official/vision/modeling/backbones/resnet_deeplab_test.py

# 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.

"""Tests for resnet_deeplab models."""

# Import libraries
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras

from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from official.vision.modeling.backbones import resnet_deeplab


class ResNetTest(parameterized.TestCase, tf.test.TestCase):

  @parameterized.parameters(
      (128, 50, 4, 8),
      (128, 101, 4, 8),
      (128, 152, 4, 8),
      (128, 200, 4, 8),
      (128, 50, 4, 16),
      (128, 101, 4, 16),
      (128, 152, 4, 16),
      (128, 200, 4, 16),
  )
  def test_network_creation(self, input_size, model_id,
                            endpoint_filter_scale, output_stride):
    """Test creation of ResNet models."""
    tf_keras.backend.set_image_data_format('channels_last')

    network = resnet_deeplab.DilatedResNet(model_id=model_id,
                                           output_stride=output_stride)
    inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
    endpoints = network(inputs)
    print(endpoints)
    self.assertAllEqual([
        1, input_size / output_stride, input_size / output_stride,
        512 * endpoint_filter_scale
    ], endpoints[str(int(np.math.log2(output_stride)))].shape.as_list())

  @parameterized.parameters(
      ('v0', None, 0.0, False, False),
      ('v1', None, 0.0, False, False),
      ('v1', 0.25, 0.0, False, False),
      ('v1', 0.25, 0.2, False, False),
      ('v1', 0.25, 0.0, True, False),
      ('v1', 0.25, 0.2, False, True),
      ('v1', None, 0.2, True, True),
  )
  def test_network_features(self, stem_type, se_ratio,
                            init_stochastic_depth_rate, resnetd_shortcut,
                            replace_stem_max_pool):
    """Test additional features of ResNet models."""
    input_size = 128
    model_id = 50
    endpoint_filter_scale = 4
    output_stride = 8

    tf_keras.backend.set_image_data_format('channels_last')

    network = resnet_deeplab.DilatedResNet(
        model_id=model_id,
        output_stride=output_stride,
        stem_type=stem_type,
        resnetd_shortcut=resnetd_shortcut,
        replace_stem_max_pool=replace_stem_max_pool,
        se_ratio=se_ratio,
        init_stochastic_depth_rate=init_stochastic_depth_rate)
    inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
    endpoints = network(inputs)
    print(endpoints)
    self.assertAllEqual([
        1, input_size / output_stride, input_size / output_stride,
        512 * endpoint_filter_scale
    ], endpoints[str(int(np.math.log2(output_stride)))].shape.as_list())

  @combinations.generate(
      combinations.combine(
          strategy=[
              strategy_combinations.cloud_tpu_strategy,
              strategy_combinations.one_device_strategy_gpu,
          ],
          use_sync_bn=[False, True],
      ))
  def test_sync_bn_multiple_devices(self, strategy, use_sync_bn):
    """Test for sync bn on TPU and GPU devices."""
    inputs = np.random.rand(64, 128, 128, 3)

    tf_keras.backend.set_image_data_format('channels_last')

    with strategy.scope():
      network = resnet_deeplab.DilatedResNet(
          model_id=50, output_stride=8, use_sync_bn=use_sync_bn)
      _ = network(inputs)

  @parameterized.parameters(1, 3, 4)
  def test_input_specs(self, input_dim):
    """Test different input feature dimensions."""
    tf_keras.backend.set_image_data_format('channels_last')

    input_specs = tf_keras.layers.InputSpec(shape=[None, None, None, input_dim])
    network = resnet_deeplab.DilatedResNet(
        model_id=50, output_stride=8, input_specs=input_specs)

    inputs = tf_keras.Input(shape=(128, 128, input_dim), batch_size=1)
    _ = network(inputs)

  def test_serialize_deserialize(self):
    # Create a network object that sets all of its config options.
    kwargs = dict(
        model_id=50,
        output_stride=8,
        stem_type='v0',
        se_ratio=0.25,
        init_stochastic_depth_rate=0.2,
        resnetd_shortcut=False,
        replace_stem_max_pool=False,
        use_sync_bn=False,
        activation='relu',
        norm_momentum=0.99,
        norm_epsilon=0.001,
        kernel_initializer='VarianceScaling',
        kernel_regularizer=None,
        bias_regularizer=None,
    )
    network = resnet_deeplab.DilatedResNet(**kwargs)

    expected_config = dict(kwargs)
    self.assertEqual(network.get_config(), expected_config)

    # Create another network object from the first object's config.
    new_network = resnet_deeplab.DilatedResNet.from_config(network.get_config())

    # Validate that the config can be forced to JSON.
    _ = new_network.to_json()

    # If the serialization was successful, the new config should match the old.
    self.assertAllEqual(network.get_config(), new_network.get_config())


if __name__ == '__main__':
  tf.test.main()