torchvision VGG model

[LiuXiaoxuanPKU]

Thanks for the repo! I'm playing with pytorch-memonger+VGG16. However, it seems not work.

Reproduce:

vgg16 = models.vgg16() # Get model from torchvision.models
vgg16.features = SublinearSequential(*list(vgg16.features.children())). # Just replace features with SublinearSequential
data = torch.randn((batch_size, 3, 224, 224), requires_grad=True).cuda() # turn on requires_grad to enable torch.autograd.backward in CheckpointFunction
label = torch.randint(0, 1000, (batch_size,)).cuda()

# start training
for iter in range(10):
    output = vgg16(data)
    loss = criterion(output, label)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Error message: RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.
All the relu in torchvision VGG16 uses in-place operations, not sure why it does not work when introducing memoger. Any comments/fixes would be greatly appreciated, thanks!

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