cls_sparse_skip_filt.py 文件源码

def forward(self, H_j_dec, input_x):
        if torch.has_cudnn:
            # Input is of the shape : (B, T, N)
            input_x = Variable(torch.from_numpy(input_x[:, self._L:-self._L, :]).cuda(), requires_grad=True)

        else:
            # Input is of the shape : (B, T, N)
            # Cropping some "un-necessary" frequency sub-bands
            input_x = Variable(torch.from_numpy(input_x[:, self._L:-self._L, :]), requires_grad=True)

        # Decode/Sparsify mask
        mask_t1 = self.relu(self.ffDec(H_j_dec))
        # Apply skip-filtering connections
        Y_j = torch.mul(mask_t1, input_x)

        return Y_j, mask_t1