Merge pull request #366 from nathansilberman/master

Full code refactor and added all networks

slim/BUILD

 # Description:
-#   Contains files for loading, training and evaluating TF-Slim 2.0-based models.
+#   Contains files for loading, training and evaluating TF-Slim-based models.
 
 package(default_visibility = [":internal"])
 
@@ -7,34 +7,41 @@ licenses(["notice"])  # Apache 2.0
 
 exports_files(["LICENSE"])
 
-package_group(
-    name = "internal",
-    packages = ["//slim/"],
-)
+package_group(name = "internal")
 
 py_library(
     name = "dataset_utils",
     srcs = ["datasets/dataset_utils.py"],
 )
 
-py_binary(
+py_library(
     name = "download_and_convert_cifar10",
     srcs = ["datasets/download_and_convert_cifar10.py"],
     deps = [":dataset_utils"],
 )
 
-py_binary(
+py_library(
     name = "download_and_convert_flowers",
     srcs = ["datasets/download_and_convert_flowers.py"],
     deps = [":dataset_utils"],
 )
 
-py_binary(
+py_library(
     name = "download_and_convert_mnist",
     srcs = ["datasets/download_and_convert_mnist.py"],
     deps = [":dataset_utils"],
 )
 
+py_binary(
+    name = "download_and_convert_data",
+    srcs = ["download_and_convert_data.py"],
+    deps = [
+        ":download_and_convert_cifar10",
+        ":download_and_convert_flowers",
+        ":download_and_convert_mnist",
+    ],
+)
+
 py_binary(
     name = "cifar10",
     srcs = ["datasets/cifar10.py"],
@@ -70,78 +77,261 @@ py_library(
     ],
 )
 
-py_binary(
-    name = "eval",
-    srcs = ["eval.py"],
-    deps = [
-        ":dataset_factory",
-        ":model_deploy",
-        ":model_factory",
-        ":preprocessing_factory",
-    ],
-)
-
 py_library(
     name = "model_deploy",
-    srcs = ["models/model_deploy.py"],
+    srcs = ["deployment/model_deploy.py"],
 )
 
 py_test(
     name = "model_deploy_test",
-    srcs = ["models/model_deploy_test.py"],
+    srcs = ["deployment/model_deploy_test.py"],
     srcs_version = "PY2AND3",
     deps = [":model_deploy"],
 )
 
 py_library(
-    name = "cifar10_preprocessing",
-    srcs = ["models/cifar10_preprocessing.py"],
+    name = "cifarnet_preprocessing",
+    srcs = ["preprocessing/cifarnet_preprocessing.py"],
 )
 
 py_library(
     name = "inception_preprocessing",
-    srcs = ["models/inception_preprocessing.py"],
+    srcs = ["preprocessing/inception_preprocessing.py"],
 )
 
 py_library(
     name = "lenet_preprocessing",
-    srcs = ["models/lenet_preprocessing.py"],
+    srcs = ["preprocessing/lenet_preprocessing.py"],
 )
 
 py_library(
     name = "vgg_preprocessing",
-    srcs = ["models/vgg_preprocessing.py"],
+    srcs = ["preprocessing/vgg_preprocessing.py"],
 )
 
 py_library(
     name = "preprocessing_factory",
-    srcs = ["models/preprocessing_factory.py"],
+    srcs = ["preprocessing/preprocessing_factory.py"],
     deps = [
-        ":cifar10_preprocessing",
+        ":cifarnet_preprocessing",
         ":inception_preprocessing",
         ":lenet_preprocessing",
         ":vgg_preprocessing",
     ],
 )
 
+# Typical networks definitions.
+
+py_library(
+    name = "nets",
+    deps = [
+        ":alexnet",
+        ":cifarnet",
+        ":inception",
+        ":lenet",
+        ":overfeat",
+        ":resnet_v1",
+        ":resnet_v2",
+        ":vgg",
+    ],
+)
+
+py_library(
+    name = "alexnet",
+    srcs = ["nets/alexnet.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_test(
+    name = "alexnet_test",
+    size = "medium",
+    srcs = ["nets/alexnet_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":alexnet"],
+)
+
+py_library(
+    name = "cifarnet",
+    srcs = ["nets/cifarnet.py"],
+)
+
+py_library(
+    name = "inception",
+    srcs = ["nets/inception.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":inception_resnet_v2",
+        ":inception_v1",
+        ":inception_v2",
+        ":inception_v3",
+    ],
+)
+
+py_library(
+    name = "inception_v1",
+    srcs = ["nets/inception_v1.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "inception_v2",
+    srcs = ["nets/inception_v2.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "inception_v3",
+    srcs = ["nets/inception_v3.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "inception_resnet_v2",
+    srcs = ["nets/inception_resnet_v2.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_test(
+    name = "inception_v1_test",
+    size = "large",
+    srcs = ["nets/inception_v1_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [":inception"],
+)
+
+py_test(
+    name = "inception_v2_test",
+    size = "large",
+    srcs = ["nets/inception_v2_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [":inception"],
+)
+
+py_test(
+    name = "inception_v3_test",
+    size = "large",
+    srcs = ["nets/inception_v3_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [":inception"],
+)
+
+py_test(
+    name = "inception_resnet_v2_test",
+    size = "large",
+    srcs = ["nets/inception_resnet_v2_test.py"],
+    shard_count = 3,
+    srcs_version = "PY2AND3",
+    deps = [":inception"],
+)
+
 py_library(
     name = "lenet",
     srcs = ["nets/lenet.py"],
 )
 
 py_library(
-    name = "model_factory",
-    srcs = ["models/model_factory.py"],
-    deps = [":lenet"],
+    name = "overfeat",
+    srcs = ["nets/overfeat.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_test(
+    name = "overfeat_test",
+    size = "medium",
+    srcs = ["nets/overfeat_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":overfeat"],
+)
+
+py_library(
+    name = "resnet_utils",
+    srcs = ["nets/resnet_utils.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_library(
+    name = "resnet_v1",
+    srcs = ["nets/resnet_v1.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":resnet_utils",
+    ],
+)
+
+py_test(
+    name = "resnet_v1_test",
+    size = "medium",
+    srcs = ["nets/resnet_v1_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":resnet_v1"],
+)
+
+py_library(
+    name = "resnet_v2",
+    srcs = ["nets/resnet_v2.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":resnet_utils",
+    ],
+)
+
+py_test(
+    name = "resnet_v2_test",
+    size = "medium",
+    srcs = ["nets/resnet_v2_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":resnet_v2"],
+)
+
+py_library(
+    name = "vgg",
+    srcs = ["nets/vgg.py"],
+    srcs_version = "PY2AND3",
+)
+
+py_test(
+    name = "vgg_test",
+    size = "medium",
+    srcs = ["nets/vgg_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":vgg"],
+)
+
+py_library(
+    name = "nets_factory",
+    srcs = ["nets/nets_factory.py"],
+    deps = [":nets"],
+)
+
+py_test(
+    name = "nets_factory_test",
+    size = "medium",
+    srcs = ["nets/nets_factory_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [":nets_factory"],
+)
+
+py_binary(
+    name = "train_image_classifier",
+    srcs = ["train_image_classifier.py"],
+    deps = [
+        ":dataset_factory",
+        ":model_deploy",
+        ":nets_factory",
+        ":preprocessing_factory",
+    ],
 )
 
 py_binary(
-    name = "train",
-    srcs = ["train.py"],
+    name = "eval_image_classifier",
+    srcs = ["eval_image_classifier.py"],
     deps = [
         ":dataset_factory",
         ":model_deploy",
-        ":model_factory",
+        ":nets_factory",
         ":preprocessing_factory",
     ],
 )

slim/datasets/__init__.py

+

slim/datasets/cifar10.py

@@ -25,7 +25,7 @@ from __future__ import print_function
 import os
 import tensorflow as tf
 
-from slim.datasets import dataset_utils
+from datasets import dataset_utils
 
 slim = tf.contrib.slim
 

slim/datasets/dataset_factory.py

@@ -18,10 +18,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from slim.datasets import cifar10
-from slim.datasets import flowers
-from slim.datasets import imagenet
-from slim.datasets import mnist
+from datasets import cifar10
+from datasets import flowers
+from datasets import imagenet
+from datasets import mnist
 
 datasets_map = {
     'cifar10': cifar10,

slim/datasets/dataset_utils.py

@@ -18,6 +18,10 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+import sys
+import tarfile
+
+from six.moves import urllib
 import tensorflow as tf
 
 LABELS_FILENAME = 'labels.txt'
@@ -59,6 +63,27 @@ def image_to_tfexample(image_data, image_format, height, width, class_id):
   }))
 
 
+def download_and_uncompress_tarball(tarball_url, dataset_dir):
+  """Downloads the `tarball_url` and uncompresses it locally.
+
+  Args:
+    tarball_url: The URL of a tarball file.
+    dataset_dir: The directory where the temporary files are stored.
+  """
+  filename = tarball_url.split('/')[-1]
+  filepath = os.path.join(dataset_dir, filename)
+
+  def _progress(count, block_size, total_size):
+    sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+        filename, float(count * block_size) / float(total_size) * 100.0))
+    sys.stdout.flush()
+  filepath, _ = urllib.request.urlretrieve(tarball_url, filepath, _progress)
+  print()
+  statinfo = os.stat(filepath)
+  print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
+  tarfile.open(filepath, 'r:gz').extractall(dataset_dir)
+
+
 def write_label_file(labels_to_class_names, dataset_dir,
                      filename=LABELS_FILENAME):
   """Writes a file with the list of class names.

slim/datasets/download_and_convert_cifar10.py

@@ -14,16 +14,13 @@
 # ==============================================================================
 r"""Downloads and converts cifar10 data to TFRecords of TF-Example protos.
 
-This script downloads the cifar10 data, uncompresses it, reads the files
+This module downloads the cifar10 data, uncompresses it, reads the files
 that make up the cifar10 data and creates two TFRecord datasets: one for train
 and one for test. Each TFRecord dataset is comprised of a set of TF-Example
 protocol buffers, each of which contain a single image and label.
 
 The script should take several minutes to run.
 
-Usage:
-$ bazel build slim:download_and_convert_cifar10
-$ .bazel-bin/slim/download_and_convert_cifar10 --dataset_dir=[DIRECTORY]
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -38,14 +35,7 @@ import numpy as np
 from six.moves import urllib
 import tensorflow as tf
 
-from slim.datasets import dataset_utils
-
-tf.app.flags.DEFINE_string(
-    'dataset_dir',
-    None,
-    'The directory where the output TFRecords and temporary files are saved.')
-
-FLAGS = tf.app.flags.FLAGS
+from datasets import dataset_utils
 
 # The URL where the CIFAR data can be downloaded.
 _DATA_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
@@ -115,16 +105,17 @@ def _add_to_tfrecord(filename, tfrecord_writer, offset=0):
   return offset + num_images
 
 
-def _get_output_filename(split_name):
+def _get_output_filename(dataset_dir, split_name):
   """Creates the output filename.
 
   Args:
+    dataset_dir: The dataset directory where the dataset is stored.
     split_name: The name of the train/test split.
 
   Returns:
     An absolute file path.
   """
-  return '%s/cifar10_%s.tfrecord' % (FLAGS.dataset_dir, split_name)
+  return '%s/cifar10_%s.tfrecord' % (dataset_dir, split_name)
 
 
 def _download_and_uncompress_dataset(dataset_dir):
@@ -162,39 +153,43 @@ def _clean_up_temporary_files(dataset_dir):
   tf.gfile.DeleteRecursively(tmp_dir)
 
 
-def main(_):
-  if not FLAGS.dataset_dir:
-    raise ValueError('You must supply the dataset directory with --dataset_dir')
+def run(dataset_dir):
+  """Runs the download and conversion operation.
 
-  if not tf.gfile.Exists(FLAGS.dataset_dir):
-    tf.gfile.MakeDirs(FLAGS.dataset_dir)
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
 
-  _download_and_uncompress_dataset(FLAGS.dataset_dir)
+  training_filename = _get_output_filename(dataset_dir, 'train')
+  testing_filename = _get_output_filename(dataset_dir, 'test')
+
+  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
 
   # First, process the training data:
-  output_file = _get_output_filename('train')
-  with tf.python_io.TFRecordWriter(output_file) as tfrecord_writer:
+  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
     offset = 0
     for i in range(_NUM_TRAIN_FILES):
-      filename = os.path.join(FLAGS.dataset_dir,
+      filename = os.path.join(dataset_dir,
                               'cifar-10-batches-py',
                               'data_batch_%d' % (i + 1))  # 1-indexed.
       offset = _add_to_tfrecord(filename, tfrecord_writer, offset)
 
   # Next, process the testing data:
-  output_file = _get_output_filename('test')
-  with tf.python_io.TFRecordWriter(output_file) as tfrecord_writer:
-    filename = os.path.join(FLAGS.dataset_dir,
+  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
+    filename = os.path.join(dataset_dir,
                             'cifar-10-batches-py',
                             'test_batch')
     _add_to_tfrecord(filename, tfrecord_writer)
 
   # Finally, write the labels file:
   labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
-  dataset_utils.write_label_file(labels_to_class_names, FLAGS.dataset_dir)
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
 
-  _clean_up_temporary_files(FLAGS.dataset_dir)
+  _clean_up_temporary_files(dataset_dir)
   print('\nFinished converting the Cifar10 dataset!')
-
-if __name__ == '__main__':
-  tf.app.run()

slim/datasets/download_and_convert_flowers.py

@@ -14,17 +14,13 @@
 # ==============================================================================
 r"""Downloads and converts Flowers data to TFRecords of TF-Example protos.
 
-This script downloads the Flowers data, uncompresses it, reads the files
+This module downloads the Flowers data, uncompresses it, reads the files
 that make up the Flowers data and creates two TFRecord datasets: one for train
 and one for test. Each TFRecord dataset is comprised of a set of TF-Example
 protocol buffers, each of which contain a single image and label.
 
 The script should take about a minute to run.
 
-Usage:
-
-$ bazel build slim:download_and_convert_flowers
-$ .bazel-bin/slim/download_and_convert_flowers --dataset_dir=[DIRECTORY]
 """
 
 from __future__ import absolute_import
@@ -35,19 +31,10 @@ import math
 import os
 import random
 import sys
-import tarfile
 
-from six.moves import urllib
 import tensorflow as tf
 
-from slim.datasets import dataset_utils
-
-tf.app.flags.DEFINE_string(
-    'dataset_dir',
-    None,
-    'The directory where the output TFRecords and temporary files are saved.')
-
-FLAGS = tf.app.flags.FLAGS
+from datasets import dataset_utils
 
 # The URL where the Flowers data can be downloaded.
 _DATA_URL = 'http://download.tensorflow.org/example_images/flower_photos.tgz'
@@ -82,27 +69,6 @@ class ImageReader(object):
     return image
 
 
-def _download_dataset(dataset_dir):
-  """Downloads the flowers data and uncompresses it locally.
-
-  Args:
-    dataset_dir: The directory where the temporary files are stored.
-  """
-  filename = _DATA_URL.split('/')[-1]
-  filepath = os.path.join(dataset_dir, filename)
-
-  if not os.path.exists(filepath):
-    def _progress(count, block_size, total_size):
-      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
-          filename, float(count * block_size) / float(total_size) * 100.0))
-      sys.stdout.flush()
-    filepath, _ = urllib.request.urlretrieve(_DATA_URL, filepath, _progress)
-    print()
-    statinfo = os.stat(filepath)
-    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
-    tarfile.open(filepath, 'r:gz').extractall(dataset_dir)
-
-
 def _get_filenames_and_classes(dataset_dir):
   """Returns a list of filenames and inferred class names.
 
@@ -132,6 +98,12 @@ def _get_filenames_and_classes(dataset_dir):
   return photo_filenames, sorted(class_names)
 
 
+def _get_dataset_filename(dataset_dir, split_name, shard_id):
+  output_filename = 'flowers_%s_%05d-of-%05d.tfrecord' % (
+      split_name, shard_id, _NUM_SHARDS)
+  return os.path.join(dataset_dir, output_filename)
+
+
 def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir):
   """Converts the given filenames to a TFRecord dataset.
 
@@ -152,9 +124,8 @@ def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir):
     with tf.Session('') as sess:
 
       for shard_id in range(_NUM_SHARDS):
-        output_filename = 'flowers_%s_%05d-of-%05d.tfrecord' % (
-            split_name, shard_id, _NUM_SHARDS)
-        output_filename = os.path.join(dataset_dir, output_filename)
+        output_filename = _get_dataset_filename(
+            dataset_dir, split_name, shard_id)
 
         with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
           start_ndx = shard_id * num_per_shard
@@ -193,15 +164,31 @@ def _clean_up_temporary_files(dataset_dir):
   tf.gfile.DeleteRecursively(tmp_dir)
 
 
-def main(_):
-  if not FLAGS.dataset_dir:
-    raise ValueError('You must supply the dataset directory with --dataset_dir')
+def _dataset_exists(dataset_dir):
+  for split_name in ['train', 'validation']:
+    for shard_id in range(_NUM_SHARDS):
+      output_filename = _get_dataset_filename(
+          dataset_dir, split_name, shard_id)
+      if not tf.gfile.Exists(output_filename):
+        return False
+  return True
+
+
+def run(dataset_dir):
+  """Runs the download and conversion operation.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
 
-  if not tf.gfile.Exists(FLAGS.dataset_dir):
-    tf.gfile.MakeDirs(FLAGS.dataset_dir)
+  if _dataset_exists(dataset_dir):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
 
-  _download_dataset(FLAGS.dataset_dir)
-  photo_filenames, class_names = _get_filenames_and_classes(FLAGS.dataset_dir)
+  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
+  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
   class_names_to_ids = dict(zip(class_names, range(len(class_names))))
 
   # Divide into train and test:
@@ -212,16 +199,14 @@ def main(_):
 
   # First, convert the training and validation sets.
   _convert_dataset('train', training_filenames, class_names_to_ids,
-                   FLAGS.dataset_dir)
+                   dataset_dir)
   _convert_dataset('validation', validation_filenames, class_names_to_ids,
-                   FLAGS.dataset_dir)
+                   dataset_dir)
 
   # Finally, write the labels file:
   labels_to_class_names = dict(zip(range(len(class_names)), class_names))
-  dataset_utils.write_label_file(labels_to_class_names, FLAGS.dataset_dir)
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
 
-  _clean_up_temporary_files(FLAGS.dataset_dir)
+  _clean_up_temporary_files(dataset_dir)
   print('\nFinished converting the Flowers dataset!')
 
-if __name__ == '__main__':
-  tf.app.run()

slim/datasets/download_and_convert_mnist.py

@@ -14,17 +14,13 @@
 # ==============================================================================
 r"""Downloads and converts MNIST data to TFRecords of TF-Example protos.
 
-This script downloads the MNIST data, uncompresses it, reads the files
+This module downloads the MNIST data, uncompresses it, reads the files
 that make up the MNIST data and creates two TFRecord datasets: one for train
 and one for test. Each TFRecord dataset is comprised of a set of TF-Example
 protocol buffers, each of which contain a single image and label.
 
 The script should take about a minute to run.
 
-Usage:
-
-$ bazel build slim:download_and_convert_mnist
-$ .bazel-bin/slim/download_and_convert_mnist --dataset_dir=[DIRECTORY]
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -38,14 +34,7 @@ import numpy as np
 from six.moves import urllib
 import tensorflow as tf
 
-from slim.datasets import dataset_utils
-
-tf.app.flags.DEFINE_string(
-    'dataset_dir',
-    None,
-    'The directory where the output TFRecords and temporary files are saved.')
-
-FLAGS = tf.app.flags.FLAGS
+from datasets import dataset_utils
 
 # The URLs where the MNIST data can be downloaded.
 _DATA_URL = 'http://yann.lecun.com/exdb/mnist/'
@@ -140,16 +129,17 @@ def _add_to_tfrecord(data_filename, labels_filename, num_images,
         tfrecord_writer.write(example.SerializeToString())
 
 
-def _get_output_filename(split_name):
+def _get_output_filename(dataset_dir, split_name):
   """Creates the output filename.
 
   Args:
+    dataset_dir: The directory where the temporary files are stored.
     split_name: The name of the train/test split.
 
   Returns:
     An absolute file path.
   """
-  return '%s/mnist_%s.tfrecord' % (FLAGS.dataset_dir, split_name)
+  return '%s/mnist_%s.tfrecord' % (dataset_dir, split_name)
 
 
 def _download_dataset(dataset_dir):
@@ -193,35 +183,39 @@ def _clean_up_temporary_files(dataset_dir):
     tf.gfile.Remove(filepath)
 
 
-def main(_):
-  if not FLAGS.dataset_dir:
-    raise ValueError('You must supply the dataset directory with --dataset_dir')
+def run(dataset_dir):
+  """Runs the download and conversion operation.
 
-  if not tf.gfile.Exists(FLAGS.dataset_dir):
-    tf.gfile.MakeDirs(FLAGS.dataset_dir)
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
+
+  training_filename = _get_output_filename(dataset_dir, 'train')
+  testing_filename = _get_output_filename(dataset_dir, 'test')
 
-  _download_dataset(FLAGS.dataset_dir)
+  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  _download_dataset(dataset_dir)
 
   # First, process the training data:
-  output_file = _get_output_filename('train')
-  with tf.python_io.TFRecordWriter(output_file) as tfrecord_writer:
-    data_filename = os.path.join(FLAGS.dataset_dir, _TRAIN_DATA_FILENAME)
-    labels_filename = os.path.join(FLAGS.dataset_dir, _TRAIN_LABELS_FILENAME)
+  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
+    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
+    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
     _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)
 
   # Next, process the testing data:
-  output_file = _get_output_filename('test')
-  with tf.python_io.TFRecordWriter(output_file) as tfrecord_writer:
-    data_filename = os.path.join(FLAGS.dataset_dir, _TEST_DATA_FILENAME)
-    labels_filename = os.path.join(FLAGS.dataset_dir, _TEST_LABELS_FILENAME)
+  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
+    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
+    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
     _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)
 
   # Finally, write the labels file:
   labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
-  dataset_utils.write_label_file(labels_to_class_names, FLAGS.dataset_dir)
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
 
-  _clean_up_temporary_files(FLAGS.dataset_dir)
+  _clean_up_temporary_files(dataset_dir)
   print('\nFinished converting the MNIST dataset!')
-
-if __name__ == '__main__':
-  tf.app.run()

slim/datasets/flowers.py

@@ -25,7 +25,7 @@ from __future__ import print_function
 import os
 import tensorflow as tf
 
-from slim.datasets import dataset_utils
+from datasets import dataset_utils
 
 slim = tf.contrib.slim
 

slim/datasets/imagenet.py

@@ -33,8 +33,11 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+from six.moves import urllib
 import tensorflow as tf
 
+from datasets import dataset_utils
+
 slim = tf.contrib.slim
 
 # TODO(nsilberman): Add tfrecord file type once the script is updated.
@@ -55,7 +58,61 @@ _ITEMS_TO_DESCRIPTIONS = {
 
 _NUM_CLASSES = 1001
 
-# TODO(nsilberman): Add _LABELS_TO_NAMES
+
+def create_readable_names_for_imagenet_labels():
+  """Create a dict mapping label id to human readable string.
+
+  Returns:
+      labels_to_names: dictionary where keys are integers from to 1000
+      and values are human-readable names.
+
+  We retrieve a synset file, which contains a list of valid synset labels used
+  by ILSVRC competition. There is one synset one per line, eg.
+          #   n01440764
+          #   n01443537
+  We also retrieve a synset_to_human_file, which contains a mapping from synsets
+  to human-readable names for every synset in Imagenet. These are stored in a
+  tsv format, as follows:
+          #   n02119247    black fox
+          #   n02119359    silver fox
+  We assign each synset (in alphabetical order) an integer, starting from 1
+  (since 0 is reserved for the background class).
+
+  Code is based on
+  https://github.com/tensorflow/models/blob/master/inception/inception/data/build_imagenet_data.py#L463
+  """
+
+  # pylint: disable=g-line-too-long
+  base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/inception/inception/data/'
+  synset_url = '{}/imagenet_lsvrc_2015_synsets.txt'.format(base_url)
+  synset_to_human_url = '{}/imagenet_metadata.txt'.format(base_url)
+
+  filename, _ = urllib.request.urlretrieve(synset_url)
+  synset_list = [s.strip() for s in open(filename).readlines()]
+  num_synsets_in_ilsvrc = len(synset_list)
+  assert num_synsets_in_ilsvrc == 1000
+
+  filename, _ = urllib.request.urlretrieve(synset_to_human_url)
+  synset_to_human_list = open(filename).readlines()
+  num_synsets_in_all_imagenet = len(synset_to_human_list)
+  assert num_synsets_in_all_imagenet == 21842
+
+  synset_to_human = {}
+  for s in synset_to_human_list:
+    parts = s.strip().split('\t')
+    assert len(parts) == 2
+    synset = parts[0]
+    human = parts[1]
+    synset_to_human[synset] = human
+
+  label_index = 1
+  labels_to_names = {0: 'background'}
+  for synset in synset_list:
+    name = synset_to_human[synset]
+    labels_to_names[label_index] = name
+    label_index += 1
+
+  return labels_to_names
 
 
 def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
@@ -119,10 +176,18 @@ def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
   decoder = slim.tfexample_decoder.TFExampleDecoder(
       keys_to_features, items_to_handlers)
 
+  labels_to_names = None
+  if dataset_utils.has_labels(dataset_dir):
+    labels_to_names = dataset_utils.read_label_file(dataset_dir)
+  else:
+    labels_to_names = create_readable_names_for_imagenet_labels()
+    dataset_utils.write_label_file(labels_to_names, dataset_dir)
+
   return slim.dataset.Dataset(
       data_sources=file_pattern,
       reader=reader,
       decoder=decoder,
       num_samples=_SPLITS_TO_SIZES[split_name],
       items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
-      num_classes=_NUM_CLASSES)
+      num_classes=_NUM_CLASSES,
+      labels_to_names=labels_to_names)

slim/datasets/mnist.py

@@ -25,7 +25,7 @@ from __future__ import print_function
 import os
 import tensorflow as tf
 
-from slim.datasets import dataset_utils
+from datasets import dataset_utils
 
 slim = tf.contrib.slim
 

slim/deployment/__init__.py

+

slim/models/model_deploy.py → slim/deployment/model_deploy.py

@@ -30,7 +30,7 @@ Usage:
   g = tf.Graph()
 
   # Set up DeploymentConfig
-  config = slim.DeploymentConfig(num_clones=2, clone_on_cpu=True)
+  config = model_deploy.DeploymentConfig(num_clones=2, clone_on_cpu=True)
 
   # Create the global step on the device storing the variables.
   with tf.device(config.variables_device()):
@@ -51,7 +51,8 @@ Usage:
     predictions = CreateNetwork(images)
     slim.losses.log_loss(predictions, labels)
 
-  model_dp = slim.deploy(config, model_fn, [inputs_queue], optimizer=optimizer)
+  model_dp = model_deploy.deploy(config, model_fn, [inputs_queue],
+                                 optimizer=optimizer)
 
   # Run training.
   slim.learning.train(model_dp.train_op, my_log_dir,
@@ -240,7 +241,7 @@ def _gather_clone_loss(clone, num_clones, regularization_losses):
 
 
 def _optimize_clone(optimizer, clone, num_clones, regularization_losses,
-                    kwargs=None):
+                    **kwargs):
   """Compute losses and gradients for a single clone.
 
   Args:
@@ -249,7 +250,7 @@ def _optimize_clone(optimizer, clone, num_clones, regularization_losses,
     num_clones: The number of clones being deployed.
     regularization_losses: Possibly empty list of regularization_losses
       to add to the clone losses.
-    kwargs: Dict of kwarg to pass to compute_gradients().
+    **kwargs: Dict of kwarg to pass to compute_gradients().
 
   Returns:
     A tuple (clone_loss, clone_grads_and_vars).
@@ -267,7 +268,7 @@ def _optimize_clone(optimizer, clone, num_clones, regularization_losses,
 
 def optimize_clones(clones, optimizer,
                     regularization_losses=None,
-                    kwargs=None):
+                    **kwargs):
   """Compute clone losses and gradients for the given list of `Clones`.
 
   Note: The regularization_losses are added to the first clone losses.
@@ -278,7 +279,7 @@ def optimize_clones(clones, optimizer,
    regularization_losses: Optional list of regularization losses. If None it
      will gather them from tf.GraphKeys.REGULARIZATION_LOSSES. Pass `[]` to
      exclude them.
-   kwargs: Optional list of keyword arguments to pass to `compute_gradients`.
+   **kwargs: Optional list of keyword arguments to pass to `compute_gradients`.
 
   Returns:
    A tuple (total_loss, grads_and_vars).
@@ -290,7 +291,6 @@ def optimize_clones(clones, optimizer,
   """
   grads_and_vars = []
   clones_losses = []
-  kwargs = kwargs or {}
   num_clones = len(clones)
   if regularization_losses is None:
     regularization_losses = tf.get_collection(
@@ -298,7 +298,7 @@ def optimize_clones(clones, optimizer,
   for clone in clones:
     with tf.name_scope(clone.scope):
       clone_loss, clone_grad = _optimize_clone(
-          optimizer, clone, num_clones, regularization_losses, kwargs)
+          optimizer, clone, num_clones, regularization_losses, **kwargs)
       if clone_loss is not None:
         clones_losses.append(clone_loss)
         grads_and_vars.append(clone_grad)

slim/models/model_deploy_test.py → slim/deployment/model_deploy_test.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import numpy as np
 import tensorflow as tf
 
-from slim.models import model_deploy
+from deployment import model_deploy
 
 slim = tf.contrib.slim
 

slim/download_and_convert_data.py

+# 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=mnist \
+    --dataset_dir=/tmp/mnist
+
+$ python download_and_convert_data.py \
+    --dataset_name=cifar10 \
+    --dataset_dir=/tmp/cifar10
+
+$ python download_and_convert_data.py \
+    --dataset_name=flowers \
+    --dataset_dir=/tmp/flowers
+```
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from datasets import download_and_convert_cifar10
+from datasets import download_and_convert_flowers
+from datasets import download_and_convert_mnist
+
+FLAGS = tf.app.flags.FLAGS
+
+tf.app.flags.DEFINE_string(
+    'dataset_name',
+    None,
+    'The name of the dataset to convert, one of "cifar10", "flowers", "mnist".')
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir',
+    None,
+    'The directory where the output TFRecords and temporary files are saved.')
+
+
+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 == 'cifar10':
+    download_and_convert_cifar10.run(FLAGS.dataset_dir)
+  elif FLAGS.dataset_name == 'flowers':
+    download_and_convert_flowers.run(FLAGS.dataset_dir)
+  elif FLAGS.dataset_name == 'mnist':
+    download_and_convert_mnist.run(FLAGS.dataset_dir)
+  else:
+    raise ValueError(
+        'dataset_name [%s] was not recognized.' % FLAGS.dataset_dir)
+
+if __name__ == '__main__':
+  tf.app.run()
+

slim/eval.py → slim/eval_image_classifier.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Generic evaluation script that trains a given model a specified dataset."""
+"""Generic evaluation script that evaluates a model using a given dataset."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -21,9 +21,9 @@ from __future__ import print_function
 import math
 import tensorflow as tf
 
-from slim.datasets import dataset_factory
-from slim.models import model_factory
-from slim.models import preprocessing_factory
+from datasets import dataset_factory
+from nets import nets_factory
+from preprocessing import preprocessing_factory
 
 slim = tf.contrib.slim
 
@@ -42,11 +42,6 @@ tf.app.flags.DEFINE_string(
     'The directory where the model was written to or an absolute path to a '
     'checkpoint file.')
 
-tf.app.flags.DEFINE_bool(
-    'restore_global_step', True,
-    'Whether or not to restore the global step. When evaluating a model '
-    'checkpoint containing ONLY weights, set this flag to `False`.')
-
 tf.app.flags.DEFINE_string(
     'eval_dir', '/tmp/tfmodel/', 'Directory where the results are saved to.')
 
@@ -58,11 +53,10 @@ tf.app.flags.DEFINE_string(
     'dataset_name', 'imagenet', 'The name of the dataset to load.')
 
 tf.app.flags.DEFINE_string(
-    'dataset_split_name', 'train', 'The name of the train/test split.')
+    'dataset_split_name', 'test', 'The name of the train/test split.')
 
 tf.app.flags.DEFINE_string(
     'dataset_dir', None, 'The directory where the dataset files are stored.')
-tf.app.flags.MarkFlagAsRequired('dataset_dir')
 
 tf.app.flags.DEFINE_integer(
     'labels_offset', 0,
@@ -82,10 +76,17 @@ tf.app.flags.DEFINE_float(
     'The decay to use for the moving average.'
     'If left as None, then moving averages are not used.')
 
+tf.app.flags.DEFINE_integer(
+    'eval_image_size', None, 'Eval image size')
+
 FLAGS = tf.app.flags.FLAGS
 
 
 def main(_):
+  if not FLAGS.dataset_dir:
+    raise ValueError('You must supply the dataset directory with --dataset_dir')
+
+  tf.logging.set_verbosity(tf.logging.INFO)
   with tf.Graph().as_default():
     tf_global_step = slim.get_or_create_global_step()
 
@@ -98,7 +99,7 @@ def main(_):
     ####################
     # Select the model #
     ####################
-    model_fn = model_factory.get_model(
+    network_fn = nets_factory.get_network_fn(
         FLAGS.model_name,
         num_classes=(dataset.num_classes - FLAGS.labels_offset),
         is_training=False)
@@ -122,9 +123,9 @@ def main(_):
         preprocessing_name,
         is_training=False)
 
-    image = image_preprocessing_fn(image,
-                                   height=model_fn.default_image_size,
-                                   width=model_fn.default_image_size)
+    eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
+
+    image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
 
     images, labels = tf.train.batch(
         [image, label],
@@ -135,19 +136,16 @@ def main(_):
     ####################
     # Define the model #
     ####################
-    logits, _ = model_fn(images)
+    logits, _ = network_fn(images)
 
     if FLAGS.moving_average_decay:
       variable_averages = tf.train.ExponentialMovingAverage(
           FLAGS.moving_average_decay, tf_global_step)
       variables_to_restore = variable_averages.variables_to_restore(
           slim.get_model_variables())
-
-      if FLAGS.restore_global_step:
-        variables_to_restore[tf_global_step.op.name] = tf_global_step
+      variables_to_restore[tf_global_step.op.name] = tf_global_step
     else:
-      exclude = None if FLAGS.restore_global_step else ['global_step']
-      variables_to_restore = slim.get_variables_to_restore(exclude=exclude)
+      variables_to_restore = slim.get_variables_to_restore()
 
     predictions = tf.argmax(logits, 1)
     labels = tf.squeeze(labels)
@@ -181,8 +179,8 @@ def main(_):
     tf.logging.info('Evaluating %s' % checkpoint_path)
 
     slim.evaluation.evaluate_once(
-        FLAGS.master,
-        checkpoint_path,
+        master=FLAGS.master,
+        checkpoint_path=checkpoint_path,
         logdir=FLAGS.eval_dir,
         num_evals=num_batches,
         eval_op=names_to_updates.values(),

slim/models/model_factory.py

-# 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.
-# ==============================================================================
-"""Contains a factory for building various models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-from tensorflow.contrib.slim import nets
-from slim.nets import lenet
-
-slim = tf.contrib.slim
-
-
-def get_model(name, num_classes, weight_decay=0.0, is_training=False):
-  """Returns a model_fn such as `logits, end_points = model_fn(images)`.
-
-  Args:
-    name: The name of the model.
-    num_classes: The number of classes to use for classification.
-    weight_decay: The l2 coefficient for the model weights.
-    is_training: `True` if the model is being used for training and `False`
-      otherwise.
-
-  Returns:
-    model_fn: A function that applies the model to a batch of images. It has
-      the following signature:
-        logits, end_points = model_fn(images)
-  Raises:
-    ValueError: If model `name` is not recognized.
-  """
-  if name == 'inception_v1':
-    default_image_size = nets.inception.inception_v1.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.inception.inception_v1_arg_scope(
-          weight_decay=weight_decay)):
-        return nets.inception.inception_v1(images,
-                                           num_classes,
-                                           is_training=is_training)
-    model_fn = func
-  elif name == 'inception_v2':
-    default_image_size = nets.inception.inception_v2.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.inception.inception_v2_arg_scope(
-          weight_decay=weight_decay)):
-        return nets.inception.inception_v2(images,
-                                           num_classes=num_classes,
-                                           is_training=is_training)
-    model_fn = func
-  elif name == 'inception_v3':
-    default_image_size = nets.inception.inception_v3.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.inception.inception_v3_arg_scope(
-          weight_decay=weight_decay)):
-        return nets.inception.inception_v3(images,
-                                           num_classes=num_classes,
-                                           is_training=is_training)
-    model_fn = func
-  elif name == 'lenet':
-    default_image_size = lenet.lenet.default_image_size
-    def func(images):
-      with slim.arg_scope(lenet.lenet_arg_scope(weight_decay=weight_decay)):
-        return lenet.lenet(images,
-                           num_classes=num_classes,
-                           is_training=is_training)
-    model_fn = func
-  elif name == 'resnet_v1_50':
-    default_image_size = nets.resnet_v1.resnet_v1.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.resnet_v1.resnet_arg_scope(
-          is_training, weight_decay=weight_decay)):
-        net, end_points = nets.resnet_v1.resnet_v1_50(
-            images, num_classes=num_classes)
-        net = tf.squeeze(net, squeeze_dims=[1, 2])
-        return net, end_points
-    model_fn = func
-  elif name == 'resnet_v1_101':
-    default_image_size = nets.resnet_v1.resnet_v1.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.resnet_v1.resnet_arg_scope(
-          is_training, weight_decay=weight_decay)):
-        net, end_points = nets.resnet_v1.resnet_v1_101(
-            images, num_classes=num_classes)
-        net = tf.squeeze(net, squeeze_dims=[1, 2])
-        return net, end_points
-    model_fn = func
-  elif name == 'resnet_v1_152':
-    default_image_size = nets.resnet_v1.resnet_v1.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.resnet_v1.resnet_arg_scope(
-          is_training, weight_decay=weight_decay)):
-        net, end_points = nets.resnet_v1.resnet_v1_152(
-            images, num_classes=num_classes)
-        net = tf.squeeze(net, squeeze_dims=[1, 2])
-        return net, end_points
-    model_fn = func
-  elif name == 'vgg_a':
-    default_image_size = nets.vgg.vgg_a.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.vgg.vgg_arg_scope(weight_decay)):
-        return nets.vgg.vgg_a(images,
-                              num_classes=num_classes,
-                              is_training=is_training)
-    model_fn = func
-  elif name == 'vgg_16':
-    default_image_size = nets.vgg.vgg_16.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.vgg.vgg_arg_scope(weight_decay)):
-        return nets.vgg.vgg_16(images,
-                               num_classes=num_classes,
-                               is_training=is_training)
-    model_fn = func
-  elif name == 'vgg_19':
-    default_image_size = nets.vgg.vgg_19.default_image_size
-    def func(images):
-      with slim.arg_scope(nets.vgg.vgg_arg_scope(weight_decay)):
-        return nets.vgg.vgg_19(images,
-                               num_classes=num_classes,
-                               is_training=is_training)
-    model_fn = func
-  else:
-    raise ValueError('Model name [%s] was not recognized' % name)
-
-  model_fn.default_image_size = default_image_size
-
-  return model_fn

slim/models/resnet_preprocessing.py

-# 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.
-# ==============================================================================
-"""Provides utilities to preprocess images for the ResNet networks."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-from tensorflow.contrib.slim import nets
-from tensorflow.python.ops import control_flow_ops
-
-slim = tf.contrib.slim
-
-_R_MEAN = 123.68
-_G_MEAN = 116.78
-_B_MEAN = 103.94
-
-_CROP_HEIGHT = nets.resnet_v1.resnet_v1.default_image_size
-_CROP_WIDTH = nets.resnet_v1.resnet_v1.default_image_size
-_RESIZE_SIDE = 256
-
-
-def _mean_image_subtraction(image, means):
-  """Subtracts the given means from each image channel.
-
-  For example:
-    means = [123.68, 116.779, 103.939]
-    image = _mean_image_subtraction(image, means)
-
-  Note that the rank of `image` must be known.
-
-  Args:
-    image: a tensor of size [height, width, C].
-    means: a C-vector of values to subtract from each channel.
-
-  Returns:
-    the centered image.
-
-  Raises:
-    ValueError: If the rank of `image` is unknown, if `image` has a rank other
-      than three or if the number of channels in `image` doesn't match the
-      number of values in `means`.
-  """
-  if image.get_shape().ndims != 3:
-    raise ValueError('Input must be of size [height, width, C>0]')
-  num_channels = image.get_shape().as_list()[-1]
-  if len(means) != num_channels:
-    raise ValueError('len(means) must match the number of channels')
-
-  channels = tf.split(2, num_channels, image)
-  for i in range(num_channels):
-    channels[i] -= means[i]
-  return tf.concat(2, channels)
-
-
-def _smallest_size_at_least(height, width, smallest_side):
-  """Computes new shape with the smallest side equal to `smallest_side`.
-
-  Computes new shape with the smallest side equal to `smallest_side` while
-  preserving the original aspect ratio.
-
-  Args:
-    height: an int32 scalar tensor indicating the current height.
-    width: an int32 scalar tensor indicating the current width.
-    smallest_side: an python integer indicating the smallest side of the new
-      shape.
-
-  Returns:
-    new_height: an int32 scalar tensor indicating the new height.
-    new_width: and int32 scalar tensor indicating the new width.
-  """
-  height = tf.to_float(height)
-  width = tf.to_float(width)
-  smallest_side = float(smallest_side)
-  scale = tf.cond(tf.greater(height, width),
-                  lambda: smallest_side / width,
-                  lambda: smallest_side / height)
-  new_height = tf.to_int32(height * scale)
-  new_width = tf.to_int32(width * scale)
-  return new_height, new_width
-
-
-def _aspect_preserving_resize(image, smallest_side):
-  """Resize images preserving the original aspect ratio.
-
-  Args:
-    image: a 3-D image tensor.
-    smallest_side: a python integer indicating the size of the smallest side
-      after resize.
-
-  Returns:
-    resized_image: a 3-D tensor containing the resized image.
-  """
-  shape = tf.shape(image)
-  height = shape[0]
-  width = shape[1]
-  new_height, new_width = _smallest_size_at_least(height, width, smallest_side)
-  image = tf.expand_dims(image, 0)
-  resized_image = tf.image.resize_bilinear(image, [new_height, new_width],
-                                           align_corners=False)
-  resized_image = tf.squeeze(resized_image)
-  resized_image.set_shape([None, None, 3])
-  return resized_image
-
-
-def _crop(image, offset_height, offset_width, crop_height, crop_width):
-  """Crops the given image using the provided offsets and sizes.
-
-  Note that the method doesn't assume we know the input image size but it does
-  assume we know the input image rank.
-
-  Args:
-    image: an image of shape [height, width, channels].
-    offset_height: a scalar tensor indicating the height offset.
-    offset_width: a scalar tensor indicating the width offset.
-    crop_height: the height of the cropped image.
-    crop_width: the width of the cropped image.
-
-  Returns:
-    the cropped (and resized) image.
-
-  Raises:
-    InvalidArgumentError: if the rank is not 3 or if the image dimensions are
-      less than the crop size.
-  """
-  original_shape = tf.shape(image)
-
-  rank_assertion = tf.Assert(
-      tf.equal(tf.rank(image), 3),
-      ['Rank of image must be equal to 3.'])
-  cropped_shape = control_flow_ops.with_dependencies(
-      [rank_assertion],
-      tf.pack([crop_height, crop_width, original_shape[2]]))
-
-  size_assertion = tf.Assert(
-      tf.logical_and(
-          tf.greater_equal(original_shape[0], crop_height),
-          tf.greater_equal(original_shape[1], crop_width)),
-      ['Crop size greater than the image size.'])
-
-  offsets = tf.to_int32(tf.pack([offset_height, offset_width, 0]))
-
-  # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
-  # define the crop size.
-  image = control_flow_ops.with_dependencies(
-      [size_assertion],
-      tf.slice(image, offsets, cropped_shape))
-  return tf.reshape(image, cropped_shape)
-
-
-def _central_crop(image_list, crop_height, crop_width):
-  """Performs central crops of the given image list.
-
-  Args:
-    image_list: a list of image tensors of the same dimension but possibly
-      varying channel.
-    crop_height: the height of the image following the crop.
-    crop_width: the width of the image following the crop.
-
-  Returns:
-    the list of cropped images.
-  """
-  outputs = []
-  for image in image_list:
-    image_height = tf.shape(image)[0]
-    image_width = tf.shape(image)[1]
-
-    offset_height = (image_height - crop_height) / 2
-    offset_width = (image_width - crop_width) / 2
-
-    outputs.append(_crop(image, offset_height, offset_width,
-                         crop_height, crop_width))
-  return outputs
-
-
-def preprocess_image(image,
-                     height=_CROP_HEIGHT,
-                     width=_CROP_WIDTH,
-                     is_training=False,  # pylint: disable=unused-argument
-                     resize_side=_RESIZE_SIDE):
-  image = _aspect_preserving_resize(image, resize_side)
-  image = _central_crop([image], height, width)[0]
-  image.set_shape([height, width, 3])
-  image = tf.to_float(image)
-  image = _mean_image_subtraction(image, [_R_MEAN, _G_MEAN, _B_MEAN])
-  return image

slim/nets/__init__.py

+