neural_network/ssd_mobilenetv1/mobilenetv1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4de4e965",
   "metadata": {},
   "source": [
    "# <center>MobileNet网络复现"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f7878c",
   "metadata": {},
   "source": [
    "## 1.MobileNet 网络结构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dffc653",
   "metadata": {},
   "source": [
    "<img src=\"arch.png\" width=40% align=center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc0f823",
   "metadata": {},
   "source": [
    "## 2.Depthwise结构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62377119",
   "metadata": {},
   "source": [
    "<img src=\"dw.png\" width=40% align=center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44bf2b20",
   "metadata": {},
   "source": [
    "## 3.代码编写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "682e0150",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchsummary import summary\n",
    "from torchvision.datasets import FashionMNIST\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23b01841",
   "metadata": {},
   "outputs": [],
   "source": [
    "#基础卷积\n",
    "class base_conv(nn.Module):\n",
    "    def __init__(self, kernel_size, in_channels, out_channels, stride):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, padding=kernel_size//2, stride=stride, bias=False)\n",
    "        self.bn = nn.BatchNorm2d(out_channels)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.bn(x)\n",
    "        x = self.relu(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d27a1b",
   "metadata": {},
   "source": [
    "dw结构将groups设为in_channels，同时out_channels也设为与in_channels相同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8cb4d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "class depth_wise(nn.Module):\n",
    "    def __init__(self, kernel_size, in_channels, out_channels, stride):\n",
    "        super().__init__()\n",
    "        self.conv3x3 = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, padding=kernel_size//2, groups=in_channels, stride=stride, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(in_channels)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv1x1 = base_conv(1, in_channels, out_channels, 1)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.conv3x3(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.conv1x1(x)\n",
    "        x = self.bn2(x)\n",
    "        x = self.relu(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87933337",
   "metadata": {},
   "source": [
    "每层卷积参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21ef45cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_cfg = [[3, 32, 2], (3, 64, 1),(3, 128, 2), (3, 128, 1), (3, 256, 2),(3, 256, 1),\n",
    "             (3, 512, 2), (3, 512, 1, 5), (3, 1024, 2),(3, 1024, 2)]#列表是第一个卷积神经网络，元组是卷积神经网络（卷积核大小，输出通道，步长，重复次数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd3c5501",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MobileNetV1(nn.Module):\n",
    "    def __init__(self, in_channels, num_classes, model_cfg):\n",
    "        super().__init__()\n",
    "        self.model_cfg = model_cfg\n",
    "        self.in_channels = in_channels\n",
    "        self.avg = nn.AdaptiveAvgPool2d(1)\n",
    "        self.fc = nn.Linear(1024, num_classes)\n",
    "        self.softmax = nn.Softmax(dim=1)#在第一个维度上进行softmax，第0个维度是batch_size\n",
    "        self.sequential = self.net()\n",
    "    def net(self):\n",
    "        in_channels = self.in_channels\n",
    "        sequential = []\n",
    "        for cfg in self.model_cfg:\n",
    "            if isinstance(cfg, list):#如果是第一个\n",
    "                sequential.append(base_conv(cfg[0], in_channels, cfg[1], cfg[2]))\n",
    "                in_channels = cfg[1]\n",
    "            elif len(cfg) != 5:#如果不是重复块\n",
    "                sequential.append(depth_wise(cfg[0], in_channels, cfg[1], cfg[2]))\n",
    "                in_channels = cfg[1]\n",
    "            else:#重复块\n",
    "                for _ in range(cfg[-1]):\n",
    "                    sequential.append(depth_wise(cfg[0], in_channels, cfg[1], cfg[2]))\n",
    "                    in_channels = cfg[1]\n",
    "        return nn.Sequential(*sequential)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.sequential(x)\n",
    "        x = self.avg(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9627dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MobileNetV1(3, 10, model_cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fddc919e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 10])\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 32, 112, 112]             864\n",
      "       BatchNorm2d-2         [-1, 32, 112, 112]              64\n",
      "              ReLU-3         [-1, 32, 112, 112]               0\n",
      "         base_conv-4         [-1, 32, 112, 112]               0\n",
      "            Conv2d-5         [-1, 32, 112, 112]             288\n",
      "       BatchNorm2d-6         [-1, 32, 112, 112]              64\n",
      "              ReLU-7         [-1, 32, 112, 112]               0\n",
      "            Conv2d-8         [-1, 64, 112, 112]           2,048\n",
      "       BatchNorm2d-9         [-1, 64, 112, 112]             128\n",
      "             ReLU-10         [-1, 64, 112, 112]               0\n",
      "        base_conv-11         [-1, 64, 112, 112]               0\n",
      "      BatchNorm2d-12         [-1, 64, 112, 112]             128\n",
      "             ReLU-13         [-1, 64, 112, 112]               0\n",
      "       depth_wise-14         [-1, 64, 112, 112]               0\n",
      "           Conv2d-15           [-1, 64, 56, 56]             576\n",
      "      BatchNorm2d-16           [-1, 64, 56, 56]             128\n",
      "             ReLU-17           [-1, 64, 56, 56]               0\n",
      "           Conv2d-18          [-1, 128, 56, 56]           8,192\n",
      "      BatchNorm2d-19          [-1, 128, 56, 56]             256\n",
      "             ReLU-20          [-1, 128, 56, 56]               0\n",
      "        base_conv-21          [-1, 128, 56, 56]               0\n",
      "      BatchNorm2d-22          [-1, 128, 56, 56]             256\n",
      "             ReLU-23          [-1, 128, 56, 56]               0\n",
      "       depth_wise-24          [-1, 128, 56, 56]               0\n",
      "           Conv2d-25          [-1, 128, 56, 56]           1,152\n",
      "      BatchNorm2d-26          [-1, 128, 56, 56]             256\n",
      "             ReLU-27          [-1, 128, 56, 56]               0\n",
      "           Conv2d-28          [-1, 128, 56, 56]          16,384\n",
      "      BatchNorm2d-29          [-1, 128, 56, 56]             256\n",
      "             ReLU-30          [-1, 128, 56, 56]               0\n",
      "        base_conv-31          [-1, 128, 56, 56]               0\n",
      "      BatchNorm2d-32          [-1, 128, 56, 56]             256\n",
      "             ReLU-33          [-1, 128, 56, 56]               0\n",
      "       depth_wise-34          [-1, 128, 56, 56]               0\n",
      "           Conv2d-35          [-1, 128, 28, 28]           1,152\n",
      "      BatchNorm2d-36          [-1, 128, 28, 28]             256\n",
      "             ReLU-37          [-1, 128, 28, 28]               0\n",
      "           Conv2d-38          [-1, 256, 28, 28]          32,768\n",
      "      BatchNorm2d-39          [-1, 256, 28, 28]             512\n",
      "             ReLU-40          [-1, 256, 28, 28]               0\n",
      "        base_conv-41          [-1, 256, 28, 28]               0\n",
      "      BatchNorm2d-42          [-1, 256, 28, 28]             512\n",
      "             ReLU-43          [-1, 256, 28, 28]               0\n",
      "       depth_wise-44          [-1, 256, 28, 28]               0\n",
      "           Conv2d-45          [-1, 256, 28, 28]           2,304\n",
      "      BatchNorm2d-46          [-1, 256, 28, 28]             512\n",
      "             ReLU-47          [-1, 256, 28, 28]               0\n",
      "           Conv2d-48          [-1, 256, 28, 28]          65,536\n",
      "      BatchNorm2d-49          [-1, 256, 28, 28]             512\n",
      "             ReLU-50          [-1, 256, 28, 28]               0\n",
      "        base_conv-51          [-1, 256, 28, 28]               0\n",
      "      BatchNorm2d-52          [-1, 256, 28, 28]             512\n",
      "             ReLU-53          [-1, 256, 28, 28]               0\n",
      "       depth_wise-54          [-1, 256, 28, 28]               0\n",
      "           Conv2d-55          [-1, 256, 14, 14]           2,304\n",
      "      BatchNorm2d-56          [-1, 256, 14, 14]             512\n",
      "             ReLU-57          [-1, 256, 14, 14]               0\n",
      "           Conv2d-58          [-1, 512, 14, 14]         131,072\n",
      "      BatchNorm2d-59          [-1, 512, 14, 14]           1,024\n",
      "             ReLU-60          [-1, 512, 14, 14]               0\n",
      "        base_conv-61          [-1, 512, 14, 14]               0\n",
      "      BatchNorm2d-62          [-1, 512, 14, 14]           1,024\n",
      "             ReLU-63          [-1, 512, 14, 14]               0\n",
      "       depth_wise-64          [-1, 512, 14, 14]               0\n",
      "           Conv2d-65          [-1, 512, 14, 14]           4,608\n",
      "      BatchNorm2d-66          [-1, 512, 14, 14]           1,024\n",
      "             ReLU-67          [-1, 512, 14, 14]               0\n",
      "           Conv2d-68          [-1, 512, 14, 14]         262,144\n",
      "      BatchNorm2d-69          [-1, 512, 14, 14]           1,024\n",
      "             ReLU-70          [-1, 512, 14, 14]               0\n",
      "        base_conv-71          [-1, 512, 14, 14]               0\n",
      "      BatchNorm2d-72          [-1, 512, 14, 14]           1,024\n",
      "             ReLU-73          [-1, 512, 14, 14]               0\n",
      "       depth_wise-74          [-1, 512, 14, 14]               0\n",
      "           Conv2d-75            [-1, 512, 7, 7]           4,608\n",
      "      BatchNorm2d-76            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-77            [-1, 512, 7, 7]               0\n",
      "           Conv2d-78           [-1, 1024, 7, 7]         524,288\n",
      "      BatchNorm2d-79           [-1, 1024, 7, 7]           2,048\n",
      "             ReLU-80           [-1, 1024, 7, 7]               0\n",
      "        base_conv-81           [-1, 1024, 7, 7]               0\n",
      "      BatchNorm2d-82           [-1, 1024, 7, 7]           2,048\n",
      "             ReLU-83           [-1, 1024, 7, 7]               0\n",
      "       depth_wise-84           [-1, 1024, 7, 7]               0\n",
      "           Conv2d-85           [-1, 1024, 4, 4]           9,216\n",
      "      BatchNorm2d-86           [-1, 1024, 4, 4]           2,048\n",
      "             ReLU-87           [-1, 1024, 4, 4]               0\n",
      "           Conv2d-88           [-1, 1024, 4, 4]       1,048,576\n",
      "      BatchNorm2d-89           [-1, 1024, 4, 4]           2,048\n",
      "             ReLU-90           [-1, 1024, 4, 4]               0\n",
      "        base_conv-91           [-1, 1024, 4, 4]               0\n",
      "      BatchNorm2d-92           [-1, 1024, 4, 4]           2,048\n",
      "             ReLU-93           [-1, 1024, 4, 4]               0\n",
      "       depth_wise-94           [-1, 1024, 4, 4]               0\n",
      "AdaptiveAvgPool2d-95           [-1, 1024, 1, 1]               0\n",
      "           Linear-96                   [-1, 10]          10,250\n",
      "          Softmax-97                   [-1, 10]               0\n",
      "================================================================\n",
      "Total params: 2,149,834\n",
      "Trainable params: 2,149,834\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 167.97\n",
      "Params size (MB): 8.20\n",
      "Estimated Total Size (MB): 176.75\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, input_size=(3, 224, 224))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47e90e3",
   "metadata": {},
   "source": [
    "总结：感觉论文中给的模型结果表有误，修改位置如下。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71074fab",
   "metadata": {},
   "source": [
    "<img src=\"modify.png\" width=40% align=center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "720904d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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