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

Before Change

            samples = [tf.gather_nd(img, knot)
                       for (img, knot) in zip(batch_inputs, batch_knots)]
        else:
            samples = []
            for img in batch_inputs:
                for knot in batch_knots:
                    samples.append(tf.gather_nd(img, knot))
        samples = tf.stack(samples, axis=1)
        // weighted average over N neighbours
        return tf.reduce_sum(
            samples * tf.expand_dims(point_weights, axis=-1), axis=0)

After Change

        if batch_size == n_coords:
            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