imagenet_main.py 11.7 KB
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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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"""Runs a ResNet model on the ImageNet dataset."""
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

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from absl import app as absl_app
from absl import flags
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import tensorflow as tf  # pylint: disable=g-bad-import-order
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from official.utils.flags import core as flags_core
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from official.utils.logs import logger
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from official.resnet import imagenet_preprocessing
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from official.resnet import resnet_model
from official.resnet import resnet_run_loop
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_DEFAULT_IMAGE_SIZE = 224
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_NUM_CHANNELS = 3
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_NUM_CLASSES = 1001
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_NUM_IMAGES = {
    'train': 1281167,
    'validation': 50000,
}
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_NUM_TRAIN_FILES = 1024
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_SHUFFLE_BUFFER = 10000
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DATASET_NAME = 'ImageNet'
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###############################################################################
# Data processing
###############################################################################
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def get_filenames(is_training, data_dir):
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  """Return filenames for dataset."""
  if is_training:
    return [
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        os.path.join(data_dir, 'train-%05d-of-01024' % i)
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        for i in range(_NUM_TRAIN_FILES)]
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  else:
    return [
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        os.path.join(data_dir, 'validation-%05d-of-00128' % i)
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        for i in range(128)]
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def _parse_example_proto(example_serialized):
  """Parses an Example proto containing a training example of an image.

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  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>
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  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.
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    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].
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  """
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),
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      'image/class/label': tf.FixedLenFeature([], dtype=tf.int64,
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                                              default_value=-1),
      'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
                                             default_value=''),
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  }
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  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']})
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  features = tf.parse_single_example(example_serialized, feature_map)
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  label = tf.cast(features['image/class/label'], dtype=tf.int32)
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  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
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def parse_record(raw_record, is_training):
  """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.
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  Returns:
    Tuple with processed image tensor and one-hot-encoded label tensor.
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  """
  image_buffer, label, bbox = _parse_example_proto(raw_record)

  image = imagenet_preprocessing.preprocess_image(
      image_buffer=image_buffer,
      bbox=bbox,
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      output_height=_DEFAULT_IMAGE_SIZE,
      output_width=_DEFAULT_IMAGE_SIZE,
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      num_channels=_NUM_CHANNELS,
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      is_training=is_training)

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  return image, label
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def input_fn(is_training, data_dir, batch_size, num_epochs=1, num_gpus=None):
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  """Input function which provides batches for train or eval.
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  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.
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    num_gpus: The number of gpus used for training.
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  Returns:
    A dataset that can be used for iteration.
  """
  filenames = get_filenames(is_training, data_dir)
  dataset = tf.data.Dataset.from_tensor_slices(filenames)
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  if is_training:
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    # Shuffle the input files
    dataset = dataset.shuffle(buffer_size=_NUM_TRAIN_FILES)
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  # 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))
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  return resnet_run_loop.process_record_dataset(
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      dataset=dataset,
      is_training=is_training,
      batch_size=batch_size,
      shuffle_buffer=_SHUFFLE_BUFFER,
      parse_record_fn=parse_record,
      num_epochs=num_epochs,
      num_gpus=num_gpus,
      examples_per_epoch=_NUM_IMAGES['train'] if is_training else None
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  )
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def get_synth_input_fn(dtype):
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  return resnet_run_loop.get_synth_input_fn(
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      _DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS, _NUM_CLASSES,
      dtype=dtype)
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###############################################################################
# Running the model
###############################################################################
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class ImagenetModel(resnet_model.Model):
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  """Model class with appropriate defaults for Imagenet data."""
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  def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
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               resnet_version=resnet_model.DEFAULT_VERSION,
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               dtype=resnet_model.DEFAULT_DTYPE):
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    """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
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        enables users to extend the same model to their own datasets.
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      resnet_version: Integer representing which version of the ResNet network
        to use. See README for details. Valid values: [1, 2]
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      dtype: The TensorFlow dtype to use for calculations.
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    """
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    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
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      bottleneck = False
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      final_size = 512
    else:
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      bottleneck = True
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      final_size = 2048

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
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        bottleneck=bottleneck,
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        num_classes=num_classes,
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        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],
        final_size=final_size,
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        resnet_version=resnet_version,
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        data_format=data_format,
        dtype=dtype
    )
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def _get_block_sizes(resnet_size):
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  """Retrieve the size of each block_layer in the ResNet model.

  The number of block layers used for the Resnet model varies according
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  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.
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  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.
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  """
  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]
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  }

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  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)
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def imagenet_model_fn(features, labels, mode, params):
  """Our model_fn for ResNet to be used with our Estimator."""
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  # 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

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  learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
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      batch_size=params['batch_size'], batch_denom=256,
      num_images=_NUM_IMAGES['train'], boundary_epochs=[30, 60, 80, 90],
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      decay_rates=[1, 0.1, 0.01, 0.001, 1e-4], warmup=warmup, base_lr=base_lr)
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  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'],
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      resnet_version=params['resnet_version'],
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      loss_scale=params['loss_scale'],
      loss_filter_fn=None,
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      dtype=params['dtype'],
      fine_tune=params['fine_tune']
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  )
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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=100)
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def run_imagenet(flags_obj):
  """Run ResNet ImageNet training and eval loop.

  Args:
    flags_obj: An object containing parsed flag values.
  """
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  input_function = (flags_obj.use_synthetic_data and
                    get_synth_input_fn(flags_core.get_tf_dtype(flags_obj)) or
                    input_fn)
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  resnet_run_loop.resnet_main(
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      flags_obj, imagenet_model_fn, input_function, DATASET_NAME,
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      shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
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def main(_):
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  with logger.benchmark_context(flags.FLAGS):
    run_imagenet(flags.FLAGS)
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if __name__ == '__main__':
  tf.logging.set_verbosity(tf.logging.INFO)
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  define_imagenet_flags()
  absl_app.run(main)