DeepLabv3_on_a_custom_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset"
      ],
      "metadata": {
        "id": "_ZnZQCdI5Nzt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download dataset\n",
        "# !gdown --id 1rbVirTmc2_NZyEHnjtEqoHFPGekApc25 -O dataset.zip"
      ],
      "metadata": {
        "id": "ddQVcA8lt41c"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Download resized dataset\n",
        "!gdown --id 1aFthbjHyKQWNoF-E6TdONuhY68ccnQaw -O dataset.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vg7cZs9IQX0z",
        "outputId": "be19bc23-6abf-4420-8832-f32a191c6fe0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:138: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1aFthbjHyKQWNoF-E6TdONuhY68ccnQaw\n",
            "To: /content/dataset.zip\n",
            "100% 3.17M/3.17M [00:00<00:00, 203MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.listdir(\"/content\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xkkxis11_byA",
        "outputId": "abc736d6-c122-42d0-e4aa-7d5739ba1051"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config', 'dataset.zip', 'sample_data']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: unzip \"/content/dataset.zip\"\n",
        "!unzip /content/dataset.zip -d /content/dataset\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fjtoq6_S_hba",
        "outputId": "0aa29d97-98d4-417a-f411-be3c89ea44ba"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_root_dir = \"/content/dataset/\""
      ],
      "metadata": {
        "id": "upc4buhy_l6E"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images_dir = dataset_root_dir + \"images/\"\n",
        "masks_dir = dataset_root_dir + \"masks/\""
      ],
      "metadata": {
        "id": "Wq6Dpuyy_tGu"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "from typing import Any, Callable, Optional\n",
        "\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from torchvision.datasets.vision import VisionDataset\n",
        "\n",
        "\n",
        "class SegmentationDataset(VisionDataset):\n",
        "    \"\"\"A PyTorch dataset for image segmentation task.\n",
        "    The dataset is compatible with torchvision transforms.\n",
        "    The transforms passed would be applied to both the Images and Masks.\n",
        "    \"\"\"\n",
        "    def __init__(self,\n",
        "                 root: str,\n",
        "                 image_folder: str,\n",
        "                 mask_folder: str,\n",
        "                 transforms: Optional[Callable] = None,\n",
        "                 seed: int = None,\n",
        "                 fraction: float = None,\n",
        "                 subset: str = None,\n",
        "                 image_color_mode: str = \"rgb\",\n",
        "                 mask_color_mode: str = \"grayscale\") -> None:\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            root (str): Root directory path.\n",
        "            image_folder (str): Name of the folder that contains the images in the root directory.\n",
        "            mask_folder (str): Name of the folder that contains the masks in the root directory.\n",
        "            transforms (Optional[Callable], optional): A function/transform that takes in\n",
        "            a sample and returns a transformed version.\n",
        "            E.g, ``transforms.ToTensor`` for images. Defaults to None.\n",
        "            seed (int, optional): Specify a seed for the train and test split for reproducible results. Defaults to None.\n",
        "            fraction (float, optional): A float value from 0 to 1 which specifies the validation split fraction. Defaults to None.\n",
        "            subset (str, optional): 'Train' or 'Test' to select the appropriate set. Defaults to None.\n",
        "            image_color_mode (str, optional): 'rgb' or 'grayscale'. Defaults to 'rgb'.\n",
        "            mask_color_mode (str, optional): 'rgb' or 'grayscale'. Defaults to 'grayscale'.\n",
        "        Raises:\n",
        "            OSError: If image folder doesn't exist in root.\n",
        "            OSError: If mask folder doesn't exist in root.\n",
        "            ValueError: If subset is not either 'Train' or 'Test'\n",
        "            ValueError: If image_color_mode and mask_color_mode are either 'rgb' or 'grayscale'\n",
        "        \"\"\"\n",
        "        super().__init__(root, transforms)\n",
        "        image_folder_path = Path(self.root) / image_folder\n",
        "        mask_folder_path = Path(self.root) / mask_folder\n",
        "        if not image_folder_path.exists():\n",
        "            raise OSError(f\"{image_folder_path} does not exist.\")\n",
        "        if not mask_folder_path.exists():\n",
        "            raise OSError(f\"{mask_folder_path} does not exist.\")\n",
        "\n",
        "        if image_color_mode not in [\"rgb\", \"grayscale\"]:\n",
        "            raise ValueError(\n",
        "                f\"{image_color_mode} is an invalid choice. Please enter from rgb grayscale.\"\n",
        "            )\n",
        "        if mask_color_mode not in [\"rgb\", \"grayscale\"]:\n",
        "            raise ValueError(\n",
        "                f\"{mask_color_mode} is an invalid choice. Please enter from rgb grayscale.\"\n",
        "            )\n",
        "\n",
        "        self.image_color_mode = image_color_mode\n",
        "        self.mask_color_mode = mask_color_mode\n",
        "\n",
        "        if not fraction:\n",
        "            self.image_names = sorted(image_folder_path.glob(\"*\"))\n",
        "            self.mask_names = sorted(mask_folder_path.glob(\"*\"))\n",
        "        else:\n",
        "            if subset not in [\"Train\", \"Test\"]:\n",
        "                raise (ValueError(\n",
        "                    f\"{subset} is not a valid input. Acceptable values are Train and Test.\"\n",
        "                ))\n",
        "            self.fraction = fraction\n",
        "            self.image_list = np.array(sorted(image_folder_path.glob(\"*\")))\n",
        "            self.mask_list = np.array(sorted(mask_folder_path.glob(\"*\")))\n",
        "            if seed:\n",
        "                np.random.seed(seed)\n",
        "                indices = np.arange(len(self.image_list))\n",
        "                np.random.shuffle(indices)\n",
        "                self.image_list = self.image_list[indices]\n",
        "                self.mask_list = self.mask_list[indices]\n",
        "            if subset == \"Train\":\n",
        "                self.image_names = self.image_list[:int(\n",
        "                    np.ceil(len(self.image_list) * (1 - self.fraction)))]\n",
        "                self.mask_names = self.mask_list[:int(\n",
        "                    np.ceil(len(self.mask_list) * (1 - self.fraction)))]\n",
        "            else:\n",
        "                self.image_names = self.image_list[\n",
        "                    int(np.ceil(len(self.image_list) * (1 - self.fraction))):]\n",
        "                self.mask_names = self.mask_list[\n",
        "                    int(np.ceil(len(self.mask_list) * (1 - self.fraction))):]\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return len(self.image_names)\n",
        "\n",
        "    def __getitem__(self, index: int) -> Any:\n",
        "        image_path = self.image_names[index]\n",
        "        mask_path = self.mask_names[index]\n",
        "        with open(image_path, \"rb\") as image_file, open(mask_path,\n",
        "                                                        \"rb\") as mask_file:\n",
        "            image = Image.open(image_file)\n",
        "            if self.image_color_mode == \"rgb\":\n",
        "                image = image.convert(\"RGB\")\n",
        "            elif self.image_color_mode == \"grayscale\":\n",
        "                image = image.convert(\"L\")\n",
        "            mask = Image.open(mask_file)\n",
        "            if self.mask_color_mode == \"rgb\":\n",
        "                mask = mask.convert(\"RGB\")\n",
        "            elif self.mask_color_mode == \"grayscale\":\n",
        "                mask = mask.convert(\"L\")\n",
        "            sample = {\"image\": image, \"mask\": mask}\n",
        "            if self.transforms:\n",
        "                sample[\"image\"] = self.transforms(sample[\"image\"])\n",
        "                sample[\"mask\"] = self.transforms(sample[\"mask\"])\n",
        "            return sample\n"
      ],
      "metadata": {
        "id": "217WshFy7AjU"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UAeGlSugAAR0"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataloaders"
      ],
      "metadata": {
        "id": "_7o_Y9ay5MG-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "\n",
        "def get_dataloader_single_folder(data_dir: str,\n",
        "                                 image_folder: str = 'Images',\n",
        "                                 mask_folder: str = 'Masks',\n",
        "                                 fraction: float = 0.2,\n",
        "                                 batch_size: int = 4):\n",
        "    \"\"\"Create train and test dataloader from a single directory containing\n",
        "    the image and mask folders.\n",
        "\n",
        "    Args:\n",
        "        data_dir (str): Data directory path or root\n",
        "        image_folder (str, optional): Image folder name. Defaults to 'Images'.\n",
        "        mask_folder (str, optional): Mask folder name. Defaults to 'Masks'.\n",
        "        fraction (float, optional): Fraction of Test set. Defaults to 0.2.\n",
        "        batch_size (int, optional): Dataloader batch size. Defaults to 4.\n",
        "\n",
        "    Returns:\n",
        "        dataloaders: Returns dataloaders dictionary containing the\n",
        "        Train and Test dataloaders.\n",
        "    \"\"\"\n",
        "    data_transforms = transforms.Compose([transforms.ToTensor()])\n",
        "\n",
        "    image_datasets = {\n",
        "        x: SegmentationDataset(data_dir,\n",
        "                               image_folder=image_folder,\n",
        "                               mask_folder=mask_folder,\n",
        "                               seed=100,\n",
        "                               fraction=fraction,\n",
        "                               subset=x,\n",
        "                               transforms=data_transforms)\n",
        "        for x in ['Train', 'Test']\n",
        "    }\n",
        "    dataloaders = {\n",
        "        x: DataLoader(image_datasets[x],\n",
        "                      batch_size=batch_size,\n",
        "                      shuffle=True,\n",
        "                      num_workers=8)\n",
        "        for x in ['Train', 'Test']\n",
        "    }\n",
        "    return dataloaders\n"
      ],
      "metadata": {
        "id": "7KY-xwKdA121"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models.segmentation.deeplabv3 import DeepLabHead\n",
        "from torchvision import models\n",
        "\n",
        "\n",
        "def createDeepLabv3(outputchannels=1):\n",
        "    \"\"\"DeepLabv3 class with custom head\n",
        "\n",
        "    Args:\n",
        "        outputchannels (int, optional): The number of output channels\n",
        "        in your dataset masks. Defaults to 1.\n",
        "\n",
        "    Returns:\n",
        "        model: Returns the DeepLabv3 model with the ResNet101 backbone.\n",
        "    \"\"\"\n",
        "    model = models.segmentation.deeplabv3_resnet101(pretrained=True,\n",
        "                                                    progress=True)\n",
        "    model.classifier = DeepLabHead(2048, outputchannels)\n",
        "    # Set the model in training mode\n",
        "    model.train()\n",
        "    return model"
      ],
      "metadata": {
        "id": "Kt_Wa-3UJ1b-"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training\n"
      ],
      "metadata": {
        "id": "kFU1r2M75YCy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import copy\n",
        "import csv\n",
        "import os\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "def train_model(model, criterion, dataloaders, optimizer, metrics, bpath,\n",
        "                num_epochs):\n",
        "    since = time.time()\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_loss = 1e10\n",
        "    # Use gpu if available\n",
        "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "    model.to(device)\n",
        "    # Initialize the log file for training and testing loss and metrics\n",
        "    fieldnames = ['epoch', 'Train_loss', 'Test_loss'] + \\\n",
        "        [f'Train_{m}' for m in metrics.keys()] + \\\n",
        "        [f'Test_{m}' for m in metrics.keys()]\n",
        "    with open(os.path.join(bpath, 'log.csv'), 'w', newline='') as csvfile:\n",
        "        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
        "        writer.writeheader()\n",
        "\n",
        "    for epoch in range(1, num_epochs + 1):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs))\n",
        "        print('-' * 10)\n",
        "        # Each epoch has a training and validation phase\n",
        "        # Initialize batch summary\n",
        "        batchsummary = {a: [0] for a in fieldnames}\n",
        "\n",
        "        for phase in ['Train', 'Test']:\n",
        "            if phase == 'Train':\n",
        "                model.train()  # Set model to training mode\n",
        "            else:\n",
        "                model.eval()  # Set model to evaluate mode\n",
        "\n",
        "            # Iterate over data.\n",
        "            for sample in tqdm(iter(dataloaders[phase])):\n",
        "                inputs = sample['image'].to(device)\n",
        "                masks = sample['mask'].to(device)\n",
        "                # zero the parameter gradients\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # track history if only in train\n",
        "                with torch.set_grad_enabled(phase == 'Train'):\n",
        "                    outputs = model(inputs)\n",
        "                    loss = criterion(outputs['out'], masks)\n",
        "                    y_pred = outputs['out'].data.cpu().numpy().ravel()\n",
        "                    y_true = masks.data.cpu().numpy().ravel()\n",
        "                    for name, metric in metrics.items():\n",
        "                        if name == 'f1_score':\n",
        "                            # Use a classification threshold of 0.1\n",
        "                            batchsummary[f'{phase}_{name}'].append(\n",
        "                                metric(y_true > 0, y_pred > 0.1))\n",
        "                        else:\n",
        "                            batchsummary[f'{phase}_{name}'].append(\n",
        "                                metric(y_true.astype('uint8'), y_pred))\n",
        "\n",
        "                    # backward + optimize only if in training phase\n",
        "                    if phase == 'Train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "            batchsummary['epoch'] = epoch\n",
        "            epoch_loss = loss\n",
        "            batchsummary[f'{phase}_loss'] = epoch_loss.item()\n",
        "            print('{} Loss: {:.4f}'.format(phase, loss))\n",
        "        for field in fieldnames[3:]:\n",
        "            batchsummary[field] = np.mean(batchsummary[field])\n",
        "        print(batchsummary)\n",
        "        with open(os.path.join(bpath, 'log.csv'), 'a', newline='') as csvfile:\n",
        "            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
        "            writer.writerow(batchsummary)\n",
        "            # deep copy the model\n",
        "            if phase == 'Test' and loss < best_loss:\n",
        "                best_loss = loss\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Lowest Loss: {:4f}'.format(best_loss))\n",
        "\n",
        "    # load best model weights\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model\n"
      ],
      "metadata": {
        "id": "P2OGAyaAId7Q"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transfer Learning\n"
      ],
      "metadata": {
        "id": "rur25dnV5glJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validation"
      ],
      "metadata": {
        "id": "UcWwR4Cs5Zol"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference"
      ],
      "metadata": {
        "id": "Qwd1_9SS5e45"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Main\n"
      ],
      "metadata": {
        "id": "1Yl4RPUWGbkF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "RaAnyVLaHJcn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = createDeepLabv3()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Lq26bZ5GarZ",
        "outputId": "4f023b00-9a84-4014-c78f-b9f5e37f44c0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1`. You can also use `weights=DeepLabV3_ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth\" to /root/.cache/torch/hub/checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth\n",
            "100%|██████████| 233M/233M [00:01<00:00, 137MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.train()"
      ],
      "metadata": {
        "id": "2WptL76D5XL5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38e9d2c4-d23b-417c-8f9c-70a929ed398a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeepLabV3(\n",
              "  (backbone): IntermediateLayerGetter(\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=(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",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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",
              "      (6): 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=(2, 2), dilation=(2, 2), 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",
              "      (7): 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=(2, 2), dilation=(2, 2), 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",
              "      (8): 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=(2, 2), dilation=(2, 2), 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",
              "      (9): 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=(2, 2), dilation=(2, 2), 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",
              "      (10): 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=(2, 2), dilation=(2, 2), 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",
              "      (11): 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=(2, 2), dilation=(2, 2), 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",
              "      (12): 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=(2, 2), dilation=(2, 2), 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",
              "      (13): 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=(2, 2), dilation=(2, 2), 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",
              "      (14): 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=(2, 2), dilation=(2, 2), 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",
              "      (15): 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=(2, 2), dilation=(2, 2), 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",
              "      (16): 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=(2, 2), dilation=(2, 2), 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",
              "      (17): 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=(2, 2), dilation=(2, 2), 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",
              "      (18): 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=(2, 2), dilation=(2, 2), 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",
              "      (19): 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=(2, 2), dilation=(2, 2), 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",
              "      (20): 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=(2, 2), dilation=(2, 2), 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",
              "      (21): 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=(2, 2), dilation=(2, 2), 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",
              "      (22): 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=(2, 2), dilation=(2, 2), 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=(1, 1), padding=(2, 2), dilation=(2, 2), 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=(1, 1), 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=(4, 4), dilation=(4, 4), 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=(4, 4), dilation=(4, 4), 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",
              "  )\n",
              "  (classifier): DeepLabHead(\n",
              "    (0): ASPP(\n",
              "      (convs): ModuleList(\n",
              "        (0): Sequential(\n",
              "          (0): Conv2d(2048, 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",
              "          (2): ReLU()\n",
              "        )\n",
              "        (1): ASPPConv(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU()\n",
              "        )\n",
              "        (2): ASPPConv(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU()\n",
              "        )\n",
              "        (3): ASPPConv(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU()\n",
              "        )\n",
              "        (4): ASPPPooling(\n",
              "          (0): AdaptiveAvgPool2d(output_size=1)\n",
              "          (1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (3): ReLU()\n",
              "        )\n",
              "      )\n",
              "      (project): Sequential(\n",
              "        (0): Conv2d(1280, 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",
              "        (2): ReLU()\n",
              "        (3): Dropout(p=0.5, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): ReLU()\n",
              "    (4): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              "  (aux_classifier): FCNHead(\n",
              "    (0): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU()\n",
              "    (3): Dropout(p=0.1, inplace=False)\n",
              "    (4): Conv2d(256, 21, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path"
      ],
      "metadata": {
        "id": "fjsTh1tYGo4G"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_directory = Path(dataset_root_dir)"
      ],
      "metadata": {
        "id": "ctvivPfZGvec"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exp_directory = \"/content/checkpoints/\"\n",
        "# Create the experiment directory if not present\n",
        "exp_directory = Path(exp_directory)\n",
        "if not exp_directory.exists():\n",
        "  exp_directory.mkdir()"
      ],
      "metadata": {
        "id": "1iY09OENJUwu"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_criterion = torch.nn.MSELoss()"
      ],
      "metadata": {
        "id": "mDhve0mNG9GC"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) # learning rate in transfer learning should be lower than the lr used in the normal training"
      ],
      "metadata": {
        "id": "vlf6n6AaHOAI"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 4"
      ],
      "metadata": {
        "id": "asfxQA_tHtKA"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataloaders = get_dataloader_single_folder(data_dir=data_directory,image_folder=\"images\",mask_folder=\"masks\", fraction=0.1, batch_size=batch_size )\n",
        "dataloaders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dqz-DxqjHj7t",
        "outputId": "4b8c92ab-c839-4836-e38a-73e631c69b5f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Train': <torch.utils.data.dataloader.DataLoader at 0x7f49a777d9c0>,\n",
              " 'Test': <torch.utils.data.dataloader.DataLoader at 0x7f49af72a8c0>}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score, roc_auc_score\n",
        "\n",
        "# Specify the evaluation metrics\n",
        "metrics = {'f1_score': f1_score, 'auroc': roc_auc_score}"
      ],
      "metadata": {
        "id": "ubQt9ht0Iv_m"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 25"
      ],
      "metadata": {
        "id": "Whc2voPrJOCF"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_ = train_model(model=model,\n",
        "                criterion=loss_criterion,\n",
        "                dataloaders=dataloaders,\n",
        "                optimizer = optimizer,\n",
        "                metrics=metrics,\n",
        "                bpath=exp_directory,\n",
        "                num_epochs=epochs\n",
        "                )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvPOZO0JIVeo",
        "outputId": "464244d4-196d-4149-9000-91aad2f4b775"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:14<00:00,  1.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.4034\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:01<00:00,  2.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.4775\n",
            "{'epoch': 1, 'Train_loss': 0.4034225344657898, 'Test_loss': 0.4775334894657135, 'Train_f1_score': 0.496820468430417, 'Train_auroc': 0.6776641754647614, 'Test_f1_score': 0.4834497733159561, 'Test_auroc': 0.5821591857722506}\n",
            "Epoch 2/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:14<00:00,  1.69it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.1383\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:01<00:00,  2.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.2622\n",
            "{'epoch': 2, 'Train_loss': 0.13826106488704681, 'Test_loss': 0.26223012804985046, 'Train_f1_score': 0.7664501399787228, 'Train_auroc': 0.7936899988231159, 'Test_f1_score': 0.6318606363175344, 'Test_auroc': 0.6300208173703707}\n",
            "Epoch 3/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.81it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.1061\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1418\n",
            "{'epoch': 3, 'Train_loss': 0.10607948899269104, 'Test_loss': 0.1417786329984665, 'Train_f1_score': 0.7557010750536397, 'Train_auroc': 0.8428118144197783, 'Test_f1_score': 0.6409632569821503, 'Test_auroc': 0.6555803076864168}\n",
            "Epoch 4/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0915\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.37it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1362\n",
            "{'epoch': 4, 'Train_loss': 0.09145771712064743, 'Test_loss': 0.13620874285697937, 'Train_f1_score': 0.7742916438672633, 'Train_auroc': 0.8752644406394494, 'Test_f1_score': 0.639000480255342, 'Test_auroc': 0.6626936166158471}\n",
            "Epoch 5/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0975\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1310\n",
            "{'epoch': 5, 'Train_loss': 0.09745423495769501, 'Test_loss': 0.1309846192598343, 'Train_f1_score': 0.7710733502594022, 'Train_auroc': 0.8860851689177968, 'Test_f1_score': 0.6304192950754961, 'Test_auroc': 0.6583719630563352}\n",
            "Epoch 6/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0840\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1006\n",
            "{'epoch': 6, 'Train_loss': 0.08397342264652252, 'Test_loss': 0.10060998797416687, 'Train_f1_score': 0.7916490919502747, 'Train_auroc': 0.8993934291183845, 'Test_f1_score': 0.6372110031194987, 'Test_auroc': 0.675288193027813}\n",
            "Epoch 7/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.92it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.1057\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:01<00:00,  2.75it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1166\n",
            "{'epoch': 7, 'Train_loss': 0.10567036271095276, 'Test_loss': 0.11663307249546051, 'Train_f1_score': 0.7831178732569236, 'Train_auroc': 0.9017200345064781, 'Test_f1_score': 0.6377210669144691, 'Test_auroc': 0.6837382033791439}\n",
            "Epoch 8/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0789\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0948\n",
            "{'epoch': 8, 'Train_loss': 0.0788654312491417, 'Test_loss': 0.09476854652166367, 'Train_f1_score': 0.8118183955507496, 'Train_auroc': 0.9090177786855187, 'Test_f1_score': 0.647839580234345, 'Test_auroc': 0.6802375598927171}\n",
            "Epoch 9/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0750\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0927\n",
            "{'epoch': 9, 'Train_loss': 0.07503051310777664, 'Test_loss': 0.09271829575300217, 'Train_f1_score': 0.8123504239200365, 'Train_auroc': 0.9125415771514376, 'Test_f1_score': 0.6432072340792622, 'Test_auroc': 0.6855295390049654}\n",
            "Epoch 10/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0512\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.51it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0955\n",
            "{'epoch': 10, 'Train_loss': 0.05121869221329689, 'Test_loss': 0.09551654011011124, 'Train_f1_score': 0.8305546058581595, 'Train_auroc': 0.9163354373041314, 'Test_f1_score': 0.6540223141735486, 'Test_auroc': 0.6831860860795325}\n",
            "Epoch 11/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0586\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.56it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1170\n",
            "{'epoch': 11, 'Train_loss': 0.05861101672053337, 'Test_loss': 0.11700096726417542, 'Train_f1_score': 0.8233262894366205, 'Train_auroc': 0.9205003588919636, 'Test_f1_score': 0.6464609412046808, 'Test_auroc': 0.6838704325694587}\n",
            "Epoch 12/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.92it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0620\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:01<00:00,  2.66it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1305\n",
            "{'epoch': 12, 'Train_loss': 0.061978649348020554, 'Test_loss': 0.1304546594619751, 'Train_f1_score': 0.8315236800680375, 'Train_auroc': 0.9213744928959748, 'Test_f1_score': 0.6442410769795057, 'Test_auroc': 0.6816625351031946}\n",
            "Epoch 13/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0583\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.59it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1716\n",
            "{'epoch': 13, 'Train_loss': 0.0582614503800869, 'Test_loss': 0.1715584099292755, 'Train_f1_score': 0.8338659672214312, 'Train_auroc': 0.9230135067268416, 'Test_f1_score': 0.6433482968149945, 'Test_auroc': 0.6715349796652142}\n",
            "Epoch 14/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0606\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1058\n",
            "{'epoch': 14, 'Train_loss': 0.06064530834555626, 'Test_loss': 0.10580003261566162, 'Train_f1_score': 0.8312503944982902, 'Train_auroc': 0.9207123815836249, 'Test_f1_score': 0.6470412693000591, 'Test_auroc': 0.6808178668385018}\n",
            "Epoch 15/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0467\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0769\n",
            "{'epoch': 15, 'Train_loss': 0.046657267957925797, 'Test_loss': 0.07688356935977936, 'Train_f1_score': 0.8443411449694896, 'Train_auroc': 0.9249764830074655, 'Test_f1_score': 0.6533645841278404, 'Test_auroc': 0.6813832510219573}\n",
            "Epoch 16/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0399\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0693\n",
            "{'epoch': 16, 'Train_loss': 0.03987574577331543, 'Test_loss': 0.06932122260332108, 'Train_f1_score': 0.8475800309023624, 'Train_auroc': 0.9266477745795956, 'Test_f1_score': 0.6497525427735709, 'Test_auroc': 0.6827645176844619}\n",
            "Epoch 17/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0617\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:01<00:00,  2.64it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1475\n",
            "{'epoch': 17, 'Train_loss': 0.06165457144379616, 'Test_loss': 0.14750805497169495, 'Train_f1_score': 0.8487805353020305, 'Train_auroc': 0.9272277884811368, 'Test_f1_score': 0.6431472215305143, 'Test_auroc': 0.6790908146671072}\n",
            "Epoch 18/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0352\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1455\n",
            "{'epoch': 18, 'Train_loss': 0.03518960624933243, 'Test_loss': 0.14551717042922974, 'Train_f1_score': 0.8544016090770771, 'Train_auroc': 0.9273233480193134, 'Test_f1_score': 0.6488550432977459, 'Test_auroc': 0.6856261925087296}\n",
            "Epoch 19/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0443\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.65it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1133\n",
            "{'epoch': 19, 'Train_loss': 0.044297486543655396, 'Test_loss': 0.11330614984035492, 'Train_f1_score': 0.8578598482491429, 'Train_auroc': 0.9283302595551662, 'Test_f1_score': 0.6518291756123097, 'Test_auroc': 0.6840422616541335}\n",
            "Epoch 20/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0393\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1233\n",
            "{'epoch': 20, 'Train_loss': 0.039343513548374176, 'Test_loss': 0.12334736436605453, 'Train_f1_score': 0.8592381990693946, 'Train_auroc': 0.9287154739323437, 'Test_f1_score': 0.6505190085378477, 'Test_auroc': 0.6818576058007795}\n",
            "Epoch 21/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0641\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0986\n",
            "{'epoch': 21, 'Train_loss': 0.06412821263074875, 'Test_loss': 0.09861250221729279, 'Train_f1_score': 0.8573068392894622, 'Train_auroc': 0.9285704200639109, 'Test_f1_score': 0.6518562258701146, 'Test_auroc': 0.6821988813013987}\n",
            "Epoch 22/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0483\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:01<00:00,  2.70it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1284\n",
            "{'epoch': 22, 'Train_loss': 0.048295799642801285, 'Test_loss': 0.12843436002731323, 'Train_f1_score': 0.8620297411617794, 'Train_auroc': 0.9292252280577511, 'Test_f1_score': 0.6530443571690565, 'Test_auroc': 0.6838862956922285}\n",
            "Epoch 23/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.87it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0566\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1465\n",
            "{'epoch': 23, 'Train_loss': 0.05661033093929291, 'Test_loss': 0.14647646248340607, 'Train_f1_score': 0.8624409631406595, 'Train_auroc': 0.9299926979354188, 'Test_f1_score': 0.6493708438994954, 'Test_auroc': 0.6828029308665409}\n",
            "Epoch 24/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0473\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.57it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.1026\n",
            "{'epoch': 24, 'Train_loss': 0.047331780195236206, 'Test_loss': 0.10257726162672043, 'Train_f1_score': 0.8617856077654632, 'Train_auroc': 0.929541499528368, 'Test_f1_score': 0.6603468882543003, 'Test_auroc': 0.684469225395747}\n",
            "Epoch 25/25\n",
            "----------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Loss: 0.0438\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "100%|██████████| 3/3 [00:00<00:00,  3.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss: 0.0800\n",
            "{'epoch': 25, 'Train_loss': 0.04379463195800781, 'Test_loss': 0.0800226703286171, 'Train_f1_score': 0.8629381735610008, 'Train_auroc': 0.9298408646093657, 'Test_f1_score': 0.6561814447549448, 'Test_auroc': 0.6882988599123044}\n",
            "Training complete in 6m 12s\n",
            "Lowest Loss: 0.069321\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: mount google drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8G-4KjSgbkTq",
        "outputId": "3a796256-eb69-4138-c663-efd9302498d4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exp_directory = Path(\"/content/gdrive/MyDrive\")"
      ],
      "metadata": {
        "id": "6o8Kq-U0b7_s"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model, exp_directory / \"transfer_learned_deeplab.pt\")"
      ],
      "metadata": {
        "id": "5syRFjl2JoTo"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: download this image using \"https://www.boxed-up.co.uk/image/data/shutterstock_1415701424.jpg\" wget as \"new_image.jpg\"\n",
        "\n",
        "!wget -O new_image.jpg https://www.boxed-up.co.uk/image/data/shutterstock_1415701424.jpg\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMy-8q8KcBdY",
        "outputId": "fa099cc6-2305-44c2-a560-00fe0c8615dd"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-30 13:00:12--  https://www.boxed-up.co.uk/image/data/shutterstock_1415701424.jpg\n",
            "Resolving www.boxed-up.co.uk (www.boxed-up.co.uk)... 104.21.94.163, 172.67.138.19, 2606:4700:3034::ac43:8a13, ...\n",
            "Connecting to www.boxed-up.co.uk (www.boxed-up.co.uk)|104.21.94.163|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [image/jpeg]\n",
            "Saving to: ‘new_image.jpg’\n",
            "\n",
            "new_image.jpg           [  <=>               ] 420.84K   993KB/s    in 0.4s    \n",
            "\n",
            "2024-03-30 13:00:14 (993 KB/s) - ‘new_image.jpg’ saved [430941]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: load image \"/content/new_image.jpg\" using Pill convert it to np array then to a tensor in pytorch\n",
        "\n",
        "import numpy as np\n",
        "image = Image.open(\"/content/new_image.jpg\")\n",
        "image = np.array(image)\n",
        "image = torch.tensor(image)\n"
      ],
      "metadata": {
        "id": "x-Otu92Gck_Q"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: show image\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(image)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "cGiNNk2KcyOj",
        "outputId": "11e22422-845e-4cb8-b7d5-f5247c0ea51a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(image)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LPRapFoc00H",
        "outputId": "8d9de53c-b223-4c2d-c1c6-8a2d82f0931c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Tensor"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fx9kneHtdmnK",
        "outputId": "837d5ff2-a90b-42d3-a0fd-1ca26f0f6081"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([801, 1200, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reshaped_image = image.permute(2, 0, 1)"
      ],
      "metadata": {
        "id": "CvmkP4-BdPbl"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dc2I4vE4ewaC",
        "outputId": "a1bc4a1f-62ea-4667-f06c-03efe4ae1f8b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeepLabV3(\n",
              "  (backbone): IntermediateLayerGetter(\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=(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",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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=(2, 2), dilation=(2, 2), 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",
              "      (6): 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=(2, 2), dilation=(2, 2), 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",
              "      (7): 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=(2, 2), dilation=(2, 2), 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",
              "      (8): 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=(2, 2), dilation=(2, 2), 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",
              "      (9): 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=(2, 2), dilation=(2, 2), 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",
              "      (10): 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=(2, 2), dilation=(2, 2), 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",
              "      (11): 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=(2, 2), dilation=(2, 2), 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",
              "      (12): 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=(2, 2), dilation=(2, 2), 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",
              "      (13): 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=(2, 2), dilation=(2, 2), 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",
              "      (14): 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=(2, 2), dilation=(2, 2), 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",
              "      (15): 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=(2, 2), dilation=(2, 2), 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",
              "      (16): 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=(2, 2), dilation=(2, 2), 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",
              "      (17): 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=(2, 2), dilation=(2, 2), 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",
              "      (18): 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=(2, 2), dilation=(2, 2), 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",
              "      (19): 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=(2, 2), dilation=(2, 2), 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",
              "      (20): 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=(2, 2), dilation=(2, 2), 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",
              "      (21): 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=(2, 2), dilation=(2, 2), 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",
              "      (22): 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=(2, 2), dilation=(2, 2), 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=(1, 1), padding=(2, 2), dilation=(2, 2), 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=(1, 1), 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=(4, 4), dilation=(4, 4), 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=(4, 4), dilation=(4, 4), 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",
              "  )\n",
              "  (classifier): DeepLabHead(\n",
              "    (0): ASPP(\n",
              "      (convs): ModuleList(\n",
              "        (0): Sequential(\n",
              "          (0): Conv2d(2048, 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",
              "          (2): ReLU()\n",
              "        )\n",
              "        (1): ASPPConv(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(12, 12), dilation=(12, 12), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU()\n",
              "        )\n",
              "        (2): ASPPConv(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(24, 24), dilation=(24, 24), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU()\n",
              "        )\n",
              "        (3): ASPPConv(\n",
              "          (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(1, 1), padding=(36, 36), dilation=(36, 36), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (2): ReLU()\n",
              "        )\n",
              "        (4): ASPPPooling(\n",
              "          (0): AdaptiveAvgPool2d(output_size=1)\n",
              "          (1): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (3): ReLU()\n",
              "        )\n",
              "      )\n",
              "      (project): Sequential(\n",
              "        (0): Conv2d(1280, 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",
              "        (2): ReLU()\n",
              "        (3): Dropout(p=0.5, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): ReLU()\n",
              "    (4): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              "  (aux_classifier): FCNHead(\n",
              "    (0): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU()\n",
              "    (3): Dropout(p=0.1, inplace=False)\n",
              "    (4): Conv2d(256, 21, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.to(\"cuda\")\n",
        "reshaped_image = reshaped_image.to(\"cuda\")"
      ],
      "metadata": {
        "id": "CQ1nTO1rd9gO"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "from PIL import Image\n",
        "\n",
        "# Preprocess the image\n",
        "def preprocess_image(image_path):\n",
        "    image = Image.open(image_path)\n",
        "    transform = transforms.Compose([\n",
        "        # transforms.Resize((224, 224)),  # Adjust size as needed\n",
        "        transforms.ToTensor(),\n",
        "        # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # Adjust normalization parameters if needed\n",
        "    ])\n",
        "    image = transform(image)\n",
        "    image = image.unsqueeze(0)  # Add batch dimension\n",
        "    return image\n",
        "\n",
        "# Run inference\n",
        "def run_inference(model, image):\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        output = model(image)\n",
        "    return output\n"
      ],
      "metadata": {
        "id": "0hKJdxHfc-HJ"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "# image_path = '/content/new_image.jpg'\n",
        "image_path = \"/content/gdrive/MyDrive/box_img.png\"\n",
        "preprocessed_image = preprocess_image(image_path).to(\"cuda\")\n",
        "output = run_inference(model, preprocessed_image)"
      ],
      "metadata": {
        "id": "t8ZzGDE8fRSd"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get keys\n",
        "keys = output.keys()\n",
        "print(\"Keys:\", keys)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8h3gUtIfYC6",
        "outputId": "26a716c4-4d6b-42cd-9d51-b168c97d1e2b"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys: odict_keys(['out', 'aux'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get values\n",
        "values = output.values()\n",
        "print(\"Values:\", values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqnkjZ8If7mZ",
        "outputId": "96ca4ebd-501e-49b2-857d-e90e433668bd"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Values: odict_values([tensor([[[[ 0.6515,  0.6515,  0.6515,  ...,  0.6528,  0.6528,  0.6528],\n",
            "          [ 0.6515,  0.6515,  0.6515,  ...,  0.6528,  0.6528,  0.6528],\n",
            "          [ 0.6515,  0.6515,  0.6515,  ...,  0.6528,  0.6528,  0.6528],\n",
            "          ...,\n",
            "          [ 0.5734,  0.5734,  0.5734,  ..., -0.0079, -0.0079, -0.0079],\n",
            "          [ 0.5734,  0.5734,  0.5734,  ..., -0.0079, -0.0079, -0.0079],\n",
            "          [ 0.5734,  0.5734,  0.5734,  ..., -0.0079, -0.0079, -0.0079]]]],\n",
            "       device='cuda:0'), tensor([[[[ 9.7353,  9.7353,  9.7353,  ...,  9.8105,  9.8105,  9.8105],\n",
            "          [ 9.7353,  9.7353,  9.7353,  ...,  9.8105,  9.8105,  9.8105],\n",
            "          [ 9.7353,  9.7353,  9.7353,  ...,  9.8105,  9.8105,  9.8105],\n",
            "          ...,\n",
            "          [ 9.4168,  9.4168,  9.4168,  ...,  6.3061,  6.3061,  6.3061],\n",
            "          [ 9.4168,  9.4168,  9.4168,  ...,  6.3061,  6.3061,  6.3061],\n",
            "          [ 9.4168,  9.4168,  9.4168,  ...,  6.3061,  6.3061,  6.3061]],\n",
            "\n",
            "         [[-0.6217, -0.6217, -0.6217,  ..., -1.2540, -1.2540, -1.2540],\n",
            "          [-0.6217, -0.6217, -0.6217,  ..., -1.2540, -1.2540, -1.2540],\n",
            "          [-0.6217, -0.6217, -0.6217,  ..., -1.2540, -1.2540, -1.2540],\n",
            "          ...,\n",
            "          [-0.6464, -0.6464, -0.6464,  ..., -1.0981, -1.0981, -1.0981],\n",
            "          [-0.6464, -0.6464, -0.6464,  ..., -1.0981, -1.0981, -1.0981],\n",
            "          [-0.6464, -0.6464, -0.6464,  ..., -1.0981, -1.0981, -1.0981]],\n",
            "\n",
            "         [[-1.9971, -1.9971, -1.9971,  ..., -1.2836, -1.2836, -1.2836],\n",
            "          [-1.9971, -1.9971, -1.9971,  ..., -1.2836, -1.2836, -1.2836],\n",
            "          [-1.9971, -1.9971, -1.9971,  ..., -1.2836, -1.2836, -1.2836],\n",
            "          ...,\n",
            "          [-1.1134, -1.1134, -1.1134,  ..., -1.4819, -1.4819, -1.4819],\n",
            "          [-1.1134, -1.1134, -1.1134,  ..., -1.4819, -1.4819, -1.4819],\n",
            "          [-1.1134, -1.1134, -1.1134,  ..., -1.4819, -1.4819, -1.4819]],\n",
            "\n",
            "         ...,\n",
            "\n",
            "         [[-1.6117, -1.6117, -1.6117,  ..., -0.3291, -0.3291, -0.3291],\n",
            "          [-1.6117, -1.6117, -1.6117,  ..., -0.3291, -0.3291, -0.3291],\n",
            "          [-1.6117, -1.6117, -1.6117,  ..., -0.3291, -0.3291, -0.3291],\n",
            "          ...,\n",
            "          [-0.2858, -0.2858, -0.2858,  ..., -0.8117, -0.8117, -0.8117],\n",
            "          [-0.2858, -0.2858, -0.2858,  ..., -0.8117, -0.8117, -0.8117],\n",
            "          [-0.2858, -0.2858, -0.2858,  ..., -0.8117, -0.8117, -0.8117]],\n",
            "\n",
            "         [[ 0.9899,  0.9899,  0.9899,  ..., -1.1273, -1.1273, -1.1273],\n",
            "          [ 0.9899,  0.9899,  0.9899,  ..., -1.1273, -1.1273, -1.1273],\n",
            "          [ 0.9899,  0.9899,  0.9899,  ..., -1.1273, -1.1273, -1.1273],\n",
            "          ...,\n",
            "          [ 0.1135,  0.1135,  0.1135,  ...,  1.1132,  1.1132,  1.1132],\n",
            "          [ 0.1135,  0.1135,  0.1135,  ...,  1.1132,  1.1132,  1.1132],\n",
            "          [ 0.1135,  0.1135,  0.1135,  ...,  1.1132,  1.1132,  1.1132]],\n",
            "\n",
            "         [[ 1.0852,  1.0852,  1.0852,  ...,  0.3961,  0.3961,  0.3961],\n",
            "          [ 1.0852,  1.0852,  1.0852,  ...,  0.3961,  0.3961,  0.3961],\n",
            "          [ 1.0852,  1.0852,  1.0852,  ...,  0.3961,  0.3961,  0.3961],\n",
            "          ...,\n",
            "          [ 0.9782,  0.9782,  0.9782,  ...,  2.4294,  2.4294,  2.4294],\n",
            "          [ 0.9782,  0.9782,  0.9782,  ...,  2.4294,  2.4294,  2.4294],\n",
            "          [ 0.9782,  0.9782,  0.9782,  ...,  2.4294,  2.4294,  2.4294]]]],\n",
            "       device='cuda:0')])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out_output = output[\"out\"]\n",
        "out_output.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xBN1ft8gC-T",
        "outputId": "33c6d48c-0eb9-4c77-90e4-2c81b2bf0828"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 1, 1024, 1024])"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out_output_0 = out_output[0]\n",
        "out_output_0.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nc_KF_GuhDv-",
        "outputId": "e6f7d981-f3fc-4ece-8def-9a70727b98c0"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 1024, 1024])"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert tensor to NumPy array and remove batch dimension\n",
        "out_output_0 = out_output_0.cpu()\n",
        "array = out_output_0.squeeze().numpy()\n",
        "\n",
        "# Plot the NumPy array\n",
        "plt.imshow(array, cmap='gray')  # Plot as grayscale\n",
        "plt.axis('off')  # Turn off axis\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "53b_0QK7gFmZ",
        "outputId": "b0feac80-d298-4949-c11a-53c854a3b6ee"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_image = preprocessed_image.cpu()"
      ],
      "metadata": {
        "id": "XWUVHCpKhnc-"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_image = preprocessed_image[0]"
      ],
      "metadata": {
        "id": "KuX78aW7iG6v"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_image= preprocessed_image"
      ],
      "metadata": {
        "id": "z5P3iMsZiS8A"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert tensor to NumPy array and reorder dimensions\n",
        "array = preprocessed_image.permute(1, 2, 0).numpy()\n",
        "\n",
        "# Plot the NumPy array\n",
        "plt.imshow(array)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "BuSB--ICh-6N",
        "outputId": "1bc8ebb2-5504-4434-f1a0-816e5e7d47ca"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}