shusentang--dive-into-dl-pytorch
shusentang
262 行
7.0 KiB
Plaintext
262 行
7.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5.11 残差网络（ResNet）"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.4.0\n",
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"cuda\n"
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]
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}
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],
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"source": [
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"import time\n",
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"import torch\n",
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"from torch import nn, optim\n",
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"import torch.nn.functional as F\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"..\") \n",
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"import d2lzh_pytorch as d2l\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"\n",
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"print(torch.__version__)\n",
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"print(device)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.11.2 残差块"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Residual(nn.Module): # 本类已保存在d2lzh_pytorch包中方便以后使用\n",
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" def __init__(self, in_channels, out_channels, use_1x1conv=False, stride=1):\n",
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" super(Residual, self).__init__()\n",
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" self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride)\n",
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" self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)\n",
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" if use_1x1conv:\n",
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" self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride)\n",
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" else:\n",
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" self.conv3 = None\n",
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" self.bn1 = nn.BatchNorm2d(out_channels)\n",
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" self.bn2 = nn.BatchNorm2d(out_channels)\n",
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"\n",
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" def forward(self, X):\n",
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" Y = F.relu(self.bn1(self.conv1(X)))\n",
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" Y = self.bn2(self.conv2(Y))\n",
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" if self.conv3:\n",
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" X = self.conv3(X)\n",
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" return F.relu(Y + X)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([4, 3, 6, 6])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"blk = Residual(3, 3)\n",
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"X = torch.rand((4, 3, 6, 6))\n",
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"blk(X).shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([4, 6, 3, 3])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"blk = Residual(3, 6, use_1x1conv=True, stride=2)\n",
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"blk(X).shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.11.2 ResNet模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"net = nn.Sequential(\n",
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" nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),\n",
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" nn.BatchNorm2d(64), \n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def resnet_block(in_channels, out_channels, num_residuals, first_block=False):\n",
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" if first_block:\n",
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" assert in_channels == out_channels # 第一个模块的通道数同输入通道数一致\n",
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" blk = []\n",
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" for i in range(num_residuals):\n",
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" if i == 0 and not first_block:\n",
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" blk.append(Residual(in_channels, out_channels, use_1x1conv=True, stride=2))\n",
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" else:\n",
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" blk.append(Residual(out_channels, out_channels))\n",
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" return nn.Sequential(*blk)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"net.add_module(\"resnet_block1\", resnet_block(64, 64, 2, first_block=True))\n",
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"net.add_module(\"resnet_block2\", resnet_block(64, 128, 2))\n",
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"net.add_module(\"resnet_block3\", resnet_block(128, 256, 2))\n",
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"net.add_module(\"resnet_block4\", resnet_block(256, 512, 2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"net.add_module(\"global_avg_pool\", d2l.GlobalAvgPool2d()) # GlobalAvgPool2d的输出: (Batch, 512, 1, 1)\n",
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"net.add_module(\"fc\", nn.Sequential(d2l.FlattenLayer(), nn.Linear(512, 10))) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 output shape:\t torch.Size([1, 64, 112, 112])\n",
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"1 output shape:\t torch.Size([1, 64, 112, 112])\n",
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"2 output shape:\t torch.Size([1, 64, 112, 112])\n",
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"3 output shape:\t torch.Size([1, 64, 56, 56])\n",
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"resnet_block1 output shape:\t torch.Size([1, 64, 56, 56])\n",
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"resnet_block2 output shape:\t torch.Size([1, 128, 28, 28])\n",
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"resnet_block3 output shape:\t torch.Size([1, 256, 14, 14])\n",
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"resnet_block4 output shape:\t torch.Size([1, 512, 7, 7])\n",
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"global_avg_pool output shape:\t torch.Size([1, 512, 1, 1])\n",
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"fc output shape:\t torch.Size([1, 10])\n"
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]
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}
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],
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"source": [
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"X = torch.rand((1, 1, 224, 224))\n",
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"for name, layer in net.named_children():\n",
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" X = layer(X)\n",
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" print(name, ' output shape:\\t', X.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.11.3 获取数据和训练模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"training on cuda\n",
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"epoch 1, loss 0.0015, train acc 0.853, test acc 0.885, time 31.0 sec\n",
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"epoch 2, loss 0.0010, train acc 0.910, test acc 0.899, time 31.8 sec\n",
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"epoch 3, loss 0.0008, train acc 0.926, test acc 0.911, time 31.6 sec\n",
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"epoch 4, loss 0.0007, train acc 0.936, test acc 0.916, time 31.8 sec\n",
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"epoch 5, loss 0.0006, train acc 0.944, test acc 0.926, time 31.5 sec\n"
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]
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}
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],
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"source": [
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"batch_size = 256\n",
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"# 如出现“out of memory”的报错信息，可减小batch_size或resize\n",
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"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)\n",
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"\n",
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"lr, num_epochs = 0.001, 5\n",
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"optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
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"d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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