imagenet_main.py 12 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
# 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.
# ==============================================================================
15
"""Runs a ResNet model on the ImageNet dataset."""
16
17
18
19
20
21
22

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

23
24
from absl import app as absl_app
from absl import flags
Karmel Allison's avatar
Karmel Allison committed
25
import tensorflow as tf  # pylint: disable=g-bad-import-order
26

27
from official.utils.flags import core as flags_core
28
from official.utils.logs import logger
29
from official.resnet import imagenet_preprocessing
30
31
from official.resnet import resnet_model
from official.resnet import resnet_run_loop
32

33
_DEFAULT_IMAGE_SIZE = 224
34
_NUM_CHANNELS = 3
35
_NUM_CLASSES = 1001
36

37
38
39
40
_NUM_IMAGES = {
    'train': 1281167,
    'validation': 50000,
}
41

42
_NUM_TRAIN_FILES = 1024
43
_SHUFFLE_BUFFER = 10000
44

45
DATASET_NAME = 'ImageNet'
46

47
48
49
###############################################################################
# Data processing
###############################################################################
50
def get_filenames(is_training, data_dir):
51
52
53
  """Return filenames for dataset."""
  if is_training:
    return [
54
        os.path.join(data_dir, 'train-%05d-of-01024' % i)
55
        for i in range(_NUM_TRAIN_FILES)]
56
57
  else:
    return [
58
        os.path.join(data_dir, 'validation-%05d-of-00128' % i)
Neal Wu's avatar
Neal Wu committed
59
        for i in range(128)]
60
61


62
63
64
def _parse_example_proto(example_serialized):
  """Parses an Example proto containing a training example of an image.

65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
  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>
84
85
86
87
88
89
90

  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.
91
92
93
94
    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].
95
96
97
98
99
  """
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),
100
      'image/class/label': tf.FixedLenFeature([], dtype=tf.int64,
101
102
103
                                              default_value=-1),
      'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
                                             default_value=''),
104
  }
105
106
107
108
109
110
111
  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']})
112

113
  features = tf.parse_single_example(example_serialized, feature_map)
114
  label = tf.cast(features['image/class/label'], dtype=tf.int32)
115

116
117
118
119
120
121
122
123
124
125
126
127
128
129
  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
130
131


132
def parse_record(raw_record, is_training, dtype):
133
134
135
136
137
138
139
140
141
  """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.
142
    dtype: data type to use for images/features.
143

144
145
  Returns:
    Tuple with processed image tensor and one-hot-encoded label tensor.
146
147
148
149
150
151
  """
  image_buffer, label, bbox = _parse_example_proto(raw_record)

  image = imagenet_preprocessing.preprocess_image(
      image_buffer=image_buffer,
      bbox=bbox,
152
153
      output_height=_DEFAULT_IMAGE_SIZE,
      output_width=_DEFAULT_IMAGE_SIZE,
154
      num_channels=_NUM_CHANNELS,
155
      is_training=is_training)
156
  image = tf.cast(image, dtype)
157

158
  return image, label
159
160


Toby Boyd's avatar
Toby Boyd committed
161
162
163
def input_fn(is_training, data_dir, batch_size, num_epochs=1,
             dtype=tf.float32, datasets_num_private_threads=None,
             num_parallel_batches=1):
164
  """Input function which provides batches for train or eval.
Karmel Allison's avatar
Karmel Allison committed
165

166
167
168
169
170
  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.
171
    dtype: Data type to use for images/features
Toby Boyd's avatar
Toby Boyd committed
172
173
    datasets_num_private_threads: Number of private threads for tf.data.
    num_parallel_batches: Number of parallel batches for tf.data.
174
175
176
177
178
179

  Returns:
    A dataset that can be used for iteration.
  """
  filenames = get_filenames(is_training, data_dir)
  dataset = tf.data.Dataset.from_tensor_slices(filenames)
180

181
  if is_training:
182
183
    # Shuffle the input files
    dataset = dataset.shuffle(buffer_size=_NUM_TRAIN_FILES)
184

185
186
187
188
189
190
191
  # 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))
192

193
  return resnet_run_loop.process_record_dataset(
Taylor Robie's avatar
Taylor Robie committed
194
195
196
197
198
199
      dataset=dataset,
      is_training=is_training,
      batch_size=batch_size,
      shuffle_buffer=_SHUFFLE_BUFFER,
      parse_record_fn=parse_record,
      num_epochs=num_epochs,
Toby Boyd's avatar
Toby Boyd committed
200
201
202
      dtype=dtype,
      datasets_num_private_threads=datasets_num_private_threads,
      num_parallel_batches=num_parallel_batches
203
  )
204
205


Toby Boyd's avatar
Toby Boyd committed
206
def get_synth_input_fn(dtype):
207
  return resnet_run_loop.get_synth_input_fn(
Toby Boyd's avatar
Toby Boyd committed
208
209
      _DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS, _NUM_CLASSES,
      dtype=dtype)
210
211


212
213
214
###############################################################################
# Running the model
###############################################################################
215
class ImagenetModel(resnet_model.Model):
Karmel Allison's avatar
Karmel Allison committed
216
  """Model class with appropriate defaults for Imagenet data."""
217

218
  def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
219
               resnet_version=resnet_model.DEFAULT_VERSION,
220
               dtype=resnet_model.DEFAULT_DTYPE):
Neal Wu's avatar
Neal Wu committed
221
222
223
224
225
226
227
    """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
228
        enables users to extend the same model to their own datasets.
229
230
      resnet_version: Integer representing which version of the ResNet network
        to use. See README for details. Valid values: [1, 2]
231
      dtype: The TensorFlow dtype to use for calculations.
Neal Wu's avatar
Neal Wu committed
232
    """
233
234
235

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
236
      bottleneck = False
237
    else:
238
      bottleneck = True
239
240
241

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
242
        bottleneck=bottleneck,
243
        num_classes=num_classes,
244
245
246
247
248
249
250
        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],
251
        resnet_version=resnet_version,
252
253
254
        data_format=data_format,
        dtype=dtype
    )
255
256
257


def _get_block_sizes(resnet_size):
Karmel Allison's avatar
Karmel Allison committed
258
259
260
  """Retrieve the size of each block_layer in the ResNet model.

  The number of block layers used for the Resnet model varies according
261
262
  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.
Karmel Allison's avatar
Karmel Allison committed
263
264
265
266
267
268
269
270
271

  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.
272
273
274
275
276
277
278
279
  """
  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]
280
281
  }

282
283
284
285
286
287
288
  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)
289
290


291
292
def imagenet_model_fn(features, labels, mode, params):
  """Our model_fn for ResNet to be used with our Estimator."""
293
294
295
296
297
298
299
300
301
302

  # 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

303
  learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
304
305
      batch_size=params['batch_size'], batch_denom=256,
      num_images=_NUM_IMAGES['train'], boundary_epochs=[30, 60, 80, 90],
306
      decay_rates=[1, 0.1, 0.01, 0.001, 1e-4], warmup=warmup, base_lr=base_lr)
307

308
309
310
311
312
313
314
315
316
317
  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'],
318
      resnet_version=params['resnet_version'],
319
320
      loss_scale=params['loss_scale'],
      loss_filter_fn=None,
Zac Wellmer's avatar
Zac Wellmer committed
321
322
      dtype=params['dtype'],
      fine_tune=params['fine_tune']
323
  )
324
325


326
327
328
329
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)
Toby Boyd's avatar
Toby Boyd committed
330
  flags_core.set_defaults(train_epochs=90)
331

332

333
334
335
336
337
338
def run_imagenet(flags_obj):
  """Run ResNet ImageNet training and eval loop.

  Args:
    flags_obj: An object containing parsed flag values.
  """
Toby Boyd's avatar
Toby Boyd committed
339
340
341
  input_function = (flags_obj.use_synthetic_data and
                    get_synth_input_fn(flags_core.get_tf_dtype(flags_obj)) or
                    input_fn)
342
343

  resnet_run_loop.resnet_main(
344
      flags_obj, imagenet_model_fn, input_function, DATASET_NAME,
345
      shape=[_DEFAULT_IMAGE_SIZE, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS])
346
347


348
def main(_):
349
350
  with logger.benchmark_context(flags.FLAGS):
    run_imagenet(flags.FLAGS)
351
352


353
354
if __name__ == '__main__':
  tf.logging.set_verbosity(tf.logging.INFO)
355
356
  define_imagenet_flags()
  absl_app.run(main)