# 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. # ============================================================================== """Runs a ResNet model on the ImageNet dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl import app as absl_app from absl import flags import tensorflow as tf # pylint: disable=g-bad-import-order from official.utils.flags import core as flags_core from official.utils.logs import logger from official.resnet import imagenet_preprocessing from official.resnet import resnet_model from official.resnet import resnet_run_loop _DEFAULT_IMAGE_SIZE = 224 _NUM_CHANNELS = 3 _NUM_CLASSES = 1001 _NUM_IMAGES = { 'train': 1281167, 'validation': 50000, } _NUM_TRAIN_FILES = 1024 _SHUFFLE_BUFFER = 10000 DATASET_NAME = 'ImageNet' ############################################################################### # Data processing ############################################################################### def get_filenames(is_training, data_dir): """Return filenames for dataset.""" if is_training: return [ os.path.join(data_dir, 'train-%05d-of-01024' % i) for i in range(_NUM_TRAIN_FILES)] else: return [ os.path.join(data_dir, 'validation-%05d-of-00128' % i) for i in range(128)] def _parse_example_proto(example_serialized): """Parses an Example proto containing a training example of an image. The output of the build_image_data.py image preprocessing script is a dataset containing serialized Example protocol buffers. Each Example proto contains the following fields (values are included as examples): image/height: 462 image/width: 581 image/colorspace: 'RGB' image/channels: 3 image/class/label: 615 image/class/synset: 'n03623198' image/class/text: 'knee pad' image/object/bbox/xmin: 0.1 image/object/bbox/xmax: 0.9 image/object/bbox/ymin: 0.2 image/object/bbox/ymax: 0.6 image/object/bbox/label: 615 image/format: 'JPEG' image/filename: 'ILSVRC2012_val_00041207.JPEG' image/encoded: <JPEG encoded string> Args: example_serialized: scalar Tensor tf.string containing a serialized Example protocol buffer. Returns: image_buffer: Tensor tf.string containing the contents of a JPEG file. label: Tensor tf.int32 containing the label. bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords] where each coordinate is [0, 1) and the coordinates are arranged as [ymin, xmin, ymax, xmax]. """ # Dense features in Example proto. feature_map = { 'image/encoded': tf.FixedLenFeature([], dtype=tf.string, default_value=''), 'image/class/label': tf.FixedLenFeature([], dtype=tf.int64, default_value=-1), 'image/class/text': tf.FixedLenFeature([], dtype=tf.string, default_value=''), } sparse_float32 = tf.VarLenFeature(dtype=tf.float32) # Sparse features in Example proto. feature_map.update( {k: sparse_float32 for k in ['image/object/bbox/xmin', 'image/object/bbox/ymin', 'image/object/bbox/xmax', 'image/object/bbox/ymax']}) features = tf.parse_single_example(example_serialized, feature_map) label = tf.cast(features['image/class/label'], dtype=tf.int32) xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0) ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0) xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0) ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0) # Note that we impose an ordering of (y, x) just to make life difficult. bbox = tf.concat([ymin, xmin, ymax, xmax], 0) # Force the variable number of bounding boxes into the shape # [1, num_boxes, coords]. bbox = tf.expand_dims(bbox, 0) bbox = tf.transpose(bbox, [0, 2, 1]) return features['image/encoded'], label, bbox def parse_record(raw_record, is_training, dtype): """Parses a record containing a training example of an image. The input record is parsed into a label and image, and the image is passed through preprocessing steps (cropping, flipping, and so on). Args: raw_record: scalar Tensor tf.string containing a serialized Example protocol buffer. is_training: A boolean denoting whether the input is for training. dtype: data type to use for images/features. Returns: Tuple with processed image tensor and one-hot-encoded label tensor. """ image_buffer, label, bbox = _parse_example_proto(raw_record) image = imagenet_preprocessing.preprocess_image( image_buffer=image_buffer, bbox=bbox, output_height=_DEFAULT_IMAGE_SIZE, output_width=_DEFAULT_IMAGE_SIZE, num_channels=_NUM_CHANNELS, is_training=is_training) image = tf.cast(image, dtype) return image, label def input_fn(is_training, data_dir, batch_size, num_epochs=1, dtype=tf.float32, datasets_num_private_threads=None, num_parallel_batches=1): """Input function which provides batches for train or eval. Args: is_training: A boolean denoting whether the input is for training. data_dir: The directory containing the input data. batch_size: The number of samples per batch. num_epochs: The number of epochs to repeat the dataset. dtype: Data type to use for images/features datasets_num_private_threads: Number of private threads for tf.data. num_parallel_batches: Number of parallel batches for tf.data. Returns: A dataset that can be used for iteration. """ filenames = get_filenames(is_training, data_dir) dataset = tf.data.Dataset.from_tensor_slices(filenames) if is_training: # Shuffle the input files dataset = dataset.shuffle(buffer_size=_NUM_TRAIN_FILES) # Convert to individual records. # cycle_length = 10 means 10 files will be read and deserialized in parallel. # This number is low enough to not cause too much contention on small systems # but high enough to provide the benefits of parallelization. You may want # to increase this number if you have a large number of CPU cores. dataset = dataset.apply(tf.contrib.data.parallel_interleave( tf.data.TFRecordDataset, cycle_length=10)) return resnet_run_loop.process_record_dataset( dataset=dataset, is_training=is_training, batch_size=batch_size, shuffle_buffer=_SHUFFLE_BUFFER, parse_record_fn=parse_record, num_epochs=num_epochs, dtype=dtype, datasets_num_private_threads=datasets_num_private_threads, num_parallel_batches=num_parallel_batches ) def get_synth_input_fn(dtype): return resnet_run_loop.get_synth_input_fn( _DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS, _NUM_CLASSES, dtype=dtype) ############################################################################### # Running the model ############################################################################### class ImagenetModel(resnet_model.Model): """Model class with appropriate defaults for Imagenet data.""" def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES, resnet_version=resnet_model.DEFAULT_VERSION, dtype=resnet_model.DEFAULT_DTYPE): """These are the parameters that work for Imagenet data. Args: resnet_size: The number of convolutional layers needed in the model. data_format: Either 'channels_first' or 'channels_last', specifying which data format to use when setting up the model. num_classes: The number of output classes needed from the model. This enables users to extend the same model to their own datasets. resnet_version: Integer representing which version of the ResNet network to use. See README for details. Valid values: [1, 2] dtype: The TensorFlow dtype to use for calculations. """ # For bigger models, we want to use "bottleneck" layers if resnet_size < 50: bottleneck = False else: bottleneck = True super(ImagenetModel, self).__init__( resnet_size=resnet_size, bottleneck=bottleneck, num_classes=num_classes, num_filters=64, kernel_size=7, conv_stride=2, first_pool_size=3, first_pool_stride=2, block_sizes=_get_block_sizes(resnet_size), block_strides=[1, 2, 2, 2], resnet_version=resnet_version, data_format=data_format, dtype=dtype ) def _get_block_sizes(resnet_size): """Retrieve the size of each block_layer in the ResNet model. The number of block layers used for the Resnet model varies according to the size of the model. This helper grabs the layer set we want, throwing an error if a non-standard size has been selected. Args: resnet_size: The number of convolutional layers needed in the model. Returns: A list of block sizes to use in building the model. Raises: KeyError: if invalid resnet_size is received. """ choices = { 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], 200: [3, 24, 36, 3] } try: return choices[resnet_size] except KeyError: err = ('Could not find layers for selected Resnet size.\n' 'Size received: {}; sizes allowed: {}.'.format( resnet_size, choices.keys())) raise ValueError(err) def imagenet_model_fn(features, labels, mode, params): """Our model_fn for ResNet to be used with our Estimator.""" # Warmup and higher lr may not be valid for fine tuning with small batches # and smaller numbers of training images. if params['fine_tune']: warmup = False base_lr = .1 else: warmup = True base_lr = .128 learning_rate_fn = resnet_run_loop.learning_rate_with_decay( batch_size=params['batch_size'], batch_denom=256, num_images=_NUM_IMAGES['train'], boundary_epochs=[30, 60, 80, 90], decay_rates=[1, 0.1, 0.01, 0.001, 1e-4], warmup=warmup, base_lr=base_lr) return resnet_run_loop.resnet_model_fn( features=features, labels=labels, mode=mode, model_class=ImagenetModel, resnet_size=params['resnet_size'], weight_decay=1e-4, learning_rate_fn=learning_rate_fn, momentum=0.9, data_format=params['data_format'], resnet_version=params['resnet_version'], loss_scale=params['loss_scale'], loss_filter_fn=None, dtype=params['dtype'], fine_tune=params['fine_tune'] ) def define_imagenet_flags(): resnet_run_loop.define_resnet_flags( resnet_size_choices=['18', '34', '50', '101', '152', '200']) flags.adopt_module_key_flags(resnet_run_loop) flags_core.set_defaults(train_epochs=90) def run_imagenet(flags_obj): """Run ResNet ImageNet training and eval loop. Args: flags_obj: An object containing parsed flag values. """ input_function = (flags_obj.use_synthetic_data and get_synth_input_fn(flags_core.get_tf_dtype(flags_obj)) or input_fn) resnet_run_loop.resnet_main( flags_obj, imagenet_model_fn, input_function, DATASET_NAME, shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS]) def main(_): with logger.benchmark_context(flags.FLAGS): run_imagenet(flags.FLAGS) if __name__ == '__main__': tf.logging.set_verbosity(tf.logging.INFO) define_imagenet_flags() absl_app.run(main)