• torchvision中的标准ResNet50网络结构

    注：仅用以记录学习

    打印出来的网络结构如下：

    1. from torchvision import models
    2. model = models.resnet50(pretrained=False)
    3. print("model: ", model)

    结构：

    1. ResNet(
    2.   (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    3.   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    4.   (relu): ReLU(inplace=True)
    5.   (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    6.   (layer1): Sequential(
    7.     (0): Bottleneck(
    8.       (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
    9.       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    10.       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    11.       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    12.       (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    13.       (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    14.       (relu): ReLU(inplace=True)
    15.       (downsample): Sequential(
    16.         (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    17.         (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    18.       )
    19.     )
    20.     (1): Bottleneck(
    21.       (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
    22.       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    23.       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    24.       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    25.       (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    26.       (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    27.       (relu): ReLU(inplace=True)
    28.     )
    29.     (2): Bottleneck(
    30.       (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
    31.       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    32.       (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    33.       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    34.       (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    35.       (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    36.       (relu): ReLU(inplace=True)
    37.     )
    38.   )
    39.   (layer2): Sequential(
    40.     (0): Bottleneck(
    41.       (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
    42.       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    43.       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    44.       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    45.       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
    46.       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    47.       (relu): ReLU(inplace=True)
    48.       (downsample): Sequential(
    49.         (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
    50.         (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    51.       )
    52.     )
    53.     (1): Bottleneck(
    54.       (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
    55.       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    56.       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    57.       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    58.       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
    59.       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    60.       (relu): ReLU(inplace=True)
    61.     )
    62.     (2): Bottleneck(
    63.       (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
    64.       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    65.       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    66.       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    67.       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
    68.       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    69.       (relu): ReLU(inplace=True)
    70.     )
    71.     (3): Bottleneck(
    72.       (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
    73.       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    74.       (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    75.       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    76.       (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
    77.       (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    78.       (relu): ReLU(inplace=True)
    79.     )
    80.   )
    81.   (layer3): Sequential(
    82.     (0): Bottleneck(
    83.       (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    84.       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    85.       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    86.       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    87.       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
    88.       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    89.       (relu): ReLU(inplace=True)
    90.       (downsample): Sequential(
    91.         (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
    92.         (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    93.       )
    94.     )
    95.     (1): Bottleneck(
    96.       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    97.       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    98.       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    99.       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    100.       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
    101.       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    102.       (relu): ReLU(inplace=True)
    103.     )
    104.     (2): Bottleneck(
    105.       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    106.       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    107.       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    108.       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    109.       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
    110.       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    111.       (relu): ReLU(inplace=True)
    112.     )
    113.     (3): Bottleneck(
    114.       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    115.       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    116.       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    117.       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    118.       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
    119.       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    120.       (relu): ReLU(inplace=True)
    121.     )
    122.     (4): Bottleneck(
    123.       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    124.       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    125.       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    126.       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    127.       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
    128.       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    129.       (relu): ReLU(inplace=True)
    130.     )
    131.     (5): Bottleneck(
    132.       (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    133.       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    134.       (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    135.       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    136.       (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
    137.       (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    138.       (relu): ReLU(inplace=True)
    139.     )
    140.   )
    141.   (layer4): Sequential(
    142.     (0): Bottleneck(
    143.       (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
    144.       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    145.       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    146.       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    147.       (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
    148.       (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    149.       (relu): ReLU(inplace=True)
    150.       (downsample): Sequential(
    151.         (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
    152.         (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    153.       )
    154.     )
    155.     (1): Bottleneck(
    156.       (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
    157.       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    158.       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    159.       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    160.       (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
    161.       (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    162.       (relu): ReLU(inplace=True)
    163.     )
    164.     (2): Bottleneck(
    165.       (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
    166.       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    167.       (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    168.       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    169.       (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
    170.       (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    171.       (relu): ReLU(inplace=True)
    172.     )
    173.   )
    174.   (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
    175.   (fc): Linear(in_features=2048, out_features=1000, bias=True)
    176. )

    修改最后一层（fc层）代码：

    用于特定的分类任务，其中最后一层全连接层的输出类别数量被指定为输入参数

    1. class ResNet50(nn.Module):
    2.     def __init__(
    3.         self,
    4.         num_classes
    5.     ):
    6.         super().__init__()
    7.         model = models.resnet50(pretrained=False)
    8.         
    9.         num_ftrs = model.fc.in_features  #获取ResNet-50模型最后一层全连接层的输入特征数量
    10.         model.fc = nn.Linear(num_ftrs, num_classes) #将原fc层替换为一个新的全连接层，其中输出特征数量为num_classes
    11.  
    12.         layers = [v for v in model.children()]   # 获取ResNet-50模型的所有子模块（层）
    13.         self.model = nn.Sequential(*layers[:-2])  # 创建一个新的nn.Sequential模块，包含ResNet-50模型的所有子模块，除了最后的两个子模块。这个nn.Sequential模块将用作新模型的主体部分。
    14.         self.pool = layers[-2]  # ResNet-50模型的倒数第二个子模块，即全局平均池化层
    15.         self.fc = layers[-1]  # 获取ResNet-50模型的最后一个子模块，即全连接层
    16.  
    17.     def forward(self, x, **kwargs):  # 定义前向传播方法
    18.         x = self.model(x) # [B, C, H, W]   # 将输入x通过ResNet-50模型的主体部分传递，得到特征表示
    19.         outs = Munch()
    20.  
    21.         x = self.fc(torch.flatten(self.pool(x), 1))  # 通过全局平均池化的特征进行扁平化，然后通过新的全连接层得到最终的输出
    22.         outs.sup = x
    23.         return outs

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  • 原文地址：https://blog.csdn.net/vivi_cin/article/details/134557726