object_detection/models/ssd_resnet_v1_ppn_feature_extractor_testbase.py

# Copyright 2018 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 resnet v1 feature extractors."""
import abc
import numpy as np
import tensorflow as tf

from object_detection.models import ssd_feature_extractor_test


class SSDResnetPpnFeatureExtractorTestBase(
    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
  """Helper test class for SSD Resnet PPN feature extractors."""

  @abc.abstractmethod
  def _scope_name(self):
    pass

  def test_extract_features_returns_correct_shapes_289(self):
    image_height = 289
    image_width = 289
    depth_multiplier = 1.0
    pad_to_multiple = 1
    expected_feature_map_shape = [(2, 19, 19, 1024), (2, 10, 10, 1024),
                                  (2, 5, 5, 1024), (2, 3, 3, 1024),
                                  (2, 2, 2, 1024), (2, 1, 1, 1024)]
    self.check_extract_features_returns_correct_shape(
        2, image_height, image_width, depth_multiplier, pad_to_multiple,
        expected_feature_map_shape)

  def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self):
    image_height = 289
    image_width = 289
    depth_multiplier = 1.0
    pad_to_multiple = 1
    expected_feature_map_shape = [(2, 19, 19, 1024), (2, 10, 10, 1024),
                                  (2, 5, 5, 1024), (2, 3, 3, 1024),
                                  (2, 2, 2, 1024), (2, 1, 1, 1024)]
    self.check_extract_features_returns_correct_shapes_with_dynamic_inputs(
        2, image_height, image_width, depth_multiplier, pad_to_multiple,
        expected_feature_map_shape)

  def test_extract_features_raises_error_with_invalid_image_size(self):
    image_height = 32
    image_width = 32
    depth_multiplier = 1.0
    pad_to_multiple = 1
    self.check_extract_features_raises_error_with_invalid_image_size(
        image_height, image_width, depth_multiplier, pad_to_multiple)

  def test_preprocess_returns_correct_value_range(self):
    image_height = 128
    image_width = 128
    depth_multiplier = 1
    pad_to_multiple = 1
    test_image = tf.constant(np.random.rand(4, image_height, image_width, 3))
    feature_extractor = self._create_feature_extractor(depth_multiplier,
                                                       pad_to_multiple)
    preprocessed_image = feature_extractor.preprocess(test_image)
    with self.test_session() as sess:
      test_image_out, preprocessed_image_out = sess.run(
          [test_image, preprocessed_image])
      self.assertAllClose(preprocessed_image_out,
                          test_image_out - [[123.68, 116.779, 103.939]])

  def test_variables_only_created_in_scope(self):
    depth_multiplier = 1
    pad_to_multiple = 1
    self.check_feature_extractor_variables_under_scope(
        depth_multiplier, pad_to_multiple, self._scope_name())