我使用的是来自PyTorch的ResNet152模型。我想从模型中去掉最后一个FC层。这是我的代码:from torchvision import datasets, transforms, models
model = models.resnet152(pretrained=True)
print(model)
当我打印模型时,最后几行如下所示:(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
我想从模型中删除最后一个fc层。list(model.modules()) # to inspect the modules of your model
my_model = nn.Sequential(*list(model.modules())[:-1]) # strips off last linear layer
所以我在代码中添加了如下代码行:model = models.resnet152(pretrained=True)
list(model.modules()) # to inspect the modules of your model
my_model = nn.Sequential(*list(model.modules())[:-1]) # strips off last linear layer
print(my_model)
但这段代码并不像宣传的那样工作——至少对我来说不是。这篇文章的其余部分详细解释了为什么这个答案不起作用,这样这个问题就不会作为一个重复而结束。
首先,打印出来的模型比以前大了近5倍。我看到的是和以前一样的模型,但是后面的模型似乎是重复的,但可能是扁平的。(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
)
)
(avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
(1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace)
(4): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(5): Sequential(
. . . this goes on for ~1600 more lines . . .
(415): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(416): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(417): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(418): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(419): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(420): ReLU(inplace)
(421): AvgPool2d(kernel_size=7, stride=1, padding=0)
)
其次,fc层仍在那里,而Conv2D层看起来就像ResNet152的第一层。
第三,如果我试图调用my_model.forward(),pytorch会抱怨大小不匹配。它需要大小[1,3,224224,224],但输入是[1,1000]。因此,看起来整个模型(减去fc层)的副本被附加到原始模型中。
总之,我找到的唯一答案实际上是行不通的。