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research/object_detection/builders/preprocessor_builder_test.py
772 строки
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Runze Wang
Automated Code Change
06 июн 2026, 19:36
06 июн 2026, 19:36
5b2b5aa
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# Copyright 2017 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 preprocessor_builder.""" from google.protobuf import text_format import tensorflow.compat.v1 as tf from object_detection.builders import preprocessor_builder from object_detection.core import preprocessor from object_detection.protos import preprocessor_pb2 class PreprocessorBuilderTest(tf.test.TestCase): def assert_dictionary_close(self, dict1, dict2): """Helper to check if two dicts with floatst or integers are close.""" self.assertEqual(sorted(dict1.keys()), sorted(dict2.keys())) for key in dict1: value = dict1[key] if isinstance(value, float): self.assertAlmostEqual(value, dict2[key]) else: self.assertEqual(value, dict2[key]) def test_build_normalize_image(self): preprocessor_text_proto = """ normalize_image { original_minval: 0.0 original_maxval: 255.0 target_minval: -1.0 target_maxval: 1.0 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.normalize_image) self.assertEqual(args, { 'original_minval': 0.0, 'original_maxval': 255.0, 'target_minval': -1.0, 'target_maxval': 1.0, }) def test_build_random_horizontal_flip(self): preprocessor_text_proto = """ random_horizontal_flip { keypoint_flip_permutation: 1 keypoint_flip_permutation: 0 keypoint_flip_permutation: 2 keypoint_flip_permutation: 3 keypoint_flip_permutation: 5 keypoint_flip_permutation: 4 probability: 0.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_horizontal_flip) self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4), 'probability': 0.5}) def test_build_random_vertical_flip(self): preprocessor_text_proto = """ random_vertical_flip { keypoint_flip_permutation: 1 keypoint_flip_permutation: 0 keypoint_flip_permutation: 2 keypoint_flip_permutation: 3 keypoint_flip_permutation: 5 keypoint_flip_permutation: 4 probability: 0.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_vertical_flip) self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4), 'probability': 0.5}) def test_build_random_rotation90(self): preprocessor_text_proto = """ random_rotation90 { keypoint_rot_permutation: 3 keypoint_rot_permutation: 0 keypoint_rot_permutation: 1 keypoint_rot_permutation: 2 probability: 0.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_rotation90) self.assertEqual(args, {'keypoint_rot_permutation': (3, 0, 1, 2), 'probability': 0.5}) def test_build_random_pixel_value_scale(self): preprocessor_text_proto = """ random_pixel_value_scale { minval: 0.8 maxval: 1.2 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_pixel_value_scale) self.assert_dictionary_close(args, {'minval': 0.8, 'maxval': 1.2}) def test_build_random_image_scale(self): preprocessor_text_proto = """ random_image_scale { min_scale_ratio: 0.8 max_scale_ratio: 2.2 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_image_scale) self.assert_dictionary_close(args, {'min_scale_ratio': 0.8, 'max_scale_ratio': 2.2}) def test_build_random_rgb_to_gray(self): preprocessor_text_proto = """ random_rgb_to_gray { probability: 0.8 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_rgb_to_gray) self.assert_dictionary_close(args, {'probability': 0.8}) def test_build_random_adjust_brightness(self): preprocessor_text_proto = """ random_adjust_brightness { max_delta: 0.2 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_adjust_brightness) self.assert_dictionary_close(args, {'max_delta': 0.2}) def test_build_random_adjust_contrast(self): preprocessor_text_proto = """ random_adjust_contrast { min_delta: 0.7 max_delta: 1.1 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_adjust_contrast) self.assert_dictionary_close(args, {'min_delta': 0.7, 'max_delta': 1.1}) def test_build_random_adjust_hue(self): preprocessor_text_proto = """ random_adjust_hue { max_delta: 0.01 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_adjust_hue) self.assert_dictionary_close(args, {'max_delta': 0.01}) def test_build_random_adjust_saturation(self): preprocessor_text_proto = """ random_adjust_saturation { min_delta: 0.75 max_delta: 1.15 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_adjust_saturation) self.assert_dictionary_close(args, {'min_delta': 0.75, 'max_delta': 1.15}) def test_build_random_distort_color(self): preprocessor_text_proto = """ random_distort_color { color_ordering: 1 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_distort_color) self.assertEqual(args, {'color_ordering': 1}) def test_build_random_jitter_boxes(self): preprocessor_text_proto = """ random_jitter_boxes { ratio: 0.1 jitter_mode: SHRINK } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_jitter_boxes) self.assert_dictionary_close(args, {'ratio': 0.1, 'jitter_mode': 'shrink'}) def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 clip_boxes: False random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'clip_boxes': False, 'random_coef': 0.125, }) def test_build_random_pad_image(self): preprocessor_text_proto = """ random_pad_image { } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_pad_image) self.assertEqual(args, { 'min_image_size': None, 'max_image_size': None, 'pad_color': None, }) def test_build_random_absolute_pad_image(self): preprocessor_text_proto = """ random_absolute_pad_image { max_height_padding: 50 max_width_padding: 100 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_absolute_pad_image) self.assertEqual(args, { 'max_height_padding': 50, 'max_width_padding': 100, 'pad_color': None, }) def test_build_random_crop_pad_image(self): preprocessor_text_proto = """ random_crop_pad_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 clip_boxes: False random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_pad_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'clip_boxes': False, 'random_coef': 0.125, 'pad_color': None, }) def test_build_random_crop_pad_image_with_optional_parameters(self): preprocessor_text_proto = """ random_crop_pad_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 clip_boxes: False random_coef: 0.125 min_padded_size_ratio: 0.5 min_padded_size_ratio: 0.75 max_padded_size_ratio: 0.5 max_padded_size_ratio: 0.75 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_pad_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'clip_boxes': False, 'random_coef': 0.125, 'min_padded_size_ratio': (0.5, 0.75), 'max_padded_size_ratio': (0.5, 0.75), 'pad_color': None, }) def test_build_random_crop_to_aspect_ratio(self): preprocessor_text_proto = """ random_crop_to_aspect_ratio { aspect_ratio: 0.85 overlap_thresh: 0.35 clip_boxes: False } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_to_aspect_ratio) self.assert_dictionary_close(args, {'aspect_ratio': 0.85, 'overlap_thresh': 0.35, 'clip_boxes': False}) def test_build_random_black_patches(self): preprocessor_text_proto = """ random_black_patches { max_black_patches: 20 probability: 0.95 size_to_image_ratio: 0.12 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_black_patches) self.assert_dictionary_close(args, {'max_black_patches': 20, 'probability': 0.95, 'size_to_image_ratio': 0.12}) def test_build_random_jpeg_quality(self): preprocessor_text_proto = """ random_jpeg_quality { random_coef: 0.5 min_jpeg_quality: 40 max_jpeg_quality: 90 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Parse(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_jpeg_quality) self.assert_dictionary_close(args, {'random_coef': 0.5, 'min_jpeg_quality': 40, 'max_jpeg_quality': 90}) def test_build_random_downscale_to_target_pixels(self): preprocessor_text_proto = """ random_downscale_to_target_pixels { random_coef: 0.5 min_target_pixels: 200 max_target_pixels: 900 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Parse(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_downscale_to_target_pixels) self.assert_dictionary_close(args, { 'random_coef': 0.5, 'min_target_pixels': 200, 'max_target_pixels': 900 }) def test_build_random_patch_gaussian(self): preprocessor_text_proto = """ random_patch_gaussian { random_coef: 0.5 min_patch_size: 10 max_patch_size: 300 min_gaussian_stddev: 0.2 max_gaussian_stddev: 1.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Parse(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_patch_gaussian) self.assert_dictionary_close(args, { 'random_coef': 0.5, 'min_patch_size': 10, 'max_patch_size': 300, 'min_gaussian_stddev': 0.2, 'max_gaussian_stddev': 1.5 }) def test_auto_augment_image(self): preprocessor_text_proto = """ autoaugment_image { policy_name: 'v0' } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.autoaugment_image) self.assert_dictionary_close(args, {'policy_name': 'v0'}) def test_drop_label_probabilistically(self): preprocessor_text_proto = """ drop_label_probabilistically{ label: 2 drop_probability: 0.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.drop_label_probabilistically) self.assert_dictionary_close(args, { 'dropped_label': 2, 'drop_probability': 0.5 }) def test_remap_labels(self): preprocessor_text_proto = """ remap_labels{ original_labels: 1 original_labels: 2 new_label: 3 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.remap_labels) self.assert_dictionary_close(args, { 'original_labels': [1, 2], 'new_label': 3 }) def test_build_random_resize_method(self): preprocessor_text_proto = """ random_resize_method { target_height: 75 target_width: 100 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_resize_method) self.assert_dictionary_close(args, {'target_size': [75, 100]}) def test_build_scale_boxes_to_pixel_coordinates(self): preprocessor_text_proto = """ scale_boxes_to_pixel_coordinates {} """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.scale_boxes_to_pixel_coordinates) self.assertEqual(args, {}) def test_build_resize_image(self): preprocessor_text_proto = """ resize_image { new_height: 75 new_width: 100 method: BICUBIC } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.resize_image) self.assertEqual(args, {'new_height': 75, 'new_width': 100, 'method': tf.image.ResizeMethod.BICUBIC}) def test_build_rgb_to_gray(self): preprocessor_text_proto = """ rgb_to_gray {} """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.rgb_to_gray) self.assertEqual(args, {}) def test_build_subtract_channel_mean(self): preprocessor_text_proto = """ subtract_channel_mean { means: [1.0, 2.0, 3.0] } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.subtract_channel_mean) self.assertEqual(args, {'means': [1.0, 2.0, 3.0]}) def test_random_self_concat_image(self): preprocessor_text_proto = """ random_self_concat_image { concat_vertical_probability: 0.5 concat_horizontal_probability: 0.25 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_self_concat_image) self.assertEqual(args, {'concat_vertical_probability': 0.5, 'concat_horizontal_probability': 0.25}) def test_build_ssd_random_crop(self): preprocessor_text_proto = """ ssd_random_crop { operations { min_object_covered: 0.0 min_aspect_ratio: 0.875 max_aspect_ratio: 1.125 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.0 clip_boxes: False random_coef: 0.375 } operations { min_object_covered: 0.25 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.25 clip_boxes: True random_coef: 0.375 } } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.ssd_random_crop) self.assertEqual(args, {'min_object_covered': [0.0, 0.25], 'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)], 'area_range': [(0.5, 1.0), (0.5, 1.0)], 'overlap_thresh': [0.0, 0.25], 'clip_boxes': [False, True], 'random_coef': [0.375, 0.375]}) def test_build_ssd_random_crop_empty_operations(self): preprocessor_text_proto = """ ssd_random_crop { } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.ssd_random_crop) self.assertEqual(args, {}) def test_build_ssd_random_crop_pad(self): preprocessor_text_proto = """ ssd_random_crop_pad { operations { min_object_covered: 0.0 min_aspect_ratio: 0.875 max_aspect_ratio: 1.125 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.0 clip_boxes: False random_coef: 0.375 min_padded_size_ratio: [1.0, 1.0] max_padded_size_ratio: [2.0, 2.0] pad_color_r: 0.5 pad_color_g: 0.5 pad_color_b: 0.5 } operations { min_object_covered: 0.25 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.25 clip_boxes: True random_coef: 0.375 min_padded_size_ratio: [1.0, 1.0] max_padded_size_ratio: [2.0, 2.0] pad_color_r: 0.5 pad_color_g: 0.5 pad_color_b: 0.5 } } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.ssd_random_crop_pad) self.assertEqual(args, {'min_object_covered': [0.0, 0.25], 'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)], 'area_range': [(0.5, 1.0), (0.5, 1.0)], 'overlap_thresh': [0.0, 0.25], 'clip_boxes': [False, True], 'random_coef': [0.375, 0.375], 'min_padded_size_ratio': [(1.0, 1.0), (1.0, 1.0)], 'max_padded_size_ratio': [(2.0, 2.0), (2.0, 2.0)], 'pad_color': [(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)]}) def test_build_ssd_random_crop_fixed_aspect_ratio(self): preprocessor_text_proto = """ ssd_random_crop_fixed_aspect_ratio { operations { min_object_covered: 0.0 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.0 clip_boxes: False random_coef: 0.375 } operations { min_object_covered: 0.25 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.25 clip_boxes: True random_coef: 0.375 } aspect_ratio: 0.875 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.ssd_random_crop_fixed_aspect_ratio) self.assertEqual(args, {'min_object_covered': [0.0, 0.25], 'aspect_ratio': 0.875, 'area_range': [(0.5, 1.0), (0.5, 1.0)], 'overlap_thresh': [0.0, 0.25], 'clip_boxes': [False, True], 'random_coef': [0.375, 0.375]}) def test_build_ssd_random_crop_pad_fixed_aspect_ratio(self): preprocessor_text_proto = """ ssd_random_crop_pad_fixed_aspect_ratio { operations { min_object_covered: 0.0 min_aspect_ratio: 0.875 max_aspect_ratio: 1.125 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.0 clip_boxes: False random_coef: 0.375 } operations { min_object_covered: 0.25 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.25 clip_boxes: True random_coef: 0.375 } aspect_ratio: 0.875 min_padded_size_ratio: [1.0, 1.0] max_padded_size_ratio: [2.0, 2.0] } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.ssd_random_crop_pad_fixed_aspect_ratio) self.assertEqual(args, {'min_object_covered': [0.0, 0.25], 'aspect_ratio': 0.875, 'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)], 'area_range': [(0.5, 1.0), (0.5, 1.0)], 'overlap_thresh': [0.0, 0.25], 'clip_boxes': [False, True], 'random_coef': [0.375, 0.375], 'min_padded_size_ratio': (1.0, 1.0), 'max_padded_size_ratio': (2.0, 2.0)}) def test_build_normalize_image_convert_class_logits_to_softmax(self): preprocessor_text_proto = """ convert_class_logits_to_softmax { temperature: 2 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.convert_class_logits_to_softmax) self.assertEqual(args, {'temperature': 2}) def test_random_crop_by_scale(self): preprocessor_text_proto = """ random_square_crop_by_scale { scale_min: 0.25 scale_max: 2.0 num_scales: 8 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_square_crop_by_scale) self.assertEqual(args, { 'scale_min': 0.25, 'scale_max': 2.0, 'num_scales': 8, 'max_border': 128 }) def test_adjust_gamma(self): preprocessor_text_proto = """ adjust_gamma { gamma: 2.2 gain: 2.0 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Parse(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.adjust_gamma) self.assert_dictionary_close(args, {'gamma': 2.2, 'gain': 2.0}) if __name__ == '__main__': tf.test.main()