chapter_modern_cnn/3_googlenet.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/ashkan-mokarian/d2l-pytorch/blob/main/chapter_modern_cnn/3_googlenet.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ],
      "metadata": {
        "id": "58pWwXQcEcqA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GoogleNet: Multi-Branch Networks"
      ],
      "metadata": {
        "id": "mH9p6pehE1z4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install d2l==1.0.0a0"
      ],
      "metadata": {
        "id": "0y8EdfOWEqay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "lhtPiBi4EZiI"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from d2l import torch as d2l"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inception Blocks"
      ],
      "metadata": {
        "id": "EA0CbxmtE_Lg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Inception(nn.Module):\n",
        "    def __init__(self, c1, c2, c3, c4, **kwargs):\n",
        "        super(Inception, self).__init__(**kwargs)\n",
        "        # Branch 1\n",
        "        self.b1 = nn.LazyConv2d(out_channels=c1, kernel_size=1)\n",
        "        # Branch 2\n",
        "        self.b2_1 = nn.LazyConv2d(c2[0], kernel_size=1)\n",
        "        self.b2_2 = nn.LazyConv2d(c2[1], kernel_size=3, padding=1)\n",
        "        # Branch 3\n",
        "        self.b3_1 = nn.LazyConv2d(c3[0], kernel_size=1)\n",
        "        self.b3_2 = nn.LazyConv2d(c3[1], kernel_size=5, padding=2)\n",
        "        # Branch 4\n",
        "        self.b4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)\n",
        "        self.b4_2 = nn.LazyConv2d(c4, kernel_size=1)\n",
        "    \n",
        "    def forward(self, X):\n",
        "        b1 = F.relu(self.b1(X))\n",
        "        b2 = F.relu(self.b2_2(F.relu(self.b2_1(X))))\n",
        "        b3 = F.relu(self.b3_2(F.relu(self.b3_1(X))))\n",
        "        b4 = F.relu(self.b4_2(self.b4_1(X)))\n",
        "        return torch.concat((b1, b2, b3, b4), dim=1)\n"
      ],
      "metadata": {
        "id": "RYGmPsqbExkg"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GoogleNet(d2l.Classifier):\n",
        "    def b1(self):\n",
        "        return nn.Sequential(\n",
        "            nn.LazyConv2d(64, kernel_size=7, stride=2, padding=3),\n",
        "            nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2, padding=1))\n",
        "    \n",
        "    def b2(self):\n",
        "        return nn.Sequential(\n",
        "            nn.LazyConv2d(64, kernel_size=1), nn.ReLU(),\n",
        "            nn.LazyConv2d(192, kernel_size=3, padding=1), nn.ReLU(),\n",
        "            nn.MaxPool2d(kernel_size=3, stride=2, padding=1))\n",
        "    # I think until here is data ingestion. similar to previous ones almost.\n",
        "\n",
        "    # Data processing with Inception blocks. multi-branch representations\n",
        "    def b3(self):\n",
        "        return nn.Sequential(Inception(64, (96, 128), (16, 32), 32),\n",
        "            Inception(128, (128, 192), (32, 96), 64),\n",
        "            nn.MaxPool2d(kernel_size=3, stride=2, padding=1))\n",
        "        \n",
        "    def b4(self):\n",
        "        return nn.Sequential(Inception(192, (96, 208), (16, 48), 64),\n",
        "                            Inception(160, (112, 224), (24, 64), 64),\n",
        "                            Inception(128, (128, 256), (24, 64), 64),\n",
        "                            Inception(112, (144, 288), (32, 64), 64),\n",
        "                            Inception(256, (160, 320), (32, 128), 128),\n",
        "                            nn.MaxPool2d(kernel_size=3, stride=2, padding=1))\n",
        "    \n",
        "    def b5(self):\n",
        "        return nn.Sequential(Inception(256, (160, 320), (32, 128), 128),\n",
        "                         Inception(384, (192, 384), (48, 128), 128),\n",
        "                         nn.AdaptiveAvgPool2d((1,1)), nn.Flatten())\n",
        "        \n",
        "    def __init__(self, lr=0.1, num_classes=10):\n",
        "        super(GoogleNet, self).__init__()\n",
        "        self.save_hyperparameters()\n",
        "        self.net = nn.Sequential(self.b1(), self.b2(), self.b3(), self.b4(),\n",
        "                                self.b5(), nn.LazyLinear(num_classes))\n",
        "        self.net.apply(d2l.init_cnn)"
      ],
      "metadata": {
        "id": "enYTr8B_IWrM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# wroking with smaller images for time and computational reasons\n",
        "model = GoogleNet().layer_summary((1, 1, 96, 96))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VifLBKtJLJDV",
        "outputId": "726dcd38-d5b0-407e-ff8b-7b348de8b011"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n",
            "  warnings.warn('Lazy modules are a new feature under heavy development '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sequential output shape:\t torch.Size([1, 64, 24, 24])\n",
            "Sequential output shape:\t torch.Size([1, 192, 12, 12])\n",
            "Sequential output shape:\t torch.Size([1, 480, 6, 6])\n",
            "Sequential output shape:\t torch.Size([1, 832, 3, 3])\n",
            "Sequential output shape:\t torch.Size([1, 1024])\n",
            "Linear output shape:\t torch.Size([1, 10])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = GoogleNet(lr=0.01)\n",
        "trainer = d2l.Trainer(max_epochs=10, num_gpus=1)\n",
        "data = d2l.FashionMNIST(batch_size=128, resize=(96, 96))\n",
        "model.apply_init([next(iter(data.get_dataloader(True)))[0]], d2l.init_cnn)\n",
        "trainer.fit(model, data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "HA2_IGddLQC3",
        "outputId": "210deac1-05c6-4ab4-c05d-9932a05e1118"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
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