"] == np.uint8 # is TFLite quantized uint8 model if int8: - scale, zero_point = input['quantization'] + scale, zero_point = input["quantization"] im = (im / scale + zero_point).astype(np.uint8) # de-scale - self.interpreter.set_tensor(input['index'], im) + self.interpreter.set_tensor(input["index"], im) self.interpreter.invoke() y = [] for output in self.output_details: - x = self.interpreter.get_tensor(output['index']) + x = self.interpreter.get_tensor(output["index"]) if int8: - scale, zero_point = output['quantization'] + scale, zero_point = output["quantization"] x = (x.astype(np.float32) - zero_point) * scale # re-scale y.append(x) y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] @@ -603,32 +627,33 @@ def from_numpy(self, x): def warmup(self, imgsz=(1, 3, 640, 640)): # Warmup model by running inference once warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton - if any(warmup_types) and (self.device.type != 'cpu' or self.triton): + if any(warmup_types) and (self.device.type != "cpu" or self.triton): im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input for _ in range(2 if self.jit else 1): # self.forward(im) # warmup @staticmethod - def _model_type(p='path/to/model.pt'): + def _model_type(p="path/to/model.pt"): # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] from export import export_formats from utils.downloads import is_url + sf = list(export_formats().Suffix) # export suffixes if not is_url(p, check=False): check_suffix(p, sf) # checks url = urlparse(p) # if url may be Triton inference server types = [s in Path(p).name for s in sf] types[8] &= not types[9] # tflite &= not edgetpu - triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc]) + triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc]) return types + [triton] @staticmethod - def _load_metadata(f=Path('path/to/meta.yaml')): + def _load_metadata(f=Path("path/to/meta.yaml")): # Load metadata from meta.yaml if it exists if f.exists(): d = yaml_load(f) - return d['stride'], d['names'] # assign stride, names + return d["stride"], d["names"] # assign stride, names return None, None @@ -645,8 +670,8 @@ class AutoShape(nn.Module): def __init__(self, model, verbose=True): super().__init__() if verbose: - LOGGER.info('Adding AutoShape... ') - copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes + LOGGER.info("Adding AutoShape... ") + copy_attr(self, model, include=("yaml", "nc", "hyp", "names", "stride", "abc"), exclude=()) # copy attributes self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance self.pt = not self.dmb or model.pt # PyTorch model self.model = model.eval() @@ -682,7 +707,7 @@ def forward(self, ims, size=640, augment=False, profile=False): if isinstance(size, int): # expand size = (size, size) p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param - autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference + autocast = self.amp and (p.device.type != "cpu") # Automatic Mixed Precision (AMP) inference if isinstance(ims, torch.Tensor): # torch with amp.autocast(autocast): return self.model(ims.to(p.device).type_as(p), augment=augment) # inference @@ -691,13 +716,13 @@ def forward(self, ims, size=640, augment=False, profile=False): n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images shape0, shape1, files = [], [], [] # image and inference shapes, filenames for i, im in enumerate(ims): - f = f'image{i}' # filename + f = f"image{i}" # filename if isinstance(im, (str, Path)): # filename or uri - im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im + im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im), im im = np.asarray(exif_transpose(im)) elif isinstance(im, Image.Image): # PIL Image - im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f - files.append(Path(f).with_suffix('.jpg').name) + im, f = np.asarray(exif_transpose(im)), getattr(im, "filename", f) or f + files.append(Path(f).with_suffix(".jpg").name) if im.shape[0] < 5: # image in CHW im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input @@ -718,13 +743,15 @@ def forward(self, ims, size=640, augment=False, profile=False): # Post-process with dt[2]: - y = non_max_suppression(y if self.dmb else y[0], - self.conf, - self.iou, - self.classes, - self.agnostic, - self.multi_label, - max_det=self.max_det) # NMS + y = non_max_suppression( + y if self.dmb else y[0], + self.conf, + self.iou, + self.classes, + self.agnostic, + self.multi_label, + max_det=self.max_det, + ) # NMS for i in range(n): scale_boxes(shape1, y[i][:, :4], shape0[i]) @@ -747,40 +774,44 @@ def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized self.n = len(self.pred) # number of images (batch size) - self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) + self.t = tuple(x.t / self.n * 1e3 for x in times) # timestamps (ms) self.s = tuple(shape) # inference BCHW shape - def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): - s, crops = '', [] + def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path("")): + s, crops = "", [] for i, (im, pred) in enumerate(zip(self.ims, self.pred)): - s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string + s += f"\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} " # string if pred.shape[0]: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() # detections per class s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string - s = s.rstrip(', ') + s = s.rstrip(", ") if show or save or render or crop: annotator = Annotator(im, example=str(self.names)) for *box, conf, cls in reversed(pred): # xyxy, confidence, class - label = f'{self.names[int(cls)]} {conf:.2f}' + label = f"{self.names[int(cls)]} {conf:.2f}" if crop: - file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None - crops.append({ - 'box': box, - 'conf': conf, - 'cls': cls, - 'label': label, - 'im': save_one_box(box, im, file=file, save=save)}) + file = save_dir / "crops" / self.names[int(cls)] / self.files[i] if save else None + crops.append( + { + "box": box, + "conf": conf, + "cls": cls, + "label": label, + "im": save_one_box(box, im, file=file, save=save), + } + ) else: # all others - annotator.box_label(box, label if labels else '', color=colors(cls)) + annotator.box_label(box, label if labels else "", color=colors(cls)) im = annotator.im else: - s += '(no detections)' + s += "(no detections)" im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np if show: if is_jupyter(): from IPython.display import display + display(im) else: im.show(self.files[i]) @@ -792,22 +823,22 @@ def _run(self, pprint=False, show=False, save=False, crop=False, render=False, l if render: self.ims[i] = np.asarray(im) if pprint: - s = s.lstrip('\n') - return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t + s = s.lstrip("\n") + return f"{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}" % self.t if crop: if save: - LOGGER.info(f'Saved results to {save_dir}\n') + LOGGER.info(f"Saved results to {save_dir}\n") return crops - @TryExcept('Showing images is not supported in this environment') + @TryExcept("Showing images is not supported in this environment") def show(self, labels=True): self._run(show=True, labels=labels) # show results - def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): + def save(self, labels=True, save_dir="runs/detect/exp", exist_ok=False): save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir self._run(save=True, labels=labels, save_dir=save_dir) # save results - def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): + def crop(self, save=True, save_dir="runs/detect/exp", exist_ok=False): save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None return self._run(crop=True, save=save, save_dir=save_dir) # crop results @@ -818,9 +849,9 @@ def render(self, labels=True): def pandas(self): # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) new = copy(self) # return copy - ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns - cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns - for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): + ca = "xmin", "ymin", "xmax", "ymax", "confidence", "class", "name" # xyxy columns + cb = "xcenter", "ycenter", "width", "height", "confidence", "class", "name" # xywh columns + for k, c in zip(["xyxy", "xyxyn", "xywh", "xywhn"], [ca, ca, cb, cb]): a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) return new @@ -844,7 +875,7 @@ def __str__(self): # override print(results) return self._run(pprint=True) # print results def __repr__(self): - return f'YOLOv5 {self.__class__} instance\n' + self.__str__() + return f"YOLOv5 {self.__class__} instance\n" + self.__str__() class Proto(nn.Module): @@ -852,7 +883,7 @@ class Proto(nn.Module): def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks super().__init__() self.cv1 = Conv(c1, c_, k=3) - self.upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.upsample = nn.Upsample(scale_factor=2, mode="nearest") self.cv2 = Conv(c_, c_, k=3) self.cv3 = Conv(c_, c2) @@ -862,14 +893,9 @@ def forward(self, x): class Classify(nn.Module): # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2) - def __init__(self, - c1, - c2, - k=1, - s=1, - p=None, - g=1, - dropout_p=0.0): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability + def __init__( + self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0 + ): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability super().__init__() c_ = 1280 # efficientnet_b0 size self.conv = Conv(c1, c_, k, s, autopad(k, p), g) diff --git a/models/experimental.py b/models/experimental.py index 11f75e2254b3..c242364bdec5 100644 --- a/models/experimental.py +++ b/models/experimental.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Experimental modules -""" +"""Experimental modules.""" import math import numpy as np @@ -38,7 +36,7 @@ def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kern super().__init__() n = len(k) # number of convolutions if equal_ch: # equal c_ per group - i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices + i = torch.linspace(0, n - 1e-6, c2).floor() # c2 indices c_ = [(i == g).sum() for g in range(n)] # intermediate channels else: # equal weight.numel() per group b = [c2] + [0] * n @@ -48,8 +46,9 @@ def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kern a[0] = 1 c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b - self.m = nn.ModuleList([ - nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) + self.m = nn.ModuleList( + [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)] + ) self.bn = nn.BatchNorm2d(c2) self.act = nn.SiLU() @@ -76,16 +75,16 @@ def attempt_load(weights, device=None, inplace=True, fuse=True): model = Ensemble() for w in weights if isinstance(weights, list) else [weights]: - ckpt = torch.load(attempt_download(w), map_location='cpu') # load - ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model + ckpt = torch.load(attempt_download(w), map_location="cpu") # load + ckpt = (ckpt.get("ema") or ckpt["model"]).to(device).float() # FP32 model # Model compatibility updates - if not hasattr(ckpt, 'stride'): - ckpt.stride = torch.tensor([32.]) - if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)): + if not hasattr(ckpt, "stride"): + ckpt.stride = torch.tensor([32.0]) + if hasattr(ckpt, "names") and isinstance(ckpt.names, (list, tuple)): ckpt.names = dict(enumerate(ckpt.names)) # convert to dict - model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode + model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, "fuse") else ckpt.eval()) # model in eval mode # Module updates for m in model.modules(): @@ -93,9 +92,9 @@ def attempt_load(weights, device=None, inplace=True, fuse=True): if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): m.inplace = inplace if t is Detect and not isinstance(m.anchor_grid, list): - delattr(m, 'anchor_grid') - setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) - elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): + delattr(m, "anchor_grid") + setattr(m, "anchor_grid", [torch.zeros(1)] * m.nl) + elif t is nn.Upsample and not hasattr(m, "recompute_scale_factor"): m.recompute_scale_factor = None # torch 1.11.0 compatibility # Return model @@ -103,9 +102,9 @@ def attempt_load(weights, device=None, inplace=True, fuse=True): return model[-1] # Return detection ensemble - print(f'Ensemble created with {weights}\n') - for k in 'names', 'nc', 'yaml': + print(f"Ensemble created with {weights}\n") + for k in "names", "nc", "yaml": setattr(model, k, getattr(model[0], k)) model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride - assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}' + assert all(model[0].nc == m.nc for m in model), f"Models have different class counts: {[m.nc for m in model]}" return model diff --git a/models/tf.py b/models/tf.py index 17cca1e54fcf..53520b52c086 100644 --- a/models/tf.py +++ b/models/tf.py @@ -27,8 +27,21 @@ import torch.nn as nn from tensorflow import keras -from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv, - DWConvTranspose2d, Focus, autopad) +from models.common import ( + C3, + SPP, + SPPF, + Bottleneck, + BottleneckCSP, + C3x, + Concat, + Conv, + CrossConv, + DWConv, + DWConvTranspose2d, + Focus, + autopad, +) from models.experimental import MixConv2d, attempt_load from models.yolo import Detect, Segment from utils.activations import SiLU @@ -44,7 +57,8 @@ def __init__(self, w=None): gamma_initializer=keras.initializers.Constant(w.weight.numpy()), moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), - epsilon=w.eps) + epsilon=w.eps, + ) def call(self, inputs): return self.bn(inputs) @@ -60,7 +74,7 @@ def __init__(self, pad): self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]]) def call(self, inputs): - return tf.pad(inputs, self.pad, mode='constant', constant_values=0) + return tf.pad(inputs, self.pad, mode="constant", constant_values=0) class TFConv(keras.layers.Layer): @@ -75,12 +89,13 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): filters=c2, kernel_size=k, strides=s, - padding='SAME' if s == 1 else 'VALID', - use_bias=not hasattr(w, 'bn'), + padding="SAME" if s == 1 else "VALID", + use_bias=not hasattr(w, "bn"), kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) + bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), + ) self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) - self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity + self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity self.act = activations(w.act) if act else tf.identity def call(self, inputs): @@ -92,17 +107,18 @@ class TFDWConv(keras.layers.Layer): def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups super().__init__() - assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels' + assert c2 % c1 == 0, f"TFDWConv() output={c2} must be a multiple of input={c1} channels" conv = keras.layers.DepthwiseConv2D( kernel_size=k, depth_multiplier=c2 // c1, strides=s, - padding='SAME' if s == 1 else 'VALID', - use_bias=not hasattr(w, 'bn'), + padding="SAME" if s == 1 else "VALID", + use_bias=not hasattr(w, "bn"), depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) + bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), + ) self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) - self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity + self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity self.act = activations(w.act) if act else tf.identity def call(self, inputs): @@ -114,19 +130,23 @@ class TFDWConvTranspose2d(keras.layers.Layer): def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups super().__init__() - assert c1 == c2, f'TFDWConv() output={c2} must be equal to input={c1} channels' - assert k == 4 and p1 == 1, 'TFDWConv() only valid for k=4 and p1=1' + assert c1 == c2, f"TFDWConv() output={c2} must be equal to input={c1} channels" + assert k == 4 and p1 == 1, "TFDWConv() only valid for k=4 and p1=1" weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy() self.c1 = c1 self.conv = [ - keras.layers.Conv2DTranspose(filters=1, - kernel_size=k, - strides=s, - padding='VALID', - output_padding=p2, - use_bias=True, - kernel_initializer=keras.initializers.Constant(weight[..., i:i + 1]), - bias_initializer=keras.initializers.Constant(bias[i])) for i in range(c1)] + keras.layers.Conv2DTranspose( + filters=1, + kernel_size=k, + strides=s, + padding="VALID", + output_padding=p2, + use_bias=True, + kernel_initializer=keras.initializers.Constant(weight[..., i : i + 1]), + bias_initializer=keras.initializers.Constant(bias[i]), + ) + for i in range(c1) + ] def call(self, inputs): return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1] @@ -176,14 +196,15 @@ class TFConv2d(keras.layers.Layer): def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): super().__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" - self.conv = keras.layers.Conv2D(filters=c2, - kernel_size=k, - strides=s, - padding='VALID', - use_bias=bias, - kernel_initializer=keras.initializers.Constant( - w.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None) + self.conv = keras.layers.Conv2D( + filters=c2, + kernel_size=k, + strides=s, + padding="VALID", + use_bias=bias, + kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, + ) def call(self, inputs): return self.conv(inputs) @@ -233,8 +254,9 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) - self.m = keras.Sequential([ - TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)]) + self.m = keras.Sequential( + [TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)] + ) def call(self, inputs): return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) @@ -247,7 +269,7 @@ def __init__(self, c1, c2, k=(5, 9, 13), w=None): c_ = c1 // 2 # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) - self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] + self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding="SAME") for x in k] def call(self, inputs): x = self.cv1(inputs) @@ -261,7 +283,7 @@ def __init__(self, c1, c2, k=5, w=None): c_ = c1 // 2 # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) - self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME') + self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding="SAME") def call(self, inputs): x = self.cv1(inputs) @@ -307,10 +329,10 @@ def call(self, inputs): # Normalize xywh to 0-1 to reduce calibration error xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) - y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1) + y = tf.concat([xy, wh, tf.sigmoid(y[..., 4 : 5 + self.nc]), y[..., 5 + self.nc :]], -1) z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) - return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), ) + return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),) @staticmethod def _make_grid(nx=20, ny=20): @@ -340,11 +362,10 @@ def call(self, x): class TFProto(keras.layers.Layer): - def __init__(self, c1, c_=256, c2=32, w=None): super().__init__() self.cv1 = TFConv(c1, c_, k=3, w=w.cv1) - self.upsample = TFUpsample(None, scale_factor=2, mode='nearest') + self.upsample = TFUpsample(None, scale_factor=2, mode="nearest") self.cv2 = TFConv(c_, c_, k=3, w=w.cv2) self.cv3 = TFConv(c_, c2, w=w.cv3) @@ -356,7 +377,7 @@ class TFUpsample(keras.layers.Layer): # TF version of torch.nn.Upsample() def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' super().__init__() - assert scale_factor % 2 == 0, 'scale_factor must be multiple of 2' + assert scale_factor % 2 == 0, "scale_factor must be multiple of 2" self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode) # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) # with default arguments: align_corners=False, half_pixel_centers=False @@ -371,7 +392,7 @@ class TFConcat(keras.layers.Layer): # TF version of torch.concat() def __init__(self, dimension=1, w=None): super().__init__() - assert dimension == 1, 'convert only NCHW to NHWC concat' + assert dimension == 1, "convert only NCHW to NHWC concat" self.d = 3 def call(self, inputs): @@ -380,15 +401,20 @@ def call(self, inputs): def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - anchors, nc, gd, gw, ch_mul = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get( - 'channel_multiple') + anchors, nc, gd, gw, ch_mul = ( + d["anchors"], + d["nc"], + d["depth_multiple"], + d["width_multiple"], + d.get("channel_multiple"), + ) na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) if not ch_mul: ch_mul = 8 layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out - for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args m_str = m m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): @@ -399,8 +425,20 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in [ - nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv, - BottleneckCSP, C3, C3x]: + nn.Conv2d, + Conv, + DWConv, + DWConvTranspose2d, + Bottleneck, + SPP, + SPPF, + MixConv2d, + Focus, + CrossConv, + BottleneckCSP, + C3, + C3x, + ]: c1, c2 = ch[f], args[0] c2 = make_divisible(c2 * gw, ch_mul) if c2 != no else c2 @@ -422,15 +460,18 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) else: c2 = ch[f] - tf_m = eval('TF' + m_str.replace('nn.', '')) - m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ - else tf_m(*args, w=model.model[i]) # module + tf_m = eval("TF" + m_str.replace("nn.", "")) + m_ = ( + keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) + if n > 1 + else tf_m(*args, w=model.model[i]) + ) # module torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module - t = str(m)[8:-2].replace('__main__.', '') # module type + t = str(m)[8:-2].replace("__main__.", "") # module type np = sum(x.numel() for x in torch_m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params - LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print + LOGGER.info(f"{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}") # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) ch.append(c2) @@ -439,30 +480,33 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) class TFModel: # TF YOLOv5 model - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes + def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes super().__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub + self.yaml_file = Path(cfg).name with open(cfg) as f: self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict # Define model - if nc and nc != self.yaml['nc']: + if nc and nc != self.yaml["nc"]: LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") - self.yaml['nc'] = nc # override yaml value + self.yaml["nc"] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) - def predict(self, - inputs, - tf_nms=False, - agnostic_nms=False, - topk_per_class=100, - topk_all=100, - iou_thres=0.45, - conf_thres=0.25): + def predict( + self, + inputs, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25, + ): y = [] # outputs x = inputs for m in self.model.layers: @@ -482,14 +526,10 @@ def predict(self, nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) else: boxes = tf.expand_dims(boxes, 2) - nms = tf.image.combined_non_max_suppression(boxes, - scores, - topk_per_class, - topk_all, - iou_thres, - conf_thres, - clip_boxes=False) - return (nms, ) + nms = tf.image.combined_non_max_suppression( + boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False + ) + return (nms,) return x # output [1,6300,85] = [xywh, conf, class0, class1, ...] # x = x[0] # [x(1,6300,85), ...] to x(6300,85) # xywh = x[..., :4] # x(6300,4) boxes @@ -508,36 +548,42 @@ class AgnosticNMS(keras.layers.Layer): # TF Agnostic NMS def call(self, input, topk_all, iou_thres, conf_thres): # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450 - return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), - input, - fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), - name='agnostic_nms') + return tf.map_fn( + lambda x: self._nms(x, topk_all, iou_thres, conf_thres), + input, + fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), + name="agnostic_nms", + ) @staticmethod def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS boxes, classes, scores = x class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) scores_inp = tf.reduce_max(scores, -1) - selected_inds = tf.image.non_max_suppression(boxes, - scores_inp, - max_output_size=topk_all, - iou_threshold=iou_thres, - score_threshold=conf_thres) + selected_inds = tf.image.non_max_suppression( + boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres + ) selected_boxes = tf.gather(boxes, selected_inds) - padded_boxes = tf.pad(selected_boxes, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], - mode='CONSTANT', - constant_values=0.0) + padded_boxes = tf.pad( + selected_boxes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], + mode="CONSTANT", + constant_values=0.0, + ) selected_scores = tf.gather(scores_inp, selected_inds) - padded_scores = tf.pad(selected_scores, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], - mode='CONSTANT', - constant_values=-1.0) + padded_scores = tf.pad( + selected_scores, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode="CONSTANT", + constant_values=-1.0, + ) selected_classes = tf.gather(class_inds, selected_inds) - padded_classes = tf.pad(selected_classes, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], - mode='CONSTANT', - constant_values=-1.0) + padded_classes = tf.pad( + selected_classes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode="CONSTANT", + constant_values=-1.0, + ) valid_detections = tf.shape(selected_inds)[0] return padded_boxes, padded_scores, padded_classes, valid_detections @@ -551,7 +597,7 @@ def activations(act=nn.SiLU): elif isinstance(act, (nn.SiLU, SiLU)): return lambda x: keras.activations.swish(x) else: - raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}') + raise Exception(f"no matching TensorFlow activation found for PyTorch activation {act}") def representative_dataset_gen(dataset, ncalib=100): @@ -566,14 +612,14 @@ def representative_dataset_gen(dataset, ncalib=100): def run( - weights=ROOT / 'yolov5s.pt', # weights path - imgsz=(640, 640), # inference size h,w - batch_size=1, # batch size - dynamic=False, # dynamic batch size + weights=ROOT / "yolov5s.pt", # weights path + imgsz=(640, 640), # inference size h,w + batch_size=1, # batch size + dynamic=False, # dynamic batch size ): # PyTorch model im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image - model = attempt_load(weights, device=torch.device('cpu'), inplace=True, fuse=False) + model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False) _ = model(im) # inference model.info() @@ -587,15 +633,15 @@ def run( keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) keras_model.summary() - LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.') + LOGGER.info("PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.") def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--dynamic', action='store_true', help='dynamic batch size') + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") + parser.add_argument("--batch-size", type=int, default=1, help="batch size") + parser.add_argument("--dynamic", action="store_true", help="dynamic batch size") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) @@ -606,6 +652,6 @@ def main(opt): run(**vars(opt)) -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/models/yolo.py b/models/yolo.py index f6cdbcb5d2d8..e98351b98691 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -1,6 +1,6 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -YOLO-specific modules +YOLO-specific modules. Usage: $ python models/yolo.py --cfg yolov5s.yaml @@ -22,18 +22,46 @@ ROOT = FILE.parents[1] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH -if platform.system() != 'Windows': +if platform.system() != "Windows": ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative -from models.common import (C3, C3SPP, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C3Ghost, C3x, Classify, Concat, - Contract, Conv, CrossConv, DetectMultiBackend, DWConv, DWConvTranspose2d, Expand, Focus, - GhostBottleneck, GhostConv, Proto) +from models.common import ( + C3, + C3SPP, + C3TR, + SPP, + SPPF, + Bottleneck, + BottleneckCSP, + C3Ghost, + C3x, + Classify, + Concat, + Contract, + Conv, + CrossConv, + DetectMultiBackend, + DWConv, + DWConvTranspose2d, + Expand, + Focus, + GhostBottleneck, + GhostConv, + Proto, +) from models.experimental import MixConv2d from utils.autoanchor import check_anchor_order from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args from utils.plots import feature_visualization -from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device, - time_sync) +from utils.torch_utils import ( + fuse_conv_and_bn, + initialize_weights, + model_info, + profile, + scale_img, + select_device, + time_sync, +) try: import thop # for FLOPs computation @@ -55,7 +83,7 @@ def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid - self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) + self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv self.inplace = inplace # use inplace ops (e.g. slice assignment) @@ -82,14 +110,14 @@ def forward(self, x): y = torch.cat((xy, wh, conf), 4) z.append(y.view(bs, self.na * nx * ny, self.no)) - return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x) + return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) - def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')): + def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")): d = self.anchors[i].device t = self.anchors[i].dtype shape = 1, self.na, ny, nx, 2 # grid shape y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) - yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility + yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) return grid, anchor_grid @@ -132,23 +160,23 @@ def _forward_once(self, x, profile=False, visualize=False): def _profile_one_layer(self, m, x, dt): c = m == self.model[-1] # is final layer, copy input as inplace fix - o = thop.profile(m, inputs=(x.copy() if c else x, ), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs + o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs t = time_sync() for _ in range(10): m(x.copy() if c else x) dt.append((time_sync() - t) * 100) if m == self.model[0]: LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") - LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') + LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}") if c: LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers - LOGGER.info('Fusing layers... ') + LOGGER.info("Fusing layers... ") for m in self.model.modules(): - if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'): + if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"): m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv - delattr(m, 'bn') # remove batchnorm + delattr(m, "bn") # remove batchnorm m.forward = m.forward_fuse # update forward self.info() return self @@ -170,27 +198,28 @@ def _apply(self, fn): class DetectionModel(BaseModel): # YOLOv5 detection model - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes + def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None): # model, input channels, number of classes super().__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub + self.yaml_file = Path(cfg).name - with open(cfg, encoding='ascii', errors='ignore') as f: + with open(cfg, encoding="ascii", errors="ignore") as f: self.yaml = yaml.safe_load(f) # model dict # Define model - ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels - if nc and nc != self.yaml['nc']: + ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels + if nc and nc != self.yaml["nc"]: LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") - self.yaml['nc'] = nc # override yaml value + self.yaml["nc"] = nc # override yaml value if anchors: - LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') - self.yaml['anchors'] = round(anchors) # override yaml value + LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}") + self.yaml["anchors"] = round(anchors) # override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist - self.names = [str(i) for i in range(self.yaml['nc'])] # default names - self.inplace = self.yaml.get('inplace', True) + self.names = [str(i) for i in range(self.yaml["nc"])] # default names + self.inplace = self.yaml.get("inplace", True) # Build strides, anchors m = self.model[-1] # Detect() @@ -207,7 +236,7 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, i # Init weights, biases initialize_weights(self) self.info() - LOGGER.info('') + LOGGER.info("") def forward(self, x, augment=False, profile=False, visualize=False): if augment: @@ -248,9 +277,9 @@ def _descale_pred(self, p, flips, scale, img_size): def _clip_augmented(self, y): # Clip YOLOv5 augmented inference tails nl = self.model[-1].nl # number of detection layers (P3-P5) - g = sum(4 ** x for x in range(nl)) # grid points + g = sum(4**x for x in range(nl)) # grid points e = 1 # exclude layer count - i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices + i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices y[0] = y[0][:, :-i] # large i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices y[-1] = y[-1][:, i:] # small @@ -263,7 +292,9 @@ def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is for mi, s in zip(m.m, m.stride): # from b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) - b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls + b.data[:, 5 : 5 + m.nc] += ( + math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) + ) # cls mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) @@ -272,7 +303,7 @@ def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class SegmentationModel(DetectionModel): # YOLOv5 segmentation model - def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None): + def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None): super().__init__(cfg, ch, nc, anchors) @@ -288,9 +319,9 @@ def _from_detection_model(self, model, nc=1000, cutoff=10): model = model.model # unwrap DetectMultiBackend model.model = model.model[:cutoff] # backbone m = model.model[-1] # last layer - ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module + ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module c = Classify(ch, nc) # Classify() - c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type + c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type model.model[-1] = c # replace self.model = model.model self.stride = model.stride @@ -305,8 +336,14 @@ def _from_yaml(self, cfg): def parse_model(d, ch): # model_dict, input_channels(3) # Parse a YOLOv5 model.yaml dictionary LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - anchors, nc, gd, gw, act, ch_mul = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get( - 'activation'), d.get('channel_multiple') + anchors, nc, gd, gw, act, ch_mul = ( + d["anchors"], + d["nc"], + d["depth_multiple"], + d["width_multiple"], + d.get("activation"), + d.get("channel_multiple"), + ) if act: Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() LOGGER.info(f"{colorstr('activation:')} {act}") # print @@ -316,7 +353,7 @@ def parse_model(d, ch): # model_dict, input_channels(3) no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out - for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): with contextlib.suppress(NameError): @@ -324,8 +361,25 @@ def parse_model(d, ch): # model_dict, input_channels(3) n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain if m in { - Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, - BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: + Conv, + GhostConv, + Bottleneck, + GhostBottleneck, + SPP, + SPPF, + DWConv, + MixConv2d, + Focus, + CrossConv, + BottleneckCSP, + C3, + C3TR, + C3SPP, + C3Ghost, + nn.ConvTranspose2d, + DWConvTranspose2d, + C3x, + }: c1, c2 = ch[f], args[0] if c2 != no: # if not output c2 = make_divisible(c2 * gw, ch_mul) @@ -353,10 +407,10 @@ def parse_model(d, ch): # model_dict, input_channels(3) c2 = ch[f] m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module - t = str(m)[8:-2].replace('__main__.', '') # module type + t = str(m)[8:-2].replace("__main__.", "") # module type np = sum(x.numel() for x in m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params - LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print + LOGGER.info(f"{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}") # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: @@ -365,14 +419,14 @@ def parse_model(d, ch): # model_dict, input_channels(3) return nn.Sequential(*layers), sorted(save) -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') - parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--profile', action='store_true', help='profile model speed') - parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer') - parser.add_argument('--test', action='store_true', help='test all yolo*.yaml') + parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml") + parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--profile", action="store_true", help="profile model speed") + parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer") + parser.add_argument("--test", action="store_true", help="test all yolo*.yaml") opt = parser.parse_args() opt.cfg = check_yaml(opt.cfg) # check YAML print_args(vars(opt)) @@ -390,11 +444,11 @@ def parse_model(d, ch): # model_dict, input_channels(3) results = profile(input=im, ops=[model], n=3) elif opt.test: # test all models - for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'): + for cfg in Path(ROOT / "models").rglob("yolo*.yaml"): try: _ = Model(cfg) except Exception as e: - print(f'Error in {cfg}: {e}') + print(f"Error in {cfg}: {e}") else: # report fused model summary model.fuse() diff --git a/segment/predict.py b/segment/predict.py index 8e3d97dfeb92..23a4e3538509 100644 --- a/segment/predict.py +++ b/segment/predict.py @@ -46,23 +46,36 @@ from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams -from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, - increment_path, non_max_suppression, print_args, scale_boxes, scale_segments, - strip_optimizer) +from utils.general import ( + LOGGER, + Profile, + check_file, + check_img_size, + check_imshow, + check_requirements, + colorstr, + cv2, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + scale_segments, + strip_optimizer, +) from utils.segment.general import masks2segments, process_mask, process_mask_native from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( - weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s) - source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) - data=ROOT / 'data/coco128.yaml', # dataset.yaml path + weights=ROOT / "yolov5s-seg.pt", # model.pt path(s) + source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) + data=ROOT / "data/coco128.yaml", # dataset.yaml path imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results save_txt=False, # save results to *.txt save_conf=False, # save confidences in --save-txt labels @@ -73,8 +86,8 @@ def run( augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models - project=ROOT / 'runs/predict-seg', # save results to project/name - name='exp', # save results to project/name + project=ROOT / "runs/predict-seg", # save results to project/name + name="exp", # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels @@ -85,17 +98,17 @@ def run( retina_masks=False, ): source = str(source) - save_img = not nosave and not source.endswith('.txt') # save inference images + save_img = not nosave and not source.endswith(".txt") # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) - is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) - webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) - screenshot = source.lower().startswith('screen') + is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) + webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) + screenshot = source.lower().startswith("screen") if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) @@ -143,14 +156,14 @@ def run( seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count - s += f'{i}: ' + s += f"{i}: " else: - p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt - s += '%gx%g ' % im.shape[2:] # print string + txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt + s += "%gx%g " % im.shape[2:] # print string imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): @@ -166,7 +179,8 @@ def run( if save_txt: segments = [ scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True) - for x in reversed(masks2segments(masks))] + for x in reversed(masks2segments(masks)) + ] # Print results for c in det[:, 5].unique(): @@ -177,39 +191,42 @@ def run( annotator.masks( masks, colors=[colors(x, True) for x in det[:, 5]], - im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / - 255 if retina_masks else im[i]) + im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() + / 255 + if retina_masks + else im[i], + ) # Write results for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): if save_txt: # Write to file seg = segments[j].reshape(-1) # (n,2) to (n*2) line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format - with open(f'{txt_path}.txt', 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') + with open(f"{txt_path}.txt", "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") if save_img or save_crop or view_img: # Add bbox to image c = int(cls) # integer class - label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}") annotator.box_label(xyxy, label, color=colors(c, True)) # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3) if save_crop: - save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) # Stream results im0 = annotator.result() if view_img: - if platform.system() == 'Linux' and p not in windows: + if platform.system() == "Linux" and p not in windows: windows.append(p) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) cv2.imshow(str(p), im0) - if cv2.waitKey(1) == ord('q'): # 1 millisecond + if cv2.waitKey(1) == ord("q"): # 1 millisecond exit() # Save results (image with detections) if save_img: - if dataset.mode == 'image': + if dataset.mode == "image": cv2.imwrite(save_path, im0) else: # 'video' or 'stream' if vid_path[i] != save_path: # new video @@ -222,18 +239,18 @@ def run( h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos - vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) vid_writer[i].write(im0) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") # Print results - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) @@ -241,34 +258,34 @@ def run( def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='show results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') - parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--visualize', action='store_true', help='visualize features') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default=ROOT / 'runs/predict-seg', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') - parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') - parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') - parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution') + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)") + parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") + parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--view-img", action="store_true", help="show results") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes") + parser.add_argument("--nosave", action="store_true", help="do not save images/videos") + parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3") + parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--visualize", action="store_true", help="visualize features") + parser.add_argument("--update", action="store_true", help="update all models") + parser.add_argument("--project", default=ROOT / "runs/predict-seg", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save results to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)") + parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels") + parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") + parser.add_argument("--retina-masks", action="store_true", help="whether to plot masks in native resolution") opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) @@ -276,10 +293,10 @@ def parse_opt(): def main(opt): - check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) run(**vars(opt)) -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/segment/train.py b/segment/train.py index 2ae09c1cbf66..fe262348fae4 100644 --- a/segment/train.py +++ b/segment/train.py @@ -1,7 +1,7 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -Train a YOLOv5 segment model on a segment dataset -Models and datasets download automatically from the latest YOLOv5 release. +Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5 +release. Usage - Single-GPU training: $ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended) @@ -47,47 +47,104 @@ from utils.autobatch import check_train_batch_size from utils.callbacks import Callbacks from utils.downloads import attempt_download, is_url -from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, - check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, - get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, - labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save) +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + check_amp, + check_dataset, + check_file, + check_git_info, + check_git_status, + check_img_size, + check_requirements, + check_suffix, + check_yaml, + colorstr, + get_latest_run, + increment_path, + init_seeds, + intersect_dicts, + labels_to_class_weights, + labels_to_image_weights, + one_cycle, + print_args, + print_mutation, + strip_optimizer, + yaml_save, +) from utils.loggers import GenericLogger from utils.plots import plot_evolve, plot_labels from utils.segment.dataloaders import create_dataloader from utils.segment.loss import ComputeLoss from utils.segment.metrics import KEYS, fitness from utils.segment.plots import plot_images_and_masks, plot_results_with_masks -from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, - smart_resume, torch_distributed_zero_first) - -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +from utils.torch_utils import ( + EarlyStopping, + ModelEMA, + de_parallel, + select_device, + smart_DDP, + smart_optimizer, + smart_resume, + torch_distributed_zero_first, +) + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary - save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, mask_ratio = \ - Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ - opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze, opt.mask_ratio + ( + save_dir, + epochs, + batch_size, + weights, + single_cls, + evolve, + data, + cfg, + resume, + noval, + nosave, + workers, + freeze, + mask_ratio, + ) = ( + Path(opt.save_dir), + opt.epochs, + opt.batch_size, + opt.weights, + opt.single_cls, + opt.evolve, + opt.data, + opt.cfg, + opt.resume, + opt.noval, + opt.nosave, + opt.workers, + opt.freeze, + opt.mask_ratio, + ) # callbacks.run('on_pretrain_routine_start') # Directories - w = save_dir / 'weights' # weights dir + w = save_dir / "weights" # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir - last, best = w / 'last.pt', w / 'best.pt' + last, best = w / "last.pt", w / "best.pt" # Hyperparameters if isinstance(hyp, str): - with open(hyp, errors='ignore') as f: + with open(hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict - LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: - yaml_save(save_dir / 'hyp.yaml', hyp) - yaml_save(save_dir / 'opt.yaml', vars(opt)) + yaml_save(save_dir / "hyp.yaml", hyp) + yaml_save(save_dir / "opt.yaml", vars(opt)) # Loggers data_dict = None @@ -97,39 +154,39 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Config plots = not evolve and not opt.noplots # create plots overlap = not opt.no_overlap - cuda = device.type != 'cpu' + cuda = device.type != "cpu" init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None - train_path, val_path = data_dict['train'], data_dict['val'] - nc = 1 if single_cls else int(data_dict['nc']) # number of classes - names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names - is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset + train_path, val_path = data_dict["train"], data_dict["val"] + nc = 1 if single_cls else int(data_dict["nc"]) # number of classes + names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names + is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset # Model - check_suffix(weights, '.pt') # check weights - pretrained = weights.endswith('.pt') + check_suffix(weights, ".pt") # check weights + pretrained = weights.endswith(".pt") if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally - ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak - model = SegmentationModel(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) - exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys - csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + ckpt = torch.load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak + model = SegmentationModel(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) + exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys + csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load - LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report + LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report else: - model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create amp = check_amp(model) # check AMP # Freeze - freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): - LOGGER.info(f'freezing {k}') + LOGGER.info(f"freezing {k}") v.requires_grad = False # Image size @@ -139,20 +196,20 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) - logger.update_params({'batch_size': batch_size}) + logger.update_params({"batch_size": batch_size}) # loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing - hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay - optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) + hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"]) # Scheduler if opt.cos_lr: - lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf'] else: - lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + lf = lambda x: (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) # EMA @@ -168,15 +225,15 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning( - 'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' - 'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.' + "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n" + "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started." ) model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and RANK != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) - LOGGER.info('Using SyncBatchNorm()') + LOGGER.info("Using SyncBatchNorm()") # Trainloader train_loader, dataset = create_dataloader( @@ -187,41 +244,43 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio single_cls, hyp=hyp, augment=True, - cache=None if opt.cache == 'val' else opt.cache, + cache=None if opt.cache == "val" else opt.cache, rect=opt.rect, rank=LOCAL_RANK, workers=workers, image_weights=opt.image_weights, quad=opt.quad, - prefix=colorstr('train: '), + prefix=colorstr("train: "), shuffle=True, mask_downsample_ratio=mask_ratio, overlap_mask=overlap, ) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class - assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' + assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}" # Process 0 if RANK in {-1, 0}: - val_loader = create_dataloader(val_path, - imgsz, - batch_size // WORLD_SIZE * 2, - gs, - single_cls, - hyp=hyp, - cache=None if noval else opt.cache, - rect=True, - rank=-1, - workers=workers * 2, - pad=0.5, - mask_downsample_ratio=mask_ratio, - overlap_mask=overlap, - prefix=colorstr('val: '))[0] + val_loader = create_dataloader( + val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + mask_downsample_ratio=mask_ratio, + overlap_mask=overlap, + prefix=colorstr("val: "), + )[0] if not resume: if not opt.noautoanchor: - check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor + check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision if plots: @@ -234,10 +293,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) - hyp['box'] *= 3 / nl # scale to layers - hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers - hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers - hyp['label_smoothing'] = opt.label_smoothing + hyp["box"] *= 3 / nl # scale to layers + hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers + hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp["label_smoothing"] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights @@ -246,7 +305,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Start training t0 = time.time() nb = len(train_loader) # number of batches - nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class @@ -256,10 +315,12 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model, overlap=overlap) # init loss class # callbacks.run('on_train_start') - LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' - f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' - f"Logging results to {colorstr('bold', save_dir)}\n" - f'Starting training for {epochs} epochs...') + LOGGER.info( + f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...' + ) for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ # callbacks.run('on_train_epoch_start') model.train() @@ -278,8 +339,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) - LOGGER.info(('\n' + '%11s' * 8) % - ('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) + LOGGER.info( + ("\n" + "%11s" * 8) + % ("Epoch", "GPU_mem", "box_loss", "seg_loss", "obj_loss", "cls_loss", "Instances", "Size") + ) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() @@ -295,9 +358,9 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 - x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) - if 'momentum' in x: - x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)]) + if "momentum" in x: + x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]) # Multi-scale if opt.multi_scale: @@ -305,7 +368,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) - imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) # Forward with torch.cuda.amp.autocast(amp): @@ -314,7 +377,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio if RANK != -1: loss *= WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: - loss *= 4. + loss *= 4.0 # Backward scaler.scale(loss).backward() @@ -333,9 +396,11 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses - mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) - pbar.set_description(('%11s' * 2 + '%11.4g' * 6) % - (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) + mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB) + pbar.set_description( + ("%11s" * 2 + "%11.4g" * 6) + % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1]) + ) # callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths) # if callbacks.stop_training: # return @@ -343,35 +408,37 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Mosaic plots if plots: if ni < 3: - plot_images_and_masks(imgs, targets, masks, paths, save_dir / f'train_batch{ni}.jpg') + plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg") if ni == 10: - files = sorted(save_dir.glob('train*.jpg')) - logger.log_images(files, 'Mosaics', epoch) + files = sorted(save_dir.glob("train*.jpg")) + logger.log_images(files, "Mosaics", epoch) # end batch ------------------------------------------------------------------------------------------------ # Scheduler - lr = [x['lr'] for x in optimizer.param_groups] # for loggers + lr = [x["lr"] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP # callbacks.run('on_train_epoch_end', epoch=epoch) - ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) + ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"]) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP - results, maps, _ = validate.run(data_dict, - batch_size=batch_size // WORLD_SIZE * 2, - imgsz=imgsz, - half=amp, - model=ema.ema, - single_cls=single_cls, - dataloader=val_loader, - save_dir=save_dir, - plots=False, - callbacks=callbacks, - compute_loss=compute_loss, - mask_downsample_ratio=mask_ratio, - overlap=overlap) + results, maps, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss, + mask_downsample_ratio=mask_ratio, + overlap=overlap, + ) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] @@ -387,23 +454,24 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { - 'epoch': epoch, - 'best_fitness': best_fitness, - 'model': deepcopy(de_parallel(model)).half(), - 'ema': deepcopy(ema.ema).half(), - 'updates': ema.updates, - 'optimizer': optimizer.state_dict(), - 'opt': vars(opt), - 'git': GIT_INFO, # {remote, branch, commit} if a git repo - 'date': datetime.now().isoformat()} + "epoch": epoch, + "best_fitness": best_fitness, + "model": deepcopy(de_parallel(model)).half(), + "ema": deepcopy(ema.ema).half(), + "updates": ema.updates, + "optimizer": optimizer.state_dict(), + "opt": vars(opt), + "git": GIT_INFO, # {remote, branch, commit} if a git repo + "date": datetime.now().isoformat(), + } # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: - torch.save(ckpt, w / f'epoch{epoch}.pt') - logger.log_model(w / f'epoch{epoch}.pt') + torch.save(ckpt, w / f"epoch{epoch}.pt") + logger.log_model(w / f"epoch{epoch}.pt") del ckpt # callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) @@ -419,12 +487,12 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: - LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') + LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.") for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: - LOGGER.info(f'\nValidating {f}...') + LOGGER.info(f"\nValidating {f}...") results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, @@ -440,7 +508,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio callbacks=callbacks, compute_loss=compute_loss, mask_downsample_ratio=mask_ratio, - overlap=overlap) # val best model with plots + overlap=overlap, + ) # val best model with plots if is_coco: # callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr)) @@ -452,56 +521,56 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio if not opt.evolve: logger.log_model(best, epoch) if plots: - plot_results_with_masks(file=save_dir / 'results.csv') # save results.png - files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] + plot_results_with_masks(file=save_dir / "results.csv") # save results.png + files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") - logger.log_images(files, 'Results', epoch + 1) - logger.log_images(sorted(save_dir.glob('val*.jpg')), 'Validation', epoch + 1) + logger.log_images(files, "Results", epoch + 1) + logger.log_images(sorted(save_dir.glob("val*.jpg")), "Validation", epoch + 1) torch.cuda.empty_cache() return results def parse_opt(known=False): parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s-seg.pt', help='initial weights path') - parser.add_argument('--cfg', type=str, default='', help='model.yaml path') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path') - parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') - parser.add_argument('--epochs', type=int, default=100, help='total training epochs') - parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') - parser.add_argument('--rect', action='store_true', help='rectangular training') - parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') - parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--noval', action='store_true', help='only validate final epoch') - parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') - parser.add_argument('--noplots', action='store_true', help='save no plot files') - parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') - parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') - parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') - parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') - parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') - parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') - parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--project', default=ROOT / 'runs/train-seg', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--quad', action='store_true', help='quad dataloader') - parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') - parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') - parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') - parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') - parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') - parser.add_argument('--seed', type=int, default=0, help='Global training seed') - parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s-seg.pt", help="initial weights path") + parser.add_argument("--cfg", type=str, default="", help="model.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path") + parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") + parser.add_argument("--epochs", type=int, default=100, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") + parser.add_argument("--rect", action="store_true", help="rectangular training") + parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--noval", action="store_true", help="only validate final epoch") + parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") + parser.add_argument("--noplots", action="store_true", help="save no plot files") + parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") + parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk") + parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") + parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") + parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") + parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train-seg", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--quad", action="store_true", help="quad dataloader") + parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") + parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") + parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") + parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") + parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") # Instance Segmentation Args - parser.add_argument('--mask-ratio', type=int, default=4, help='Downsample the truth masks to saving memory') - parser.add_argument('--no-overlap', action='store_true', help='Overlap masks train faster at slightly less mAP') + parser.add_argument("--mask-ratio", type=int, default=4, help="Downsample the truth masks to saving memory") + parser.add_argument("--no-overlap", action="store_true", help="Overlap masks train faster at slightly less mAP") return parser.parse_known_args()[0] if known else parser.parse_args() @@ -511,46 +580,51 @@ def main(opt, callbacks=Callbacks()): if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() - check_requirements(ROOT / 'requirements.txt') + check_requirements(ROOT / "requirements.txt") # Resume if opt.resume and not opt.evolve: # resume from specified or most recent last.pt last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) - opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml + opt_yaml = last.parent.parent / "opt.yaml" # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): - with open(opt_yaml, errors='ignore') as f: + with open(opt_yaml, errors="ignore") as f: d = yaml.safe_load(f) else: - d = torch.load(last, map_location='cpu')['opt'] + d = torch.load(last, map_location="cpu")["opt"] opt = argparse.Namespace(**d) # replace - opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate + opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: - opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ - check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks - assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = ( + check_file(opt.data), + check_yaml(opt.cfg), + check_yaml(opt.hyp), + str(opt.weights), + str(opt.project), + ) # checks + assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified" if opt.evolve: - if opt.project == str(ROOT / 'runs/train-seg'): # if default project name, rename to runs/evolve-seg - opt.project = str(ROOT / 'runs/evolve-seg') + if opt.project == str(ROOT / "runs/train-seg"): # if default project name, rename to runs/evolve-seg + opt.project = str(ROOT / "runs/evolve-seg") opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume - if opt.name == 'cfg': + if opt.name == "cfg": opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: - msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' - assert not opt.image_weights, f'--image-weights {msg}' - assert not opt.evolve, f'--evolve {msg}' - assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' - assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' - assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + msg = "is not compatible with YOLOv5 Multi-GPU DDP training" + assert not opt.image_weights, f"--image-weights {msg}" + assert not opt.evolve, f"--evolve {msg}" + assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size" + assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" + assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" torch.cuda.set_device(LOCAL_RANK) - device = torch.device('cuda', LOCAL_RANK) - dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + device = torch.device("cuda", LOCAL_RANK) + dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") # Train if not opt.evolve: @@ -560,65 +634,69 @@ def main(opt, callbacks=Callbacks()): else: # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) meta = { - 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) - 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) - 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 - 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay - 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) - 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum - 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr - 'box': (1, 0.02, 0.2), # box loss gain - 'cls': (1, 0.2, 4.0), # cls loss gain - 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight - 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) - 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight - 'iou_t': (0, 0.1, 0.7), # IoU training threshold - 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold - 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) - 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) - 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) - 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) - 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) - 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) - 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) - 'scale': (1, 0.0, 0.9), # image scale (+/- gain) - 'shear': (1, 0.0, 10.0), # image shear (+/- deg) - 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 - 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) - 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) - 'mosaic': (1, 0.0, 1.0), # image mixup (probability) - 'mixup': (1, 0.0, 1.0), # image mixup (probability) - 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) - - with open(opt.hyp, errors='ignore') as f: + "lr0": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + "lrf": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + "momentum": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + "weight_decay": (1, 0.0, 0.001), # optimizer weight decay + "warmup_epochs": (1, 0.0, 5.0), # warmup epochs (fractions ok) + "warmup_momentum": (1, 0.0, 0.95), # warmup initial momentum + "warmup_bias_lr": (1, 0.0, 0.2), # warmup initial bias lr + "box": (1, 0.02, 0.2), # box loss gain + "cls": (1, 0.2, 4.0), # cls loss gain + "cls_pw": (1, 0.5, 2.0), # cls BCELoss positive_weight + "obj": (1, 0.2, 4.0), # obj loss gain (scale with pixels) + "obj_pw": (1, 0.5, 2.0), # obj BCELoss positive_weight + "iou_t": (0, 0.1, 0.7), # IoU training threshold + "anchor_t": (1, 2.0, 8.0), # anchor-multiple threshold + "anchors": (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + "fl_gamma": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + "hsv_h": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + "hsv_s": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + "hsv_v": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + "degrees": (1, 0.0, 45.0), # image rotation (+/- deg) + "translate": (1, 0.0, 0.9), # image translation (+/- fraction) + "scale": (1, 0.0, 0.9), # image scale (+/- gain) + "shear": (1, 0.0, 10.0), # image shear (+/- deg) + "perspective": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + "flipud": (1, 0.0, 1.0), # image flip up-down (probability) + "fliplr": (0, 0.0, 1.0), # image flip left-right (probability) + "mosaic": (1, 0.0, 1.0), # image mixup (probability) + "mixup": (1, 0.0, 1.0), # image mixup (probability) + "copy_paste": (1, 0.0, 1.0), + } # segment copy-paste (probability) + + with open(opt.hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict - if 'anchors' not in hyp: # anchors commented in hyp.yaml - hyp['anchors'] = 3 + if "anchors" not in hyp: # anchors commented in hyp.yaml + hyp["anchors"] = 3 if opt.noautoanchor: - del hyp['anchors'], meta['anchors'] + del hyp["anchors"], meta["anchors"] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices - evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' + evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv" if opt.bucket: # download evolve.csv if exists - subprocess.run([ - 'gsutil', - 'cp', - f'gs://{opt.bucket}/evolve.csv', - str(evolve_csv), ]) + subprocess.run( + [ + "gsutil", + "cp", + f"gs://{opt.bucket}/evolve.csv", + str(evolve_csv), + ] + ) for _ in range(opt.evolve): # generations to evolve if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate # Select parent(s) - parent = 'single' # parent selection method: 'single' or 'weighted' - x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) + parent = "single" # parent selection method: 'single' or 'weighted' + x = np.loadtxt(evolve_csv, ndmin=2, delimiter=",", skiprows=1) n = min(5, len(x)) # number of previous results to consider x = x[np.argsort(-fitness(x))][:n] # top n mutations - w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) - if parent == 'single' or len(x) == 1: + w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0) + if parent == "single" or len(x) == 1: # x = x[random.randint(0, n - 1)] # random selection x = x[random.choices(range(n), weights=w)[0]] # weighted selection - elif parent == 'weighted': + elif parent == "weighted": x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination # Mutate @@ -647,9 +725,11 @@ def main(opt, callbacks=Callbacks()): # Plot results plot_evolve(evolve_csv) - LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' - f"Results saved to {colorstr('bold', save_dir)}\n" - f'Usage example: $ python train.py --hyp {evolve_yaml}') + LOGGER.info( + f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}' + ) def run(**kwargs): @@ -661,6 +741,6 @@ def run(**kwargs): return opt -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/segment/val.py b/segment/val.py index 304d0c751314..1e5159c710ed 100644 --- a/segment/val.py +++ b/segment/val.py @@ -1,6 +1,6 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -Validate a trained YOLOv5 segment model on a segment dataset +Validate a trained YOLOv5 segment model on a segment dataset. Usage: $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images) @@ -43,9 +43,24 @@ from models.common import DetectMultiBackend from models.yolo import SegmentationModel from utils.callbacks import Callbacks -from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, - check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path, - non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh) +from utils.general import ( + LOGGER, + NUM_THREADS, + TQDM_BAR_FORMAT, + Profile, + check_dataset, + check_img_size, + check_requirements, + check_yaml, + coco80_to_coco91_class, + colorstr, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + xywh2xyxy, + xyxy2xywh, +) from utils.metrics import ConfusionMatrix, box_iou from utils.plots import output_to_target, plot_val_study from utils.segment.dataloaders import create_dataloader @@ -61,8 +76,8 @@ def save_one_txt(predn, save_conf, shape, file): for *xyxy, conf, cls in predn.tolist(): xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format - with open(file, 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') + with open(file, "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") def save_one_json(predn, jdict, path, class_map, pred_masks): @@ -70,8 +85,8 @@ def save_one_json(predn, jdict, path, class_map, pred_masks): from pycocotools.mask import encode def single_encode(x): - rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0] - rle['counts'] = rle['counts'].decode('utf-8') + rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0] + rle["counts"] = rle["counts"].decode("utf-8") return rle image_id = int(path.stem) if path.stem.isnumeric() else path.stem @@ -81,12 +96,15 @@ def single_encode(x): with ThreadPool(NUM_THREADS) as pool: rles = pool.map(single_encode, pred_masks) for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())): - jdict.append({ - 'image_id': image_id, - 'category_id': class_map[int(p[5])], - 'bbox': [round(x, 3) for x in b], - 'score': round(p[4], 5), - 'segmentation': rles[i]}) + jdict.append( + { + "image_id": image_id, + "category_id": class_map[int(p[5])], + "bbox": [round(x, 3) for x in b], + "score": round(p[4], 5), + "segmentation": rles[i], + } + ) def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False): @@ -105,7 +123,7 @@ def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, over gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640) gt_masks = torch.where(gt_masks == index, 1.0, 0.0) if gt_masks.shape[1:] != pred_masks.shape[1:]: - gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0] + gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0] gt_masks = gt_masks.gt_(0.5) iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1)) else: # boxes @@ -128,39 +146,39 @@ def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, over @smart_inference_mode() def run( - data, - weights=None, # model.pt path(s) - batch_size=32, # batch size - imgsz=640, # inference size (pixels) - conf_thres=0.001, # confidence threshold - iou_thres=0.6, # NMS IoU threshold - max_det=300, # maximum detections per image - task='val', # train, val, test, speed or study - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - workers=8, # max dataloader workers (per RANK in DDP mode) - single_cls=False, # treat as single-class dataset - augment=False, # augmented inference - verbose=False, # verbose output - save_txt=False, # save results to *.txt - save_hybrid=False, # save label+prediction hybrid results to *.txt - save_conf=False, # save confidences in --save-txt labels - save_json=False, # save a COCO-JSON results file - project=ROOT / 'runs/val-seg', # save to project/name - name='exp', # save to project/name - exist_ok=False, # existing project/name ok, do not increment - half=True, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - model=None, - dataloader=None, - save_dir=Path(''), - plots=True, - overlap=False, - mask_downsample_ratio=1, - compute_loss=None, - callbacks=Callbacks(), + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task="val", # train, val, test, speed or study + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / "runs/val-seg", # save to project/name + name="exp", # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(""), + plots=True, + overlap=False, + mask_downsample_ratio=1, + compute_loss=None, + callbacks=Callbacks(), ): if save_json: - check_requirements('pycocotools>=2.0.6') + check_requirements("pycocotools>=2.0.6") process = process_mask_native # more accurate else: process = process_mask # faster @@ -169,7 +187,7 @@ def run( training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model - half &= device.type != 'cpu' # half precision only supported on CUDA + half &= device.type != "cpu" # half precision only supported on CUDA model.half() if half else model.float() nm = de_parallel(model).model[-1].nm # number of masks else: # called directly @@ -177,7 +195,7 @@ def run( # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) @@ -191,16 +209,16 @@ def run( device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 - LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") # Data data = check_dataset(data) # check # Configure model.eval() - cuda = device.type != 'cpu' - is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset - nc = 1 if single_cls else int(data['nc']) # number of classes + cuda = device.type != "cpu" + is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset + nc = 1 if single_cls else int(data["nc"]) # number of classes iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() @@ -208,31 +226,46 @@ def run( if not training: if pt and not single_cls: # check --weights are trained on --data ncm = model.model.nc - assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ - f'classes). Pass correct combination of --weights and --data that are trained together.' + assert ncm == nc, ( + f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} " + f"classes). Pass correct combination of --weights and --data that are trained together." + ) model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup - pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks - task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images - dataloader = create_dataloader(data[task], - imgsz, - batch_size, - stride, - single_cls, - pad=pad, - rect=rect, - workers=workers, - prefix=colorstr(f'{task}: '), - overlap_mask=overlap, - mask_downsample_ratio=mask_downsample_ratio)[0] + pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks + task = task if task in ("train", "val", "test") else "val" # path to train/val/test images + dataloader = create_dataloader( + data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f"{task}: "), + overlap_mask=overlap, + mask_downsample_ratio=mask_downsample_ratio, + )[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) - names = model.names if hasattr(model, 'names') else model.module.names # get class names + names = model.names if hasattr(model, "names") else model.module.names # get class names if isinstance(names, (list, tuple)): # old format names = dict(enumerate(names)) class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) - s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R', - 'mAP50', 'mAP50-95)') + s = ("%22s" + "%11s" * 10) % ( + "Class", + "Images", + "Instances", + "Box(P", + "R", + "mAP50", + "mAP50-95)", + "Mask(P", + "R", + "mAP50", + "mAP50-95)", + ) dt = Profile(device=device), Profile(device=device), Profile(device=device) metrics = Metrics() loss = torch.zeros(4, device=device) @@ -263,14 +296,9 @@ def run( targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling with dt[2]: - preds = non_max_suppression(preds, - conf_thres, - iou_thres, - labels=lb, - multi_label=True, - agnostic=single_cls, - max_det=max_det, - nm=nm) + preds = non_max_suppression( + preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm + ) # Metrics plot_masks = [] # masks for plotting @@ -317,10 +345,11 @@ def run( # Save/log if save_txt: - save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') + save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt") if save_json: - pred_masks = scale_image(im[si].shape[1:], - pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]) + pred_masks = scale_image( + im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1] + ) save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary # callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) @@ -328,9 +357,15 @@ def run( if plots and batch_i < 3: if len(plot_masks): plot_masks = torch.cat(plot_masks, dim=0) - plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) - plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths, - save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred + plot_images_and_masks(im, targets, masks, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) + plot_images_and_masks( + im, + output_to_target(preds, max_det=15), + plot_masks, + paths, + save_dir / f"val_batch{batch_i}_pred.jpg", + names, + ) # pred # callbacks.run('on_val_batch_end') @@ -342,10 +377,10 @@ def run( nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class # Print results - pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format - LOGGER.info(pf % ('all', seen, nt.sum(), *metrics.mean_results())) + pf = "%22s" + "%11i" * 2 + "%11.3g" * 8 # print format + LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results())) if nt.sum() == 0: - LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels') + LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels") # Print results per class if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): @@ -353,10 +388,10 @@ def run( LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i))) # Print speeds - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image if not training: shape = (batch_size, 3, imgsz, imgsz) - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t) # Plots if plots: @@ -367,11 +402,11 @@ def run( # Save JSON if save_json and len(jdict): - w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights - anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations - pred_json = str(save_dir / f'{w}_predictions.json') # predictions - LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') - with open(pred_json, 'w') as f: + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights + anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations + pred_json = str(save_dir / f"{w}_predictions.json") # predictions + LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...") + with open(pred_json, "w") as f: json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb @@ -381,7 +416,7 @@ def run( anno = COCO(anno_json) # init annotations api pred = anno.loadRes(pred_json) # init predictions api results = [] - for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'): + for eval in COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm"): if is_coco: eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate eval.evaluate() @@ -390,12 +425,12 @@ def run( results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5) map_bbox, map50_bbox, map_mask, map50_mask = results except Exception as e: - LOGGER.info(f'pycocotools unable to run: {e}') + LOGGER.info(f"pycocotools unable to run: {e}") # Return results model.float() # for training if not training: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t @@ -403,28 +438,28 @@ def run( def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path') - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') - parser.add_argument('--batch-size', type=int, default=32, help='batch size') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image') - parser.add_argument('--task', default='val', help='train, val, test, speed or study') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--verbose', action='store_true', help='report mAP by class') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') - parser.add_argument('--project', default=ROOT / 'runs/val-seg', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)") + parser.add_argument("--batch-size", type=int, default=32, help="batch size") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") + parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image") + parser.add_argument("--task", default="val", help="train, val, test, speed or study") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--verbose", action="store_true", help="report mAP by class") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file") + parser.add_argument("--project", default=ROOT / "runs/val-seg", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML # opt.save_json |= opt.data.endswith('coco.yaml') @@ -434,40 +469,40 @@ def parse_opt(): def main(opt): - check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) - if opt.task in ('train', 'val', 'test'): # run normally + if opt.task in ("train", "val", "test"): # run normally if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 - LOGGER.warning(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results') + LOGGER.warning(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results") if opt.save_hybrid: - LOGGER.warning('WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone') + LOGGER.warning("WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone") run(**vars(opt)) else: weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] - opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results - if opt.task == 'speed': # speed benchmarks + opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results + if opt.task == "speed": # speed benchmarks # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False for opt.weights in weights: run(**vars(opt), plots=False) - elif opt.task == 'study': # speed vs mAP benchmarks + elif opt.task == "study": # speed vs mAP benchmarks # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... for opt.weights in weights: - f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to + f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis for opt.imgsz in x: # img-size - LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') + LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...") r, _, t = run(**vars(opt), plots=False) y.append(r + t) # results and times - np.savetxt(f, y, fmt='%10.4g') # save - subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt']) + np.savetxt(f, y, fmt="%10.4g") # save + subprocess.run(["zip", "-r", "study.zip", "study_*.txt"]) plot_val_study(x=x) # plot else: raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/train.py b/train.py index 378ff4bd30ea..73297d204393 100644 --- a/train.py +++ b/train.py @@ -1,7 +1,6 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -Train a YOLOv5 model on a custom dataset. -Models and datasets download automatically from the latest YOLOv5 release. +Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release. Usage - Single-GPU training: $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) @@ -53,56 +52,97 @@ from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader from utils.downloads import attempt_download, is_url -from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, - check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, - get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, - labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, - yaml_save) +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + check_amp, + check_dataset, + check_file, + check_git_info, + check_git_status, + check_img_size, + check_requirements, + check_suffix, + check_yaml, + colorstr, + get_latest_run, + increment_path, + init_seeds, + intersect_dicts, + labels_to_class_weights, + labels_to_image_weights, + methods, + one_cycle, + print_args, + print_mutation, + strip_optimizer, + yaml_save, +) from utils.loggers import LOGGERS, Loggers from utils.loggers.comet.comet_utils import check_comet_resume from utils.loss import ComputeLoss from utils.metrics import fitness from utils.plots import plot_evolve -from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, - smart_resume, torch_distributed_zero_first) - -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +from utils.torch_utils import ( + EarlyStopping, + ModelEMA, + de_parallel, + select_device, + smart_DDP, + smart_optimizer, + smart_resume, + torch_distributed_zero_first, +) + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) GIT_INFO = check_git_info() def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary - save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ - Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ - opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze - callbacks.run('on_pretrain_routine_start') + save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = ( + Path(opt.save_dir), + opt.epochs, + opt.batch_size, + opt.weights, + opt.single_cls, + opt.evolve, + opt.data, + opt.cfg, + opt.resume, + opt.noval, + opt.nosave, + opt.workers, + opt.freeze, + ) + callbacks.run("on_pretrain_routine_start") # Directories - w = save_dir / 'weights' # weights dir + w = save_dir / "weights" # weights dir (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir - last, best = w / 'last.pt', w / 'best.pt' + last, best = w / "last.pt", w / "best.pt" # Hyperparameters if isinstance(hyp, str): - with open(hyp, errors='ignore') as f: + with open(hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict - LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items())) opt.hyp = hyp.copy() # for saving hyps to checkpoints # Save run settings if not evolve: - yaml_save(save_dir / 'hyp.yaml', hyp) - yaml_save(save_dir / 'opt.yaml', vars(opt)) + yaml_save(save_dir / "hyp.yaml", hyp) + yaml_save(save_dir / "opt.yaml", vars(opt)) # Loggers data_dict = None if RANK in {-1, 0}: include_loggers = list(LOGGERS) - if getattr(opt, 'ndjson_console', False): - include_loggers.append('ndjson_console') - if getattr(opt, 'ndjson_file', False): - include_loggers.append('ndjson_file') + if getattr(opt, "ndjson_console", False): + include_loggers.append("ndjson_console") + if getattr(opt, "ndjson_file", False): + include_loggers.append("ndjson_file") loggers = Loggers( save_dir=save_dir, @@ -124,39 +164,39 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Config plots = not evolve and not opt.noplots # create plots - cuda = device.type != 'cpu' + cuda = device.type != "cpu" init_seeds(opt.seed + 1 + RANK, deterministic=True) with torch_distributed_zero_first(LOCAL_RANK): data_dict = data_dict or check_dataset(data) # check if None - train_path, val_path = data_dict['train'], data_dict['val'] - nc = 1 if single_cls else int(data_dict['nc']) # number of classes - names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names - is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset + train_path, val_path = data_dict["train"], data_dict["val"] + nc = 1 if single_cls else int(data_dict["nc"]) # number of classes + names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names + is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset # Model - check_suffix(weights, '.pt') # check weights - pretrained = weights.endswith('.pt') + check_suffix(weights, ".pt") # check weights + pretrained = weights.endswith(".pt") if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally - ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak - model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create - exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys - csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + ckpt = torch.load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak + model = Model(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create + exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys + csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load - LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report + LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report else: - model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + model = Model(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create amp = check_amp(model) # check AMP # Freeze - freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze for k, v in model.named_parameters(): v.requires_grad = True # train all layers # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) if any(x in k for x in freeze): - LOGGER.info(f'freezing {k}') + LOGGER.info(f"freezing {k}") v.requires_grad = False # Image size @@ -166,19 +206,19 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Batch size if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size batch_size = check_train_batch_size(model, imgsz, amp) - loggers.on_params_update({'batch_size': batch_size}) + loggers.on_params_update({"batch_size": batch_size}) # Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing - hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay - optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) + hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"]) # Scheduler if opt.cos_lr: - lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf'] else: - lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + lf = lambda x: (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) # EMA @@ -194,58 +234,62 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # DP mode if cuda and RANK == -1 and torch.cuda.device_count() > 1: LOGGER.warning( - 'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' - 'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.' + "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n" + "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started." ) model = torch.nn.DataParallel(model) # SyncBatchNorm if opt.sync_bn and cuda and RANK != -1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) - LOGGER.info('Using SyncBatchNorm()') + LOGGER.info("Using SyncBatchNorm()") # Trainloader - train_loader, dataset = create_dataloader(train_path, - imgsz, - batch_size // WORLD_SIZE, - gs, - single_cls, - hyp=hyp, - augment=True, - cache=None if opt.cache == 'val' else opt.cache, - rect=opt.rect, - rank=LOCAL_RANK, - workers=workers, - image_weights=opt.image_weights, - quad=opt.quad, - prefix=colorstr('train: '), - shuffle=True, - seed=opt.seed) + train_loader, dataset = create_dataloader( + train_path, + imgsz, + batch_size // WORLD_SIZE, + gs, + single_cls, + hyp=hyp, + augment=True, + cache=None if opt.cache == "val" else opt.cache, + rect=opt.rect, + rank=LOCAL_RANK, + workers=workers, + image_weights=opt.image_weights, + quad=opt.quad, + prefix=colorstr("train: "), + shuffle=True, + seed=opt.seed, + ) labels = np.concatenate(dataset.labels, 0) mlc = int(labels[:, 0].max()) # max label class - assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' + assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}" # Process 0 if RANK in {-1, 0}: - val_loader = create_dataloader(val_path, - imgsz, - batch_size // WORLD_SIZE * 2, - gs, - single_cls, - hyp=hyp, - cache=None if noval else opt.cache, - rect=True, - rank=-1, - workers=workers * 2, - pad=0.5, - prefix=colorstr('val: '))[0] + val_loader = create_dataloader( + val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + prefix=colorstr("val: "), + )[0] if not resume: if not opt.noautoanchor: - check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor + check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor model.half().float() # pre-reduce anchor precision - callbacks.run('on_pretrain_routine_end', labels, names) + callbacks.run("on_pretrain_routine_end", labels, names) # DDP mode if cuda and RANK != -1: @@ -253,10 +297,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Model attributes nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) - hyp['box'] *= 3 / nl # scale to layers - hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers - hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers - hyp['label_smoothing'] = opt.label_smoothing + hyp["box"] *= 3 / nl # scale to layers + hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers + hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp["label_smoothing"] = opt.label_smoothing model.nc = nc # attach number of classes to model model.hyp = hyp # attach hyperparameters to model model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights @@ -265,7 +309,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Start training t0 = time.time() nb = len(train_loader) # number of batches - nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training last_opt_step = -1 maps = np.zeros(nc) # mAP per class @@ -274,13 +318,15 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio scaler = torch.cuda.amp.GradScaler(enabled=amp) stopper, stop = EarlyStopping(patience=opt.patience), False compute_loss = ComputeLoss(model) # init loss class - callbacks.run('on_train_start') - LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' - f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' - f"Logging results to {colorstr('bold', save_dir)}\n" - f'Starting training for {epochs} epochs...') + callbacks.run("on_train_start") + LOGGER.info( + f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...' + ) for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ - callbacks.run('on_train_epoch_start') + callbacks.run("on_train_epoch_start") model.train() # Update image weights (optional, single-GPU only) @@ -297,12 +343,12 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio if RANK != -1: train_loader.sampler.set_epoch(epoch) pbar = enumerate(train_loader) - LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) + LOGGER.info(("\n" + "%11s" * 7) % ("Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size")) if RANK in {-1, 0}: pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- - callbacks.run('on_train_batch_start') + callbacks.run("on_train_batch_start") ni = i + nb * epoch # number integrated batches (since train start) imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 @@ -313,9 +359,9 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 - x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) - if 'momentum' in x: - x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)]) + if "momentum" in x: + x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]) # Multi-scale if opt.multi_scale: @@ -323,7 +369,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio sf = sz / max(imgs.shape[2:]) # scale factor if sf != 1: ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) - imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) # Forward with torch.cuda.amp.autocast(amp): @@ -332,7 +378,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio if RANK != -1: loss *= WORLD_SIZE # gradient averaged between devices in DDP mode if opt.quad: - loss *= 4. + loss *= 4.0 # Backward scaler.scale(loss).backward() @@ -351,35 +397,39 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # Log if RANK in {-1, 0}: mloss = (mloss * i + loss_items) / (i + 1) # update mean losses - mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) - pbar.set_description(('%11s' * 2 + '%11.4g' * 5) % - (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) - callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss)) + mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB) + pbar.set_description( + ("%11s" * 2 + "%11.4g" * 5) + % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1]) + ) + callbacks.run("on_train_batch_end", model, ni, imgs, targets, paths, list(mloss)) if callbacks.stop_training: return # end batch ------------------------------------------------------------------------------------------------ # Scheduler - lr = [x['lr'] for x in optimizer.param_groups] # for loggers + lr = [x["lr"] for x in optimizer.param_groups] # for loggers scheduler.step() if RANK in {-1, 0}: # mAP - callbacks.run('on_train_epoch_end', epoch=epoch) - ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) + callbacks.run("on_train_epoch_end", epoch=epoch) + ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"]) final_epoch = (epoch + 1 == epochs) or stopper.possible_stop if not noval or final_epoch: # Calculate mAP - results, maps, _ = validate.run(data_dict, - batch_size=batch_size // WORLD_SIZE * 2, - imgsz=imgsz, - half=amp, - model=ema.ema, - single_cls=single_cls, - dataloader=val_loader, - save_dir=save_dir, - plots=False, - callbacks=callbacks, - compute_loss=compute_loss) + results, maps, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss, + ) # Update best mAP fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] @@ -387,29 +437,30 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio if fi > best_fitness: best_fitness = fi log_vals = list(mloss) + list(results) + lr - callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) + callbacks.run("on_fit_epoch_end", log_vals, epoch, best_fitness, fi) # Save model if (not nosave) or (final_epoch and not evolve): # if save ckpt = { - 'epoch': epoch, - 'best_fitness': best_fitness, - 'model': deepcopy(de_parallel(model)).half(), - 'ema': deepcopy(ema.ema).half(), - 'updates': ema.updates, - 'optimizer': optimizer.state_dict(), - 'opt': vars(opt), - 'git': GIT_INFO, # {remote, branch, commit} if a git repo - 'date': datetime.now().isoformat()} + "epoch": epoch, + "best_fitness": best_fitness, + "model": deepcopy(de_parallel(model)).half(), + "ema": deepcopy(ema.ema).half(), + "updates": ema.updates, + "optimizer": optimizer.state_dict(), + "opt": vars(opt), + "git": GIT_INFO, # {remote, branch, commit} if a git repo + "date": datetime.now().isoformat(), + } # Save last, best and delete torch.save(ckpt, last) if best_fitness == fi: torch.save(ckpt, best) if opt.save_period > 0 and epoch % opt.save_period == 0: - torch.save(ckpt, w / f'epoch{epoch}.pt') + torch.save(ckpt, w / f"epoch{epoch}.pt") del ckpt - callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) + callbacks.run("on_model_save", last, epoch, final_epoch, best_fitness, fi) # EarlyStopping if RANK != -1: # if DDP training @@ -423,12 +474,12 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio # end epoch ---------------------------------------------------------------------------------------------------- # end training ----------------------------------------------------------------------------------------------------- if RANK in {-1, 0}: - LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') + LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.") for f in last, best: if f.exists(): strip_optimizer(f) # strip optimizers if f is best: - LOGGER.info(f'\nValidating {f}...') + LOGGER.info(f"\nValidating {f}...") results, _, _ = validate.run( data_dict, batch_size=batch_size // WORLD_SIZE * 2, @@ -442,11 +493,12 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio verbose=True, plots=plots, callbacks=callbacks, - compute_loss=compute_loss) # val best model with plots + compute_loss=compute_loss, + ) # val best model with plots if is_coco: - callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) + callbacks.run("on_fit_epoch_end", list(mloss) + list(results) + lr, epoch, best_fitness, fi) - callbacks.run('on_train_end', last, best, epoch, results) + callbacks.run("on_train_end", last, best, epoch, results) torch.cuda.empty_cache() return results @@ -454,55 +506,54 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio def parse_opt(known=False): parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') - parser.add_argument('--cfg', type=str, default='', help='model.yaml path') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') - parser.add_argument('--epochs', type=int, default=100, help='total training epochs') - parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') - parser.add_argument('--rect', action='store_true', help='rectangular training') - parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') - parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--noval', action='store_true', help='only validate final epoch') - parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') - parser.add_argument('--noplots', action='store_true', help='save no plot files') - parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') - parser.add_argument('--evolve_population', - type=str, - default=ROOT / 'data/hyps', - help='location for loading population') - parser.add_argument('--resume_evolve', type=str, default=None, help='resume evolve from last generation') - parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') - parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') - parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') - parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') - parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') - parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--quad', action='store_true', help='quad dataloader') - parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') - parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') - parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') - parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') - parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') - parser.add_argument('--seed', type=int, default=0, help='Global training seed') - parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") + parser.add_argument("--cfg", type=str, default="", help="model.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") + parser.add_argument("--epochs", type=int, default=100, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") + parser.add_argument("--rect", action="store_true", help="rectangular training") + parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--noval", action="store_true", help="only validate final epoch") + parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") + parser.add_argument("--noplots", action="store_true", help="save no plot files") + parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") + parser.add_argument( + "--evolve_population", type=str, default=ROOT / "data/hyps", help="location for loading population" + ) + parser.add_argument("--resume_evolve", type=str, default=None, help="resume evolve from last generation") + parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk") + parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") + parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") + parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") + parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--quad", action="store_true", help="quad dataloader") + parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") + parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") + parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") + parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") + parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") # Logger arguments - parser.add_argument('--entity', default=None, help='Entity') - parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') - parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') - parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') + parser.add_argument("--entity", default=None, help="Entity") + parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='Upload data, "val" option') + parser.add_argument("--bbox_interval", type=int, default=-1, help="Set bounding-box image logging interval") + parser.add_argument("--artifact_alias", type=str, default="latest", help="Version of dataset artifact to use") # NDJSON logging - parser.add_argument('--ndjson-console', action='store_true', help='Log ndjson to console') - parser.add_argument('--ndjson-file', action='store_true', help='Log ndjson to file') + parser.add_argument("--ndjson-console", action="store_true", help="Log ndjson to console") + parser.add_argument("--ndjson-file", action="store_true", help="Log ndjson to file") return parser.parse_known_args()[0] if known else parser.parse_args() @@ -512,47 +563,53 @@ def main(opt, callbacks=Callbacks()): if RANK in {-1, 0}: print_args(vars(opt)) check_git_status() - check_requirements(ROOT / 'requirements.txt') + check_requirements(ROOT / "requirements.txt") # Resume (from specified or most recent last.pt) if opt.resume and not check_comet_resume(opt) and not opt.evolve: last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) - opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml + opt_yaml = last.parent.parent / "opt.yaml" # train options yaml opt_data = opt.data # original dataset if opt_yaml.is_file(): - with open(opt_yaml, errors='ignore') as f: + with open(opt_yaml, errors="ignore") as f: d = yaml.safe_load(f) else: - d = torch.load(last, map_location='cpu')['opt'] + d = torch.load(last, map_location="cpu")["opt"] opt = argparse.Namespace(**d) # replace - opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate + opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate if is_url(opt_data): opt.data = check_file(opt_data) # avoid HUB resume auth timeout else: - opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ - check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks - assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = ( + check_file(opt.data), + check_yaml(opt.cfg), + check_yaml(opt.hyp), + str(opt.weights), + str(opt.project), + ) # checks + assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified" if opt.evolve: - if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve - opt.project = str(ROOT / 'runs/evolve') + if opt.project == str(ROOT / "runs/train"): # if default project name, rename to runs/evolve + opt.project = str(ROOT / "runs/evolve") opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume - if opt.name == 'cfg': + if opt.name == "cfg": opt.name = Path(opt.cfg).stem # use model.yaml as name opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # DDP mode device = select_device(opt.device, batch_size=opt.batch_size) if LOCAL_RANK != -1: - msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' - assert not opt.image_weights, f'--image-weights {msg}' - assert not opt.evolve, f'--evolve {msg}' - assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' - assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' - assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + msg = "is not compatible with YOLOv5 Multi-GPU DDP training" + assert not opt.image_weights, f"--image-weights {msg}" + assert not opt.evolve, f"--evolve {msg}" + assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size" + assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" + assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" torch.cuda.set_device(LOCAL_RANK) - device = torch.device('cuda', LOCAL_RANK) - dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo', - timeout=timedelta(seconds=10800)) + device = torch.device("cuda", LOCAL_RANK) + dist.init_process_group( + backend="nccl" if dist.is_nccl_available() else "gloo", timeout=timedelta(seconds=10800) + ) # Train if not opt.evolve: @@ -562,35 +619,36 @@ def main(opt, callbacks=Callbacks()): else: # Hyperparameter evolution metadata (including this hyperparameter True-False, lower_limit, upper_limit) meta = { - 'lr0': (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) - 'lrf': (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) - 'momentum': (False, 0.6, 0.98), # SGD momentum/Adam beta1 - 'weight_decay': (False, 0.0, 0.001), # optimizer weight decay - 'warmup_epochs': (False, 0.0, 5.0), # warmup epochs (fractions ok) - 'warmup_momentum': (False, 0.0, 0.95), # warmup initial momentum - 'warmup_bias_lr': (False, 0.0, 0.2), # warmup initial bias lr - 'box': (False, 0.02, 0.2), # box loss gain - 'cls': (False, 0.2, 4.0), # cls loss gain - 'cls_pw': (False, 0.5, 2.0), # cls BCELoss positive_weight - 'obj': (False, 0.2, 4.0), # obj loss gain (scale with pixels) - 'obj_pw': (False, 0.5, 2.0), # obj BCELoss positive_weight - 'iou_t': (False, 0.1, 0.7), # IoU training threshold - 'anchor_t': (False, 2.0, 8.0), # anchor-multiple threshold - 'anchors': (False, 2.0, 10.0), # anchors per output grid (0 to ignore) - 'fl_gamma': (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) - 'hsv_h': (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction) - 'hsv_s': (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) - 'hsv_v': (True, 0.0, 0.9), # image HSV-Value augmentation (fraction) - 'degrees': (True, 0.0, 45.0), # image rotation (+/- deg) - 'translate': (True, 0.0, 0.9), # image translation (+/- fraction) - 'scale': (True, 0.0, 0.9), # image scale (+/- gain) - 'shear': (True, 0.0, 10.0), # image shear (+/- deg) - 'perspective': (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 - 'flipud': (True, 0.0, 1.0), # image flip up-down (probability) - 'fliplr': (True, 0.0, 1.0), # image flip left-right (probability) - 'mosaic': (True, 0.0, 1.0), # image mixup (probability) - 'mixup': (True, 0.0, 1.0), # image mixup (probability) - 'copy_paste': (True, 0.0, 1.0)} # segment copy-paste (probability) + "lr0": (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + "lrf": (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + "momentum": (False, 0.6, 0.98), # SGD momentum/Adam beta1 + "weight_decay": (False, 0.0, 0.001), # optimizer weight decay + "warmup_epochs": (False, 0.0, 5.0), # warmup epochs (fractions ok) + "warmup_momentum": (False, 0.0, 0.95), # warmup initial momentum + "warmup_bias_lr": (False, 0.0, 0.2), # warmup initial bias lr + "box": (False, 0.02, 0.2), # box loss gain + "cls": (False, 0.2, 4.0), # cls loss gain + "cls_pw": (False, 0.5, 2.0), # cls BCELoss positive_weight + "obj": (False, 0.2, 4.0), # obj loss gain (scale with pixels) + "obj_pw": (False, 0.5, 2.0), # obj BCELoss positive_weight + "iou_t": (False, 0.1, 0.7), # IoU training threshold + "anchor_t": (False, 2.0, 8.0), # anchor-multiple threshold + "anchors": (False, 2.0, 10.0), # anchors per output grid (0 to ignore) + "fl_gamma": (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + "hsv_h": (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + "hsv_s": (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + "hsv_v": (True, 0.0, 0.9), # image HSV-Value augmentation (fraction) + "degrees": (True, 0.0, 45.0), # image rotation (+/- deg) + "translate": (True, 0.0, 0.9), # image translation (+/- fraction) + "scale": (True, 0.0, 0.9), # image scale (+/- gain) + "shear": (True, 0.0, 10.0), # image shear (+/- deg) + "perspective": (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + "flipud": (True, 0.0, 1.0), # image flip up-down (probability) + "fliplr": (True, 0.0, 1.0), # image flip left-right (probability) + "mosaic": (True, 0.0, 1.0), # image mixup (probability) + "mixup": (True, 0.0, 1.0), # image mixup (probability) + "copy_paste": (True, 0.0, 1.0), + } # segment copy-paste (probability) # GA configs pop_size = 50 @@ -603,22 +661,25 @@ def main(opt, callbacks=Callbacks()): tournament_size_min = 2 tournament_size_max = 10 - with open(opt.hyp, errors='ignore') as f: + with open(opt.hyp, errors="ignore") as f: hyp = yaml.safe_load(f) # load hyps dict - if 'anchors' not in hyp: # anchors commented in hyp.yaml - hyp['anchors'] = 3 + if "anchors" not in hyp: # anchors commented in hyp.yaml + hyp["anchors"] = 3 if opt.noautoanchor: - del hyp['anchors'], meta['anchors'] + del hyp["anchors"], meta["anchors"] opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices - evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' + evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv" if opt.bucket: # download evolve.csv if exists - subprocess.run([ - 'gsutil', - 'cp', - f'gs://{opt.bucket}/evolve.csv', - str(evolve_csv), ]) + subprocess.run( + [ + "gsutil", + "cp", + f"gs://{opt.bucket}/evolve.csv", + str(evolve_csv), + ] + ) # Delete the items in meta dictionary whose first value is False del_ = [] @@ -644,8 +705,8 @@ def main(opt, callbacks=Callbacks()): # If resuming evolution from a previous checkpoint if opt.resume_evolve is not None: - assert os.path.isfile(ROOT / opt.resume_evolve), 'evolve population path is wrong!' - with open(ROOT / opt.resume_evolve, errors='ignore') as f: + assert os.path.isfile(ROOT / opt.resume_evolve), "evolve population path is wrong!" + with open(ROOT / opt.resume_evolve, errors="ignore") as f: evolve_population = yaml.safe_load(f) for value in evolve_population.values(): value = np.array([value[k] for k in hyp_GA.keys()]) @@ -653,7 +714,7 @@ def main(opt, callbacks=Callbacks()): # If not resuming from a previous checkpoint, generate initial values from .yaml files in opt.evolve_population else: - yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith('.yaml')] + yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith(".yaml")] for file_name in yaml_files: with open(os.path.join(opt.evolve_population, file_name)) as yaml_file: value = yaml.safe_load(yaml_file) @@ -661,27 +722,28 @@ def main(opt, callbacks=Callbacks()): initial_values.append(list(value)) # Generate random values within the search space for the rest of the population - if (initial_values is None): + if initial_values is None: population = [generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size)] else: - if (pop_size > 1): + if pop_size > 1: population = [ - generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size - len(initial_values))] + generate_individual(gene_ranges, len(hyp_GA)) for i in range(pop_size - len(initial_values)) + ] for initial_value in initial_values: population = [initial_value] + population # Run the genetic algorithm for a fixed number of generations list_keys = list(hyp_GA.keys()) for generation in range(opt.evolve): - if (generation >= 1): + if generation >= 1: save_dict = {} for i in range(len(population)): little_dict = {} for j in range(len(population[i])): little_dict[list_keys[j]] = float(population[i][j]) - save_dict['gen' + str(generation) + 'number' + str(i)] = little_dict + save_dict["gen" + str(generation) + "number" + str(i)] = little_dict - with open(save_dir / 'evolve_population.yaml', 'w') as outfile: + with open(save_dir / "evolve_population.yaml", "w") as outfile: yaml.dump(save_dict, outfile, default_flow_style=False) # Adaptive elite size @@ -695,8 +757,15 @@ def main(opt, callbacks=Callbacks()): results = train(hyp.copy(), opt, device, callbacks) callbacks = Callbacks() # Write mutation results - keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', - 'val/box_loss', 'val/obj_loss', 'val/cls_loss') + keys = ( + "metrics/precision", + "metrics/recall", + "metrics/mAP_0.5", + "metrics/mAP_0.5:0.95", + "val/box_loss", + "val/obj_loss", + "val/cls_loss", + ) print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) fitness_scores.append(results[2]) @@ -704,8 +773,10 @@ def main(opt, callbacks=Callbacks()): selected_indices = [] for i in range(pop_size - elite_size): # Adaptive tournament size - tournament_size = max(max(2, tournament_size_min), - int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10)))) + tournament_size = max( + max(2, tournament_size_min), + int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10))), + ) # Perform tournament selection to choose the best individual tournament_indices = random.sample(range(pop_size), tournament_size) tournament_fitness = [fitness_scores[j] for j in tournament_indices] @@ -721,16 +792,18 @@ def main(opt, callbacks=Callbacks()): parent1_index = selected_indices[random.randint(0, pop_size - 1)] parent2_index = selected_indices[random.randint(0, pop_size - 1)] # Adaptive crossover rate - crossover_rate = max(crossover_rate_min, - min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve))) + crossover_rate = max( + crossover_rate_min, min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve)) + ) if random.uniform(0, 1) < crossover_rate: crossover_point = random.randint(1, len(hyp_GA) - 1) child = population[parent1_index][:crossover_point] + population[parent2_index][crossover_point:] else: child = population[parent1_index] # Adaptive mutation rate - mutation_rate = max(mutation_rate_min, - min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve))) + mutation_rate = max( + mutation_rate_min, min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve)) + ) for j in range(len(hyp_GA)): if random.uniform(0, 1) < mutation_rate: child[j] += random.uniform(-0.1, 0.1) @@ -741,12 +814,14 @@ def main(opt, callbacks=Callbacks()): # Print the best solution found best_index = fitness_scores.index(max(fitness_scores)) best_individual = population[best_index] - print('Best solution found:', best_individual) + print("Best solution found:", best_individual) # Plot results plot_evolve(evolve_csv) - LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' - f"Results saved to {colorstr('bold', save_dir)}\n" - f'Usage example: $ python train.py --hyp {evolve_yaml}') + LOGGER.info( + f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}' + ) def generate_individual(input_ranges, individual_length): @@ -766,6 +841,6 @@ def run(**kwargs): return opt -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) diff --git a/utils/__init__.py b/utils/__init__.py index 4c7379c87466..eff756e2b90e 100644 --- a/utils/__init__.py +++ b/utils/__init__.py @@ -1,21 +1,19 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -utils/initialization -""" +"""utils/initialization.""" import contextlib import platform import threading -def emojis(str=''): +def emojis(str=""): # Return platform-dependent emoji-safe version of string - return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str + return str.encode().decode("ascii", "ignore") if platform.system() == "Windows" else str class TryExcept(contextlib.ContextDecorator): # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager - def __init__(self, msg=''): + def __init__(self, msg=""): self.msg = msg def __enter__(self): @@ -43,13 +41,13 @@ def join_threads(verbose=False): for t in threading.enumerate(): if t is not main_thread: if verbose: - print(f'Joining thread {t.name}') + print(f"Joining thread {t.name}") t.join() def notebook_init(verbose=True): # Check system software and hardware - print('Checking setup...') + print("Checking setup...") import os import shutil @@ -63,24 +61,25 @@ def notebook_init(verbose=True): import psutil - if check_requirements('wandb', install=False): - os.system('pip uninstall -y wandb') # eliminate unexpected account creation prompt with infinite hang + if check_requirements("wandb", install=False): + os.system("pip uninstall -y wandb") # eliminate unexpected account creation prompt with infinite hang if is_colab(): - shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory + shutil.rmtree("/content/sample_data", ignore_errors=True) # remove colab /sample_data directory # System info display = None if verbose: gb = 1 << 30 # bytes to GiB (1024 ** 3) ram = psutil.virtual_memory().total - total, used, free = shutil.disk_usage('/') + total, used, free = shutil.disk_usage("/") with contextlib.suppress(Exception): # clear display if ipython is installed from IPython import display + display.clear_output() - s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)' + s = f"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)" else: - s = '' + s = "" select_device(newline=False) - print(emojis(f'Setup complete ✅ {s}')) + print(emojis(f"Setup complete ✅ {s}")) return display diff --git a/utils/activations.py b/utils/activations.py index e4d4bbde5ec8..616002f06a73 100644 --- a/utils/activations.py +++ b/utils/activations.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Activation functions -""" +"""Activation functions.""" import torch import torch.nn as nn @@ -33,7 +31,6 @@ def forward(x): class MemoryEfficientMish(nn.Module): # Mish activation memory-efficient class F(torch.autograd.Function): - @staticmethod def forward(ctx, x): ctx.save_for_backward(x) @@ -62,7 +59,7 @@ def forward(self, x): class AconC(nn.Module): - r""" ACON activation (activate or not) + r"""ACON activation (activate or not) AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter according to "Activate or Not: Learning Customized Activation" . """ @@ -79,7 +76,7 @@ def forward(self, x): class MetaAconC(nn.Module): - r""" ACON activation (activate or not) + r"""ACON activation (activate or not) MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network according to "Activate or Not: Learning Customized Activation" . """ diff --git a/utils/augmentations.py b/utils/augmentations.py index 1e609303e209..b3b9524320d0 100644 --- a/utils/augmentations.py +++ b/utils/augmentations.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Image augmentation functions -""" +"""Image augmentation functions.""" import math import random @@ -23,10 +21,11 @@ class Albumentations: # YOLOv5 Albumentations class (optional, only used if package is installed) def __init__(self, size=640): self.transform = None - prefix = colorstr('albumentations: ') + prefix = colorstr("albumentations: ") try: import albumentations as A - check_version(A.__version__, '1.0.3', hard=True) # version requirement + + check_version(A.__version__, "1.0.3", hard=True) # version requirement T = [ A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), @@ -36,19 +35,20 @@ def __init__(self, size=640): A.CLAHE(p=0.01), A.RandomBrightnessContrast(p=0.0), A.RandomGamma(p=0.0), - A.ImageCompression(quality_lower=75, p=0.0)] # transforms - self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) + A.ImageCompression(quality_lower=75, p=0.0), + ] # transforms + self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"])) - LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) + LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) except ImportError: # package not installed, skip pass except Exception as e: - LOGGER.info(f'{prefix}{e}') + LOGGER.info(f"{prefix}{e}") def __call__(self, im, labels, p=1.0): if self.transform and random.random() < p: new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed - im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) + im, labels = new["image"], np.array([[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])]) return im, labels @@ -97,7 +97,7 @@ def replicate(im, labels): boxes = labels[:, 1:].astype(int) x1, y1, x2, y2 = boxes.T s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) - for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices + for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices x1b, y1b, x2b, y2b = boxes[i] bh, bw = y2b - y1b, x2b - x1b yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y @@ -141,15 +141,9 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF return im, ratio, (dw, dh) -def random_perspective(im, - targets=(), - segments=(), - degrees=10, - translate=.1, - scale=.1, - shear=10, - perspective=0.0, - border=(0, 0)): +def random_perspective( + im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) +): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] @@ -303,50 +297,52 @@ def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): def classify_albumentations( - augment=True, - size=224, - scale=(0.08, 1.0), - ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 - hflip=0.5, - vflip=0.0, - jitter=0.4, - mean=IMAGENET_MEAN, - std=IMAGENET_STD, - auto_aug=False): + augment=True, + size=224, + scale=(0.08, 1.0), + ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 + hflip=0.5, + vflip=0.0, + jitter=0.4, + mean=IMAGENET_MEAN, + std=IMAGENET_STD, + auto_aug=False, +): # YOLOv5 classification Albumentations (optional, only used if package is installed) - prefix = colorstr('albumentations: ') + prefix = colorstr("albumentations: ") try: import albumentations as A from albumentations.pytorch import ToTensorV2 - check_version(A.__version__, '1.0.3', hard=True) # version requirement + + check_version(A.__version__, "1.0.3", hard=True) # version requirement if augment: # Resize and crop T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] if auto_aug: # TODO: implement AugMix, AutoAug & RandAug in albumentation - LOGGER.info(f'{prefix}auto augmentations are currently not supported') + LOGGER.info(f"{prefix}auto augmentations are currently not supported") else: if hflip > 0: T += [A.HorizontalFlip(p=hflip)] if vflip > 0: T += [A.VerticalFlip(p=vflip)] if jitter > 0: - color_jitter = (float(jitter), ) * 3 # repeat value for brightness, contrast, satuaration, 0 hue + color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue T += [A.ColorJitter(*color_jitter, 0)] else: # Use fixed crop for eval set (reproducibility) T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor - LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) + LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) return A.Compose(T) except ImportError: # package not installed, skip - LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)') + LOGGER.warning(f"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)") except Exception as e: - LOGGER.info(f'{prefix}{e}') + LOGGER.info(f"{prefix}{e}") def classify_transforms(size=224): # Transforms to apply if albumentations not installed - assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' + assert isinstance(size, int), f"ERROR: classify_transforms size {size} must be integer, not (list, tuple)" # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) @@ -366,7 +362,7 @@ def __call__(self, im): # im = np.array HWC hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) - im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) + im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) return im_out @@ -380,7 +376,7 @@ def __call__(self, im): # im = np.array HWC imh, imw = im.shape[:2] m = min(imh, imw) # min dimension top, left = (imh - m) // 2, (imw - m) // 2 - return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) + return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) class ToTensor: diff --git a/utils/autoanchor.py b/utils/autoanchor.py index 4c11ab3decec..89e4d97fdcd5 100644 --- a/utils/autoanchor.py +++ b/utils/autoanchor.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -AutoAnchor utils -""" +"""AutoAnchor utils.""" import random @@ -13,7 +11,7 @@ from utils import TryExcept from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr -PREFIX = colorstr('AutoAnchor: ') +PREFIX = colorstr("AutoAnchor: ") def check_anchor_order(m): @@ -22,14 +20,14 @@ def check_anchor_order(m): da = a[-1] - a[0] # delta a ds = m.stride[-1] - m.stride[0] # delta s if da and (da.sign() != ds.sign()): # same order - LOGGER.info(f'{PREFIX}Reversing anchor order') + LOGGER.info(f"{PREFIX}Reversing anchor order") m.anchors[:] = m.anchors.flip(0) -@TryExcept(f'{PREFIX}ERROR') +@TryExcept(f"{PREFIX}ERROR") def check_anchors(dataset, model, thr=4.0, imgsz=640): # Check anchor fit to data, recompute if necessary - m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() + m = model.module.model[-1] if hasattr(model, "module") else model.model[-1] # Detect() shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh @@ -45,11 +43,11 @@ def metric(k): # compute metric stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides anchors = m.anchors.clone() * stride # current anchors bpr, aat = metric(anchors.cpu().view(-1, 2)) - s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). ' + s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). " if bpr > 0.98: # threshold to recompute - LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅') + LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅") else: - LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...') + LOGGER.info(f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...") na = m.anchors.numel() // 2 # number of anchors anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) new_bpr = metric(anchors)[0] @@ -58,28 +56,29 @@ def metric(k): # compute metric m.anchors[:] = anchors.clone().view_as(m.anchors) check_anchor_order(m) # must be in pixel-space (not grid-space) m.anchors /= stride - s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)' + s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)" else: - s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)' + s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)" LOGGER.info(s) -def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): - """ Creates kmeans-evolved anchors from training dataset +def kmean_anchors(dataset="./data/coco128.yaml", n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ + Creates kmeans-evolved anchors from training dataset. - Arguments: - dataset: path to data.yaml, or a loaded dataset - n: number of anchors - img_size: image size used for training - thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 - gen: generations to evolve anchors using genetic algorithm - verbose: print all results + Arguments: + dataset: path to data.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results - Return: - k: kmeans evolved anchors + Return: + k: kmeans evolved anchors - Usage: - from utils.autoanchor import *; _ = kmean_anchors() + Usage: + from utils.autoanchor import *; _ = kmean_anchors() """ from scipy.cluster.vq import kmeans @@ -100,20 +99,23 @@ def print_results(k, verbose=True): k = k[np.argsort(k.prod(1))] # sort small to large x, best = metric(k, wh0) bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr - s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \ - f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \ - f'past_thr={x[x > thr].mean():.3f}-mean: ' + s = ( + f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n" + f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, " + f"past_thr={x[x > thr].mean():.3f}-mean: " + ) for x in k: - s += '%i,%i, ' % (round(x[0]), round(x[1])) + s += "%i,%i, " % (round(x[0]), round(x[1])) if verbose: LOGGER.info(s[:-2]) return k if isinstance(dataset, str): # *.yaml file - with open(dataset, errors='ignore') as f: + with open(dataset, errors="ignore") as f: data_dict = yaml.safe_load(f) # model dict from utils.dataloaders import LoadImagesAndLabels - dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) + + dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True) # Get label wh shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) @@ -122,19 +124,19 @@ def print_results(k, verbose=True): # Filter i = (wh0 < 3.0).any(1).sum() if i: - LOGGER.info(f'{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size') + LOGGER.info(f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size") wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 # Kmeans init try: - LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...') + LOGGER.info(f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...") assert n <= len(wh) # apply overdetermined constraint s = wh.std(0) # sigmas for whitening k = kmeans(wh / s, n, iter=30)[0] * s # points assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar except Exception: - LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init') + LOGGER.warning(f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init") k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) k = print_results(k, verbose=False) @@ -162,7 +164,7 @@ def print_results(k, verbose=True): fg = anchor_fitness(kg) if fg > f: f, k = fg, kg.copy() - pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' + pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}" if verbose: print_results(k, verbose) diff --git a/utils/autobatch.py b/utils/autobatch.py index aa763b888462..396dbed1dda4 100644 --- a/utils/autobatch.py +++ b/utils/autobatch.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Auto-batch utils -""" +"""Auto-batch utils.""" from copy import deepcopy @@ -27,14 +25,14 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): # print(autobatch(model)) # Check device - prefix = colorstr('AutoBatch: ') - LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}') + prefix = colorstr("AutoBatch: ") + LOGGER.info(f"{prefix}Computing optimal batch size for --imgsz {imgsz}") device = next(model.parameters()).device # get model device - if device.type == 'cpu': - LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') + if device.type == "cpu": + LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}") return batch_size if torch.backends.cudnn.benchmark: - LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}') + LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}") return batch_size # Inspect CUDA memory @@ -45,7 +43,7 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): r = torch.cuda.memory_reserved(device) / gb # GiB reserved a = torch.cuda.memory_allocated(device) / gb # GiB allocated f = t - (r + a) # GiB free - LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') + LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free") # Profile batch sizes batch_sizes = [1, 2, 4, 8, 16] @@ -53,11 +51,11 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] results = profile(img, model, n=3, device=device) except Exception as e: - LOGGER.warning(f'{prefix}{e}') + LOGGER.warning(f"{prefix}{e}") # Fit a solution y = [x[2] for x in results if x] # memory [2] - p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit + p = np.polyfit(batch_sizes[: len(y)], y, deg=1) # first degree polynomial fit b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) if None in results: # some sizes failed i = results.index(None) # first fail index @@ -65,8 +63,8 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): b = batch_sizes[max(i - 1, 0)] # select prior safe point if b < 1 or b > 1024: # b outside of safe range b = batch_size - LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.') + LOGGER.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.") fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted - LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅') + LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅") return b diff --git a/utils/aws/resume.py b/utils/aws/resume.py index b21731c979a1..4525ba96749a 100644 --- a/utils/aws/resume.py +++ b/utils/aws/resume.py @@ -14,27 +14,27 @@ sys.path.append(str(ROOT)) # add ROOT to PATH port = 0 # --master_port -path = Path('').resolve() -for last in path.rglob('*/**/last.pt'): +path = Path("").resolve() +for last in path.rglob("*/**/last.pt"): ckpt = torch.load(last) - if ckpt['optimizer'] is None: + if ckpt["optimizer"] is None: continue # Load opt.yaml - with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: + with open(last.parent.parent / "opt.yaml", errors="ignore") as f: opt = yaml.safe_load(f) # Get device count - d = opt['device'].split(',') # devices + d = opt["device"].split(",") # devices nd = len(d) # number of devices ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel if ddp: # multi-GPU port += 1 - cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}" else: # single-GPU - cmd = f'python train.py --resume {last}' + cmd = f"python train.py --resume {last}" - cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread print(cmd) os.system(cmd) diff --git a/utils/callbacks.py b/utils/callbacks.py index c90fa824cdb4..ab7befdb0b3b 100644 --- a/utils/callbacks.py +++ b/utils/callbacks.py @@ -1,43 +1,40 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Callback utils -""" +"""Callback utils.""" import threading class Callbacks: - """" - Handles all registered callbacks for YOLOv5 Hooks - """ + """" Handles all registered callbacks for YOLOv5 Hooks.""" def __init__(self): # Define the available callbacks self._callbacks = { - 'on_pretrain_routine_start': [], - 'on_pretrain_routine_end': [], - 'on_train_start': [], - 'on_train_epoch_start': [], - 'on_train_batch_start': [], - 'optimizer_step': [], - 'on_before_zero_grad': [], - 'on_train_batch_end': [], - 'on_train_epoch_end': [], - 'on_val_start': [], - 'on_val_batch_start': [], - 'on_val_image_end': [], - 'on_val_batch_end': [], - 'on_val_end': [], - 'on_fit_epoch_end': [], # fit = train + val - 'on_model_save': [], - 'on_train_end': [], - 'on_params_update': [], - 'teardown': [], } + "on_pretrain_routine_start": [], + "on_pretrain_routine_end": [], + "on_train_start": [], + "on_train_epoch_start": [], + "on_train_batch_start": [], + "optimizer_step": [], + "on_before_zero_grad": [], + "on_train_batch_end": [], + "on_train_epoch_end": [], + "on_val_start": [], + "on_val_batch_start": [], + "on_val_image_end": [], + "on_val_batch_end": [], + "on_val_end": [], + "on_fit_epoch_end": [], # fit = train + val + "on_model_save": [], + "on_train_end": [], + "on_params_update": [], + "teardown": [], + } self.stop_training = False # set True to interrupt training - def register_action(self, hook, name='', callback=None): + def register_action(self, hook, name="", callback=None): """ - Register a new action to a callback hook + Register a new action to a callback hook. Args: hook: The callback hook name to register the action to @@ -46,11 +43,11 @@ def register_action(self, hook, name='', callback=None): """ assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" assert callable(callback), f"callback '{callback}' is not callable" - self._callbacks[hook].append({'name': name, 'callback': callback}) + self._callbacks[hook].append({"name": name, "callback": callback}) def get_registered_actions(self, hook=None): - """" - Returns all the registered actions by callback hook + """ + " Returns all the registered actions by callback hook. Args: hook: The name of the hook to check, defaults to all @@ -59,7 +56,7 @@ def get_registered_actions(self, hook=None): def run(self, hook, *args, thread=False, **kwargs): """ - Loop through the registered actions and fire all callbacks on main thread + Loop through the registered actions and fire all callbacks on main thread. Args: hook: The name of the hook to check, defaults to all @@ -71,6 +68,6 @@ def run(self, hook, *args, thread=False, **kwargs): assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" for logger in self._callbacks[hook]: if thread: - threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start() + threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start() else: - logger['callback'](*args, **kwargs) + logger["callback"](*args, **kwargs) diff --git a/utils/dataloaders.py b/utils/dataloaders.py index d422ef0711cb..c821e917ed38 100644 --- a/utils/dataloaders.py +++ b/utils/dataloaders.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Dataloaders and dataset utils -""" +"""Dataloaders and dataset utils.""" import contextlib import glob @@ -28,25 +26,49 @@ from torch.utils.data import DataLoader, Dataset, dataloader, distributed from tqdm import tqdm -from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste, - letterbox, mixup, random_perspective) -from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements, - check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, - xywh2xyxy, xywhn2xyxy, xyxy2xywhn) +from utils.augmentations import ( + Albumentations, + augment_hsv, + classify_albumentations, + classify_transforms, + copy_paste, + letterbox, + mixup, + random_perspective, +) +from utils.general import ( + DATASETS_DIR, + LOGGER, + NUM_THREADS, + TQDM_BAR_FORMAT, + check_dataset, + check_requirements, + check_yaml, + clean_str, + cv2, + is_colab, + is_kaggle, + segments2boxes, + unzip_file, + xyn2xy, + xywh2xyxy, + xywhn2xyxy, + xyxy2xywhn, +) from utils.torch_utils import torch_distributed_zero_first # Parameters -HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data' -IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes -VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) -PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders +HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data" +IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # include image suffixes +VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv" # include video suffixes +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) +PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders # Get orientation exif tag for orientation in ExifTags.TAGS.keys(): - if ExifTags.TAGS[orientation] == 'Orientation': + if ExifTags.TAGS[orientation] == "Orientation": break @@ -54,7 +76,7 @@ def get_hash(paths): # Returns a single hash value of a list of paths (files or dirs) size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes h = hashlib.sha256(str(size).encode()) # hash sizes - h.update(''.join(paths).encode()) # hash paths + h.update("".join(paths).encode()) # hash paths return h.hexdigest() # return hash @@ -86,17 +108,18 @@ def exif_transpose(image): 5: Image.TRANSPOSE, 6: Image.ROTATE_270, 7: Image.TRANSVERSE, - 8: Image.ROTATE_90}.get(orientation) + 8: Image.ROTATE_90, + }.get(orientation) if method is not None: image = image.transpose(method) del exif[0x0112] - image.info['exif'] = exif.tobytes() + image.info["exif"] = exif.tobytes() return image def seed_worker(worker_id): # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader - worker_seed = torch.initial_seed() % 2 ** 32 + worker_seed = torch.initial_seed() % 2**32 np.random.seed(worker_seed) random.seed(worker_seed) @@ -104,7 +127,6 @@ def seed_worker(worker_id): # Inherit from DistributedSampler and override iterator # https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py class SmartDistributedSampler(distributed.DistributedSampler): - def __iter__(self): # deterministically shuffle based on epoch and seed g = torch.Generator() @@ -118,7 +140,7 @@ def __iter__(self): idx = idx.tolist() if self.drop_last: - idx = idx[:self.num_samples] + idx = idx[: self.num_samples] else: padding_size = self.num_samples - len(idx) if padding_size <= len(idx): @@ -129,25 +151,27 @@ def __iter__(self): return iter(idx) -def create_dataloader(path, - imgsz, - batch_size, - stride, - single_cls=False, - hyp=None, - augment=False, - cache=False, - pad=0.0, - rect=False, - rank=-1, - workers=8, - image_weights=False, - quad=False, - prefix='', - shuffle=False, - seed=0): +def create_dataloader( + path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix="", + shuffle=False, + seed=0, +): if rect and shuffle: - LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') + LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") shuffle = False with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = LoadImagesAndLabels( @@ -163,7 +187,8 @@ def create_dataloader(path, pad=pad, image_weights=image_weights, prefix=prefix, - rank=rank) + rank=rank, + ) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices @@ -172,26 +197,29 @@ def create_dataloader(path, loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates generator = torch.Generator() generator.manual_seed(6148914691236517205 + seed + RANK) - return loader(dataset, - batch_size=batch_size, - shuffle=shuffle and sampler is None, - num_workers=nw, - sampler=sampler, - pin_memory=PIN_MEMORY, - collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, - worker_init_fn=seed_worker, - generator=generator), dataset + return loader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, + worker_init_fn=seed_worker, + generator=generator, + ), dataset class InfiniteDataLoader(dataloader.DataLoader): - """ Dataloader that reuses workers + """ + Dataloader that reuses workers. Uses same syntax as vanilla DataLoader """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) + object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler)) self.iterator = super().__iter__() def __len__(self): @@ -203,7 +231,8 @@ def __iter__(self): class _RepeatSampler: - """ Sampler that repeats forever + """ + Sampler that repeats forever. Args: sampler (Sampler) @@ -221,7 +250,7 @@ class LoadScreenshots: # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): # source = [screen_number left top width height] (pixels) - check_requirements('mss') + check_requirements("mss") import mss source, *params = source.split() @@ -236,17 +265,17 @@ def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): self.stride = stride self.transforms = transforms self.auto = auto - self.mode = 'stream' + self.mode = "stream" self.frame = 0 self.sct = mss.mss() # Parse monitor shape monitor = self.sct.monitors[self.screen] - self.top = monitor['top'] if top is None else (monitor['top'] + top) - self.left = monitor['left'] if left is None else (monitor['left'] + left) - self.width = width or monitor['width'] - self.height = height or monitor['height'] - self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height} + self.top = monitor["top"] if top is None else (monitor["top"] + top) + self.left = monitor["left"] if left is None else (monitor["left"] + left) + self.width = width or monitor["width"] + self.height = height or monitor["height"] + self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height} def __iter__(self): return self @@ -254,7 +283,7 @@ def __iter__(self): def __next__(self): # mss screen capture: get raw pixels from the screen as np array im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR - s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: ' + s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: " if self.transforms: im = self.transforms(im0) # transforms @@ -269,22 +298,22 @@ def __next__(self): class LoadImages: # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): - if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line + if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line path = Path(path).read_text().rsplit() files = [] for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: p = str(Path(p).resolve()) - if '*' in p: + if "*" in p: files.extend(sorted(glob.glob(p, recursive=True))) # glob elif os.path.isdir(p): - files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir + files.extend(sorted(glob.glob(os.path.join(p, "*.*")))) # dir elif os.path.isfile(p): files.append(p) # files else: - raise FileNotFoundError(f'{p} does not exist') + raise FileNotFoundError(f"{p} does not exist") - images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS] - videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS] + images = [x for x in files if x.split(".")[-1].lower() in IMG_FORMATS] + videos = [x for x in files if x.split(".")[-1].lower() in VID_FORMATS] ni, nv = len(images), len(videos) self.img_size = img_size @@ -292,7 +321,7 @@ def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vi self.files = images + videos self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv - self.mode = 'image' + self.mode = "image" self.auto = auto self.transforms = transforms # optional self.vid_stride = vid_stride # video frame-rate stride @@ -300,8 +329,10 @@ def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vi self._new_video(videos[0]) # new video else: self.cap = None - assert self.nf > 0, f'No images or videos found in {p}. ' \ - f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}' + assert self.nf > 0, ( + f"No images or videos found in {p}. " + f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}" + ) def __iter__(self): self.count = 0 @@ -314,7 +345,7 @@ def __next__(self): if self.video_flag[self.count]: # Read video - self.mode = 'video' + self.mode = "video" for _ in range(self.vid_stride): self.cap.grab() ret_val, im0 = self.cap.retrieve() @@ -329,14 +360,14 @@ def __next__(self): self.frame += 1 # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False - s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ' + s = f"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: " else: # Read image self.count += 1 im0 = cv2.imread(path) # BGR - assert im0 is not None, f'Image Not Found {path}' - s = f'image {self.count}/{self.nf} {path}: ' + assert im0 is not None, f"Image Not Found {path}" + s = f"image {self.count}/{self.nf} {path}: " if self.transforms: im = self.transforms(im0) # transforms @@ -371,9 +402,9 @@ def __len__(self): class LoadStreams: # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` - def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): torch.backends.cudnn.benchmark = True # faster for fixed-size inference - self.mode = 'stream' + self.mode = "stream" self.img_size = img_size self.stride = stride self.vid_stride = vid_stride # video frame-rate stride @@ -383,29 +414,30 @@ def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, t self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n for i, s in enumerate(sources): # index, source # Start thread to read frames from video stream - st = f'{i + 1}/{n}: {s}... ' - if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video + st = f"{i + 1}/{n}: {s}... " + if urlparse(s).hostname in ("www.youtube.com", "youtube.com", "youtu.be"): # if source is YouTube video # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4' - check_requirements(('pafy', 'youtube_dl==2020.12.2')) + check_requirements(("pafy", "youtube_dl==2020.12.2")) import pafy - s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL + + s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam if s == 0: - assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.' - assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.' + assert not is_colab(), "--source 0 webcam unsupported on Colab. Rerun command in a local environment." + assert not is_kaggle(), "--source 0 webcam unsupported on Kaggle. Rerun command in a local environment." cap = cv2.VideoCapture(s) - assert cap.isOpened(), f'{st}Failed to open {s}' + assert cap.isOpened(), f"{st}Failed to open {s}" w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan - self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback + self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float("inf") # infinite stream fallback self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback _, self.imgs[i] = cap.read() # guarantee first frame self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) - LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)') + LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)") self.threads[i].start() - LOGGER.info('') # newline + LOGGER.info("") # newline # check for common shapes s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) @@ -413,7 +445,7 @@ def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, t self.auto = auto and self.rect self.transforms = transforms # optional if not self.rect: - LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.') + LOGGER.warning("WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.") def update(self, i, cap, stream): # Read stream `i` frames in daemon thread @@ -426,7 +458,7 @@ def update(self, i, cap, stream): if success: self.imgs[i] = im else: - LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.') + LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.") self.imgs[i] = np.zeros_like(self.imgs[i]) cap.open(stream) # re-open stream if signal was lost time.sleep(0.0) # wait time @@ -437,7 +469,7 @@ def __iter__(self): def __next__(self): self.count += 1 - if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit + if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord("q"): # q to quit cv2.destroyAllWindows() raise StopIteration @@ -449,7 +481,7 @@ def __next__(self): im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW im = np.ascontiguousarray(im) # contiguous - return self.sources, im, im0, None, '' + return self.sources, im, im0, None, "" def __len__(self): return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years @@ -457,8 +489,8 @@ def __len__(self): def img2label_paths(img_paths): # Define label paths as a function of image paths - sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings - return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths] + sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings + return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths] class LoadImagesAndLabels(Dataset): @@ -466,22 +498,24 @@ class LoadImagesAndLabels(Dataset): cache_version = 0.6 # dataset labels *.cache version rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] - def __init__(self, - path, - img_size=640, - batch_size=16, - augment=False, - hyp=None, - rect=False, - image_weights=False, - cache_images=False, - single_cls=False, - stride=32, - pad=0.0, - min_items=0, - prefix='', - rank=-1, - seed=0): + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0.0, + min_items=0, + prefix="", + rank=-1, + seed=0, + ): self.img_size = img_size self.augment = augment self.hyp = hyp @@ -498,46 +532,46 @@ def __init__(self, for p in path if isinstance(path, list) else [path]: p = Path(p) # os-agnostic if p.is_dir(): # dir - f += glob.glob(str(p / '**' / '*.*'), recursive=True) + f += glob.glob(str(p / "**" / "*.*"), recursive=True) # f = list(p.rglob('*.*')) # pathlib elif p.is_file(): # file with open(p) as t: t = t.read().strip().splitlines() parent = str(p.parent) + os.sep - f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t] # to global path + f += [x.replace("./", parent, 1) if x.startswith("./") else x for x in t] # to global path # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) else: - raise FileNotFoundError(f'{prefix}{p} does not exist') - self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS) + raise FileNotFoundError(f"{prefix}{p} does not exist") + self.im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS) # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib - assert self.im_files, f'{prefix}No images found' + assert self.im_files, f"{prefix}No images found" except Exception as e: - raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e + raise Exception(f"{prefix}Error loading data from {path}: {e}\n{HELP_URL}") from e # Check cache self.label_files = img2label_paths(self.im_files) # labels - cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix(".cache") try: cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict - assert cache['version'] == self.cache_version # matches current version - assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash + assert cache["version"] == self.cache_version # matches current version + assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash except Exception: cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops # Display cache - nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total + nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total if exists and LOCAL_RANK in {-1, 0}: - d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt' + d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt" tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results - if cache['msgs']: - LOGGER.info('\n'.join(cache['msgs'])) # display warnings - assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}' + if cache["msgs"]: + LOGGER.info("\n".join(cache["msgs"])) # display warnings + assert nf > 0 or not augment, f"{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}" # Read cache - [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items + [cache.pop(k) for k in ("hash", "version", "msgs")] # remove items labels, shapes, self.segments = zip(*cache.values()) nl = len(np.concatenate(labels, 0)) # number of labels - assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}' + assert nl > 0 or not augment, f"{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}" self.labels = list(labels) self.shapes = np.array(shapes) self.im_files = list(cache.keys()) # update @@ -546,7 +580,7 @@ def __init__(self, # Filter images if min_items: include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) - LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset') + LOGGER.info(f"{prefix}{n - len(include)}/{n} images filtered from dataset") self.im_files = [self.im_files[i] for i in include] self.label_files = [self.label_files[i] for i in include] self.labels = [self.labels[i] for i in include] @@ -603,52 +637,56 @@ def __init__(self, self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride # Cache images into RAM/disk for faster training - if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix): + if cache_images == "ram" and not self.check_cache_ram(prefix=prefix): cache_images = False self.ims = [None] * n - self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files] + self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files] if cache_images: b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes self.im_hw0, self.im_hw = [None] * n, [None] * n - fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image + fcn = self.cache_images_to_disk if cache_images == "disk" else self.load_image results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices) pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) for i, x in pbar: - if cache_images == 'disk': + if cache_images == "disk": b += self.npy_files[i].stat().st_size else: # 'ram' self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) b += self.ims[i].nbytes * WORLD_SIZE - pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})' + pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})" pbar.close() - def check_cache_ram(self, safety_margin=0.1, prefix=''): + def check_cache_ram(self, safety_margin=0.1, prefix=""): # Check image caching requirements vs available memory b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes n = min(self.n, 30) # extrapolate from 30 random images for _ in range(n): im = cv2.imread(random.choice(self.im_files)) # sample image ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio - b += im.nbytes * ratio ** 2 + b += im.nbytes * ratio**2 mem_required = b * self.n / n # GB required to cache dataset into RAM mem = psutil.virtual_memory() cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question if not cache: - LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, ' - f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, ' - f"{'caching images ✅' if cache else 'not caching images ⚠️'}") + LOGGER.info( + f'{prefix}{mem_required / gb:.1f}GB RAM required, ' + f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, ' + f"{'caching images ✅' if cache else 'not caching images ⚠️'}" + ) return cache - def cache_labels(self, path=Path('./labels.cache'), prefix=''): + def cache_labels(self, path=Path("./labels.cache"), prefix=""): # Cache dataset labels, check images and read shapes x = {} # dict nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages - desc = f'{prefix}Scanning {path.parent / path.stem}...' + desc = f"{prefix}Scanning {path.parent / path.stem}..." with Pool(NUM_THREADS) as pool: - pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), - desc=desc, - total=len(self.im_files), - bar_format=TQDM_BAR_FORMAT) + pbar = tqdm( + pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), + desc=desc, + total=len(self.im_files), + bar_format=TQDM_BAR_FORMAT, + ) for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: nm += nm_f nf += nf_f @@ -658,23 +696,23 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''): x[im_file] = [lb, shape, segments] if msg: msgs.append(msg) - pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt' + pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt" pbar.close() if msgs: - LOGGER.info('\n'.join(msgs)) + LOGGER.info("\n".join(msgs)) if nf == 0: - LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}') - x['hash'] = get_hash(self.label_files + self.im_files) - x['results'] = nf, nm, ne, nc, len(self.im_files) - x['msgs'] = msgs # warnings - x['version'] = self.cache_version # cache version + LOGGER.warning(f"{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}") + x["hash"] = get_hash(self.label_files + self.im_files) + x["results"] = nf, nm, ne, nc, len(self.im_files) + x["msgs"] = msgs # warnings + x["version"] = self.cache_version # cache version try: np.save(path, x) # save cache for next time - path.with_suffix('.cache.npy').rename(path) # remove .npy suffix - LOGGER.info(f'{prefix}New cache created: {path}') + path.with_suffix(".cache.npy").rename(path) # remove .npy suffix + LOGGER.info(f"{prefix}New cache created: {path}") except Exception as e: - LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}') # not writeable + LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}") # not writeable return x def __len__(self): @@ -690,14 +728,14 @@ def __getitem__(self, index): index = self.indices[index] # linear, shuffled, or image_weights hyp = self.hyp - mosaic = self.mosaic and random.random() < hyp['mosaic'] + mosaic = self.mosaic and random.random() < hyp["mosaic"] if mosaic: # Load mosaic img, labels = self.load_mosaic(index) shapes = None # MixUp augmentation - if random.random() < hyp['mixup']: + if random.random() < hyp["mixup"]: img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices))) else: @@ -714,17 +752,19 @@ def __getitem__(self, index): labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) if self.augment: - img, labels = random_perspective(img, - labels, - degrees=hyp['degrees'], - translate=hyp['translate'], - scale=hyp['scale'], - shear=hyp['shear'], - perspective=hyp['perspective']) + img, labels = random_perspective( + img, + labels, + degrees=hyp["degrees"], + translate=hyp["translate"], + scale=hyp["scale"], + shear=hyp["shear"], + perspective=hyp["perspective"], + ) nl = len(labels) # number of labels if nl: - labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3) + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) if self.augment: # Albumentations @@ -732,16 +772,16 @@ def __getitem__(self, index): nl = len(labels) # update after albumentations # HSV color-space - augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) # Flip up-down - if random.random() < hyp['flipud']: + if random.random() < hyp["flipud"]: img = np.flipud(img) if nl: labels[:, 2] = 1 - labels[:, 2] # Flip left-right - if random.random() < hyp['fliplr']: + if random.random() < hyp["fliplr"]: img = np.fliplr(img) if nl: labels[:, 1] = 1 - labels[:, 1] @@ -762,13 +802,17 @@ def __getitem__(self, index): def load_image(self, i): # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw) - im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i], + im, f, fn = ( + self.ims[i], + self.im_files[i], + self.npy_files[i], + ) if im is None: # not cached in RAM if fn.exists(): # load npy im = np.load(fn) else: # read image im = cv2.imread(f) # BGR - assert im is not None, f'Image Not Found {f}' + assert im is not None, f"Image Not Found {f}" h0, w0 = im.shape[:2] # orig hw r = self.img_size / max(h0, w0) # ratio if r != 1: # if sizes are not equal @@ -828,16 +872,18 @@ def load_mosaic(self, index): # img4, labels4 = replicate(img4, labels4) # replicate # Augment - img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) - img4, labels4 = random_perspective(img4, - labels4, - segments4, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) + img4, labels4 = random_perspective( + img4, + labels4, + segments4, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove return img4, labels4 @@ -886,12 +932,12 @@ def load_mosaic9(self, index): segments9.extend(segments) # Image - img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] + img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :] # img9[ymin:ymax, xmin:xmax] hp, wp = h, w # height, width previous # Offset yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y - img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] + img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s] # Concat/clip labels labels9 = np.concatenate(labels9, 0) @@ -905,16 +951,18 @@ def load_mosaic9(self, index): # img9, labels9 = replicate(img9, labels9) # replicate # Augment - img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste']) - img9, labels9 = random_perspective(img9, - labels9, - segments9, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove + img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp["copy_paste"]) + img9, labels9 = random_perspective( + img9, + labels9, + segments9, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove return img9, labels9 @@ -937,8 +985,9 @@ def collate_fn4(batch): for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW i *= 4 if random.random() < 0.5: - im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', - align_corners=False)[0].type(im[i].type()) + im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode="bilinear", align_corners=False)[ + 0 + ].type(im[i].type()) lb = label[i] else: im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) @@ -953,21 +1002,21 @@ def collate_fn4(batch): # Ancillary functions -------------------------------------------------------------------------------------------------- -def flatten_recursive(path=DATASETS_DIR / 'coco128'): +def flatten_recursive(path=DATASETS_DIR / "coco128"): # Flatten a recursive directory by bringing all files to top level - new_path = Path(f'{str(path)}_flat') + new_path = Path(f"{str(path)}_flat") if os.path.exists(new_path): shutil.rmtree(new_path) # delete output folder os.makedirs(new_path) # make new output folder - for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)): + for file in tqdm(glob.glob(f"{str(Path(path))}/**/*.*", recursive=True)): shutil.copyfile(file, new_path / Path(file).name) -def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders import *; extract_boxes() +def extract_boxes(path=DATASETS_DIR / "coco128"): # from utils.dataloaders import *; extract_boxes() # Convert detection dataset into classification dataset, with one directory per class path = Path(path) # images dir - shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None # remove existing - files = list(path.rglob('*.*')) + shutil.rmtree(path / "classification") if (path / "classification").is_dir() else None # remove existing + files = list(path.rglob("*.*")) n = len(files) # number of files for im_file in tqdm(files, total=n): if im_file.suffix[1:] in IMG_FORMATS: @@ -983,7 +1032,7 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders impo for j, x in enumerate(lb): c = int(x[0]) # class - f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename + f = (path / "classifier") / f"{c}" / f"{path.stem}_{im_file.stem}_{j}.jpg" # new filename if not f.parent.is_dir(): f.parent.mkdir(parents=True) @@ -994,11 +1043,11 @@ def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders impo b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image b[[1, 3]] = np.clip(b[[1, 3]], 0, h) - assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' + assert cv2.imwrite(str(f), im[b[1] : b[3], b[0] : b[2]]), f"box failure in {f}" -def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False): - """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files +def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False): + """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files Usage: from utils.dataloaders import *; autosplit() Arguments path: Path to images directory @@ -1006,40 +1055,40 @@ def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), ann annotated_only: Only use images with an annotated txt file """ path = Path(path) # images dir - files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only + files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS) # image files only n = len(files) # number of files random.seed(0) # for reproducibility indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split - txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files + txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"] # 3 txt files for x in txt: if (path.parent / x).exists(): (path.parent / x).unlink() # remove existing - print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) + print(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only) for i, img in tqdm(zip(indices, files), total=n): if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label - with open(path.parent / txt[i], 'a') as f: - f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file + with open(path.parent / txt[i], "a") as f: + f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n") # add image to txt file def verify_image_label(args): # Verify one image-label pair im_file, lb_file, prefix = args - nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments + nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, "", [] # number (missing, found, empty, corrupt), message, segments try: # verify images im = Image.open(im_file) im.verify() # PIL verify shape = exif_size(im) # image size - assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' - assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}' - if im.format.lower() in ('jpg', 'jpeg'): - with open(im_file, 'rb') as f: + assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels" + assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}" + if im.format.lower() in ("jpg", "jpeg"): + with open(im_file, "rb") as f: f.seek(-2, 2) - if f.read() != b'\xff\xd9': # corrupt JPEG - ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100) - msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved' + if f.read() != b"\xff\xd9": # corrupt JPEG + ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100) + msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved" # verify labels if os.path.isfile(lb_file): @@ -1053,15 +1102,15 @@ def verify_image_label(args): lb = np.array(lb, dtype=np.float32) nl = len(lb) if nl: - assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected' - assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}' - assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}' + assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected" + assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}" + assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" _, i = np.unique(lb, axis=0, return_index=True) if len(i) < nl: # duplicate row check lb = lb[i] # remove duplicates if segments: segments = [segments[x] for x in i] - msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed' + msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed" else: ne = 1 # label empty lb = np.zeros((0, 5), dtype=np.float32) @@ -1071,12 +1120,13 @@ def verify_image_label(args): return im_file, lb, shape, segments, nm, nf, ne, nc, msg except Exception as e: nc = 1 - msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}' + msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}" return [None, None, None, None, nm, nf, ne, nc, msg] -class HUBDatasetStats(): - """ Class for generating HUB dataset JSON and `-hub` dataset directory +class HUBDatasetStats: + """ + Class for generating HUB dataset JSON and `-hub` dataset directory. Arguments path: Path to data.yaml or data.zip (with data.yaml inside data.zip) @@ -1090,43 +1140,43 @@ class HUBDatasetStats(): stats.process_images() """ - def __init__(self, path='coco128.yaml', autodownload=False): + def __init__(self, path="coco128.yaml", autodownload=False): # Initialize class zipped, data_dir, yaml_path = self._unzip(Path(path)) try: - with open(check_yaml(yaml_path), errors='ignore') as f: + with open(check_yaml(yaml_path), errors="ignore") as f: data = yaml.safe_load(f) # data dict if zipped: - data['path'] = data_dir + data["path"] = data_dir except Exception as e: - raise Exception('error/HUB/dataset_stats/yaml_load') from e + raise Exception("error/HUB/dataset_stats/yaml_load") from e check_dataset(data, autodownload) # download dataset if missing - self.hub_dir = Path(data['path'] + '-hub') - self.im_dir = self.hub_dir / 'images' + self.hub_dir = Path(data["path"] + "-hub") + self.im_dir = self.hub_dir / "images" self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images - self.stats = {'nc': data['nc'], 'names': list(data['names'].values())} # statistics dictionary + self.stats = {"nc": data["nc"], "names": list(data["names"].values())} # statistics dictionary self.data = data @staticmethod def _find_yaml(dir): # Return data.yaml file - files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive - assert files, f'No *.yaml file found in {dir}' + files = list(dir.glob("*.yaml")) or list(dir.rglob("*.yaml")) # try root level first and then recursive + assert files, f"No *.yaml file found in {dir}" if len(files) > 1: files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name - assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed' - assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}' + assert files, f"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed" + assert len(files) == 1, f"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}" return files[0] def _unzip(self, path): # Unzip data.zip - if not str(path).endswith('.zip'): # path is data.yaml + if not str(path).endswith(".zip"): # path is data.yaml return False, None, path - assert Path(path).is_file(), f'Error unzipping {path}, file not found' + assert Path(path).is_file(), f"Error unzipping {path}, file not found" unzip_file(path, path=path.parent) - dir = path.with_suffix('') # dataset directory == zip name - assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/' + dir = path.with_suffix("") # dataset directory == zip name + assert dir.is_dir(), f"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/" return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path def _hub_ops(self, f, max_dim=1920): @@ -1137,9 +1187,9 @@ def _hub_ops(self, f, max_dim=1920): r = max_dim / max(im.height, im.width) # ratio if r < 1.0: # image too large im = im.resize((int(im.width * r), int(im.height * r))) - im.save(f_new, 'JPEG', quality=50, optimize=True) # save + im.save(f_new, "JPEG", quality=50, optimize=True) # save except Exception as e: # use OpenCV - LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}') + LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}") im = cv2.imread(f) im_height, im_width = im.shape[:2] r = max_dim / max(im_height, im_width) # ratio @@ -1153,30 +1203,32 @@ def _round(labels): # Update labels to integer class and 6 decimal place floats return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] - for split in 'train', 'val', 'test': + for split in "train", "val", "test": if self.data.get(split) is None: self.stats[split] = None # i.e. no test set continue dataset = LoadImagesAndLabels(self.data[split]) # load dataset - x = np.array([ - np.bincount(label[:, 0].astype(int), minlength=self.data['nc']) - for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')]) # shape(128x80) + x = np.array( + [ + np.bincount(label[:, 0].astype(int), minlength=self.data["nc"]) + for label in tqdm(dataset.labels, total=dataset.n, desc="Statistics") + ] + ) # shape(128x80) self.stats[split] = { - 'instance_stats': { - 'total': int(x.sum()), - 'per_class': x.sum(0).tolist()}, - 'image_stats': { - 'total': dataset.n, - 'unlabelled': int(np.all(x == 0, 1).sum()), - 'per_class': (x > 0).sum(0).tolist()}, - 'labels': [{ - str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]} + "instance_stats": {"total": int(x.sum()), "per_class": x.sum(0).tolist()}, + "image_stats": { + "total": dataset.n, + "unlabelled": int(np.all(x == 0, 1).sum()), + "per_class": (x > 0).sum(0).tolist(), + }, + "labels": [{str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)], + } # Save, print and return if save: - stats_path = self.hub_dir / 'stats.json' - print(f'Saving {stats_path.resolve()}...') - with open(stats_path, 'w') as f: + stats_path = self.hub_dir / "stats.json" + print(f"Saving {stats_path.resolve()}...") + with open(stats_path, "w") as f: json.dump(self.stats, f) # save stats.json if verbose: print(json.dumps(self.stats, indent=2, sort_keys=False)) @@ -1184,14 +1236,14 @@ def _round(labels): def process_images(self): # Compress images for Ultralytics HUB - for split in 'train', 'val', 'test': + for split in "train", "val", "test": if self.data.get(split) is None: continue dataset = LoadImagesAndLabels(self.data[split]) # load dataset - desc = f'{split} images' + desc = f"{split} images" for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): pass - print(f'Done. All images saved to {self.im_dir}') + print(f"Done. All images saved to {self.im_dir}") return self.im_dir @@ -1199,6 +1251,7 @@ def process_images(self): class ClassificationDataset(torchvision.datasets.ImageFolder): """ YOLOv5 Classification Dataset. + Arguments root: Dataset path transform: torchvision transforms, used by default @@ -1209,9 +1262,9 @@ def __init__(self, root, augment, imgsz, cache=False): super().__init__(root=root) self.torch_transforms = classify_transforms(imgsz) self.album_transforms = classify_albumentations(augment, imgsz) if augment else None - self.cache_ram = cache is True or cache == 'ram' - self.cache_disk = cache == 'disk' - self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im + self.cache_ram = cache is True or cache == "ram" + self.cache_disk = cache == "disk" + self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im def __getitem__(self, i): f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image @@ -1224,20 +1277,15 @@ def __getitem__(self, i): else: # read image im = cv2.imread(f) # BGR if self.album_transforms: - sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image'] + sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"] else: sample = self.torch_transforms(im) return sample, j -def create_classification_dataloader(path, - imgsz=224, - batch_size=16, - augment=True, - cache=False, - rank=-1, - workers=8, - shuffle=True): +def create_classification_dataloader( + path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True +): # Returns Dataloader object to be used with YOLOv5 Classifier with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) @@ -1247,11 +1295,13 @@ def create_classification_dataloader(path, sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) generator = torch.Generator() generator.manual_seed(6148914691236517205 + RANK) - return InfiniteDataLoader(dataset, - batch_size=batch_size, - shuffle=shuffle and sampler is None, - num_workers=nw, - sampler=sampler, - pin_memory=PIN_MEMORY, - worker_init_fn=seed_worker, - generator=generator) # or DataLoader(persistent_workers=True) + return InfiniteDataLoader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + worker_init_fn=seed_worker, + generator=generator, + ) # or DataLoader(persistent_workers=True) diff --git a/utils/downloads.py b/utils/downloads.py index 9298259d4ab1..ee700acb618b 100644 --- a/utils/downloads.py +++ b/utils/downloads.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Download utils -""" +"""Download utils.""" import logging import subprocess @@ -23,89 +21,90 @@ def is_url(url, check=True): return False -def gsutil_getsize(url=''): +def gsutil_getsize(url=""): # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du - output = subprocess.check_output(['gsutil', 'du', url], shell=True, encoding='utf-8') + output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8") if output: return int(output.split()[0]) return 0 -def url_getsize(url='https://ultralytics.com/images/bus.jpg'): +def url_getsize(url="https://ultralytics.com/images/bus.jpg"): # Return downloadable file size in bytes response = requests.head(url, allow_redirects=True) - return int(response.headers.get('content-length', -1)) + return int(response.headers.get("content-length", -1)) def curl_download(url, filename, *, silent: bool = False) -> bool: - """ - Download a file from a url to a filename using curl. - """ - silent_option = 'sS' if silent else '' # silent - proc = subprocess.run([ - 'curl', - '-#', - f'-{silent_option}L', - url, - '--output', - filename, - '--retry', - '9', - '-C', - '-', ]) + """Download a file from a url to a filename using curl.""" + silent_option = "sS" if silent else "" # silent + proc = subprocess.run( + [ + "curl", + "-#", + f"-{silent_option}L", + url, + "--output", + filename, + "--retry", + "9", + "-C", + "-", + ] + ) return proc.returncode == 0 -def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): +def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""): # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes from utils.general import LOGGER file = Path(file) assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" try: # url1 - LOGGER.info(f'Downloading {url} to {file}...') + LOGGER.info(f"Downloading {url} to {file}...") torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check except Exception as e: # url2 if file.exists(): file.unlink() # remove partial downloads - LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...') + LOGGER.info(f"ERROR: {e}\nRe-attempting {url2 or url} to {file}...") # curl download, retry and resume on fail curl_download(url2 or url, file) finally: if not file.exists() or file.stat().st_size < min_bytes: # check if file.exists(): file.unlink() # remove partial downloads - LOGGER.info(f'ERROR: {assert_msg}\n{error_msg}') - LOGGER.info('') + LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}") + LOGGER.info("") -def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'): +def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"): # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc. from utils.general import LOGGER - def github_assets(repository, version='latest'): + def github_assets(repository, version="latest"): # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...]) - if version != 'latest': - version = f'tags/{version}' # i.e. tags/v7.0 - response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api - return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets + if version != "latest": + version = f"tags/{version}" # i.e. tags/v7.0 + response = requests.get(f"https://api.github.com/repos/{repository}/releases/{version}").json() # github api + return response["tag_name"], [x["name"] for x in response["assets"]] # tag, assets - file = Path(str(file).strip().replace("'", '')) + file = Path(str(file).strip().replace("'", "")) if not file.exists(): # URL specified name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. - if str(file).startswith(('http:/', 'https:/')): # download - url = str(file).replace(':/', '://') # Pathlib turns :// -> :/ - file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth... + if str(file).startswith(("http:/", "https:/")): # download + url = str(file).replace(":/", "://") # Pathlib turns :// -> :/ + file = name.split("?")[0] # parse authentication https://url.com/file.txt?auth... if Path(file).is_file(): - LOGGER.info(f'Found {url} locally at {file}') # file already exists + LOGGER.info(f"Found {url} locally at {file}") # file already exists else: - safe_download(file=file, url=url, min_bytes=1E5) + safe_download(file=file, url=url, min_bytes=1e5) return file # GitHub assets - assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default + assets = [f"yolov5{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "6", "-cls", "-seg")] # default try: tag, assets = github_assets(repo, release) except Exception: @@ -113,15 +112,17 @@ def github_assets(repository, version='latest'): tag, assets = github_assets(repo) # latest release except Exception: try: - tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] + tag = subprocess.check_output("git tag", shell=True, stderr=subprocess.STDOUT).decode().split()[-1] except Exception: tag = release if name in assets: file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) - safe_download(file, - url=f'https://github.com/{repo}/releases/download/{tag}/{name}', - min_bytes=1E5, - error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}') + safe_download( + file, + url=f"https://github.com/{repo}/releases/download/{tag}/{name}", + min_bytes=1e5, + error_msg=f"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}", + ) return str(file) diff --git a/utils/flask_rest_api/example_request.py b/utils/flask_rest_api/example_request.py index 256ad1319c82..7b850051cca0 100644 --- a/utils/flask_rest_api/example_request.py +++ b/utils/flask_rest_api/example_request.py @@ -1,19 +1,17 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Perform test request -""" +"""Perform test request.""" import pprint import requests -DETECTION_URL = 'http://localhost:5000/v1/object-detection/yolov5s' -IMAGE = 'zidane.jpg' +DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" +IMAGE = "zidane.jpg" # Read image -with open(IMAGE, 'rb') as f: +with open(IMAGE, "rb") as f: image_data = f.read() -response = requests.post(DETECTION_URL, files={'image': image_data}).json() +response = requests.post(DETECTION_URL, files={"image": image_data}).json() pprint.pprint(response) diff --git a/utils/flask_rest_api/restapi.py b/utils/flask_rest_api/restapi.py index ae4756b276e4..e62c7ebd709f 100644 --- a/utils/flask_rest_api/restapi.py +++ b/utils/flask_rest_api/restapi.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Run a Flask REST API exposing one or more YOLOv5s models -""" +"""Run a Flask REST API exposing one or more YOLOv5s models.""" import argparse import io @@ -13,36 +11,36 @@ app = Flask(__name__) models = {} -DETECTION_URL = '/v1/object-detection/' +DETECTION_URL = "/v1/object-detection/" -@app.route(DETECTION_URL, methods=['POST']) +@app.route(DETECTION_URL, methods=["POST"]) def predict(model): - if request.method != 'POST': + if request.method != "POST": return - if request.files.get('image'): + if request.files.get("image"): # Method 1 # with request.files["image"] as f: # im = Image.open(io.BytesIO(f.read())) # Method 2 - im_file = request.files['image'] + im_file = request.files["image"] im_bytes = im_file.read() im = Image.open(io.BytesIO(im_bytes)) if model in models: results = models[model](im, size=640) # reduce size=320 for faster inference - return results.pandas().xyxy[0].to_json(orient='records') + return results.pandas().xyxy[0].to_json(orient="records") -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='Flask API exposing YOLOv5 model') - parser.add_argument('--port', default=5000, type=int, help='port number') - parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s') +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") + parser.add_argument("--port", default=5000, type=int, help="port number") + parser.add_argument("--model", nargs="+", default=["yolov5s"], help="model(s) to run, i.e. --model yolov5n yolov5s") opt = parser.parse_args() for m in opt.model: - models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True) + models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True) - app.run(host='0.0.0.0', port=opt.port) # debug=True causes Restarting with stat + app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat diff --git a/utils/general.py b/utils/general.py index 73925ce5fb95..47ab656e5a3f 100644 --- a/utils/general.py +++ b/utils/general.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -General utils -""" +"""General utils.""" import contextlib import glob @@ -40,9 +38,9 @@ try: import ultralytics - assert hasattr(ultralytics, '__version__') # verify package is not directory + assert hasattr(ultralytics, "__version__") # verify package is not directory except (ImportError, AssertionError): - os.system('pip install -U ultralytics') + os.system("pip install -U ultralytics") import ultralytics from ultralytics.utils.checks import check_requirements @@ -53,67 +51,67 @@ FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv5 root directory -RANK = int(os.getenv('RANK', -1)) +RANK = int(os.getenv("RANK", -1)) # Settings NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads -DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory -AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode -VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode -TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format -FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf - -torch.set_printoptions(linewidth=320, precision=5, profile='long') -np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +DATASETS_DIR = Path(os.getenv("YOLOv5_DATASETS_DIR", ROOT.parent / "datasets")) # global datasets directory +AUTOINSTALL = str(os.getenv("YOLOv5_AUTOINSTALL", True)).lower() == "true" # global auto-install mode +VERBOSE = str(os.getenv("YOLOv5_VERBOSE", True)).lower() == "true" # global verbose mode +TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}" # tqdm bar format +FONT = "Arial.ttf" # https://ultralytics.com/assets/Arial.ttf + +torch.set_printoptions(linewidth=320, precision=5, profile="long") +np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format}) # format short g, %precision=5 pd.options.display.max_columns = 10 cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) -os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads -os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress verbose TF compiler warnings in Colab +os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS) # NumExpr max threads +os.environ["OMP_NUM_THREADS"] = "1" if platform.system() == "darwin" else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # suppress verbose TF compiler warnings in Colab -def is_ascii(s=''): +def is_ascii(s=""): # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) s = str(s) # convert list, tuple, None, etc. to str - return len(s.encode().decode('ascii', 'ignore')) == len(s) + return len(s.encode().decode("ascii", "ignore")) == len(s) -def is_chinese(s='人工智能'): +def is_chinese(s="人工智能"): # Is string composed of any Chinese characters? - return bool(re.search('[\u4e00-\u9fff]', str(s))) + return bool(re.search("[\u4e00-\u9fff]", str(s))) def is_colab(): # Is environment a Google Colab instance? - return 'google.colab' in sys.modules + return "google.colab" in sys.modules def is_jupyter(): """ - Check if the current script is running inside a Jupyter Notebook. - Verified on Colab, Jupyterlab, Kaggle, Paperspace. + Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace. Returns: bool: True if running inside a Jupyter Notebook, False otherwise. """ with contextlib.suppress(Exception): from IPython import get_ipython + return get_ipython() is not None return False def is_kaggle(): # Is environment a Kaggle Notebook? - return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com' + return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com" def is_docker() -> bool: """Check if the process runs inside a docker container.""" - if Path('/.dockerenv').exists(): + if Path("/.dockerenv").exists(): return True try: # check if docker is in control groups - with open('/proc/self/cgroup') as file: - return any('docker' in line for line in file) + with open("/proc/self/cgroup") as file: + return any("docker" in line for line in file) except OSError: return False @@ -122,9 +120,9 @@ def is_writeable(dir, test=False): # Return True if directory has write permissions, test opening a file with write permissions if test=True if not test: return os.access(dir, os.W_OK) # possible issues on Windows - file = Path(dir) / 'tmp.txt' + file = Path(dir) / "tmp.txt" try: - with open(file, 'w'): # open file with write permissions + with open(file, "w"): # open file with write permissions pass file.unlink() # remove file return True @@ -132,47 +130,52 @@ def is_writeable(dir, test=False): return False -LOGGING_NAME = 'yolov5' +LOGGING_NAME = "yolov5" def set_logging(name=LOGGING_NAME, verbose=True): # sets up logging for the given name - rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings + rank = int(os.getenv("RANK", -1)) # rank in world for Multi-GPU trainings level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR - logging.config.dictConfig({ - 'version': 1, - 'disable_existing_loggers': False, - 'formatters': { - name: { - 'format': '%(message)s'}}, - 'handlers': { - name: { - 'class': 'logging.StreamHandler', - 'formatter': name, - 'level': level, }}, - 'loggers': { - name: { - 'level': level, - 'handlers': [name], - 'propagate': False, }}}) + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": {name: {"format": "%(message)s"}}, + "handlers": { + name: { + "class": "logging.StreamHandler", + "formatter": name, + "level": level, + } + }, + "loggers": { + name: { + "level": level, + "handlers": [name], + "propagate": False, + } + }, + } + ) set_logging(LOGGING_NAME) # run before defining LOGGER LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) -if platform.system() == 'Windows': +if platform.system() == "Windows": for fn in LOGGER.info, LOGGER.warning: setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging -def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): +def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"): # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required. env = os.getenv(env_var) if env: path = Path(env) # use environment variable else: - cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs - path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir - path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable + cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"} # 3 OS dirs + path = Path.home() / cfg.get(platform.system(), "") # OS-specific config dir + path = (path if is_writeable(path) else Path("/tmp")) / dir # GCP and AWS lambda fix, only /tmp is writeable path.mkdir(exist_ok=True) # make if required return path @@ -185,7 +188,7 @@ class Profile(contextlib.ContextDecorator): def __init__(self, t=0.0, device: torch.device = None): self.t = t self.device = device - self.cuda = True if (device and str(device)[:4] == 'cuda') else False + self.cuda = True if (device and str(device)[:4] == "cuda") else False def __enter__(self): self.start = self.time() @@ -203,7 +206,7 @@ def time(self): class Timeout(contextlib.ContextDecorator): # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager - def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True): + def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True): self.seconds = int(seconds) self.timeout_message = timeout_msg self.suppress = bool(suppress_timeout_errors) @@ -212,12 +215,12 @@ def _timeout_handler(self, signum, frame): raise TimeoutError(self.timeout_message) def __enter__(self): - if platform.system() != 'Windows': # not supported on Windows + if platform.system() != "Windows": # not supported on Windows signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM signal.alarm(self.seconds) # start countdown for SIGALRM to be raised def __exit__(self, exc_type, exc_val, exc_tb): - if platform.system() != 'Windows': + if platform.system() != "Windows": signal.alarm(0) # Cancel SIGALRM if it's scheduled if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError return True @@ -238,7 +241,7 @@ def __exit__(self, exc_type, exc_val, exc_tb): def methods(instance): # Get class/instance methods - return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith('__')] + return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")] def print_args(args: Optional[dict] = None, show_file=True, show_func=False): @@ -249,11 +252,11 @@ def print_args(args: Optional[dict] = None, show_file=True, show_func=False): args, _, _, frm = inspect.getargvalues(x) args = {k: v for k, v in frm.items() if k in args} try: - file = Path(file).resolve().relative_to(ROOT).with_suffix('') + file = Path(file).resolve().relative_to(ROOT).with_suffix("") except ValueError: file = Path(file).stem - s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '') - LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items())) + s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "") + LOGGER.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items())) def init_seeds(seed=0, deterministic=False): @@ -264,11 +267,11 @@ def init_seeds(seed=0, deterministic=False): torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 - if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213 + if deterministic and check_version(torch.__version__, "1.12.0"): # https://github.com/ultralytics/yolov5/pull/8213 torch.use_deterministic_algorithms(True) torch.backends.cudnn.deterministic = True - os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' - os.environ['PYTHONHASHSEED'] = str(seed) + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + os.environ["PYTHONHASHSEED"] = str(seed) def intersect_dicts(da, db, exclude=()): @@ -282,22 +285,22 @@ def get_default_args(func): return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} -def get_latest_run(search_dir='.'): +def get_latest_run(search_dir="."): # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) - last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) - return max(last_list, key=os.path.getctime) if last_list else '' + last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True) + return max(last_list, key=os.path.getctime) if last_list else "" def file_age(path=__file__): # Return days since last file update - dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta + dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime) # delta return dt.days # + dt.seconds / 86400 # fractional days def file_date(path=__file__): # Return human-readable file modification date, i.e. '2021-3-26' t = datetime.fromtimestamp(Path(path).stat().st_mtime) - return f'{t.year}-{t.month}-{t.day}' + return f"{t.year}-{t.month}-{t.day}" def file_size(path): @@ -307,7 +310,7 @@ def file_size(path): if path.is_file(): return path.stat().st_size / mb elif path.is_dir(): - return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb + return sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) / mb else: return 0.0 @@ -319,7 +322,7 @@ def check_online(): def run_once(): # Check once try: - socket.create_connection(('1.1.1.1', 443), 5) # check host accessibility + socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility return True except OSError: return False @@ -330,68 +333,69 @@ def run_once(): def git_describe(path=ROOT): # path must be a directory # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe try: - assert (Path(path) / '.git').is_dir() - return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1] + assert (Path(path) / ".git").is_dir() + return check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1] except Exception: - return '' + return "" @TryExcept() @WorkingDirectory(ROOT) -def check_git_status(repo='ultralytics/yolov5', branch='master'): +def check_git_status(repo="ultralytics/yolov5", branch="master"): # YOLOv5 status check, recommend 'git pull' if code is out of date - url = f'https://github.com/{repo}' - msg = f', for updates see {url}' - s = colorstr('github: ') # string - assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg - assert check_online(), s + 'skipping check (offline)' + msg + url = f"https://github.com/{repo}" + msg = f", for updates see {url}" + s = colorstr("github: ") # string + assert Path(".git").exists(), s + "skipping check (not a git repository)" + msg + assert check_online(), s + "skipping check (offline)" + msg - splits = re.split(pattern=r'\s', string=check_output('git remote -v', shell=True).decode()) + splits = re.split(pattern=r"\s", string=check_output("git remote -v", shell=True).decode()) matches = [repo in s for s in splits] if any(matches): remote = splits[matches.index(True) - 1] else: - remote = 'ultralytics' - check_output(f'git remote add {remote} {url}', shell=True) - check_output(f'git fetch {remote}', shell=True, timeout=5) # git fetch - local_branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out - n = int(check_output(f'git rev-list {local_branch}..{remote}/{branch} --count', shell=True)) # commits behind + remote = "ultralytics" + check_output(f"git remote add {remote} {url}", shell=True) + check_output(f"git fetch {remote}", shell=True, timeout=5) # git fetch + local_branch = check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip() # checked out + n = int(check_output(f"git rev-list {local_branch}..{remote}/{branch} --count", shell=True)) # commits behind if n > 0: - pull = 'git pull' if remote == 'origin' else f'git pull {remote} {branch}' + pull = "git pull" if remote == "origin" else f"git pull {remote} {branch}" s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." else: - s += f'up to date with {url} ✅' + s += f"up to date with {url} ✅" LOGGER.info(s) @WorkingDirectory(ROOT) -def check_git_info(path='.'): +def check_git_info(path="."): # YOLOv5 git info check, return {remote, branch, commit} - check_requirements('gitpython') + check_requirements("gitpython") import git + try: repo = git.Repo(path) - remote = repo.remotes.origin.url.replace('.git', '') # i.e. 'https://github.com/ultralytics/yolov5' + remote = repo.remotes.origin.url.replace(".git", "") # i.e. 'https://github.com/ultralytics/yolov5' commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' try: branch = repo.active_branch.name # i.e. 'main' except TypeError: # not on any branch branch = None # i.e. 'detached HEAD' state - return {'remote': remote, 'branch': branch, 'commit': commit} + return {"remote": remote, "branch": branch, "commit": commit} except git.exc.InvalidGitRepositoryError: # path is not a git dir - return {'remote': None, 'branch': None, 'commit': None} + return {"remote": None, "branch": None, "commit": None} -def check_python(minimum='3.8.0'): +def check_python(minimum="3.8.0"): # Check current python version vs. required python version - check_version(platform.python_version(), minimum, name='Python ', hard=True) + check_version(platform.python_version(), minimum, name="Python ", hard=True) -def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False): +def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False): # Check version vs. required version current, minimum = (pkg.parse_version(x) for x in (current, minimum)) result = (current == minimum) if pinned else (current >= minimum) # bool - s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string + s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed" # string if hard: assert result, emojis(s) # assert min requirements met if verbose and not result: @@ -407,7 +411,7 @@ def check_img_size(imgsz, s=32, floor=0): imgsz = list(imgsz) # convert to list if tuple new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] if new_size != imgsz: - LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}') + LOGGER.warning(f"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}") return new_size @@ -416,18 +420,18 @@ def check_imshow(warn=False): try: assert not is_jupyter() assert not is_docker() - cv2.imshow('test', np.zeros((1, 1, 3))) + cv2.imshow("test", np.zeros((1, 1, 3))) cv2.waitKey(1) cv2.destroyAllWindows() cv2.waitKey(1) return True except Exception as e: if warn: - LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}') + LOGGER.warning(f"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}") return False -def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''): +def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""): # Check file(s) for acceptable suffix if file and suffix: if isinstance(suffix, str): @@ -435,38 +439,40 @@ def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''): for f in file if isinstance(file, (list, tuple)) else [file]: s = Path(f).suffix.lower() # file suffix if len(s): - assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}' + assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}" -def check_yaml(file, suffix=('.yaml', '.yml')): +def check_yaml(file, suffix=(".yaml", ".yml")): # Search/download YAML file (if necessary) and return path, checking suffix return check_file(file, suffix) -def check_file(file, suffix=''): +def check_file(file, suffix=""): # Search/download file (if necessary) and return path check_suffix(file, suffix) # optional file = str(file) # convert to str() if os.path.isfile(file) or not file: # exists return file - elif file.startswith(('http:/', 'https:/')): # download + elif file.startswith(("http:/", "https:/")): # download url = file # warning: Pathlib turns :// -> :/ - file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth + file = Path(urllib.parse.unquote(file).split("?")[0]).name # '%2F' to '/', split https://url.com/file.txt?auth if os.path.isfile(file): - LOGGER.info(f'Found {url} locally at {file}') # file already exists + LOGGER.info(f"Found {url} locally at {file}") # file already exists else: - LOGGER.info(f'Downloading {url} to {file}...') + LOGGER.info(f"Downloading {url} to {file}...") torch.hub.download_url_to_file(url, file) - assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check + assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}" # check return file - elif file.startswith('clearml://'): # ClearML Dataset ID - assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." + elif file.startswith("clearml://"): # ClearML Dataset ID + assert ( + "clearml" in sys.modules + ), "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." return file else: # search files = [] - for d in 'data', 'models', 'utils': # search directories - files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file - assert len(files), f'File not found: {file}' # assert file was found + for d in "data", "models", "utils": # search directories + files.extend(glob.glob(str(ROOT / d / "**" / file), recursive=True)) # find file + assert len(files), f"File not found: {file}" # assert file was found assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique return files[0] # return file @@ -476,8 +482,8 @@ def check_font(font=FONT, progress=False): font = Path(font) file = CONFIG_DIR / font.name if not font.exists() and not file.exists(): - url = f'https://ultralytics.com/assets/{font.name}' - LOGGER.info(f'Downloading {url} to {file}...') + url = f"https://ultralytics.com/assets/{font.name}" + LOGGER.info(f"Downloading {url} to {file}...") torch.hub.download_url_to_file(url, str(file), progress=progress) @@ -485,10 +491,10 @@ def check_dataset(data, autodownload=True): # Download, check and/or unzip dataset if not found locally # Download (optional) - extract_dir = '' + extract_dir = "" if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): - download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1) - data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml')) + download(data, dir=f"{DATASETS_DIR}/{Path(data).stem}", unzip=True, delete=False, curl=False, threads=1) + data = next((DATASETS_DIR / Path(data).stem).rglob("*.yaml")) extract_dir, autodownload = data.parent, False # Read yaml (optional) @@ -496,54 +502,54 @@ def check_dataset(data, autodownload=True): data = yaml_load(data) # dictionary # Checks - for k in 'train', 'val', 'names': + for k in "train", "val", "names": assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") - if isinstance(data['names'], (list, tuple)): # old array format - data['names'] = dict(enumerate(data['names'])) # convert to dict - assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car' - data['nc'] = len(data['names']) + if isinstance(data["names"], (list, tuple)): # old array format + data["names"] = dict(enumerate(data["names"])) # convert to dict + assert all(isinstance(k, int) for k in data["names"].keys()), "data.yaml names keys must be integers, i.e. 2: car" + data["nc"] = len(data["names"]) # Resolve paths - path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.' + path = Path(extract_dir or data.get("path") or "") # optional 'path' default to '.' if not path.is_absolute(): path = (ROOT / path).resolve() - data['path'] = path # download scripts - for k in 'train', 'val', 'test': + data["path"] = path # download scripts + for k in "train", "val", "test": if data.get(k): # prepend path if isinstance(data[k], str): x = (path / data[k]).resolve() - if not x.exists() and data[k].startswith('../'): + if not x.exists() and data[k].startswith("../"): x = (path / data[k][3:]).resolve() data[k] = str(x) else: data[k] = [str((path / x).resolve()) for x in data[k]] # Parse yaml - train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download')) + train, val, test, s = (data.get(x) for x in ("train", "val", "test", "download")) if val: val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path if not all(x.exists() for x in val): - LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]) + LOGGER.info("\nDataset not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]) if not s or not autodownload: - raise Exception('Dataset not found ❌') + raise Exception("Dataset not found ❌") t = time.time() - if s.startswith('http') and s.endswith('.zip'): # URL + if s.startswith("http") and s.endswith(".zip"): # URL f = Path(s).name # filename - LOGGER.info(f'Downloading {s} to {f}...') + LOGGER.info(f"Downloading {s} to {f}...") torch.hub.download_url_to_file(s, f) Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root unzip_file(f, path=DATASETS_DIR) # unzip Path(f).unlink() # remove zip r = None # success - elif s.startswith('bash '): # bash script - LOGGER.info(f'Running {s} ...') + elif s.startswith("bash "): # bash script + LOGGER.info(f"Running {s} ...") r = subprocess.run(s, shell=True) else: # python script - r = exec(s, {'yaml': data}) # return None - dt = f'({round(time.time() - t, 1)}s)' - s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌' - LOGGER.info(f'Dataset download {s}') - check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts + r = exec(s, {"yaml": data}) # return None + dt = f"({round(time.time() - t, 1)}s)" + s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌" + LOGGER.info(f"Dataset download {s}") + check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf", progress=True) # download fonts return data # dictionary @@ -559,35 +565,35 @@ def amp_allclose(model, im): b = m(im).xywhn[0] # AMP inference return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance - prefix = colorstr('AMP: ') + prefix = colorstr("AMP: ") device = next(model.parameters()).device # get model device - if device.type in ('cpu', 'mps'): + if device.type in ("cpu", "mps"): return False # AMP only used on CUDA devices - f = ROOT / 'data' / 'images' / 'bus.jpg' # image to check - im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3)) + f = ROOT / "data" / "images" / "bus.jpg" # image to check + im = f if f.exists() else "https://ultralytics.com/images/bus.jpg" if check_online() else np.ones((640, 640, 3)) try: - assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im) - LOGGER.info(f'{prefix}checks passed ✅') + assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend("yolov5n.pt", device), im) + LOGGER.info(f"{prefix}checks passed ✅") return True except Exception: - help_url = 'https://github.com/ultralytics/yolov5/issues/7908' - LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}') + help_url = "https://github.com/ultralytics/yolov5/issues/7908" + LOGGER.warning(f"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}") return False -def yaml_load(file='data.yaml'): +def yaml_load(file="data.yaml"): # Single-line safe yaml loading - with open(file, errors='ignore') as f: + with open(file, errors="ignore") as f: return yaml.safe_load(f) -def yaml_save(file='data.yaml', data={}): +def yaml_save(file="data.yaml", data={}): # Single-line safe yaml saving - with open(file, 'w') as f: + with open(file, "w") as f: yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) -def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): +def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")): # Unzip a *.zip file to path/, excluding files containing strings in exclude list if path is None: path = Path(file).parent # default path @@ -599,11 +605,11 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): def url2file(url): # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt - url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/ - return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth + url = str(Path(url)).replace(":/", "://") # Pathlib turns :// -> :/ + return Path(urllib.parse.unquote(url)).name.split("?")[0] # '%2F' to '/', split https://url.com/file.txt?auth -def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3): +def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3): # Multithreaded file download and unzip function, used in data.yaml for autodownload def download_one(url, dir): # Download 1 file @@ -612,7 +618,7 @@ def download_one(url, dir): f = Path(url) # filename else: # does not exist f = dir / Path(url).name - LOGGER.info(f'Downloading {url} to {f}...') + LOGGER.info(f"Downloading {url} to {f}...") for i in range(retry + 1): if curl: success = curl_download(url, f, silent=(threads > 1)) @@ -622,18 +628,18 @@ def download_one(url, dir): if success: break elif i < retry: - LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...') + LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...") else: - LOGGER.warning(f'❌ Failed to download {url}...') + LOGGER.warning(f"❌ Failed to download {url}...") - if unzip and success and (f.suffix == '.gz' or is_zipfile(f) or is_tarfile(f)): - LOGGER.info(f'Unzipping {f}...') + if unzip and success and (f.suffix == ".gz" or is_zipfile(f) or is_tarfile(f)): + LOGGER.info(f"Unzipping {f}...") if is_zipfile(f): unzip_file(f, dir) # unzip elif is_tarfile(f): - subprocess.run(['tar', 'xf', f, '--directory', f.parent], check=True) # unzip - elif f.suffix == '.gz': - subprocess.run(['tar', 'xfz', f, '--directory', f.parent], check=True) # unzip + subprocess.run(["tar", "xf", f, "--directory", f.parent], check=True) # unzip + elif f.suffix == ".gz": + subprocess.run(["tar", "xfz", f, "--directory", f.parent], check=True) # unzip if delete: f.unlink() # remove zip @@ -658,7 +664,7 @@ def make_divisible(x, divisor): def clean_str(s): # Cleans a string by replacing special characters with underscore _ - return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s) + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) def one_cycle(y1=0.0, y2=1.0, steps=100): @@ -668,28 +674,29 @@ def one_cycle(y1=0.0, y2=1.0, steps=100): def colorstr(*input): # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') - *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string + *args, string = input if len(input) > 1 else ("blue", "bold", input[0]) # color arguments, string colors = { - 'black': '\033[30m', # basic colors - 'red': '\033[31m', - 'green': '\033[32m', - 'yellow': '\033[33m', - 'blue': '\033[34m', - 'magenta': '\033[35m', - 'cyan': '\033[36m', - 'white': '\033[37m', - 'bright_black': '\033[90m', # bright colors - 'bright_red': '\033[91m', - 'bright_green': '\033[92m', - 'bright_yellow': '\033[93m', - 'bright_blue': '\033[94m', - 'bright_magenta': '\033[95m', - 'bright_cyan': '\033[96m', - 'bright_white': '\033[97m', - 'end': '\033[0m', # misc - 'bold': '\033[1m', - 'underline': '\033[4m'} - return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] + "black": "\033[30m", # basic colors + "red": "\033[31m", + "green": "\033[32m", + "yellow": "\033[33m", + "blue": "\033[34m", + "magenta": "\033[35m", + "cyan": "\033[36m", + "white": "\033[37m", + "bright_black": "\033[90m", # bright colors + "bright_red": "\033[91m", + "bright_green": "\033[92m", + "bright_yellow": "\033[93m", + "bright_blue": "\033[94m", + "bright_magenta": "\033[95m", + "bright_cyan": "\033[96m", + "bright_white": "\033[97m", + "end": "\033[0m", # misc + "bold": "\033[1m", + "underline": "\033[4m", + } + return "".join(colors[x] for x in args) + f"{string}" + colors["end"] def labels_to_class_weights(labels, nc=80): @@ -725,9 +732,87 @@ def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet return [ - 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, - 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 27, + 28, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 67, + 70, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + ] def xyxy2xywh(x): @@ -784,7 +869,10 @@ def segment2box(segment, width=640, height=640): # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) x, y = segment.T # segment xy inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) - x, y, = x[inside], y[inside] + ( + x, + y, + ) = x[inside], y[inside] return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy @@ -865,30 +953,31 @@ def clip_segments(segments, shape): def non_max_suppression( - prediction, - conf_thres=0.25, - iou_thres=0.45, - classes=None, - agnostic=False, - multi_label=False, - labels=(), - max_det=300, - nm=0, # number of masks + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks ): - """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections + """ + Non-Maximum Suppression (NMS) on inference results to reject overlapping detections. Returns: list of detections, on (n,6) tensor per image [xyxy, conf, cls] """ # Checks - assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' - assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0" + assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0" if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) prediction = prediction[0] # select only inference output device = prediction.device - mps = 'mps' in device.type # Apple MPS + mps = "mps" in device.type # Apple MPS if mps: # MPS not fully supported yet, convert tensors to CPU before NMS prediction = prediction.cpu() bs = prediction.shape[0] # batch size @@ -959,7 +1048,7 @@ def non_max_suppression( boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS i = i[:max_det] # limit detections - if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix weights = iou * scores[None] # box weights @@ -971,31 +1060,31 @@ def non_max_suppression( if mps: output[xi] = output[xi].to(device) if (time.time() - t) > time_limit: - LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') + LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded") break # time limit exceeded return output -def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() +def strip_optimizer(f="best.pt", s=""): # from utils.general import *; strip_optimizer() # Strip optimizer from 'f' to finalize training, optionally save as 's' - x = torch.load(f, map_location=torch.device('cpu')) - if x.get('ema'): - x['model'] = x['ema'] # replace model with ema - for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys + x = torch.load(f, map_location=torch.device("cpu")) + if x.get("ema"): + x["model"] = x["ema"] # replace model with ema + for k in "optimizer", "best_fitness", "ema", "updates": # keys x[k] = None - x['epoch'] = -1 - x['model'].half() # to FP16 - for p in x['model'].parameters(): + x["epoch"] = -1 + x["model"].half() # to FP16 + for p in x["model"].parameters(): p.requires_grad = False torch.save(x, s or f) - mb = os.path.getsize(s or f) / 1E6 # filesize + mb = os.path.getsize(s or f) / 1e6 # filesize LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") -def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')): - evolve_csv = save_dir / 'evolve.csv' - evolve_yaml = save_dir / 'hyp_evolve.yaml' +def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr("evolve: ")): + evolve_csv = save_dir / "evolve.csv" + evolve_yaml = save_dir / "hyp_evolve.yaml" keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] keys = tuple(x.strip() for x in keys) vals = results + tuple(hyp.values()) @@ -1003,33 +1092,48 @@ def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve # Download (optional) if bucket: - url = f'gs://{bucket}/evolve.csv' + url = f"gs://{bucket}/evolve.csv" if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): - subprocess.run(['gsutil', 'cp', f'{url}', f'{save_dir}']) # download evolve.csv if larger than local + subprocess.run(["gsutil", "cp", f"{url}", f"{save_dir}"]) # download evolve.csv if larger than local # Log to evolve.csv - s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header - with open(evolve_csv, 'a') as f: - f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n') + s = "" if evolve_csv.exists() else (("%20s," * n % keys).rstrip(",") + "\n") # add header + with open(evolve_csv, "a") as f: + f.write(s + ("%20.5g," * n % vals).rstrip(",") + "\n") # Save yaml - with open(evolve_yaml, 'w') as f: + with open(evolve_yaml, "w") as f: data = pd.read_csv(evolve_csv, skipinitialspace=True) data = data.rename(columns=lambda x: x.strip()) # strip keys i = np.argmax(fitness(data.values[:, :4])) # generations = len(data) - f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' + - f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + - '\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n') + f.write( + "# YOLOv5 Hyperparameter Evolution Results\n" + + f"# Best generation: {i}\n" + + f"# Last generation: {generations - 1}\n" + + "# " + + ", ".join(f"{x.strip():>20s}" for x in keys[:7]) + + "\n" + + "# " + + ", ".join(f"{x:>20.5g}" for x in data.values[i, :7]) + + "\n\n" + ) yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) # Print to screen - LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix + - ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}' - for x in vals) + '\n\n') + LOGGER.info( + prefix + + f"{generations} generations finished, current result:\n" + + prefix + + ", ".join(f"{x.strip():>20s}" for x in keys) + + "\n" + + prefix + + ", ".join(f"{x:20.5g}" for x in vals) + + "\n\n" + ) if bucket: - subprocess.run(['gsutil', 'cp', f'{evolve_csv}', f'{evolve_yaml}', f'gs://{bucket}']) # upload + subprocess.run(["gsutil", "cp", f"{evolve_csv}", f"{evolve_yaml}", f"gs://{bucket}"]) # upload def apply_classifier(x, model, img, im0): @@ -1053,7 +1157,7 @@ def apply_classifier(x, model, img, im0): pred_cls1 = d[:, 5].long() ims = [] for a in d: - cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] + cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])] im = cv2.resize(cutout, (224, 224)) # BGR im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 @@ -1067,15 +1171,15 @@ def apply_classifier(x, model, img, im0): return x -def increment_path(path, exist_ok=False, sep='', mkdir=False): +def increment_path(path, exist_ok=False, sep="", mkdir=False): # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. path = Path(path) # os-agnostic if path.exists() and not exist_ok: - path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '') + path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "") # Method 1 for n in range(2, 9999): - p = f'{path}{sep}{n}{suffix}' # increment path + p = f"{path}{sep}{n}{suffix}" # increment path if not os.path.exists(p): # break path = Path(p) @@ -1110,7 +1214,7 @@ def imwrite(filename, img): def imshow(path, im): - imshow_(path.encode('unicode_escape').decode(), im) + imshow_(path.encode("unicode_escape").decode(), im) if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename: diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index 381d477d127c..df67e45c8221 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Logging utils -""" +"""Logging utils.""" import json import os import warnings @@ -16,8 +14,8 @@ from utils.plots import plot_images, plot_labels, plot_results from utils.torch_utils import de_parallel -LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.csv, TensorBoard, Weights & Biases, ClearML -RANK = int(os.getenv('RANK', -1)) +LOGGERS = ("csv", "tb", "wandb", "clearml", "comet") # *.csv, TensorBoard, Weights & Biases, ClearML +RANK = int(os.getenv("RANK", -1)) try: from torch.utils.tensorboard import SummaryWriter @@ -27,8 +25,8 @@ try: import wandb - assert hasattr(wandb, '__version__') # verify package import not local dir - if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}: + assert hasattr(wandb, "__version__") # verify package import not local dir + if pkg.parse_version(wandb.__version__) >= pkg.parse_version("0.12.2") and RANK in {0, -1}: try: wandb_login_success = wandb.login(timeout=30) except wandb.errors.UsageError: # known non-TTY terminal issue @@ -41,7 +39,7 @@ try: import clearml - assert hasattr(clearml, '__version__') # verify package import not local dir + assert hasattr(clearml, "__version__") # verify package import not local dir except (ImportError, AssertionError): clearml = None @@ -49,7 +47,7 @@ if RANK in {0, -1}: import comet_ml - assert hasattr(comet_ml, '__version__') # verify package import not local dir + assert hasattr(comet_ml, "__version__") # verify package import not local dir from utils.loggers.comet import CometLogger else: @@ -59,7 +57,11 @@ def _json_default(value): - """Format `value` for JSON serialization (e.g. unwrap tensors). Fall back to strings.""" + """ + Format `value` for JSON serialization (e.g. unwrap tensors). + + Fall back to strings. + """ if isinstance(value, torch.Tensor): try: value = value.item() @@ -70,7 +72,7 @@ def _json_default(value): return str(value) -class Loggers(): +class Loggers: # YOLOv5 Loggers class def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): self.save_dir = save_dir @@ -81,62 +83,65 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, self.logger = logger # for printing results to console self.include = include self.keys = [ - 'train/box_loss', - 'train/obj_loss', - 'train/cls_loss', # train loss - 'metrics/precision', - 'metrics/recall', - 'metrics/mAP_0.5', - 'metrics/mAP_0.5:0.95', # metrics - 'val/box_loss', - 'val/obj_loss', - 'val/cls_loss', # val loss - 'x/lr0', - 'x/lr1', - 'x/lr2'] # params - self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95'] + "train/box_loss", + "train/obj_loss", + "train/cls_loss", # train loss + "metrics/precision", + "metrics/recall", + "metrics/mAP_0.5", + "metrics/mAP_0.5:0.95", # metrics + "val/box_loss", + "val/obj_loss", + "val/cls_loss", # val loss + "x/lr0", + "x/lr1", + "x/lr2", + ] # params + self.best_keys = ["best/epoch", "best/precision", "best/recall", "best/mAP_0.5", "best/mAP_0.5:0.95"] for k in LOGGERS: setattr(self, k, None) # init empty logger dictionary self.csv = True # always log to csv - self.ndjson_console = ('ndjson_console' in self.include) # log ndjson to console - self.ndjson_file = ('ndjson_file' in self.include) # log ndjson to file + self.ndjson_console = "ndjson_console" in self.include # log ndjson to console + self.ndjson_file = "ndjson_file" in self.include # log ndjson to file # Messages if not comet_ml: - prefix = colorstr('Comet: ') + prefix = colorstr("Comet: ") s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet" self.logger.info(s) # TensorBoard s = self.save_dir - if 'tb' in self.include and not self.opt.evolve: - prefix = colorstr('TensorBoard: ') + if "tb" in self.include and not self.opt.evolve: + prefix = colorstr("TensorBoard: ") self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") self.tb = SummaryWriter(str(s)) # W&B - if wandb and 'wandb' in self.include: + if wandb and "wandb" in self.include: self.opt.hyp = self.hyp # add hyperparameters self.wandb = WandbLogger(self.opt) else: self.wandb = None # ClearML - if clearml and 'clearml' in self.include: + if clearml and "clearml" in self.include: try: self.clearml = ClearmlLogger(self.opt, self.hyp) except Exception: self.clearml = None - prefix = colorstr('ClearML: ') - LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' - f' See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme') + prefix = colorstr("ClearML: ") + LOGGER.warning( + f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging." + f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme" + ) else: self.clearml = None # Comet - if comet_ml and 'comet' in self.include: - if isinstance(self.opt.resume, str) and self.opt.resume.startswith('comet://'): - run_id = self.opt.resume.split('/')[-1] + if comet_ml and "comet" in self.include: + if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"): + run_id = self.opt.resume.split("/")[-1] self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) else: @@ -170,9 +175,9 @@ def on_pretrain_routine_end(self, labels, names): # Callback runs on pre-train routine end if self.plots: plot_labels(labels, names, self.save_dir) - paths = self.save_dir.glob('*labels*.jpg') # training labels + paths = self.save_dir.glob("*labels*.jpg") # training labels if self.wandb: - self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]}) + self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]}) if self.comet_logger: self.comet_logger.on_pretrain_routine_end(paths) if self.clearml: @@ -185,16 +190,16 @@ def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): # ni: number integrated batches (since train start) if self.plots: if ni < 3: - f = self.save_dir / f'train_batch{ni}.jpg' # filename + f = self.save_dir / f"train_batch{ni}.jpg" # filename plot_images(imgs, targets, paths, f) if ni == 0 and self.tb and not self.opt.sync_bn: log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) if ni == 10 and (self.wandb or self.clearml): - files = sorted(self.save_dir.glob('train*.jpg')) + files = sorted(self.save_dir.glob("train*.jpg")) if self.wandb: - self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) + self.wandb.log({"Mosaics": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) if self.clearml: - self.clearml.log_debug_samples(files, title='Mosaics') + self.clearml.log_debug_samples(files, title="Mosaics") if self.comet_logger: self.comet_logger.on_train_batch_end(log_dict, step=ni) @@ -225,11 +230,11 @@ def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): # Callback runs on val end if self.wandb or self.clearml: - files = sorted(self.save_dir.glob('val*.jpg')) + files = sorted(self.save_dir.glob("val*.jpg")) if self.wandb: - self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.name) for f in files]}) + self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]}) if self.clearml: - self.clearml.log_debug_samples(files, title='Validation') + self.clearml.log_debug_samples(files, title="Validation") if self.comet_logger: self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) @@ -238,18 +243,18 @@ def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): # Callback runs at the end of each fit (train+val) epoch x = dict(zip(self.keys, vals)) if self.csv: - file = self.save_dir / 'results.csv' + file = self.save_dir / "results.csv" n = len(x) + 1 # number of cols - s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header - with open(file, 'a') as f: - f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') + s = "" if file.exists() else (("%20s," * n % tuple(["epoch"] + self.keys)).rstrip(",") + "\n") # add header + with open(file, "a") as f: + f.write(s + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n") if self.ndjson_console or self.ndjson_file: json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default) if self.ndjson_console: print(json_data) if self.ndjson_file: - file = self.save_dir / 'results.ndjson' - with open(file, 'a') as f: + file = self.save_dir / "results.ndjson" + with open(file, "a") as f: print(json_data, file=f) if self.tb: @@ -279,9 +284,9 @@ def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): if self.wandb: self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) if self.clearml: - self.clearml.task.update_output_model(model_path=str(last), - model_name='Latest Model', - auto_delete_file=False) + self.clearml.task.update_output_model( + model_path=str(last), model_name="Latest Model", auto_delete_file=False + ) if self.comet_logger: self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) @@ -289,31 +294,34 @@ def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): def on_train_end(self, last, best, epoch, results): # Callback runs on training end, i.e. saving best model if self.plots: - plot_results(file=self.save_dir / 'results.csv') # save results.png - files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] + plot_results(file=self.save_dir / "results.csv") # save results.png + files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}") if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles for f in files: - self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") if self.wandb: self.wandb.log(dict(zip(self.keys[3:10], results))) - self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for f in files]}) + self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]}) # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model if not self.opt.evolve: - wandb.log_artifact(str(best if best.exists() else last), - type='model', - name=f'run_{self.wandb.wandb_run.id}_model', - aliases=['latest', 'best', 'stripped']) + wandb.log_artifact( + str(best if best.exists() else last), + type="model", + name=f"run_{self.wandb.wandb_run.id}_model", + aliases=["latest", "best", "stripped"], + ) self.wandb.finish_run() if self.clearml and not self.opt.evolve: self.clearml.log_summary(dict(zip(self.keys[3:10], results))) [self.clearml.log_plot(title=f.stem, plot_path=f) for f in files] - self.clearml.log_model(str(best if best.exists() else last), - "Best Model" if best.exists() else "Last Model", epoch) + self.clearml.log_model( + str(best if best.exists() else last), "Best Model" if best.exists() else "Last Model", epoch + ) if self.comet_logger: final_results = dict(zip(self.keys[3:10], results)) @@ -339,38 +347,41 @@ class GenericLogger: include: loggers to include """ - def __init__(self, opt, console_logger, include=('tb', 'wandb', 'clearml')): + def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")): # init default loggers self.save_dir = Path(opt.save_dir) self.include = include self.console_logger = console_logger - self.csv = self.save_dir / 'results.csv' # CSV logger - if 'tb' in self.include: - prefix = colorstr('TensorBoard: ') + self.csv = self.save_dir / "results.csv" # CSV logger + if "tb" in self.include: + prefix = colorstr("TensorBoard: ") self.console_logger.info( - f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/") + f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/" + ) self.tb = SummaryWriter(str(self.save_dir)) - if wandb and 'wandb' in self.include: - self.wandb = wandb.init(project=web_project_name(str(opt.project)), - name=None if opt.name == 'exp' else opt.name, - config=opt) + if wandb and "wandb" in self.include: + self.wandb = wandb.init( + project=web_project_name(str(opt.project)), name=None if opt.name == "exp" else opt.name, config=opt + ) else: self.wandb = None - if clearml and 'clearml' in self.include: + if clearml and "clearml" in self.include: try: # Hyp is not available in classification mode - if 'hyp' not in opt: + if "hyp" not in opt: hyp = {} else: hyp = opt.hyp self.clearml = ClearmlLogger(opt, hyp) except Exception: self.clearml = None - prefix = colorstr('ClearML: ') - LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' - f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme') + prefix = colorstr("ClearML: ") + LOGGER.warning( + f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging." + f" See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme" + ) else: self.clearml = None @@ -379,9 +390,9 @@ def log_metrics(self, metrics, epoch): if self.csv: keys, vals = list(metrics.keys()), list(metrics.values()) n = len(metrics) + 1 # number of cols - s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header - with open(self.csv, 'a') as f: - f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') + s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n") # header + with open(self.csv, "a") as f: + f.write(s + ("%23.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n") if self.tb: for k, v in metrics.items(): @@ -393,20 +404,20 @@ def log_metrics(self, metrics, epoch): if self.clearml: self.clearml.log_scalars(metrics, epoch) - def log_images(self, files, name='Images', epoch=0): + def log_images(self, files, name="Images", epoch=0): # Log images to all loggers files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path files = [f for f in files if f.exists()] # filter by exists if self.tb: for f in files: - self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") if self.wandb: self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) if self.clearml: - if name == 'Results': + if name == "Results": [self.clearml.log_plot(f.stem, f) for f in files] else: self.clearml.log_debug_samples(files, title=name) @@ -419,7 +430,7 @@ def log_graph(self, model, imgsz=(640, 640)): def log_model(self, model_path, epoch=0, metadata={}): # Log model to all loggers if self.wandb: - art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata) + art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata) art.add_file(str(model_path)) wandb.log_artifact(art) if self.clearml: @@ -440,15 +451,15 @@ def log_tensorboard_graph(tb, model, imgsz=(640, 640)): imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress jit trace warning + warnings.simplefilter("ignore") # suppress jit trace warning tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) except Exception as e: - LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}') + LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}") def web_project_name(project): # Convert local project name to web project name - if not project.startswith('runs/train'): + if not project.startswith("runs/train"): return project - suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else '' - return f'YOLOv5{suffix}' + suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else "" + return f"YOLOv5{suffix}" diff --git a/utils/loggers/clearml/clearml_utils.py b/utils/loggers/clearml/clearml_utils.py index c7627a261186..8b141d177afd 100644 --- a/utils/loggers/clearml/clearml_utils.py +++ b/utils/loggers/clearml/clearml_utils.py @@ -13,55 +13,63 @@ import clearml from clearml import Dataset, Task - assert hasattr(clearml, '__version__') # verify package import not local dir + assert hasattr(clearml, "__version__") # verify package import not local dir except (ImportError, AssertionError): clearml = None def construct_dataset(clearml_info_string): - """Load in a clearml dataset and fill the internal data_dict with its contents. - """ - dataset_id = clearml_info_string.replace('clearml://', '') + """Load in a clearml dataset and fill the internal data_dict with its contents.""" + dataset_id = clearml_info_string.replace("clearml://", "") dataset = Dataset.get(dataset_id=dataset_id) dataset_root_path = Path(dataset.get_local_copy()) # We'll search for the yaml file definition in the dataset - yaml_filenames = list(glob.glob(str(dataset_root_path / '*.yaml')) + glob.glob(str(dataset_root_path / '*.yml'))) + yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml"))) if len(yaml_filenames) > 1: - raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains ' - 'the dataset definition this way.') + raise ValueError( + "More than one yaml file was found in the dataset root, cannot determine which one contains " + "the dataset definition this way." + ) elif len(yaml_filenames) == 0: - raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file ' - 'inside the dataset root path.') + raise ValueError( + "No yaml definition found in dataset root path, check that there is a correct yaml file " + "inside the dataset root path." + ) with open(yaml_filenames[0]) as f: dataset_definition = yaml.safe_load(f) - assert set(dataset_definition.keys()).issuperset( - {'train', 'test', 'val', 'nc', 'names'} + assert set( + dataset_definition.keys() + ).issuperset( + {"train", "test", "val", "nc", "names"} ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" data_dict = dict() - data_dict['train'] = str( - (dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None - data_dict['test'] = str( - (dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None - data_dict['val'] = str( - (dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None - data_dict['nc'] = dataset_definition['nc'] - data_dict['names'] = dataset_definition['names'] + data_dict["train"] = ( + str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None + ) + data_dict["test"] = ( + str((dataset_root_path / dataset_definition["test"]).resolve()) if dataset_definition["test"] else None + ) + data_dict["val"] = ( + str((dataset_root_path / dataset_definition["val"]).resolve()) if dataset_definition["val"] else None + ) + data_dict["nc"] = dataset_definition["nc"] + data_dict["names"] = dataset_definition["names"] return data_dict class ClearmlLogger: - """Log training runs, datasets, models, and predictions to ClearML. + """ + Log training runs, datasets, models, and predictions to ClearML. - This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, - this information includes hyperparameters, system configuration and metrics, model metrics, code information and - basic data metrics and analyses. + This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information + includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics + and analyses. - By providing additional command line arguments to train.py, datasets, - models and predictions can also be logged. + By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. """ def __init__(self, opt, hyp): @@ -81,36 +89,36 @@ def __init__(self, opt, hyp): self.max_imgs_to_log_per_epoch = 16 # Get the interval of epochs when bounding box images should be logged # Only for detection task though! - if 'bbox_interval' in opt: + if "bbox_interval" in opt: self.bbox_interval = opt.bbox_interval self.clearml = clearml self.task = None self.data_dict = None if self.clearml: self.task = Task.init( - project_name=opt.project if not str(opt.project).startswith('runs/') else 'YOLOv5', - task_name=opt.name if opt.name != 'exp' else 'Training', - tags=['YOLOv5'], + project_name=opt.project if not str(opt.project).startswith("runs/") else "YOLOv5", + task_name=opt.name if opt.name != "exp" else "Training", + tags=["YOLOv5"], output_uri=True, reuse_last_task_id=opt.exist_ok, - auto_connect_frameworks={ - 'pytorch': False, - 'matplotlib': False} + auto_connect_frameworks={"pytorch": False, "matplotlib": False}, # We disconnect pytorch auto-detection, because we added manual model save points in the code ) # ClearML's hooks will already grab all general parameters # Only the hyperparameters coming from the yaml config file # will have to be added manually! - self.task.connect(hyp, name='Hyperparameters') - self.task.connect(opt, name='Args') + self.task.connect(hyp, name="Hyperparameters") + self.task.connect(opt, name="Args") # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent - self.task.set_base_docker('ultralytics/yolov5:latest', - docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', - docker_setup_bash_script='pip install clearml') + self.task.set_base_docker( + "ultralytics/yolov5:latest", + docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', + docker_setup_bash_script="pip install clearml", + ) # Get ClearML Dataset Version if requested - if opt.data.startswith('clearml://'): + if opt.data.startswith("clearml://"): # data_dict should have the following keys: # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) self.data_dict = construct_dataset(opt.data) @@ -120,33 +128,32 @@ def __init__(self, opt, hyp): def log_scalars(self, metrics, epoch): """ - Log scalars/metrics to ClearML + Log scalars/metrics to ClearML. arguments: metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} epoch (int) iteration number for the current set of metrics """ for k, v in metrics.items(): - title, series = k.split('/') + title, series = k.split("/") self.task.get_logger().report_scalar(title, series, v, epoch) def log_model(self, model_path, model_name, epoch=0): """ - Log model weights to ClearML + Log model weights to ClearML. arguments: model_path (PosixPath or str) Path to the model weights model_name (str) Name of the model visible in ClearML epoch (int) Iteration / epoch of the model weights """ - self.task.update_output_model(model_path=str(model_path), - name=model_name, - iteration=epoch, - auto_delete_file=False) + self.task.update_output_model( + model_path=str(model_path), name=model_name, iteration=epoch, auto_delete_file=False + ) def log_summary(self, metrics): """ - Log final metrics to a summary table + Log final metrics to a summary table. arguments: metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} @@ -156,7 +163,7 @@ def log_summary(self, metrics): def log_plot(self, title, plot_path): """ - Log image as plot in the plot section of ClearML + Log image as plot in the plot section of ClearML. arguments: title (str) Title of the plot @@ -164,12 +171,12 @@ def log_plot(self, title, plot_path): """ img = mpimg.imread(plot_path) fig = plt.figure() - ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[]) # no ticks + ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[]) # no ticks ax.imshow(img) self.task.get_logger().report_matplotlib_figure(title, "", figure=fig, report_interactive=False) - def log_debug_samples(self, files, title='Debug Samples'): + def log_debug_samples(self, files, title="Debug Samples"): """ Log files (images) as debug samples in the ClearML task. @@ -179,12 +186,11 @@ def log_debug_samples(self, files, title='Debug Samples'): """ for f in files: if f.exists(): - it = re.search(r'_batch(\d+)', f.name) + it = re.search(r"_batch(\d+)", f.name) iteration = int(it.groups()[0]) if it else 0 - self.task.get_logger().report_image(title=title, - series=f.name.replace(f"_batch{iteration}", ''), - local_path=str(f), - iteration=iteration) + self.task.get_logger().report_image( + title=title, series=f.name.replace(f"_batch{iteration}", ""), local_path=str(f), iteration=iteration + ) def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): """ @@ -206,15 +212,14 @@ def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_thres class_name = class_names[int(class_nr)] confidence_percentage = round(float(conf) * 100, 2) - label = f'{class_name}: {confidence_percentage}%' + label = f"{class_name}: {confidence_percentage}%" if conf > conf_threshold: annotator.rectangle(box.cpu().numpy(), outline=color) annotator.box_label(box.cpu().numpy(), label=label, color=color) annotated_image = annotator.result() - self.task.get_logger().report_image(title='Bounding Boxes', - series=image_path.name, - iteration=self.current_epoch, - image=annotated_image) + self.task.get_logger().report_image( + title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image + ) self.current_epoch_logged_images.add(image_path) diff --git a/utils/loggers/clearml/hpo.py b/utils/loggers/clearml/hpo.py index ee518b0fbfc8..4e314ea868df 100644 --- a/utils/loggers/clearml/hpo.py +++ b/utils/loggers/clearml/hpo.py @@ -1,18 +1,21 @@ from clearml import Task + # Connecting ClearML with the current process, # from here on everything is logged automatically from clearml.automation import HyperParameterOptimizer, UniformParameterRange from clearml.automation.optuna import OptimizerOptuna -task = Task.init(project_name='Hyper-Parameter Optimization', - task_name='YOLOv5', - task_type=Task.TaskTypes.optimizer, - reuse_last_task_id=False) +task = Task.init( + project_name="Hyper-Parameter Optimization", + task_name="YOLOv5", + task_type=Task.TaskTypes.optimizer, + reuse_last_task_id=False, +) # Example use case: optimizer = HyperParameterOptimizer( # This is the experiment we want to optimize - base_task_id='', + base_task_id="", # here we define the hyper-parameters to optimize # Notice: The parameter name should exactly match what you see in the UI: / # For Example, here we see in the base experiment a section Named: "General" @@ -20,39 +23,40 @@ # If you have `argparse` for example, then arguments will appear under the "Args" section, # and you should instead pass "Args/batch_size" hyper_parameters=[ - UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1), - UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0), - UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98), - UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001), - UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0), - UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95), - UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2), - UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2), - UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0), - UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0), - UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0), - UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0), - UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7), - UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0), - UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0), - UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1), - UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0), - UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9), - UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0), - UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001), - UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0), - UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)], + UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1), + UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0), + UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98), + UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001), + UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0), + UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95), + UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2), + UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2), + UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0), + UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0), + UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0), + UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0), + UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7), + UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0), + UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0), + UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1), + UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0), + UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0), + UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001), + UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0), + ], # this is the objective metric we want to maximize/minimize - objective_metric_title='metrics', - objective_metric_series='mAP_0.5', + objective_metric_title="metrics", + objective_metric_series="mAP_0.5", # now we decide if we want to maximize it or minimize it (accuracy we maximize) - objective_metric_sign='max', + objective_metric_sign="max", # let us limit the number of concurrent experiments, # this in turn will make sure we do dont bombard the scheduler with experiments. # if we have an auto-scaler connected, this, by proxy, will limit the number of machine @@ -81,4 +85,4 @@ # make sure background optimization stopped optimizer.stop() -print('We are done, good bye') +print("We are done, good bye") diff --git a/utils/loggers/comet/__init__.py b/utils/loggers/comet/__init__.py index c14a5f885696..bdf81f63982e 100644 --- a/utils/loggers/comet/__init__.py +++ b/utils/loggers/comet/__init__.py @@ -17,7 +17,7 @@ # Project Configuration config = comet_ml.config.get_config() - COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') + COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") except ImportError: comet_ml = None COMET_PROJECT_NAME = None @@ -31,42 +31,40 @@ from utils.general import check_dataset, scale_boxes, xywh2xyxy from utils.metrics import box_iou -COMET_PREFIX = 'comet://' +COMET_PREFIX = "comet://" -COMET_MODE = os.getenv('COMET_MODE', 'online') +COMET_MODE = os.getenv("COMET_MODE", "online") # Model Saving Settings -COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') +COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") # Dataset Artifact Settings -COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true' +COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true" # Evaluation Settings -COMET_LOG_CONFUSION_MATRIX = (os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true') -COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true' -COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100)) +COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true" +COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true" +COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100)) # Confusion Matrix Settings -CONF_THRES = float(os.getenv('CONF_THRES', 0.001)) -IOU_THRES = float(os.getenv('IOU_THRES', 0.6)) +CONF_THRES = float(os.getenv("CONF_THRES", 0.001)) +IOU_THRES = float(os.getenv("IOU_THRES", 0.6)) # Batch Logging Settings -COMET_LOG_BATCH_METRICS = (os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true') -COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1) -COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1) -COMET_LOG_PER_CLASS_METRICS = (os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true') +COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true" +COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1) +COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1) +COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true" -RANK = int(os.getenv('RANK', -1)) +RANK = int(os.getenv("RANK", -1)) to_pil = T.ToPILImage() class CometLogger: - """Log metrics, parameters, source code, models and much more - with Comet - """ + """Log metrics, parameters, source code, models and much more with Comet.""" - def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None: + def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None: self.job_type = job_type self.opt = opt self.hyp = hyp @@ -87,57 +85,58 @@ def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwar # Default parameters to pass to Experiment objects self.default_experiment_kwargs = { - 'log_code': False, - 'log_env_gpu': True, - 'log_env_cpu': True, - 'project_name': COMET_PROJECT_NAME, } + "log_code": False, + "log_env_gpu": True, + "log_env_cpu": True, + "project_name": COMET_PROJECT_NAME, + } self.default_experiment_kwargs.update(experiment_kwargs) self.experiment = self._get_experiment(self.comet_mode, run_id) self.experiment.set_name(self.opt.name) self.data_dict = self.check_dataset(self.opt.data) - self.class_names = self.data_dict['names'] - self.num_classes = self.data_dict['nc'] + self.class_names = self.data_dict["names"] + self.num_classes = self.data_dict["nc"] self.logged_images_count = 0 self.max_images = COMET_MAX_IMAGE_UPLOADS if run_id is None: - self.experiment.log_other('Created from', 'YOLOv5') + self.experiment.log_other("Created from", "YOLOv5") if not isinstance(self.experiment, comet_ml.OfflineExperiment): - workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:] + workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:] self.experiment.log_other( - 'Run Path', - f'{workspace}/{project_name}/{experiment_id}', + "Run Path", + f"{workspace}/{project_name}/{experiment_id}", ) self.log_parameters(vars(opt)) self.log_parameters(self.opt.hyp) self.log_asset_data( self.opt.hyp, - name='hyperparameters.json', - metadata={'type': 'hyp-config-file'}, + name="hyperparameters.json", + metadata={"type": "hyp-config-file"}, ) self.log_asset( - f'{self.opt.save_dir}/opt.yaml', - metadata={'type': 'opt-config-file'}, + f"{self.opt.save_dir}/opt.yaml", + metadata={"type": "opt-config-file"}, ) self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX - if hasattr(self.opt, 'conf_thres'): + if hasattr(self.opt, "conf_thres"): self.conf_thres = self.opt.conf_thres else: self.conf_thres = CONF_THRES - if hasattr(self.opt, 'iou_thres'): + if hasattr(self.opt, "iou_thres"): self.iou_thres = self.opt.iou_thres else: self.iou_thres = IOU_THRES - self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres}) + self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres}) self.comet_log_predictions = COMET_LOG_PREDICTIONS if self.opt.bbox_interval == -1: - self.comet_log_prediction_interval = (1 if self.opt.epochs < 10 else self.opt.epochs // 10) + self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10 else: self.comet_log_prediction_interval = self.opt.bbox_interval @@ -147,30 +146,35 @@ def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwar self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS - self.experiment.log_others({ - 'comet_mode': COMET_MODE, - 'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS, - 'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS, - 'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS, - 'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX, - 'comet_model_name': COMET_MODEL_NAME, }) + self.experiment.log_others( + { + "comet_mode": COMET_MODE, + "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS, + "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS, + "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS, + "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX, + "comet_model_name": COMET_MODEL_NAME, + } + ) # Check if running the Experiment with the Comet Optimizer - if hasattr(self.opt, 'comet_optimizer_id'): - self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id) - self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective) - self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric) - self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp)) + if hasattr(self.opt, "comet_optimizer_id"): + self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id) + self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective) + self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric) + self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp)) def _get_experiment(self, mode, experiment_id=None): - if mode == 'offline': + if mode == "offline": if experiment_id is not None: return comet_ml.ExistingOfflineExperiment( previous_experiment=experiment_id, **self.default_experiment_kwargs, ) - return comet_ml.OfflineExperiment(**self.default_experiment_kwargs, ) + return comet_ml.OfflineExperiment( + **self.default_experiment_kwargs, + ) else: try: @@ -183,11 +187,13 @@ def _get_experiment(self, mode, experiment_id=None): return comet_ml.Experiment(**self.default_experiment_kwargs) except ValueError: - logger.warning('COMET WARNING: ' - 'Comet credentials have not been set. ' - 'Comet will default to offline logging. ' - 'Please set your credentials to enable online logging.') - return self._get_experiment('offline', experiment_id) + logger.warning( + "COMET WARNING: " + "Comet credentials have not been set. " + "Comet will default to offline logging. " + "Please set your credentials to enable online logging." + ) + return self._get_experiment("offline", experiment_id) return @@ -211,12 +217,13 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False): return model_metadata = { - 'fitness_score': fitness_score[-1], - 'epochs_trained': epoch + 1, - 'save_period': opt.save_period, - 'total_epochs': opt.epochs, } + "fitness_score": fitness_score[-1], + "epochs_trained": epoch + 1, + "save_period": opt.save_period, + "total_epochs": opt.epochs, + } - model_files = glob.glob(f'{path}/*.pt') + model_files = glob.glob(f"{path}/*.pt") for model_path in model_files: name = Path(model_path).name @@ -232,14 +239,14 @@ def check_dataset(self, data_file): with open(data_file) as f: data_config = yaml.safe_load(f) - path = data_config.get('path') + path = data_config.get("path") if path and path.startswith(COMET_PREFIX): - path = data_config['path'].replace(COMET_PREFIX, '') + path = data_config["path"].replace(COMET_PREFIX, "") data_dict = self.download_dataset_artifact(path) return data_dict - self.log_asset(self.opt.data, metadata={'type': 'data-config-file'}) + self.log_asset(self.opt.data, metadata={"type": "data-config-file"}) return check_dataset(data_file) @@ -255,8 +262,8 @@ def log_predictions(self, image, labelsn, path, shape, predn): filtered_detections = detections[mask] filtered_labels = labelsn[mask] - image_id = path.split('/')[-1].split('.')[0] - image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}' + image_id = path.split("/")[-1].split(".")[0] + image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}" if image_name not in self.logged_image_names: native_scale_image = PIL.Image.open(path) self.log_image(native_scale_image, name=image_name) @@ -264,23 +271,21 @@ def log_predictions(self, image, labelsn, path, shape, predn): metadata = [] for cls, *xyxy in filtered_labels.tolist(): - metadata.append({ - 'label': f'{self.class_names[int(cls)]}-gt', - 'score': 100, - 'box': { - 'x': xyxy[0], - 'y': xyxy[1], - 'x2': xyxy[2], - 'y2': xyxy[3]}, }) + metadata.append( + { + "label": f"{self.class_names[int(cls)]}-gt", + "score": 100, + "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, + } + ) for *xyxy, conf, cls in filtered_detections.tolist(): - metadata.append({ - 'label': f'{self.class_names[int(cls)]}', - 'score': conf * 100, - 'box': { - 'x': xyxy[0], - 'y': xyxy[1], - 'x2': xyxy[2], - 'y2': xyxy[3]}, }) + metadata.append( + { + "label": f"{self.class_names[int(cls)]}", + "score": conf * 100, + "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, + } + ) self.metadata_dict[image_name] = metadata self.logged_images_count += 1 @@ -307,7 +312,7 @@ def preprocess_prediction(self, image, labels, shape, pred): return predn, labelsn def add_assets_to_artifact(self, artifact, path, asset_path, split): - img_paths = sorted(glob.glob(f'{asset_path}/*')) + img_paths = sorted(glob.glob(f"{asset_path}/*")) label_paths = img2label_paths(img_paths) for image_file, label_file in zip(img_paths, label_paths): @@ -317,33 +322,33 @@ def add_assets_to_artifact(self, artifact, path, asset_path, split): artifact.add( image_file, logical_path=image_logical_path, - metadata={'split': split}, + metadata={"split": split}, ) artifact.add( label_file, logical_path=label_logical_path, - metadata={'split': split}, + metadata={"split": split}, ) except ValueError as e: - logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.') - logger.error(f'COMET ERROR: {e}') + logger.error("COMET ERROR: Error adding file to Artifact. Skipping file.") + logger.error(f"COMET ERROR: {e}") continue return artifact def upload_dataset_artifact(self): - dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset') - path = str((ROOT / Path(self.data_dict['path'])).resolve()) + dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset") + path = str((ROOT / Path(self.data_dict["path"])).resolve()) metadata = self.data_dict.copy() - for key in ['train', 'val', 'test']: + for key in ["train", "val", "test"]: split_path = metadata.get(key) if split_path is not None: - metadata[key] = split_path.replace(path, '') + metadata[key] = split_path.replace(path, "") - artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata) + artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata) for key in metadata.keys(): - if key in ['train', 'val', 'test']: + if key in ["train", "val", "test"]: if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): continue @@ -362,26 +367,27 @@ def download_dataset_artifact(self, artifact_path): metadata = logged_artifact.metadata data_dict = metadata.copy() - data_dict['path'] = artifact_save_dir + data_dict["path"] = artifact_save_dir - metadata_names = metadata.get('names') + metadata_names = metadata.get("names") if isinstance(metadata_names, dict): - data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()} + data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()} elif isinstance(metadata_names, list): - data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} + data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} else: raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" return self.update_data_paths(data_dict) def update_data_paths(self, data_dict): - path = data_dict.get('path', '') + path = data_dict.get("path", "") - for split in ['train', 'val', 'test']: + for split in ["train", "val", "test"]: if data_dict.get(split): split_path = data_dict.get(split) - data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [ - f'{path}/{x}' for x in split_path]) + data_dict[split] = ( + f"{path}/{split_path}" if isinstance(split, str) else [f"{path}/{x}" for x in split_path] + ) return data_dict @@ -422,11 +428,11 @@ def on_train_batch_end(self, log_dict, step): def on_train_end(self, files, save_dir, last, best, epoch, results): if self.comet_log_predictions: curr_epoch = self.experiment.curr_epoch - self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch) + self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch) for f in files: - self.log_asset(f, metadata={'epoch': epoch}) - self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch}) + self.log_asset(f, metadata={"epoch": epoch}) + self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch}) if not self.opt.evolve: model_path = str(best if best.exists() else last) @@ -440,9 +446,9 @@ def on_train_end(self, files, save_dir, last, best, epoch, results): ) # Check if running Experiment with Comet Optimizer - if hasattr(self.opt, 'comet_optimizer_id'): + if hasattr(self.opt, "comet_optimizer_id"): metric = results.get(self.opt.comet_optimizer_metric) - self.experiment.log_other('optimizer_metric_value', metric) + self.experiment.log_other("optimizer_metric_value", metric) self.finish_run() @@ -477,21 +483,22 @@ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) class_name = self.class_names[c] self.experiment.log_metrics( { - 'mAP@.5': ap50[i], - 'mAP@.5:.95': ap[i], - 'precision': p[i], - 'recall': r[i], - 'f1': f1[i], - 'true_positives': tp[i], - 'false_positives': fp[i], - 'support': nt[c], }, + "mAP@.5": ap50[i], + "mAP@.5:.95": ap[i], + "precision": p[i], + "recall": r[i], + "f1": f1[i], + "true_positives": tp[i], + "false_positives": fp[i], + "support": nt[c], + }, prefix=class_name, ) if self.comet_log_confusion_matrix: epoch = self.experiment.curr_epoch class_names = list(self.class_names.values()) - class_names.append('background') + class_names.append("background") num_classes = len(class_names) self.experiment.log_confusion_matrix( @@ -499,9 +506,9 @@ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) max_categories=num_classes, labels=class_names, epoch=epoch, - column_label='Actual Category', - row_label='Predicted Category', - file_name=f'confusion-matrix-epoch-{epoch}.json', + column_label="Actual Category", + row_label="Predicted Category", + file_name=f"confusion-matrix-epoch-{epoch}.json", ) def on_fit_epoch_end(self, result, epoch): diff --git a/utils/loggers/comet/comet_utils.py b/utils/loggers/comet/comet_utils.py index 27600761ad28..f7b56dddd5f7 100644 --- a/utils/loggers/comet/comet_utils.py +++ b/utils/loggers/comet/comet_utils.py @@ -11,28 +11,28 @@ logger = logging.getLogger(__name__) -COMET_PREFIX = 'comet://' -COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') -COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt') +COMET_PREFIX = "comet://" +COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") +COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt") def download_model_checkpoint(opt, experiment): - model_dir = f'{opt.project}/{experiment.name}' + model_dir = f"{opt.project}/{experiment.name}" os.makedirs(model_dir, exist_ok=True) model_name = COMET_MODEL_NAME model_asset_list = experiment.get_model_asset_list(model_name) if len(model_asset_list) == 0: - logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}') + logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}") return model_asset_list = sorted( model_asset_list, - key=lambda x: x['step'], + key=lambda x: x["step"], reverse=True, ) - logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list} + logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list} resource_url = urlparse(opt.weights) checkpoint_filename = resource_url.query @@ -44,28 +44,28 @@ def download_model_checkpoint(opt, experiment): checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME if asset_id is None: - logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment') + logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment") return try: - logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}') + logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}") asset_filename = checkpoint_filename - model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) - model_download_path = f'{model_dir}/{asset_filename}' - with open(model_download_path, 'wb') as f: + model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) + model_download_path = f"{model_dir}/{asset_filename}" + with open(model_download_path, "wb") as f: f.write(model_binary) opt.weights = model_download_path except Exception as e: - logger.warning('COMET WARNING: Unable to download checkpoint from Comet') + logger.warning("COMET WARNING: Unable to download checkpoint from Comet") logger.exception(e) def set_opt_parameters(opt, experiment): - """Update the opts Namespace with parameters - from Comet's ExistingExperiment when resuming a run + """ + Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run. Args: opt (argparse.Namespace): Namespace of command line options @@ -75,9 +75,9 @@ def set_opt_parameters(opt, experiment): resume_string = opt.resume for asset in asset_list: - if asset['fileName'] == 'opt.yaml': - asset_id = asset['assetId'] - asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) + if asset["fileName"] == "opt.yaml": + asset_id = asset["assetId"] + asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) opt_dict = yaml.safe_load(asset_binary) for key, value in opt_dict.items(): setattr(opt, key, value) @@ -85,18 +85,18 @@ def set_opt_parameters(opt, experiment): # Save hyperparameters to YAML file # Necessary to pass checks in training script - save_dir = f'{opt.project}/{experiment.name}' + save_dir = f"{opt.project}/{experiment.name}" os.makedirs(save_dir, exist_ok=True) - hyp_yaml_path = f'{save_dir}/hyp.yaml' - with open(hyp_yaml_path, 'w') as f: + hyp_yaml_path = f"{save_dir}/hyp.yaml" + with open(hyp_yaml_path, "w") as f: yaml.dump(opt.hyp, f) opt.hyp = hyp_yaml_path def check_comet_weights(opt): - """Downloads model weights from Comet and updates the - weights path to point to saved weights location + """ + Downloads model weights from Comet and updates the weights path to point to saved weights location. Args: opt (argparse.Namespace): Command Line arguments passed @@ -113,7 +113,7 @@ def check_comet_weights(opt): if opt.weights.startswith(COMET_PREFIX): api = comet_ml.API() resource = urlparse(opt.weights) - experiment_path = f'{resource.netloc}{resource.path}' + experiment_path = f"{resource.netloc}{resource.path}" experiment = api.get(experiment_path) download_model_checkpoint(opt, experiment) return True @@ -122,8 +122,8 @@ def check_comet_weights(opt): def check_comet_resume(opt): - """Restores run parameters to its original state based on the model checkpoint - and logged Experiment parameters. + """ + Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters. Args: opt (argparse.Namespace): Command Line arguments passed @@ -140,7 +140,7 @@ def check_comet_resume(opt): if opt.resume.startswith(COMET_PREFIX): api = comet_ml.API() resource = urlparse(opt.resume) - experiment_path = f'{resource.netloc}{resource.path}' + experiment_path = f"{resource.netloc}{resource.path}" experiment = api.get(experiment_path) set_opt_parameters(opt, experiment) download_model_checkpoint(opt, experiment) diff --git a/utils/loggers/comet/hpo.py b/utils/loggers/comet/hpo.py index fc49115c1358..a9e6fabec1cd 100644 --- a/utils/loggers/comet/hpo.py +++ b/utils/loggers/comet/hpo.py @@ -21,77 +21,79 @@ # Project Configuration config = comet_ml.config.get_config() -COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') +COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") def get_args(known=False): parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') - parser.add_argument('--cfg', type=str, default='', help='model.yaml path') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') - parser.add_argument('--epochs', type=int, default=300, help='total training epochs') - parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') - parser.add_argument('--rect', action='store_true', help='rectangular training') - parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') - parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--noval', action='store_true', help='only validate final epoch') - parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') - parser.add_argument('--noplots', action='store_true', help='save no plot files') - parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') - parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') - parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') - parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') - parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') - parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') - parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--quad', action='store_true', help='quad dataloader') - parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') - parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') - parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') - parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') - parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') - parser.add_argument('--seed', type=int, default=0, help='Global training seed') - parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") + parser.add_argument("--cfg", type=str, default="", help="model.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") + parser.add_argument("--epochs", type=int, default=300, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") + parser.add_argument("--rect", action="store_true", help="rectangular training") + parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--noval", action="store_true", help="only validate final epoch") + parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") + parser.add_argument("--noplots", action="store_true", help="save no plot files") + parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") + parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"') + parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") + parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") + parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") + parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--quad", action="store_true", help="quad dataloader") + parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") + parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") + parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") + parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") + parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") # Weights & Biases arguments - parser.add_argument('--entity', default=None, help='W&B: Entity') - parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option') - parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval') - parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use') + parser.add_argument("--entity", default=None, help="W&B: Entity") + parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='W&B: Upload data, "val" option') + parser.add_argument("--bbox_interval", type=int, default=-1, help="W&B: Set bounding-box image logging interval") + parser.add_argument("--artifact_alias", type=str, default="latest", help="W&B: Version of dataset artifact to use") # Comet Arguments - parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.') - parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.') - parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.") - parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.') - parser.add_argument('--comet_optimizer_workers', - type=int, - default=1, - help='Comet: Number of Parallel Workers to use with the Comet Optimizer.') + parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.") + parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.") + parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.") + parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.") + parser.add_argument( + "--comet_optimizer_workers", + type=int, + default=1, + help="Comet: Number of Parallel Workers to use with the Comet Optimizer.", + ) return parser.parse_known_args()[0] if known else parser.parse_args() def run(parameters, opt): - hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']} + hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]} opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) - opt.batch_size = parameters.get('batch_size') - opt.epochs = parameters.get('epochs') + opt.batch_size = parameters.get("batch_size") + opt.epochs = parameters.get("epochs") device = select_device(opt.device, batch_size=opt.batch_size) train(hyp_dict, opt, device, callbacks=Callbacks()) -if __name__ == '__main__': +if __name__ == "__main__": opt = get_args(known=True) opt.weights = str(opt.weights) @@ -99,7 +101,7 @@ def run(parameters, opt): opt.data = str(opt.data) opt.project = str(opt.project) - optimizer_id = os.getenv('COMET_OPTIMIZER_ID') + optimizer_id = os.getenv("COMET_OPTIMIZER_ID") if optimizer_id is None: with open(opt.comet_optimizer_config) as f: optimizer_config = json.load(f) @@ -110,9 +112,9 @@ def run(parameters, opt): opt.comet_optimizer_id = optimizer.id status = optimizer.status() - opt.comet_optimizer_objective = status['spec']['objective'] - opt.comet_optimizer_metric = status['spec']['metric'] + opt.comet_optimizer_objective = status["spec"]["objective"] + opt.comet_optimizer_metric = status["spec"]["metric"] - logger.info('COMET INFO: Starting Hyperparameter Sweep') + logger.info("COMET INFO: Starting Hyperparameter Sweep") for parameter in optimizer.get_parameters(): - run(parameter['parameters'], opt) + run(parameter["parameters"], opt) diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py index 4ea32b1d4c6e..f8d49a33d00f 100644 --- a/utils/loggers/wandb/wandb_utils.py +++ b/utils/loggers/wandb/wandb_utils.py @@ -15,34 +15,35 @@ ROOT = FILE.parents[3] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH -RANK = int(os.getenv('RANK', -1)) -DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \ - f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.' +RANK = int(os.getenv("RANK", -1)) +DEPRECATION_WARNING = ( + f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " + f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.' +) try: import wandb - assert hasattr(wandb, '__version__') # verify package import not local dir + assert hasattr(wandb, "__version__") # verify package import not local dir LOGGER.warning(DEPRECATION_WARNING) except (ImportError, AssertionError): wandb = None -class WandbLogger(): - """Log training runs, datasets, models, and predictions to Weights & Biases. +class WandbLogger: + """ + Log training runs, datasets, models, and predictions to Weights & Biases. - This logger sends information to W&B at wandb.ai. By default, this information - includes hyperparameters, system configuration and metrics, model metrics, - and basic data metrics and analyses. + This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system + configuration and metrics, model metrics, and basic data metrics and analyses. - By providing additional command line arguments to train.py, datasets, - models and predictions can also be logged. + By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. For more on how this logger is used, see the Weights & Biases documentation: https://docs.wandb.com/guides/integrations/yolov5 """ - def __init__(self, opt, run_id=None, job_type='Training'): + def __init__(self, opt, run_id=None, job_type="Training"): """ - Initialize WandbLogger instance - Upload dataset if opt.upload_dataset is True @@ -53,7 +54,7 @@ def __init__(self, opt, run_id=None, job_type='Training'): run_id (str) -- Run ID of W&B run to be resumed job_type (str) -- To set the job_type for this run - """ + """ # Pre-training routine -- self.job_type = job_type self.wandb, self.wandb_run = wandb, wandb.run if wandb else None @@ -64,17 +65,23 @@ def __init__(self, opt, run_id=None, job_type='Training'): self.max_imgs_to_log = 16 self.data_dict = None if self.wandb: - self.wandb_run = wandb.init(config=opt, - resume='allow', - project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, - entity=opt.entity, - name=opt.name if opt.name != 'exp' else None, - job_type=job_type, - id=run_id, - allow_val_change=True) if not wandb.run else wandb.run + self.wandb_run = ( + wandb.init( + config=opt, + resume="allow", + project="YOLOv5" if opt.project == "runs/train" else Path(opt.project).stem, + entity=opt.entity, + name=opt.name if opt.name != "exp" else None, + job_type=job_type, + id=run_id, + allow_val_change=True, + ) + if not wandb.run + else wandb.run + ) if self.wandb_run: - if self.job_type == 'Training': + if self.job_type == "Training": if isinstance(opt.data, dict): # This means another dataset manager has already processed the dataset info (e.g. ClearML) # and they will have stored the already processed dict in opt.data @@ -97,11 +104,17 @@ def setup_training(self, opt): if isinstance(opt.resume, str): model_dir, _ = self.download_model_artifact(opt) if model_dir: - self.weights = Path(model_dir) / 'last.pt' + self.weights = Path(model_dir) / "last.pt" config = self.wandb_run.config - opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str( - self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \ - config.hyp, config.imgsz + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = ( + str(self.weights), + config.save_period, + config.batch_size, + config.bbox_interval, + config.epochs, + config.hyp, + config.imgsz, + ) if opt.bbox_interval == -1: self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 @@ -110,7 +123,7 @@ def setup_training(self, opt): def log_model(self, path, opt, epoch, fitness_score, best_model=False): """ - Log the model checkpoint as W&B artifact + Log the model checkpoint as W&B artifact. arguments: path (Path) -- Path of directory containing the checkpoints @@ -119,26 +132,30 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False): fitness_score (float) -- fitness score for current epoch best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. """ - model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', - type='model', - metadata={ - 'original_url': str(path), - 'epochs_trained': epoch + 1, - 'save period': opt.save_period, - 'project': opt.project, - 'total_epochs': opt.epochs, - 'fitness_score': fitness_score}) - model_artifact.add_file(str(path / 'last.pt'), name='last.pt') - wandb.log_artifact(model_artifact, - aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) - LOGGER.info(f'Saving model artifact on epoch {epoch + 1}') + model_artifact = wandb.Artifact( + "run_" + wandb.run.id + "_model", + type="model", + metadata={ + "original_url": str(path), + "epochs_trained": epoch + 1, + "save period": opt.save_period, + "project": opt.project, + "total_epochs": opt.epochs, + "fitness_score": fitness_score, + }, + ) + model_artifact.add_file(str(path / "last.pt"), name="last.pt") + wandb.log_artifact( + model_artifact, aliases=["latest", "last", "epoch " + str(self.current_epoch), "best" if best_model else ""] + ) + LOGGER.info(f"Saving model artifact on epoch {epoch + 1}") def val_one_image(self, pred, predn, path, names, im): pass def log(self, log_dict): """ - save the metrics to the logging dictionary + Save the metrics to the logging dictionary. arguments: log_dict (Dict) -- metrics/media to be logged in current step @@ -149,7 +166,7 @@ def log(self, log_dict): def end_epoch(self): """ - commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. + Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. arguments: best_result (boolean): Boolean representing if the result of this evaluation is best or not @@ -160,16 +177,14 @@ def end_epoch(self): wandb.log(self.log_dict) except BaseException as e: LOGGER.info( - f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}' + f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}" ) self.wandb_run.finish() self.wandb_run = None self.log_dict = {} def finish_run(self): - """ - Log metrics if any and finish the current W&B run - """ + """Log metrics if any and finish the current W&B run.""" if self.wandb_run: if self.log_dict: with all_logging_disabled(): @@ -180,7 +195,7 @@ def finish_run(self): @contextmanager def all_logging_disabled(highest_level=logging.CRITICAL): - """ source - https://gist.github.com/simon-weber/7853144 + """source - https://gist.github.com/simon-weber/7853144 A context manager that will prevent any logging messages triggered during the body from being processed. :param highest_level: the maximum logging level in use. This would only need to be changed if a custom level greater than CRITICAL is defined. diff --git a/utils/loss.py b/utils/loss.py index 26cca8797315..26b8c06bf333 100644 --- a/utils/loss.py +++ b/utils/loss.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Loss functions -""" +"""Loss functions.""" import torch import torch.nn as nn @@ -19,7 +17,7 @@ class BCEBlurWithLogitsLoss(nn.Module): # BCEwithLogitLoss() with reduced missing label effects. def __init__(self, alpha=0.05): super().__init__() - self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none") # must be nn.BCEWithLogitsLoss() self.alpha = alpha def forward(self, pred, true): @@ -40,7 +38,7 @@ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element + self.loss_fcn.reduction = "none" # required to apply FL to each element def forward(self, pred, true): loss = self.loss_fcn(pred, true) @@ -54,9 +52,9 @@ def forward(self, pred, true): modulating_factor = (1.0 - p_t) ** self.gamma loss *= alpha_factor * modulating_factor - if self.reduction == 'mean': + if self.reduction == "mean": return loss.mean() - elif self.reduction == 'sum': + elif self.reduction == "sum": return loss.sum() else: # 'none' return loss @@ -70,7 +68,7 @@ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element + self.loss_fcn.reduction = "none" # required to apply FL to each element def forward(self, pred, true): loss = self.loss_fcn(pred, true) @@ -80,9 +78,9 @@ def forward(self, pred, true): modulating_factor = torch.abs(true - pred_prob) ** self.gamma loss *= alpha_factor * modulating_factor - if self.reduction == 'mean': + if self.reduction == "mean": return loss.mean() - elif self.reduction == 'sum': + elif self.reduction == "sum": return loss.sum() else: # 'none' return loss @@ -97,14 +95,14 @@ def __init__(self, model, autobalance=False): h = model.hyp # hyperparameters # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets # Focal loss - g = h['fl_gamma'] # focal loss gamma + g = h["fl_gamma"] # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) @@ -167,9 +165,9 @@ def __call__(self, p, targets): # predictions, targets if self.autobalance: self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] bs = tobj.shape[0] # batch size return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() @@ -183,16 +181,20 @@ def build_targets(self, p, targets): targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices g = 0.5 # bias - off = torch.tensor( - [ - [0, 0], - [1, 0], - [0, 1], - [-1, 0], - [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], - device=self.device).float() * g # offsets + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device, + ).float() + * g + ) # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape @@ -203,7 +205,7 @@ def build_targets(self, p, targets): if nt: # Matches r = t[..., 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare + j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) t = t[j] # filter diff --git a/utils/metrics.py b/utils/metrics.py index 5646f40e9860..5f45621dc372 100644 --- a/utils/metrics.py +++ b/utils/metrics.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Model validation metrics -""" +"""Model validation metrics.""" import math import warnings @@ -25,11 +23,13 @@ def smooth(y, f=0.05): nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) p = np.ones(nf // 2) # ones padding yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded - return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed + return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed -def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''): - """ Compute the average precision, given the recall and precision curves. +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names=(), eps=1e-16, prefix=""): + """ + Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments tp: True positives (nparray, nx1 or nx10). @@ -83,10 +83,10 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data names = dict(enumerate(names)) # to dict if plot: - plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names) - plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1') - plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision') - plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall') + plot_pr_curve(px, py, ap, Path(save_dir) / f"{prefix}PR_curve.png", names) + plot_mc_curve(px, f1, Path(save_dir) / f"{prefix}F1_curve.png", names, ylabel="F1") + plot_mc_curve(px, p, Path(save_dir) / f"{prefix}P_curve.png", names, ylabel="Precision") + plot_mc_curve(px, r, Path(save_dir) / f"{prefix}R_curve.png", names, ylabel="Recall") i = smooth(f1.mean(0), 0.1).argmax() # max F1 index p, r, f1 = p[:, i], r[:, i], f1[:, i] @@ -96,7 +96,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names def compute_ap(recall, precision): - """ Compute the average precision, given the recall and precision curves + """Compute the average precision, given the recall and precision curves # Arguments recall: The recall curve (list) precision: The precision curve (list) @@ -112,8 +112,8 @@ def compute_ap(recall, precision): mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) # Integrate area under curve - method = 'interp' # methods: 'continuous', 'interp' - if method == 'interp': + method = "interp" # methods: 'continuous', 'interp' + if method == "interp": x = np.linspace(0, 1, 101) # 101-point interp (COCO) ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate else: # 'continuous' @@ -134,6 +134,7 @@ def __init__(self, nc, conf=0.25, iou_thres=0.45): def process_batch(self, detections, labels): """ Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: detections (Array[N, 6]), x1, y1, x2, y2, conf, class @@ -183,40 +184,41 @@ def tp_fp(self): # fn = self.matrix.sum(0) - tp # false negatives (missed detections) return tp[:-1], fp[:-1] # remove background class - @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure') - def plot(self, normalize=True, save_dir='', names=()): + @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure") + def plot(self, normalize=True, save_dir="", names=()): import seaborn as sn - array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns + array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) nc, nn = self.nc, len(names) # number of classes, names sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels - ticklabels = (names + ['background']) if labels else 'auto' + ticklabels = (names + ["background"]) if labels else "auto" with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered - sn.heatmap(array, - ax=ax, - annot=nc < 30, - annot_kws={ - 'size': 8}, - cmap='Blues', - fmt='.2f', - square=True, - vmin=0.0, - xticklabels=ticklabels, - yticklabels=ticklabels).set_facecolor((1, 1, 1)) - ax.set_xlabel('True') - ax.set_ylabel('Predicted') - ax.set_title('Confusion Matrix') - fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) + warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered + sn.heatmap( + array, + ax=ax, + annot=nc < 30, + annot_kws={"size": 8}, + cmap="Blues", + fmt=".2f", + square=True, + vmin=0.0, + xticklabels=ticklabels, + yticklabels=ticklabels, + ).set_facecolor((1, 1, 1)) + ax.set_xlabel("True") + ax.set_ylabel("Predicted") + ax.set_title("Confusion Matrix") + fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250) plt.close(fig) def print(self): for i in range(self.nc + 1): - print(' '.join(map(str, self.matrix[i]))) + print(" ".join(map(str, self.matrix[i]))) def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): @@ -235,8 +237,9 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7 w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps) # Intersection area - inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * \ - (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp(0) + inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * ( + b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1) + ).clamp(0) # Union Area union = w1 * h1 + w2 * h2 - inter + eps @@ -247,10 +250,10 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7 cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 - c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + c2 = cw**2 + ch**2 + eps # convex diagonal squared rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 - v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) + v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) with torch.no_grad(): alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU @@ -264,6 +267,7 @@ def box_iou(box1, box2, eps=1e-7): # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py """ Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments: box1 (Tensor[N, 4]) @@ -282,7 +286,10 @@ def box_iou(box1, box2, eps=1e-7): def bbox_ioa(box1, box2, eps=1e-7): - """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 + """ + Returns the intersection over box2 area given box1, box2. + + Boxes are x1y1x2y2 box1: np.array of shape(4) box2: np.array of shape(nx4) returns: np.array of shape(n) @@ -293,8 +300,9 @@ def bbox_ioa(box1, box2, eps=1e-7): b2_x1, b2_y1, b2_x2, b2_y2 = box2.T # Intersection area - inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ - (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * ( + np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1) + ).clip(0) # box2 area box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps @@ -315,46 +323,46 @@ def wh_iou(wh1, wh2, eps=1e-7): @threaded -def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()): +def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()): # Precision-recall curve fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) py = np.stack(py, axis=1) if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py.T): - ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) + ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision) else: - ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) + ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) - ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) - ax.set_xlabel('Recall') - ax.set_ylabel('Precision') + ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean()) + ax.set_xlabel("Recall") + ax.set_ylabel("Precision") ax.set_xlim(0, 1) ax.set_ylim(0, 1) - ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') - ax.set_title('Precision-Recall Curve') + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title("Precision-Recall Curve") fig.savefig(save_dir, dpi=250) plt.close(fig) @threaded -def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'): +def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric"): # Metric-confidence curve fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) if 0 < len(names) < 21: # display per-class legend if < 21 classes for i, y in enumerate(py): - ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) + ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) else: - ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) + ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) y = smooth(py.mean(0), 0.05) - ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') + ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}") ax.set_xlabel(xlabel) ax.set_ylabel(ylabel) ax.set_xlim(0, 1) ax.set_ylim(0, 1) - ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') - ax.set_title(f'{ylabel}-Confidence Curve') + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title(f"{ylabel}-Confidence Curve") fig.savefig(save_dir, dpi=250) plt.close(fig) diff --git a/utils/plots.py b/utils/plots.py index 5901ca2dbfaa..11c96a6372c3 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Plotting utils -""" +"""Plotting utils.""" import contextlib import math @@ -25,18 +23,38 @@ from utils.metrics import fitness # Settings -RANK = int(os.getenv('RANK', -1)) -matplotlib.rc('font', **{'size': 11}) -matplotlib.use('Agg') # for writing to files only +RANK = int(os.getenv("RANK", -1)) +matplotlib.rc("font", **{"size": 11}) +matplotlib.use("Agg") # for writing to files only class Colors: # Ultralytics color palette https://ultralytics.com/ def __init__(self): # hex = matplotlib.colors.TABLEAU_COLORS.values() - hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', - '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') - self.palette = [self.hex2rgb(f'#{c}') for c in hexs] + hexs = ( + "FF3838", + "FF9D97", + "FF701F", + "FFB21D", + "CFD231", + "48F90A", + "92CC17", + "3DDB86", + "1A9334", + "00D4BB", + "2C99A8", + "00C2FF", + "344593", + "6473FF", + "0018EC", + "8438FF", + "520085", + "CB38FF", + "FF95C8", + "FF37C7", + ) + self.palette = [self.hex2rgb(f"#{c}") for c in hexs] self.n = len(self.palette) def __call__(self, i, bgr=False): @@ -45,13 +63,13 @@ def __call__(self, i, bgr=False): @staticmethod def hex2rgb(h): # rgb order (PIL) - return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4)) colors = Colors() # create instance for 'from utils.plots import colors' -def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')): +def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detect/exp")): """ x: Features to be visualized module_type: Module type @@ -59,9 +77,9 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec n: Maximum number of feature maps to plot save_dir: Directory to save results """ - if ('Detect' - not in module_type) and ('Segment' - not in module_type): # 'Detect' for Object Detect task,'Segment' for Segment task + if ("Detect" not in module_type) and ( + "Segment" not in module_type + ): # 'Detect' for Object Detect task,'Segment' for Segment task batch, channels, height, width = x.shape # batch, channels, height, width if height > 1 and width > 1: f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename @@ -73,12 +91,12 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec plt.subplots_adjust(wspace=0.05, hspace=0.05) for i in range(n): ax[i].imshow(blocks[i].squeeze()) # cmap='gray' - ax[i].axis('off') + ax[i].axis("off") - LOGGER.info(f'Saving {f}... ({n}/{channels})') - plt.savefig(f, dpi=300, bbox_inches='tight') + LOGGER.info(f"Saving {f}... ({n}/{channels})") + plt.savefig(f, dpi=300, bbox_inches="tight") plt.close() - np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save + np.save(str(f.with_suffix(".npy")), x[0].cpu().numpy()) # npy save def hist2d(x, y, n=100): @@ -97,7 +115,7 @@ def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): def butter_lowpass(cutoff, fs, order): nyq = 0.5 * fs normal_cutoff = cutoff / nyq - return butter(order, normal_cutoff, btype='low', analog=False) + return butter(order, normal_cutoff, btype="low", analog=False) b, a = butter_lowpass(cutoff, fs, order=order) return filtfilt(b, a, data) # forward-backward filter @@ -114,7 +132,7 @@ def output_to_target(output, max_det=300): @threaded -def plot_images(images, targets, paths=None, fname='images.jpg', names=None): +def plot_images(images, targets, paths=None, fname="images.jpg", names=None): # Plot image grid with labels if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() @@ -125,7 +143,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None): max_subplots = 16 # max image subplots, i.e. 4x4 bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images - ns = np.ceil(bs ** 0.5) # number of subplots (square) + ns = np.ceil(bs**0.5) # number of subplots (square) if np.max(images[0]) <= 1: images *= 255 # de-normalise (optional) @@ -136,7 +154,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None): break x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin im = im.transpose(1, 2, 0) - mosaic[y:y + h, x:x + w, :] = im + mosaic[y : y + h, x : x + w, :] = im # Resize (optional) scale = max_size / ns / max(h, w) @@ -156,7 +174,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None): if len(targets) > 0: ti = targets[targets[:, 0] == i] # image targets boxes = xywh2xyxy(ti[:, 2:6]).T - classes = ti[:, 1].astype('int') + classes = ti[:, 1].astype("int") labels = ti.shape[1] == 6 # labels if no conf column conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) @@ -173,59 +191,59 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None): color = colors(cls) cls = names[cls] if names else cls if labels or conf[j] > 0.25: # 0.25 conf thresh - label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' + label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" annotator.box_label(box, label, color=color) annotator.im.save(fname) # save -def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""): # Plot LR simulating training for full epochs optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals y = [] for _ in range(epochs): scheduler.step() - y.append(optimizer.param_groups[0]['lr']) - plt.plot(y, '.-', label='LR') - plt.xlabel('epoch') - plt.ylabel('LR') + y.append(optimizer.param_groups[0]["lr"]) + plt.plot(y, ".-", label="LR") + plt.xlabel("epoch") + plt.ylabel("LR") plt.grid() plt.xlim(0, epochs) plt.ylim(0) - plt.savefig(Path(save_dir) / 'LR.png', dpi=200) + plt.savefig(Path(save_dir) / "LR.png", dpi=200) plt.close() def plot_val_txt(): # from utils.plots import *; plot_val() # Plot val.txt histograms - x = np.loadtxt('val.txt', dtype=np.float32) + x = np.loadtxt("val.txt", dtype=np.float32) box = xyxy2xywh(x[:, :4]) cx, cy = box[:, 0], box[:, 1] fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) - ax.set_aspect('equal') - plt.savefig('hist2d.png', dpi=300) + ax.set_aspect("equal") + plt.savefig("hist2d.png", dpi=300) fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) ax[0].hist(cx, bins=600) ax[1].hist(cy, bins=600) - plt.savefig('hist1d.png', dpi=200) + plt.savefig("hist1d.png", dpi=200) def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() # Plot targets.txt histograms - x = np.loadtxt('targets.txt', dtype=np.float32).T - s = ['x targets', 'y targets', 'width targets', 'height targets'] + x = np.loadtxt("targets.txt", dtype=np.float32).T + s = ["x targets", "y targets", "width targets", "height targets"] fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) ax = ax.ravel() for i in range(4): - ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}') + ax[i].hist(x[i], bins=100, label=f"{x[i].mean():.3g} +/- {x[i].std():.3g}") ax[i].legend() ax[i].set_title(s[i]) - plt.savefig('targets.jpg', dpi=200) + plt.savefig("targets.jpg", dpi=200) -def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study() +def plot_val_study(file="", dir="", x=None): # from utils.plots import *; plot_val_study() # Plot file=study.txt generated by val.py (or plot all study*.txt in dir) save_dir = Path(file).parent if file else Path(dir) plot2 = False # plot additional results @@ -234,69 +252,74 @@ def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_ fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: - for f in sorted(save_dir.glob('study*.txt')): + for f in sorted(save_dir.glob("study*.txt")): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) if plot2: - s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)'] + s = ["P", "R", "mAP@.5", "mAP@.5:.95", "t_preprocess (ms/img)", "t_inference (ms/img)", "t_NMS (ms/img)"] for i in range(7): - ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + ax[i].plot(x, y[i], ".-", linewidth=2, markersize=8) ax[i].set_title(s[i]) j = y[3].argmax() + 1 - ax2.plot(y[5, 1:j], - y[3, 1:j] * 1E2, - '.-', - linewidth=2, - markersize=8, - label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) - - ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], - 'k.-', - linewidth=2, - markersize=8, - alpha=.25, - label='EfficientDet') + ax2.plot( + y[5, 1:j], + y[3, 1:j] * 1e2, + ".-", + linewidth=2, + markersize=8, + label=f.stem.replace("study_coco_", "").replace("yolo", "YOLO"), + ) + + ax2.plot( + 1e3 / np.array([209, 140, 97, 58, 35, 18]), + [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + "k.-", + linewidth=2, + markersize=8, + alpha=0.25, + label="EfficientDet", + ) ax2.grid(alpha=0.2) ax2.set_yticks(np.arange(20, 60, 5)) ax2.set_xlim(0, 57) ax2.set_ylim(25, 55) - ax2.set_xlabel('GPU Speed (ms/img)') - ax2.set_ylabel('COCO AP val') - ax2.legend(loc='lower right') - f = save_dir / 'study.png' - print(f'Saving {f}...') + ax2.set_xlabel("GPU Speed (ms/img)") + ax2.set_ylabel("COCO AP val") + ax2.legend(loc="lower right") + f = save_dir / "study.png" + print(f"Saving {f}...") plt.savefig(f, dpi=300) @TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 -def plot_labels(labels, names=(), save_dir=Path('')): +def plot_labels(labels, names=(), save_dir=Path("")): # plot dataset labels LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes nc = int(c.max() + 1) # number of classes - x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) + x = pd.DataFrame(b.transpose(), columns=["x", "y", "width", "height"]) # seaborn correlogram - sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) - plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) + sn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200) plt.close() # matplotlib labels - matplotlib.use('svg') # faster + matplotlib.use("svg") # faster ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) with contextlib.suppress(Exception): # color histogram bars by class [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 - ax[0].set_ylabel('instances') + ax[0].set_ylabel("instances") if 0 < len(names) < 30: ax[0].set_xticks(range(len(names))) ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10) else: - ax[0].set_xlabel('classes') - sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) - sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) + ax[0].set_xlabel("classes") + sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9) + sn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9) # rectangles labels[:, 1:3] = 0.5 # center @@ -305,47 +328,48 @@ def plot_labels(labels, names=(), save_dir=Path('')): for cls, *box in labels[:1000]: ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot ax[1].imshow(img) - ax[1].axis('off') + ax[1].axis("off") for a in [0, 1, 2, 3]: - for s in ['top', 'right', 'left', 'bottom']: + for s in ["top", "right", "left", "bottom"]: ax[a].spines[s].set_visible(False) - plt.savefig(save_dir / 'labels.jpg', dpi=200) - matplotlib.use('Agg') + plt.savefig(save_dir / "labels.jpg", dpi=200) + matplotlib.use("Agg") plt.close() -def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')): +def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")): # Show classification image grid with labels (optional) and predictions (optional) from utils.augmentations import denormalize - names = names or [f'class{i}' for i in range(1000)] - blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im), - dim=0) # select batch index 0, block by channels + names = names or [f"class{i}" for i in range(1000)] + blocks = torch.chunk( + denormalize(im.clone()).cpu().float(), len(im), dim=0 + ) # select batch index 0, block by channels n = min(len(blocks), nmax) # number of plots - m = min(8, round(n ** 0.5)) # 8 x 8 default + m = min(8, round(n**0.5)) # 8 x 8 default fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols ax = ax.ravel() if m > 1 else [ax] # plt.subplots_adjust(wspace=0.05, hspace=0.05) for i in range(n): ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) - ax[i].axis('off') + ax[i].axis("off") if labels is not None: - s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '') - ax[i].set_title(s, fontsize=8, verticalalignment='top') - plt.savefig(f, dpi=300, bbox_inches='tight') + s = names[labels[i]] + (f"—{names[pred[i]]}" if pred is not None else "") + ax[i].set_title(s, fontsize=8, verticalalignment="top") + plt.savefig(f, dpi=300, bbox_inches="tight") plt.close() if verbose: - LOGGER.info(f'Saving {f}') + LOGGER.info(f"Saving {f}") if labels is not None: - LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax])) + LOGGER.info("True: " + " ".join(f"{names[i]:3s}" for i in labels[:nmax])) if pred is not None: - LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax])) + LOGGER.info("Predicted:" + " ".join(f"{names[i]:3s}" for i in pred[:nmax])) return f -def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve() +def plot_evolve(evolve_csv="path/to/evolve.csv"): # from utils.plots import *; plot_evolve() # Plot evolve.csv hyp evolution results evolve_csv = Path(evolve_csv) data = pd.read_csv(evolve_csv) @@ -354,83 +378,83 @@ def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; f = fitness(x) j = np.argmax(f) # max fitness index plt.figure(figsize=(10, 12), tight_layout=True) - matplotlib.rc('font', **{'size': 8}) - print(f'Best results from row {j} of {evolve_csv}:') + matplotlib.rc("font", **{"size": 8}) + print(f"Best results from row {j} of {evolve_csv}:") for i, k in enumerate(keys[7:]): v = x[:, 7 + i] mu = v[j] # best single result plt.subplot(6, 5, i + 1) - plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none') - plt.plot(mu, f.max(), 'k+', markersize=15) - plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters + plt.scatter(v, f, c=hist2d(v, f, 20), cmap="viridis", alpha=0.8, edgecolors="none") + plt.plot(mu, f.max(), "k+", markersize=15) + plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9}) # limit to 40 characters if i % 5 != 0: plt.yticks([]) - print(f'{k:>15}: {mu:.3g}') - f = evolve_csv.with_suffix('.png') # filename + print(f"{k:>15}: {mu:.3g}") + f = evolve_csv.with_suffix(".png") # filename plt.savefig(f, dpi=200) plt.close() - print(f'Saved {f}') + print(f"Saved {f}") -def plot_results(file='path/to/results.csv', dir=''): +def plot_results(file="path/to/results.csv", dir=""): # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) ax = ax.ravel() - files = list(save_dir.glob('results*.csv')) - assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' + files = list(save_dir.glob("results*.csv")) + assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." for f in files: try: data = pd.read_csv(f) s = [x.strip() for x in data.columns] x = data.values[:, 0] for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): - y = data.values[:, j].astype('float') + y = data.values[:, j].astype("float") # y[y == 0] = np.nan # don't show zero values - ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) # actual results - ax[i].plot(x, gaussian_filter1d(y, sigma=3), ':', label='smooth', linewidth=2) # smoothing line + ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=8) # actual results + ax[i].plot(x, gaussian_filter1d(y, sigma=3), ":", label="smooth", linewidth=2) # smoothing line ax[i].set_title(s[j], fontsize=12) # if j in [8, 9, 10]: # share train and val loss y axes # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) except Exception as e: - LOGGER.info(f'Warning: Plotting error for {f}: {e}') + LOGGER.info(f"Warning: Plotting error for {f}: {e}") ax[1].legend() - fig.savefig(save_dir / 'results.png', dpi=200) + fig.savefig(save_dir / "results.png", dpi=200) plt.close() -def profile_idetection(start=0, stop=0, labels=(), save_dir=''): +def profile_idetection(start=0, stop=0, labels=(), save_dir=""): # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() - s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] - files = list(Path(save_dir).glob('frames*.txt')) + s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"] + files = list(Path(save_dir).glob("frames*.txt")) for fi, f in enumerate(files): try: results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows n = results.shape[1] # number of rows x = np.arange(start, min(stop, n) if stop else n) results = results[:, x] - t = (results[0] - results[0].min()) # set t0=0s + t = results[0] - results[0].min() # set t0=0s results[0] = x for i, a in enumerate(ax): if i < len(results): - label = labels[fi] if len(labels) else f.stem.replace('frames_', '') - a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) + label = labels[fi] if len(labels) else f.stem.replace("frames_", "") + a.plot(t, results[i], marker=".", label=label, linewidth=1, markersize=5) a.set_title(s[i]) - a.set_xlabel('time (s)') + a.set_xlabel("time (s)") # if fi == len(files) - 1: # a.set_ylim(bottom=0) - for side in ['top', 'right']: + for side in ["top", "right"]: a.spines[side].set_visible(False) else: a.remove() except Exception as e: - print(f'Warning: Plotting error for {f}; {e}') + print(f"Warning: Plotting error for {f}; {e}") ax[1].legend() - plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) + plt.savefig(Path(save_dir) / "idetection_profile.png", dpi=200) -def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True): +def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True): # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop xyxy = torch.tensor(xyxy).view(-1, 4) b = xyxy2xywh(xyxy) # boxes @@ -439,10 +463,10 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad xyxy = xywh2xyxy(b).long() clip_boxes(xyxy, im.shape) - crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] + crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)] if save: file.parent.mkdir(parents=True, exist_ok=True) # make directory - f = str(increment_path(file).with_suffix('.jpg')) + f = str(increment_path(file).with_suffix(".jpg")) # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB return crop diff --git a/utils/segment/augmentations.py b/utils/segment/augmentations.py index f8154b834869..56636b65d93a 100644 --- a/utils/segment/augmentations.py +++ b/utils/segment/augmentations.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Image augmentation functions -""" +"""Image augmentation functions.""" import math import random @@ -22,15 +20,9 @@ def mixup(im, labels, segments, im2, labels2, segments2): return im, labels, segments -def random_perspective(im, - targets=(), - segments=(), - degrees=10, - translate=.1, - scale=.1, - shear=10, - perspective=0.0, - border=(0, 0)): +def random_perspective( + im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) +): # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) # targets = [cls, xyxy] @@ -62,8 +54,8 @@ def random_perspective(im, # Translation T = np.eye(3) - T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels) - T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) # Combined rotation matrix M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT @@ -89,7 +81,7 @@ def random_perspective(im, xy = np.ones((len(segment), 3)) xy[:, :2] = segment xy = xy @ M.T # transform - xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine # clip new[i] = segment2box(xy, width, height) diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py index 5398617eef68..b0b3a7424216 100644 --- a/utils/segment/dataloaders.py +++ b/utils/segment/dataloaders.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Dataloaders -""" +"""Dataloaders.""" import os import random @@ -17,30 +15,32 @@ from ..torch_utils import torch_distributed_zero_first from .augmentations import mixup, random_perspective -RANK = int(os.getenv('RANK', -1)) - - -def create_dataloader(path, - imgsz, - batch_size, - stride, - single_cls=False, - hyp=None, - augment=False, - cache=False, - pad=0.0, - rect=False, - rank=-1, - workers=8, - image_weights=False, - quad=False, - prefix='', - shuffle=False, - mask_downsample_ratio=1, - overlap_mask=False, - seed=0): +RANK = int(os.getenv("RANK", -1)) + + +def create_dataloader( + path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix="", + shuffle=False, + mask_downsample_ratio=1, + overlap_mask=False, + seed=0, +): if rect and shuffle: - LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') + LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") shuffle = False with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = LoadImagesAndLabelsAndMasks( @@ -58,7 +58,8 @@ def create_dataloader(path, prefix=prefix, downsample_ratio=mask_downsample_ratio, overlap=overlap_mask, - rank=rank) + rank=rank, + ) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices @@ -81,7 +82,6 @@ def create_dataloader(path, class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing - def __init__( self, path, @@ -96,14 +96,29 @@ def __init__( stride=32, pad=0, min_items=0, - prefix='', + prefix="", downsample_ratio=1, overlap=False, rank=-1, seed=0, ): - super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls, - stride, pad, min_items, prefix, rank, seed) + super().__init__( + path, + img_size, + batch_size, + augment, + hyp, + rect, + image_weights, + cache_images, + single_cls, + stride, + pad, + min_items, + prefix, + rank, + seed, + ) self.downsample_ratio = downsample_ratio self.overlap = overlap @@ -111,7 +126,7 @@ def __getitem__(self, index): index = self.indices[index] # linear, shuffled, or image_weights hyp = self.hyp - mosaic = self.mosaic and random.random() < hyp['mosaic'] + mosaic = self.mosaic and random.random() < hyp["mosaic"] masks = [] if mosaic: # Load mosaic @@ -119,7 +134,7 @@ def __getitem__(self, index): shapes = None # MixUp augmentation - if random.random() < hyp['mixup']: + if random.random() < hyp["mixup"]: img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1))) else: @@ -147,30 +162,36 @@ def __getitem__(self, index): labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) if self.augment: - img, labels, segments = random_perspective(img, - labels, - segments=segments, - degrees=hyp['degrees'], - translate=hyp['translate'], - scale=hyp['scale'], - shear=hyp['shear'], - perspective=hyp['perspective']) + img, labels, segments = random_perspective( + img, + labels, + segments=segments, + degrees=hyp["degrees"], + translate=hyp["translate"], + scale=hyp["scale"], + shear=hyp["shear"], + perspective=hyp["perspective"], + ) nl = len(labels) # number of labels if nl: labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) if self.overlap: - masks, sorted_idx = polygons2masks_overlap(img.shape[:2], - segments, - downsample_ratio=self.downsample_ratio) + masks, sorted_idx = polygons2masks_overlap( + img.shape[:2], segments, downsample_ratio=self.downsample_ratio + ) masks = masks[None] # (640, 640) -> (1, 640, 640) labels = labels[sorted_idx] else: masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio) - masks = (torch.from_numpy(masks) if len(masks) else torch.zeros(1 if self.overlap else nl, img.shape[0] // - self.downsample_ratio, img.shape[1] // - self.downsample_ratio)) + masks = ( + torch.from_numpy(masks) + if len(masks) + else torch.zeros( + 1 if self.overlap else nl, img.shape[0] // self.downsample_ratio, img.shape[1] // self.downsample_ratio + ) + ) # TODO: albumentations support if self.augment: # Albumentations @@ -180,17 +201,17 @@ def __getitem__(self, index): nl = len(labels) # update after albumentations # HSV color-space - augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) # Flip up-down - if random.random() < hyp['flipud']: + if random.random() < hyp["flipud"]: img = np.flipud(img) if nl: labels[:, 2] = 1 - labels[:, 2] masks = torch.flip(masks, dims=[1]) # Flip left-right - if random.random() < hyp['fliplr']: + if random.random() < hyp["fliplr"]: img = np.fliplr(img) if nl: labels[:, 1] = 1 - labels[:, 1] @@ -254,16 +275,18 @@ def load_mosaic(self, index): # img4, labels4 = replicate(img4, labels4) # replicate # Augment - img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) - img4, labels4, segments4 = random_perspective(img4, - labels4, - segments4, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) + img4, labels4, segments4 = random_perspective( + img4, + labels4, + segments4, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove return img4, labels4, segments4 @staticmethod @@ -312,8 +335,10 @@ def polygons2masks(img_size, polygons, color, downsample_ratio=1): def polygons2masks_overlap(img_size, segments, downsample_ratio=1): """Return a (640, 640) overlap mask.""" - masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), - dtype=np.int32 if len(segments) > 255 else np.uint8) + masks = np.zeros( + (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), + dtype=np.int32 if len(segments) > 255 else np.uint8, + ) areas = [] ms = [] for si in range(len(segments)): diff --git a/utils/segment/general.py b/utils/segment/general.py index f1b2f1dd120f..8cbc745b4a90 100644 --- a/utils/segment/general.py +++ b/utils/segment/general.py @@ -6,8 +6,7 @@ def crop_mask(masks, boxes): """ - "Crop" predicted masks by zeroing out everything not in the predicted bbox. - Vectorized by Chong (thanks Chong). + "Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong). Args: - masks should be a size [n, h, w] tensor of masks @@ -35,7 +34,7 @@ def process_mask_upsample(protos, masks_in, bboxes, shape): c, mh, mw = protos.shape # CHW masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) - masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW masks = crop_mask(masks, bboxes) # CHW return masks.gt_(0.5) @@ -63,7 +62,7 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False): masks = crop_mask(masks, downsampled_bboxes) # CHW if upsample: - masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW return masks.gt_(0.5) @@ -85,7 +84,7 @@ def process_mask_native(protos, masks_in, bboxes, shape): bottom, right = int(mh - pad[1]), int(mw - pad[0]) masks = masks[:, top:bottom, left:right] - masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW masks = crop_mask(masks, bboxes) # CHW return masks.gt_(0.5) @@ -144,17 +143,17 @@ def masks_iou(mask1, mask2, eps=1e-7): return intersection / (union + eps) -def masks2segments(masks, strategy='largest'): +def masks2segments(masks, strategy="largest"): # Convert masks(n,160,160) into segments(n,xy) segments = [] - for x in masks.int().cpu().numpy().astype('uint8'): + for x in masks.int().cpu().numpy().astype("uint8"): c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] if c: - if strategy == 'concat': # concatenate all segments + if strategy == "concat": # concatenate all segments c = np.concatenate([x.reshape(-1, 2) for x in c]) - elif strategy == 'largest': # select largest segment + elif strategy == "largest": # select largest segment c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) else: c = np.zeros((0, 2)) # no segments found - segments.append(c.astype('float32')) + segments.append(c.astype("float32")) return segments diff --git a/utils/segment/loss.py b/utils/segment/loss.py index caeff3cad586..1e007271fa9c 100644 --- a/utils/segment/loss.py +++ b/utils/segment/loss.py @@ -18,14 +18,14 @@ def __init__(self, model, autobalance=False, overlap=False): h = model.hyp # hyperparameters # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets # Focal loss - g = h['fl_gamma'] # focal loss gamma + g = h["fl_gamma"] # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) @@ -82,7 +82,7 @@ def __call__(self, preds, targets, masks): # predictions, targets, model # Mask regression if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample - masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0] + masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0] marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) for bi in b.unique(): @@ -100,10 +100,10 @@ def __call__(self, preds, targets, masks): # predictions, targets, model if self.autobalance: self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - lseg *= self.hyp['box'] / bs + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + lseg *= self.hyp["box"] / bs loss = lbox + lobj + lcls + lseg return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() @@ -111,7 +111,7 @@ def __call__(self, preds, targets, masks): # predictions, targets, model def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): # Mask loss for one image pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) - loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none') + loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none") return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() def build_targets(self, p, targets): @@ -132,16 +132,20 @@ def build_targets(self, p, targets): targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices g = 0.5 # bias - off = torch.tensor( - [ - [0, 0], - [1, 0], - [0, 1], - [-1, 0], - [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], - device=self.device).float() * g # offsets + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device, + ).float() + * g + ) # offsets for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape @@ -152,7 +156,7 @@ def build_targets(self, p, targets): if nt: # Matches r = t[..., 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare + j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) t = t[j] # filter diff --git a/utils/segment/metrics.py b/utils/segment/metrics.py index 787961bee1bf..222a749b5986 100644 --- a/utils/segment/metrics.py +++ b/utils/segment/metrics.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Model validation metrics -""" +"""Model validation metrics.""" import numpy as np @@ -15,14 +13,14 @@ def fitness(x): def ap_per_class_box_and_mask( - tp_m, - tp_b, - conf, - pred_cls, - target_cls, - plot=False, - save_dir='.', - names=(), + tp_m, + tp_b, + conf, + pred_cls, + target_cls, + plot=False, + save_dir=".", + names=(), ): """ Args: @@ -30,41 +28,33 @@ def ap_per_class_box_and_mask( tp_m: tp of masks. other arguments see `func: ap_per_class`. """ - results_boxes = ap_per_class(tp_b, - conf, - pred_cls, - target_cls, - plot=plot, - save_dir=save_dir, - names=names, - prefix='Box')[2:] - results_masks = ap_per_class(tp_m, - conf, - pred_cls, - target_cls, - plot=plot, - save_dir=save_dir, - names=names, - prefix='Mask')[2:] + results_boxes = ap_per_class( + tp_b, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Box" + )[2:] + results_masks = ap_per_class( + tp_m, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Mask" + )[2:] results = { - 'boxes': { - 'p': results_boxes[0], - 'r': results_boxes[1], - 'ap': results_boxes[3], - 'f1': results_boxes[2], - 'ap_class': results_boxes[4]}, - 'masks': { - 'p': results_masks[0], - 'r': results_masks[1], - 'ap': results_masks[3], - 'f1': results_masks[2], - 'ap_class': results_masks[4]}} + "boxes": { + "p": results_boxes[0], + "r": results_boxes[1], + "ap": results_boxes[3], + "f1": results_boxes[2], + "ap_class": results_boxes[4], + }, + "masks": { + "p": results_masks[0], + "r": results_masks[1], + "ap": results_masks[3], + "f1": results_masks[2], + "ap_class": results_masks[4], + }, + } return results class Metric: - def __init__(self) -> None: self.p = [] # (nc, ) self.r = [] # (nc, ) @@ -74,7 +64,9 @@ def __init__(self) -> None: @property def ap50(self): - """AP@0.5 of all classes. + """ + AP@0.5 of all classes. + Return: (nc, ) or []. """ @@ -90,7 +82,9 @@ def ap(self): @property def mp(self): - """mean precision of all classes. + """ + Mean precision of all classes. + Return: float. """ @@ -98,7 +92,9 @@ def mp(self): @property def mr(self): - """mean recall of all classes. + """ + Mean recall of all classes. + Return: float. """ @@ -106,7 +102,9 @@ def mr(self): @property def map50(self): - """Mean AP@0.5 of all classes. + """ + Mean AP@0.5 of all classes. + Return: float. """ @@ -114,18 +112,20 @@ def map50(self): @property def map(self): - """Mean AP@0.5:0.95 of all classes. + """ + Mean AP@0.5:0.95 of all classes. + Return: float. """ return self.all_ap.mean() if len(self.all_ap) else 0.0 def mean_results(self): - """Mean of results, return mp, mr, map50, map""" + """Mean of results, return mp, mr, map50, map.""" return (self.mp, self.mr, self.map50, self.map) def class_result(self, i): - """class-aware result, return p[i], r[i], ap50[i], ap[i]""" + """Class-aware result, return p[i], r[i], ap50[i], ap[i]""" return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) def get_maps(self, nc): @@ -159,8 +159,8 @@ def update(self, results): Args: results: Dict{'boxes': Dict{}, 'masks': Dict{}} """ - self.metric_box.update(list(results['boxes'].values())) - self.metric_mask.update(list(results['masks'].values())) + self.metric_box.update(list(results["boxes"].values())) + self.metric_mask.update(list(results["masks"].values())) def mean_results(self): return self.metric_box.mean_results() + self.metric_mask.mean_results() @@ -178,33 +178,35 @@ def ap_class_index(self): KEYS = [ - 'train/box_loss', - 'train/seg_loss', # train loss - 'train/obj_loss', - 'train/cls_loss', - 'metrics/precision(B)', - 'metrics/recall(B)', - 'metrics/mAP_0.5(B)', - 'metrics/mAP_0.5:0.95(B)', # metrics - 'metrics/precision(M)', - 'metrics/recall(M)', - 'metrics/mAP_0.5(M)', - 'metrics/mAP_0.5:0.95(M)', # metrics - 'val/box_loss', - 'val/seg_loss', # val loss - 'val/obj_loss', - 'val/cls_loss', - 'x/lr0', - 'x/lr1', - 'x/lr2', ] + "train/box_loss", + "train/seg_loss", # train loss + "train/obj_loss", + "train/cls_loss", + "metrics/precision(B)", + "metrics/recall(B)", + "metrics/mAP_0.5(B)", + "metrics/mAP_0.5:0.95(B)", # metrics + "metrics/precision(M)", + "metrics/recall(M)", + "metrics/mAP_0.5(M)", + "metrics/mAP_0.5:0.95(M)", # metrics + "val/box_loss", + "val/seg_loss", # val loss + "val/obj_loss", + "val/cls_loss", + "x/lr0", + "x/lr1", + "x/lr2", +] BEST_KEYS = [ - 'best/epoch', - 'best/precision(B)', - 'best/recall(B)', - 'best/mAP_0.5(B)', - 'best/mAP_0.5:0.95(B)', - 'best/precision(M)', - 'best/recall(M)', - 'best/mAP_0.5(M)', - 'best/mAP_0.5:0.95(M)', ] + "best/epoch", + "best/precision(B)", + "best/recall(B)", + "best/mAP_0.5(B)", + "best/mAP_0.5:0.95(B)", + "best/precision(M)", + "best/recall(M)", + "best/mAP_0.5(M)", + "best/mAP_0.5:0.95(M)", +] diff --git a/utils/segment/plots.py b/utils/segment/plots.py index f9938cd1b06a..0e30c61be66f 100644 --- a/utils/segment/plots.py +++ b/utils/segment/plots.py @@ -14,7 +14,7 @@ @threaded -def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None): +def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None): # Plot image grid with labels if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() @@ -27,7 +27,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg' max_subplots = 16 # max image subplots, i.e. 4x4 bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images - ns = np.ceil(bs ** 0.5) # number of subplots (square) + ns = np.ceil(bs**0.5) # number of subplots (square) if np.max(images[0]) <= 1: images *= 255 # de-normalise (optional) @@ -38,7 +38,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg' break x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin im = im.transpose(1, 2, 0) - mosaic[y:y + h, x:x + w, :] = im + mosaic[y : y + h, x : x + w, :] = im # Resize (optional) scale = max_size / ns / max(h, w) @@ -60,7 +60,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg' ti = targets[idx] # image targets boxes = xywh2xyxy(ti[:, 2:6]).T - classes = ti[:, 1].astype('int') + classes = ti[:, 1].astype("int") labels = ti.shape[1] == 6 # labels if no conf column conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) @@ -77,7 +77,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg' color = colors(cls) cls = names[cls] if names else cls if labels or conf[j] > 0.25: # 0.25 conf thresh - label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' + label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" annotator.box_label(box, label, color=color) # Plot masks @@ -103,41 +103,44 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg' else: mask = image_masks[j].astype(bool) with contextlib.suppress(Exception): - im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6 + im[y : y + h, x : x + w, :][mask] = ( + im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6 + ) annotator.fromarray(im) annotator.im.save(fname) # save -def plot_results_with_masks(file='path/to/results.csv', dir='', best=True): +def plot_results_with_masks(file="path/to/results.csv", dir="", best=True): # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) ax = ax.ravel() - files = list(save_dir.glob('results*.csv')) - assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' + files = list(save_dir.glob("results*.csv")) + assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." for f in files: try: data = pd.read_csv(f) - index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + - 0.1 * data.values[:, 11]) + index = np.argmax( + 0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11] + ) s = [x.strip() for x in data.columns] x = data.values[:, 0] for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): y = data.values[:, j] # y[y == 0] = np.nan # don't show zero values - ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=2) + ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2) if best: # best - ax[i].scatter(index, y[index], color='r', label=f'best:{index}', marker='*', linewidth=3) - ax[i].set_title(s[j] + f'\n{round(y[index], 5)}') + ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3) + ax[i].set_title(s[j] + f"\n{round(y[index], 5)}") else: # last - ax[i].scatter(x[-1], y[-1], color='r', label='last', marker='*', linewidth=3) - ax[i].set_title(s[j] + f'\n{round(y[-1], 5)}') + ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3) + ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}") # if j in [8, 9, 10]: # share train and val loss y axes # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) except Exception as e: - print(f'Warning: Plotting error for {f}: {e}') + print(f"Warning: Plotting error for {f}: {e}") ax[1].legend() - fig.savefig(save_dir / 'results.png', dpi=200) + fig.savefig(save_dir / "results.png", dpi=200) plt.close() diff --git a/utils/torch_utils.py b/utils/torch_utils.py index 13a356f3238c..6bc4b4c7fd04 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -1,7 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -PyTorch utils -""" +"""PyTorch utils.""" import math import os @@ -21,9 +19,9 @@ from utils.general import LOGGER, check_version, colorstr, file_date, git_describe -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) try: import thop # for FLOPs computation @@ -31,11 +29,11 @@ thop = None # Suppress PyTorch warnings -warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling') -warnings.filterwarnings('ignore', category=UserWarning) +warnings.filterwarnings("ignore", message="User provided device_type of 'cuda', but CUDA is not available. Disabling") +warnings.filterwarnings("ignore", category=UserWarning) -def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')): +def smart_inference_mode(torch_1_9=check_version(torch.__version__, "1.9.0")): # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator def decorate(fn): return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) @@ -45,19 +43,20 @@ def decorate(fn): def smartCrossEntropyLoss(label_smoothing=0.0): # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0 - if check_version(torch.__version__, '1.10.0'): + if check_version(torch.__version__, "1.10.0"): return nn.CrossEntropyLoss(label_smoothing=label_smoothing) if label_smoothing > 0: - LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0') + LOGGER.warning(f"WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0") return nn.CrossEntropyLoss() def smart_DDP(model): # Model DDP creation with checks - assert not check_version(torch.__version__, '1.12.0', pinned=True), \ - 'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \ - 'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395' - if check_version(torch.__version__, '1.11.0'): + assert not check_version(torch.__version__, "1.12.0", pinned=True), ( + "torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. " + "Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395" + ) + if check_version(torch.__version__, "1.11.0"): return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) else: return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) @@ -66,7 +65,8 @@ def smart_DDP(model): def reshape_classifier_output(model, n=1000): # Update a TorchVision classification model to class count 'n' if required from models.common import Classify - name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module + + name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module if isinstance(m, Classify): # YOLOv5 Classify() head if m.linear.out_features != n: m.linear = nn.Linear(m.linear.in_features, n) @@ -97,43 +97,44 @@ def torch_distributed_zero_first(local_rank: int): def device_count(): # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows - assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows' + assert platform.system() in ("Linux", "Windows"), "device_count() only supported on Linux or Windows" try: - cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows + cmd = "nvidia-smi -L | wc -l" if platform.system() == "Linux" else 'nvidia-smi -L | find /c /v ""' # Windows return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) except Exception: return 0 -def select_device(device='', batch_size=0, newline=True): +def select_device(device="", batch_size=0, newline=True): # device = None or 'cpu' or 0 or '0' or '0,1,2,3' - s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} ' - device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0' - cpu = device == 'cpu' - mps = device == 'mps' # Apple Metal Performance Shaders (MPS) + s = f"YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} " + device = str(device).strip().lower().replace("cuda:", "").replace("none", "") # to string, 'cuda:0' to '0' + cpu = device == "cpu" + mps = device == "mps" # Apple Metal Performance Shaders (MPS) if cpu or mps: - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False elif device: # non-cpu device requested - os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available() - assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ - f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" + os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available() + assert torch.cuda.is_available() and torch.cuda.device_count() >= len( + device.replace(",", "") + ), f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available - devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 + devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7 n = len(devices) # device count if n > 1 and batch_size > 0: # check batch_size is divisible by device_count - assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' - space = ' ' * (len(s) + 1) + assert batch_size % n == 0, f"batch-size {batch_size} not multiple of GPU count {n}" + space = " " * (len(s) + 1) for i, d in enumerate(devices): p = torch.cuda.get_device_properties(i) s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB - arg = 'cuda:0' - elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available - s += 'MPS\n' - arg = 'mps' + arg = "cuda:0" + elif mps and getattr(torch, "has_mps", False) and torch.backends.mps.is_available(): # prefer MPS if available + s += "MPS\n" + arg = "mps" else: # revert to CPU - s += 'CPU\n' - arg = 'cpu' + s += "CPU\n" + arg = "cpu" if not newline: s = s.rstrip() @@ -149,7 +150,7 @@ def time_sync(): def profile(input, ops, n=10, device=None): - """ YOLOv5 speed/memory/FLOPs profiler + """YOLOv5 speed/memory/FLOPs profiler Usage: input = torch.randn(16, 3, 640, 640) m1 = lambda x: x * torch.sigmoid(x) @@ -159,18 +160,20 @@ def profile(input, ops, n=10, device=None): results = [] if not isinstance(device, torch.device): device = select_device(device) - print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" - f"{'input':>24s}{'output':>24s}") + print( + f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" + f"{'input':>24s}{'output':>24s}" + ) for x in input if isinstance(input, list) else [input]: x = x.to(device) x.requires_grad = True for m in ops if isinstance(ops, list) else [ops]: - m = m.to(device) if hasattr(m, 'to') else m # device - m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m + m = m.to(device) if hasattr(m, "to") else m # device + m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward try: - flops = thop.profile(m, inputs=(x, ), verbose=False)[0] / 1E9 * 2 # GFLOPs + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1e9 * 2 # GFLOPs except Exception: flops = 0 @@ -184,13 +187,13 @@ def profile(input, ops, n=10, device=None): t[2] = time_sync() except Exception: # no backward method # print(e) # for debug - t[2] = float('nan') + t[2] = float("nan") tf += (t[1] - t[0]) * 1000 / n # ms per op forward tb += (t[2] - t[1]) * 1000 / n # ms per op backward - mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB) - s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes + mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0 # (GB) + s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else "list" for x in (x, y)) # shapes p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters - print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}') + print(f"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}") results.append([p, flops, mem, tf, tb, s_in, s_out]) except Exception as e: print(e) @@ -238,23 +241,30 @@ def sparsity(model): def prune(model, amount=0.3): # Prune model to requested global sparsity import torch.nn.utils.prune as prune + for name, m in model.named_modules(): if isinstance(m, nn.Conv2d): - prune.l1_unstructured(m, name='weight', amount=amount) # prune - prune.remove(m, 'weight') # make permanent - LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity') + prune.l1_unstructured(m, name="weight", amount=amount) # prune + prune.remove(m, "weight") # make permanent + LOGGER.info(f"Model pruned to {sparsity(model):.3g} global sparsity") def fuse_conv_and_bn(conv, bn): # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ - fusedconv = nn.Conv2d(conv.in_channels, - conv.out_channels, - kernel_size=conv.kernel_size, - stride=conv.stride, - padding=conv.padding, - dilation=conv.dilation, - groups=conv.groups, - bias=True).requires_grad_(False).to(conv.weight.device) + fusedconv = ( + nn.Conv2d( + conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + dilation=conv.dilation, + groups=conv.groups, + bias=True, + ) + .requires_grad_(False) + .to(conv.weight.device) + ) # Prepare filters w_conv = conv.weight.clone().view(conv.out_channels, -1) @@ -276,22 +286,24 @@ def model_info(model, verbose=False, imgsz=640): if verbose: print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") for i, (name, p) in enumerate(model.named_parameters()): - name = name.replace('module_list.', '') - print('%5g %40s %9s %12g %20s %10.3g %10.3g' % - (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + name = name.replace("module_list.", "") + print( + "%5g %40s %9s %12g %20s %10.3g %10.3g" + % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()) + ) try: # FLOPs p = next(model.parameters()) - stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride + stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 # max stride im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format - flops = thop.profile(deepcopy(model), inputs=(im, ), verbose=False)[0] / 1E9 * 2 # stride GFLOPs + flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1e9 * 2 # stride GFLOPs imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float - fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs + fs = f", {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs" # 640x640 GFLOPs except Exception: - fs = '' + fs = "" - name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model' - LOGGER.info(f'{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}') + name = Path(model.yaml_file).stem.replace("yolov5", "YOLOv5") if hasattr(model, "yaml_file") else "Model" + LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) @@ -300,7 +312,7 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) return img h, w = img.shape[2:] s = (int(h * ratio), int(w * ratio)) # new size - img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize + img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize if not same_shape: # pad/crop img h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean @@ -309,72 +321,76 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) def copy_attr(a, b, include=(), exclude=()): # Copy attributes from b to a, options to only include [...] and to exclude [...] for k, v in b.__dict__.items(): - if (len(include) and k not in include) or k.startswith('_') or k in exclude: + if (len(include) and k not in include) or k.startswith("_") or k in exclude: continue else: setattr(a, k, v) -def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5): +def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5): # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay g = [], [], [] # optimizer parameter groups - bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d() + bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d() for v in model.modules(): for p_name, p in v.named_parameters(recurse=0): - if p_name == 'bias': # bias (no decay) + if p_name == "bias": # bias (no decay) g[2].append(p) - elif p_name == 'weight' and isinstance(v, bn): # weight (no decay) + elif p_name == "weight" and isinstance(v, bn): # weight (no decay) g[1].append(p) else: g[0].append(p) # weight (with decay) - if name == 'Adam': + if name == "Adam": optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum - elif name == 'AdamW': + elif name == "AdamW": optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) - elif name == 'RMSProp': + elif name == "RMSProp": optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) - elif name == 'SGD': + elif name == "SGD": optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) else: - raise NotImplementedError(f'Optimizer {name} not implemented.') - - optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay - optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights) - LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " - f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias') + raise NotImplementedError(f"Optimizer {name} not implemented.") + + optimizer.add_param_group({"params": g[0], "weight_decay": decay}) # add g0 with weight_decay + optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights) + LOGGER.info( + f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " + f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias' + ) return optimizer -def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs): +def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs): # YOLOv5 torch.hub.load() wrapper with smart error/issue handling - if check_version(torch.__version__, '1.9.1'): - kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors - if check_version(torch.__version__, '1.12.0'): - kwargs['trust_repo'] = True # argument required starting in torch 0.12 + if check_version(torch.__version__, "1.9.1"): + kwargs["skip_validation"] = True # validation causes GitHub API rate limit errors + if check_version(torch.__version__, "1.12.0"): + kwargs["trust_repo"] = True # argument required starting in torch 0.12 try: return torch.hub.load(repo, model, **kwargs) except Exception: return torch.hub.load(repo, model, force_reload=True, **kwargs) -def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True): +def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, resume=True): # Resume training from a partially trained checkpoint best_fitness = 0.0 - start_epoch = ckpt['epoch'] + 1 - if ckpt['optimizer'] is not None: - optimizer.load_state_dict(ckpt['optimizer']) # optimizer - best_fitness = ckpt['best_fitness'] - if ema and ckpt.get('ema'): - ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA - ema.updates = ckpt['updates'] + start_epoch = ckpt["epoch"] + 1 + if ckpt["optimizer"] is not None: + optimizer.load_state_dict(ckpt["optimizer"]) # optimizer + best_fitness = ckpt["best_fitness"] + if ema and ckpt.get("ema"): + ema.ema.load_state_dict(ckpt["ema"].float().state_dict()) # EMA + ema.updates = ckpt["updates"] if resume: - assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \ - f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" - LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs') + assert start_epoch > 0, ( + f"{weights} training to {epochs} epochs is finished, nothing to resume.\n" + f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" + ) + LOGGER.info(f"Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs") if epochs < start_epoch: LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") - epochs += ckpt['epoch'] # finetune additional epochs + epochs += ckpt["epoch"] # finetune additional epochs return best_fitness, start_epoch, epochs @@ -383,7 +399,7 @@ class EarlyStopping: def __init__(self, patience=30): self.best_fitness = 0.0 # i.e. mAP self.best_epoch = 0 - self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop + self.patience = patience or float("inf") # epochs to wait after fitness stops improving to stop self.possible_stop = False # possible stop may occur next epoch def __call__(self, epoch, fitness): @@ -394,15 +410,17 @@ def __call__(self, epoch, fitness): self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch stop = delta >= self.patience # stop training if patience exceeded if stop: - LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. ' - f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n' - f'To update EarlyStopping(patience={self.patience}) pass a new patience value, ' - f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.') + LOGGER.info( + f"Stopping training early as no improvement observed in last {self.patience} epochs. " + f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n" + f"To update EarlyStopping(patience={self.patience}) pass a new patience value, " + f"i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping." + ) return stop class ModelEMA: - """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models + """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models Keeps a moving average of everything in the model state_dict (parameters and buffers) For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage """ @@ -427,6 +445,6 @@ def update(self, model): v += (1 - d) * msd[k].detach() # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' - def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + def update_attr(self, model, include=(), exclude=("process_group", "reducer")): # Update EMA attributes copy_attr(self.ema, model, include, exclude) diff --git a/utils/triton.py b/utils/triton.py index b5153dad940d..9584d07fbcf0 100644 --- a/utils/triton.py +++ b/utils/triton.py @@ -1,6 +1,5 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" Utils to interact with the Triton Inference Server -""" +"""Utils to interact with the Triton Inference Server.""" import typing from urllib.parse import urlparse @@ -9,9 +8,11 @@ class TritonRemoteModel: - """ A wrapper over a model served by the Triton Inference Server. It can - be configured to communicate over GRPC or HTTP. It accepts Torch Tensors - as input and returns them as outputs. + """ + A wrapper over a model served by the Triton Inference Server. + + It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as + outputs. """ def __init__(self, url: str): @@ -21,7 +22,7 @@ def __init__(self, url: str): """ parsed_url = urlparse(url) - if parsed_url.scheme == 'grpc': + if parsed_url.scheme == "grpc": from tritonclient.grpc import InferenceServerClient, InferInput self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client @@ -31,51 +32,55 @@ def __init__(self, url: str): def create_input_placeholders() -> typing.List[InferInput]: return [ - InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] + InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] + ] else: from tritonclient.http import InferenceServerClient, InferInput self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client model_repository = self.client.get_model_repository_index() - self.model_name = model_repository[0]['name'] + self.model_name = model_repository[0]["name"] self.metadata = self.client.get_model_metadata(self.model_name) def create_input_placeholders() -> typing.List[InferInput]: return [ - InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] + InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] + ] self._create_input_placeholders_fn = create_input_placeholders @property def runtime(self): - """Returns the model runtime""" - return self.metadata.get('backend', self.metadata.get('platform')) + """Returns the model runtime.""" + return self.metadata.get("backend", self.metadata.get("platform")) def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]: - """ Invokes the model. Parameters can be provided via args or kwargs. - args, if provided, are assumed to match the order of inputs of the model. - kwargs are matched with the model input names. + """ + Invokes the model. + + Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of + the model. kwargs are matched with the model input names. """ inputs = self._create_inputs(*args, **kwargs) response = self.client.infer(model_name=self.model_name, inputs=inputs) result = [] - for output in self.metadata['outputs']: - tensor = torch.as_tensor(response.as_numpy(output['name'])) + for output in self.metadata["outputs"]: + tensor = torch.as_tensor(response.as_numpy(output["name"])) result.append(tensor) return result[0] if len(result) == 1 else result def _create_inputs(self, *args, **kwargs): args_len, kwargs_len = len(args), len(kwargs) if not args_len and not kwargs_len: - raise RuntimeError('No inputs provided.') + raise RuntimeError("No inputs provided.") if args_len and kwargs_len: - raise RuntimeError('Cannot specify args and kwargs at the same time') + raise RuntimeError("Cannot specify args and kwargs at the same time") placeholders = self._create_input_placeholders_fn() if args_len: if args_len != len(placeholders): - raise RuntimeError(f'Expected {len(placeholders)} inputs, got {args_len}.') + raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.") for input, value in zip(placeholders, args): input.set_data_from_numpy(value.cpu().numpy()) else: diff --git a/val.py b/val.py index 1a4219c38962..6cc1d37a0a26 100644 --- a/val.py +++ b/val.py @@ -1,6 +1,6 @@ # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ -Validate a trained YOLOv5 detection model on a detection dataset +Validate a trained YOLOv5 detection model on a detection dataset. Usage: $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640 @@ -39,9 +39,23 @@ from models.common import DetectMultiBackend from utils.callbacks import Callbacks from utils.dataloaders import create_dataloader -from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements, - check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, - print_args, scale_boxes, xywh2xyxy, xyxy2xywh) +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + Profile, + check_dataset, + check_img_size, + check_requirements, + check_yaml, + coco80_to_coco91_class, + colorstr, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + xywh2xyxy, + xyxy2xywh, +) from utils.metrics import ConfusionMatrix, ap_per_class, box_iou from utils.plots import output_to_target, plot_images, plot_val_study from utils.torch_utils import select_device, smart_inference_mode @@ -53,8 +67,8 @@ def save_one_txt(predn, save_conf, shape, file): for *xyxy, conf, cls in predn.tolist(): xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format - with open(file, 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') + with open(file, "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") def save_one_json(predn, jdict, path, class_map): @@ -63,11 +77,14 @@ def save_one_json(predn, jdict, path, class_map): box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(predn.tolist(), box.tolist()): - jdict.append({ - 'image_id': image_id, - 'category_id': class_map[int(p[5])], - 'bbox': [round(x, 3) for x in b], - 'score': round(p[4], 5)}) + jdict.append( + { + "image_id": image_id, + "category_id": class_map[int(p[5])], + "bbox": [round(x, 3) for x in b], + "score": round(p[4], 5), + } + ) def process_batch(detections, labels, iouv): @@ -98,47 +115,47 @@ def process_batch(detections, labels, iouv): @smart_inference_mode() def run( - data, - weights=None, # model.pt path(s) - batch_size=32, # batch size - imgsz=640, # inference size (pixels) - conf_thres=0.001, # confidence threshold - iou_thres=0.6, # NMS IoU threshold - max_det=300, # maximum detections per image - task='val', # train, val, test, speed or study - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - workers=8, # max dataloader workers (per RANK in DDP mode) - single_cls=False, # treat as single-class dataset - augment=False, # augmented inference - verbose=False, # verbose output - save_txt=False, # save results to *.txt - save_hybrid=False, # save label+prediction hybrid results to *.txt - save_conf=False, # save confidences in --save-txt labels - save_json=False, # save a COCO-JSON results file - project=ROOT / 'runs/val', # save to project/name - name='exp', # save to project/name - exist_ok=False, # existing project/name ok, do not increment - half=True, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - model=None, - dataloader=None, - save_dir=Path(''), - plots=True, - callbacks=Callbacks(), - compute_loss=None, + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task="val", # train, val, test, speed or study + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / "runs/val", # save to project/name + name="exp", # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(""), + plots=True, + callbacks=Callbacks(), + compute_loss=None, ): # Initialize/load model and set device training = model is not None if training: # called by train.py device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model - half &= device.type != 'cpu' # half precision only supported on CUDA + half &= device.type != "cpu" # half precision only supported on CUDA model.half() if half else model.float() else: # called directly device = select_device(device, batch_size=batch_size) # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) @@ -151,16 +168,16 @@ def run( device = model.device if not (pt or jit): batch_size = 1 # export.py models default to batch-size 1 - LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") # Data data = check_dataset(data) # check # Configure model.eval() - cuda = device.type != 'cpu' - is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset - nc = 1 if single_cls else int(data['nc']) # number of classes + cuda = device.type != "cpu" + is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset + nc = 1 if single_cls else int(data["nc"]) # number of classes iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 niou = iouv.numel() @@ -168,36 +185,40 @@ def run( if not training: if pt and not single_cls: # check --weights are trained on --data ncm = model.model.nc - assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ - f'classes). Pass correct combination of --weights and --data that are trained together.' + assert ncm == nc, ( + f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} " + f"classes). Pass correct combination of --weights and --data that are trained together." + ) model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup - pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks - task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images - dataloader = create_dataloader(data[task], - imgsz, - batch_size, - stride, - single_cls, - pad=pad, - rect=rect, - workers=workers, - prefix=colorstr(f'{task}: '))[0] + pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks + task = task if task in ("train", "val", "test") else "val" # path to train/val/test images + dataloader = create_dataloader( + data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f"{task}: "), + )[0] seen = 0 confusion_matrix = ConfusionMatrix(nc=nc) - names = model.names if hasattr(model, 'names') else model.module.names # get class names + names = model.names if hasattr(model, "names") else model.module.names # get class names if isinstance(names, (list, tuple)): # old format names = dict(enumerate(names)) class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) - s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95') + s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95") tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 dt = Profile(device=device), Profile(device=device), Profile(device=device) # profiling times loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] - callbacks.run('on_val_start') + callbacks.run("on_val_start") pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar for batch_i, (im, targets, paths, shapes) in enumerate(pbar): - callbacks.run('on_val_batch_start') + callbacks.run("on_val_batch_start") with dt[0]: if cuda: im = im.to(device, non_blocking=True) @@ -218,13 +239,9 @@ def run( targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling with dt[2]: - preds = non_max_suppression(preds, - conf_thres, - iou_thres, - labels=lb, - multi_label=True, - agnostic=single_cls, - max_det=max_det) + preds = non_max_suppression( + preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det + ) # Metrics for si, pred in enumerate(preds): @@ -259,18 +276,18 @@ def run( # Save/log if save_txt: - (save_dir / 'labels').mkdir(parents=True, exist_ok=True) - save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') + (save_dir / "labels").mkdir(parents=True, exist_ok=True) + save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt") if save_json: save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary - callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) + callbacks.run("on_val_image_end", pred, predn, path, names, im[si]) # Plot images if plots and batch_i < 3: - plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels - plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred + plot_images(im, targets, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) # labels + plot_images(im, output_to_target(preds), paths, save_dir / f"val_batch{batch_i}_pred.jpg", names) # pred - callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds) + callbacks.run("on_val_batch_end", batch_i, im, targets, paths, shapes, preds) # Compute metrics stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy @@ -281,10 +298,10 @@ def run( nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class # Print results - pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format - LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) + pf = "%22s" + "%11i" * 2 + "%11.3g" * 4 # print format + LOGGER.info(pf % ("all", seen, nt.sum(), mp, mr, map50, map)) if nt.sum() == 0: - LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels') + LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels") # Print results per class if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): @@ -292,35 +309,35 @@ def run( LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) # Print speeds - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image if not training: shape = (batch_size, 3, imgsz, imgsz) - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t) # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) - callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + callbacks.run("on_val_end", nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) # Save JSON if save_json and len(jdict): - w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights - anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights + anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations if not os.path.exists(anno_json): - anno_json = os.path.join(data['path'], 'annotations', 'instances_val2017.json') - pred_json = str(save_dir / f'{w}_predictions.json') # predictions - LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') - with open(pred_json, 'w') as f: + anno_json = os.path.join(data["path"], "annotations", "instances_val2017.json") + pred_json = str(save_dir / f"{w}_predictions.json") # predictions + LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...") + with open(pred_json, "w") as f: json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb - check_requirements('pycocotools>=2.0.6') + check_requirements("pycocotools>=2.0.6") from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval anno = COCO(anno_json) # init annotations api pred = anno.loadRes(pred_json) # init predictions api - eval = COCOeval(anno, pred, 'bbox') + eval = COCOeval(anno, pred, "bbox") if is_coco: eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate eval.evaluate() @@ -328,12 +345,12 @@ def run( eval.summarize() map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) except Exception as e: - LOGGER.info(f'pycocotools unable to run: {e}') + LOGGER.info(f"pycocotools unable to run: {e}") # Return results model.float() # for training if not training: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") maps = np.zeros(nc) + map for i, c in enumerate(ap_class): @@ -343,71 +360,71 @@ def run( def parse_opt(): parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)') - parser.add_argument('--batch-size', type=int, default=32, help='batch size') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image') - parser.add_argument('--task', default='val', help='train, val, test, speed or study') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--verbose', action='store_true', help='report mAP by class') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') - parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path(s)") + parser.add_argument("--batch-size", type=int, default=32, help="batch size") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") + parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image") + parser.add_argument("--task", default="val", help="train, val, test, speed or study") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--verbose", action="store_true", help="report mAP by class") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file") + parser.add_argument("--project", default=ROOT / "runs/val", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML - opt.save_json |= opt.data.endswith('coco.yaml') + opt.save_json |= opt.data.endswith("coco.yaml") opt.save_txt |= opt.save_hybrid print_args(vars(opt)) return opt def main(opt): - check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) - if opt.task in ('train', 'val', 'test'): # run normally + if opt.task in ("train", "val", "test"): # run normally if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 - LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results') + LOGGER.info(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results") if opt.save_hybrid: - LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone') + LOGGER.info("WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone") run(**vars(opt)) else: weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] - opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results - if opt.task == 'speed': # speed benchmarks + opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results + if opt.task == "speed": # speed benchmarks # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False for opt.weights in weights: run(**vars(opt), plots=False) - elif opt.task == 'study': # speed vs mAP benchmarks + elif opt.task == "study": # speed vs mAP benchmarks # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... for opt.weights in weights: - f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to + f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis for opt.imgsz in x: # img-size - LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') + LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...") r, _, t = run(**vars(opt), plots=False) y.append(r + t) # results and times - np.savetxt(f, y, fmt='%10.4g') # save - subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt']) + np.savetxt(f, y, fmt="%10.4g") # save + subprocess.run(["zip", "-r", "study.zip", "study_*.txt"]) plot_val_study(x=x) # plot else: raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') -if __name__ == '__main__': +if __name__ == "__main__": opt = parse_opt() main(opt) From 4733b4df90d9a359601c4e481b0312a5ca521fc5 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 8 Jan 2024 18:55:43 +0100 Subject: [PATCH 63/79] Update links.yml (#12601) Signed-off-by: Glenn Jocher --- .github/workflows/links.yml | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml index 1c7b28257d80..cb0ad264cc13 100644 --- a/.github/workflows/links.yml +++ b/.github/workflows/links.yml @@ -34,13 +34,13 @@ jobs: retry_wait_seconds: 60 max_attempts: 3 command: | - lychee - --accept 403,429,500,502,999 - --exclude-loopback - --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' - --exclude-path '**/ci.yaml' - --github-token ${{ secrets.GITHUB_TOKEN }} - './**/*.md' + lychee \ + --accept 403,429,500,502,999 \ + --exclude-loopback \ + --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ + --exclude-path '**/ci.yaml' \ + --github-token ${{ secrets.GITHUB_TOKEN }} \ + './**/*.md' \ './**/*.html' - name: Test Markdown, HTML, YAML, Python and Notebook links with retry @@ -51,15 +51,15 @@ jobs: retry_wait_seconds: 60 max_attempts: 3 command: | - lychee - --accept 429,999 - --exclude-loopback - --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' - --exclude-path '**/ci.yaml' - --github-token ${{ secrets.GITHUB_TOKEN }} - './**/*.md' - './**/*.html' - './**/*.yml' - './**/*.yaml' - './**/*.py' + lychee \ + --accept 429,999 \ + --exclude-loopback \ + --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ + --exclude-path '**/ci.yaml' \ + --github-token ${{ secrets.GITHUB_TOKEN }} \ + './**/*.md' \ + './**/*.html' \ + './**/*.yml' \ + './**/*.yaml' \ + './**/*.py' \ './**/*.ipynb' From 9abbef522f6e36b36c7d1ce91afa3f58ac4e7fde Mon Sep 17 00:00:00 2001 From: Muhammad Rizwan Munawar Date: Wed, 10 Jan 2024 20:16:40 +0500 Subject: [PATCH 64/79] Update banners for YOLOv8 release v8.1.0 (#12605) * Auto-format by Ultralytics actions * updated git banner * Update README.md Signed-off-by: Glenn Jocher * Update README.zh-CN.md Signed-off-by: Glenn Jocher --------- Signed-off-by: Glenn Jocher Co-authored-by: UltralyticsAssistant Co-authored-by: Glenn Jocher --- README.md | 4 ++-- README.zh-CN.md | 4 ++-- utils/flask_rest_api/README.md | 7 ++----- utils/loggers/clearml/README.md | 23 ++++++++++------------- utils/loggers/comet/README.md | 26 +++++++++----------------- 5 files changed, 25 insertions(+), 39 deletions(-) diff --git a/README.md b/README.md index c778a17258e9..6d94bcaea761 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@

- - + + runs/exp{sep}2, runs/exp{sep}3, ... etc. + """ + Generates an incremented file or directory path if it exists, with optional mkdir; args: path, exist_ok=False, + sep="", mkdir=False. + + Example: runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc + """ path = Path(path) # os-agnostic if path.exists() and not exist_ok: path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "") @@ -1202,10 +1250,14 @@ def increment_path(path, exist_ok=False, sep="", mkdir=False): def imread(filename, flags=cv2.IMREAD_COLOR): + """Reads an image from a file and returns it as a numpy array, using OpenCV's imdecode to support multilanguage + paths. + """ return cv2.imdecode(np.fromfile(filename, np.uint8), flags) def imwrite(filename, img): + """Writes an image to a file, returns True on success and False on failure, supports multilanguage paths.""" try: cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) return True @@ -1214,6 +1266,7 @@ def imwrite(filename, img): def imshow(path, im): + """Displays an image using Unicode path, requires encoded path and image matrix as input.""" imshow_(path.encode("unicode_escape").decode(), im) diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index 36792979913a..c3fbded50a3c 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -73,6 +73,7 @@ def _json_default(value): class Loggers: # YOLOv5 Loggers class def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): + """Initializes loggers for YOLOv5 training and validation metrics, paths, and options.""" self.save_dir = save_dir self.weights = weights self.opt = opt @@ -150,7 +151,7 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, @property def remote_dataset(self): - # Get data_dict if custom dataset artifact link is provided + """Fetches dataset dictionary from remote logging services like ClearML, Weights & Biases, or Comet ML.""" data_dict = None if self.clearml: data_dict = self.clearml.data_dict @@ -162,15 +163,17 @@ def remote_dataset(self): return data_dict def on_train_start(self): + """Initializes the training process for Comet ML logger if it's configured.""" if self.comet_logger: self.comet_logger.on_train_start() def on_pretrain_routine_start(self): + """Invokes pre-training routine start hook for Comet ML logger if available.""" if self.comet_logger: self.comet_logger.on_pretrain_routine_start() def on_pretrain_routine_end(self, labels, names): - # Callback runs on pre-train routine end + """Callback that runs at the end of pre-training routine, logging label plots if enabled.""" if self.plots: plot_labels(labels, names, self.save_dir) paths = self.save_dir.glob("*labels*.jpg") # training labels @@ -183,6 +186,7 @@ def on_pretrain_routine_end(self, labels, names): self.clearml.log_plot(title=path.stem, plot_path=path) def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): + """Logs training batch end events, plots images, and updates external loggers with batch-end data.""" log_dict = dict(zip(self.keys[:3], vals)) # Callback runs on train batch end # ni: number integrated batches (since train start) @@ -203,7 +207,7 @@ def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): self.comet_logger.on_train_batch_end(log_dict, step=ni) def on_train_epoch_end(self, epoch): - # Callback runs on train epoch end + """Callback that updates the current epoch in Weights & Biases at the end of a training epoch.""" if self.wandb: self.wandb.current_epoch = epoch + 1 @@ -211,22 +215,24 @@ def on_train_epoch_end(self, epoch): self.comet_logger.on_train_epoch_end(epoch) def on_val_start(self): + """Callback that signals the start of a validation phase to the Comet logger.""" if self.comet_logger: self.comet_logger.on_val_start() def on_val_image_end(self, pred, predn, path, names, im): - # Callback runs on val image end + """Callback that logs a validation image and its predictions to WandB or ClearML.""" if self.wandb: self.wandb.val_one_image(pred, predn, path, names, im) if self.clearml: self.clearml.log_image_with_boxes(path, pred, names, im) def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): + """Logs validation batch results to Comet ML during training at the end of each validation batch.""" if self.comet_logger: self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): - # Callback runs on val end + """Logs validation results to WandB or ClearML at the end of the validation process.""" if self.wandb or self.clearml: files = sorted(self.save_dir.glob("val*.jpg")) if self.wandb: @@ -238,7 +244,7 @@ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): - # Callback runs at the end of each fit (train+val) epoch + """Callback that logs metrics and saves them to CSV or NDJSON at the end of each fit (train+val) epoch.""" x = dict(zip(self.keys, vals)) if self.csv: file = self.save_dir / "results.csv" @@ -277,7 +283,7 @@ def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): self.comet_logger.on_fit_epoch_end(x, epoch=epoch) def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): - # Callback runs on model save event + """Callback that handles model saving events, logging to Weights & Biases or ClearML if enabled.""" if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: if self.wandb: self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) @@ -290,7 +296,7 @@ def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) def on_train_end(self, last, best, epoch, results): - # Callback runs on training end, i.e. saving best model + """Callback that runs at the end of training to save plots and log results.""" if self.plots: plot_results(file=self.save_dir / "results.csv") # save results.png files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] @@ -326,7 +332,7 @@ def on_train_end(self, last, best, epoch, results): self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) def on_params_update(self, params: dict): - # Update hyperparams or configs of the experiment + """Updates experiment hyperparameters or configurations in WandB, Comet, or ClearML.""" if self.wandb: self.wandb.wandb_run.config.update(params, allow_val_change=True) if self.comet_logger: @@ -346,7 +352,7 @@ class GenericLogger: """ def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")): - # init default loggers + """Initializes a generic logger with optional TensorBoard, W&B, and ClearML support.""" self.save_dir = Path(opt.save_dir) self.include = include self.console_logger = console_logger @@ -381,7 +387,7 @@ def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")): self.clearml = None def log_metrics(self, metrics, epoch): - # Log metrics dictionary to all loggers + """Logs metrics to CSV, TensorBoard, W&B, and ClearML; `metrics` is a dict, `epoch` is an int.""" if self.csv: keys, vals = list(metrics.keys()), list(metrics.values()) n = len(metrics) + 1 # number of cols @@ -400,7 +406,7 @@ def log_metrics(self, metrics, epoch): self.clearml.log_scalars(metrics, epoch) def log_images(self, files, name="Images", epoch=0): - # Log images to all loggers + """Logs images to all loggers with optional naming and epoch specification.""" files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path files = [f for f in files if f.exists()] # filter by exists @@ -418,11 +424,12 @@ def log_images(self, files, name="Images", epoch=0): self.clearml.log_debug_samples(files, title=name) def log_graph(self, model, imgsz=(640, 640)): - # Log model graph to all loggers + """Logs model graph to all configured loggers with specified input image size.""" if self.tb: log_tensorboard_graph(self.tb, model, imgsz) def log_model(self, model_path, epoch=0, metadata=None): + """Logs the model to all configured loggers with optional epoch and metadata.""" if metadata is None: metadata = {} # Log model to all loggers @@ -434,7 +441,7 @@ def log_model(self, model_path, epoch=0, metadata=None): self.clearml.log_model(model_path=model_path, model_name=model_path.stem) def update_params(self, params): - # Update the parameters logged + """Updates logged parameters in WandB and/or ClearML if enabled.""" if self.wandb: wandb.run.config.update(params, allow_val_change=True) if self.clearml: @@ -442,7 +449,7 @@ def update_params(self, params): def log_tensorboard_graph(tb, model, imgsz=(640, 640)): - # Log model graph to TensorBoard + """Logs the model graph to TensorBoard with specified image size and model.""" try: p = next(model.parameters()) # for device, type imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand @@ -455,7 +462,7 @@ def log_tensorboard_graph(tb, model, imgsz=(640, 640)): def web_project_name(project): - # Convert local project name to web project name + """Converts a local project name to a standardized web project name with optional suffixes.""" if not project.startswith("runs/train"): return project suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else "" diff --git a/utils/loggers/comet/__init__.py b/utils/loggers/comet/__init__.py index cec46f5af1fb..076eb3ccecab 100644 --- a/utils/loggers/comet/__init__.py +++ b/utils/loggers/comet/__init__.py @@ -165,6 +165,7 @@ def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwar self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp)) def _get_experiment(self, mode, experiment_id=None): + """Returns a new or existing Comet.ml experiment based on mode and optional experiment_id.""" if mode == "offline": return ( comet_ml.ExistingOfflineExperiment( @@ -197,21 +198,27 @@ def _get_experiment(self, mode, experiment_id=None): return def log_metrics(self, log_dict, **kwargs): + """Logs metrics to the current experiment, accepting a dictionary of metric names and values.""" self.experiment.log_metrics(log_dict, **kwargs) def log_parameters(self, log_dict, **kwargs): + """Logs parameters to the current experiment, accepting a dictionary of parameter names and values.""" self.experiment.log_parameters(log_dict, **kwargs) def log_asset(self, asset_path, **kwargs): + """Logs a file or directory as an asset to the current experiment.""" self.experiment.log_asset(asset_path, **kwargs) def log_asset_data(self, asset, **kwargs): + """Logs in-memory data as an asset to the current experiment, with optional kwargs.""" self.experiment.log_asset_data(asset, **kwargs) def log_image(self, img, **kwargs): + """Logs an image to the current experiment with optional kwargs.""" self.experiment.log_image(img, **kwargs) def log_model(self, path, opt, epoch, fitness_score, best_model=False): + """Logs model checkpoint to experiment with path, options, epoch, fitness, and best model flag.""" if not self.save_model: return @@ -235,6 +242,7 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False): ) def check_dataset(self, data_file): + """Validates the dataset configuration by loading the YAML file specified in `data_file`.""" with open(data_file) as f: data_config = yaml.safe_load(f) @@ -247,6 +255,7 @@ def check_dataset(self, data_file): return check_dataset(data_file) def log_predictions(self, image, labelsn, path, shape, predn): + """Logs predictions with IOU filtering, given image, labels, path, shape, and predictions.""" if self.logged_images_count >= self.max_images: return detections = predn[predn[:, 4] > self.conf_thres] @@ -287,6 +296,7 @@ def log_predictions(self, image, labelsn, path, shape, predn): return def preprocess_prediction(self, image, labels, shape, pred): + """Processes prediction data, resizing labels and adding dataset metadata.""" nl, _ = labels.shape[0], pred.shape[0] # Predictions @@ -306,6 +316,7 @@ def preprocess_prediction(self, image, labels, shape, pred): return predn, labelsn def add_assets_to_artifact(self, artifact, path, asset_path, split): + """Adds image and label assets to a wandb artifact given dataset split and paths.""" img_paths = sorted(glob.glob(f"{asset_path}/*")) label_paths = img2label_paths(img_paths) @@ -331,6 +342,7 @@ def add_assets_to_artifact(self, artifact, path, asset_path, split): return artifact def upload_dataset_artifact(self): + """Uploads a YOLOv5 dataset as an artifact to the Comet.ml platform.""" dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset") path = str((ROOT / Path(self.data_dict["path"])).resolve()) @@ -355,6 +367,7 @@ def upload_dataset_artifact(self): return def download_dataset_artifact(self, artifact_path): + """Downloads a dataset artifact to a specified directory using the experiment's logged artifact.""" logged_artifact = self.experiment.get_artifact(artifact_path) artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) logged_artifact.download(artifact_save_dir) @@ -374,6 +387,7 @@ def download_dataset_artifact(self, artifact_path): return self.update_data_paths(data_dict) def update_data_paths(self, data_dict): + """Updates data paths in the dataset dictionary, defaulting 'path' to an empty string if not present.""" path = data_dict.get("path", "") for split in ["train", "val", "test"]: @@ -386,6 +400,7 @@ def update_data_paths(self, data_dict): return data_dict def on_pretrain_routine_end(self, paths): + """Called at the end of pretraining routine to handle paths if training is not being resumed.""" if self.opt.resume: return @@ -398,20 +413,25 @@ def on_pretrain_routine_end(self, paths): return def on_train_start(self): + """Logs hyperparameters at the start of training.""" self.log_parameters(self.hyp) def on_train_epoch_start(self): + """Called at the start of each training epoch.""" return def on_train_epoch_end(self, epoch): + """Updates the current epoch in the experiment tracking at the end of each epoch.""" self.experiment.curr_epoch = epoch return def on_train_batch_start(self): + """Called at the start of each training batch.""" return def on_train_batch_end(self, log_dict, step): + """Callback function that updates and logs metrics at the end of each training batch if conditions are met.""" self.experiment.curr_step = step if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): self.log_metrics(log_dict, step=step) @@ -419,6 +439,7 @@ def on_train_batch_end(self, log_dict, step): return def on_train_end(self, files, save_dir, last, best, epoch, results): + """Logs metadata and optionally saves model files at the end of training.""" if self.comet_log_predictions: curr_epoch = self.experiment.curr_epoch self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch) @@ -446,12 +467,15 @@ def on_train_end(self, files, save_dir, last, best, epoch, results): self.finish_run() def on_val_start(self): + """Called at the start of validation, currently a placeholder with no functionality.""" return def on_val_batch_start(self): + """Placeholder called at the start of a validation batch with no current functionality.""" return def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): + """Callback executed at the end of a validation batch, conditionally logs predictions to Comet ML.""" if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): return @@ -470,6 +494,7 @@ def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): return def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + """Logs per-class metrics to Comet.ml after validation if enabled and more than one class exists.""" if self.comet_log_per_class_metrics and self.num_classes > 1: for i, c in enumerate(ap_class): class_name = self.class_names[c] @@ -504,14 +529,18 @@ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) ) def on_fit_epoch_end(self, result, epoch): + """Logs metrics at the end of each training epoch.""" self.log_metrics(result, epoch=epoch) def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + """Callback to save model checkpoints periodically if conditions are met.""" if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) def on_params_update(self, params): + """Logs updated parameters during training.""" self.log_parameters(params) def finish_run(self): + """Ends the current experiment and logs its completion.""" self.experiment.end() diff --git a/utils/loggers/comet/comet_utils.py b/utils/loggers/comet/comet_utils.py index 6e8fad68c6cc..7eca1f504d69 100644 --- a/utils/loggers/comet/comet_utils.py +++ b/utils/loggers/comet/comet_utils.py @@ -17,6 +17,7 @@ def download_model_checkpoint(opt, experiment): + """Downloads YOLOv5 model checkpoint from Comet ML experiment, updating `opt.weights` with download path.""" model_dir = f"{opt.project}/{experiment.name}" os.makedirs(model_dir, exist_ok=True) diff --git a/utils/loggers/comet/hpo.py b/utils/loggers/comet/hpo.py index a9e6fabec1cd..8ca08ddc858a 100644 --- a/utils/loggers/comet/hpo.py +++ b/utils/loggers/comet/hpo.py @@ -25,6 +25,9 @@ def get_args(known=False): + """Parses command-line arguments for YOLOv5 training, supporting configuration of weights, data paths, + hyperparameters, and more. + """ parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") parser.add_argument("--cfg", type=str, default="", help="model.yaml path") @@ -83,6 +86,7 @@ def get_args(known=False): def run(parameters, opt): + """Executes YOLOv5 training with given hyperparameters and options, setting up device and training directories.""" hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]} opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py index 0af8bda12d85..4083312e6a59 100644 --- a/utils/loggers/wandb/wandb_utils.py +++ b/utils/loggers/wandb/wandb_utils.py @@ -152,6 +152,7 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False): LOGGER.info(f"Saving model artifact on epoch {epoch + 1}") def val_one_image(self, pred, predn, path, names, im): + """Evaluates model prediction for a single image, returning metrics and visualizations.""" pass def log(self, log_dict): diff --git a/utils/loss.py b/utils/loss.py index 26b8c06bf333..8a910e12ad6f 100644 --- a/utils/loss.py +++ b/utils/loss.py @@ -16,11 +16,17 @@ def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#iss class BCEBlurWithLogitsLoss(nn.Module): # BCEwithLogitLoss() with reduced missing label effects. def __init__(self, alpha=0.05): + """Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing + parameter. + """ super().__init__() self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none") # must be nn.BCEWithLogitsLoss() self.alpha = alpha def forward(self, pred, true): + """Computes modified BCE loss for YOLOv5 with reduced missing label effects, taking pred and true tensors, + returns mean loss. + """ loss = self.loss_fcn(pred, true) pred = torch.sigmoid(pred) # prob from logits dx = pred - true # reduce only missing label effects @@ -33,6 +39,9 @@ def forward(self, pred, true): class FocalLoss(nn.Module): # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to + 'none'. + """ super().__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma @@ -41,6 +50,7 @@ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): self.loss_fcn.reduction = "none" # required to apply FL to each element def forward(self, pred, true): + """Calculates the focal loss between predicted and true labels using a modified BCEWithLogitsLoss.""" loss = self.loss_fcn(pred, true) # p_t = torch.exp(-loss) # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability @@ -63,6 +73,7 @@ def forward(self, pred, true): class QFocalLoss(nn.Module): # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + """Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'.""" super().__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma @@ -71,6 +82,9 @@ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): self.loss_fcn.reduction = "none" # required to apply FL to each element def forward(self, pred, true): + """Computes the focal loss between `pred` and `true` using BCEWithLogitsLoss, adjusting for imbalance with + `gamma` and `alpha`. + """ loss = self.loss_fcn(pred, true) pred_prob = torch.sigmoid(pred) # prob from logits @@ -91,6 +105,7 @@ class ComputeLoss: # Compute losses def __init__(self, model, autobalance=False): + """Initializes ComputeLoss with model and autobalance option, autobalances losses if True.""" device = next(model.parameters()).device # get model device h = model.hyp # hyperparameters @@ -173,7 +188,9 @@ def __call__(self, p, targets): # predictions, targets return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + """Prepares model targets from input targets (image,class,x,y,w,h) for loss computation, returning class, box, + indices, and anchors. + """ na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch = [], [], [], [] gain = torch.ones(7, device=self.device) # normalized to gridspace gain diff --git a/utils/metrics.py b/utils/metrics.py index 5f45621dc372..e572355fec1e 100644 --- a/utils/metrics.py +++ b/utils/metrics.py @@ -13,13 +13,13 @@ def fitness(x): - # Model fitness as a weighted combination of metrics + """Calculates fitness of a model using weighted sum of metrics P, R, mAP@0.5, mAP@0.5:0.95.""" w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] return (x[:, :4] * w).sum(1) def smooth(y, f=0.05): - # Box filter of fraction f + """Applies box filter smoothing to array `y` with fraction `f`, yielding a smoothed array.""" nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) p = np.ones(nf // 2) # ones padding yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded @@ -126,6 +126,7 @@ def compute_ap(recall, precision): class ConfusionMatrix: # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix def __init__(self, nc, conf=0.25, iou_thres=0.45): + """Initializes ConfusionMatrix with given number of classes, confidence, and IoU threshold.""" self.matrix = np.zeros((nc + 1, nc + 1)) self.nc = nc # number of classes self.conf = conf @@ -179,6 +180,9 @@ def process_batch(self, detections, labels): self.matrix[dc, self.nc] += 1 # predicted background def tp_fp(self): + """Calculates true positives (tp) and false positives (fp) excluding the background class from the confusion + matrix. + """ tp = self.matrix.diagonal() # true positives fp = self.matrix.sum(1) - tp # false positives # fn = self.matrix.sum(0) - tp # false negatives (missed detections) @@ -186,6 +190,7 @@ def tp_fp(self): @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure") def plot(self, normalize=True, save_dir="", names=()): + """Plots confusion matrix using seaborn, optional normalization; can save plot to specified directory.""" import seaborn as sn array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns @@ -217,12 +222,17 @@ def plot(self, normalize=True, save_dir="", names=()): plt.close(fig) def print(self): + """Prints the confusion matrix row-wise, with each class and its predictions separated by spaces.""" for i in range(self.nc + 1): print(" ".join(map(str, self.matrix[i]))) def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): - # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4) + """ + Calculates IoU, GIoU, DIoU, or CIoU between two boxes, supporting xywh/xyxy formats. + + Input shapes are box1(1,4) to box2(n,4). + """ # Get the coordinates of bounding boxes if xywh: # transform from xywh to xyxy @@ -312,7 +322,9 @@ def bbox_ioa(box1, box2, eps=1e-7): def wh_iou(wh1, wh2, eps=1e-7): - # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + """Calculates the Intersection over Union (IoU) for two sets of widths and heights; `wh1` and `wh2` should be nx2 + and mx2 tensors. + """ wh1 = wh1[:, None] # [N,1,2] wh2 = wh2[None] # [1,M,2] inter = torch.min(wh1, wh2).prod(2) # [N,M] @@ -324,7 +336,9 @@ def wh_iou(wh1, wh2, eps=1e-7): @threaded def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()): - # Precision-recall curve + """Plots precision-recall curve, optionally per class, saving to `save_dir`; `px`, `py` are lists, `ap` is Nx2 + array, `names` optional. + """ fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) py = np.stack(py, axis=1) @@ -347,7 +361,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()): @threaded def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric"): - # Metric-confidence curve + """Plots a metric-confidence curve for model predictions, supporting per-class visualization and smoothing.""" fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) if 0 < len(names) < 21: # display per-class legend if < 21 classes diff --git a/utils/plots.py b/utils/plots.py index 11c96a6372c3..e1b073dfb1ad 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -31,7 +31,11 @@ class Colors: # Ultralytics color palette https://ultralytics.com/ def __init__(self): - # hex = matplotlib.colors.TABLEAU_COLORS.values() + """ + Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB. + + Colors derived from `hex = matplotlib.colors.TABLEAU_COLORS.values()`. + """ hexs = ( "FF3838", "FF9D97", @@ -58,6 +62,7 @@ def __init__(self): self.n = len(self.palette) def __call__(self, i, bgr=False): + """Returns color from palette by index `i`, in BGR format if `bgr=True`, else RGB; `i` is an integer index.""" c = self.palette[int(i) % self.n] return (c[2], c[1], c[0]) if bgr else c @@ -100,7 +105,11 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detec def hist2d(x, y, n=100): - # 2d histogram used in labels.png and evolve.png + """ + Generates a logarithmic 2D histogram, useful for visualizing label or evolution distributions. + + Used in used in labels.png and evolve.png. + """ xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) @@ -109,6 +118,7 @@ def hist2d(x, y, n=100): def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + """Applies a low-pass Butterworth filter to `data` with specified `cutoff`, `fs`, and `order`.""" from scipy.signal import butter, filtfilt # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy @@ -122,7 +132,9 @@ def butter_lowpass(cutoff, fs, order): def output_to_target(output, max_det=300): - # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting + """Converts YOLOv5 model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, limiting detections + to `max_det`. + """ targets = [] for i, o in enumerate(output): box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) @@ -133,7 +145,7 @@ def output_to_target(output, max_det=300): @threaded def plot_images(images, targets, paths=None, fname="images.jpg", names=None): - # Plot image grid with labels + """Plots an image grid with labels from YOLOv5 predictions or targets, saving to `fname`.""" if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(targets, torch.Tensor): @@ -197,7 +209,7 @@ def plot_images(images, targets, paths=None, fname="images.jpg", names=None): def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""): - # Plot LR simulating training for full epochs + """Plots learning rate schedule for given optimizer and scheduler, saving plot to `save_dir`.""" optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals y = [] for _ in range(epochs): @@ -295,7 +307,7 @@ def plot_val_study(file="", dir="", x=None): # from utils.plots import *; plot_ @TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 def plot_labels(labels, names=(), save_dir=Path("")): - # plot dataset labels + """Plots dataset labels, saving correlogram and label images, handles classes, and visualizes bounding boxes.""" LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes nc = int(c.max() + 1) # number of classes @@ -340,7 +352,7 @@ def plot_labels(labels, names=(), save_dir=Path("")): def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")): - # Show classification image grid with labels (optional) and predictions (optional) + """Displays a grid of images with optional labels and predictions, saving to a file.""" from utils.augmentations import denormalize names = names or [f"class{i}" for i in range(1000)] @@ -397,7 +409,11 @@ def plot_evolve(evolve_csv="path/to/evolve.csv"): # from utils.plots import *; def plot_results(file="path/to/results.csv", dir=""): - # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') + """ + Plots training results from a 'results.csv' file; accepts file path and directory as arguments. + + Example: from utils.plots import *; plot_results('path/to/results.csv') + """ save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) ax = ax.ravel() @@ -424,7 +440,11 @@ def plot_results(file="path/to/results.csv", dir=""): def profile_idetection(start=0, stop=0, labels=(), save_dir=""): - # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + """ + Plots per-image iDetection logs, comparing metrics like storage and performance over time. + + Example: from utils.plots import *; profile_idetection() + """ ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"] files = list(Path(save_dir).glob("frames*.txt")) @@ -455,7 +475,9 @@ def profile_idetection(start=0, stop=0, labels=(), save_dir=""): def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True): - # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop + """Crops and saves an image from bounding box `xyxy`, applied with `gain` and `pad`, optionally squares and adjusts + for BGR. + """ xyxy = torch.tensor(xyxy).view(-1, 4) b = xyxy2xywh(xyxy) # boxes if square: diff --git a/utils/segment/augmentations.py b/utils/segment/augmentations.py index 56636b65d93a..e13a53d34821 100644 --- a/utils/segment/augmentations.py +++ b/utils/segment/augmentations.py @@ -12,7 +12,11 @@ def mixup(im, labels, segments, im2, labels2, segments2): - # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf + """ + Applies MixUp augmentation blending two images, labels, and segments with a random ratio. + + See https://arxiv.org/pdf/1710.09412.pdf + """ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 im = (im * r + im2 * (1 - r)).astype(np.uint8) labels = np.concatenate((labels, labels2), 0) diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py index b0b3a7424216..9d2e9bef0b09 100644 --- a/utils/segment/dataloaders.py +++ b/utils/segment/dataloaders.py @@ -123,6 +123,7 @@ def __init__( self.overlap = overlap def __getitem__(self, index): + """Returns a transformed item from the dataset at the specified index, handling indexing and image weighting.""" index = self.indices[index] # linear, shuffled, or image_weights hyp = self.hyp @@ -230,7 +231,7 @@ def __getitem__(self, index): return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks) def load_mosaic(self, index): - # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic + """Loads 1 image + 3 random images into a 4-image YOLOv5 mosaic, adjusting labels and segments accordingly.""" labels4, segments4 = [], [] s = self.img_size yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y @@ -291,6 +292,7 @@ def load_mosaic(self, index): @staticmethod def collate_fn(batch): + """Custom collation function for DataLoader, batches images, labels, paths, shapes, and segmentation masks.""" img, label, path, shapes, masks = zip(*batch) # transposed batched_masks = torch.cat(masks, 0) for i, l in enumerate(label): diff --git a/utils/segment/general.py b/utils/segment/general.py index 8cbc745b4a90..f292496c0da9 100644 --- a/utils/segment/general.py +++ b/utils/segment/general.py @@ -144,7 +144,9 @@ def masks_iou(mask1, mask2, eps=1e-7): def masks2segments(masks, strategy="largest"): - # Convert masks(n,160,160) into segments(n,xy) + """Converts binary (n,160,160) masks to polygon segments with options for concatenation or selecting the largest + segment. + """ segments = [] for x in masks.int().cpu().numpy().astype("uint8"): c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] diff --git a/utils/segment/loss.py b/utils/segment/loss.py index 1e007271fa9c..29f1bcbb7e77 100644 --- a/utils/segment/loss.py +++ b/utils/segment/loss.py @@ -12,6 +12,9 @@ class ComputeLoss: # Compute losses def __init__(self, model, autobalance=False, overlap=False): + """Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap + handling. + """ self.sort_obj_iou = False self.overlap = overlap device = next(model.parameters()).device # get model device @@ -109,13 +112,15 @@ def __call__(self, preds, targets, masks): # predictions, targets, model return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): - # Mask loss for one image + """Calculates and normalizes single mask loss for YOLOv5 between predicted and ground truth masks.""" pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none") return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + """Prepares YOLOv5 targets for loss computation; inputs targets (image, class, x, y, w, h), output target + classes/boxes. + """ na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], [] gain = torch.ones(8, device=self.device) # normalized to gridspace gain diff --git a/utils/segment/metrics.py b/utils/segment/metrics.py index 7811e7eb364a..973b398eb6b9 100644 --- a/utils/segment/metrics.py +++ b/utils/segment/metrics.py @@ -7,7 +7,7 @@ def fitness(x): - # Model fitness as a weighted combination of metrics + """Evaluates model fitness by a weighted sum of 8 metrics, `x`: [N,8] array, weights: [0.1, 0.9] for mAP and F1.""" w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9] return (x[:, :8] * w).sum(1) @@ -128,6 +128,7 @@ def class_result(self, i): return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) def get_maps(self, nc): + """Calculates and returns mean Average Precision (mAP) for each class given number of classes `nc`.""" maps = np.zeros(nc) + self.map for i, c in enumerate(self.ap_class_index): maps[c] = self.ap[i] @@ -162,17 +163,22 @@ def update(self, results): self.metric_mask.update(list(results["masks"].values())) def mean_results(self): + """Computes and returns the mean results for both box and mask metrics by summing their individual means.""" return self.metric_box.mean_results() + self.metric_mask.mean_results() def class_result(self, i): + """Returns the sum of box and mask metric results for a specified class index `i`.""" return self.metric_box.class_result(i) + self.metric_mask.class_result(i) def get_maps(self, nc): + """Calculates and returns the sum of mean average precisions (mAPs) for both box and mask metrics for `nc` + classes. + """ return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) @property def ap_class_index(self): - # boxes and masks have the same ap_class_index + """Returns the class index for average precision, shared by both box and mask metrics.""" return self.metric_box.ap_class_index diff --git a/utils/segment/plots.py b/utils/segment/plots.py index 0e30c61be66f..ce01988be937 100644 --- a/utils/segment/plots.py +++ b/utils/segment/plots.py @@ -15,7 +15,7 @@ @threaded def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None): - # Plot image grid with labels + """Plots a grid of images, their labels, and masks with optional resizing and annotations, saving to fname.""" if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(targets, torch.Tensor): @@ -111,7 +111,11 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg" def plot_results_with_masks(file="path/to/results.csv", dir="", best=True): - # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') + """ + Plots training results from CSV files, plotting best or last result highlights based on `best` parameter. + + Example: from utils.plots import *; plot_results('path/to/results.csv') + """ save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) ax = ax.ravel() diff --git a/utils/torch_utils.py b/utils/torch_utils.py index 6bc4b4c7fd04..c2c760efa404 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -34,7 +34,8 @@ def smart_inference_mode(torch_1_9=check_version(torch.__version__, "1.9.0")): - # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator + """Applies torch.inference_mode() if torch>=1.9.0, else torch.no_grad() as a decorator for functions.""" + def decorate(fn): return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) @@ -42,7 +43,9 @@ def decorate(fn): def smartCrossEntropyLoss(label_smoothing=0.0): - # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0 + """Returns a CrossEntropyLoss with optional label smoothing for torch>=1.10.0; warns if smoothing on lower + versions. + """ if check_version(torch.__version__, "1.10.0"): return nn.CrossEntropyLoss(label_smoothing=label_smoothing) if label_smoothing > 0: @@ -51,7 +54,7 @@ def smartCrossEntropyLoss(label_smoothing=0.0): def smart_DDP(model): - # Model DDP creation with checks + """Initializes DistributedDataParallel (DDP) for model training, respecting torch version constraints.""" assert not check_version(torch.__version__, "1.12.0", pinned=True), ( "torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. " "Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395" @@ -63,7 +66,7 @@ def smart_DDP(model): def reshape_classifier_output(model, n=1000): - # Update a TorchVision classification model to class count 'n' if required + """Reshapes last layer of model to match class count 'n', supporting Classify, Linear, Sequential types.""" from models.common import Classify name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module @@ -87,7 +90,9 @@ def reshape_classifier_output(model, n=1000): @contextmanager def torch_distributed_zero_first(local_rank: int): - # Decorator to make all processes in distributed training wait for each local_master to do something + """Context manager ensuring ordered operations in distributed training by making all processes wait for the leading + process. + """ if local_rank not in [-1, 0]: dist.barrier(device_ids=[local_rank]) yield @@ -96,7 +101,7 @@ def torch_distributed_zero_first(local_rank: int): def device_count(): - # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows + """Returns the number of available CUDA devices; works on Linux and Windows by invoking `nvidia-smi`.""" assert platform.system() in ("Linux", "Windows"), "device_count() only supported on Linux or Windows" try: cmd = "nvidia-smi -L | wc -l" if platform.system() == "Linux" else 'nvidia-smi -L | find /c /v ""' # Windows @@ -106,7 +111,7 @@ def device_count(): def select_device(device="", batch_size=0, newline=True): - # device = None or 'cpu' or 0 or '0' or '0,1,2,3' + """Selects computing device (CPU, CUDA GPU, MPS) for YOLOv5 model deployment, logging device info.""" s = f"YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} " device = str(device).strip().lower().replace("cuda:", "").replace("none", "") # to string, 'cuda:0' to '0' cpu = device == "cpu" @@ -143,7 +148,7 @@ def select_device(device="", batch_size=0, newline=True): def time_sync(): - # PyTorch-accurate time + """Synchronizes PyTorch for accurate timing, leveraging CUDA if available, and returns the current time.""" if torch.cuda.is_available(): torch.cuda.synchronize() return time.time() @@ -203,16 +208,19 @@ def profile(input, ops, n=10, device=None): def is_parallel(model): - # Returns True if model is of type DP or DDP + """Checks if the model is using Data Parallelism (DP) or Distributed Data Parallelism (DDP).""" return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) def de_parallel(model): - # De-parallelize a model: returns single-GPU model if model is of type DP or DDP + """Returns a single-GPU model by removing Data Parallelism (DP) or Distributed Data Parallelism (DDP) if applied.""" return model.module if is_parallel(model) else model def initialize_weights(model): + """Initializes weights of Conv2d, BatchNorm2d, and activations (Hardswish, LeakyReLU, ReLU, ReLU6, SiLU) in the + model. + """ for m in model.modules(): t = type(m) if t is nn.Conv2d: @@ -225,12 +233,14 @@ def initialize_weights(model): def find_modules(model, mclass=nn.Conv2d): - # Finds layer indices matching module class 'mclass' + """Finds and returns list of layer indices in `model.module_list` matching the specified `mclass`.""" return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] def sparsity(model): - # Return global model sparsity + """Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total + parameters. + """ a, b = 0, 0 for p in model.parameters(): a += p.numel() @@ -239,7 +249,7 @@ def sparsity(model): def prune(model, amount=0.3): - # Prune model to requested global sparsity + """Prunes Conv2d layers in a model to a specified sparsity using L1 unstructured pruning.""" import torch.nn.utils.prune as prune for name, m in model.named_modules(): @@ -250,7 +260,11 @@ def prune(model, amount=0.3): def fuse_conv_and_bn(conv, bn): - # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + """ + Fuses Conv2d and BatchNorm2d layers into a single Conv2d layer. + + See https://tehnokv.com/posts/fusing-batchnorm-and-conv/. + """ fusedconv = ( nn.Conv2d( conv.in_channels, @@ -280,7 +294,11 @@ def fuse_conv_and_bn(conv, bn): def model_info(model, verbose=False, imgsz=640): - # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + """ + Prints model summary including layers, parameters, gradients, and FLOPs; imgsz may be int or list. + + Example: img_size=640 or img_size=[640, 320] + """ n_p = sum(x.numel() for x in model.parameters()) # number parameters n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients if verbose: @@ -319,7 +337,7 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) def copy_attr(a, b, include=(), exclude=()): - # Copy attributes from b to a, options to only include [...] and to exclude [...] + """Copies attributes from object b to a, optionally filtering with include and exclude lists.""" for k, v in b.__dict__.items(): if (len(include) and k not in include) or k.startswith("_") or k in exclude: continue @@ -328,7 +346,11 @@ def copy_attr(a, b, include=(), exclude=()): def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5): - # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay + """ + Initializes YOLOv5 smart optimizer with 3 parameter groups for different decay configurations. + + Groups are 0) weights with decay, 1) weights no decay, 2) biases no decay. + """ g = [], [], [] # optimizer parameter groups bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d() for v in model.modules(): @@ -361,7 +383,7 @@ def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5): def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs): - # YOLOv5 torch.hub.load() wrapper with smart error/issue handling + """YOLOv5 torch.hub.load() wrapper with smart error handling, adjusting torch arguments for compatibility.""" if check_version(torch.__version__, "1.9.1"): kwargs["skip_validation"] = True # validation causes GitHub API rate limit errors if check_version(torch.__version__, "1.12.0"): @@ -373,7 +395,7 @@ def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs): def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, resume=True): - # Resume training from a partially trained checkpoint + """Resumes training from a checkpoint, updating optimizer, ema, and epochs, with optional resume verification.""" best_fitness = 0.0 start_epoch = ckpt["epoch"] + 1 if ckpt["optimizer"] is not None: @@ -397,12 +419,14 @@ def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, re class EarlyStopping: # YOLOv5 simple early stopper def __init__(self, patience=30): + """Initializes simple early stopping mechanism for YOLOv5, with adjustable patience for non-improving epochs.""" self.best_fitness = 0.0 # i.e. mAP self.best_epoch = 0 self.patience = patience or float("inf") # epochs to wait after fitness stops improving to stop self.possible_stop = False # possible stop may occur next epoch def __call__(self, epoch, fitness): + """Evaluates if training should stop based on fitness improvement and patience, returning a boolean.""" if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training self.best_epoch = epoch self.best_fitness = fitness @@ -426,7 +450,9 @@ class ModelEMA: """ def __init__(self, model, decay=0.9999, tau=2000, updates=0): - # Create EMA + """Initializes EMA with model parameters, decay rate, tau for decay adjustment, and update count; sets model to + evaluation mode. + """ self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA self.updates = updates # number of EMA updates self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) @@ -434,7 +460,7 @@ def __init__(self, model, decay=0.9999, tau=2000, updates=0): p.requires_grad_(False) def update(self, model): - # Update EMA parameters + """Updates the Exponential Moving Average (EMA) parameters based on the current model's parameters.""" self.updates += 1 d = self.decay(self.updates) @@ -446,5 +472,7 @@ def update(self, model): # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' def update_attr(self, model, include=(), exclude=("process_group", "reducer")): - # Update EMA attributes + """Updates EMA attributes by copying specified attributes from model to EMA, excluding certain attributes by + default. + """ copy_attr(self.ema, model, include, exclude) diff --git a/utils/triton.py b/utils/triton.py index 9584d07fbcf0..87524c9c7801 100644 --- a/utils/triton.py +++ b/utils/triton.py @@ -71,6 +71,7 @@ def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[t return result[0] if len(result) == 1 else result def _create_inputs(self, *args, **kwargs): + """Creates input tensors from args or kwargs, not both; raises error if none or both are provided.""" args_len, kwargs_len = len(args), len(kwargs) if not args_len and not kwargs_len: raise RuntimeError("No inputs provided.") diff --git a/val.py b/val.py index 6cc1d37a0a26..1c8c65ba89aa 100644 --- a/val.py +++ b/val.py @@ -62,7 +62,7 @@ def save_one_txt(predn, save_conf, shape, file): - # Save one txt result + """Saves one detection result to a txt file in normalized xywh format, optionally including confidence.""" gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh for *xyxy, conf, cls in predn.tolist(): xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh @@ -72,7 +72,11 @@ def save_one_txt(predn, save_conf, shape, file): def save_one_json(predn, jdict, path, class_map): - # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} + """ + Saves one JSON detection result with image ID, category ID, bounding box, and score. + + Example: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} + """ image_id = int(path.stem) if path.stem.isnumeric() else path.stem box = xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner @@ -359,6 +363,7 @@ def run( def parse_opt(): + """Parses command-line options for YOLOv5 model inference configuration.""" parser = argparse.ArgumentParser() parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path(s)") @@ -391,6 +396,9 @@ def parse_opt(): def main(opt): + """Executes YOLOv5 tasks like training, validation, testing, speed, and study benchmarks based on provided + options. + """ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) if opt.task in ("train", "val", "test"): # run normally