walk_pytorch_params.py

def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                pass # leave the default PyTorch init
                # m.weight.data.zero_()
                # if m.bias is not None:
                #     m.bias.data.zero_()
            if isinstance(m, nn.ConvTranspose2d):
                assert m.kernel_size[0] == m.kernel_size[1]
                initial_weight = get_upsampling_weight(
                    m.in_channels, m.out_channels, m.kernel_size[0])
                m.weight.data.copy_(initial_weight)