slim-transfer-learning

# Copyright 2016 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.# =============================================================================="""Provides data for the flowers dataset.The dataset scripts used to create the dataset can be found at:tensorflow/models/research/slim/datasets/download_and_convert_flowers.py"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow.compat.v1 as tfimport tf_slim as slimimport osfrom datasets import dataset_utils_FILE_PATTERN = '%s_*.tfrecord'SPLITS_TO_SIZES = {'train': 3320, 'validation': 350}_NUM_CLASSES = 6_ITEMS_TO_DESCRIPTIONS = {    'image': 'A color image of varying size.',    'label': 'A single integer between 0 and 4',}def get_split(split_name, dataset_dir, file_pattern=None, reader=None):  """Gets a dataset tuple with instructions for reading flowers.  Args:    split_name: A train/validation split name.    dataset_dir: The base directory of the dataset sources.    file_pattern: The file pattern to use when matching the dataset sources.      It is assumed that the pattern contains a '%s' string so that the split      name can be inserted.    reader: The TensorFlow reader type.  Returns:    A `Dataset` namedtuple.  Raises:    ValueError: if `split_name` is not a valid train/validation split.  """  if split_name not in SPLITS_TO_SIZES:    raise ValueError('split name %s was not recognized.' % split_name)  if not file_pattern:    file_pattern = _FILE_PATTERN  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)  # Allowing None in the signature so that dataset_factory can use the default.  if reader is None:    reader = tf.TFRecordReader  keys_to_features = {      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),      'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),      'image/class/label': tf.FixedLenFeature(          [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),  }  items_to_handlers = {      'image': slim.tfexample_decoder.Image(),      'label': slim.tfexample_decoder.Tensor('image/class/label'),  }  decoder = slim.tfexample_decoder.TFExampleDecoder(      keys_to_features, items_to_handlers)  labels_to_names = None  if dataset_utils.has_labels(dataset_dir):    labels_to_names = dataset_utils.read_label_file(dataset_dir)  return slim.dataset.Dataset(      data_sources=file_pattern,      reader=reader,      decoder=decoder,      num_samples=SPLITS_TO_SIZES[split_name],      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,      num_classes=_NUM_CLASSES,      labels_to_names=labels_to_names)