Bike_and_Motorbike.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "34pv7vYvq60z"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "b7vKPKVNjjm9"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.transforms import functional as TF\n",
        "import random\n",
        "import os\n",
        "from torchvision import transforms\n",
        "import shutil\n",
        "from tqdm.notebook import tqdm\n",
        "from PIL import Image, ImageFile\n",
        "from torch.utils.data import Dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "w0NfdPyFjsZD"
      },
      "outputs": [],
      "source": [
        "class BikeBicycleDataset(Dataset):\n",
        "    def __init__(self, root_dir, transform=None, train=True, val_split=0.2):\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "        self.train = train\n",
        "        self.images = []\n",
        "        self.labels = []\n",
        "\n",
        "        for label, folder in enumerate(['bike', 'motorbike']):\n",
        "            folder_path = os.path.join(self.root_dir, folder)\n",
        "            for image_name in os.listdir(folder_path):\n",
        "                self.images.append(os.path.join(folder_path, image_name))\n",
        "                self.labels.append(label)\n",
        "\n",
        "        self.train_images, self.val_images, self.train_labels, self.val_labels = train_test_split(\n",
        "            self.images, self.labels, test_size=val_split, stratify=self.labels)\n",
        "\n",
        "        self.data = self.train_images if self.train else self.val_images\n",
        "        self.labels = self.train_labels if self.train else self.val_labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        try:\n",
        "          image_path = self.data[idx]\n",
        "          label = self.labels[idx]\n",
        "          image = Image.open(image_path)\n",
        "\n",
        "          if image.mode == 'P':\n",
        "              image = image.convert('RGBA')\n",
        "\n",
        "          image = image.convert('RGB')\n",
        "\n",
        "          if self.transform:\n",
        "              image = self.transform(image)\n",
        "        except (IOError, OSError):\n",
        "          print(f'Error loading image {image_path}, skipping.')\n",
        "          idx = (idx + 1) % len(self.data) \n",
        "\n",
        "        return image, label\n",
        "\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "transform_val = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "train_dataset = BikeBicycleDataset(root_dir='/content/drive/MyDrive/Bike_and_motorbike', transform=transform_train, train=True)\n",
        "val_dataset = BikeBicycleDataset(root_dir='/content/drive/MyDrive/Bike_and_motorbike', transform=transform_val, train=False)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=0)\n",
        "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "OHFQUmQvjl11"
      },
      "outputs": [],
      "source": [
        "class BasicBlock(nn.Module):\n",
        "    expansion = 1\n",
        "\n",
        "    def __init__(self, in_channels, out_channels, stride=1, downsample=None):\n",
        "        super(BasicBlock, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
        "        self.downsample = downsample\n",
        "        self.stride = stride\n",
        "\n",
        "    def forward(self, x):\n",
        "        identity = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "\n",
        "        if self.downsample is not None:\n",
        "            identity = self.downsample(x)\n",
        "\n",
        "        out += identity\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out\n",
        "\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, layers, num_classes=1000):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_channels = 64\n",
        "        self.conv1 = nn.Conv2d(3, self.in_channels, kernel_size=7, stride=2, padding=3, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(self.in_channels)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "        self.layer1 = self._make_layer(block, 64, layers[0])\n",
        "        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n",
        "        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n",
        "        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
        "\n",
        "    def _make_layer(self, block, out_channels, blocks, stride=1):\n",
        "        downsample = None\n",
        "        if stride != 1 or self.in_channels != out_channels * block.expansion:\n",
        "            downsample = nn.Sequential(\n",
        "                nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(out_channels * block.expansion),\n",
        "            )\n",
        "\n",
        "        layers = []\n",
        "        layers.append(block(self.in_channels, out_channels, stride, downsample))\n",
        "        self.in_channels = out_channels * block.expansion\n",
        "        for _ in range(1, blocks):\n",
        "            layers.append(block(self.in_channels, out_channels))\n",
        "\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.maxpool(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.layer4(x)\n",
        "\n",
        "        x = self.avgpool(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.fc(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "def resnet18(num_classes=2):\n",
        "    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "_bsVIOKdkNWE"
      },
      "outputs": [],
      "source": [
        "model = resnet18(num_classes=2)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "# Loss Function\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Optimizer\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# Function to save a checkpoint\n",
        "def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):\n",
        "    torch.save(state, filename)\n",
        "    if is_best:\n",
        "        shutil.copyfile(filename, 'model_best.pth.tar')\n",
        "\n",
        "# Function to save the final model\n",
        "def save_model(model, filename='final_model.pth'):\n",
        "    torch.save(model.state_dict(), filename)\n",
        "\n",
        "# Training function\n",
        "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=25):\n",
        "    best_acc = 0.0\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        running_loss = 0.0\n",
        "        correct = 0\n",
        "        total = 0\n",
        "\n",
        "        with tqdm(enumerate(train_loader), total=len(train_loader), unit=\"batch\") as tepoch:\n",
        "            for i, (inputs, labels) in tepoch:\n",
        "                tepoch.set_description(f\"Epoch {epoch+1}\")\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                optimizer.zero_grad()\n",
        "                outputs = model(inputs)\n",
        "                loss = criterion(outputs, labels)\n",
        "                loss.backward()\n",
        "                optimizer.step()\n",
        "\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                _, predicted = torch.max(outputs.data, 1)\n",
        "                total += labels.size(0)\n",
        "                correct += (predicted == labels).sum().item()\n",
        "                tepoch.set_postfix(loss=running_loss/total, accuracy=100. * correct / total)\n",
        "\n",
        "        val_loss, val_acc = validate(model, val_loader, criterion)\n",
        "\n",
        "        is_best = val_acc > best_acc\n",
        "        best_acc = max(val_acc, best_acc)\n",
        "        save_checkpoint({\n",
        "            'epoch': epoch + 1,\n",
        "            'state_dict': model.state_dict(),\n",
        "            'optimizer': optimizer.state_dict(),\n",
        "            'best_acc': best_acc,\n",
        "        }, is_best)\n",
        "\n",
        "        print(f'Epoch [{epoch+1}/{num_epochs}] Train Loss: {running_loss/total:.4f} Acc: {100. * correct / total:.4f} Val Loss: {val_loss:.4f} Val Acc: {val_acc:.4f}')\n",
        "\n",
        "    save_model(model, 'final_model.pth')\n",
        "\n",
        "# Validation function\n",
        "def validate(model, val_loader, criterion):\n",
        "    model.eval()\n",
        "    running_loss = 0.0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        with tqdm(val_loader, total=len(val_loader), unit=\"batch\") as tepoch:\n",
        "            for inputs, labels in tepoch:\n",
        "                tepoch.set_description(\"Validation\")\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                outputs = model(inputs)\n",
        "                loss = criterion(outputs, labels)\n",
        "\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                _, predicted = torch.max(outputs.data, 1)\n",
        "                total += labels.size(0)\n",
        "                correct += (predicted == labels).sum().item()\n",
        "                tepoch.set_postfix(loss=running_loss/total, accuracy=100. * correct / total)\n",
        "\n",
        "    return running_loss / total, correct / total\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
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