CODEWITHSUNDEEP
torchnonetypeautograd
许文嘉
许文嘉

Reputation: 1

Gradient flow stopped on a combined model

I meet with a problem that the gradient cannot backpropagate on a combined network. I checked lots of answers but cannot find a relevant solution to this problem. I would appreciate it so much if we can solve this.

I wanted to calculate the gradient for input data in this code:

for i, (input, target, impath) in tqdm(enumerate(data_loader)):
# print(‘input.shape:’, input.shape)
input = Variable(input.cuda(), requires_grad=True)
output = model(input)
loss = criterion(output, target.cuda())
loss = Variable(loss, requires_grad=True)
loss.backward()
print(‘input:’, input.grad.data)

but I got errror:

print(‘input:’, input.grad.data)
AttributeError: ‘NoneType’ object has no attribute ‘data’

and my model is a combined model that I loaded the parameters from two pretrained models. I checked the requires_grad state-dict of model weights, it is true, however, the gradient of the model weights is None. Is it because I load the state-dict that caused the gradient block?

How can I deal with this problem?

The model structure is attached below:

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

        resnet = models.resnet101()
        num_ftrs = resnet.fc.in_features
        resnet.fc = nn.Linear(num_ftrs, 1000)

        if opt.resnet_path != None:
            state_dict = torch.load(opt.resnet_path)
            resnet.load_state_dict(state_dict)
            print("resnet load state dict from {}".format(opt.resnet_path))

        self.model1 = torch.nn.Sequential()

        for chd in resnet.named_children():
            if chd[0] != 'fc':
                self.model1.add_module(chd[0], chd[1])

        self.model2 = torch.nn.Sequential()

        self.classifier = LINEAR_LOGSOFTMAX(input_dim=2048, nclass=200)
        if opt.pretrained != None:
            self.classifier_state_dict = torch.load('../checkpoint/{}_cls.pth'.format(opt.pretrained))
            print("classifier load state dict from ../checkpoint/{}_cls.pth".format(opt.pretrained))
        self.classifier.load_state_dict(self.classifier_state_dict)

        for chd in self.classifier.named_children():
            self.model2.add_module(chd[0], chd[1])

    def forward(self, x):
        x = self.model1(x)

        x = x.view(-1, 2048)

        x = self.model2(x)
        return x

Upvotes: 0

Views: 94

Answers (1)

许文嘉
许文嘉

Reputation: 1

The problem is solved with this comment:

Why do you have this line: loss = Variable(loss, requires_grad=True) ? Variable should not be used anymore. So the line above should be deleted and to mark a Tensor for which you want gradients, you can use: input = input.cuda().requires_grad_().

Upvotes: 0

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