预训练模型从resnet18换成resnet50之后，为什么pytorch出现size mismatch错误？

Question

class SVHN_Model2(nn.Module):
    def __init__(self):
        super(SVHN_Model2, self).__init__()

        # resnet18
        model_conv = models.resnet50(pretrained=True)
        model_conv.avgpool = nn.AdaptiveAvgPool2d(1)
        model_conv = nn.Sequential(*list(model_conv.children())[:-1])  # 去除最后一个fc layer
        self.cnn = model_conv

        self.hd_fc1 = nn.Linear(512, 128)
        self.hd_fc2 = nn.Linear(512, 128)
        self.hd_fc3 = nn.Linear(512, 128)
        self.hd_fc4 = nn.Linear(512, 128)
        self.hd_fc5 = nn.Linear(512, 128)
        self.dropout_1 = nn.Dropout(0.25)
        self.dropout_2 = nn.Dropout(0.25)
        self.dropout_3 = nn.Dropout(0.25)
        self.dropout_4 = nn.Dropout(0.25)
        self.dropout_5 = nn.Dropout(0.25)
        self.fc1 = nn.Linear(128, 11)
        self.fc2 = nn.Linear(128, 11)
        self.fc3 = nn.Linear(128, 11)
        self.fc4 = nn.Linear(128, 11)
        self.fc5 = nn.Linear(128, 11)

    def forward(self, img):
        feat = self.cnn(img)
        feat = feat.view(feat.shape[0], -1)

        feat1 = self.hd_fc1(feat)
        feat2 = self.hd_fc2(feat)
        feat3 = self.hd_fc3(feat)
        feat4 = self.hd_fc4(feat)
        feat5 = self.hd_fc5(feat)
        feat1 = self.dropout_1(feat1)
        feat2 = self.dropout_2(feat2)
        feat3 = self.dropout_3(feat3)
        feat4 = self.dropout_4(feat4)
        feat5 = self.dropout_5(feat5)

        c1 = self.fc1(feat1)
        c2 = self.fc2(feat2)
        c3 = self.fc3(feat3)
        c4 = self.fc4(feat4)
        c5 = self.fc5(feat5)

        return c1, c2, c3, c4,c5

Answer 1

size mismatch意思就是：大小不匹配