可以写一个使用ResNet训练模型的代码吗
时间: 2023-02-26 16:39:13 浏览: 47
当然可以,这是一个使用PyTorch实现ResNet的示例代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNetBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResNetBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.bn = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self.make_layer(block, 64, layers[0])
self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
self.layer4 = self.make_layer(block, 512, layers[3], stride=2)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if (stride != 1) or (self.in_channels != out_channels):
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels, kernel_size=3, stride=stride, padding=1),
nn.BatchNorm2d(out_channels)
)
layers = []
layers.append(