Comments (2)
Thanks for your attention. The code and pretrained models are only tested on PyTorch1.1. Thus it may encounter some errors when running with pytorch of higher version due to the incompatibility. As your problem, I advise you to use MMAction2 ^_^.
from temporal-adaptive-module.
Hi I think here the error is that for higher pytorch version, you need to decorate the forward and backward function of the torch.autograd.Function as @staticmethod
.
But my question, why do we need a torch.autograd.Function as the SegmentConsensus function here? can't we just simply implment this as a normal torch.nn.Module as in the following?
class ConsensusModule(torch.nn.Module): # contains no parameters
def __init__(self, consensus_type, dim=1):
super(ConsensusModule, self).__init__()
self.consensus_type = consensus_type if consensus_type != 'rnn' else 'identity'
self.dim = dim
# assert self.dim == 1
def forward(self, input): # input_tensor (bz, T, n_class)
if self.consensus_type == 'avg':
return input.mean(dim=self.dim, keepdim=True)
elif self.consensus_type == 'identity':
return input
from temporal-adaptive-module.
Related Issues (14)
- 代码何时能开源? HOT 2
- Make it more compact
- awesome works HOT 1
- n_segment? HOT 1
- Training time on Kinetics400 and GPU? HOT 2
- Learning rate of nn.Linear HOT 2
- The pretrained models seem don't work
- About n_segment HOT 2
- About MobileNetV2 arch HOT 4
- Could please provide a small demo dataset used to debug? HOT 2
- swapping labels for training data of Something-something v2 HOT 1
- The application of the algorithm in some 2D-CNN image classification networks
- TAM module in the ResBlock
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