标签：nn 实现 self ResNet resnet channels block out

残差块

import time
import torch
from torch import nn,optim
import sys
sys.path.append("./Dive-into-DL-PyTorch-master/Dive-into-DL-PyTorch-master/code/") 
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
import torch.nn.functional as F

class Residual(nn.Module): 
    def __init__(self, in_channels, out_channels, use_1x1conv=False, stride=1):
        super(Residual, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        if use_1x1conv:
            self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, X):
        Y = F.relu(self.bn1(self.conv1(X)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3:
            X = self.conv3(X)
        return F.relu(Y + X)

blk = Residual(3,3)
blk

ResNet模型

net = nn.Sequential(nn.Conv2d(1,64,kernel_size=7,stride=2,padding=3),
                   nn.BatchNorm2d(64),
                   nn.ReLU(),
                   nn.MaxPool2d(kernel_size=3,stride=2,padding=1))

def resnet_block(in_channels,out_channels,num_residuals,first_block=False):
    if first_block:
        # 第一个模块的通道数同输入通道数一致
        assert in_channels == out_channels
    blk= []
    for i in range(num_residuals):
        if i == 0 and not first_block:
            blk.append(Residual(in_channels,out_channels,use_1x1conv=True,
                               stride=2))
        else:
            blk.append(Residual(out_channels,out_channels))
    return nn.Sequential(*blk)

net.add_module("resnet_block1", resnet_block(64, 64, 2, first_block=True))
net.add_module("resnet_block2", resnet_block(64, 128, 2))
net.add_module("resnet_block3", resnet_block(128, 256, 2))
net.add_module("resnet_block4", resnet_block(256, 512, 2))

net.add_module("global_avg_pool", d2l.GlobalAvgPool2d()) # GlobalAvgPool2d的输出: (Batch, 512, 1, 1)
net.add_module("fc", nn.Sequential(d2l.FlattenLayer(), nn.Linear(512, 10))) 

X = torch.rand((1, 1, 224, 224))
for name, layer in net.named_children():
    X = layer(X)
    print(name, ' output shape:\t', X.shape) 

标签：nn,实现,self,ResNet,resnet,channels,block,out
来源： https://www.cnblogs.com/coline/p/16493283.html

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