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Updating TF pets training example (#179)
* Add tf_pets training example * Adding dockerfile and readme changes * Resolving review comments * Resolving review comments --------- Co-authored-by: SundarRajan98 <[email protected]>
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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. | ||
| # ============================================================================== | ||
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| r"""Convert the Oxford pet dataset to TFRecord for object_detection. | ||
| See: O. M. Parkhi, A. Vedaldi, A. Zisserman, C. V. Jawahar | ||
| Cats and Dogs | ||
| IEEE Conference on Computer Vision and Pattern Recognition, 2012 | ||
| http://www.robots.ox.ac.uk/~vgg/data/pets/ | ||
| Example usage: | ||
| python object_detection/dataset_tools/create_pet_tf_record.py \ | ||
| --data_dir=/home/user/pet \ | ||
| --output_dir=/home/user/pet/output | ||
| """ | ||
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| import hashlib | ||
| import io | ||
| import logging | ||
| import os | ||
| import random | ||
| import re | ||
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| import contextlib2 | ||
| from lxml import etree | ||
| import PIL.Image | ||
| from six.moves import range | ||
| import tensorflow.compat.v1 as tf | ||
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| flags = tf.app.flags | ||
| flags.DEFINE_string('data_dir', '', 'Root directory to raw pet dataset.') | ||
| flags.DEFINE_string('output_dir', '', 'Path to directory to output TFRecords.') | ||
| flags.DEFINE_string('label_map_path', 'pet_label_map.pbtxt', | ||
| 'Path to label map proto') | ||
| flags.DEFINE_integer('num_shards', 10, 'Number of TFRecord shards') | ||
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| FLAGS = flags.FLAGS | ||
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| def open_sharded_output_tfrecords(exit_stack, base_path, num_shards): | ||
| """Opens all TFRecord shards for writing and adds them to an exit stack. | ||
| Args: | ||
| exit_stack: A context2.ExitStack used to automatically closed the TFRecords | ||
| opened in this function. | ||
| base_path: The base path for all shards | ||
| num_shards: The number of shards | ||
| Returns: | ||
| The list of opened TFRecords. Position k in the list corresponds to shard k. | ||
| """ | ||
| tf_record_output_filenames = [ | ||
| '{}-{:05d}-of-{:05d}'.format(base_path, idx, num_shards) | ||
| for idx in range(num_shards) | ||
| ] | ||
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| tfrecords = [ | ||
| exit_stack.enter_context(tf.python_io.TFRecordWriter(file_name)) | ||
| for file_name in tf_record_output_filenames | ||
| ] | ||
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| return tfrecords | ||
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| def int64_feature(value): | ||
| return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) | ||
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| def int64_list_feature(value): | ||
| return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) | ||
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| def bytes_feature(value): | ||
| return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) | ||
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| def bytes_list_feature(value): | ||
| return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) | ||
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| def float_feature(value): | ||
| return tf.train.Feature(float_list=tf.train.FloatList(value=[value])) | ||
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| def float_list_feature(value): | ||
| return tf.train.Feature(float_list=tf.train.FloatList(value=value)) | ||
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| def get_class_name_from_filename(file_name): | ||
| """Gets the class name from a file. | ||
| Args: | ||
| file_name: The file name to get the class name from. | ||
| ie. "american_pit_bull_terrier_105.jpg" | ||
| Returns: | ||
| A string of the class name. | ||
| """ | ||
| match = re.match(r'([A-Za-z_]+)(_[0-9]+\.jpg)', file_name, re.I) | ||
| return match.groups()[0] | ||
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| def read_examples_list(path): | ||
| """Read list of training or validation examples. | ||
| The file is assumed to contain a single example per line where the first | ||
| token in the line is an identifier that allows us to find the image and | ||
| annotation xml for that example. | ||
| For example, the line: | ||
| xyz 3 | ||
| would allow us to find files xyz.jpg and xyz.xml (the 3 would be ignored). | ||
| Args: | ||
| path: absolute path to examples list file. | ||
| Returns: | ||
| list of example identifiers (strings). | ||
| """ | ||
| with tf.gfile.GFile(path) as fid: | ||
| lines = fid.readlines() | ||
| return [line.strip().split(' ')[0] for line in lines] | ||
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| def recursive_parse_xml_to_dict(xml): | ||
| """Recursively parses XML contents to python dict. | ||
| We assume that `object` tags are the only ones that can appear | ||
| multiple times at the same level of a tree. | ||
| Args: | ||
| xml: xml tree obtained by parsing XML file contents using lxml.etree | ||
| Returns: | ||
| Python dictionary holding XML contents. | ||
| """ | ||
| if not xml: | ||
| return {xml.tag: xml.text} | ||
| result = {} | ||
| for child in xml: | ||
| child_result = recursive_parse_xml_to_dict(child) | ||
| if child.tag != 'object': | ||
| result[child.tag] = child_result[child.tag] | ||
| else: | ||
| if child.tag not in result: | ||
| result[child.tag] = [] | ||
| result[child.tag].append(child_result[child.tag]) | ||
| return {xml.tag: result} | ||
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| def dict_to_tf_example(data, | ||
| mask_path, | ||
| label_map_dict, | ||
| image_subdirectory): | ||
| """Convert XML derived dict to tf.Example proto. | ||
| Notice that this function normalizes the bounding box coordinates provided | ||
| by the raw data. | ||
| Args: | ||
| data: dict holding PASCAL XML fields for a single image (obtained by | ||
| running recursive_parse_xml_to_dict) | ||
| mask_path: String path to PNG encoded mask. | ||
| label_map_dict: A map from string label names to integers ids. | ||
| image_subdirectory: String specifying subdirectory within the | ||
| Pascal dataset directory holding the actual image data. | ||
| Returns: | ||
| example: The converted tf.Example. | ||
| Raises: | ||
| ValueError: if the image pointed to by data['filename'] is not a valid JPEG | ||
| """ | ||
| img_path = os.path.join(image_subdirectory, data['filename']) | ||
| with tf.gfile.GFile(img_path, 'rb') as fid: | ||
| encoded_jpg = fid.read() | ||
| encoded_jpg_io = io.BytesIO(encoded_jpg) | ||
| image = PIL.Image.open(encoded_jpg_io) | ||
| if image.format != 'JPEG': | ||
| raise ValueError('Image format not JPEG') | ||
| key = hashlib.sha256(encoded_jpg).hexdigest() | ||
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| width = int(data['size']['width']) | ||
| height = int(data['size']['height']) | ||
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| classes = [] | ||
| classes_text = [] | ||
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| if 'object' in data: | ||
| class_name = get_class_name_from_filename(data['filename']) | ||
| classes_text.append(class_name.encode('utf8')) | ||
| classes.append(label_map_dict[class_name]) | ||
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| feature_dict = { | ||
| 'image/height': int64_feature(height), | ||
| 'image/width': int64_feature(width), | ||
| 'image/filename': bytes_feature( | ||
| data['filename'].encode('utf8')), | ||
| 'image/source_id': bytes_feature( | ||
| data['filename'].encode('utf8')), | ||
| 'image/key/sha256': bytes_feature(key.encode('utf8')), | ||
| 'image/encoded': bytes_feature(encoded_jpg), | ||
| 'image/format': bytes_feature('jpeg'.encode('utf8')), | ||
| 'image/object/class/text': bytes_list_feature(classes_text), | ||
| 'image/object/class/label': int64_list_feature(classes) | ||
| } | ||
| example = tf.train.Example( | ||
| features=tf.train.Features(feature=feature_dict)) | ||
| return example | ||
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| def create_tf_record(output_filename, | ||
| num_shards, | ||
| label_map_dict, | ||
| annotations_dir, | ||
| image_dir, | ||
| examples): | ||
| """Creates a TFRecord file from examples. | ||
| Args: | ||
| output_filename: Path to where output file is saved. | ||
| num_shards: Number of shards for output file. | ||
| label_map_dict: The label map dictionary. | ||
| annotations_dir: Directory where annotation files are stored. | ||
| image_dir: Directory where image files are stored. | ||
| examples: Examples to parse and save to tf record. | ||
| """ | ||
| with contextlib2.ExitStack() as tf_record_close_stack: | ||
| output_tfrecords = open_sharded_output_tfrecords( | ||
| tf_record_close_stack, output_filename, num_shards) | ||
| for idx, example in enumerate(examples): | ||
| if idx % 100 == 0: | ||
| logging.info('On image %d of %d', idx, len(examples)) | ||
| xml_path = os.path.join(annotations_dir, 'xmls', example + '.xml') | ||
| mask_path = os.path.join( | ||
| annotations_dir, 'trimaps', example + '.png') | ||
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| if not os.path.exists(xml_path): | ||
| logging.warning( | ||
| 'Could not find %s, ignoring example.', xml_path) | ||
| continue | ||
| with tf.gfile.GFile(xml_path, 'r') as fid: | ||
| xml_str = fid.read() | ||
| xml = etree.fromstring(xml_str) | ||
| data = recursive_parse_xml_to_dict(xml)['annotation'] | ||
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| try: | ||
| tf_example = dict_to_tf_example( | ||
| data, | ||
| mask_path, | ||
| label_map_dict, | ||
| image_dir) | ||
| if tf_example: | ||
| shard_idx = idx % num_shards | ||
| output_tfrecords[shard_idx].write( | ||
| tf_example.SerializeToString()) | ||
| except ValueError: | ||
| logging.warning('Invalid example: %s, ignoring.', xml_path) | ||
|
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| def main(_): | ||
| data_dir = FLAGS.data_dir | ||
| label_map_dict = {'Abyssinian': 1, 'american_bulldog': 2, 'american_pit_bull_terrier': 3, 'basset_hound': 4, 'beagle': 5, 'Bengal': 6, 'Birman': 7, 'Bombay': 8, 'boxer': 9, 'British_Shorthair': 10, 'chihuahua': 11, 'Egyptian_Mau': 12, 'english_cocker_spaniel': 13, 'english_setter': 14, 'german_shorthaired': 15, 'great_pyrenees': 16, 'havanese': 17, 'japanese_chin': 18, | ||
| 'keeshond': 19, 'leonberger': 20, 'Maine_Coon': 21, 'miniature_pinscher': 22, 'newfoundland': 23, 'Persian': 24, 'pomeranian': 25, 'pug': 26, 'Ragdoll': 27, 'Russian_Blue': 28, 'saint_bernard': 29, 'samoyed': 30, 'scottish_terrier': 31, 'shiba_inu': 32, 'Siamese': 33, 'Sphynx': 34, 'staffordshire_bull_terrier': 35, 'wheaten_terrier': 36, 'yorkshire_terrier': 37} | ||
| logging.info('Reading from Pet dataset.') | ||
| image_dir = os.path.join(data_dir, 'images') | ||
| annotations_dir = os.path.join(data_dir, 'annotations') | ||
| examples_path = os.path.join(annotations_dir, 'trainval.txt') | ||
| examples_list = read_examples_list(examples_path) | ||
|
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| # Test images are not included in the downloaded data set, so we shall perform | ||
| # our own split. | ||
| random.seed(42) | ||
| random.shuffle(examples_list) | ||
| num_examples = len(examples_list) | ||
| num_train = int(0.7 * num_examples) | ||
| train_examples = examples_list[:num_train] | ||
| val_examples = examples_list[num_train:] | ||
| logging.info('%d training and %d validation examples.', | ||
| len(train_examples), len(val_examples)) | ||
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| train_output_path = os.path.join( | ||
| FLAGS.output_dir, 'pet_faces_train.record') | ||
| val_output_path = os.path.join(FLAGS.output_dir, 'pet_faces_val.record') | ||
| create_tf_record( | ||
| train_output_path, | ||
| FLAGS.num_shards, | ||
| label_map_dict, | ||
| annotations_dir, | ||
| image_dir, | ||
| train_examples) | ||
| create_tf_record( | ||
| val_output_path, | ||
| FLAGS.num_shards, | ||
| label_map_dict, | ||
| annotations_dir, | ||
| image_dir, | ||
| val_examples) | ||
|
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| if __name__ == '__main__': | ||
| tf.app.run() |
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