chrishkchris commented on issue #674: Autograd Layer constructor URL: https://github.com/apache/singa/issues/674#issuecomment-612570401 > > > I think the Module class is quite similar to Layer, we can think about combining them together. At present, users can't get params from the Module, they can only get them from the Layer. If the Layer class is the subclass of Module, this problem will be solved. > > > > > > Yes, when we buffer the ops, there won't be any actual run, but all the parameters size will be obtained after the call function. > > > > > > I think the Module class is quite similar to Layer, we can think about combining them together. At present, users can't get params from the Module, they can only get them from the Layer. If the Layer class is the subclass of Module, this problem will be solved. > > > > > > > > > > > > Yes, when we buffer the ops, there won't be any actual run, but all the parameters size will be obtained after the call function. > > > > > > > > > After calling the constructor of Layer class. > > > > > > Constructor might not be enough, because in the case of RNN the CudnnRNNHandle.weight_size is inferred from the input size. So this can only be obtained from the forward function. FYI, this is the new Shicong's code in his PR (I guess this issue is for solving this PR problem): > > https://github.com/apache/singa/blob/19f2020fda36032fdc26dc5910dacbfcff638e7f/python/singa/autograd.py#L3406 > > Sorry, I haven't checked the newest code. Handles in the past code like ConvHandle are initialized in the constructor, so I thought CudnnRNNHandle was the same. @XJDKC FYI Even ConvHandle is not initialized in the constructor of Layer but in the call function, but the RNN is different from conv2d just because there is tensor initization in the call function https://github.com/apache/singa/blob/master/python/singa/autograd.py#L1758
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