Merge branch 'master' of github.com:SamSamhuns/yolov5_adversarial

adv_patch_gen/conv_visdrone_2_yolo/conv_visdrone_2_yolo_fmt.py

@@ -4,14 +4,15 @@
 YOLOv7 requires an additional txt file (Same name as the first parent directory) with paths to the images for the train,
 val & test splits
 """
+
+import argparse
+import glob
 import os
 import os.path as osp
-import glob
-import argparse
 from typing import Optional
 
-import tqdm
 import imagesize
+import tqdm
 
 # VisDrone annot fmt
 # <bbox_left>, <bbox_top>, <bbox_width>, <bbox_height>, <score>, <object_category>, <truncation>, <occlusion>

adv_patch_gen/conv_visdrone_2_yolo/disp_visdrone.py

@@ -4,8 +4,8 @@
 sys.path.append(".")
 
 import cv2
-from adv_patch_gen.conv_visdrone_2_yolo.utils import get_annot_img_paths, load_visdrone_annots_as_np
 
+from adv_patch_gen.conv_visdrone_2_yolo.utils import get_annot_img_paths, load_visdrone_annots_as_np
 
 # visdrone dataset classes
 # ignore(0), pedestrian(1), people(2), bicycle(3),

adv_patch_gen/conv_visdrone_2_yolo/disp_yolo.py

@@ -4,8 +4,8 @@
 sys.path.append(".")
 
 import cv2
-from adv_patch_gen.conv_visdrone_2_yolo.utils import get_annot_img_paths, load_yolo_annots_as_np
 
+from adv_patch_gen.conv_visdrone_2_yolo.utils import get_annot_img_paths, load_yolo_annots_as_np
 
 # visdrone dataset classes
 # ignore(0), pedestrian(1), people(2), bicycle(3),

adv_patch_gen/conv_visdrone_2_yolo/utils.py

@@ -1,6 +1,6 @@
-import os
 import glob
-from typing import List, Tuple, Set
+import os
+from typing import List, Set, Tuple
 
 import numpy as np
 

adv_patch_gen/utils/common.py

@@ -1,11 +1,11 @@
 """Common utils."""
+
 import socket
 from typing import Tuple, Union
 
 import numpy as np
 from PIL import Image
 
-
 IMG_EXTNS = {".png", ".jpg", ".jpeg"}
 
 

adv_patch_gen/utils/config_parser.py

@@ -1,6 +1,8 @@
 """Create argparse options for config files."""
-import json
+
 import argparse
+import json
+
 from easydict import EasyDict as edict
 
 

adv_patch_gen/utils/dataset.py

@@ -1,18 +1,18 @@
 """Dataset Class for loading YOLO format datasets where the source data dir has the image and labels subdirs where each
 image must have a corresponding label file with the same name.
 """
+
 import glob
 import os.path as osp
-from typing import Tuple, Optional
+from typing import Optional, Tuple
 
 import numpy as np
-from PIL import Image
 import torch
 import torch.nn.functional as F
+from PIL import Image
 from torch.utils.data import Dataset
 from torchvision import transforms
 
-
 IMG_EXTNS = {".png", ".jpg", ".jpeg"}
 
 

adv_patch_gen/utils/loss.py

@@ -1,4 +1,5 @@
 """Loss functions used in patch generation."""
+
 from typing import Tuple
 
 import torch

adv_patch_gen/utils/patch.py

@@ -1,6 +1,7 @@
 """Modules for creating adversarial object patch."""
+
 import math
-from typing import Union, Tuple
+from typing import Tuple, Union
 
 import numpy as np
 import torch

adv_patch_gen/utils/scheduler.py

@@ -3,8 +3,8 @@
 # Gradual increase in learning rate by a constant amount to avoid sudden increase in lr
 
 import warnings
-from torch.optim.lr_scheduler import _LRScheduler, ReduceLROnPlateau
 
+from torch.optim.lr_scheduler import ReduceLROnPlateau, _LRScheduler
 
 EPOCH_DEPRECATION_WARNING = (
  "The epoch parameter in `scheduler.step()` was not necessary and is being "

adv_patch_gen/utils/video.py

@@ -1,6 +1,6 @@
 import os.path as osp
+from subprocess import PIPE, Popen
 from typing import Optional
-from subprocess import Popen, PIPE
 
 
 def ffmpeg_create_video_from_image_dir(

detect_oak.py

@@ -5,15 +5,16 @@
 requirements:
  depthai-sdk==1.9.4
 """
+
 import time
-from typing import Tuple
 from threading import Thread
+from typing import Tuple
 
 import cv2
-import torch
-import numpy as np
 import depthai
+import numpy as np
 import onnxruntime
+import torch
 
 from models.common import DetectMultiBackend
 from utils.augmentations import letterbox

explainer.py

@@ -17,16 +17,16 @@
 from pytorch_grad_cam import (
  AblationCAM,
  EigenCAM,
+ EigenGradCAM,
  FullGrad,
  GradCAM,
+ GradCAMElementWise,
  GradCAMPlusPlus,
  HiResCAM,
- ScoreCAM,
- XGradCAM,
- EigenGradCAM,
- GradCAMElementWise,
  LayerCAM,
  RandomCAM,
+ ScoreCAM,
+ XGradCAM,
 )
 from pytorch_grad_cam.utils.image import scale_cam_image, show_cam_on_image
 
@@ -36,13 +36,11 @@
  sys.path.append(str(ROOT)) # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
 
-from utils.general import print_args, check_file, non_max_suppression
-from utils.torch_utils import select_device
-
 from models.common import DetectMultiBackend
-from utils.dataloaders import LoadImages, IMG_FORMATS, VID_FORMATS
-from utils.general import check_img_size, scale_boxes
+from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages
+from utils.general import check_file, check_img_size, non_max_suppression, print_args, scale_boxes
 from utils.plots import Annotator, colors
+from utils.torch_utils import select_device
 
 
 def yolo_reshape_transform(x):

export.py

@@ -742,9 +742,9 @@ def pipeline_coreml(model, im, file, names, y, prefix=colorstr("CoreML Pipeline:
  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.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

models/experimental.py

@@ -1,5 +1,6 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """Experimental modules."""
+
 import math
 
 import numpy as np

test_patch.py

@@ -1,38 +1,38 @@
 """Testing code for evaluating Adversarial patches against object detection."""
+
+import glob
 import io
+import json
 import os
 import os.path as osp
-import time
-import json
-import glob
 import random
-from pathlib import Path
-from typing import Optional, List, Tuple
+import time
 from contextlib import redirect_stdout
+from pathlib import Path
+from typing import List, Optional, Tuple
 
-import tqdm
 import numpy as np
-from PIL import Image
-from easydict import EasyDict as edict
 import torch
-from torchvision import transforms
+import tqdm
+from easydict import EasyDict as edict
+from PIL import Image
 from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval
+from torchvision import transforms
 
-from models.common import DetectMultiBackend
-from utils.metrics import ConfusionMatrix
-from utils.general import non_max_suppression, xyxy2xywh
-from utils.torch_utils import select_device
-from utils.plots import Annotator, colors
-
+from adv_patch_gen.utils.common import IMG_EXTNS, BColors, pad_to_square
 from adv_patch_gen.utils.config_parser import get_argparser, load_config_object
 from adv_patch_gen.utils.patch import PatchApplier, PatchTransformer
-from adv_patch_gen.utils.common import pad_to_square, BColors, IMG_EXTNS
 from adv_patch_gen.utils.video import (
- ffmpeg_create_video_from_image_dir,
- ffmpeg_combine_two_vids,
  ffmpeg_combine_three_vids,
+ ffmpeg_combine_two_vids,
+ ffmpeg_create_video_from_image_dir,
 )
+from models.common import DetectMultiBackend
+from utils.general import non_max_suppression, xyxy2xywh
+from utils.metrics import ConfusionMatrix
+from utils.plots import Annotator, colors
+from utils.torch_utils import select_device
 
 # optionally set seed for repeatability
 SEED = 42

train_patch.py

@@ -3,38 +3,36 @@
 
 python train_patch.py --cfg config_json_file
 """
+
+import glob
+import json
 import os
 import os.path as osp
+import random
 import time
-import json
 from contextlib import nullcontext
 
-import glob
-import random
 import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from easydict import EasyDict as edict
-
 import torch
 import torch.nn.functional as F
-from torch import optim, autograd
+from easydict import EasyDict as edict
+from PIL import Image
+from tensorboard import program
+from torch import autograd, optim
 from torch.cuda.amp import autocast
-from torchvision import transforms as T
-
 from torch.utils.tensorboard import SummaryWriter
-from tensorboard import program
-
-from models.common import DetectMultiBackend
-from utils.torch_utils import select_device
-from utils.general import non_max_suppression, xyxy2xywh
+from torchvision import transforms as T
+from tqdm import tqdm
 
-from test_patch import PatchTester
+from adv_patch_gen.utils.common import IMG_EXTNS, is_port_in_use, pad_to_square
 from adv_patch_gen.utils.config_parser import get_argparser, load_config_object
-from adv_patch_gen.utils.common import is_port_in_use, pad_to_square, IMG_EXTNS
 from adv_patch_gen.utils.dataset import YOLODataset
+from adv_patch_gen.utils.loss import MaxProbExtractor, NPSLoss, SaliencyLoss, TotalVariationLoss
 from adv_patch_gen.utils.patch import PatchApplier, PatchTransformer
-from adv_patch_gen.utils.loss import MaxProbExtractor, SaliencyLoss, TotalVariationLoss, NPSLoss
+from models.common import DetectMultiBackend
+from test_patch import PatchTester
+from utils.general import non_max_suppression, xyxy2xywh
+from utils.torch_utils import select_device
 
 # optionally set seed for repeatability
 SEED = None

utils/augmentations.py

@@ -1,9 +1,9 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """Image augmentation functions."""
 
-import os
 import glob
 import math
+import os
 import random
 
 import cv2

utils/dataloaders.py

@@ -27,8 +27,8 @@
 from tqdm import tqdm
 
 from utils.augmentations import (
- BboxPatcher,
  Albumentations,
+ BboxPatcher,
  augment_hsv,
  classify_albumentations,
  classify_transforms,

utils/loggers/__init__.py

@@ -1,5 +1,6 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 """Logging utils."""
+
 import json
 import os
 import warnings

utils/loggers/clearml/clearml_utils.py

@@ -1,4 +1,5 @@
 """Main Logger class for ClearML experiment tracking."""
+
 import glob
 import re
 from pathlib import Path