tensorflow/models

# Copyright 2024 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 nn_blocks."""

from typing import Any, Iterable, Tuple
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras

from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from official.projects.qat.vision.n_bit import nn_blocks


def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
  """Returns the combinations of end-to-end tests to run."""
  return combinations.combine(
      distribution=[
          strategy_combinations.default_strategy,
          strategy_combinations.cloud_tpu_strategy,
          strategy_combinations.one_device_strategy_gpu,
      ],
  )


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

  @parameterized.parameters(
      (nn_blocks.BottleneckBlockNBitQuantized, 1, False, 0.0, None, 4, 4),
      (nn_blocks.BottleneckBlockNBitQuantized, 2, True, 0.2, 0.25, 4, 4),
  )
  def test_bottleneck_block_creation(self, block_fn, strides, use_projection,
                                     stochastic_depth_drop_rate, se_ratio,
                                     num_bits_weight, num_bits_activation):
    input_size = 128
    filter_size = 256
    inputs = tf_keras.Input(
        shape=(input_size, input_size, filter_size * 4), batch_size=1)
    block = block_fn(
        filter_size,
        strides,
        use_projection=use_projection,
        se_ratio=se_ratio,
        stochastic_depth_drop_rate=stochastic_depth_drop_rate,
        num_bits_weight=num_bits_weight,
        num_bits_activation=num_bits_activation)

    features = block(inputs)

    self.assertAllEqual(
        [1, input_size // strides, input_size // strides, filter_size * 4],
        features.shape.as_list())

  @parameterized.parameters(
      (nn_blocks.InvertedBottleneckBlockNBitQuantized, 1, 1, None, None, 4, 4),
      (nn_blocks.InvertedBottleneckBlockNBitQuantized, 6, 1, None, None, 4, 4),
      (nn_blocks.InvertedBottleneckBlockNBitQuantized, 1, 2, None, None, 4, 4),
      (nn_blocks.InvertedBottleneckBlockNBitQuantized, 1, 1, 0.2, None, 4, 4),
      (nn_blocks.InvertedBottleneckBlockNBitQuantized, 1, 1, None, 0.2, 4, 4),
  )
  def test_invertedbottleneck_block_creation(
      self, block_fn, expand_ratio, strides, se_ratio,
      stochastic_depth_drop_rate, num_bits_weight, num_bits_activation):
    input_size = 128
    in_filters = 24
    out_filters = 40
    inputs = tf_keras.Input(
        shape=(input_size, input_size, in_filters), batch_size=1)
    block = block_fn(
        in_filters=in_filters,
        out_filters=out_filters,
        expand_ratio=expand_ratio,
        strides=strides,
        se_ratio=se_ratio,
        stochastic_depth_drop_rate=stochastic_depth_drop_rate,
        num_bits_weight=num_bits_weight,
        num_bits_activation=num_bits_activation)

    features = block(inputs)

    self.assertAllEqual(
        [1, input_size // strides, input_size // strides, out_filters],
        features.shape.as_list())


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