ResNet34.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.models as models\n",
    "from PIL import Image\n",
    "from torchvision.models import resnet50, ResNet50_Weights\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = \"/home/khushi/Testing_testing/Data/Updated_Data\"\n",
    "categories = [\"Anaesthesia_machine\",\"baby_incubator\",\"Bone_saws\",\"C_arm\",\"colonoscope\",\"Curved_Mayo_Scissor\",\"difibrillator\",\"Electrocautery_devices\",\"fetal_doppler\",\"forceps\",\"Heart_Lung_Machine\",\"IABP\",\"IMRT\",\"infusion_pump\",\"Laryngoscopes\",\"mayfield_clamp\",\"Needle_Biopsy_Device\",\"phacoemulsification\",\"Radiofrequency_Ablation_Device\",\"Scalpel\",\"Straight_Dissection_Clamp\",\"Straight_Mayo_Scissor\",\"Suction_Machine\",\"ventilator\",\"x_ray\"]\n",
    "input_size = (224, 224)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, data_dir, categories, input_size, transform=None):\n",
    "        self.data_dir = data_dir\n",
    "        self.categories = categories\n",
    "        self.input_size = input_size\n",
    "        self.transform = transform\n",
    "        self.data = []\n",
    "        self.labels = []\n",
    "        self.load_data()\n",
    "\n",
    "    def load_data(self):\n",
    "        for cat in self.categories:\n",
    "            path = os.path.join(self.data_dir, cat)\n",
    "            class_num = self.categories.index(cat)\n",
    "            for img_name in os.listdir(path):\n",
    "                try:\n",
    "                    img_path = os.path.join(path, img_name)\n",
    "                    img = Image.open(img_path).convert(\"RGB\")\n",
    "                    img = img.resize(self.input_size)\n",
    "                    if self.transform:\n",
    "                        img = self.transform(img)\n",
    "                    self.data.append(img)\n",
    "                    self.labels.append(class_num)\n",
    "                except Exception as e:\n",
    "                    pass\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img = self.data[idx]\n",
    "        label = self.labels[idx]\n",
    "        return img, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "    \n",
    "    transforms.RandomVerticalFlip(),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "   \n",
    "])\n",
    "\n",
    "dataset = CustomDataset(DATADIR, categories, input_size, transform=transform)\n",
    "\n",
    "train_size = int(0.80 * len(dataset))\n",
    "test_size = len(dataset) - train_size\n",
    "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size)\n",
    "\n",
    "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torchvision.models as models\n",
    "'''\n",
    "class CustomResNet(nn.Module):\n",
    "    def __init__(self, num_classes, fine_tune_layers=3):\n",
    "        super(CustomResNet, self).__init__()\n",
    "        res = models.resnet34(pretrained=True)\n",
    "        self.res = nn.Sequential(*list(res.children())[:-1])\n",
    "\n",
    "        # Freeze all layers by default\n",
    "        for param in res.parameters():\n",
    "            param.requires_grad = True\n",
    "        \n",
    "\n",
    "        # Define your custom layers\n",
    "        self.convlayers = nn.Sequential(\n",
    "            nn.Conv2d(512,2048, kernel_size=3, padding=1, stride=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(2048, 2048, kernel_size=3, padding=1, stride=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Conv2d(2048, 1024, kernel_size=3, padding=1, stride=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(1024, 1024),\n",
    "            nn.BatchNorm2d(1024),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Conv2d(1024, 1024, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(1024,512, kernel_size=1),\n",
    "            nn.Linear(512, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(512,64, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            \n",
    "            nn.Linear(64, num_classes)  # Adjust the output size to match the number of classes\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.res(x)\n",
    "        x = self.convlayers(x)\n",
    "        return x\n",
    "'''\n",
    "\n",
    "class CustomResNet(nn.Module):\n",
    "    def __init__(self, num_classes, fine_tune_layers=3):\n",
    "        super(CustomResNet, self).__init__()\n",
    "        res = models.resnet34(pretrained=True)\n",
    "        self.res = nn.Sequential(*list(res.children())[:-2])\n",
    "\n",
    "        # Freeze all layers by default\n",
    "        for param in res.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "        # Define your custom layers\n",
    "        self.convlayers = nn.Sequential(\n",
    "            nn.Conv2d(512, 2048, kernel_size=3, padding=1, stride=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm2d(2048),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),  # Max pooling layer added\n",
    "            \n",
    "            nn.Conv2d(2048, 1024, kernel_size=3, padding=1, stride=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm2d(1024),\n",
    "            nn.Conv2d(1024, 512, kernel_size=3, padding=1, stride=1),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.Dropout(0.5),\n",
    "        )\n",
    "\n",
    "        self.fc_layers = nn.Sequential(\n",
    "            nn.Linear(512 * 3 * 3, 126),  # Adjust the output size after max pooling\n",
    "            nn.ReLU(),            \n",
    "            nn.Linear(126, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.res(x)\n",
    "        x = self.convlayers(x)\n",
    "        x = x.view(x.size(0), -1)  # Flatten the tensor to [batch_size, num_features]\n",
    "        x = self.fc_layers(x)\n",
    "        return x\n",
    "\n",
    "       \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "num_classes = len(categories)\n",
    "model = CustomResNet(num_classes).to(device)\n",
    "# for name, param in model.named_parameters():\n",
    "#     print(f\"{name}: requires_grad={param.requires_grad}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25 | Train Loss: 2.2758 | Train Acc: 0.2823 | Val Loss: 14.4269 | Val Acc: 0.3096\n",
      "Epoch 2/25 | Train Loss: 1.4848 | Train Acc: 0.5246 | Val Loss: 11.7626 | Val Acc: 0.4341\n",
      "Epoch 3/25 | Train Loss: 1.0629 | Train Acc: 0.6622 | Val Loss: 10.9440 | Val Acc: 0.5115\n",
      "Epoch 4/25 | Train Loss: 0.8445 | Train Acc: 0.7342 | Val Loss: 16.0683 | Val Acc: 0.5262\n",
      "Epoch 5/25 | Train Loss: 0.7124 | Train Acc: 0.7737 | Val Loss: 13.0875 | Val Acc: 0.5533\n",
      "Epoch 6/25 | Train Loss: 0.5984 | Train Acc: 0.8111 | Val Loss: 16.6732 | Val Acc: 0.5303\n",
      "Epoch 7/25 | Train Loss: 0.4527 | Train Acc: 0.8522 | Val Loss: 36.0037 | Val Acc: 0.4592\n",
      "Epoch 8/25 | Train Loss: 0.4062 | Train Acc: 0.8666 | Val Loss: 29.2487 | Val Acc: 0.4874\n",
      "Early stopping! No improvement in 5 epochs.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_acc_history=[]\n",
    "test_acc_history=[]\n",
    "test_loss_history=[]\n",
    "train_loss_history=[]\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.00105, weight_decay=1e-5)\n",
    "\n",
    "# Define the number of epochs and early stopping parameters\n",
    "num_epochs = 25\n",
    "patience = 5\n",
    "best_val_loss = float('inf')\n",
    "epochs_without_improvement = 0\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    train_loss, train_corrects = 0.0, 0.0\n",
    "\n",
    "    for images, labels in train_loader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "        labels = torch.squeeze(labels)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_loss += loss.item() * images.size(0)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        train_corrects += torch.sum(preds == labels).item()\n",
    "\n",
    "    train_loss = train_loss / len(train_dataset)\n",
    "    train_acc = train_corrects / len(train_dataset)\n",
    "    train_acc_history.append(train_acc)\n",
    "    train_loss_history.append(train_loss)\n",
    "\n",
    "    model.eval()\n",
    "    val_loss, val_corrects = 0.0, 0.0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in test_loader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "\n",
    "            val_loss += loss.item() * images.size(0)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            val_corrects += torch.sum(preds == labels).item()\n",
    "\n",
    "        val_loss = val_loss / len(test_dataset)\n",
    "        val_acc = val_corrects / len(test_dataset)\n",
    "\n",
    "        test_acc_history.append(val_acc)\n",
    "        test_loss_history.append(val_loss)\n",
    "\n",
    "        print(f\"Epoch {epoch+1}/{num_epochs} | Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f} | Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f}\")\n",
    "\n",
    "        # Check for early stopping\n",
    "        if val_loss < best_val_loss:\n",
    "            best_val_loss = val_loss\n",
    "            epochs_without_improvement = 0\n",
    "            # Save the best model if needed\n",
    "            torch.save(model.state_dict(), 'best_model.pt')\n",
    "        else:\n",
    "            epochs_without_improvement += 1\n",
    "            if epochs_without_improvement >= patience:\n",
    "                print(f\"Early stopping! No improvement in {patience} epochs.\")\n",
    "                break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (25,) and (8,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[56], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m plt\u001b[39m.\u001b[39;49mplot(\u001b[39mrange\u001b[39;49m(\u001b[39m1\u001b[39;49m, num_epochs\u001b[39m+\u001b[39;49m\u001b[39m1\u001b[39;49m), train_acc_history, label\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mTrain\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      4\u001b[0m plt\u001b[39m.\u001b[39mplot(\u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m, num_epochs\u001b[39m+\u001b[39m\u001b[39m1\u001b[39m), test_acc_history, label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mValidation\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mEpoch\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/matplotlib/pyplot.py:2812\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2810\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mplot)\n\u001b[1;32m   2811\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot\u001b[39m(\u001b[39m*\u001b[39margs, scalex\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, scaley\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 2812\u001b[0m     \u001b[39mreturn\u001b[39;00m gca()\u001b[39m.\u001b[39;49mplot(\n\u001b[1;32m   2813\u001b[0m         \u001b[39m*\u001b[39;49margs, scalex\u001b[39m=\u001b[39;49mscalex, scaley\u001b[39m=\u001b[39;49mscaley,\n\u001b[1;32m   2814\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1688\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1446\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1447\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1685\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1686\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1687\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m   1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m   1690\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/matplotlib/axes/_base.py:311\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m     this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m    310\u001b[0m     args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 311\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m    312\u001b[0m     this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/matplotlib/axes/_base.py:504\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    501\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mupdate_units(y)\n\u001b[1;32m    503\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m y\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 504\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y must have same first dimension, but \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    505\u001b[0m                      \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhave shapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    506\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m y\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[1;32m    507\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y can be no greater than 2D, but have \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    508\u001b[0m                      \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mshapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (25,) and (8,)"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(range(1, num_epochs+1), train_acc_history, label='Train')\n",
    "plt.plot(range(1, num_epochs+1), test_acc_history, label='Validation')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.plot(range(1, num_epochs+1), train_loss_history, label='Train')\n",
    "plt.plot(range(1, num_epochs+1), test_loss_history, label='Validation')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "\n",
    "# Load the custom ResNet model\n",
    "model = CustomResNet(num_classes=len(categories))  # Replace NUM_CLASSES with the number of classes in your dataset\n",
    "\n",
    "# Load the pre-trained weights (if any)\n",
    "# If you have saved your model state_dict, you can load it using:\n",
    "# model.load_state_dict(torch.load('path_to_your_model_weights.pth'))\n",
    "# Otherwise, you can skip this step if you trained the model in the same session\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# Load and preprocess the image\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "# Load the image using PIL\n",
    "image = Image.open(\"/home/khushi/Testing_testing/Data/Updated_Data/Straight_Mayo_Scissor/tesourareta4_resized_.jpeg\")\n",
    "# Preprocess the image\n",
    "input_tensor = transform(image).unsqueeze(0)  # Add batch dimension\n",
    "\n",
    "# Make predictions\n",
    "with torch.no_grad():\n",
    "    output = model(input_tensor)\n",
    "    probabilities = torch.softmax(output, dim=1)\n",
    "\n",
    "# Get the predicted class index\n",
    "predicted_class_index = torch.argmax(probabilities, dim=1).item()\n",
    "\n",
    "# Optionally, get the class label based on the index\n",
    "# Assuming you have a list of class labels\n",
    "class_labels = categories  # Replace with your actual class labels\n",
    "predicted_class_label = class_labels[predicted_class_index]\n",
    "\n",
    "print(\"Predicted Class Index:\", predicted_class_index)\n",
    "print(\"Predicted Class Label:\", predicted_class_label)\n",
    "print(\"Class Probabilities:\", probabilities)\n"
   ]
  }
 ],
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