我无法使我的数据集适应VGG-net，导致大小不匹配。

Question

我试图在我的脚本中实现经过预先训练的VGG网，以便在RGB 256 256中识别数据集中的面孔，但我得到了一个“大小不匹配，m1: 1 x 2622，m2: 4096 x 2”，即使我调整图像大小，它也不工作，因为您可以看到我的代码与resnet和alexnet一起工作。

我试过用插值函数调整图像大小，但是大小不匹配仍然存在。

def training(model_conv, learning_rate, wd, net):

criterion = nn.CrossEntropyLoss(weight= torch.FloatTensor([1,1]))
optimizer = torch.optim.Adam(model_conv.fc.parameters(),         lr=learning_rate, weight_decay = wd)
total_step = len(train_loader)
loss_list = []
acc_list = []
print("Inizio il training")

for epoch in range(num_epochs):
    for i, (im, labels) in enumerate(train_loader):  

        images = torch.nn.functional.interpolate(im, 224, mode = 'bilinear')
        outputs = model_conv(images)
        loss = criterion(outputs, labels)
        loss_list.append(loss.item())
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if (i + 1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))
torch.save(model_conv, 'TrainedModel.pt')
return images, labels

def main():

net = "vgg"
learning_rate = 10e-6
wd = 10e-4

if net == "vgg":
    print("Hai selezionato VGG")
    model_conv = VGG_FACE.vgg_face
    data = torch.load("VGG_FACE.pth")
    model_conv.load_state_dict(data) 

    model_conv.fc = nn.Linear(4096, 2)
    model_conv[-1] = model_conv.fc

if __name__ == '__main__':
main()

例如，这是另一段代码，在这段代码中，我正确地使用了VGG和一些随机图像。

def test():
N=5
net = VGG_FACE.vgg_face
data = torch.load("VGG_FACE.pth")
net.load_state_dict(data)
net.eval()
names = open("names.txt").read().split()

with torch.no_grad():
    mean = np.array([93.5940, 104.7624, 129.1863])
    images = scipy.misc.imread("cooper2.jpg", mode="RGB")
    images = scipy.misc.imresize(images, [224, 224])
    images = images.astype(np.float32)
    images -= mean[np.newaxis, np.newaxis, :]
    images = np.transpose(images, (2, 0, 1))
    images = images[np.newaxis, ...]
    images = torch.tensor(images, dtype=torch.float32)


    y = net(images)
    y = torch.nn.functional.softmax(y, 1)
    rank = torch.topk(y[0, :], N)
    for i in range(N):
        index = rank[1][i].item()
        score = rank[0][i].item()
        print("{}) {} ({:.2f})".format(i + 1, names[index], score))
    print()


numero_classi = 2
net[-1] = torch.nn.Linear(4096, numero_classi)


if __name__ == "__main__":
test()

我所犯的错误是

  File "/Users/danieleligato/PycharmProjects/parametral/VGGTEST.py", line 53, in training
outputs = model_conv(images)
RuntimeError: size mismatch, m1: [4 x 2622], m2: [4096 x 2] at /Users/soumith/code/builder/wheel/pytorch-src/aten/src/TH/generic/THTensorMath.cpp:2070

--这是我使用的的VGG网

class LambdaBase(nn.Sequential):
def __init__(self, fn, *args):
    super(LambdaBase, self).__init__(*args)
    self.lambda_func = fn

def forward_prepare(self, input):
    output = []
    for module in self._modules.values():
        output.append(module(input))
    return output if output else input

class Lambda(LambdaBase):
def forward(self, input):
    return self.lambda_func(self.forward_prepare(input))

class LambdaMap(LambdaBase):
def forward(self, input):
    return map(self.lambda_func,self.forward_prepare(input))

class LambdaReduce(LambdaBase):
def forward(self, input):
    return reduce(self.lambda_func,self.forward_prepare(input))


vgg_face = nn.Sequential( # Sequential,
nn.Conv2d(3,64,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
nn.Conv2d(64,128,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
nn.Conv2d(128,256,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
nn.Conv2d(256,512,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1)),
nn.ReLU(),
nn.MaxPool2d((2, 2),(2, 2),(0, 0),ceil_mode=True),
Lambda(lambda x: x.view(x.size(0),-1)), # View,
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(25088,4096)), # Linear,
nn.ReLU(),
nn.Dropout(0.5),
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(4096,4096)), # Linear,
nn.ReLU(),
nn.Dropout(0.5),
nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(4096,2622)), # Linear,
)

Answer 1

错误来自于这一行：

model_conv.fc = nn.Linear(4096, 2)

改为：

model_conv.fc = nn.Linear(2622, 2)