删除无效代码
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yoiannis
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@ -7,7 +7,7 @@ nc: 38 # number of classes
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# YOLOv8.0n backbone
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, ResNet18, []] # 0-P1/2
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- [-1, 1, resnet18, [False]] # 0-P1/2
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# YOLOv8.0n head
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head:
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@ -1,182 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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__all__ = [ 'ResNet18','ResNet34','ResNet50']
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'''-------------一、BasicBlock模块-----------------------------'''
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# 用于ResNet18和ResNet34基本残差结构块
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class BasicBlock(nn.Module):
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def __init__(self, inchannel, outchannel, stride=1):
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super(BasicBlock, self).__init__()
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self.left = nn.Sequential(
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nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(outchannel),
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nn.ReLU(inplace=True), #inplace=True表示进行原地操作，一般默认为False，表示新建一个变量存储操作
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nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(outchannel)
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)
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self.shortcut = nn.Sequential()
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#论文中模型架构的虚线部分，需要下采样
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if stride != 1 or inchannel != outchannel:
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self.shortcut = nn.Sequential(
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nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(outchannel)
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)
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def forward(self, x):
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out = self.left(x) #这是由于残差块需要保留原始输入
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out += self.shortcut(x)#这是ResNet的核心，在输出上叠加了输入x
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out = F.relu(out)
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return out
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'''-------------二、Bottleneck模块-----------------------------'''
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# 用于ResNet50及以上的残差结构块
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class Bottleneck(nn.Module):
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def __init__(self, inchannel, outchannel, stride=1):
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super(Bottleneck, self).__init__()
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self.left = nn.Sequential(
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nn.Conv2d(inchannel, int(outchannel / 4), kernel_size=1, stride=stride, padding=0, bias=False),
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nn.BatchNorm2d(int(outchannel / 4)),
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nn.ReLU(inplace=True),
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nn.Conv2d(int(outchannel / 4), int(outchannel / 4), kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(int(outchannel / 4)),
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nn.ReLU(inplace=True),
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nn.Conv2d(int(outchannel / 4), outchannel, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(outchannel),
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)
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self.shortcut = nn.Sequential()
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if stride != 1 or inchannel != outchannel:
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self.shortcut = nn.Sequential(
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nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(outchannel)
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)
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def forward(self, x):
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out = self.left(x)
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y = self.shortcut(x)
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out += self.shortcut(x)
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out = F.relu(out)
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return out
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'''-------------ResNet18---------------'''
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class ResNet_18(nn.Module):
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def __init__(self, ResidualBlock, num_classes=10):
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super(ResNet_18, self).__init__()
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self.inchannel = 64
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(),
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)
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self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1)
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self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
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self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
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self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
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self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 224, 224))]
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def make_layer(self, block, channels, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1) # strides=[1,1]
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layers = []
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for stride in strides:
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layers.append(block(self.inchannel, channels, stride))
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self.inchannel = channels
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return nn.Sequential(*layers)
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def forward(self, x): # 3*32*32
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out = self.conv1(x) # 64*32*32
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out = self.layer1(out) # 64*32*32
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out = self.layer2(out) # 128*16*16
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out = self.layer3(out) # 256*8*8
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out = self.layer4(out) # 512*4*4
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out = F.avg_pool2d(out, 4) # 512*1*1
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return out
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'''-------------ResNet34---------------'''
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class ResNet_34(nn.Module):
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def __init__(self, ResidualBlock, num_classes=10):
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super(ResNet_34, self).__init__()
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self.inchannel = 64
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(),
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)
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self.layer1 = self.make_layer(ResidualBlock, 64, 3, stride=1)
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self.layer2 = self.make_layer(ResidualBlock, 128, 4, stride=2)
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self.layer3 = self.make_layer(ResidualBlock, 256, 6, stride=2)
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self.layer4 = self.make_layer(ResidualBlock, 512, 3, stride=2)
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self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 224, 224))]
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def make_layer(self, block, channels, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1) # strides=[1,1]
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layers = []
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for stride in strides:
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layers.append(block(self.inchannel, channels, stride))
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self.inchannel = channels
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return nn.Sequential(*layers)
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def forward(self, x): # 3*32*32
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out = self.conv1(x) # 64*32*32
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out = self.layer1(out) # 64*32*32
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out = self.layer2(out) # 128*16*16
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out = self.layer3(out) # 256*8*8
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out = self.layer4(out) # 512*4*4
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out = F.avg_pool2d(out, 4) # 512*1*1
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out = out.view(out.size(0), -1) # 512
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return out
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'''-------------ResNet50---------------'''
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class ResNet_50(nn.Module):
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def __init__(self, ResidualBlock, num_classes=10):
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super(ResNet_50, self).__init__()
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self.inchannel = 64
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(),
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)
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self.layer1 = self.make_layer(ResidualBlock, 256, 3, stride=1)
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self.layer2 = self.make_layer(ResidualBlock, 512, 4, stride=2)
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self.layer3 = self.make_layer(ResidualBlock, 1024, 6, stride=2)
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self.layer4 = self.make_layer(ResidualBlock, 2048, 3, stride=2)
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self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 224, 224))]
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# **************************
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def make_layer(self, block, channels, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1) # strides=[1,1]
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layers = []
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for stride in strides:
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layers.append(block(self.inchannel, channels, stride))
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self.inchannel = channels
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return nn.Sequential(*layers)
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def forward(self, x): # 3*32*32
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out = self.conv1(x) # 64*32*32
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out = self.layer1(out) # 64*32*32
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out = self.layer2(out) # 128*16*16
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out = self.layer3(out) # 256*8*8
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out = self.layer4(out) # 512*4*4
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out = F.avg_pool2d(out, 4) # 512*1*1
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# print(out.size())
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return out
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def ResNet18():
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return ResNet_18(BasicBlock)
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def ResNet34():
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return ResNet_34(BasicBlock)
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def ResNet50():
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return ResNet_50(Bottleneck)
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if __name__ == '__main__':
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model = ResNet18()
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inputs = torch.randn((1, 3, 224, 224))
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res = model(inputs)
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for i in res:
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print(i.size())
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