82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class ResBlock(nn.Module):
|
|
def __init__(self, in_channel, out_channel, stride=1):
|
|
super(ResBlock, self).__init__()
|
|
|
|
self.layer = nn.Sequential(
|
|
nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=1),
|
|
nn.BatchNorm2d(out_channel),
|
|
nn.ReLU(),
|
|
nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1),
|
|
nn.BatchNorm2d(out_channel)
|
|
)
|
|
|
|
if in_channel != out_channel or stride > 1:
|
|
self.shortcut = nn.Sequential(
|
|
nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=stride),
|
|
nn.BatchNorm2d(out_channel),
|
|
)
|
|
else:
|
|
self.shortcut = nn.Sequential()
|
|
|
|
def forward(self, x):
|
|
out = self.layer(x)
|
|
shortcut = self.shortcut(x)
|
|
out = out + shortcut
|
|
out = F.relu(out)
|
|
return out
|
|
|
|
|
|
class ResNet(nn.Module):
|
|
def make_layer(self, block, out_channel, stride, num_block):
|
|
layer_list = []
|
|
for i in range(num_block):
|
|
if i == 0:
|
|
in_stride = stride
|
|
else:
|
|
in_stride = 1
|
|
|
|
layer_list.append(block(self.in_channel, out_channel, in_stride))
|
|
self.in_channel = out_channel
|
|
|
|
return nn.Sequential(*layer_list)
|
|
|
|
def __init__(self, num_classes=67):
|
|
super(ResNet, self).__init__()
|
|
self.in_channel = 32
|
|
|
|
self.conv1 = nn.Sequential(
|
|
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
|
|
nn.BatchNorm2d(32),
|
|
nn.ReLU()
|
|
)
|
|
|
|
self.layer1 = self.make_layer(ResBlock, 64, 2, 2) # 32 -> 64
|
|
self.layer2 = self.make_layer(ResBlock, 128, 2, 2) # 64 -> 128
|
|
self.layer3 = self.make_layer(ResBlock, 256, 2, 2) # 128 -> 256
|
|
self.layer4 = self.make_layer(ResBlock, 512, 2, 2) # 256 -> 512
|
|
|
|
# 添加全局平均池化
|
|
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
|
self.fc = nn.Linear(512, num_classes)
|
|
|
|
def forward(self, x):
|
|
out = self.conv1(x)
|
|
out = self.layer1(out)
|
|
out = self.layer2(out)
|
|
out = self.layer3(out)
|
|
out = self.layer4(out)
|
|
|
|
# 使用全局平均池化
|
|
out = self.avgpool(out)
|
|
out = out.view(out.size(0), -1)
|
|
out = self.fc(out)
|
|
|
|
return out
|
|
|
|
|
|
def resnet():
|
|
return ResNet() |