ac2a794577087259da7200eefc792dbba40f8600,niftynet/layer/resampler.py,ResamplerLayer,_resample_nearest,#ResamplerLayer#Any#Any#,60

Before Change

        spatial_coords = tf.cast(spatial_coords, COORDINATES_TYPE)
        output = tf.stack([
            tf.gather_nd(img, coords) for (img, coords) in
            zip(tf.unstack(inputs), tf.unstack(spatial_coords))])

        if self.boundary == "ZERO" and not partial_shape:
            scale = 1. / (tf.constant(in_spatial_size, dtype=tf.float32) - 1)

After Change

            spatial_coords = tf.round(sample_coords)
        spatial_coords = tf.cast(spatial_coords, COORDINATES_TYPE)

        batch_inputs = tf.unstack(inputs)
        batch_coords = tf.unstack(spatial_coords)
        if len(batch_inputs) == len(batch_coords):
            gathered_image = [tf.gather_nd(img, coord) for (img, coord) in
                              zip(batch_inputs, batch_coords)]
        else:
            gathered_image = []
            for img in batch_inputs:
                for coord in batch_coords:
                    gathered_image.append(tf.gather_nd(img, coord))
        output = tf.stack(gathered_image)

        if self.boundary == "ZERO" and not partial_shape:
            scale = 1. / (tf.constant(in_spatial_size, dtype=tf.float32) - 1)
            mask = tf.logical_and(

Instances