# Copyright 2017 Google Inc. # # 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. """A factory-pattern class which returns image/label pairs.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # Dependency imports import tensorflow as tf from slim.datasets import mnist from domain_adaptation.datasets import mnist_m slim = tf.contrib.slim def get_dataset(dataset_name, split_name, dataset_dir, file_pattern=None, reader=None): """Given a dataset name and a split_name returns a Dataset. Args: dataset_name: String, the name of the dataset. split_name: A train/test split name. dataset_dir: The directory where the dataset files are stored. file_pattern: The file pattern to use for matching the dataset source files. reader: The subclass of tf.ReaderBase. If left as `None`, then the default reader defined by each dataset is used. Returns: A tf-slim `Dataset` class. Raises: ValueError: if `dataset_name` isn't recognized. """ dataset_name_to_module = {'mnist': mnist, 'mnist_m': mnist_m} if dataset_name not in dataset_name_to_module: raise ValueError('Name of dataset unknown %s.' % dataset_name) return dataset_name_to_module[dataset_name].get_split(split_name, dataset_dir, file_pattern, reader) def provide_batch(dataset_name, split_name, dataset_dir, num_readers, batch_size, num_preprocessing_threads): """Provides a batch of images and corresponding labels. Args: dataset_name: String, the name of the dataset. split_name: A train/test split name. dataset_dir: The directory where the dataset files are stored. num_readers: The number of readers used by DatasetDataProvider. batch_size: The size of the batch requested. num_preprocessing_threads: The number of preprocessing threads for tf.train.batch. file_pattern: The file pattern to use for matching the dataset source files. reader: The subclass of tf.ReaderBase. If left as `None`, then the default reader defined by each dataset is used. Returns: A batch of images: tensor of [batch_size, height, width, channels]. labels: dictionary of labels. """ dataset = get_dataset(dataset_name, split_name, dataset_dir) provider = slim.dataset_data_provider.DatasetDataProvider( dataset, num_readers=num_readers, common_queue_capacity=20 * batch_size, common_queue_min=10 * batch_size) [image, label] = provider.get(['image', 'label']) # Convert images to float32 image = tf.image.convert_image_dtype(image, tf.float32) image -= 0.5 image *= 2 # Load the data. labels = {} images, labels['classes'] = tf.train.batch( [image, label], batch_size=batch_size, num_threads=num_preprocessing_threads, capacity=5 * batch_size) labels['classes'] = slim.one_hot_encoding(labels['classes'], dataset.num_classes) # Convert mnist to RGB and 32x32 so that it can match mnist_m. if dataset_name == 'mnist': images = tf.image.grayscale_to_rgb(images) images = tf.image.resize_images(images, [32, 32]) return images, labels