def test_merge_dict(self): target_dict = {'a': 1, 'b': 2} source_dict = {'c': 4} data_utils.merge_dict(source_dict, target_dict) self.assertDictEqual(target_dict, {'a': 1, 'b': 2, 'c': 4}) target_dict = {'a': 1, 'b': 2} source_dict = {'b': 3, 'c': 4} with self.assertRaisesRegexp(ValueError, 'Key conflict: `b`.'): data_utils.merge_dict(source_dict, target_dict)
def create_inputs(): """Creates pipeline and model inputs.""" inputs = pipeline_utils.read_batch_from_dataset_tables( FLAGS.input_table, batch_sizes=[int(x) for x in FLAGS.batch_size], num_instances_per_record=2, shuffle=True, num_epochs=None, keypoint_names_3d=configs['keypoint_profile_3d']. keypoint_names, keypoint_names_2d=configs['keypoint_profile_2d']. keypoint_names, min_keypoint_score_2d=FLAGS.min_input_keypoint_score_2d, shuffle_buffer_size=FLAGS.input_shuffle_buffer_size, common_module=common_module, dataset_class=input_dataset_class, input_example_parser_creator=input_example_parser_creator) (inputs[common_module.KEY_KEYPOINTS_3D], keypoint_preprocessor_side_outputs_3d ) = keypoint_preprocessor_3d( inputs[common_module.KEY_KEYPOINTS_3D], keypoint_profile_3d=configs['keypoint_profile_3d'], normalize_keypoints_3d=True) inputs.update(keypoint_preprocessor_side_outputs_3d) inputs['model_inputs'], side_inputs = configs[ 'create_model_input_fn']( inputs[common_module.KEY_KEYPOINTS_2D], inputs[common_module.KEY_KEYPOINT_MASKS_2D], inputs[common_module.KEY_PREPROCESSED_KEYPOINTS_3D], model_input_keypoint_type=FLAGS. model_input_keypoint_type, normalize_keypoints_2d=True, keypoint_profile_2d=configs['keypoint_profile_2d'], keypoint_profile_3d=configs['keypoint_profile_3d'], azimuth_range=configs[ 'random_projection_azimuth_range'], elevation_range=configs[ 'random_projection_elevation_range'], roll_range=configs['random_projection_roll_range'], normalized_camera_depth_range=( configs['random_projection_camera_depth_range'])) data_utils.merge_dict(side_inputs, inputs) return inputs