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

Before Change

        knots_shape = [0] + list(range(2, out_rank + 1)) + [1]
        knots_id = tf.cast(knots_id, COORDINATES_TYPE)
        knots_id = tf.transpose(knots_id, knots_shape)
        knots_shape = knots_id.get_shape().as_list()

        // get values of N neighbours
        batch_inputs = tf.unstack(inputs, axis=0)
        batch_knots = tf.unstack(knots_id, axis=1)

After Change

            samples = [tf.gather_nd(img, knot)
                       for (img, knot) in zip(batch_inputs, batch_knots)]
        elif n_coords == 1 and batch_size > 1:
            samples = [tf.gather_nd(img, batch_knots[0])
                       for img in batch_inputs]
        else:
            raise NotImplementedError
        samples = tf.stack(samples, axis=1)
        // weighted average over N neighbours

Instances