Transfer learning on TensorFlow-Slim image classification model library
# 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.# ==============================================================================r"""Downloads and converts a particular dataset.Usage:```shell$ python download_and_convert_data.py \ --dataset_name=flowers \ --dataset_dir=/tmp/flowers$ python download_and_convert_data.py \ --dataset_name=cifar10 \ --dataset_dir=/tmp/cifar10$ python download_and_convert_data.py \ --dataset_name=mnist \ --dataset_dir=/tmp/mnist$ python download_and_convert_data.py \ --dataset_name=visualwakewords \ --dataset_dir=/tmp/visualwakewords```"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow.compat.v1 as tffrom datasets import download_and_convert_cifar10from datasets import download_and_convert_flowersfrom datasets import download_and_convert_mnistfrom datasets import download_and_convert_visualwakewordsfrom datasets import convert_naturalFLAGS = tf.app.flags.FLAGStf.app.flags.DEFINE_string( 'dataset_name', None, 'The name of the dataset to convert, one of "flowers", "cifar10", "mnist", "visualwakewords"' )tf.app.flags.DEFINE_string( 'dataset_dir', None, 'The directory where the output TFRecords and temporary files are saved.')tf.flags.DEFINE_float( 'small_object_area_threshold', 0.005, 'For --dataset_name=visualwakewords only. Threshold of fraction of image ' 'area below which small objects are filtered')tf.flags.DEFINE_string( 'foreground_class_of_interest', 'person', 'For --dataset_name=visualwakewords only. Build a binary classifier based ' 'on the presence or absence of this object in the image.')def main(_): if not FLAGS.dataset_name: raise ValueError('You must supply the dataset name with --dataset_name') if not FLAGS.dataset_dir: raise ValueError('You must supply the dataset directory with --dataset_dir') if FLAGS.dataset_name == 'flowers': download_and_convert_flowers.run(FLAGS.dataset_dir) elif FLAGS.dataset_name == 'cifar10': download_and_convert_cifar10.run(FLAGS.dataset_dir) elif FLAGS.dataset_name == 'mnist': download_and_convert_mnist.run(FLAGS.dataset_dir) elif FLAGS.dataset_name == "natural": convert_natural.run(FLAGS.dataset_dir) elif FLAGS.dataset_name == 'visualwakewords': download_and_convert_visualwakewords.run( FLAGS.dataset_dir, FLAGS.small_object_area_threshold, FLAGS.foreground_class_of_interest) else: raise ValueError( 'dataset_name [%s] was not recognized.' % FLAGS.dataset_name)if __name__ == '__main__': tf.app.run()