ResNet/ResNet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/garrisonz/reproduce/blob/main/ResNet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install d2l\n",
        "!pip install ipython-autotime\n",
        "%load_ext autotime"
      ],
      "metadata": {
        "id": "4B_dMbhNlHQ0"
      },
      "id": "4B_dMbhNlHQ0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ddc7bbc",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-24T01:42:16.615248Z",
          "start_time": "2022-06-24T01:42:15.439363Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ddc7bbc",
        "outputId": "34d928b1-869c-4011-c724-2e6a967ec792"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 3.67 s (started: 2022-06-24 07:20:19 +00:00)\n"
          ]
        }
      ],
      "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": "code",
      "execution_count": null,
      "id": "418dde04",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-24T01:42:20.973575Z",
          "start_time": "2022-06-24T01:42:20.969473Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "418dde04",
        "outputId": "58d8ffa1-a5a8-4082-e519-0fd8f8c92dfd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 11.7 ms (started: 2022-06-24 07:20:22 +00:00)\n"
          ]
        }
      ],
      "source": [
        "class Residual(nn.Module):\n",
        "    def __init__(self, input_channels, num_channels,\n",
        "                 use_1x1conv=False, strides=1):\n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(input_channels, num_channels,\n",
        "                               kernel_size=3, padding=1, stride=strides)\n",
        "        self.conv2 = nn.Conv2d(num_channels, num_channels,\n",
        "                               kernel_size=3, padding=1)\n",
        "        if use_1x1conv: # 此变量可以不要，直接根据 input_channels 和 num_channels 是否相等 \n",
        "                        # 或 strides 是否大于 1，决定是否需要 1x1 卷积 ?? zyp. \n",
        "            self.conv3 = nn.Conv2d(input_channels, num_channels,\n",
        "                                   kernel_size=1, stride=strides)\n",
        "        else:\n",
        "            self.conv3 = None\n",
        "        self.bn1 = nn.BatchNorm2d(num_channels)\n",
        "        self.bn2 = nn.BatchNorm2d(num_channels)\n",
        "\n",
        "    def forward(self, X):\n",
        "        Y = F.relu(self.bn1(self.conv1(X)))\n",
        "        Y = self.bn2(self.conv2(Y))\n",
        "        if self.conv3:\n",
        "            X = self.conv3(X)\n",
        "        Y += X\n",
        "        return F.relu(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a43a320",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-24T01:59:05.297524Z",
          "start_time": "2022-06-24T01:59:05.294398Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3a43a320",
        "outputId": "23657544-b20b-42ca-e842-b664f0ec490d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 4.38 ms (started: 2022-06-24 07:20:22 +00:00)\n"
          ]
        }
      ],
      "source": [
        "b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3), nn.BatchNorm2d(64), nn.ReLU(),\n",
        "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f8ac332",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-24T02:04:07.801685Z",
          "start_time": "2022-06-24T02:04:07.799063Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3f8ac332",
        "outputId": "423f8ab5-e25d-4912-e007-5cab7ee8dd08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 3.88 ms (started: 2022-06-24 07:20:22 +00:00)\n"
          ]
        }
      ],
      "source": [
        "def resnet_block(input_channels, num_channels, num_residuals,\n",
        "                 first_block=False):\n",
        "    blk = []\n",
        "    for i in range(num_residuals):\n",
        "        if i == 0 and not first_block:\n",
        "            blk.append(Residual(input_channels, num_channels,\n",
        "                                use_1x1conv=True, strides=2))\n",
        "        else:\n",
        "            blk.append(Residual(num_channels, num_channels))\n",
        "    return blk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6417153d",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-24T02:04:09.170249Z",
          "start_time": "2022-06-24T02:04:09.112145Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6417153d",
        "outputId": "ea5ab972-8b63-41f8-df69-7b670717915e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 111 ms (started: 2022-06-24 07:20:22 +00:00)\n"
          ]
        }
      ],
      "source": [
        "b2 = nn.Sequential(*resnet_block(64, 64, 2, first_block=True))\n",
        "b3 = nn.Sequential(*resnet_block(64, 128, 2))\n",
        "b4 = nn.Sequential(*resnet_block(128, 256, 2))\n",
        "b5 = nn.Sequential(*resnet_block(256, 512, 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67ca2bb5",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-24T02:04:10.915534Z",
          "start_time": "2022-06-24T02:04:10.912585Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67ca2bb5",
        "outputId": "a455efd8-c096-4bf4-a368-01efef547afc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time: 2.39 ms (started: 2022-06-24 07:20:22 +00:00)\n"
          ]
        }
      ],
      "source": [
        "net = nn.Sequential(b1, b2, b3, b4, b5,\n",
        "                    nn.AdaptiveAvgPool2d((1,1)),\n",
        "                    nn.Flatten(), nn.Linear(512, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b783c940",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-06-24T02:04:21.790228Z",
          "start_time": "2022-06-24T02:04:21.748573Z"
        },
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b783c940",
        "outputId": "a433f00a-4f88-479f-cdad-92b21859e2db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sequential output shape:\t torch.Size([1, 64, 56, 56])\n",
            "Sequential output shape:\t torch.Size([1, 64, 56, 56])\n",
            "Sequential output shape:\t torch.Size([1, 128, 28, 28])\n",
            "Sequential output shape:\t torch.Size([1, 256, 14, 14])\n",
            "Sequential output shape:\t torch.Size([1, 512, 7, 7])\n",
            "AdaptiveAvgPool2d output shape:\t torch.Size([1, 512, 1, 1])\n",
            "Flatten output shape:\t torch.Size([1, 512])\n",
            "Linear output shape:\t torch.Size([1, 10])\n",
            "time: 179 ms (started: 2022-06-24 07:20:23 +00:00)\n"
          ]
        }
      ],
      "source": [
        "X = torch.rand(size=(1, 1, 224, 224))\n",
        "for layer in net:\n",
        "    X = layer(X)\n",
        "    print(layer.__class__.__name__,'output shape:\\t', X.shape)"
      ]
    },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss 0.010, train acc 0.998, test acc 0.919\n",
            "1461.7 examples/sec on cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
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            "time: 7min 45s (started: 2022-06-24 07:20:23 +00:00)\n"
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