wide resnet101-2的结构

Question

画出wide resnet101-2网络模型，包括他的层还是输出大小，尽量详细一点。

Answer 1

Gpt辅助回答，我来为你把关

import torch
import torch.nn as nn
import torch.nn.functional as F 
class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample 
    def forward(self, x):
        identity = x 
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out) 
        out = self.conv2(out)
        out = self.bn2(out) 
        if self.downsample is not None:
            identity = self.downsample(x) 
        out += identity
        out = self.relu(out) 
        return out 
class WideResNet(nn.Module):
    def __init__(self, block, num_blocks, k=2, num_classes=10):
        super(WideResNet, self).__init__()
        self.in_channels = 16
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16*k, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 32*k, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 64*k, num_blocks[2], stride=2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64*k, num_classes) 
    def _make_layer(self, block, out_channels, num_blocks, stride):
        downsample = None
        if stride != 1 or self.in_channels != out_channels:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels
        for _ in range(1, num_blocks):
            layers.append(block(out_channels, out_channels))
        return nn.Sequential(*layers) 
    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        
        return out 
# Create a WideResNet-101-2 model
model = WideResNet(BasicBlock, [3, 4, 23, 3], k=2, num_classes=1000)
print(model)

在上面的代码中，我们定义了一个WideResNet模型，包含了基本块BasicBlock和WideResNet结构。WideResNet具有101层和增长因子k为2，共有4个阶段，每个阶段的BasicBlock数量分别为3、4、23、3。输入通道为3，输出类别数为1000。WideResNet模型的架构非常深，但由于加入了宽度因子k，可以在不增加深度的情况下增加模型的复杂度和准确性。

有问题你别着急，评论留言都可以，看到马上就回复，尽量及时补充齐