Task_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f38d3afb-58f8-4335-8fac-074db8adeaa7",
   "metadata": {},
   "source": [
    "# Task 2: Image Classification with CNNs (50%)\n",
    "\n",
    "In this task, you'll dive into the world of Convolutional Neural Networks (CNNs) by working with the CIFAR-10 dataset, a staple in image classification challenges. Your goal is to build and evaluate two different CNN models to classify images into one of the ten categories accurately.\n",
    "\n",
    "The dataset is availabel in pytorch and keras.\n",
    "\n",
    "## Part 1: Custom CNN Model (20%)\n",
    "\n",
    "- Design and train a CNN model from scratch tailored for the CIFAR-10 dataset.\n",
    "- Focus on the architecture that you believe will perform best for this specific task.\n",
    "- Integrate various techniques such as batch normalization, dropout, learning rate schedulers, and early stopping to improve model training. Experiment with these methods and finetune them to see how they affect training stability, convergence speed, and overall performance.\n",
    "\n",
    "## Part 2: Transfer Learning Model (20%)\n",
    "\n",
    "- Implement a transfer learning approach using a pre-trained model of your choice.\n",
    "- Fine-tune the model on the CIFAR-10 dataset to achieve the best possible performance.\n",
    "\n",
    "## Evaluation (10%)\n",
    "\n",
    "Ensure that both models are robust and generalized well to unseen data.\n",
    "\n",
    "After training both models, you will evaluate them on a provided test dataset.\n",
    "\n",
    "Compare your models based on:\n",
    "- **AUC-ROC**: How well does each model discriminate between classes?\n",
    "- **Model Size**: Consider the trade-offs in model complexity.\n",
    "- **Inference Speed**: Evaluate how quickly your model can predict classes for new images.\n",
    "\n",
    "Reflect on the performance, size, and inference speed of both models. What insights can you draw from these comparisons?\n",
    "\n",
    "### Learning Objectives\n",
    "\n",
    "- Understand and apply CNNs for image classification.\n",
    "- Explore the impact of model architecture on performance and efficiency.\n",
    "- Learn the process and benefits of transfer learning in deep learning.\n",
    "\n",
    "Remember, the key to this task is not just about achieving the highest accuracy but also understanding the strengths and limitations of different approaches in machine learning model development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad2ea604",
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch framework itself\n",
    "import torch\n",
    "\n",
    "from torch.optim import SGD  # optimizers\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau  # learning rate schedulers\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "\n",
    "# image tranformations and computer vision\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# base libraries\n",
    "import numpy as np\n",
    "from copy import deepcopy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74212a16",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5f03163",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can increase these values if you've enough computational power\n",
    "TRAIN_BATCH_SIZE = TEST_BATCH_SIZE = VAL_BATCH_SIZE = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3551cb79",
   "metadata": {},
   "source": [
    "### Data Tranforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a557ed6",
   "metadata": {},
   "source": [
    "#### Custom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19892a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# put augmentations to train\n",
    "train_transforms = transforms.Compose(\n",
    "    [\n",
    "        # tensorigy and normalize\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# do not modify val transforms, because it will corrupt test data\n",
    "val_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# transoformations for test data\n",
    "test_transforms = transforms.Compose(\n",
    "    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59738a1a",
   "metadata": {},
   "source": [
    "#### Pretrained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7176da08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# put augmentations to train\n",
    "resnet_train_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# do not modify val transforms, because it will corrupt test data\n",
    "resnet_val_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# transoformations for test data\n",
    "resnet_test_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5e1662",
   "metadata": {},
   "source": [
    "### Data Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8263bc",
   "metadata": {},
   "source": [
    "#### Custom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14a7b3fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# specify path of downloaded set in root, if you've loaded it\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root=\"cifar10\",\n",
    "    train=True,\n",
    "    transform=train_transforms,\n",
    "    download=True,\n",
    ")\n",
    "\n",
    "# do the same for val part of CIFAR10, apply val_transforms\n",
    "val_dataset = datasets.CIFAR10(\n",
    "    root=\"cifar10\", train=False, transform=val_transforms, download=True\n",
    ")\n",
    "\n",
    "# load given test data and targets\n",
    "test_images = np.load(\"task_2_test_images.npy\")\n",
    "test_labels = np.load(\"task_2_test_labels.npy\")\n",
    "\n",
    "test_images = np.array(\n",
    "    [test_transforms(test_image).numpy() for test_image in test_images]\n",
    ")\n",
    "test_dataset = TensorDataset(\n",
    "    torch.from_numpy(test_images), torch.from_numpy(test_labels)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c74d7f",
   "metadata": {},
   "source": [
    "#### Pretrained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71c0a4ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# specify path of downloaded set in root, if you've loaded it\n",
    "resnet_train_dataset = datasets.CIFAR10(\n",
    "    root=\"cifar10\", train=True, transform=resnet_train_transforms, download=True\n",
    ")\n",
    "\n",
    "# do the same for val part of CIFAR10, apply val_transforms\n",
    "resnet_val_dataset = datasets.CIFAR10(\n",
    "    root=\"cifar10\", train=False, transform=resnet_val_transforms, download=True\n",
    ")\n",
    "\n",
    "resnet_test_images: np.ndarray = np.load(\"task_2_test_images.npy\")\n",
    "resnet_test_labels: np.ndarray = np.load(\"task_2_test_labels.npy\")\n",
    "\n",
    "# compose to a tensor dataset with images as data and labels as targets\n",
    "resnet_test_images = np.array([resnet_test_transforms(test_image).numpy() for test_image in resnet_test_images])  # type: ignore\n",
    "resnet_test_dataset = TensorDataset(\n",
    "    torch.from_numpy(resnet_test_images), torch.from_numpy(resnet_test_labels)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f992201",
   "metadata": {},
   "source": [
    "### Data Loaders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "463edefd",
   "metadata": {},
   "source": [
    "#### Custom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d1f1f2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the loader of train data set: use train_batch_size as batch_size\n",
    "train_data_loader = DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=TRAIN_BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    drop_last=True,\n",
    "    num_workers=2,\n",
    ")\n",
    "\n",
    "# define the loader of val data set: use test_batch_size\n",
    "val_data_loader = DataLoader(\n",
    "    val_dataset, batch_size=VAL_BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2\n",
    ")\n",
    "\n",
    "# create test data loader for evaluation\n",
    "test_data_loader = DataLoader(\n",
    "    test_dataset,\n",
    "    batch_size=TEST_BATCH_SIZE,\n",
    "    shuffle=False,\n",
    "    num_workers=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b190922",
   "metadata": {},
   "source": [
    "#### Pretrained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eadb4e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the loader of train data set: use train_batch_size as batch_size\n",
    "resnet_train_data_loader = DataLoader(\n",
    "    resnet_train_dataset,\n",
    "    batch_size=TRAIN_BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    drop_last=True,\n",
    "    num_workers=2,\n",
    ")\n",
    "\n",
    "# define the loader of val data set: use test_batch_size\n",
    "resnet_val_data_loader = DataLoader(\n",
    "    resnet_val_dataset,\n",
    "    batch_size=VAL_BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    drop_last=True,\n",
    "    num_workers=2,\n",
    ")\n",
    "\n",
    "# create test data loader for evaluation\n",
    "resnet_test_data_loader = DataLoader(\n",
    "    resnet_test_dataset,\n",
    "    batch_size=TEST_BATCH_SIZE,\n",
    "    shuffle=False,\n",
    "    num_workers=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed594b4",
   "metadata": {},
   "source": [
    "### Show data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d05964a",
   "metadata": {},
   "source": [
    "Plot first 4 images to perceive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0653a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# map of number classes and number names\n",
    "labels_maps = {\n",
    "    -1: \"None\",\n",
    "    0: \"airplane\",\n",
    "    1: \"automobile\",\n",
    "    2: \"bird\",\n",
    "    3: \"cat\",\n",
    "    4: \"deer\",\n",
    "    5: \"dog\",\n",
    "    6: \"frog\",\n",
    "    7: \"horse\",\n",
    "    8: \"ship\",\n",
    "    9: \"truck\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f51f266e",
   "metadata": {},
   "source": [
    "Cifar10 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d7fb4e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, labels = next(iter(train_data_loader))\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=4)\n",
    "\n",
    "for i in range(4):\n",
    "    ax = axs[i]\n",
    "\n",
    "    title = labels_maps.get(labels[i].item(), -1)\n",
    "    ax.set_title(title)\n",
    "\n",
    "    ax.imshow(images[i].numpy().transpose(1, 2, 0))\n",
    "    ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d9a5ed",
   "metadata": {},
   "source": [
    "Secret dataset given to us samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66d0f9ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, labels = next(iter(test_data_loader))\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=4)\n",
    "\n",
    "for i in range(4):\n",
    "    ax = axs[i]\n",
    "\n",
    "    title = labels_maps.get(labels[i].item(), -1)\n",
    "    ax.set_title(title)\n",
    "\n",
    "    ax.imshow(images[i].numpy().transpose(1, 2, 0))\n",
    "    ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0521fe0d",
   "metadata": {},
   "source": [
    "## Useful Functions and Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16c56483",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_device() -> torch.device:\n",
    "    use_cuda = torch.cuda.is_available()\n",
    "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
    "    return device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8f2e3a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import time\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "def train(\n",
    "    model,\n",
    "    device: torch.device,\n",
    "    train_loader: DataLoader,\n",
    "    criterion,\n",
    "    optimizer: torch.optim.Optimizer,\n",
    "    epoch: int,\n",
    "):\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    start_time = time()\n",
    "    correct = 0\n",
    "    iteration = 0\n",
    "\n",
    "    bar = tqdm(train_loader)  # to print a progress bar simultaneously\n",
    "    for data, target in bar:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "\n",
    "        # get the index of the max log-probability\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        iteration += 1\n",
    "        bar.set_postfix({\"Loss\": format(epoch_loss / iteration, \".6f\")})\n",
    "\n",
    "    acc = 100.0 * correct / len(train_loader.dataset)  # type: ignore\n",
    "    print(f\"\\rTrain Epoch: {epoch}, elapsed time:{time()-start_time:.2f}s\")\n",
    "    return epoch_loss, acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1efbb2f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, device, test_loader, criterion):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += criterion(output, target).item()  # sum up batch loss\n",
    "            pred = output.argmax(\n",
    "                dim=1, keepdim=True\n",
    "            )  # get the index of the max log-probability\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    acc = 100.0 * correct / len(test_loader.dataset)\n",
    "    return test_loss, acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ab96a395",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill this class to stop when a certain value stop improving\n",
    "import operator\n",
    "\n",
    "\n",
    "class EarlyStopping:\n",
    "    def __init__(self, tolerance=5, min_delta=0, mode=\"min\"):\n",
    "        \"\"\"\n",
    "        :param tolerance: number of epochs that the metric doesn't improve\n",
    "        :param min_delta: minimum improvement\n",
    "        :param mode: 'min' or 'max' to minimize or maximize the metric\n",
    "        \"\"\"\n",
    "\n",
    "        \"\"\"\n",
    "        You should keep these parameters,\n",
    "        define a counter of __call__ falses and the previous best value of metric\n",
    "        \"\"\"\n",
    "        self.tolerance = tolerance\n",
    "        self.min_delta = min_delta\n",
    "        self.mode = mode\n",
    "        self.counter = 0\n",
    "        self.early_stop = False\n",
    "        self.prev_metric = np.inf if mode == \"min\" else -np.inf\n",
    "\n",
    "        # the correct operators after debugging\n",
    "        self.operation = operator.lt if mode == \"min\" else operator.gt\n",
    "\n",
    "    def __call__(self, metric) -> bool:\n",
    "        \"\"\"This function should return True if `metric` is not improving for\n",
    "        'tolerance' calls\n",
    "        \"\"\"\n",
    "        delta = self.prev_metric - metric  # don't need abs not to allow to increase\n",
    "\n",
    "        if self.operation(delta, self.min_delta):\n",
    "            self.counter += 1\n",
    "        else:\n",
    "            self.counter = 0\n",
    "            self.prev_metric = metric\n",
    "\n",
    "        if self.counter >= self.tolerance:\n",
    "            self.early_stop = True\n",
    "\n",
    "        return self.early_stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d74e1483",
   "metadata": {},
   "outputs": [],
   "source": [
    "def training(\n",
    "    model,\n",
    "    device: torch.device,\n",
    "    train_data_loader: DataLoader,\n",
    "    val_data_loader: DataLoader,\n",
    "    criterion,\n",
    "    optimizer: torch.optim.Optimizer,\n",
    "    scheduler,\n",
    "    early_stopping: EarlyStopping,\n",
    "    epochs: int,\n",
    "    train_accuracies: list[float],\n",
    "    test_accuracies: list[float],\n",
    "    train_losses: list[float],\n",
    "    test_losses: list[float],\n",
    "):\n",
    "    # clear statistics lists\n",
    "    train_accuracies.clear()\n",
    "    test_accuracies.clear()\n",
    "    train_losses.clear()\n",
    "    test_losses.clear()\n",
    "\n",
    "    # training initial values\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(1, epochs + 1):\n",
    "        # training one epoch\n",
    "        train_loss, train_acc = train(\n",
    "            model=model,\n",
    "            device=device,\n",
    "            train_loader=train_data_loader,\n",
    "            criterion=criterion,\n",
    "            optimizer=optimizer,\n",
    "            epoch=epoch,\n",
    "        )\n",
    "\n",
    "        # update learning rate if needed\n",
    "        scheduler.step(train_loss)\n",
    "\n",
    "        # test one after on epoch\n",
    "        test_loss, test_acc = test(model, device, val_data_loader, criterion)\n",
    "\n",
    "        # keep data for plotting\n",
    "        train_accuracies.append(train_acc)\n",
    "        test_accuracies.append(test_acc)\n",
    "        train_losses.append(train_loss)\n",
    "        test_losses.append(test_loss)\n",
    "\n",
    "        # Terminate training if loss stopped to decrease\n",
    "        if early_stopping(test_loss):\n",
    "            print(\"\\nEarly stopping\\n\")\n",
    "            break\n",
    "\n",
    "        # deep copy the weight of model if its accuracy is the best for now\n",
    "        if test_acc > best_acc:\n",
    "            best_acc = test_acc\n",
    "            best_model_wts = deepcopy(model.state_dict())\n",
    "\n",
    "        # log results after each epoch\n",
    "        print(f\"Accuracies:\\nTrain: {train_acc:.5f}%\\n  Val: {test_acc:.5f}%\\n\")\n",
    "        print(f\"Losses:\\nTrain: {train_loss}\\n  Val: {test_loss}\\n\")\n",
    "\n",
    "    # save last and best models weights\n",
    "    torch.save(model.state_dict(), \"model.pt\")\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    torch.save(model.state_dict(), \"best_model.pt\")\n",
    "\n",
    "    return model, train_accuracies, test_accuracies, train_losses, test_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fd5f5cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_acc_loss(\n",
    "    epochs_range, train_accuracies, test_accuracies, train_losses, test_losses\n",
    "):\n",
    "    # 2 sublots and limit figsize\n",
    "    _, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "    # accuracies\n",
    "    axs[0].plot(epochs_range, train_accuracies, label=\"train_acc\", color=\"blue\")\n",
    "    axs[0].plot(epochs_range, test_accuracies, label=\"val_acc\", color=\"orange\")\n",
    "    axs[0].set(xlabel=\"epochs\", ylabel=\"acc%\")\n",
    "    axs[0].set_title(\"Accuracy\")\n",
    "    axs[0].legend()\n",
    "\n",
    "    # losses\n",
    "    axs[1].plot(epochs_range, train_losses, label=\"train_loss\", color=\"blue\")\n",
    "    axs[1].plot(epochs_range, test_losses, label=\"val_loss\", color=\"orange\")\n",
    "    axs[1].set(xlabel=\"epochs\", ylabel=\"loss\")\n",
    "    axs[1].set_title(\"Loss\")\n",
    "    axs[1].legend()\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf9fea1",
   "metadata": {},
   "source": [
    "## Custom CNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bcbc807",
   "metadata": {},
   "source": [
    "### Architecture\n",
    "\n",
    "I took structure from lab 10 and slightly edited it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7b365738",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "\n",
    "        # Build your model\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "            nn.BatchNorm2d(16),\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(16, 32, 3), nn.ReLU(), nn.BatchNorm2d(32), nn.Dropout(0.25)\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(32, 64, 3),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm2d(64),\n",
    "        )\n",
    "        self.linear1 = nn.Sequential(\n",
    "            nn.Linear(64 * 11 * 11, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(256),\n",
    "            nn.Dropout(0.1),\n",
    "            nn.Linear(256, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.linear1(x)\n",
    "\n",
    "        return F.log_softmax(x, dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607a5f1b",
   "metadata": {},
   "source": [
    "Check the availability of GPU to facilitate training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce220f64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n"
     ]
    }
   ],
   "source": [
    "device = get_device()\n",
    "print(f\"Device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8bb62a",
   "metadata": {},
   "source": [
    "Transfer model to device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "902a4280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (3): Dropout(p=0.25, inplace=False)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (linear1): Sequential(\n",
      "    (0): Linear(in_features=7744, out_features=256, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (3): Dropout(p=0.1, inplace=False)\n",
      "    (4): Linear(in_features=256, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = CNN().to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a0a05e",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cdfa38e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many times the learning algorithm will work through the entire training dataset\n",
    "epochs = 10\n",
    "\n",
    "# loss function to evaluate model's performance and help learning\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# algorithm used to tune parameters\n",
    "# optimizer = Adam(model.parameters(), lr=0.1)\n",
    "optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
    "\n",
    "# how the learning rate is being adjusted\n",
    "scheduler = ReduceLROnPlateau(optimizer, \"min\", factor=0.2, patience=5, min_lr=0.001)\n",
    "\n",
    "# stop training when metric does not change for tolerance number of epochs using given mode\n",
    "early_stopping = EarlyStopping(tolerance=5, min_delta=1, mode=\"min\")\n",
    "\n",
    "# var to store best model's weights\n",
    "best_model_wts = deepcopy(model.state_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237c7407",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b368e634",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_train_accuracies, model_test_accuracies = [], []\n",
    "model_train_losses, model_test_losses = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d12d9aec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/390 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:16<00:00, 24.13it/s, Loss=1.264139]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1, elapsed time:16.19s\n",
      "Accuracies:\n",
      "Train: 54.79200%\n",
      "  Val: 64.29000%\n",
      "\n",
      "Losses:\n",
      "Train: 493.0141392350197\n",
      "  Val: 78.35937517881393\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:13<00:00, 29.92it/s, Loss=0.904140]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2, elapsed time:13.04s\n",
      "Accuracies:\n",
      "Train: 68.07400%\n",
      "  Val: 69.40000%\n",
      "\n",
      "Losses:\n",
      "Train: 352.6145211458206\n",
      "  Val: 66.60367524623871\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:12<00:00, 32.00it/s, Loss=0.738805]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3, elapsed time:12.19s\n",
      "Accuracies:\n",
      "Train: 73.96200%\n",
      "  Val: 72.84000%\n",
      "\n",
      "Losses:\n",
      "Train: 288.1341110467911\n",
      "  Val: 61.0249080657959\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:12<00:00, 32.48it/s, Loss=0.612067]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4, elapsed time:12.01s\n",
      "Accuracies:\n",
      "Train: 78.32800%\n",
      "  Val: 72.50000%\n",
      "\n",
      "Losses:\n",
      "Train: 238.70622700452805\n",
      "  Val: 64.54899597167969\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:11<00:00, 32.68it/s, Loss=0.502575]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 5, elapsed time:11.94s\n",
      "Accuracies:\n",
      "Train: 82.20400%\n",
      "  Val: 73.59000%\n",
      "\n",
      "Losses:\n",
      "Train: 196.00440448522568\n",
      "  Val: 61.15220785140991\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:14<00:00, 26.14it/s, Loss=0.416810]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6, elapsed time:14.92s\n",
      "Accuracies:\n",
      "Train: 85.15800%\n",
      "  Val: 74.21000%\n",
      "\n",
      "Losses:\n",
      "Train: 162.55605340003967\n",
      "  Val: 62.446273028850555\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:13<00:00, 29.36it/s, Loss=0.321139]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 7, elapsed time:13.29s\n",
      "Accuracies:\n",
      "Train: 88.66600%\n",
      "  Val: 74.33000%\n",
      "\n",
      "Losses:\n",
      "Train: 125.24403630197048\n",
      "  Val: 64.4566320180893\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [00:12<00:00, 31.49it/s, Loss=0.256607]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 8, elapsed time:12.39s\n",
      "\n",
      "Early stopping\n",
      "\n"
     ]
    }
   ],
   "source": [
    "(\n",
    "    model,\n",
    "    model_train_accuracies,\n",
    "    model_test_accuracies,\n",
    "    model_train_losses,\n",
    "    model_test_losses,\n",
    ") = training(\n",
    "    model=model,\n",
    "    device=device,\n",
    "    train_data_loader=train_data_loader,\n",
    "    val_data_loader=val_data_loader,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer,\n",
    "    scheduler=scheduler,\n",
    "    early_stopping=early_stopping,\n",
    "    epochs=epochs,\n",
    "    train_accuracies=model_train_accuracies,\n",
    "    test_accuracies=model_test_accuracies,\n",
    "    train_losses=model_train_losses,\n",
    "    test_losses=model_test_losses,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a598f321",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "37847d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in case when early stopping worked\n",
    "epochs_range = range(1, len(model_train_accuracies) + 1)\n",
    "\n",
    "plot_acc_loss(\n",
    "    epochs_range,\n",
    "    model_train_accuracies,\n",
    "    model_test_accuracies,\n",
    "    model_train_losses,\n",
    "    model_test_losses,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145a30c0",
   "metadata": {},
   "source": [
    "### Test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d67c760",
   "metadata": {},
   "source": [
    "Test prediction on single sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c38d797c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for (\n",
    "        images,\n",
    "        labels,\n",
    "    ) in test_data_loader:  # zip(test_data_loader, test_labels_loader):\n",
    "        test_image, test_label = images[:1], labels[:1]\n",
    "        test_image, test_label = test_image.to(device), test_label.to(device)\n",
    "\n",
    "        output = model(test_image)\n",
    "        pred = output.argmax(dim=1, keepdim=True)  # returns 2 dim tensor\n",
    "        pred = pred[0][0].item()\n",
    "\n",
    "        plt.imshow(images[0].numpy().transpose(1, 2, 0))\n",
    "        plt.title(\n",
    "            f\"{pred = } ({labels_maps.get(pred, -1)}), true = {test_labels[0]} ({labels_maps.get(test_labels[0], -1)})\"\n",
    "        )\n",
    "\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c95747",
   "metadata": {},
   "source": [
    "The whole test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fdf426b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.851%\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "test_loss, test_acc = test(model, device, test_data_loader, criterion)\n",
    "model_inference_speed = time() - start_time\n",
    "print(f\"{test_acc:.5}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359643f9",
   "metadata": {},
   "source": [
    "## Transfer Learning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f014e785",
   "metadata": {},
   "source": [
    "### Architecure\n",
    "\n",
    "I chose ResNet50 as best model for the first transfer learing experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4624fdd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.models import resnet50, ResNet50_Weights\n",
    "\n",
    "# DEFAULT means the improved weights (more advanced recipe)\n",
    "weights = ResNet50_Weights.DEFAULT\n",
    "resnet = resnet50(weights=weights)\n",
    "\n",
    "# freeze the parameters\n",
    "for param in resnet.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "04377538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(resnet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd3c2d5",
   "metadata": {},
   "source": [
    "### Attach sequential head \n",
    "\n",
    "Modify resnet50 to suit cifar10 on input and output layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8e1644fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make final dense layer according to the number of classes\n",
    "num_classes = 10\n",
    "num_features = resnet.fc.in_features\n",
    "\n",
    "resnet.fc = nn.Sequential(  # type: ignore\n",
    "    nn.Linear(num_features, 512),\n",
    "    nn.ReLU(),\n",
    "    nn.BatchNorm1d(512),\n",
    "    nn.Linear(512, 256),\n",
    "    nn.ReLU(),\n",
    "    nn.BatchNorm1d(256),\n",
    "    nn.Linear(256, 128),\n",
    "    nn.ReLU(),\n",
    "    nn.BatchNorm1d(128),\n",
    "    nn.Linear(128, 10),\n",
    "    nn.LogSoftmax(dim=1),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f0e6f355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n"
     ]
    }
   ],
   "source": [
    "# check GPU availability again and move to device\n",
    "device = get_device()\n",
    "resnet = resnet.to(device)\n",
    "print(f\"Device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06385974",
   "metadata": {},
   "source": [
    "### Show amount of learnable parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "43002e36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learnable params: 1216394  => 4.92%\n",
      "Total params:    24724426 => 100.00%\n"
     ]
    }
   ],
   "source": [
    "pytorch_total_params = sum(p.numel() for p in resnet.parameters())\n",
    "pytorch_learnable_params = sum(\n",
    "    p.numel() for p in resnet.parameters() if p.requires_grad\n",
    ")\n",
    "\n",
    "\n",
    "print(\n",
    "    f\"Learnable params: {pytorch_learnable_params}  => {pytorch_learnable_params / pytorch_total_params * 100:.2f}%\"\n",
    ")\n",
    "print(\n",
    "    f\"Total params: {pytorch_total_params:-11} => {pytorch_total_params / pytorch_total_params * 100:.2f}%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d209330b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "Layer (type:depth-idx)                   Output Shape              Param #\n",
      "==========================================================================================\n",
      "ResNet                                   [128, 10]                 --\n",
      "├─Conv2d: 1-1                            [128, 64, 16, 16]         (9,408)\n",
      "│    └─weight                                                      └─9,408\n",
      "├─BatchNorm2d: 1-2                       [128, 64, 16, 16]         (128)\n",
      "│    └─weight                                                      ├─64\n",
      "│    └─bias                                                        └─64\n",
      "├─ReLU: 1-3                              [128, 64, 16, 16]         --\n",
      "├─MaxPool2d: 1-4                         [128, 64, 8, 8]           --\n",
      "├─Sequential: 1-5                        [128, 256, 8, 8]          --\n",
      "│    └─0.conv1.weight                                              ├─4,096\n",
      "│    └─0.bn1.weight                                                ├─64\n",
      "│    └─0.bn1.bias                                                  ├─64\n",
      "│    └─0.conv2.weight                                              ├─36,864\n",
      "│    └─0.bn2.weight                                                ├─64\n",
      "│    └─0.bn2.bias                                                  ├─64\n",
      "│    └─0.conv3.weight                                              ├─16,384\n",
      "│    └─0.bn3.weight                                                ├─256\n",
      "│    └─0.bn3.bias                                                  ├─256\n",
      "│    └─0.downsample.0.weight                                       ├─16,384\n",
      "│    └─0.downsample.1.weight                                       ├─256\n",
      "│    └─0.downsample.1.bias                                         ├─256\n",
      "│    └─1.conv1.weight                                              ├─16,384\n",
      "│    └─1.bn1.weight                                                ├─64\n",
      "│    └─1.bn1.bias                                                  ├─64\n",
      "│    └─1.conv2.weight                                              ├─36,864\n",
      "│    └─1.bn2.weight                                                ├─64\n",
      "│    └─1.bn2.bias                                                  ├─64\n",
      "│    └─1.conv3.weight                                              ├─16,384\n",
      "│    └─1.bn3.weight                                                ├─256\n",
      "│    └─1.bn3.bias                                                  ├─256\n",
      "│    └─2.conv1.weight                                              ├─16,384\n",
      "│    └─2.bn1.weight                                                ├─64\n",
      "│    └─2.bn1.bias                                                  ├─64\n",
      "│    └─2.conv2.weight                                              ├─36,864\n",
      "│    └─2.bn2.weight                                                ├─64\n",
      "│    └─2.bn2.bias                                                  ├─64\n",
      "│    └─2.conv3.weight                                              ├─16,384\n",
      "│    └─2.bn3.weight                                                ├─256\n",
      "│    └─2.bn3.bias                                                  └─256\n",
      "│    └─Bottleneck: 2-1                   [128, 256, 8, 8]          --\n",
      "│    │    └─conv1.weight                                           ├─4,096\n",
      "│    │    └─bn1.weight                                             ├─64\n",
      "│    │    └─bn1.bias                                               ├─64\n",
      "│    │    └─conv2.weight                                           ├─36,864\n",
      "│    │    └─bn2.weight                                             ├─64\n",
      "│    │    └─bn2.bias                                               ├─64\n",
      "│    │    └─conv3.weight                                           ├─16,384\n",
      "│    │    └─bn3.weight                                             ├─256\n",
      "│    │    └─bn3.bias                                               ├─256\n",
      "│    │    └─downsample.0.weight                                    ├─16,384\n",
      "│    │    └─downsample.1.weight                                    ├─256\n",
      "│    │    └─downsample.1.bias                                      └─256\n",
      "│    │    └─Conv2d: 3-1                  [128, 64, 8, 8]           (4,096)\n",
      "│    │    │    └─weight                                            └─4,096\n",
      "│    │    └─BatchNorm2d: 3-2             [128, 64, 8, 8]           (128)\n",
      "│    │    │    └─weight                                            ├─64\n",
      "│    │    │    └─bias                                              └─64\n",
      "│    │    └─ReLU: 3-3                    [128, 64, 8, 8]           --\n",
      "│    │    └─Conv2d: 3-4                  [128, 64, 8, 8]           (36,864)\n",
      "│    │    │    └─weight                                            └─36,864\n",
      "│    │    └─BatchNorm2d: 3-5             [128, 64, 8, 8]           (128)\n",
      "│    │    │    └─weight                                            ├─64\n",
      "│    │    │    └─bias                                              └─64\n",
      "│    │    └─ReLU: 3-6                    [128, 64, 8, 8]           --\n",
      "│    │    └─Conv2d: 3-7                  [128, 256, 8, 8]          (16,384)\n",
      "│    │    │    └─weight                                            └─16,384\n",
      "│    │    └─BatchNorm2d: 3-8             [128, 256, 8, 8]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─Sequential: 3-9              [128, 256, 8, 8]          (16,896)\n",
      "│    │    │    └─0.weight                                          ├─16,384\n",
      "│    │    │    └─1.weight                                          ├─256\n",
      "│    │    │    └─1.bias                                            └─256\n",
      "│    │    └─ReLU: 3-10                   [128, 256, 8, 8]          --\n",
      "│    └─Bottleneck: 2-2                   [128, 256, 8, 8]          --\n",
      "│    │    └─conv1.weight                                           ├─16,384\n",
      "│    │    └─bn1.weight                                             ├─64\n",
      "│    │    └─bn1.bias                                               ├─64\n",
      "│    │    └─conv2.weight                                           ├─36,864\n",
      "│    │    └─bn2.weight                                             ├─64\n",
      "│    │    └─bn2.bias                                               ├─64\n",
      "│    │    └─conv3.weight                                           ├─16,384\n",
      "│    │    └─bn3.weight                                             ├─256\n",
      "│    │    └─bn3.bias                                               └─256\n",
      "│    │    └─Conv2d: 3-11                 [128, 64, 8, 8]           (16,384)\n",
      "│    │    │    └─weight                                            └─16,384\n",
      "│    │    └─BatchNorm2d: 3-12            [128, 64, 8, 8]           (128)\n",
      "│    │    │    └─weight                                            ├─64\n",
      "│    │    │    └─bias                                              └─64\n",
      "│    │    └─ReLU: 3-13                   [128, 64, 8, 8]           --\n",
      "│    │    └─Conv2d: 3-14                 [128, 64, 8, 8]           (36,864)\n",
      "│    │    │    └─weight                                            └─36,864\n",
      "│    │    └─BatchNorm2d: 3-15            [128, 64, 8, 8]           (128)\n",
      "│    │    │    └─weight                                            ├─64\n",
      "│    │    │    └─bias                                              └─64\n",
      "│    │    └─ReLU: 3-16                   [128, 64, 8, 8]           --\n",
      "│    │    └─Conv2d: 3-17                 [128, 256, 8, 8]          (16,384)\n",
      "│    │    │    └─weight                                            └─16,384\n",
      "│    │    └─BatchNorm2d: 3-18            [128, 256, 8, 8]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-19                   [128, 256, 8, 8]          --\n",
      "│    └─Bottleneck: 2-3                   [128, 256, 8, 8]          --\n",
      "│    │    └─conv1.weight                                           ├─16,384\n",
      "│    │    └─bn1.weight                                             ├─64\n",
      "│    │    └─bn1.bias                                               ├─64\n",
      "│    │    └─conv2.weight                                           ├─36,864\n",
      "│    │    └─bn2.weight                                             ├─64\n",
      "│    │    └─bn2.bias                                               ├─64\n",
      "│    │    └─conv3.weight                                           ├─16,384\n",
      "│    │    └─bn3.weight                                             ├─256\n",
      "│    │    └─bn3.bias                                               └─256\n",
      "│    │    └─Conv2d: 3-20                 [128, 64, 8, 8]           (16,384)\n",
      "│    │    │    └─weight                                            └─16,384\n",
      "│    │    └─BatchNorm2d: 3-21            [128, 64, 8, 8]           (128)\n",
      "│    │    │    └─weight                                            ├─64\n",
      "│    │    │    └─bias                                              └─64\n",
      "│    │    └─ReLU: 3-22                   [128, 64, 8, 8]           --\n",
      "│    │    └─Conv2d: 3-23                 [128, 64, 8, 8]           (36,864)\n",
      "│    │    │    └─weight                                            └─36,864\n",
      "│    │    └─BatchNorm2d: 3-24            [128, 64, 8, 8]           (128)\n",
      "│    │    │    └─weight                                            ├─64\n",
      "│    │    │    └─bias                                              └─64\n",
      "│    │    └─ReLU: 3-25                   [128, 64, 8, 8]           --\n",
      "│    │    └─Conv2d: 3-26                 [128, 256, 8, 8]          (16,384)\n",
      "│    │    │    └─weight                                            └─16,384\n",
      "│    │    └─BatchNorm2d: 3-27            [128, 256, 8, 8]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-28                   [128, 256, 8, 8]          --\n",
      "├─Sequential: 1-6                        [128, 512, 4, 4]          --\n",
      "│    └─0.conv1.weight                                              ├─32,768\n",
      "│    └─0.bn1.weight                                                ├─128\n",
      "│    └─0.bn1.bias                                                  ├─128\n",
      "│    └─0.conv2.weight                                              ├─147,456\n",
      "│    └─0.bn2.weight                                                ├─128\n",
      "│    └─0.bn2.bias                                                  ├─128\n",
      "│    └─0.conv3.weight                                              ├─65,536\n",
      "│    └─0.bn3.weight                                                ├─512\n",
      "│    └─0.bn3.bias                                                  ├─512\n",
      "│    └─0.downsample.0.weight                                       ├─131,072\n",
      "│    └─0.downsample.1.weight                                       ├─512\n",
      "│    └─0.downsample.1.bias                                         ├─512\n",
      "│    └─1.conv1.weight                                              ├─65,536\n",
      "│    └─1.bn1.weight                                                ├─128\n",
      "│    └─1.bn1.bias                                                  ├─128\n",
      "│    └─1.conv2.weight                                              ├─147,456\n",
      "│    └─1.bn2.weight                                                ├─128\n",
      "│    └─1.bn2.bias                                                  ├─128\n",
      "│    └─1.conv3.weight                                              ├─65,536\n",
      "│    └─1.bn3.weight                                                ├─512\n",
      "│    └─1.bn3.bias                                                  ├─512\n",
      "│    └─2.conv1.weight                                              ├─65,536\n",
      "│    └─2.bn1.weight                                                ├─128\n",
      "│    └─2.bn1.bias                                                  ├─128\n",
      "│    └─2.conv2.weight                                              ├─147,456\n",
      "│    └─2.bn2.weight                                                ├─128\n",
      "│    └─2.bn2.bias                                                  ├─128\n",
      "│    └─2.conv3.weight                                              ├─65,536\n",
      "│    └─2.bn3.weight                                                ├─512\n",
      "│    └─2.bn3.bias                                                  ├─512\n",
      "│    └─3.conv1.weight                                              ├─65,536\n",
      "│    └─3.bn1.weight                                                ├─128\n",
      "│    └─3.bn1.bias                                                  ├─128\n",
      "│    └─3.conv2.weight                                              ├─147,456\n",
      "│    └─3.bn2.weight                                                ├─128\n",
      "│    └─3.bn2.bias                                                  ├─128\n",
      "│    └─3.conv3.weight                                              ├─65,536\n",
      "│    └─3.bn3.weight                                                ├─512\n",
      "│    └─3.bn3.bias                                                  └─512\n",
      "│    └─Bottleneck: 2-4                   [128, 512, 4, 4]          --\n",
      "│    │    └─conv1.weight                                           ├─32,768\n",
      "│    │    └─bn1.weight                                             ├─128\n",
      "│    │    └─bn1.bias                                               ├─128\n",
      "│    │    └─conv2.weight                                           ├─147,456\n",
      "│    │    └─bn2.weight                                             ├─128\n",
      "│    │    └─bn2.bias                                               ├─128\n",
      "│    │    └─conv3.weight                                           ├─65,536\n",
      "│    │    └─bn3.weight                                             ├─512\n",
      "│    │    └─bn3.bias                                               ├─512\n",
      "│    │    └─downsample.0.weight                                    ├─131,072\n",
      "│    │    └─downsample.1.weight                                    ├─512\n",
      "│    │    └─downsample.1.bias                                      └─512\n",
      "│    │    └─Conv2d: 3-29                 [128, 128, 8, 8]          (32,768)\n",
      "│    │    │    └─weight                                            └─32,768\n",
      "│    │    └─BatchNorm2d: 3-30            [128, 128, 8, 8]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-31                   [128, 128, 8, 8]          --\n",
      "│    │    └─Conv2d: 3-32                 [128, 128, 4, 4]          (147,456)\n",
      "│    │    │    └─weight                                            └─147,456\n",
      "│    │    └─BatchNorm2d: 3-33            [128, 128, 4, 4]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-34                   [128, 128, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-35                 [128, 512, 4, 4]          (65,536)\n",
      "│    │    │    └─weight                                            └─65,536\n",
      "│    │    └─BatchNorm2d: 3-36            [128, 512, 4, 4]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─Sequential: 3-37             [128, 512, 4, 4]          (132,096)\n",
      "│    │    │    └─0.weight                                          ├─131,072\n",
      "│    │    │    └─1.weight                                          ├─512\n",
      "│    │    │    └─1.bias                                            └─512\n",
      "│    │    └─ReLU: 3-38                   [128, 512, 4, 4]          --\n",
      "│    └─Bottleneck: 2-5                   [128, 512, 4, 4]          --\n",
      "│    │    └─conv1.weight                                           ├─65,536\n",
      "│    │    └─bn1.weight                                             ├─128\n",
      "│    │    └─bn1.bias                                               ├─128\n",
      "│    │    └─conv2.weight                                           ├─147,456\n",
      "│    │    └─bn2.weight                                             ├─128\n",
      "│    │    └─bn2.bias                                               ├─128\n",
      "│    │    └─conv3.weight                                           ├─65,536\n",
      "│    │    └─bn3.weight                                             ├─512\n",
      "│    │    └─bn3.bias                                               └─512\n",
      "│    │    └─Conv2d: 3-39                 [128, 128, 4, 4]          (65,536)\n",
      "│    │    │    └─weight                                            └─65,536\n",
      "│    │    └─BatchNorm2d: 3-40            [128, 128, 4, 4]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-41                   [128, 128, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-42                 [128, 128, 4, 4]          (147,456)\n",
      "│    │    │    └─weight                                            └─147,456\n",
      "│    │    └─BatchNorm2d: 3-43            [128, 128, 4, 4]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-44                   [128, 128, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-45                 [128, 512, 4, 4]          (65,536)\n",
      "│    │    │    └─weight                                            └─65,536\n",
      "│    │    └─BatchNorm2d: 3-46            [128, 512, 4, 4]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-47                   [128, 512, 4, 4]          --\n",
      "│    └─Bottleneck: 2-6                   [128, 512, 4, 4]          --\n",
      "│    │    └─conv1.weight                                           ├─65,536\n",
      "│    │    └─bn1.weight                                             ├─128\n",
      "│    │    └─bn1.bias                                               ├─128\n",
      "│    │    └─conv2.weight                                           ├─147,456\n",
      "│    │    └─bn2.weight                                             ├─128\n",
      "│    │    └─bn2.bias                                               ├─128\n",
      "│    │    └─conv3.weight                                           ├─65,536\n",
      "│    │    └─bn3.weight                                             ├─512\n",
      "│    │    └─bn3.bias                                               └─512\n",
      "│    │    └─Conv2d: 3-48                 [128, 128, 4, 4]          (65,536)\n",
      "│    │    │    └─weight                                            └─65,536\n",
      "│    │    └─BatchNorm2d: 3-49            [128, 128, 4, 4]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-50                   [128, 128, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-51                 [128, 128, 4, 4]          (147,456)\n",
      "│    │    │    └─weight                                            └─147,456\n",
      "│    │    └─BatchNorm2d: 3-52            [128, 128, 4, 4]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-53                   [128, 128, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-54                 [128, 512, 4, 4]          (65,536)\n",
      "│    │    │    └─weight                                            └─65,536\n",
      "│    │    └─BatchNorm2d: 3-55            [128, 512, 4, 4]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-56                   [128, 512, 4, 4]          --\n",
      "│    └─Bottleneck: 2-7                   [128, 512, 4, 4]          --\n",
      "│    │    └─conv1.weight                                           ├─65,536\n",
      "│    │    └─bn1.weight                                             ├─128\n",
      "│    │    └─bn1.bias                                               ├─128\n",
      "│    │    └─conv2.weight                                           ├─147,456\n",
      "│    │    └─bn2.weight                                             ├─128\n",
      "│    │    └─bn2.bias                                               ├─128\n",
      "│    │    └─conv3.weight                                           ├─65,536\n",
      "│    │    └─bn3.weight                                             ├─512\n",
      "│    │    └─bn3.bias                                               └─512\n",
      "│    │    └─Conv2d: 3-57                 [128, 128, 4, 4]          (65,536)\n",
      "│    │    │    └─weight                                            └─65,536\n",
      "│    │    └─BatchNorm2d: 3-58            [128, 128, 4, 4]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-59                   [128, 128, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-60                 [128, 128, 4, 4]          (147,456)\n",
      "│    │    │    └─weight                                            └─147,456\n",
      "│    │    └─BatchNorm2d: 3-61            [128, 128, 4, 4]          (256)\n",
      "│    │    │    └─weight                                            ├─128\n",
      "│    │    │    └─bias                                              └─128\n",
      "│    │    └─ReLU: 3-62                   [128, 128, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-63                 [128, 512, 4, 4]          (65,536)\n",
      "│    │    │    └─weight                                            └─65,536\n",
      "│    │    └─BatchNorm2d: 3-64            [128, 512, 4, 4]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-65                   [128, 512, 4, 4]          --\n",
      "├─Sequential: 1-7                        [128, 1024, 2, 2]         --\n",
      "│    └─0.conv1.weight                                              ├─131,072\n",
      "│    └─0.bn1.weight                                                ├─256\n",
      "│    └─0.bn1.bias                                                  ├─256\n",
      "│    └─0.conv2.weight                                              ├─589,824\n",
      "│    └─0.bn2.weight                                                ├─256\n",
      "│    └─0.bn2.bias                                                  ├─256\n",
      "│    └─0.conv3.weight                                              ├─262,144\n",
      "│    └─0.bn3.weight                                                ├─1,024\n",
      "│    └─0.bn3.bias                                                  ├─1,024\n",
      "│    └─0.downsample.0.weight                                       ├─524,288\n",
      "│    └─0.downsample.1.weight                                       ├─1,024\n",
      "│    └─0.downsample.1.bias                                         ├─1,024\n",
      "│    └─1.conv1.weight                                              ├─262,144\n",
      "│    └─1.bn1.weight                                                ├─256\n",
      "│    └─1.bn1.bias                                                  ├─256\n",
      "│    └─1.conv2.weight                                              ├─589,824\n",
      "│    └─1.bn2.weight                                                ├─256\n",
      "│    └─1.bn2.bias                                                  ├─256\n",
      "│    └─1.conv3.weight                                              ├─262,144\n",
      "│    └─1.bn3.weight                                                ├─1,024\n",
      "│    └─1.bn3.bias                                                  ├─1,024\n",
      "│    └─2.conv1.weight                                              ├─262,144\n",
      "│    └─2.bn1.weight                                                ├─256\n",
      "│    └─2.bn1.bias                                                  ├─256\n",
      "│    └─2.conv2.weight                                              ├─589,824\n",
      "│    └─2.bn2.weight                                                ├─256\n",
      "│    └─2.bn2.bias                                                  ├─256\n",
      "│    └─2.conv3.weight                                              ├─262,144\n",
      "│    └─2.bn3.weight                                                ├─1,024\n",
      "│    └─2.bn3.bias                                                  ├─1,024\n",
      "│    └─3.conv1.weight                                              ├─262,144\n",
      "│    └─3.bn1.weight                                                ├─256\n",
      "│    └─3.bn1.bias                                                  ├─256\n",
      "│    └─3.conv2.weight                                              ├─589,824\n",
      "│    └─3.bn2.weight                                                ├─256\n",
      "│    └─3.bn2.bias                                                  ├─256\n",
      "│    └─3.conv3.weight                                              ├─262,144\n",
      "│    └─3.bn3.weight                                                ├─1,024\n",
      "│    └─3.bn3.bias                                                  ├─1,024\n",
      "│    └─4.conv1.weight                                              ├─262,144\n",
      "│    └─4.bn1.weight                                                ├─256\n",
      "│    └─4.bn1.bias                                                  ├─256\n",
      "│    └─4.conv2.weight                                              ├─589,824\n",
      "│    └─4.bn2.weight                                                ├─256\n",
      "│    └─4.bn2.bias                                                  ├─256\n",
      "│    └─4.conv3.weight                                              ├─262,144\n",
      "│    └─4.bn3.weight                                                ├─1,024\n",
      "│    └─4.bn3.bias                                                  ├─1,024\n",
      "│    └─5.conv1.weight                                              ├─262,144\n",
      "│    └─5.bn1.weight                                                ├─256\n",
      "│    └─5.bn1.bias                                                  ├─256\n",
      "│    └─5.conv2.weight                                              ├─589,824\n",
      "│    └─5.bn2.weight                                                ├─256\n",
      "│    └─5.bn2.bias                                                  ├─256\n",
      "│    └─5.conv3.weight                                              ├─262,144\n",
      "│    └─5.bn3.weight                                                ├─1,024\n",
      "│    └─5.bn3.bias                                                  └─1,024\n",
      "│    └─Bottleneck: 2-8                   [128, 1024, 2, 2]         --\n",
      "│    │    └─conv1.weight                                           ├─131,072\n",
      "│    │    └─bn1.weight                                             ├─256\n",
      "│    │    └─bn1.bias                                               ├─256\n",
      "│    │    └─conv2.weight                                           ├─589,824\n",
      "│    │    └─bn2.weight                                             ├─256\n",
      "│    │    └─bn2.bias                                               ├─256\n",
      "│    │    └─conv3.weight                                           ├─262,144\n",
      "│    │    └─bn3.weight                                             ├─1,024\n",
      "│    │    └─bn3.bias                                               ├─1,024\n",
      "│    │    └─downsample.0.weight                                    ├─524,288\n",
      "│    │    └─downsample.1.weight                                    ├─1,024\n",
      "│    │    └─downsample.1.bias                                      └─1,024\n",
      "│    │    └─Conv2d: 3-66                 [128, 256, 4, 4]          (131,072)\n",
      "│    │    │    └─weight                                            └─131,072\n",
      "│    │    └─BatchNorm2d: 3-67            [128, 256, 4, 4]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-68                   [128, 256, 4, 4]          --\n",
      "│    │    └─Conv2d: 3-69                 [128, 256, 2, 2]          (589,824)\n",
      "│    │    │    └─weight                                            └─589,824\n",
      "│    │    └─BatchNorm2d: 3-70            [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-71                   [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-72                 [128, 1024, 2, 2]         (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-73            [128, 1024, 2, 2]         (2,048)\n",
      "│    │    │    └─weight                                            ├─1,024\n",
      "│    │    │    └─bias                                              └─1,024\n",
      "│    │    └─Sequential: 3-74             [128, 1024, 2, 2]         (526,336)\n",
      "│    │    │    └─0.weight                                          ├─524,288\n",
      "│    │    │    └─1.weight                                          ├─1,024\n",
      "│    │    │    └─1.bias                                            └─1,024\n",
      "│    │    └─ReLU: 3-75                   [128, 1024, 2, 2]         --\n",
      "│    └─Bottleneck: 2-9                   [128, 1024, 2, 2]         --\n",
      "│    │    └─conv1.weight                                           ├─262,144\n",
      "│    │    └─bn1.weight                                             ├─256\n",
      "│    │    └─bn1.bias                                               ├─256\n",
      "│    │    └─conv2.weight                                           ├─589,824\n",
      "│    │    └─bn2.weight                                             ├─256\n",
      "│    │    └─bn2.bias                                               ├─256\n",
      "│    │    └─conv3.weight                                           ├─262,144\n",
      "│    │    └─bn3.weight                                             ├─1,024\n",
      "│    │    └─bn3.bias                                               └─1,024\n",
      "│    │    └─Conv2d: 3-76                 [128, 256, 2, 2]          (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-77            [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-78                   [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-79                 [128, 256, 2, 2]          (589,824)\n",
      "│    │    │    └─weight                                            └─589,824\n",
      "│    │    └─BatchNorm2d: 3-80            [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-81                   [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-82                 [128, 1024, 2, 2]         (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-83            [128, 1024, 2, 2]         (2,048)\n",
      "│    │    │    └─weight                                            ├─1,024\n",
      "│    │    │    └─bias                                              └─1,024\n",
      "│    │    └─ReLU: 3-84                   [128, 1024, 2, 2]         --\n",
      "│    └─Bottleneck: 2-10                  [128, 1024, 2, 2]         --\n",
      "│    │    └─conv1.weight                                           ├─262,144\n",
      "│    │    └─bn1.weight                                             ├─256\n",
      "│    │    └─bn1.bias                                               ├─256\n",
      "│    │    └─conv2.weight                                           ├─589,824\n",
      "│    │    └─bn2.weight                                             ├─256\n",
      "│    │    └─bn2.bias                                               ├─256\n",
      "│    │    └─conv3.weight                                           ├─262,144\n",
      "│    │    └─bn3.weight                                             ├─1,024\n",
      "│    │    └─bn3.bias                                               └─1,024\n",
      "│    │    └─Conv2d: 3-85                 [128, 256, 2, 2]          (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-86            [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-87                   [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-88                 [128, 256, 2, 2]          (589,824)\n",
      "│    │    │    └─weight                                            └─589,824\n",
      "│    │    └─BatchNorm2d: 3-89            [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-90                   [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-91                 [128, 1024, 2, 2]         (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-92            [128, 1024, 2, 2]         (2,048)\n",
      "│    │    │    └─weight                                            ├─1,024\n",
      "│    │    │    └─bias                                              └─1,024\n",
      "│    │    └─ReLU: 3-93                   [128, 1024, 2, 2]         --\n",
      "│    └─Bottleneck: 2-11                  [128, 1024, 2, 2]         --\n",
      "│    │    └─conv1.weight                                           ├─262,144\n",
      "│    │    └─bn1.weight                                             ├─256\n",
      "│    │    └─bn1.bias                                               ├─256\n",
      "│    │    └─conv2.weight                                           ├─589,824\n",
      "│    │    └─bn2.weight                                             ├─256\n",
      "│    │    └─bn2.bias                                               ├─256\n",
      "│    │    └─conv3.weight                                           ├─262,144\n",
      "│    │    └─bn3.weight                                             ├─1,024\n",
      "│    │    └─bn3.bias                                               └─1,024\n",
      "│    │    └─Conv2d: 3-94                 [128, 256, 2, 2]          (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-95            [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-96                   [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-97                 [128, 256, 2, 2]          (589,824)\n",
      "│    │    │    └─weight                                            └─589,824\n",
      "│    │    └─BatchNorm2d: 3-98            [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-99                   [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-100                [128, 1024, 2, 2]         (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-101           [128, 1024, 2, 2]         (2,048)\n",
      "│    │    │    └─weight                                            ├─1,024\n",
      "│    │    │    └─bias                                              └─1,024\n",
      "│    │    └─ReLU: 3-102                  [128, 1024, 2, 2]         --\n",
      "│    └─Bottleneck: 2-12                  [128, 1024, 2, 2]         --\n",
      "│    │    └─conv1.weight                                           ├─262,144\n",
      "│    │    └─bn1.weight                                             ├─256\n",
      "│    │    └─bn1.bias                                               ├─256\n",
      "│    │    └─conv2.weight                                           ├─589,824\n",
      "│    │    └─bn2.weight                                             ├─256\n",
      "│    │    └─bn2.bias                                               ├─256\n",
      "│    │    └─conv3.weight                                           ├─262,144\n",
      "│    │    └─bn3.weight                                             ├─1,024\n",
      "│    │    └─bn3.bias                                               └─1,024\n",
      "│    │    └─Conv2d: 3-103                [128, 256, 2, 2]          (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-104           [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-105                  [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-106                [128, 256, 2, 2]          (589,824)\n",
      "│    │    │    └─weight                                            └─589,824\n",
      "│    │    └─BatchNorm2d: 3-107           [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-108                  [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-109                [128, 1024, 2, 2]         (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-110           [128, 1024, 2, 2]         (2,048)\n",
      "│    │    │    └─weight                                            ├─1,024\n",
      "│    │    │    └─bias                                              └─1,024\n",
      "│    │    └─ReLU: 3-111                  [128, 1024, 2, 2]         --\n",
      "│    └─Bottleneck: 2-13                  [128, 1024, 2, 2]         --\n",
      "│    │    └─conv1.weight                                           ├─262,144\n",
      "│    │    └─bn1.weight                                             ├─256\n",
      "│    │    └─bn1.bias                                               ├─256\n",
      "│    │    └─conv2.weight                                           ├─589,824\n",
      "│    │    └─bn2.weight                                             ├─256\n",
      "│    │    └─bn2.bias                                               ├─256\n",
      "│    │    └─conv3.weight                                           ├─262,144\n",
      "│    │    └─bn3.weight                                             ├─1,024\n",
      "│    │    └─bn3.bias                                               └─1,024\n",
      "│    │    └─Conv2d: 3-112                [128, 256, 2, 2]          (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-113           [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-114                  [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-115                [128, 256, 2, 2]          (589,824)\n",
      "│    │    │    └─weight                                            └─589,824\n",
      "│    │    └─BatchNorm2d: 3-116           [128, 256, 2, 2]          (512)\n",
      "│    │    │    └─weight                                            ├─256\n",
      "│    │    │    └─bias                                              └─256\n",
      "│    │    └─ReLU: 3-117                  [128, 256, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-118                [128, 1024, 2, 2]         (262,144)\n",
      "│    │    │    └─weight                                            └─262,144\n",
      "│    │    └─BatchNorm2d: 3-119           [128, 1024, 2, 2]         (2,048)\n",
      "│    │    │    └─weight                                            ├─1,024\n",
      "│    │    │    └─bias                                              └─1,024\n",
      "│    │    └─ReLU: 3-120                  [128, 1024, 2, 2]         --\n",
      "├─Sequential: 1-8                        [128, 2048, 1, 1]         --\n",
      "│    └─0.conv1.weight                                              ├─524,288\n",
      "│    └─0.bn1.weight                                                ├─512\n",
      "│    └─0.bn1.bias                                                  ├─512\n",
      "│    └─0.conv2.weight                                              ├─2,359,296\n",
      "│    └─0.bn2.weight                                                ├─512\n",
      "│    └─0.bn2.bias                                                  ├─512\n",
      "│    └─0.conv3.weight                                              ├─1,048,576\n",
      "│    └─0.bn3.weight                                                ├─2,048\n",
      "│    └─0.bn3.bias                                                  ├─2,048\n",
      "│    └─0.downsample.0.weight                                       ├─2,097,152\n",
      "│    └─0.downsample.1.weight                                       ├─2,048\n",
      "│    └─0.downsample.1.bias                                         ├─2,048\n",
      "│    └─1.conv1.weight                                              ├─1,048,576\n",
      "│    └─1.bn1.weight                                                ├─512\n",
      "│    └─1.bn1.bias                                                  ├─512\n",
      "│    └─1.conv2.weight                                              ├─2,359,296\n",
      "│    └─1.bn2.weight                                                ├─512\n",
      "│    └─1.bn2.bias                                                  ├─512\n",
      "│    └─1.conv3.weight                                              ├─1,048,576\n",
      "│    └─1.bn3.weight                                                ├─2,048\n",
      "│    └─1.bn3.bias                                                  ├─2,048\n",
      "│    └─2.conv1.weight                                              ├─1,048,576\n",
      "│    └─2.bn1.weight                                                ├─512\n",
      "│    └─2.bn1.bias                                                  ├─512\n",
      "│    └─2.conv2.weight                                              ├─2,359,296\n",
      "│    └─2.bn2.weight                                                ├─512\n",
      "│    └─2.bn2.bias                                                  ├─512\n",
      "│    └─2.conv3.weight                                              ├─1,048,576\n",
      "│    └─2.bn3.weight                                                ├─2,048\n",
      "│    └─2.bn3.bias                                                  └─2,048\n",
      "│    └─Bottleneck: 2-14                  [128, 2048, 1, 1]         --\n",
      "│    │    └─conv1.weight                                           ├─524,288\n",
      "│    │    └─bn1.weight                                             ├─512\n",
      "│    │    └─bn1.bias                                               ├─512\n",
      "│    │    └─conv2.weight                                           ├─2,359,296\n",
      "│    │    └─bn2.weight                                             ├─512\n",
      "│    │    └─bn2.bias                                               ├─512\n",
      "│    │    └─conv3.weight                                           ├─1,048,576\n",
      "│    │    └─bn3.weight                                             ├─2,048\n",
      "│    │    └─bn3.bias                                               ├─2,048\n",
      "│    │    └─downsample.0.weight                                    ├─2,097,152\n",
      "│    │    └─downsample.1.weight                                    ├─2,048\n",
      "│    │    └─downsample.1.bias                                      └─2,048\n",
      "│    │    └─Conv2d: 3-121                [128, 512, 2, 2]          (524,288)\n",
      "│    │    │    └─weight                                            └─524,288\n",
      "│    │    └─BatchNorm2d: 3-122           [128, 512, 2, 2]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-123                  [128, 512, 2, 2]          --\n",
      "│    │    └─Conv2d: 3-124                [128, 512, 1, 1]          (2,359,296)\n",
      "│    │    │    └─weight                                            └─2,359,296\n",
      "│    │    └─BatchNorm2d: 3-125           [128, 512, 1, 1]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-126                  [128, 512, 1, 1]          --\n",
      "│    │    └─Conv2d: 3-127                [128, 2048, 1, 1]         (1,048,576)\n",
      "│    │    │    └─weight                                            └─1,048,576\n",
      "│    │    └─BatchNorm2d: 3-128           [128, 2048, 1, 1]         (4,096)\n",
      "│    │    │    └─weight                                            ├─2,048\n",
      "│    │    │    └─bias                                              └─2,048\n",
      "│    │    └─Sequential: 3-129            [128, 2048, 1, 1]         (2,101,248)\n",
      "│    │    │    └─0.weight                                          ├─2,097,152\n",
      "│    │    │    └─1.weight                                          ├─2,048\n",
      "│    │    │    └─1.bias                                            └─2,048\n",
      "│    │    └─ReLU: 3-130                  [128, 2048, 1, 1]         --\n",
      "│    └─Bottleneck: 2-15                  [128, 2048, 1, 1]         --\n",
      "│    │    └─conv1.weight                                           ├─1,048,576\n",
      "│    │    └─bn1.weight                                             ├─512\n",
      "│    │    └─bn1.bias                                               ├─512\n",
      "│    │    └─conv2.weight                                           ├─2,359,296\n",
      "│    │    └─bn2.weight                                             ├─512\n",
      "│    │    └─bn2.bias                                               ├─512\n",
      "│    │    └─conv3.weight                                           ├─1,048,576\n",
      "│    │    └─bn3.weight                                             ├─2,048\n",
      "│    │    └─bn3.bias                                               └─2,048\n",
      "│    │    └─Conv2d: 3-131                [128, 512, 1, 1]          (1,048,576)\n",
      "│    │    │    └─weight                                            └─1,048,576\n",
      "│    │    └─BatchNorm2d: 3-132           [128, 512, 1, 1]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-133                  [128, 512, 1, 1]          --\n",
      "│    │    └─Conv2d: 3-134                [128, 512, 1, 1]          (2,359,296)\n",
      "│    │    │    └─weight                                            └─2,359,296\n",
      "│    │    └─BatchNorm2d: 3-135           [128, 512, 1, 1]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-136                  [128, 512, 1, 1]          --\n",
      "│    │    └─Conv2d: 3-137                [128, 2048, 1, 1]         (1,048,576)\n",
      "│    │    │    └─weight                                            └─1,048,576\n",
      "│    │    └─BatchNorm2d: 3-138           [128, 2048, 1, 1]         (4,096)\n",
      "│    │    │    └─weight                                            ├─2,048\n",
      "│    │    │    └─bias                                              └─2,048\n",
      "│    │    └─ReLU: 3-139                  [128, 2048, 1, 1]         --\n",
      "│    └─Bottleneck: 2-16                  [128, 2048, 1, 1]         --\n",
      "│    │    └─conv1.weight                                           ├─1,048,576\n",
      "│    │    └─bn1.weight                                             ├─512\n",
      "│    │    └─bn1.bias                                               ├─512\n",
      "│    │    └─conv2.weight                                           ├─2,359,296\n",
      "│    │    └─bn2.weight                                             ├─512\n",
      "│    │    └─bn2.bias                                               ├─512\n",
      "│    │    └─conv3.weight                                           ├─1,048,576\n",
      "│    │    └─bn3.weight                                             ├─2,048\n",
      "│    │    └─bn3.bias                                               └─2,048\n",
      "│    │    └─Conv2d: 3-140                [128, 512, 1, 1]          (1,048,576)\n",
      "│    │    │    └─weight                                            └─1,048,576\n",
      "│    │    └─BatchNorm2d: 3-141           [128, 512, 1, 1]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-142                  [128, 512, 1, 1]          --\n",
      "│    │    └─Conv2d: 3-143                [128, 512, 1, 1]          (2,359,296)\n",
      "│    │    │    └─weight                                            └─2,359,296\n",
      "│    │    └─BatchNorm2d: 3-144           [128, 512, 1, 1]          (1,024)\n",
      "│    │    │    └─weight                                            ├─512\n",
      "│    │    │    └─bias                                              └─512\n",
      "│    │    └─ReLU: 3-145                  [128, 512, 1, 1]          --\n",
      "│    │    └─Conv2d: 3-146                [128, 2048, 1, 1]         (1,048,576)\n",
      "│    │    │    └─weight                                            └─1,048,576\n",
      "│    │    └─BatchNorm2d: 3-147           [128, 2048, 1, 1]         (4,096)\n",
      "│    │    │    └─weight                                            ├─2,048\n",
      "│    │    │    └─bias                                              └─2,048\n",
      "│    │    └─ReLU: 3-148                  [128, 2048, 1, 1]         --\n",
      "├─AdaptiveAvgPool2d: 1-9                 [128, 2048, 1, 1]         --\n",
      "├─Sequential: 1-10                       [128, 10]                 --\n",
      "│    └─0.weight                                                    ├─1,048,576\n",
      "│    └─0.bias                                                      ├─512\n",
      "│    └─2.weight                                                    ├─512\n",
      "│    └─2.bias                                                      ├─512\n",
      "│    └─3.weight                                                    ├─131,072\n",
      "│    └─3.bias                                                      ├─256\n",
      "│    └─5.weight                                                    ├─256\n",
      "│    └─5.bias                                                      ├─256\n",
      "│    └─6.weight                                                    ├─32,768\n",
      "│    └─6.bias                                                      ├─128\n",
      "│    └─8.weight                                                    ├─128\n",
      "│    └─8.bias                                                      ├─128\n",
      "│    └─9.weight                                                    ├─1,280\n",
      "│    └─9.bias                                                      └─10\n",
      "│    └─Linear: 2-17                      [128, 512]                1,049,088\n",
      "│    │    └─weight                                                 ├─1,048,576\n",
      "│    │    └─bias                                                   └─512\n",
      "│    └─ReLU: 2-18                        [128, 512]                --\n",
      "│    └─BatchNorm1d: 2-19                 [128, 512]                1,024\n",
      "│    │    └─weight                                                 ├─512\n",
      "│    │    └─bias                                                   └─512\n",
      "│    └─Linear: 2-20                      [128, 256]                131,328\n",
      "│    │    └─weight                                                 ├─131,072\n",
      "│    │    └─bias                                                   └─256\n",
      "│    └─ReLU: 2-21                        [128, 256]                --\n",
      "│    └─BatchNorm1d: 2-22                 [128, 256]                512\n",
      "│    │    └─weight                                                 ├─256\n",
      "│    │    └─bias                                                   └─256\n",
      "│    └─Linear: 2-23                      [128, 128]                32,896\n",
      "│    │    └─weight                                                 ├─32,768\n",
      "│    │    └─bias                                                   └─128\n",
      "│    └─ReLU: 2-24                        [128, 128]                --\n",
      "│    └─BatchNorm1d: 2-25                 [128, 128]                256\n",
      "│    │    └─weight                                                 ├─128\n",
      "│    │    └─bias                                                   └─128\n",
      "│    └─Linear: 2-26                      [128, 10]                 1,290\n",
      "│    │    └─weight                                                 ├─1,280\n",
      "│    │    └─bias                                                   └─10\n",
      "│    └─LogSoftmax: 2-27                  [128, 10]                 --\n",
      "==========================================================================================\n",
      "Total params: 24,724,426\n",
      "Trainable params: 1,216,394\n",
      "Non-trainable params: 23,508,032\n",
      "Total mult-adds (Units.GIGABYTES): 10.84\n",
      "==========================================================================================\n",
      "Input size (MB): 1.57\n",
      "Forward/backward pass size (MB): 466.36\n",
      "Params size (MB): 98.90\n",
      "Estimated Total Size (MB): 566.84\n",
      "==========================================================================================\n"
     ]
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "summary(resnet, input_size=(TRAIN_BATCH_SIZE, 3, 32, 32), verbose=2)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b2bd03",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "54efacb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many times the learning algorithm will work through the entire training dataset\n",
    "epochs = 5\n",
    "\n",
    "# loss function to evaluate model's performance and help learning\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# algorithm used to tune parameters\n",
    "optimizer = SGD(resnet.parameters(), lr=1e-1, momentum=0.9)\n",
    "# optimizer = Adam(model.parameters(), lr=1e-3)\n",
    "\n",
    "# how the learning rate is being adjusted\n",
    "# scheduler = CosineAnnealingLR(optimizer, T_max=200, verbose=True)\n",
    "scheduler = ReduceLROnPlateau(optimizer, \"min\", factor=0.1, patience=3, min_lr=10e-5)\n",
    "\n",
    "# stop training when metric does not change for tolerance number of epochs using given mode\n",
    "early_stopping = EarlyStopping(tolerance=5, min_delta=1, mode=\"min\")\n",
    "\n",
    "# var to store best model's weights\n",
    "best_model_wts = deepcopy(resnet.state_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11122a9",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "307915d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet_train_accuracies, resnet_test_accuracies = [], []\n",
    "resnet_train_losses, resnet_test_losses = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cd199427",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [03:07<00:00,  2.08it/s, Loss=0.740577]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1, elapsed time:187.57s\n",
      "Accuracies:\n",
      "Train: 74.95000%\n",
      "  Val: 78.90000%\n",
      "\n",
      "Losses:\n",
      "Train: 288.82485085725784\n",
      "  Val: 47.07477888464928\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [03:11<00:00,  2.04it/s, Loss=0.552090]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2, elapsed time:191.49s\n",
      "Accuracies:\n",
      "Train: 80.82400%\n",
      "  Val: 79.88000%\n",
      "\n",
      "Losses:\n",
      "Train: 215.31503209471703\n",
      "  Val: 45.514909237623215\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [03:07<00:00,  2.08it/s, Loss=0.487771]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3, elapsed time:187.13s\n",
      "Accuracies:\n",
      "Train: 82.91000%\n",
      "  Val: 80.88000%\n",
      "\n",
      "Losses:\n",
      "Train: 190.2306403517723\n",
      "  Val: 42.8132746219635\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [03:15<00:00,  2.00it/s, Loss=0.447891]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4, elapsed time:195.45s\n",
      "Accuracies:\n",
      "Train: 84.25000%\n",
      "  Val: 80.38000%\n",
      "\n",
      "Losses:\n",
      "Train: 174.67747896909714\n",
      "  Val: 43.82504314184189\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 390/390 [03:09<00:00,  2.06it/s, Loss=0.413995]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 5, elapsed time:189.76s\n",
      "Accuracies:\n",
      "Train: 85.54400%\n",
      "  Val: 80.63000%\n",
      "\n",
      "Losses:\n",
      "Train: 161.4579010605812\n",
      "  Val: 43.447905510663986\n",
      "\n"
     ]
    }
   ],
   "source": [
    "(\n",
    "    resnet,\n",
    "    resnet_train_accuracies,\n",
    "    resnet_test_accuracies,\n",
    "    resnet_train_losses,\n",
    "    resnet_test_losses,\n",
    ") = training(\n",
    "    model=resnet,\n",
    "    device=device,\n",
    "    train_data_loader=resnet_train_data_loader,\n",
    "    val_data_loader=resnet_val_data_loader,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer,\n",
    "    scheduler=scheduler,\n",
    "    early_stopping=early_stopping,\n",
    "    epochs=epochs,\n",
    "    train_accuracies=resnet_train_accuracies,\n",
    "    test_accuracies=resnet_test_accuracies,\n",
    "    train_losses=resnet_train_losses,\n",
    "    test_losses=resnet_test_losses,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcf88f6",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "89ee822a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# in case when early stopping worked\n",
    "epochs_range = range(1, len(resnet_train_accuracies) + 1)\n",
    "\n",
    "plot_acc_loss(\n",
    "    epochs_range,\n",
    "    resnet_train_accuracies,\n",
    "    resnet_test_accuracies,\n",
    "    resnet_train_losses,\n",
    "    resnet_test_losses,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec4d123",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b6549e1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58.98%\n"
     ]
    }
   ],
   "source": [
    "start_time = time()\n",
    "test_loss, test_acc = test(resnet, device, resnet_test_data_loader, criterion)\n",
    "resnet_inference_speed = time() - start_time\n",
    "print(f\"{test_acc:.5}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c77125",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464cb426",
   "metadata": {},
   "source": [
    "#### ROC-AUC\n",
    "\n",
    "Custom model turned to learn better on cifar10 and due to its relative light weight is learnt faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e4d01c1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming `model` is your trained PyTorch model and `test_loader` is your test data loader\n",
    "model.eval()\n",
    "\n",
    "# Placeholder for storing true labels and predicted probabilities\n",
    "model_true_labels_list = []\n",
    "model_predicted_probs_list = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data, target in test_data_loader:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        output = model(data)\n",
    "\n",
    "        # output is a probability distribution over classes\n",
    "        model_true_labels_list.extend(target.cpu().numpy())\n",
    "        model_predicted_probs_list.extend(output.cpu().numpy())\n",
    "\n",
    "\n",
    "# Convert lists to numpy arrays\n",
    "model_true_labels: np.ndarray = np.array(model_true_labels_list)\n",
    "model_predicted_probs: np.ndarray = np.array(model_predicted_probs_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "de963352",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming `model` is your trained PyTorch model and `test_loader` is your test data loader\n",
    "resnet.eval()\n",
    "\n",
    "# Placeholder for storing true labels and predicted probabilities\n",
    "resnet_true_labels_list = []\n",
    "resnet_predicted_probs_list = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data, target in test_data_loader:\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        output = resnet(data)\n",
    "\n",
    "        # output is a probability distribution over classes\n",
    "        resnet_true_labels_list.extend(target.cpu().numpy())\n",
    "        resnet_predicted_probs_list.extend(output.cpu().numpy())\n",
    "\n",
    "\n",
    "# Convert lists to numpy arrays\n",
    "resnet_true_labels: np.ndarray = np.array(resnet_true_labels_list)\n",
    "resnet_predicted_probs: np.ndarray = np.array(resnet_predicted_probs_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "df1c5e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import auc, roc_curve, roc_auc_score\n",
    "\n",
    "# var to store custom model data\n",
    "model_tpr, model_fpr, model_roc_auc = dict(), dict(), dict()\n",
    "\n",
    "# tuned resnet variables\n",
    "resnet_tpr, resnet_fpr, resnet_roc_auc = dict(), dict(), dict()\n",
    "\n",
    "for i in range(10):\n",
    "    model_fpr[i], model_tpr[i], _ = roc_curve(model_true_labels == i, model_predicted_probs[:, i])\n",
    "    model_roc_auc[i] = auc(model_fpr[i], model_tpr[i])\n",
    "\n",
    "    resnet_fpr[i], resnet_tpr[i], _ = roc_curve(resnet_true_labels == i, resnet_predicted_probs[:, i])\n",
    "    resnet_roc_auc[i] = auc(resnet_fpr[i], resnet_tpr[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec2215d",
   "metadata": {},
   "source": [
    "ROC-AUC for custom model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a83cba05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "for i in range(10):\n",
    "    plt.plot(\n",
    "        model_fpr[i],\n",
    "        model_tpr[i],\n",
    "        lw=2,\n",
    "        label=f'ROC curve of class {labels_maps[i]} (area = {model_roc_auc[i]:.2f})'\n",
    "    )\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title(f'Receiver operating characteristic for custom ({roc_auc_score(model_true_labels, np.exp(model_predicted_probs), multi_class=\"ovr\"):.2f})')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8a915316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "for i in range(10):\n",
    "    plt.plot(\n",
    "        resnet_fpr[i],\n",
    "        resnet_tpr[i],\n",
    "        lw=2,\n",
    "        label=f'ROC curve of class {labels_maps[i]} (area = {resnet_roc_auc[i]:.2f})'\n",
    "    )\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title(f'Receiver operating characteristic for resnet ({roc_auc_score(resnet_true_labels, np.exp(resnet_predicted_probs), multi_class=\"ovr\"):.2f})')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6a142c",
   "metadata": {},
   "source": [
    "#### Complexity\n",
    "\n",
    "Resnet is a larger model and it was design to classify more than 10 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c50f7044",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_total_params = sum(p.numel() for p in model.parameters())\n",
    "model_learnable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "resnet_total_params = sum(p.numel() for p in resnet.parameters())\n",
    "resnet_learnable_params = sum(p.numel() for p in resnet.parameters() if p.requires_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f7c54622",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom model params: Total (2009610), Learnable (2009610), 100.00%\n",
      "ReNet model params: Total (24724426), Learnable (1216394), 4.92%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Custom model params: Total ({model_total_params}), Learnable ({model_learnable_params}), {model_learnable_params / model_total_params * 100:.2f}%\")\n",
    "print(f\"ReNet model params: Total ({resnet_total_params}), Learnable ({resnet_learnable_params}), {resnet_learnable_params / resnet_total_params * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6981c73a",
   "metadata": {},
   "source": [
    "#### Inference Speed\n",
    "\n",
    "Resnet model is significatly less efficient on inference due to its complexity of +-25 millions of params, whereas custom model is only 2 millions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b5ce0b49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom: 4.48606 seconds\n",
      "ResNet: 101.79931 seconds\n"
     ]
    }
   ],
   "source": [
    "print(f\"Custom: {model_inference_speed:.5f} seconds\")\n",
    "print(f\"ResNet: {resnet_inference_speed:.5f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd875d4d",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Despite on fact that huge pretrained models performs well, their fine-tuning won't help if the unseen data differs much.\n",
    "\n",
    "Here the ResNet model result appeared worse because of its heavy weight and small epoch amount due to train complexity cost. "
   ]
  }
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