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# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python2, python3
"""Minimal Reference implementation for the Frechet Video Distance (FVD).
FVD is a metric for the quality of video generation models. It is inspired by
the FID (Frechet Inception Distance) used for images, but uses a different
embedding to be better suitable for videos.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import tensorflow.compat.v1 as tf
import tensorflow_gan as tfgan
import tensorflow_hub as hub
def preprocess(videos, target_resolution):
"""Runs some preprocessing on the videos for I3D model.
Args:
videos: <T>[batch_size, num_frames, height, width, depth] The videos to be
preprocessed. We don't care about the specific dtype of the videos, it can
be anything that tf.image.resize_bilinear accepts. Values are expected to
be in the range 0-255.
target_resolution: (width, height): target video resolution
Returns:
videos: <float32>[batch_size, num_frames, height, width, depth]
"""
videos_shape = list(videos.shape)
all_frames = tf.reshape(videos, [-1] + videos_shape[-3:])
resized_videos = tf.image.resize_bilinear(all_frames, size=target_resolution)
target_shape = [videos_shape[0], -1] + list(target_resolution) + [3]
output_videos = tf.reshape(resized_videos, target_shape)
scaled_videos = 2. * tf.cast(output_videos, tf.float32) / 255. - 1
return scaled_videos
def _is_in_graph(tensor_name):
"""Checks whether a given tensor does exists in the graph."""
try:
tf.get_default_graph().get_tensor_by_name(tensor_name)
except KeyError:
return False
return True
def create_id3_embedding(videos,warmup=False,batch_size=16):
"""Embeds the given videos using the Inflated 3D Convolution ne twork.
Downloads the graph of the I3D from tf.hub and adds it to the graph on the
first call.
Args:
videos: <float32>[batch_size, num_frames, height=224, width=224, depth=3].
Expected range is [-1, 1].
Returns:
embedding: <float32>[batch_size, embedding_size]. embedding_size depends
on the model used.
Raises:
ValueError: when a provided embedding_layer is not supported.
"""
# batch_size = 16
module_spec = "https://tfhub.dev/deepmind/i3d-kinetics-400/1"
# Making sure that we import the graph separately for
# each different input video tensor.
module_name = "fvd_kinetics-400_id3_module_" + six.ensure_str(
videos.name).replace(":", "_")
assert_ops = [
tf.Assert(
tf.reduce_max(videos) <= 1.001,
["max value in frame is > 1", videos]),
tf.Assert(
tf.reduce_min(videos) >= -1.001,
["min value in frame is < -1", videos]),
tf.assert_equal(
tf.shape(videos)[0],
batch_size, ["invalid frame batch size: ",
tf.shape(videos)],
summarize=6),
]
with tf.control_dependencies(assert_ops):
videos = tf.identity(videos)
module_scope = "%s_apply_default/" % module_name
# To check whether the module has already been loaded into the graph, we look
# for a given tensor name. If this tensor name exists, we assume the function
# has been called before and the graph was imported. Otherwise we import it.
# Note: in theory, the tensor could exist, but have wrong shapes.
# This will happen if create_id3_embedding is called with a frames_placehoder
# of wrong size/batch size, because even though that will throw a tf.Assert
# on graph-execution time, it will insert the tensor (with wrong shape) into
# the graph. This is why we need the following assert.
if warmup:
video_batch_size = int(videos.shape[0])
assert video_batch_size in [batch_size, -1, None], f"Invalid batch size {video_batch_size}"
tensor_name = module_scope + "RGB/inception_i3d/Mean:0"
if not _is_in_graph(tensor_name):
i3d_model = hub.Module(module_spec, name=module_name)
i3d_model(videos)
# gets the kinetics-i3d-400-logits layer
tensor_name = module_scope + "RGB/inception_i3d/Mean:0"
tensor = tf.get_default_graph().get_tensor_by_name(tensor_name)
return tensor
def calculate_fvd(real_activations,
generated_activations):
"""Returns a list of ops that compute metrics as funcs of activations.
Args:
real_activations: <float32>[num_samples, embedding_size]
generated_activations: <float32>[num_samples, embedding_size]
Returns:
A scalar that contains the requested FVD.
"""
return tfgan.eval.frechet_classifier_distance_from_activations(
real_activations, generated_activations)
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