8aaabd6dfcd2f4ae5618ef08657bbba82e8bd69f,mesh_renderer/rasterize_triangles.py,,rasterize_clip_space,#Any#Any#Any#Any#Any#Any#,69

Before Change

    raise ValueError("Image height must be > 0.")
  if len(clip_space_vertices.shape) != 3:
    raise ValueError("The vertex buffer must be 3D.")
  batch_size = clip_space_vertices.shape[].value
  vertex_count = clip_space_vertices.shape[1].value

  per_image_barycentric_coordinates = []

After Change

  vertex_count = clip_space_vertices.shape[1].value

  batch_size = tf.shape(clip_space_vertices)[0]
  
  per_image_barycentric_coordinates = tf.TensorArray(dtype=tf.float32, size=batch_size)
  per_image_vertex_ids = tf.TensorArray(dtype=tf.int32, size=batch_size)

  def condition(b, *args):
    return b < batch_size

  def iteration(b, per_image_barycentric_coordinates, per_image_vertex_ids):
    barycentric_coords, triangle_ids, _ = (
        rasterize_triangles_module.rasterize_triangles(
            clip_space_vertices[b, :, :], triangles, image_width,
            image_height))
    per_image_barycentric_coordinates = per_image_barycentric_coordinates.write(b, tf.reshape(barycentric_coords, [-1, 3]))

    vertex_ids = tf.gather(triangles, tf.reshape(triangle_ids, [-1]))
    reindexed_ids = tf.add(vertex_ids, b * clip_space_vertices.shape[1].value)
    per_image_vertex_ids = per_image_vertex_ids.write(b, reindexed_ids)

    return b+1, per_image_barycentric_coordinates, per_image_vertex_ids

  b = tf.placeholder_with_default(0, shape=[])
  _, per_image_barycentric_coordinates, per_image_vertex_ids = tf.while_loop(condition, iteration, [b, per_image_barycentric_coordinates, per_image_vertex_ids])

  barycentric_coordinates = tf.reshape(per_image_barycentric_coordinates.stack(), [-1, 3])
  vertex_ids = tf.reshape(per_image_vertex_ids.stack(), [-1, 3])

Instances