diff --git a/ultralytics/cfg/models/mtl/yolov8-cls.yaml b/ultralytics/cfg/models/mtl/resnet18-cls.yaml similarity index 88% rename from ultralytics/cfg/models/mtl/yolov8-cls.yaml rename to ultralytics/cfg/models/mtl/resnet18-cls.yaml index fd0a8e4..d9fd42c 100644 --- a/ultralytics/cfg/models/mtl/yolov8-cls.yaml +++ b/ultralytics/cfg/models/mtl/resnet18-cls.yaml @@ -7,7 +7,7 @@ nc: 38 # number of classes # YOLOv8.0n backbone backbone: # [from, repeats, module, args] - - [-1, 1, ResNet18, []] # 0-P1/2 + - [-1, 1, resnet18, [False]] # 0-P1/2 # YOLOv8.0n head head: diff --git a/ultralytics/nn/backbone/resnet.py b/ultralytics/nn/backbone/resnet.py deleted file mode 100644 index 882cf01..0000000 --- a/ultralytics/nn/backbone/resnet.py +++ /dev/null @@ -1,182 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -__all__ = [ 'ResNet18','ResNet34','ResNet50'] - -'''-------------一、BasicBlock模块-----------------------------''' -# 用于ResNet18和ResNet34基本残差结构块 -class BasicBlock(nn.Module): - def __init__(self, inchannel, outchannel, stride=1): - super(BasicBlock, self).__init__() - self.left = nn.Sequential( - nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False), - nn.BatchNorm2d(outchannel), - 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 - nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(outchannel) - ) - self.shortcut = nn.Sequential() - #论文中模型架构的虚线部分，需要下采样 - if stride != 1 or inchannel != outchannel: - self.shortcut = nn.Sequential( - nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(outchannel) - ) - - def forward(self, x): - out = self.left(x) #这是由于残差块需要保留原始输入 - out += self.shortcut(x)#这是ResNet的核心，在输出上叠加了输入x - out = F.relu(out) - return out - - -'''-------------二、Bottleneck模块-----------------------------''' -# 用于ResNet50及以上的残差结构块 -class Bottleneck(nn.Module): - def __init__(self, inchannel, outchannel, stride=1): - super(Bottleneck, self).__init__() - self.left = nn.Sequential( - nn.Conv2d(inchannel, int(outchannel / 4), kernel_size=1, stride=stride, padding=0, bias=False), - nn.BatchNorm2d(int(outchannel / 4)), - nn.ReLU(inplace=True), - nn.Conv2d(int(outchannel / 4), int(outchannel / 4), kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(int(outchannel / 4)), - nn.ReLU(inplace=True), - nn.Conv2d(int(outchannel / 4), outchannel, kernel_size=1, stride=1, padding=0, bias=False), - nn.BatchNorm2d(outchannel), - ) - self.shortcut = nn.Sequential() - if stride != 1 or inchannel != outchannel: - self.shortcut = nn.Sequential( - nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(outchannel) - ) - - def forward(self, x): - out = self.left(x) - y = self.shortcut(x) - out += self.shortcut(x) - out = F.relu(out) - return out - -'''-------------ResNet18---------------''' -class ResNet_18(nn.Module): - def __init__(self, ResidualBlock, num_classes=10): - super(ResNet_18, self).__init__() - self.inchannel = 64 - self.conv1 = nn.Sequential( - nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(64), - nn.ReLU(), - ) - self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1) - self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2) - self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2) - self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2) - self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 224, 224))] - - def make_layer(self, block, channels, num_blocks, stride): - strides = [stride] + [1] * (num_blocks - 1) # strides=[1,1] - layers = [] - for stride in strides: - layers.append(block(self.inchannel, channels, stride)) - self.inchannel = channels - return nn.Sequential(*layers) - - def forward(self, x): # 3*32*32 - out = self.conv1(x) # 64*32*32 - out = self.layer1(out) # 64*32*32 - out = self.layer2(out) # 128*16*16 - out = self.layer3(out) # 256*8*8 - out = self.layer4(out) # 512*4*4 - out = F.avg_pool2d(out, 4) # 512*1*1 - return out - -'''-------------ResNet34---------------''' -class ResNet_34(nn.Module): - def __init__(self, ResidualBlock, num_classes=10): - super(ResNet_34, self).__init__() - self.inchannel = 64 - self.conv1 = nn.Sequential( - nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(64), - nn.ReLU(), - ) - self.layer1 = self.make_layer(ResidualBlock, 64, 3, stride=1) - self.layer2 = self.make_layer(ResidualBlock, 128, 4, stride=2) - self.layer3 = self.make_layer(ResidualBlock, 256, 6, stride=2) - self.layer4 = self.make_layer(ResidualBlock, 512, 3, stride=2) - self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 224, 224))] - - def make_layer(self, block, channels, num_blocks, stride): - strides = [stride] + [1] * (num_blocks - 1) # strides=[1,1] - layers = [] - for stride in strides: - layers.append(block(self.inchannel, channels, stride)) - self.inchannel = channels - return nn.Sequential(*layers) - - def forward(self, x): # 3*32*32 - out = self.conv1(x) # 64*32*32 - out = self.layer1(out) # 64*32*32 - out = self.layer2(out) # 128*16*16 - out = self.layer3(out) # 256*8*8 - out = self.layer4(out) # 512*4*4 - out = F.avg_pool2d(out, 4) # 512*1*1 - out = out.view(out.size(0), -1) # 512 - return out - -'''-------------ResNet50---------------''' -class ResNet_50(nn.Module): - def __init__(self, ResidualBlock, num_classes=10): - super(ResNet_50, self).__init__() - self.inchannel = 64 - self.conv1 = nn.Sequential( - nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False), - nn.BatchNorm2d(64), - nn.ReLU(), - ) - self.layer1 = self.make_layer(ResidualBlock, 256, 3, stride=1) - self.layer2 = self.make_layer(ResidualBlock, 512, 4, stride=2) - self.layer3 = self.make_layer(ResidualBlock, 1024, 6, stride=2) - self.layer4 = self.make_layer(ResidualBlock, 2048, 3, stride=2) - self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 224, 224))] - # ************************** - - def make_layer(self, block, channels, num_blocks, stride): - strides = [stride] + [1] * (num_blocks - 1) # strides=[1,1] - layers = [] - for stride in strides: - layers.append(block(self.inchannel, channels, stride)) - self.inchannel = channels - return nn.Sequential(*layers) - - def forward(self, x): # 3*32*32 - out = self.conv1(x) # 64*32*32 - out = self.layer1(out) # 64*32*32 - out = self.layer2(out) # 128*16*16 - out = self.layer3(out) # 256*8*8 - out = self.layer4(out) # 512*4*4 - out = F.avg_pool2d(out, 4) # 512*1*1 - # print(out.size()) - return out - - -def ResNet18(): - return ResNet_18(BasicBlock) - - -def ResNet34(): - return ResNet_34(BasicBlock) - - -def ResNet50(): - return ResNet_50(Bottleneck) - -if __name__ == '__main__': - model = ResNet18() - inputs = torch.randn((1, 3, 224, 224)) - res = model(inputs) - for i in res: - print(i.size()) \ No newline at end of file