nih-chest-vit (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e169098",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:00:41.118917Z",
     "iopub.status.busy": "2024-01-28T06:00:41.118566Z",
     "iopub.status.idle": "2024-01-28T06:00:45.205603Z",
     "shell.execute_reply": "2024-01-28T06:00:45.204662Z"
    },
    "papermill": {
     "duration": 4.098413,
     "end_time": "2024-01-28T06:00:45.208398",
     "exception": false,
     "start_time": "2024-01-28T06:00:41.109985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import h5py as h5py \n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import torch\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6988cfc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:00:45.230233Z",
     "iopub.status.busy": "2024-01-28T06:00:45.229592Z",
     "iopub.status.idle": "2024-01-28T06:00:45.281584Z",
     "shell.execute_reply": "2024-01-28T06:00:45.280712Z"
    },
    "papermill": {
     "duration": 0.06515,
     "end_time": "2024-01-28T06:00:45.283495",
     "exception": false,
     "start_time": "2024-01-28T06:00:45.218345",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "368b67a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:00:45.298343Z",
     "iopub.status.busy": "2024-01-28T06:00:45.298043Z",
     "iopub.status.idle": "2024-01-28T06:00:45.310164Z",
     "shell.execute_reply": "2024-01-28T06:00:45.308931Z"
    },
    "papermill": {
     "duration": 0.021495,
     "end_time": "2024-01-28T06:00:45.311954",
     "exception": false,
     "start_time": "2024-01-28T06:00:45.290459",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['images_003', 'images_012', 'LOG_CHESTXRAY.pdf', 'README_CHESTXRAY.pdf', 'BBox_List_2017.csv', 'images_009', 'images_008', 'images_007', 'test_list.txt', 'images_010', 'ARXIV_V5_CHESTXRAY.pdf', 'images_002', 'images_011', 'Data_Entry_2017.csv', 'images_001', 'train_val_list.txt', 'images_005', 'FAQ_CHESTXRAY.pdf', 'images_004', 'images_006']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(\"/kaggle/input/data\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8d4bd95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:00:45.327013Z",
     "iopub.status.busy": "2024-01-28T06:00:45.326735Z",
     "iopub.status.idle": "2024-01-28T06:00:46.217661Z",
     "shell.execute_reply": "2024-01-28T06:00:46.216777Z"
    },
    "papermill": {
     "duration": 0.900768,
     "end_time": "2024-01-28T06:00:46.219757",
     "exception": false,
     "start_time": "2024-01-28T06:00:45.318989",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding Labels\n",
      "No Finding            60361\n",
      "Infiltration          11795\n",
      "Atelectasis           11545\n",
      "Effusion               8040\n",
      "Consolidation          3331\n",
      "Nodule                 3009\n",
      "Mass                   2926\n",
      "Cardiomegaly           2404\n",
      "Pneumothorax           2200\n",
      "Edema                  1862\n",
      "Emphysema              1734\n",
      "Pleural_Thickening     1217\n",
      "Fibrosis               1215\n",
      "Pneumonia               325\n",
      "Hernia                  156\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(r\"/kaggle/input/data/Data_Entry_2017.csv\")\n",
    "\n",
    "# Remove the line that replaces 'No Finding'\n",
    "# df['Finding Labels'] = df['Finding Labels'].map(lambda x: x.replace('No Finding', ''))\n",
    "\n",
    "df['Finding Labels'] = df['Finding Labels'].map(lambda x: x.split('|')[0])\n",
    "df['Finding Labels'] = df['Finding Labels'].map(lambda x: x.split(','))\n",
    "df['Finding Labels'] = df['Finding Labels'].map(lambda x: x[0])\n",
    "df['Finding Labels'] = df['Finding Labels'].map(lambda x: x.strip())\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "df['Finding Labels'].value_counts().plot(kind='bar')\n",
    "plt.title('Distribution of Labels')\n",
    "plt.xlabel('Labels')\n",
    "plt.ylabel('Count')\n",
    "plt.show()\n",
    "\n",
    "# Print the count of all labels\n",
    "print(df['Finding Labels'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2b2dae4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:00:46.237156Z",
     "iopub.status.busy": "2024-01-28T06:00:46.236857Z",
     "iopub.status.idle": "2024-01-28T06:00:47.176864Z",
     "shell.execute_reply": "2024-01-28T06:00:47.175966Z"
    },
    "papermill": {
     "duration": 0.95152,
     "end_time": "2024-01-28T06:00:47.179120",
     "exception": false,
     "start_time": "2024-01-28T06:00:46.227600",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of labels are : 13\n",
      "Label Emphysema: 1734 images, weight: 63.18339100346021\n",
      "Label Effusion: 8040 images, weight: 13.626865671641792\n",
      "Label Nodule: 3009 images, weight: 36.410767696909275\n",
      "Label Pneumothorax: 2200 images, weight: 49.8\n",
      "Label Atelectasis: 11545 images, weight: 9.489822433954092\n",
      "Label Pleural_Thickening: 1217 images, weight: 90.02465078060806\n",
      "Label Mass: 2926 images, weight: 37.443609022556394\n",
      "Label Edema: 1862 images, weight: 58.83995703544576\n",
      "Label Consolidation: 3331 images, weight: 32.89102371660162\n",
      "Label Infiltration: 11795 images, weight: 9.288681644764731\n",
      "Label Fibrosis: 1215 images, weight: 90.17283950617283\n",
      "Label Pneumonia: 325 images, weight: 337.10769230769233\n",
      "Label No Finding: 60361 images, weight: 1.8150792730405394\n"
     ]
    }
   ],
   "source": [
    "# Initialize a dictionary to store the counts\n",
    "import torch.nn as nn\n",
    "label_counts = {}\n",
    "label_map={'Emphysema':0,\n",
    "           'Effusion':1,\n",
    "           'Nodule':2,\n",
    "           'Pneumothorax':3,\n",
    "           'Atelectasis':4,\n",
    "           'Pleural_Thickening':5,\n",
    "           'Mass':6,\n",
    "           'Edema':7,\n",
    "           'Consolidation':8,\n",
    "           'Infiltration':9,\n",
    "           'Fibrosis':10,\n",
    "           'Pneumonia':11,\n",
    "           'No Finding':12\n",
    "          }\n",
    "print(\"Length of labels are :\",len(label_map))\n",
    "\n",
    "# Iterate over the labels\n",
    "for label in label_map.keys():\n",
    "    # Count the number of times each label appears in the 'Finding Labels' column\n",
    "    label_counts[label] = df['Finding Labels'].str.contains(label).sum()\n",
    "\n",
    "# Calculate class weights\n",
    "total_count = sum(label_counts.values())\n",
    "class_weights = {k: total_count/v for k, v in label_counts.items()}\n",
    "\n",
    "# Convert class weights to tensor\n",
    "# weights = [class_weights[i] for i in range(len(class_weights))]\n",
    "# class_weights_tensor = torch.FloatTensor(weights).to(device)\n",
    "\n",
    "# Convert class weights to tensor\n",
    "weights = [class_weights[label] for label in label_map.keys()]\n",
    "class_weights_tensor = torch.FloatTensor(weights).to(device)\n",
    "\n",
    "# Define your criterion with the class weights\n",
    "criterion = nn.BCEWithLogitsLoss(pos_weight=class_weights_tensor)\n",
    "\n",
    "# Print the counts and class weights\n",
    "for label, count in label_counts.items():\n",
    "    print(f'Label {label}: {count} images, weight: {class_weights[label]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ad43557",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:00:47.196233Z",
     "iopub.status.busy": "2024-01-28T06:00:47.195922Z",
     "iopub.status.idle": "2024-01-28T06:00:47.206980Z",
     "shell.execute_reply": "2024-01-28T06:00:47.206096Z"
    },
    "papermill": {
     "duration": 0.021709,
     "end_time": "2024-01-28T06:00:47.208917",
     "exception": false,
     "start_time": "2024-01-28T06:00:47.187208",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "class NIH_Chest_Xray_Dataset(Dataset):\n",
    "    def __init__(self, data_dir, df, label_map, transform=None):\n",
    "        self.data_dir = data_dir\n",
    "        self.transform = transform\n",
    "        self.image_paths = []\n",
    "        self.labels = []\n",
    "        self.df = df\n",
    "        self.label_map = label_map\n",
    "\n",
    "        for i, row in self.df.iterrows():\n",
    "            img_name = row['Image Index']\n",
    "            labels = row['Finding Labels'].split(\"|\")\n",
    "            # Only include the image if its label is in label_map\n",
    "            if any(label in self.label_map for label in labels):\n",
    "                for i in range(1, 13):\n",
    "                    img_path = os.path.join(self.data_dir, f'images_{str(i).zfill(3)}/images/', img_name)\n",
    "                    if os.path.isfile(img_path):\n",
    "                        self.image_paths.append(img_path)\n",
    "                        self.labels.append(labels)\n",
    "                        break\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.image_paths)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path = self.image_paths[idx]\n",
    "        labels = self.labels[idx]\n",
    "\n",
    "        img = Image.open(img_path).convert('RGB')\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "\n",
    "        num_labels = len(self.label_map)\n",
    "        binary_label = torch.zeros(num_labels)\n",
    "\n",
    "        for label in labels:\n",
    "            if label in self.label_map:\n",
    "                binary_label[self.label_map[label]] = 1\n",
    "\n",
    "        return img, binary_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0186f1a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:00:47.225482Z",
     "iopub.status.busy": "2024-01-28T06:00:47.225227Z",
     "iopub.status.idle": "2024-01-28T06:11:45.513697Z",
     "shell.execute_reply": "2024-01-28T06:11:45.512636Z"
    },
    "papermill": {
     "duration": 658.307478,
     "end_time": "2024-01-28T06:11:45.524297",
     "exception": false,
     "start_time": "2024-01-28T06:00:47.216819",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DF size (Total)  112120\n",
      "Train dataset size: 99811\n",
      "Test dataset size: 11104\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from torchvision import transforms\n",
    "data_dir = '/kaggle/input/data'\n",
    "df = pd.read_csv(os.path.join(data_dir, 'Data_Entry_2017.csv'))\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.RandomRotation(degrees=10),\n",
    "    transforms.RandomHorizontalFlip(p=0.5),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "\n",
    "# Split the DataFrame into a training set and a test set\n",
    "train_df, test_df = train_test_split(df, test_size=0.1, random_state=42)\n",
    "\n",
    "# Create the train and test datasets \n",
    "train_dataset = NIH_Chest_Xray_Dataset(data_dir, train_df, label_map, transform=transform)\n",
    "test_dataset = NIH_Chest_Xray_Dataset(data_dir, test_df, label_map, transform=transform)\n",
    "\n",
    "print('DF size (Total) ',len(df))\n",
    "print('Train dataset size:', len(train_dataset))\n",
    "print('Test dataset size:', len(test_dataset))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf6c1526",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:11:45.541838Z",
     "iopub.status.busy": "2024-01-28T06:11:45.541541Z",
     "iopub.status.idle": "2024-01-28T06:11:45.546717Z",
     "shell.execute_reply": "2024-01-28T06:11:45.545892Z"
    },
    "papermill": {
     "duration": 0.016445,
     "end_time": "2024-01-28T06:11:45.548762",
     "exception": false,
     "start_time": "2024-01-28T06:11:45.532317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=2)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3d627b24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:11:45.565727Z",
     "iopub.status.busy": "2024-01-28T06:11:45.565443Z",
     "iopub.status.idle": "2024-01-28T06:11:52.581652Z",
     "shell.execute_reply": "2024-01-28T06:11:52.580871Z"
    },
    "papermill": {
     "duration": 7.027548,
     "end_time": "2024-01-28T06:11:52.584118",
     "exception": false,
     "start_time": "2024-01-28T06:11:45.556570",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vit_b_16-c867db91.pth\" to /root/.cache/torch/hub/checkpoints/vit_b_16-c867db91.pth\n",
      "100%|██████████| 330M/330M [00:05<00:00, 63.6MB/s]\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "from torchvision.models import vision_transformer\n",
    "\n",
    "#model.head = torch.nn.Linear(out_features=14,in_features=768)  #14\n",
    "# Get pretrained weights for ViT-Base\n",
    "pretrained_vit_weights = torchvision.models.ViT_B_16_Weights.DEFAULT \n",
    "#pretrained_vit_weights=torchvision.models.vit_l_16(pretrained=True)\n",
    "\n",
    "# Setup a ViT model instance with pretrained weights\n",
    "#model = torchvision.models.vit_l_16(weights=pretrained_vit_weights)\n",
    "model = torchvision.models.vit_b_16(weights=pretrained_vit_weights)\n",
    "\n",
    "# Freeze the base parameters\n",
    "for parameter in model.parameters():\n",
    "    parameter.requires_grad = False\n",
    "    \n",
    "# change your out_features according to number of classes defined, we have 14 classes \n",
    "#model.heads = nn.Linear(in_features=1024, out_features=13)\n",
    "model.heads = nn.Linear(in_features=768, out_features=13)\n",
    "\n",
    "model.to(device)  # to put the whole architecture to gpu memory\n",
    "#print(model)\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d91bda",
   "metadata": {
    "papermill": {
     "duration": 0.012317,
     "end_time": "2024-01-28T06:11:52.610284",
     "exception": false,
     "start_time": "2024-01-28T06:11:52.597967",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# run this next code cell for scratch training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bf595bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:11:52.635642Z",
     "iopub.status.busy": "2024-01-28T06:11:52.635320Z",
     "iopub.status.idle": "2024-01-28T06:11:52.640160Z",
     "shell.execute_reply": "2024-01-28T06:11:52.639326Z"
    },
    "papermill": {
     "duration": 0.019895,
     "end_time": "2024-01-28T06:11:52.642170",
     "exception": false,
     "start_time": "2024-01-28T06:11:52.622275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import torch.nn as nn\n",
    "# import torch.optim as optim\n",
    "# from torchvision.models import vit_b_16\n",
    "# from torchvision.models import vit_l_16\n",
    "\n",
    "# # Setup a ViT model instance without pretrained weights\n",
    "# model = vit_l_16(pretrained=False)\n",
    "\n",
    "# # Freeze the base parameters\n",
    "# for parameter in model.parameters():\n",
    "#     parameter.requires_grad = False\n",
    "    \n",
    "# # Change your out_features according to number of classes defined, we have 14 classes \n",
    "# #model.heads = nn.Linear(in_features=768, out_features=13) #for vit_b_16\n",
    "# model.heads = nn.Linear(in_features=1024, out_features=13) #for vit_l_16\n",
    "\n",
    "# model.to(device)  # To put the whole architecture to GPU memory\n",
    "# print(model)\n",
    "# criterion = nn.BCEWithLogitsLoss()\n",
    "# optimizer = optim.Adam(model.parameters(), lr=1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be944324",
   "metadata": {
    "papermill": {
     "duration": 0.011717,
     "end_time": "2024-01-28T06:11:52.665619",
     "exception": false,
     "start_time": "2024-01-28T06:11:52.653902",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c375178",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T06:11:52.691283Z",
     "iopub.status.busy": "2024-01-28T06:11:52.690505Z",
     "iopub.status.idle": "2024-01-28T08:52:08.012404Z",
     "shell.execute_reply": "2024-01-28T08:52:08.011108Z"
    },
    "papermill": {
     "duration": 9615.340057,
     "end_time": "2024-01-28T08:52:08.017571",
     "exception": false,
     "start_time": "2024-01-28T06:11:52.677514",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3120/3120 [27:59<00:00,  1.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/6], Loss: 1.5270, Accuracy: [0.571550190448761, 0.5707988142967224, 0.1845988780260086, 0.38862448930740356, 0.6011361479759216, 0.2008696347475052, 0.2663734257221222, 0.7087194323539734, 0.5639959573745728, 0.31723958253860474, 0.49879270792007446, 0.06688641011714935, 0.6226868629455566]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3120/3120 [25:19<00:00,  2.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2/6], Loss: 1.4995, Accuracy: [0.6042720675468445, 0.5868892073631287, 0.207822784781456, 0.4264259338378906, 0.6094117760658264, 0.22443416714668274, 0.29272323846817017, 0.7169349789619446, 0.5753373503684998, 0.326066255569458, 0.5244912505149841, 0.11036859452724457, 0.6250613331794739]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3120/3120 [25:55<00:00,  2.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [3/6], Loss: 1.4933, Accuracy: [0.6002143621444702, 0.5873700976371765, 0.21729066967964172, 0.4392201006412506, 0.609431803226471, 0.2434501051902771, 0.2962799668312073, 0.7191792130470276, 0.5763092041015625, 0.3237919509410858, 0.5334081053733826, 0.11494724452495575, 0.6278566122055054]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3120/3120 [27:39<00:00,  1.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [4/6], Loss: 1.4943, Accuracy: [0.6011962294578552, 0.5894440412521362, 0.20559857785701752, 0.43417057394981384, 0.6093215942382812, 0.23133721947669983, 0.29578903317451477, 0.7191391587257385, 0.5769404172897339, 0.323471337556839, 0.5328971743583679, 0.1224915012717247, 0.6254721283912659]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3120/3120 [26:31<00:00,  1.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/6], Loss: 1.4946, Accuracy: [0.6082395315170288, 0.5853462815284729, 0.2146957665681839, 0.4352726638317108, 0.6081494092941284, 0.23222890496253967, 0.29198184609413147, 0.7205818891525269, 0.5779122114181519, 0.32085639238357544, 0.5358126759529114, 0.10760336369276047, 0.625963032245636]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3120/3120 [26:49<00:00,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [6/6], Loss: 1.4939, Accuracy: [0.6009557843208313, 0.58919358253479, 0.21692998707294464, 0.42704710364341736, 0.6063058972358704, 0.23010489344596863, 0.294656902551651, 0.7201811075210571, 0.5772910714149475, 0.3291320502758026, 0.5348508358001709, 0.11324402689933777, 0.6249611377716064]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "model.train()\n",
    "num_epochs = 6\n",
    "\n",
    "train_losses = []  # List to store training losses\n",
    "train_accuracies = []  # List to store training accuracies\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    running_loss = 0.0\n",
    "    corrects = torch.zeros(13).to(device)  # change torch.zeros(No.classes) according to classes defined above\n",
    "    total_samples = 0\n",
    "    \n",
    "    for images, labels in tqdm(train_dataloader):\n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item() * images.size(0)\n",
    "        \n",
    "        # Calculate training accuracy\n",
    "        predicted_labels = torch.sigmoid(outputs) > 0.5\n",
    "        corrects += (predicted_labels == labels).sum(dim=0).float()\n",
    "        total_samples += images.size(0)\n",
    "    \n",
    "    epoch_loss = running_loss / len(train_dataset)\n",
    "    epoch_accuracy = corrects / total_samples\n",
    "    \n",
    "    # Print and save training loss and accuracy\n",
    "    print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {}'.format(epoch+1, num_epochs, epoch_loss, epoch_accuracy.tolist()))\n",
    "    \n",
    "    train_losses.append(epoch_loss)\n",
    "    train_accuracies.append(epoch_accuracy.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6631998b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T08:52:11.101839Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot training loss curve\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(range(1, num_epochs+1), train_losses, marker='o', label='Training Loss')\n",
    "plt.title('Training Loss Curve')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot training accuracy curve\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i in range(2):  # Plot accuracy for each label\n",
    "    plt.plot(range(1, num_epochs+1), [acc[i] for acc in train_accuracies], marker='o', label=f'Training Accuracy (Label {i+1})')\n",
    "\n",
    "plt.title('Training Accuracy Curve')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ac1acbf9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T08:52:14.884373Z",
     "iopub.status.busy": "2024-01-28T08:52:14.883981Z",
     "iopub.status.idle": "2024-01-28T08:52:15.386445Z",
     "shell.execute_reply": "2024-01-28T08:52:15.385457Z"
    },
    "papermill": {
     "duration": 2.000503,
     "end_time": "2024-01-28T08:52:15.388835",
     "exception": false,
     "start_time": "2024-01-28T08:52:13.388332",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'weights.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b716ef73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T08:52:18.402074Z",
     "iopub.status.busy": "2024-01-28T08:52:18.401409Z",
     "iopub.status.idle": "2024-01-28T08:55:35.090959Z",
     "shell.execute_reply": "2024-01-28T08:55:35.089852Z"
    },
    "papermill": {
     "duration": 198.162747,
     "end_time": "2024-01-28T08:55:35.094732",
     "exception": false,
     "start_time": "2024-01-28T08:52:16.931985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 347/347 [03:16<00:00,  1.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 1.4910 | Validation Acc: [0.40381842851638794, 0.48297908902168274, 0.11338256299495697, 0.4843299686908722, 0.746577799320221, 0.07627881318330765, 0.36473342776298523, 0.7151476740837097, 0.5989733338356018, 0.19434437155723572, 0.5887967944145203, 0.1255403459072113, 0.6569704413414001] | AUC score: 0.7389\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Evaluate\n",
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model.eval()\n",
    "auc_scores = []    \n",
    "running_loss = 0.0\n",
    "running_corrects = torch.zeros(13).to(device)\n",
    "total_samples = 0\n",
    "num_labels = 13  # change lables here\n",
    "with torch.no_grad():\n",
    "    y_true = []\n",
    "    y_pred = []\n",
    "    for inputs, labels in tqdm(test_dataloader):\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        predicted_labels = torch.sigmoid(outputs) > 0.5\n",
    "        predicted_probs = torch.sigmoid(outputs)\n",
    "                \n",
    "        running_corrects += (predicted_labels == labels).sum(dim=0).float()\n",
    "        total_samples += inputs.size(0)\n",
    "        y_pred.append(predicted_probs.cpu().numpy())\n",
    "        y_true.append(labels.cpu().numpy())\n",
    "\n",
    "epoch_loss = running_loss / total_samples\n",
    "epoch_acc = running_corrects / total_samples\n",
    "y_pred = np.concatenate(y_pred)\n",
    "y_true = np.concatenate(y_true)\n",
    "fpr, tpr, thresholds = roc_curve(y_true.ravel(), y_pred.ravel())\n",
    "auc_score = roc_auc_score(y_true, y_pred, average='macro')\n",
    "\n",
    "#auc_score = roc_auc_score(y_true, y_pred, multi_class='ovr') \n",
    "print(f'Validation Loss: {epoch_loss:.4f} | Validation Acc: {epoch_acc.tolist()} | AUC score: {auc_score:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73576da3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T08:55:38.215120Z",
     "iopub.status.busy": "2024-01-28T08:55:38.214735Z",
     "iopub.status.idle": "2024-01-28T08:58:42.923937Z",
     "shell.execute_reply": "2024-01-28T08:58:42.922854Z"
    },
    "papermill": {
     "duration": 186.299371,
     "end_time": "2024-01-28T08:58:42.926516",
     "exception": false,
     "start_time": "2024-01-28T08:55:36.627145",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 347/347 [03:03<00:00,  1.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Emphysema': {'precision': 0.03237044563797358, 'recall': 0.9291666666666667, 'f1-score': 0.06256136905596858, 'support': 240}, 'Effusion': {'precision': 0.1838351822503962, 'recall': 0.9444855662472242, 'f1-score': 0.3077665219488664, 'support': 1351}, 'Nodule': {'precision': 0.058266360505166474, 'recall': 0.9886363636363636, 'f1-score': 0.1100469822912902, 'support': 616}, 'Pneumothorax': {'precision': 0.0773002124530152, 'recall': 0.9131274131274131, 'f1-score': 0.1425342775350309, 'support': 518}, 'Atelectasis': {'precision': 0.21236559139784947, 'recall': 0.5683453237410072, 'f1-score': 0.30919765166340507, 'support': 1112}, 'Pleural_Thickening': {'precision': 0.03023983315954119, 'recall': 0.996875, 'f1-score': 0.05869905235072223, 'support': 320}, 'Mass': {'precision': 0.06665776115415442, 'recall': 0.8633217993079585, 'f1-score': 0.12375992063492063, 'support': 578}, 'Edema': {'precision': 0.05893129770992366, 'recall': 0.8654708520179372, 'f1-score': 0.11034877072612921, 'support': 223}, 'Consolidation': {'precision': 0.06672369546621043, 'recall': 0.7536231884057971, 'f1-score': 0.12259332023575638, 'support': 414}, 'Infiltration': {'precision': 0.18252516010978956, 'recall': 0.9960059910134798, 'f1-score': 0.3085131060078868, 'support': 2003}, 'Fibrosis': {'precision': 0.02967741935483871, 'recall': 0.8117647058823529, 'f1-score': 0.05726141078838174, 'support': 170}, 'Pneumonia': {'precision': 0.01394543973941368, 'recall': 0.958041958041958, 'f1-score': 0.02749071937393398, 'support': 143}, 'No Finding': {'precision': 0.63889903787602, 'recall': 0.8612707273025776, 'f1-score': 0.733603691791358, 'support': 6091}, 'micro avg': {'precision': 0.12962206113274108, 'recall': 0.8746643442920387, 'f1-score': 0.22578378935339136, 'support': 13779}, 'macro avg': {'precision': 0.12705672590879175, 'recall': 0.8807796581069798, 'f1-score': 0.19033667649258845, 'support': 13779}, 'weighted avg': {'precision': 0.357163501007035, 'recall': 0.8746643442920387, 'f1-score': 0.448648855308719, 'support': 13779}, 'samples avg': {'precision': 0.1378995291751956, 'recall': 0.8704377229998628, 'f1-score': 0.23296832130049708, 'support': 13779}}\n",
      "Average F1 Score: 0.19033667649258845\n",
      "Average Recall: 0.8807796581069798\n",
      "Average Specificity: 0.11922034189302035\n",
      "Validation Loss: 1.4888 | Validation Acc: [0.39814481139183044, 0.48306915163993835, 0.11293227225542068, 0.4874819815158844, 0.7456772327423096, 0.0786203145980835, 0.3636527359485626, 0.719740629196167, 0.5978025794029236, 0.19461454451084137, 0.5907780528068542, 0.1270713210105896, 0.6568803787231445] | AUC score: 0.7387\n",
      "Validation Loss: 1.4888 | Validation Acc: [0.39814481139183044, 0.48306915163993835, 0.11293227225542068, 0.4874819815158844, 0.7456772327423096, 0.0786203145980835, 0.3636527359485626, 0.719740629196167, 0.5978025794029236, 0.19461454451084137, 0.5907780528068542, 0.1270713210105896, 0.6568803787231445] | AUC score: 0.7387\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "model.eval()\n",
    "auc_scores = []    \n",
    "running_loss = 0.0\n",
    "running_corrects = torch.zeros(13).to(device)\n",
    "total_samples = 0\n",
    "num_labels = 13  # Change the number of labels here\n",
    "\n",
    "with torch.no_grad():\n",
    "    y_true = []\n",
    "    y_pred = []\n",
    "    for inputs, labels in tqdm(test_dataloader):\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        predicted_labels = torch.sigmoid(outputs) > 0.5\n",
    "        predicted_probs = torch.sigmoid(outputs)\n",
    "                \n",
    "        running_corrects += (predicted_labels == labels).sum(dim=0).float()\n",
    "        total_samples += inputs.size(0)\n",
    "        y_pred.append(predicted_probs.cpu().numpy())\n",
    "        y_true.append(labels.cpu().numpy())\n",
    "\n",
    "    epoch_loss = running_loss / total_samples\n",
    "    epoch_acc = running_corrects / total_samples\n",
    "    y_pred = np.concatenate(y_pred)\n",
    "    y_true = np.concatenate(y_true)\n",
    "    fpr, tpr, thresholds = roc_curve(y_true.ravel(), y_pred.ravel())\n",
    "    auc_score = roc_auc_score(y_true, y_pred, average='macro')\n",
    "\n",
    "    \n",
    "    \n",
    "# Convert probabilities to class labels\n",
    "    y_pred_labels = (y_pred > 0.5).astype(int)\n",
    "\n",
    "# Calculate and print the classification report\n",
    "    report = classification_report(y_true, y_pred_labels, target_names=label_map.keys(), output_dict=True)\n",
    "    print(report)\n",
    "\n",
    "# Calculate and print the average F1 score, recall, and specificity\n",
    "    avg_f1 = np.mean([report[label]['f1-score'] for label in label_map.keys()])\n",
    "    avg_recall = np.mean([report[label]['recall'] for label in label_map.keys()])\n",
    "    avg_specificity = np.mean([1 - report[label]['recall'] for label in label_map.keys()])\n",
    "\n",
    "    print(f'Average F1 Score: {avg_f1}')\n",
    "    print(f'Average Recall: {avg_recall}')\n",
    "    print(f'Average Specificity: {avg_specificity}')\n",
    "\n",
    "# Print validation loss, accuracy, and AUC score\n",
    "    print(f'Validation Loss: {epoch_loss:.4f} | Validation Acc: {epoch_acc.tolist()} | AUC score: {auc_score:.4f}')\n",
    "\n",
    "\n",
    "    # Print validation loss, accuracy, and AUC score\n",
    "    print(f'Validation Loss: {epoch_loss:.4f} | Validation Acc: {epoch_acc.tolist()} | AUC score: {auc_score:.4f}')\n",
    "\n",
    "    # Plotting accuracy and loss curves\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(range(1, num_epochs+1), train_losses, marker='o', label='Training Loss')\n",
    "    plt.title('Validation Loss Curve')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    for i in range(num_labels):  # Plot accuracy for each label\n",
    "        plt.plot(range(1, num_epochs+1), [acc[i] for acc in train_accuracies], marker='o', label=f'Validation Accuracy (Label {i+1})')\n",
    "\n",
    "    plt.title('Validation Accuracy Curve')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b3e9b0",
   "metadata": {
    "papermill": {
     "duration": 1.630471,
     "end_time": "2024-01-28T08:58:46.111622",
     "exception": false,
     "start_time": "2024-01-28T08:58:44.481151",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4988a693",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T08:58:49.251980Z",
     "iopub.status.busy": "2024-01-28T08:58:49.251593Z",
     "iopub.status.idle": "2024-01-28T08:58:49.257483Z",
     "shell.execute_reply": "2024-01-28T08:58:49.256554Z"
    },
    "papermill": {
     "duration": 1.627882,
     "end_time": "2024-01-28T08:58:49.259382",
     "exception": false,
     "start_time": "2024-01-28T08:58:47.631500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # plot the ROC curve\n",
    "# plt.figure()\n",
    "# plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (AUC = %0.2f)' % auc_score)\n",
    "# plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "# plt.xlim([0.0, 1.0])\n",
    "# plt.ylim([0.0, 1.05])\n",
    "# plt.xlabel('False Positive Rate')\n",
    "# plt.ylabel('True Positive Rate')\n",
    "# plt.title('ROC Plot Curve')\n",
    "# plt.legend(loc=\"lower right\")\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "# # Confusion matrix\n",
    "# from sklearn.metrics import confusion_matrix\n",
    "# import seaborn as sns\n",
    "# import numpy as np\n",
    "\n",
    "# # Convert lists to numpy arrays\n",
    "# y_true_np = np.array(y_true)\n",
    "# y_pred_np = np.array(y_pred)\n",
    "\n",
    "# # Calculate confusion matrix\n",
    "# #conf_mat = confusion_matrix(y_true_np.argmax(axis=1), y_pred_np.argmax(axis=1))\n",
    "\n",
    "# #conf_mat = confusion_matrix(y_true.argmax(axis=1), y_pred.argmax(axis=1))\n",
    "\n",
    "# plt.figure(figsize=(10, 10))\n",
    "# sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g')\n",
    "# plt.title('Confusion Matrix')\n",
    "# plt.xlabel('Predicted Labels')\n",
    "# plt.ylabel('True Labels')\n",
    "# plt.show()\n",
    "\n",
    "# # Precision-recall curve\n",
    "# from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# precision, recall, _ = precision_recall_curve(y_true.ravel(), y_pred.ravel())\n",
    "\n",
    "# plt.figure()\n",
    "# plt.plot(recall, precision, color='darkorange', lw=2)\n",
    "# plt.xlim([0.0, 1.0])\n",
    "# plt.ylim([0.0, 1.05])\n",
    "# plt.xlabel('Recall')\n",
    "# plt.ylabel('Precision')\n",
    "# plt.title('Precision-Recall Curve')\n",
    "# plt.show()\n",
    "\n",
    "# # Histogram of predicted probabilities\n",
    "# plt.figure()\n",
    "# plt.hist(y_pred.ravel(), bins=20)\n",
    "# plt.xlabel('Predicted Probability')\n",
    "# plt.ylabel('Frequency')\n",
    "# plt.title('Histogram of Predicted Probabilities')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e96c63d",
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2d983ccd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-28T08:58:58.760790Z",
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     "shell.execute_reply": "2024-01-28T08:59:26.946262Z"
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     "duration": 29.804989,
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     "exception": false,
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting grad-cam\r\n",
      "  Downloading grad-cam-1.5.0.tar.gz (7.8 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m54.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \bdone\r\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from grad-cam) (1.24.3)\r\n",
      "Requirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from grad-cam) (10.1.0)\r\n",
      "Requirement already satisfied: torch>=1.7.1 in /opt/conda/lib/python3.10/site-packages (from grad-cam) (2.0.0)\r\n",
      "Requirement already satisfied: torchvision>=0.8.2 in /opt/conda/lib/python3.10/site-packages (from grad-cam) (0.15.1)\r\n",
      "Collecting ttach (from grad-cam)\r\n",
      "  Downloading ttach-0.0.3-py3-none-any.whl (9.8 kB)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from grad-cam) (4.66.1)\r\n",
      "Requirement already satisfied: opencv-python in /opt/conda/lib/python3.10/site-packages (from grad-cam) (4.8.1.78)\r\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from grad-cam) (3.7.4)\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from grad-cam) (1.2.2)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.1->grad-cam) (3.12.2)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.1->grad-cam) (4.5.0)\r\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.1->grad-cam) (1.12)\r\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.1->grad-cam) (3.1)\r\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.7.1->grad-cam) (3.1.2)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision>=0.8.2->grad-cam) (2.31.0)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->grad-cam) (1.1.0)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib->grad-cam) (0.11.0)\r\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->grad-cam) (4.42.1)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->grad-cam) (1.4.4)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->grad-cam) (21.3)\r\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->grad-cam) (3.0.9)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib->grad-cam) (2.8.2)\r\n",
      "Requirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->grad-cam) (1.11.4)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->grad-cam) (1.3.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->grad-cam) (3.2.0)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->grad-cam) (1.16.0)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.7.1->grad-cam) (2.1.3)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision>=0.8.2->grad-cam) (3.2.0)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision>=0.8.2->grad-cam) (3.4)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision>=0.8.2->grad-cam) (1.26.15)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision>=0.8.2->grad-cam) (2023.11.17)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.7.1->grad-cam) (1.3.0)\r\n",
      "Building wheels for collected packages: grad-cam\r\n",
      "  Building wheel for grad-cam (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for grad-cam: filename=grad_cam-1.5.0-py3-none-any.whl size=38070 sha256=61d3b0a0fff6bfce32b1d71af0392fe47cea935cf59511636373452752b4b24a\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/5b/e5/3d/8548241d5cffe53ad1476c566a61ad9bf09cc61a9430f09726\r\n",
      "Successfully built grad-cam\r\n",
      "Installing collected packages: ttach, grad-cam\r\n",
      "Successfully installed grad-cam-1.5.0 ttach-0.0.3\r\n"
     ]
    }
   ],
   "source": [
    "!pip install grad-cam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ce72f7a9",
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: timm in /opt/conda/lib/python3.10/site-packages (0.9.12)\r\n",
      "Requirement already satisfied: torch>=1.7 in /opt/conda/lib/python3.10/site-packages (from timm) (2.0.0)\r\n",
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (from timm) (0.15.1)\r\n",
      "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from timm) (6.0.1)\r\n",
      "Requirement already satisfied: huggingface-hub in /opt/conda/lib/python3.10/site-packages (from timm) (0.19.4)\r\n",
      "Requirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from timm) (0.4.1)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (3.12.2)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (4.5.0)\r\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (1.12)\r\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (3.1)\r\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (3.1.2)\r\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (2023.12.2)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (2.31.0)\r\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (4.66.1)\r\n",
      "Requirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (21.3)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision->timm) (1.24.3)\r\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision->timm) (10.1.0)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.9->huggingface-hub->timm) (3.0.9)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.7->timm) (2.1.3)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (3.2.0)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (3.4)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (1.26.15)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (2023.11.17)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.7->timm) (1.3.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install timm"
   ]
  },
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     "text": [
      "Using GPU for acceleration\n"
     ]
    },
    {
     "data": {
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       "model_id": "f8fd6cb34ae245c0bd377071472c1a8b",
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       "version_minor": 0
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      "text/plain": [
       "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
      ]
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   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import torch\n",
    "from torchvision import transforms\n",
    "import timm\n",
    "#from your_module import ViT_B_16  # Import your ViT model\n",
    "from torchvision.models import vision_transformer as vit\n",
    "from torchvision.models import vision_transformer\n",
    "from pytorch_grad_cam import GradCAM    \n",
    "from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image\n",
    "from pytorch_grad_cam.ablation_layer import AblationLayerVit\n",
    "\n",
    "def reshape_transform(tensor, height=14, width=14):\n",
    "    result = tensor[:, 1:, :].reshape(tensor.size(0),\n",
    "                                      height, width, tensor.size(2))\n",
    "    result = result.transpose(2, 3).transpose(1, 2)\n",
    "    return result\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    use_cuda = torch.cuda.is_available()\n",
    "    image_path = r'/kaggle/input/data/images_004/images/00006585_008.png'\n",
    "    aug_smooth = False\n",
    "    eigen_smooth = False\n",
    "    method = 'gradcam'\n",
    "\n",
    "    if use_cuda:\n",
    "        print('Using GPU for acceleration')\n",
    "    else:\n",
    "        print('Using CPU for computation')\n",
    "\n",
    "    # Load your ViT model\n",
    "    #model = ViT_B_16(pretrained=True) \n",
    "    # Adjust the parameters accordingly\n",
    "    #model = vit.vit_base_patch16_224(pretrained=True)\n",
    "    # Get pretrained weights for ViT-Base\n",
    "    pretrained_vit_weights = torchvision.models.ViT_B_16_Weights.DEFAULT \n",
    "\n",
    "# Setup a ViT model instance with pretrained weights\n",
    "    #model = torchvision.models.vit_b_16(weights=pretrained_vit_weights)\n",
    "    model = timm.create_model('vit_base_patch16_224', pretrained=True)  # Adjust the parameters accordingly\n",
    "\n",
    "    model.eval()\n",
    "\n",
    "    if use_cuda:\n",
    "        model = model.cuda()\n",
    "\n",
    "    target_layers = [model.blocks[-1].norm1]\n",
    "\n",
    "    methods = {\"gradcam\": GradCAM}\n",
    "\n",
    "    if method not in list(methods.keys()):\n",
    "        raise Exception(f\"method should be one of {list(methods.keys())}\")\n",
    "\n",
    "    if method == \"ablationcam\":\n",
    "        cam = methods[method](model=model,\n",
    "                              target_layers=target_layers,\n",
    "                              reshape_transform=reshape_transform,\n",
    "                              ablation_layer=AblationLayerVit())\n",
    "    else:\n",
    "        cam = methods[method](model=model,\n",
    "                              target_layers=target_layers,\n",
    "                              reshape_transform=reshape_transform)\n",
    "\n",
    "    rgb_img = cv2.imread(image_path, 1)[:, :, ::-1]\n",
    "    rgb_img = cv2.resize(rgb_img, (224, 224))\n",
    "    rgb_img = np.float32(rgb_img) / 255\n",
    "    input_tensor = preprocess_image(rgb_img, mean=[0.5, 0.5, 0.5],\n",
    "                                    std=[0.5, 0.5, 0.5])\n",
    "\n",
    "    targets = None\n",
    "\n",
    "    cam.batch_size = 32\n",
    "\n",
    "    grayscale_cam = cam(input_tensor=input_tensor,\n",
    "                        targets=targets,\n",
    "                        eigen_smooth=eigen_smooth,\n",
    "                        aug_smooth=aug_smooth)\n",
    "\n",
    "    grayscale_cam = grayscale_cam[0, :]\n",
    "\n",
    "    cam_image = show_cam_on_image(rgb_img, grayscale_cam)\n",
    "    cv2.imwrite(f'{method}_cams8.jpg', cam_image)\n",
    "    #cv2.imshow(f'{method}_cams.jpg', cam_image)\n"
   ]
  }
 ],
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