# 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. # ============================================================================== """tf.data.Dataset builder. Creates data sources for DetectionModels from an InputReader config. See input_reader.proto for options. Note: If users wishes to also use their own InputReaders with the Object Detection configuration framework, they should define their own builder function that wraps the build function. """ import functools import tensorflow as tf from object_detection.core import standard_fields as fields from object_detection.data_decoders import tf_example_decoder from object_detection.protos import input_reader_pb2 from object_detection.utils import dataset_util def _get_padding_shapes(dataset, max_num_boxes=None, num_classes=None, spatial_image_shape=None): """Returns shapes to pad dataset tensors to before batching. Args: dataset: tf.data.Dataset object. max_num_boxes: Max number of groundtruth boxes needed to computes shapes for padding. num_classes: Number of classes in the dataset needed to compute shapes for padding. spatial_image_shape: A list of two integers of the form [height, width] containing expected spatial shape of the image. Returns: A dictionary keyed by fields.InputDataFields containing padding shapes for tensors in the dataset. Raises: ValueError: If groundtruth classes is neither rank 1 nor rank 2. """ if not spatial_image_shape or spatial_image_shape == [-1, -1]: height, width = None, None else: height, width = spatial_image_shape # pylint: disable=unpacking-non-sequence padding_shapes = { fields.InputDataFields.image: [height, width, 3], fields.InputDataFields.source_id: [], fields.InputDataFields.filename: [], fields.InputDataFields.key: [], fields.InputDataFields.groundtruth_difficult: [max_num_boxes], fields.InputDataFields.groundtruth_boxes: [max_num_boxes, 4], fields.InputDataFields.groundtruth_instance_masks: [max_num_boxes, height, width], fields.InputDataFields.groundtruth_is_crowd: [max_num_boxes], fields.InputDataFields.groundtruth_group_of: [max_num_boxes], fields.InputDataFields.groundtruth_area: [max_num_boxes], fields.InputDataFields.groundtruth_weights: [max_num_boxes], fields.InputDataFields.num_groundtruth_boxes: [], fields.InputDataFields.groundtruth_label_types: [max_num_boxes], fields.InputDataFields.groundtruth_label_scores: [max_num_boxes], fields.InputDataFields.true_image_shape: [3], fields.InputDataFields.multiclass_scores: [ max_num_boxes, num_classes + 1 if num_classes is not None else None], } # Determine whether groundtruth_classes are integers or one-hot encodings, and # apply batching appropriately. classes_shape = dataset.output_shapes[ fields.InputDataFields.groundtruth_classes] if len(classes_shape) == 1: # Class integers. padding_shapes[fields.InputDataFields.groundtruth_classes] = [max_num_boxes] elif len(classes_shape) == 2: # One-hot or k-hot encoding. padding_shapes[fields.InputDataFields.groundtruth_classes] = [ max_num_boxes, num_classes] else: raise ValueError('Groundtruth classes must be a rank 1 tensor (classes) or ' 'rank 2 tensor (one-hot encodings)') if fields.InputDataFields.original_image in dataset.output_shapes: padding_shapes[fields.InputDataFields.original_image] = [None, None, 3] if fields.InputDataFields.groundtruth_keypoints in dataset.output_shapes: tensor_shape = dataset.output_shapes[fields.InputDataFields. groundtruth_keypoints] padding_shape = [max_num_boxes, tensor_shape[1].value, tensor_shape[2].value] padding_shapes[fields.InputDataFields.groundtruth_keypoints] = padding_shape if (fields.InputDataFields.groundtruth_keypoint_visibilities in dataset.output_shapes): tensor_shape = dataset.output_shapes[fields.InputDataFields. groundtruth_keypoint_visibilities] padding_shape = [max_num_boxes, tensor_shape[1].value] padding_shapes[fields.InputDataFields. groundtruth_keypoint_visibilities] = padding_shape return {tensor_key: padding_shapes[tensor_key] for tensor_key, _ in dataset.output_shapes.items()} def build(input_reader_config, transform_input_data_fn=None, batch_size=None, max_num_boxes=None, num_classes=None, spatial_image_shape=None): """Builds a tf.data.Dataset. Builds a tf.data.Dataset by applying the `transform_input_data_fn` on all records. Applies a padded batch to the resulting dataset. Args: input_reader_config: A input_reader_pb2.InputReader object. transform_input_data_fn: Function to apply to all records, or None if no extra decoding is required. batch_size: Batch size. If None, batching is not performed. max_num_boxes: Max number of groundtruth boxes needed to compute shapes for padding. If None, will use a dynamic shape. num_classes: Number of classes in the dataset needed to compute shapes for padding. If None, will use a dynamic shape. spatial_image_shape: A list of two integers of the form [height, width] containing expected spatial shape of the image after applying transform_input_data_fn. If None, will use dynamic shapes. Returns: A tf.data.Dataset based on the input_reader_config. Raises: ValueError: On invalid input reader proto. ValueError: If no input paths are specified. """ if not isinstance(input_reader_config, input_reader_pb2.InputReader): raise ValueError('input_reader_config not of type ' 'input_reader_pb2.InputReader.') if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader': config = input_reader_config.tf_record_input_reader if not config.input_path: raise ValueError('At least one input path must be specified in ' '`input_reader_config`.') label_map_proto_file = None if input_reader_config.HasField('label_map_path'): label_map_proto_file = input_reader_config.label_map_path decoder = tf_example_decoder.TfExampleDecoder( load_instance_masks=input_reader_config.load_instance_masks, instance_mask_type=input_reader_config.mask_type, label_map_proto_file=label_map_proto_file) def process_fn(value): processed = decoder.decode(value) if transform_input_data_fn is not None: return transform_input_data_fn(processed) return processed dataset = dataset_util.read_dataset( functools.partial(tf.data.TFRecordDataset, buffer_size=8 * 1000 * 1000), process_fn, config.input_path[:], input_reader_config) if batch_size: padding_shapes = _get_padding_shapes(dataset, max_num_boxes, num_classes, spatial_image_shape) dataset = dataset.apply( tf.contrib.data.padded_batch_and_drop_remainder(batch_size, padding_shapes)) return dataset raise ValueError('Unsupported input_reader_config.')