def test_raises_error_with_no_input_paths(self): input_reader_text_proto = """ shuffle: false num_readers: 1 load_instance_masks: true """ input_reader_proto = input_reader_pb2.InputReader() text_format.Merge(input_reader_text_proto, input_reader_proto) with self.assertRaises(ValueError): dataset_builder.build(input_reader_proto)
def test_build_tf_record_input_reader(self): tf_record_path = self.create_tf_record() input_reader_text_proto = """ shuffle: false num_readers: 1 tf_record_input_reader {{ input_path: '{0}' }} """.format(tf_record_path) input_reader_proto = input_reader_pb2.InputReader() text_format.Merge(input_reader_text_proto, input_reader_proto) tensor_dict = dataset_util.make_initializable_iterator( dataset_builder.build(input_reader_proto)).get_next() sv = tf.train.Supervisor(logdir=self.get_temp_dir()) with sv.prepare_or_wait_for_session() as sess: sv.start_queue_runners(sess) output_dict = sess.run(tensor_dict) self.assertTrue( fields.InputDataFields.groundtruth_instance_masks not in output_dict) self.assertEquals((4, 5, 3), output_dict[fields.InputDataFields.image].shape) self.assertEquals([2], output_dict[fields.InputDataFields.groundtruth_classes]) self.assertEquals( (1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape) self.assertAllEqual( [0.0, 0.0, 1.0, 1.0], output_dict[fields.InputDataFields.groundtruth_boxes][0])
def test_build_tf_record_input_reader_with_batch_size_two_and_masks(self): tf_record_path = self.create_tf_record() input_reader_text_proto = """ shuffle: false num_readers: 1 load_instance_masks: true tf_record_input_reader {{ input_path: '{0}' }} """.format(tf_record_path) input_reader_proto = input_reader_pb2.InputReader() text_format.Merge(input_reader_text_proto, input_reader_proto) def one_hot_class_encoding_fn(tensor_dict): tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot( tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3) return tensor_dict tensor_dict = dataset_builder.make_initializable_iterator( dataset_builder.build( input_reader_proto, transform_input_data_fn=one_hot_class_encoding_fn, batch_size=2)).get_next() sv = tf.train.Supervisor(logdir=self.get_temp_dir()) with sv.prepare_or_wait_for_session() as sess: sv.start_queue_runners(sess) output_dict = sess.run(tensor_dict) self.assertAllEqual( [2, 1, 4, 5], output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
def test_build_tf_record_input_reader_with_batch_size_two(self): tf_record_path = self.create_tf_record() input_reader_text_proto = """ shuffle: false num_readers: 1 tf_record_input_reader {{ input_path: '{0}' }} """.format(tf_record_path) input_reader_proto = input_reader_pb2.InputReader() text_format.Merge(input_reader_text_proto, input_reader_proto) def one_hot_class_encoding_fn(tensor_dict): tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot( tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3) return tensor_dict tensor_dict = dataset_util.make_initializable_iterator( dataset_builder.build( input_reader_proto, transform_input_data_fn=one_hot_class_encoding_fn, batch_size=2, max_num_boxes=2, num_classes=3, spatial_image_shape=[4, 5])).get_next() sv = tf.train.Supervisor(logdir=self.get_temp_dir()) with sv.prepare_or_wait_for_session() as sess: sv.start_queue_runners(sess) output_dict = sess.run(tensor_dict) self.assertAllEqual([2, 4, 5, 3], output_dict[fields.InputDataFields.image].shape) self.assertAllEqual([2, 2, 3], output_dict[fields.InputDataFields.groundtruth_classes]. shape) self.assertAllEqual([2, 2, 4], output_dict[fields.InputDataFields.groundtruth_boxes]. shape) self.assertAllEqual( [[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]], [[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]]], output_dict[fields.InputDataFields.groundtruth_boxes])
def test_build_tf_record_input_reader_and_load_instance_masks(self): tf_record_path = self.create_tf_record() input_reader_text_proto = """ shuffle: false num_readers: 1 load_instance_masks: true tf_record_input_reader {{ input_path: '{0}' }} """.format(tf_record_path) input_reader_proto = input_reader_pb2.InputReader() text_format.Merge(input_reader_text_proto, input_reader_proto) tensor_dict = dataset_builder.make_initializable_iterator( dataset_builder.build(input_reader_proto, batch_size=1)).get_next() sv = tf.train.Supervisor(logdir=self.get_temp_dir()) with sv.prepare_or_wait_for_session() as sess: sv.start_queue_runners(sess) output_dict = sess.run(tensor_dict) self.assertAllEqual( (1, 1, 4, 5), output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
def test_build_tf_record_input_reader_with_additional_channels(self): tf_record_path = self.create_tf_record(has_additional_channels=True) input_reader_text_proto = """ shuffle: false num_readers: 1 tf_record_input_reader {{ input_path: '{0}' }} """.format(tf_record_path) input_reader_proto = input_reader_pb2.InputReader() text_format.Merge(input_reader_text_proto, input_reader_proto) tensor_dict = dataset_util.make_initializable_iterator( dataset_builder.build( input_reader_proto, batch_size=2, num_additional_channels=2)).get_next() sv = tf.train.Supervisor(logdir=self.get_temp_dir()) with sv.prepare_or_wait_for_session() as sess: sv.start_queue_runners(sess) output_dict = sess.run(tensor_dict) self.assertEquals((2, 4, 5, 5), output_dict[fields.InputDataFields.image].shape)
def _train_input_fn(params=None): """Returns `features` and `labels` tensor dictionaries for training. Args: params: Parameter dictionary passed from the estimator. Returns: features: Dictionary of feature tensors. features[fields.InputDataFields.image] is a [batch_size, H, W, C] float32 tensor with preprocessed images. features[HASH_KEY] is a [batch_size] int32 tensor representing unique identifiers for the images. features[fields.InputDataFields.true_image_shape] is a [batch_size, 3] int32 tensor representing the true image shapes, as preprocessed images could be padded. labels: Dictionary of groundtruth tensors. labels[fields.InputDataFields.num_groundtruth_boxes] is a [batch_size] int32 tensor indicating the number of groundtruth boxes. labels[fields.InputDataFields.groundtruth_boxes] is a [batch_size, num_boxes, 4] float32 tensor containing the corners of the groundtruth boxes. labels[fields.InputDataFields.groundtruth_classes] is a [batch_size, num_boxes, num_classes] float32 one-hot tensor of classes. labels[fields.InputDataFields.groundtruth_weights] is a [batch_size, num_boxes] float32 tensor containing groundtruth weights for the boxes. -- Optional -- labels[fields.InputDataFields.groundtruth_instance_masks] is a [batch_size, num_boxes, H, W] float32 tensor containing only binary values, which represent instance masks for objects. labels[fields.InputDataFields.groundtruth_keypoints] is a [batch_size, num_boxes, num_keypoints, 2] float32 tensor containing keypoints for each box. Raises: TypeError: if the `train_config` or `train_input_config` are not of the correct type. """ if not isinstance(train_config, train_pb2.TrainConfig): raise TypeError('For training mode, the `train_config` must be a ' 'train_pb2.TrainConfig.') if not isinstance(train_input_config, input_reader_pb2.InputReader): raise TypeError('The `train_input_config` must be a ' 'input_reader_pb2.InputReader.') if not isinstance(model_config, model_pb2.DetectionModel): raise TypeError('The `model_config` must be a ' 'model_pb2.DetectionModel.') data_augmentation_options = [ preprocessor_builder.build(step) for step in train_config.data_augmentation_options ] data_augmentation_fn = functools.partial( augment_input_data, data_augmentation_options=data_augmentation_options) model = model_builder.build(model_config, is_training=True) image_resizer_config = config_util.get_image_resizer_config(model_config) image_resizer_fn = image_resizer_builder.build(image_resizer_config) transform_data_fn = functools.partial( transform_input_data, model_preprocess_fn=model.preprocess, image_resizer_fn=image_resizer_fn, num_classes=config_util.get_number_of_classes(model_config), data_augmentation_fn=data_augmentation_fn) dataset = dataset_builder.build( train_input_config, transform_input_data_fn=transform_data_fn, batch_size=params['batch_size'] if params else train_config.batch_size, max_num_boxes=train_config.max_number_of_boxes, num_classes=config_util.get_number_of_classes(model_config), spatial_image_shape=config_util.get_spatial_image_size( image_resizer_config)) tensor_dict = dataset_util.make_initializable_iterator(dataset).get_next() hash_from_source_id = tf.string_to_hash_bucket_fast( tensor_dict[fields.InputDataFields.source_id], HASH_BINS) features = { fields.InputDataFields.image: tensor_dict[fields.InputDataFields.image], HASH_KEY: tf.cast(hash_from_source_id, tf.int32), fields.InputDataFields.true_image_shape: tensor_dict[ fields.InputDataFields.true_image_shape] } labels = { fields.InputDataFields.num_groundtruth_boxes: tensor_dict[ fields.InputDataFields.num_groundtruth_boxes], fields.InputDataFields.groundtruth_boxes: tensor_dict[ fields.InputDataFields.groundtruth_boxes], fields.InputDataFields.groundtruth_classes: tensor_dict[ fields.InputDataFields.groundtruth_classes], fields.InputDataFields.groundtruth_weights: tensor_dict[ fields.InputDataFields.groundtruth_weights] } if fields.InputDataFields.groundtruth_keypoints in tensor_dict: labels[fields.InputDataFields.groundtruth_keypoints] = tensor_dict[ fields.InputDataFields.groundtruth_keypoints] if fields.InputDataFields.groundtruth_instance_masks in tensor_dict: labels[fields.InputDataFields.groundtruth_instance_masks] = tensor_dict[ fields.InputDataFields.groundtruth_instance_masks] return features, labels
def _eval_input_fn(params=None): """Returns `features` and `labels` tensor dictionaries for evaluation. Args: params: Parameter dictionary passed from the estimator. Returns: features: Dictionary of feature tensors. features[fields.InputDataFields.image] is a [1, H, W, C] float32 tensor with preprocessed images. features[HASH_KEY] is a [1] int32 tensor representing unique identifiers for the images. features[fields.InputDataFields.true_image_shape] is a [1, 3] int32 tensor representing the true image shapes, as preprocessed images could be padded. features[fields.InputDataFields.original_image] is a [1, H', W', C] float32 tensor with the original image. labels: Dictionary of groundtruth tensors. labels[fields.InputDataFields.groundtruth_boxes] is a [1, num_boxes, 4] float32 tensor containing the corners of the groundtruth boxes. labels[fields.InputDataFields.groundtruth_classes] is a [num_boxes, num_classes] float32 one-hot tensor of classes. labels[fields.InputDataFields.groundtruth_area] is a [1, num_boxes] float32 tensor containing object areas. labels[fields.InputDataFields.groundtruth_is_crowd] is a [1, num_boxes] bool tensor indicating if the boxes enclose a crowd. labels[fields.InputDataFields.groundtruth_difficult] is a [1, num_boxes] int32 tensor indicating if the boxes represent difficult instances. -- Optional -- labels[fields.InputDataFields.groundtruth_instance_masks] is a [1, num_boxes, H, W] float32 tensor containing only binary values, which represent instance masks for objects. Raises: TypeError: if the `eval_config` or `eval_input_config` are not of the correct type. """ del params if not isinstance(eval_config, eval_pb2.EvalConfig): raise TypeError('For eval mode, the `eval_config` must be a ' 'train_pb2.EvalConfig.') if not isinstance(eval_input_config, input_reader_pb2.InputReader): raise TypeError('The `eval_input_config` must be a ' 'input_reader_pb2.InputReader.') if not isinstance(model_config, model_pb2.DetectionModel): raise TypeError('The `model_config` must be a ' 'model_pb2.DetectionModel.') num_classes = config_util.get_number_of_classes(model_config) model = model_builder.build(model_config, is_training=False) image_resizer_config = config_util.get_image_resizer_config( model_config) image_resizer_fn = image_resizer_builder.build(image_resizer_config) transform_data_fn = functools.partial( transform_input_data, model_preprocess_fn=model.preprocess, image_resizer_fn=image_resizer_fn, num_classes=num_classes, data_augmentation_fn=None, retain_original_image=True) dataset = dataset_builder.build( eval_input_config, transform_input_data_fn=transform_data_fn) input_dict = dataset_util.make_initializable_iterator( dataset).get_next() hash_from_source_id = tf.string_to_hash_bucket_fast( input_dict[fields.InputDataFields.source_id], HASH_BINS) features = { fields.InputDataFields.image: input_dict[fields.InputDataFields.image], fields.InputDataFields.original_image: input_dict[fields.InputDataFields.original_image], HASH_KEY: tf.cast(hash_from_source_id, tf.int32), fields.InputDataFields.true_image_shape: input_dict[fields.InputDataFields.true_image_shape] } labels = { fields.InputDataFields.groundtruth_boxes: input_dict[fields.InputDataFields.groundtruth_boxes], fields.InputDataFields.groundtruth_classes: input_dict[fields.InputDataFields.groundtruth_classes], fields.InputDataFields.groundtruth_area: input_dict[fields.InputDataFields.groundtruth_area], fields.InputDataFields.groundtruth_is_crowd: input_dict[fields.InputDataFields.groundtruth_is_crowd], fields.InputDataFields.groundtruth_difficult: tf.cast(input_dict[fields.InputDataFields.groundtruth_difficult], tf.int32) } if fields.InputDataFields.groundtruth_instance_masks in input_dict: labels[fields.InputDataFields. groundtruth_instance_masks] = input_dict[ fields.InputDataFields.groundtruth_instance_masks] # Add a batch dimension to the tensors. features = { key: tf.expand_dims(features[key], axis=0) for key, feature in features.items() } labels = { key: tf.expand_dims(labels[key], axis=0) for key, label in labels.items() } return features, labels
def get_next(config): return dataset_builder.make_initializable_iterator( dataset_builder.build(config)).get_next()
def graph_fn_last_batch(): dataset = dataset_builder.build(input_reader_proto, batch_size=8, input_context=input_context) dataset = dataset.skip(8) return get_iterator_next_for_testing(dataset, self.is_tf2())
def graph_fn(): dataset = dataset_builder.build(input_reader_proto, batch_size=8) dataset = dataset.skip(i) return get_iterator_next_for_testing(dataset, self.is_tf2())
def graph_fn(): return dataset_builder.make_initializable_iterator( dataset_builder.build( input_reader_proto, transform_input_data_fn=one_hot_class_encoding_fn, batch_size=2)).get_next()
def graph_fn(): return get_iterator_next_for_testing( dataset_builder.build(input_reader_proto, batch_size=1), self.is_tf2())
def main(_): assert FLAGS.train_dir, '`train_dir` is missing.' if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) if FLAGS.task == 0: tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.train_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, train_config_path=FLAGS.train_config_path, train_input_config_path=FLAGS.input_config_path) if FLAGS.task == 0: for name, config in [('model.config', FLAGS.model_config_path), ('train.config', FLAGS.train_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name), overwrite=True) model_config = configs['model'] train_config = configs['train_config'] input_config = configs['train_input_config'] model_fn = functools.partial( model_builder.build, model_config=model_config, is_training=True) #iterator = dataset_util.make_initializable_iterator(dataset_builder.build(input_config)) datasetmy = dataset_builder.build(input_config) iterator = datasetmy.make_initializable_iterator() def get_next(config): return iterator.get_next() create_input_dict_fn = functools.partial(get_next, input_config) data_augmentation_options = [ preprocessor_builder.build(step) for step in train_config.data_augmentation_options] input_queue = trainer.create_input_queue( train_config.batch_size, create_input_dict_fn, train_config.batch_queue_capacity, train_config.num_batch_queue_threads, train_config.prefetch_queue_capacity, data_augmentation_options) tensors = input_queue.dequeue() #print all tensors in tfrecord print(tensors) groundtruth_difficult = tensors[0]['groundtruth_difficult'] groundtruth_group_of = tensors[0]['groundtruth_group_of'] groundtruth_weights = tensors[0]['groundtruth_weights'] groundtruth_is_crowd = tensors[0]['groundtruth_is_crowd'] key = tensors[0]['key'] groundtruth_boxes = tensors[0]['groundtruth_boxes'] image = tensors[0]['image'] groundtruth_area = tensors[0]['groundtruth_area'] groundtruth_classes = tensors[0]['groundtruth_classes'] filename = tensors[0]['filename'] num_groundtruth_boxes = tensors[0]['num_groundtruth_boxes'] source_id = tensors[0]['source_id'] init_op=tf.initialize_all_variables() with tf.Session() as sess: sess.run(iterator.initializer) sess.run(tf.tables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) sess.run(init_op) for i in range(10): groundtruth_weights_val,groundtruth_difficult_val,groundtruth_group_of_val,groundtruth_is_crowd_val,key_val,groundtruth_boxes_val,image_val,groundtruth_area_val,groundtruth_classes_val,filename_val,num_groundtruth_boxes_val,source_id_val = \ sess.run([groundtruth_weights,groundtruth_difficult,groundtruth_group_of,groundtruth_is_crowd,key,groundtruth_boxes,image,groundtruth_area,groundtruth_classes,filename,num_groundtruth_boxes,source_id]) # print(groundtruth_weights_val) print(groundtruth_boxes_val) # print(groundtruth_difficult_val) # print(groundtruth_group_of_val) # print(groundtruth_is_crowd_val) # print(key_val) # print(image_val) # print(groundtruth_area_val) print(groundtruth_classes_val) print(filename_val) print(num_groundtruth_boxes_val) # print(source_id_val) image_val = image_val[0] image_val = image_val.astype(np.uint8) # cv2.imshow('image', image_val) # cv2.waitKey() # plt.imshow(image_val) # plt.show() print('finish') #plot bbox on image plt.switch_backend("TkAgg") classes_val = groundtruth_classes_val boxes_val = groundtruth_boxes_val scores_val = [1.0]*num_groundtruth_boxes_val image_np = image_val image_np_origin = image_val.copy() NUM_CLASSES = 90 IMAGE_SIZE = (12, 8) PATH_TO_LABELS = '../../data/mscoco_label_map.pbtxt' label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) category_index = label_map_util.create_category_index(categories) vis_util.visualize_boxes_and_labels_on_image_array( image_np, boxes_val, np.squeeze(classes_val).astype(np.int32), np.squeeze(scores_val), category_index, use_normalized_coordinates=True, line_thickness=8) plt.figure(figsize=IMAGE_SIZE) plt.subplot(121) plt.imshow(image_np) plt.subplot(122) plt.imshow(image_np_origin) plt.show() print('finish') pass coord.request_stop() coord.join(threads)
def _eval_input_fn(params=None): """Returns `features` and `labels` tensor dictionaries for evaluation. Args: params: Parameter dictionary passed from the estimator. Returns: features: Dictionary of feature tensors. features[fields.InputDataFields.image] is a [1, H, W, C] float32 tensor with preprocessed images. features[HASH_KEY] is a [1] int32 tensor representing unique identifiers for the images. features[fields.InputDataFields.true_image_shape] is a [1, 3] int32 tensor representing the true image shapes, as preprocessed images could be padded. features[fields.InputDataFields.original_image] is a [1, H', W', C] float32 tensor with the original image. labels: Dictionary of groundtruth tensors. labels[fields.InputDataFields.groundtruth_boxes] is a [1, num_boxes, 4] float32 tensor containing the corners of the groundtruth boxes. labels[fields.InputDataFields.groundtruth_classes] is a [num_boxes, num_classes] float32 one-hot tensor of classes. labels[fields.InputDataFields.groundtruth_area] is a [1, num_boxes] float32 tensor containing object areas. labels[fields.InputDataFields.groundtruth_is_crowd] is a [1, num_boxes] bool tensor indicating if the boxes enclose a crowd. labels[fields.InputDataFields.groundtruth_difficult] is a [1, num_boxes] int32 tensor indicating if the boxes represent difficult instances. -- Optional -- labels[fields.InputDataFields.groundtruth_instance_masks] is a [1, num_boxes, H, W] float32 tensor containing only binary values, which represent instance masks for objects. Raises: TypeError: if the `eval_config` or `eval_input_config` are not of the correct type. """ del params if not isinstance(eval_config, eval_pb2.EvalConfig): raise TypeError('For eval mode, the `eval_config` must be a ' 'train_pb2.EvalConfig.') if not isinstance(eval_input_config, input_reader_pb2.InputReader): raise TypeError('The `eval_input_config` must be a ' 'input_reader_pb2.InputReader.') if not isinstance(model_config, model_pb2.DetectionModel): raise TypeError('The `model_config` must be a ' 'model_pb2.DetectionModel.') num_classes = config_util.get_number_of_classes(model_config) model = model_builder.build(model_config, is_training=False) image_resizer_config = config_util.get_image_resizer_config(model_config) image_resizer_fn = image_resizer_builder.build(image_resizer_config) transform_data_fn = functools.partial( transform_input_data, model_preprocess_fn=model.preprocess, image_resizer_fn=image_resizer_fn, num_classes=num_classes, data_augmentation_fn=None, retain_original_image=True) dataset = dataset_builder.build(eval_input_config, transform_input_data_fn=transform_data_fn) input_dict = dataset_util.make_initializable_iterator(dataset).get_next() hash_from_source_id = tf.string_to_hash_bucket_fast( input_dict[fields.InputDataFields.source_id], HASH_BINS) features = { fields.InputDataFields.image: input_dict[fields.InputDataFields.image], fields.InputDataFields.original_image: input_dict[fields.InputDataFields.original_image], HASH_KEY: tf.cast(hash_from_source_id, tf.int32), fields.InputDataFields.true_image_shape: input_dict[fields.InputDataFields.true_image_shape] } labels = { fields.InputDataFields.groundtruth_boxes: input_dict[fields.InputDataFields.groundtruth_boxes], fields.InputDataFields.groundtruth_classes: input_dict[fields.InputDataFields.groundtruth_classes], fields.InputDataFields.groundtruth_area: input_dict[fields.InputDataFields.groundtruth_area], fields.InputDataFields.groundtruth_is_crowd: input_dict[fields.InputDataFields.groundtruth_is_crowd], fields.InputDataFields.groundtruth_difficult: tf.cast(input_dict[fields.InputDataFields.groundtruth_difficult], tf.int32) } if fields.InputDataFields.groundtruth_instance_masks in input_dict: labels[fields.InputDataFields.groundtruth_instance_masks] = input_dict[ fields.InputDataFields.groundtruth_instance_masks] # Add a batch dimension to the tensors. features = { key: tf.expand_dims(features[key], axis=0) for key, feature in features.items() } labels = { key: tf.expand_dims(labels[key], axis=0) for key, label in labels.items() } return features, labels
def get_next(config): return dataset_util.make_initializable_iterator( dataset_builder.build(config)).get_next()
def _train_input_fn(params=None): """Returns `features` and `labels` tensor dictionaries for training. Args: params: Parameter dictionary passed from the estimator. Returns: features: Dictionary of feature tensors. features[fields.InputDataFields.image] is a [batch_size, H, W, C] float32 tensor with preprocessed images. features[HASH_KEY] is a [batch_size] int32 tensor representing unique identifiers for the images. features[fields.InputDataFields.true_image_shape] is a [batch_size, 3] int32 tensor representing the true image shapes, as preprocessed images could be padded. labels: Dictionary of groundtruth tensors. labels[fields.InputDataFields.num_groundtruth_boxes] is a [batch_size] int32 tensor indicating the number of groundtruth boxes. labels[fields.InputDataFields.groundtruth_boxes] is a [batch_size, num_boxes, 4] float32 tensor containing the corners of the groundtruth boxes. labels[fields.InputDataFields.groundtruth_classes] is a [batch_size, num_boxes, num_classes] float32 one-hot tensor of classes. labels[fields.InputDataFields.groundtruth_weights] is a [batch_size, num_boxes] float32 tensor containing groundtruth weights for the boxes. -- Optional -- labels[fields.InputDataFields.groundtruth_instance_masks] is a [batch_size, num_boxes, H, W] float32 tensor containing only binary values, which represent instance masks for objects. labels[fields.InputDataFields.groundtruth_keypoints] is a [batch_size, num_boxes, num_keypoints, 2] float32 tensor containing keypoints for each box. Raises: TypeError: if the `train_config` or `train_input_config` are not of the correct type. """ if not isinstance(train_config, train_pb2.TrainConfig): raise TypeError('For training mode, the `train_config` must be a ' 'train_pb2.TrainConfig.') if not isinstance(train_input_config, input_reader_pb2.InputReader): raise TypeError('The `train_input_config` must be a ' 'input_reader_pb2.InputReader.') if not isinstance(model_config, model_pb2.DetectionModel): raise TypeError('The `model_config` must be a ' 'model_pb2.DetectionModel.') data_augmentation_options = [ preprocessor_builder.build(step) for step in train_config.data_augmentation_options ] data_augmentation_fn = functools.partial( augment_input_data, data_augmentation_options=data_augmentation_options) model = model_builder.build(model_config, is_training=True) image_resizer_config = config_util.get_image_resizer_config( model_config) image_resizer_fn = image_resizer_builder.build(image_resizer_config) transform_data_fn = functools.partial( transform_input_data, model_preprocess_fn=model.preprocess, image_resizer_fn=image_resizer_fn, num_classes=config_util.get_number_of_classes(model_config), data_augmentation_fn=data_augmentation_fn) dataset = dataset_builder.build( train_input_config, transform_input_data_fn=transform_data_fn, batch_size=params['batch_size'] if params else train_config.batch_size, max_num_boxes=train_config.max_number_of_boxes, num_classes=config_util.get_number_of_classes(model_config), spatial_image_shape=config_util.get_spatial_image_size( image_resizer_config)) tensor_dict = dataset_util.make_initializable_iterator( dataset).get_next() hash_from_source_id = tf.string_to_hash_bucket_fast( tensor_dict[fields.InputDataFields.source_id], HASH_BINS) features = { fields.InputDataFields.image: tensor_dict[fields.InputDataFields.image], HASH_KEY: tf.cast(hash_from_source_id, tf.int32), fields.InputDataFields.true_image_shape: tensor_dict[fields.InputDataFields.true_image_shape] } labels = { fields.InputDataFields.num_groundtruth_boxes: tensor_dict[fields.InputDataFields.num_groundtruth_boxes], fields.InputDataFields.groundtruth_boxes: tensor_dict[fields.InputDataFields.groundtruth_boxes], fields.InputDataFields.groundtruth_classes: tensor_dict[fields.InputDataFields.groundtruth_classes], fields.InputDataFields.groundtruth_weights: tensor_dict[fields.InputDataFields.groundtruth_weights] } if fields.InputDataFields.groundtruth_keypoints in tensor_dict: labels[fields.InputDataFields.groundtruth_keypoints] = tensor_dict[ fields.InputDataFields.groundtruth_keypoints] if fields.InputDataFields.groundtruth_instance_masks in tensor_dict: labels[fields.InputDataFields. groundtruth_instance_masks] = tensor_dict[ fields.InputDataFields.groundtruth_instance_masks] return features, labels