534c4dc7423681faab78d1829c1ec3c31ffb8330,niftynet/layer/resampler.py,ResamplerLayer,_resample_inv_dst_weighting,#ResamplerLayer#Any#Any#,319

Before Change

        // get values of N neighbours
        batch_inputs = tf.unstack(inputs, axis=0)
        batch_knots = tf.unstack(knots_id, axis=1)
        if len(batch_inputs) == len(batch_knots):
            samples = [tf.gather_nd(img, knot)
                       for (img, knot) in zip(batch_inputs, batch_knots)]
        else:

After Change

        partial_shape = not in_size.is_fully_defined()
        try:
            batch_size = int(in_size[0])
            n_coords = int(sample_coords.shape[0])
            in_spatial_rank = infer_spatial_rank(inputs)
            in_spatial_size = \
                None if partial_shape else in_size.as_list()[1:-1]
        except (TypeError, AssertionError, ValueError):

Instances