我是Pytorch的新手！我在尝试微调VGG16模型以预测3个不同的类别。我的工作的一部分涉及将全连接层（FC layers）转换为卷积层（CONV layers）。然而，我的预测值并未落在0到2之间（即3个类别）。
有人能推荐一些关于如何计算最终层正确维度的优质资源吗？
以下是VGG16的原始全连接层：
(classifier): Sequential( (0): Linear(in_features=25088, out_features=4096, bias=True) (1): ReLU(inplace) (2): Dropout(p=0.5) (3): Linear(in_features=4096, out_features=4096, bias=True) (4): ReLU(inplace) (5): Dropout(p=0.5) (6): Linear(in_features=4096, out_features=1000, bias=True) )我用于将全连接层转换为卷积层的代码：
def convert_fc_to_conv(self, fc_layers): # Replace first FC layer with CONV layer fc = fc_layers[0].state_dict() in_ch = 512 out_ch = fc["weight"].size(0) first_conv = nn.Conv2d(512, out_ch, kernel_size=(1, 1), stride=(1, 1)) conv_list = [first_conv] for idx, layer in enumerate(fc_layers[1:]): if isinstance(layer, nn.Linear): fc = layer.state_dict() in_ch = fc["weight"].size(1) out_ch = fc["weight"].size(0) if idx == len(fc_layers)-4: in_ch = 3 conv = nn.Conv2d(out_ch, in_ch, kernel_size=(1, 1), stride=(1, 1)) conv_list += [conv] else: conv_list += [layer] gc.collect() avg_pool = nn.AvgPool2d(kernel_size=2, stride=1, ceil_mode=False) conv_list += [avg_pool, nn.Softmax()] top_layers = nn.Sequential(*conv_list) return top_layers最终模型架构：
Model( (features): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU(inplace) (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): ReLU(inplace) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): ReLU(inplace) (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): ReLU(inplace) (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): ReLU(inplace) (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): ReLU(inplace) (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (18): ReLU(inplace) (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (20): ReLU(inplace) (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): ReLU(inplace) (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): ReLU(inplace) (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): ReLU(inplace) (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): ReLU(inplace) (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)) (classifier): Sequential( (0): Conv2d(512, 4096, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace) (2): Dropout(p=0.5) (3): Conv2d(4096, 3, kernel_size=(1, 1), stride=(1, 1)) (4): ReLU(inplace) (5): Dropout(p=0.5) (6): AvgPool2d(kernel_size=2, stride=1, padding=0) (7): Softmax() ))模型摘要：
Conv2d-1 [-1, 64, 224, 224] 1,792 ReLU-2 [-1, 64, 224, 224] 0 Conv2d-3 [-1, 64, 224, 224] 36,928 ReLU-4 [-1, 64, 224, 224] 0 MaxPool2d-5 [-1, 64, 112, 112] 0 Conv2d-6 [-1, 128, 112, 112] 73,856 ReLU-7 [-1, 128, 112, 112] 0 Conv2d-8 [-1, 128, 112, 112] 147,584 ReLU-9 [-1, 128, 112, 112] 0 MaxPool2d-10 [-1, 128, 56, 56] 0 Conv2d-11 [-1, 256, 56, 56] 295,168 ReLU-12 [-1, 256, 56, 56] 0 Conv2d-13 [-1, 256, 56, 56] 590,080 ReLU-14 [-1, 256, 56, 56] 0 Conv2d-15 [-1, 256, 56, 56] 590,080 ReLU-16 [-1, 256, 56, 56] 0 MaxPool2d-17 [-1, 256, 28, 28] 0 Conv2d-18 [-1, 512, 28, 28] 1,180,160 ReLU-19 [-1, 512, 28, 28] 0 Conv2d-20 [-1, 512, 28, 28] 2,359,808 ReLU-21 [-1, 512, 28, 28] 0 Conv2d-22 [-1, 512, 28, 28] 2,359,808 ReLU-23 [-1, 512, 28, 28] 0 MaxPool2d-24 [-1, 512, 14, 14] 0 Conv2d-25 [-1, 512, 14, 14] 2,359,808 ReLU-26 [-1, 512, 14, 14] 0 Conv2d-27 [-1, 512, 14, 14] 2,359,808 ReLU-28 [-1, 512, 14, 14] 0 Conv2d-29 [-1, 512, 14, 14] 2,359,808 ReLU-30 [-1, 512, 14, 14] 0 MaxPool2d-31 [-1, 512, 7, 7] 0 Conv2d-32 [-1, 4096, 7, 7] 2,101,248 ReLU-33 [-1, 4096, 7, 7] 0 Dropout-34 [-1, 4096, 7, 7] 0 Conv2d-35 [-1, 3, 7, 7] 12,291 ReLU-36 [-1, 3, 7, 7] 0 Dropout-37 [-1, 3, 7, 7] 0 AvgPool2d-38 [-1, 3, 6, 6] 0 Softmax-39 [-1, 3, 6, 6] 0回答：
我编写了一个函数，该函数接受Pytorch模型作为输入，并将分类层转换为卷积层。目前它适用于VGG和Alexnet，但您也可以将其扩展到其他模型。
import torchimport torch.nn as nnfrom torchvision.models import alexnet, vgg16def convolutionize(model, num_classes, input_size=(3, 224, 224)): '''Converts the classification layers of VGG & Alexnet to convolutions Input: model: torch.models num_classes: number of output classes input_size: size of input tensor to the model Returns: model: converted model with convolutions ''' features = model.features classifier = model.classifier # create a dummy input tensor and add a dim for batch-size x = torch.zeros(input_size).unsqueeze_(dim=0) # change the last layer output to the num_classes classifier[-1] = nn.Linear(in_features=classifier[-1].in_features, out_features=num_classes) # pass the dummy input tensor through the features layer to compute the output size for layer in features: x = layer(x) conv_classifier = [] for layer in classifier: if isinstance(layer, nn.Linear): # create a convolution equivalent of linear layer conv_layer = nn.Conv2d(in_channels=x.size(1), out_channels=layer.weight.size(0), kernel_size=(x.size(2), x.size(3))) # transfer the weights conv_layer.weight.data.view(-1).copy_(layer.weight.data.view(-1)) conv_layer.bias.data.view(-1).copy_(layer.bias.data.view(-1)) layer = conv_layer x = layer(x) conv_classifier.append(layer) # replace the model.classifier with newly created convolution layers model.classifier = nn.Sequential(*conv_classifier) return modeldef visualize(model, input_size=(3, 224, 224)): '''Visualize the input size though the layers of the model''' x = torch.zeros(input_size).unsqueeze_(dim=0) print(x.size()) for layer in list(model.features) + list(model.classifier): x = layer(x) print(x.size())这是输入通过模型时的样子
_vgg = vgg16()vgg = convolutionize(_vgg, 100)print('\n\nVGG')visualize(vgg)…
VGGtorch.Size([1, 3, 224, 224])torch.Size([1, 64, 224, 224])torch.Size([1, 64, 224, 224])torch.Size([1, 64, 224, 224])torch.Size([1, 64, 224, 224])torch.Size([1, 64, 112, 112])torch.Size([1, 128, 112, 112])torch.Size([1, 128, 112, 112])torch.Size([1, 128, 112, 112])torch.Size([1, 128, 112, 112])torch.Size([1, 128, 56, 56])torch.Size([1, 256, 56, 56])torch.Size([1, 256, 56, 56])torch.Size([1, 256, 56, 56])torch.Size([1, 256, 56, 56])torch.Size([1, 256, 56, 56])torch.Size([1, 256, 56, 56])torch.Size([1, 256, 28, 28])torch.Size([1, 512, 28, 28])torch.Size([1, 512, 28, 28])torch.Size([1, 512, 28, 28])torch.Size([1, 512, 28, 28])torch.Size([1, 512, 28, 28])torch.Size([1, 512, 28, 28])torch.Size([1, 512, 14, 14])torch.Size([1, 512, 14, 14])torch.Size([1, 512, 14, 14])torch.Size([1, 512, 14, 14])torch.Size([1, 512, 14, 14])torch.Size([1, 512, 14, 14])torch.Size([1, 512, 14, 14])torch.Size([1, 512, 7, 7])torch.Size([1, 4096, 1, 1])torch.Size([1, 4096, 1, 1])torch.Size([1, 4096, 1, 1])torch.Size([1, 4096, 1, 1])torch.Size([1, 4096, 1, 1])torch.Size([1, 4096, 1, 1])torch.Size([1, 100, 1, 1])