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
size mismatch意思就是：大小不匹配