# Copyright 2019 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 ssd_mobilenet_v3_feature_extractor."""
import unittest
import tensorflow.compat.v1 as tf

from object_detection.models import ssd_mobilenet_v3_feature_extractor
from object_detection.models import ssd_mobilenet_v3_feature_extractor_testbase
from object_detection.utils import tf_version


@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SsdMobilenetV3LargeFeatureExtractorTest(
    ssd_mobilenet_v3_feature_extractor_testbase
    ._SsdMobilenetV3FeatureExtractorTestBase):

  def _get_input_sizes(self):
    """Return first two input feature map sizes."""
    return [672, 480]

  def _create_feature_extractor(self,
                                depth_multiplier,
                                pad_to_multiple,
                                use_explicit_padding=False,
                                use_keras=False):
    """Constructs a new Mobilenet V3-Large feature extractor.

    Args:
      depth_multiplier: float depth multiplier for feature extractor
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      use_explicit_padding: use 'VALID' padding for convolutions, but prepad
        inputs so that the output dimensions are the same as if 'SAME' padding
        were used.
      use_keras: if True builds a keras-based feature extractor, if False builds
        a slim-based one.

    Returns:
      an ssd_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    return (
        ssd_mobilenet_v3_feature_extractor.SSDMobileNetV3LargeFeatureExtractor(
            False,
            depth_multiplier,
            min_depth,
            pad_to_multiple,
            self.conv_hyperparams_fn,
            use_explicit_padding=use_explicit_padding))


@unittest.skipIf(tf_version.is_tf2(), 'Skipping TF1.X only test.')
class SsdMobilenetV3SmallFeatureExtractorTest(
    ssd_mobilenet_v3_feature_extractor_testbase
    ._SsdMobilenetV3FeatureExtractorTestBase):

  def _get_input_sizes(self):
    """Return first two input feature map sizes."""
    return [288, 288]

  def _create_feature_extractor(self,
                                depth_multiplier,
                                pad_to_multiple,
                                use_explicit_padding=False,
                                use_keras=False):
    """Constructs a new Mobilenet V3-Small feature extractor.

    Args:
      depth_multiplier: float depth multiplier for feature extractor
      pad_to_multiple: the nearest multiple to zero pad the input height and
        width dimensions to.
      use_explicit_padding: use 'VALID' padding for convolutions, but prepad
        inputs so that the output dimensions are the same as if 'SAME' padding
        were used.
      use_keras: if True builds a keras-based feature extractor, if False builds
        a slim-based one.

    Returns:
      an ssd_meta_arch.SSDFeatureExtractor object.
    """
    min_depth = 32
    return (
        ssd_mobilenet_v3_feature_extractor.SSDMobileNetV3SmallFeatureExtractor(
            False,
            depth_multiplier,
            min_depth,
            pad_to_multiple,
            self.conv_hyperparams_fn,
            use_explicit_padding=use_explicit_padding))


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