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resnet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
# 用于ResNet18和34的残差块，用的是2个3x3的卷积
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
# 经过处理后的x要与x的维度相同(尺寸和深度)
# 如果不相同，需要添加卷积+BN来变换为同一维度
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
# print('x',x.shape)
out = F.relu(self.bn1(self.conv1(x)))
# print(out.shape)
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
# 用于ResNet50,101和152的残差块，用的是1x1+3x3+1x1的卷积
class Bottleneck(nn.Module):
# 前面1x1和3x3卷积的filter个数相等，最后1x1卷积是其expansion倍
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion * planes,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, in_channel,block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
# print('--',out.shape)
out_512 = self.layer1(out)
# print('--', out.shape)
out_256 = self.layer2(out_512)
# print('--', out.shape)
out_128 = self.layer3(out_256)
# print('--', out.shape)
out_64 = self.layer4(out_128)
# print('--', out.shape)
# print('1', out.shape)
# out = F.avg_pool2d(out, 2) #torch.Size([1, 512, 64, 64])
# print('2',out.shape)
# out = out.view(out.size(0), -1)
# out = self.linear(out)
return out_64,out_128,out_256,out_512
def ResNet18():
return ResNet(2,BasicBlock, [2, 2, 2, 2])
def ResNet34():
return ResNet(1,BasicBlock, [3, 4, 6, 3])
def ResNet50():
return ResNet(Bottleneck, [3, 4, 6, 3])
def ResNet101():
return ResNet(Bottleneck, [3, 4, 23, 3])
def ResNet152():
return ResNet(Bottleneck, [3, 8, 36, 3])
if __name__ == '__main__':
def test():
net = ResNet18()
y = net(torch.randn(1, 3, 512, 512))
# print(y.size())
test()