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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# import io
import os
import random
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.core import exp_factory
from official.vision import registry_imports # pylint: disable=unused-import
from official.vision.dataloaders import tfexample_utils
from official.vision.serving import video_classification
class VideoClassificationTest(tf.test.TestCase, parameterized.TestCase):
def _get_classification_module(self):
params = exp_factory.get_exp_config('video_classification_ucf101')
params.task.train_data.feature_shape = (8, 64, 64, 3)
params.task.validation_data.feature_shape = (8, 64, 64, 3)
params.task.model.backbone.resnet_3d.model_id = 50
classification_module = video_classification.VideoClassificationModule(
params, batch_size=1, input_image_size=[8, 64, 64])
return classification_module
def _export_from_module(self, module, input_type, save_directory):
signatures = module.get_inference_signatures(
{input_type: 'serving_default'})
tf.saved_model.save(module, save_directory, signatures=signatures)
def _get_dummy_input(self, input_type, module=None):
"""Get dummy input for the given input type."""
if input_type == 'image_tensor':
images = np.random.randint(
low=0, high=255, size=(1, 8, 64, 64, 3), dtype=np.uint8)
# images = np.zeros((1, 8, 64, 64, 3), dtype=np.uint8)
return images, images
elif input_type == 'tf_example':
example = tfexample_utils.make_video_test_example(
image_shape=(64, 64, 3),
audio_shape=(20, 128),
label=random.randint(0, 100)).SerializeToString()
images = tf.nest.map_structure(
tf.stop_gradient,
tf.map_fn(
module._decode_tf_example,
elems=tf.constant([example]),
fn_output_signature={
video_classification.video_input.IMAGE_KEY: tf.string,
}))
images = images[video_classification.video_input.IMAGE_KEY]
return [example], images
else:
raise ValueError(f'{input_type}')
@parameterized.parameters(
{'input_type': 'image_tensor'},
{'input_type': 'tf_example'},
)
def test_export(self, input_type):
tmp_dir = self.get_temp_dir()
module = self._get_classification_module()
self._export_from_module(module, input_type, tmp_dir)
self.assertTrue(os.path.exists(os.path.join(tmp_dir, 'saved_model.pb')))
self.assertTrue(
os.path.exists(os.path.join(tmp_dir, 'variables', 'variables.index')))
self.assertTrue(
os.path.exists(
os.path.join(tmp_dir, 'variables',
'variables.data-00000-of-00001')))
imported = tf.saved_model.load(tmp_dir)
classification_fn = imported.signatures['serving_default']
images, images_tensor = self._get_dummy_input(input_type, module)
processed_images = tf.nest.map_structure(
tf.stop_gradient,
tf.map_fn(
module._preprocess_image,
elems=images_tensor,
fn_output_signature={
'image': tf.float32,
}))
expected_logits = module.model(processed_images, training=False)
expected_prob = tf.nn.softmax(expected_logits)
out = classification_fn(tf.constant(images))
# The imported model should contain any trackable attrs that the original
# model had.
self.assertAllClose(out['logits'].numpy(), expected_logits.numpy())
self.assertAllClose(out['probs'].numpy(), expected_prob.numpy())
if __name__ == '__main__':
tf.test.main()
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