# Copyright 2023 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 tf_example_decoder.py.""" # Import libraries from absl.testing import parameterized import numpy as np import tensorflow as tf, tf_keras from official.vision.dataloaders import tf_example_decoder from official.vision.dataloaders import tfexample_utils class TfExampleDecoderTest(tf.test.TestCase, parameterized.TestCase): @parameterized.parameters( (100, 100, 0, True, True), (100, 100, 1, True, True), (100, 100, 2, True, True), (100, 100, 0, False, True), (100, 100, 1, False, True), (100, 100, 2, False, True), (100, 100, 0, True, False), (100, 100, 1, True, False), (100, 100, 2, True, False), (100, 100, 0, False, False), (100, 100, 1, False, False), (100, 100, 2, False, False), ) def test_result_shape(self, image_height, image_width, num_instances, regenerate_source_id, fill_image_size): decoder = tf_example_decoder.TfExampleDecoder( include_mask=True, regenerate_source_id=regenerate_source_id) serialized_example = tfexample_utils.create_detection_test_example( image_height=image_height, image_width=image_width, image_channel=3, num_instances=num_instances, fill_image_size=fill_image_size, ).SerializeToString() decoded_tensors = decoder.decode( tf.convert_to_tensor(value=serialized_example)) results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors) self.assertAllEqual( (image_height, image_width, 3), results['image'].shape) if not regenerate_source_id: self.assertEqual(tfexample_utils.DUMP_SOURCE_ID, results['source_id']) self.assertEqual(image_height, results['height']) self.assertEqual(image_width, results['width']) self.assertAllEqual( (num_instances,), results['groundtruth_classes'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_is_crowd'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_area'].shape) self.assertAllEqual( (num_instances, 4), results['groundtruth_boxes'].shape) self.assertAllEqual( (num_instances, image_height, image_width), results['groundtruth_instance_masks'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_instance_masks_png'].shape) def test_result_content(self): decoder = tf_example_decoder.TfExampleDecoder( include_mask=True, attribute_names=['attr1', 'attr2'] ) image_content = [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]], [[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]] image = tfexample_utils.encode_image(np.uint8(image_content), fmt='PNG') image_height = 4 image_width = 4 num_instances = 2 xmins = [0, 0.25] xmaxs = [0.5, 1.0] ymins = [0, 0] ymaxs = [0.5, 1.0] labels = [3, 1] attr1 = np.array([[0], [2]]) attr2 = np.array([[1], [3]]) areas = [ 0.25 * image_height * image_width, 0.75 * image_height * image_width ] is_crowds = [1, 0] mask_content = [[[255, 255, 0, 0], [255, 255, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 255, 255, 255], [0, 255, 255, 255], [0, 255, 255, 255], [0, 255, 255, 255]]] masks = [ tfexample_utils.encode_image(np.uint8(m), fmt='PNG') for m in list(mask_content) ] serialized_example = tf.train.Example( features=tf.train.Features( feature={ 'image/encoded': tf.train.Feature( bytes_list=tf.train.BytesList(value=[image]) ), 'image/source_id': tf.train.Feature( bytes_list=tf.train.BytesList( value=[tfexample_utils.DUMP_SOURCE_ID] ) ), 'image/height': tf.train.Feature( int64_list=tf.train.Int64List(value=[image_height]) ), 'image/width': tf.train.Feature( int64_list=tf.train.Int64List(value=[image_width]) ), 'image/object/bbox/xmin': tf.train.Feature( float_list=tf.train.FloatList(value=xmins) ), 'image/object/bbox/xmax': tf.train.Feature( float_list=tf.train.FloatList(value=xmaxs) ), 'image/object/bbox/ymin': tf.train.Feature( float_list=tf.train.FloatList(value=ymins) ), 'image/object/bbox/ymax': tf.train.Feature( float_list=tf.train.FloatList(value=ymaxs) ), 'image/object/class/label': tf.train.Feature( int64_list=tf.train.Int64List(value=labels) ), 'image/object/is_crowd': tf.train.Feature( int64_list=tf.train.Int64List(value=is_crowds) ), 'image/object/area': tf.train.Feature( float_list=tf.train.FloatList(value=areas) ), 'image/object/mask': tf.train.Feature( bytes_list=tf.train.BytesList(value=masks) ), 'image/object/attribute/attr1': tf.train.Feature( int64_list=tf.train.Int64List(value=attr1.flatten()) ), 'image/object/attribute/attr2': tf.train.Feature( int64_list=tf.train.Int64List(value=attr2.flatten()) ), } ) ).SerializeToString() decoded_tensors = decoder.decode( tf.convert_to_tensor(value=serialized_example)) results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors) self.assertAllEqual( (image_height, image_width, 3), results['image'].shape) self.assertAllEqual(image_content, results['image']) self.assertEqual(tfexample_utils.DUMP_SOURCE_ID, results['source_id']) self.assertEqual(image_height, results['height']) self.assertEqual(image_width, results['width']) self.assertAllEqual( (num_instances,), results['groundtruth_classes'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_is_crowd'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_area'].shape) self.assertAllEqual( (num_instances, 4), results['groundtruth_boxes'].shape) self.assertAllEqual( (num_instances, image_height, image_width), results['groundtruth_instance_masks'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_instance_masks_png'].shape) self.assertAllEqual( [3, 1], results['groundtruth_classes']) np.testing.assert_array_equal( attr1, results['groundtruth_attributes']['attr1'] ) np.testing.assert_array_equal( attr2, results['groundtruth_attributes']['attr2'] ) self.assertAllEqual([True, False], results['groundtruth_is_crowd']) self.assertNDArrayNear( [0.25 * image_height * image_width, 0.75 * image_height * image_width], results['groundtruth_area'], 1e-4) self.assertNDArrayNear( [[0, 0, 0.5, 0.5], [0, 0.25, 1.0, 1.0]], results['groundtruth_boxes'], 1e-4) self.assertNDArrayNear( mask_content, results['groundtruth_instance_masks'], 1e-4) self.assertAllEqual( masks, results['groundtruth_instance_masks_png']) def test_handling_missing_fields(self): decoder = tf_example_decoder.TfExampleDecoder(include_mask=True) image_content = [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]], [[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]] image = tfexample_utils.encode_image(np.uint8(image_content), fmt='PNG') image_height = 4 image_width = 4 num_instances = 2 xmins = [0, 0.25] xmaxs = [0.5, 1.0] ymins = [0, 0] ymaxs = [0.5, 1.0] labels = [3, 1] mask_content = [[[255, 255, 0, 0], [255, 255, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 255, 255, 255], [0, 255, 255, 255], [0, 255, 255, 255], [0, 255, 255, 255]]] masks = [ tfexample_utils.encode_image(np.uint8(m), fmt='PNG') for m in list(mask_content) ] serialized_example = tf.train.Example( features=tf.train.Features( feature={ 'image/encoded': (tf.train.Feature( bytes_list=tf.train.BytesList(value=[image]))), 'image/source_id': (tf.train.Feature( bytes_list=tf.train.BytesList( value=[tfexample_utils.DUMP_SOURCE_ID]))), 'image/height': (tf.train.Feature( int64_list=tf.train.Int64List(value=[image_height]))), 'image/width': (tf.train.Feature( int64_list=tf.train.Int64List(value=[image_width]))), 'image/object/bbox/xmin': (tf.train.Feature( float_list=tf.train.FloatList(value=xmins))), 'image/object/bbox/xmax': (tf.train.Feature( float_list=tf.train.FloatList(value=xmaxs))), 'image/object/bbox/ymin': (tf.train.Feature( float_list=tf.train.FloatList(value=ymins))), 'image/object/bbox/ymax': (tf.train.Feature( float_list=tf.train.FloatList(value=ymaxs))), 'image/object/class/label': (tf.train.Feature( int64_list=tf.train.Int64List(value=labels))), 'image/object/mask': (tf.train.Feature( bytes_list=tf.train.BytesList(value=masks))), })).SerializeToString() decoded_tensors = decoder.decode( tf.convert_to_tensor(serialized_example)) results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors) self.assertAllEqual( (image_height, image_width, 3), results['image'].shape) self.assertAllEqual(image_content, results['image']) self.assertEqual(tfexample_utils.DUMP_SOURCE_ID, results['source_id']) self.assertEqual(image_height, results['height']) self.assertEqual(image_width, results['width']) self.assertAllEqual( (num_instances,), results['groundtruth_classes'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_is_crowd'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_area'].shape) self.assertAllEqual( (num_instances, 4), results['groundtruth_boxes'].shape) self.assertAllEqual( (num_instances, image_height, image_width), results['groundtruth_instance_masks'].shape) self.assertAllEqual( (num_instances,), results['groundtruth_instance_masks_png'].shape) self.assertAllEqual( [3, 1], results['groundtruth_classes']) self.assertAllEqual( [False, False], results['groundtruth_is_crowd']) self.assertNDArrayNear( [0.25 * image_height * image_width, 0.75 * image_height * image_width], results['groundtruth_area'], 1e-4) self.assertNDArrayNear( [[0, 0, 0.5, 0.5], [0, 0.25, 1.0, 1.0]], results['groundtruth_boxes'], 1e-4) self.assertNDArrayNear( mask_content, results['groundtruth_instance_masks'], 1e-4) self.assertAllEqual( masks, results['groundtruth_instance_masks_png']) if __name__ == '__main__': tf.test.main()