# Copyright 2025 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."""

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


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

  @parameterized.parameters(
      (128, 10, 1),
      (128, 18, 1),
      (128, 26, 1),
      (128, 34, 1),
      (128, 50, 4),
      (128, 101, 4),
      (128, 152, 4),
  )
  def test_network_creation(self, input_size, model_id,
                            endpoint_filter_scale):
    """Test creation of ResNet family models."""
    resnet_params = {
        10: 4915904,
        18: 11190464,
        26: 17465024,
        34: 21306048,
        50: 23561152,
        101: 42605504,
        152: 58295232,
    }
    tf_keras.backend.set_image_data_format('channels_last')

    network = resnet.ResNet(model_id=model_id)
    self.assertEqual(network.count_params(), resnet_params[model_id])

    inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
    endpoints = network(inputs)

    self.assertAllEqual(
        [1, input_size / 2**2, input_size / 2**2, 64 * endpoint_filter_scale],
        endpoints['2'].shape.as_list())
    self.assertAllEqual(
        [1, input_size / 2**3, input_size / 2**3, 128 * endpoint_filter_scale],
        endpoints['3'].shape.as_list())
    self.assertAllEqual(
        [1, input_size / 2**4, input_size / 2**4, 256 * endpoint_filter_scale],
        endpoints['4'].shape.as_list())
    self.assertAllEqual(
        [1, input_size / 2**5, input_size / 2**5, 512 * endpoint_filter_scale],
        endpoints['5'].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.ResNet(model_id=50, use_sync_bn=use_sync_bn)
      _ = network(inputs)

  @parameterized.parameters(
      (128, 34, 1, 'v0', None, 0.0, 1.0, False, False),
      (128, 34, 1, 'v1', 0.25, 0.2, 1.25, True, True),
      (128, 50, 4, 'v0', None, 0.0, 1.5, False, False),
      (128, 50, 4, 'v1', 0.25, 0.2, 2.0, True, True),
  )
  def test_resnet_rs(self, input_size, model_id, endpoint_filter_scale,
                     stem_type, se_ratio, init_stochastic_depth_rate,
                     depth_multiplier, resnetd_shortcut, replace_stem_max_pool):
    """Test creation of ResNet family models."""
    tf_keras.backend.set_image_data_format('channels_last')
    network = resnet.ResNet(
        model_id=model_id,
        depth_multiplier=depth_multiplier,
        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)
    _ = 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.ResNet(model_id=50, 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,
        depth_multiplier=1.0,
        stem_type='v0',
        se_ratio=None,
        resnetd_shortcut=False,
        replace_stem_max_pool=False,
        init_stochastic_depth_rate=0.0,
        scale_stem=True,
        use_sync_bn=False,
        activation='relu',
        norm_momentum=0.99,
        norm_epsilon=0.001,
        kernel_initializer='VarianceScaling',
        kernel_regularizer=None,
        bias_regularizer=None,
        bn_trainable=True)
    network = resnet.ResNet(**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.ResNet.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()
