update

update_parameters.py

    # set new weights from loaded tf values
    with torch.no_grad():
        for (name, param), (tf_name, tf_param) in zip(m.named_parameters(), tf_weights.items()):
            # convert NHWC to NCHW format and copy to change memory layout
            tf_param = np.transpose(tf_param, (3, 2, 0, 1)).copy() if len(tf_param.shape) == 4 else tf_param
            assert tf_param.shape == param.detach().numpy().shape, name

            # https://discuss.pytorch.org/t/how-to-assign-an-arbitrary-tensor-to-models-parameter/44082/3
            param.copy_(torch.tensor(tf_param, requires_grad=True, dtype=param.dtype))