Session_8/(3) ResNets Notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "implementing_resnet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import torchvision.models as models\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision\n",
        "from torchsummary import summary\n",
        "print(torch.__version__)\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "b_-428Szfifa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "680fd487-043d-407a-96a9-36dc1dec7881"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.12.0+cu113\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class resblock(nn.Module):\n",
        "    def __init__(self, in_planes, out_planes, downsample):\n",
        "        super().__init__()\n",
        "        if downsample: #if the number of channels changes in the residual block(in_planes != out_planes)\n",
        "            self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=2, padding=1) #change the number of channels from in_planes \n",
        "                        #to out_planes and halving the height and width of each channel using stride of 2.\n",
        "            self.identity = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=2), #use 1x1 convolution to compensate for the change in number of channels\n",
        "                nn.BatchNorm2d(out_planes)\n",
        "            )\n",
        "        else: #if there is no change in the number of channels(in_planes = out_planes)\n",
        "            self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1)\n",
        "            self.identity = nn.Sequential() # pass the input of residual block to output directly\n",
        "\n",
        "        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1)\n",
        "        self.batch_norm1 = nn.BatchNorm2d(out_planes)\n",
        "        self.batch_norm2 = nn.BatchNorm2d(out_planes)\n",
        "\n",
        "    def forward(self, input):\n",
        "        identity = self.identity(input)\n",
        "        input = self.conv1(input)\n",
        "        input = self.batch_norm1(input)\n",
        "        input = nn.ReLU()(input)\n",
        "        input = self.conv2(input)\n",
        "        input = self.batch_norm2(input)\n",
        "        #input = nn.ReLU()(input)\n",
        "        input = input + identity\n",
        "        output = nn.ReLU()(input)\n",
        "        return output\n",
        "\n"
      ],
      "metadata": {
        "id": "zK2T7wLk1Y3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
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      ],
      "metadata": {
        "id": "mNxHyOwFgKuO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class resnet(nn.Module):\n",
        "    def __init__(self, in_planes, resblock, outputs):\n",
        "        super().__init__()\n",
        "        self.layer1 = nn.Sequential(\n",
        "            nn.Conv2d(in_planes, 64, kernel_size=7, stride=2, padding=3),\n",
        "            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(inplace = True)\n",
        "        )\n",
        "\n",
        "        self.layer2 = nn.Sequential(\n",
        "            resblock(64, 64, downsample=False),\n",
        "            resblock(64, 64, downsample=False)\n",
        "        )\n",
        "\n",
        "        self.layer3 = nn.Sequential(\n",
        "            resblock(64, 128, downsample=True),\n",
        "            resblock(128, 128, downsample=False)\n",
        "        )\n",
        "\n",
        "        self.layer4 = nn.Sequential(\n",
        "            resblock(128, 256, downsample=True),\n",
        "            resblock(256, 256, downsample=False)\n",
        "        )\n",
        "        self.layer5 = nn.Sequential(\n",
        "            resblock(256, 512, downsample=True),\n",
        "            resblock(512, 512, downsample=False)\n",
        "        )\n",
        "\n",
        "        self.avg_pooling = nn.AdaptiveAvgPool2d(1)\n",
        "        self.fully_connected = nn.Linear(512, outputs)\n",
        "\n",
        "    def forward(self, input):\n",
        "        input = self.layer1(input)\n",
        "        input = self.layer2(input)\n",
        "        input = self.layer3(input)\n",
        "        input = self.layer4(input)\n",
        "        input = self.layer5(input)\n",
        "        input = self.avg_pooling(input)\n",
        "        input = input.view(input.size(0),-1)\n",
        "        input = self.fully_connected(input)\n",
        "        return input\n",
        "\n",
        "\n",
        "\n",
        "model = resnet(3, resblock, outputs=10)\n",
        "model.to(device)"
      ],
      "metadata": {
        "id": "i39C7-QEfqxR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "255b86e5-d4a3-471c-fe92-4a725db022af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "resnet(\n",
              "  (layer1): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n",
              "    (1): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): ReLU(inplace=True)\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): resblock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (identity): Sequential()\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): resblock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (identity): Sequential()\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): resblock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (identity): Sequential(\n",
              "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))\n",
              "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): resblock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (identity): Sequential()\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): resblock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (identity): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): resblock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (identity): Sequential()\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer5): Sequential(\n",
              "    (0): resblock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (identity): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): resblock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (identity): Sequential()\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (batch_norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (batch_norm2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avg_pooling): AdaptiveAvgPool2d(output_size=1)\n",
              "  (fully_connected): Linear(in_features=512, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "KXF4drkGLKFO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 100\n",
        "learning_rate = 0.001\n",
        "\n",
        "# Image preprocessing modules\n",
        "transform = transforms.Compose([\n",
        "    transforms.Pad(4),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.RandomCrop(32),\n",
        "    transforms.ToTensor()])\n",
        "\n",
        "# CIFAR-10 dataset\n",
        "train_dataset = torchvision.datasets.CIFAR10(root='../data/',\n",
        "                                             train=True, \n",
        "                                             transform=transform,\n",
        "                                             download=True)\n",
        "\n",
        "test_dataset = torchvision.datasets.CIFAR10(root='../data/',\n",
        "                                            train=False, \n",
        "                                            transform=transforms.ToTensor())\n",
        "\n",
        "# Data loader\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                           batch_size=100, \n",
        "                                           shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                          batch_size=100, \n",
        "                                          shuffle=False)\n"
      ],
      "metadata": {
        "id": "saX-hZ7m-MgX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7740c7bb-59e0-4f45-9047-237a72a319b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "metadata": {
        "id": "_AKcHWPVfzgK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "total_step = len(train_loader)\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(train_loader):\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if (i+1) % 100 == 0:\n",
        "            print (\"Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}\"\n",
        "                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))"
      ],
      "metadata": {
        "id": "l0XnWDlZf3ep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b41d4263-4744-4213-9c03-b5aad75ca803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Epoch [99/100], Step [500/500] Loss: 0.3051\n",
            "Epoch [100/100], Step [100/500] Loss: 0.1056\n",
            "Epoch [100/100], Step [200/500] Loss: 0.1094\n",
            "Epoch [100/100], Step [300/500] Loss: 0.0382\n",
            "Epoch [100/100], Step [400/500] Loss: 0.1259\n",
            "Epoch [100/100], Step [500/500] Loss: 0.0839\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in test_loader:\n",
        "        images = images.to(device)\n",
        "        labels = labels.to(device)\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))\n"
      ],
      "metadata": {
        "id": "dbv6YLUugOqL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da647091-aa2c-41b8-a8a3-c3d0a370d741"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the model on the test images: 85.16 %\n"
          ]
        }
      ]
    }
  ]
}