Glint everything (#3654)

* Glint everything

* Adding rcfile and pylinting

* Extra newline

* Few last lints

official/__init__.py

-# 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.
-# ==============================================================================
\ No newline at end of file

official/mnist/dataset.py

@@ -17,9 +17,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import gzip
 import os
 import shutil
-import gzip
 
 import numpy as np
 from six.moves import urllib
@@ -36,7 +36,7 @@ def check_image_file_header(filename):
   """Validate that filename corresponds to images for the MNIST dataset."""
   with tf.gfile.Open(filename, 'rb') as f:
     magic = read32(f)
-    num_images = read32(f)
+    read32(f)  # num_images, unused
     rows = read32(f)
     cols = read32(f)
     if magic != 2051:
@@ -52,7 +52,7 @@ def check_labels_file_header(filename):
   """Validate that filename corresponds to labels for the MNIST dataset."""
   with tf.gfile.Open(filename, 'rb') as f:
     magic = read32(f)
-    num_items = read32(f)
+    read32(f)  # num_items, unused
     if magic != 2049:
       raise ValueError('Invalid magic number %d in MNIST file %s' % (magic,
                                                                      f.name))
@@ -77,6 +77,8 @@ def download(directory, filename):
 
 
 def dataset(directory, images_file, labels_file):
+  """Download and parse MNIST dataset."""
+
   images_file = download(directory, images_file)
   labels_file = download(directory, labels_file)
 

official/mnist/mnist.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 import argparse
 import sys
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.mnist import dataset
 from official.utils.arg_parsers import parsers
@@ -28,6 +28,7 @@ from official.utils.logging import hooks_helper
 
 LEARNING_RATE = 1e-4
 
+
 class Model(tf.keras.Model):
   """Model to recognize digits in the MNIST dataset.
 
@@ -145,14 +146,19 @@ def model_fn(features, labels, mode, params):
 
 
 def validate_batch_size_for_multi_gpu(batch_size):
-  """For multi-gpu, batch-size must be a multiple of the number of
-  available GPUs.
+  """For multi-gpu, batch-size must be a multiple of the number of GPUs.
 
   Note that this should eventually be handled by replicate_model_fn
   directly. Multi-GPU support is currently experimental, however,
   so doing the work here until that feature is in place.
+
+  Args:
+    batch_size: the number of examples processed in each training batch.
+
+  Raises:
+    ValueError: if no GPUs are found, or selected batch_size is invalid.
   """
-  from tensorflow.python.client import device_lib
+  from tensorflow.python.client import device_lib  # pylint: disable=g-import-not-at-top
 
   local_device_protos = device_lib.list_local_devices()
   num_gpus = sum([1 for d in local_device_protos if d.device_type == 'GPU'])
@@ -169,7 +175,7 @@ def validate_batch_size_for_multi_gpu(batch_size):
     raise ValueError(err)
 
 
-def main(unused_argv):
+def main(_):
   model_function = model_fn
 
   if FLAGS.multi_gpu:
@@ -195,6 +201,8 @@ def main(unused_argv):
 
   # Set up training and evaluation input functions.
   def train_input_fn():
+    """Prepare data for training."""
+
     # When choosing shuffle buffer sizes, larger sizes result in better
     # randomness, while smaller sizes use less memory. MNIST is a small
     # enough dataset that we can easily shuffle the full epoch.
@@ -215,7 +223,7 @@ def main(unused_argv):
       FLAGS.hooks, batch_size=FLAGS.batch_size)
 
   # Train and evaluate model.
-  for n in range(FLAGS.train_epochs // FLAGS.epochs_between_evals):
+  for _ in range(FLAGS.train_epochs // FLAGS.epochs_between_evals):
     mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
     eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
     print('\nEvaluation results:\n\t%s\n' % eval_results)
@@ -231,6 +239,7 @@ def main(unused_argv):
 
 class MNISTArgParser(argparse.ArgumentParser):
   """Argument parser for running MNIST model."""
+
   def __init__(self):
     super(MNISTArgParser, self).__init__(parents=[
         parsers.BaseParser(),

official/mnist/mnist_eager.py

@@ -31,11 +31,11 @@ import os
 import sys
 import time
 
-import tensorflow as tf
-import tensorflow.contrib.eager as tfe
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+import tensorflow.contrib.eager as tfe  # pylint: disable=g-bad-import-order
 
+from official.mnist import dataset as mnist_dataset
 from official.mnist import mnist
-from official.mnist import dataset
 from official.utils.arg_parsers import parsers
 
 FLAGS = None
@@ -110,9 +110,9 @@ def main(_):
   print('Using device %s, and data format %s.' % (device, data_format))
 
   # Load the datasets
-  train_ds = dataset.train(FLAGS.data_dir).shuffle(60000).batch(
+  train_ds = mnist_dataset.train(FLAGS.data_dir).shuffle(60000).batch(
       FLAGS.batch_size)
-  test_ds = dataset.test(FLAGS.data_dir).batch(FLAGS.batch_size)
+  test_ds = mnist_dataset.test(FLAGS.data_dir).batch(FLAGS.batch_size)
 
   # Create the model and optimizer
   model = mnist.Model(data_format)
@@ -159,11 +159,12 @@ def main(_):
 
 
 class MNISTEagerArgParser(argparse.ArgumentParser):
-  """Argument parser for running MNIST model with eager trainng loop."""
+  """Argument parser for running MNIST model with eager training loop."""
+
   def __init__(self):
     super(MNISTEagerArgParser, self).__init__(parents=[
-      parsers.BaseParser(epochs_between_evals=False, multi_gpu=False,
-                         hooks=False),
+        parsers.BaseParser(
+            epochs_between_evals=False, multi_gpu=False, hooks=False),
         parsers.ImageModelParser()])
 
     self.add_argument(

official/mnist/mnist_eager_test.py

@@ -17,8 +17,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow as tf
-import tensorflow.contrib.eager as tfe
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+import tensorflow.contrib.eager as tfe  # pylint: disable=g-bad-import-order
 
 from official.mnist import mnist
 from official.mnist import mnist_eager
@@ -60,6 +60,7 @@ def evaluate(defun=False):
 
 
 class MNISTTest(tf.test.TestCase):
+  """Run tests for MNIST eager loop."""
 
   def test_train(self):
     train(defun=False)

official/mnist/mnist_test.py

@@ -17,9 +17,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow as tf
 import time
 
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+
 from official.mnist import mnist
 
 BATCH_SIZE = 100
@@ -42,6 +43,7 @@ def make_estimator():
 
 
 class Tests(tf.test.TestCase):
+  """Run tests for MNIST model."""
 
   def test_mnist(self):
     classifier = make_estimator()
@@ -57,7 +59,7 @@ class Tests(tf.test.TestCase):
 
     input_fn = lambda: tf.random_uniform([3, 784])
     predictions_generator = classifier.predict(input_fn)
-    for i in range(3):
+    for _ in range(3):
       predictions = next(predictions_generator)
       self.assertEqual(predictions['probabilities'].shape, (10,))
       self.assertEqual(predictions['classes'].shape, ())
@@ -103,6 +105,7 @@ class Tests(tf.test.TestCase):
 
 
 class Benchmarks(tf.test.Benchmark):
+  """Simple speed benchmarking for MNIST."""
 
   def benchmark_train_step_time(self):
     classifier = make_estimator()

official/mnist/mnist_tpu.py

@@ -23,7 +23,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+
 from official.mnist import dataset
 from official.mnist import mnist
 

official/resnet/cifar10_download_and_extract.py

@@ -36,7 +36,7 @@ parser.add_argument(
     help='Directory to download data and extract the tarball')
 
 
-def main(unused_argv):
+def main(_):
   """Download and extract the tarball from Alex's website."""
   if not os.path.exists(FLAGS.data_dir):
     os.makedirs(FLAGS.data_dir)

official/resnet/cifar10_main.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import os
 import sys
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.resnet import resnet_model
 from official.resnet import resnet_run_loop
@@ -127,19 +127,22 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1,
 
   num_images = is_training and _NUM_IMAGES['train'] or _NUM_IMAGES['validation']
 
-  return resnet_run_loop.process_record_dataset(dataset, is_training, batch_size,
-                                                _NUM_IMAGES['train'], parse_record, num_epochs, num_parallel_calls,
+  return resnet_run_loop.process_record_dataset(
+      dataset, is_training, batch_size, _NUM_IMAGES['train'],
+      parse_record, num_epochs, num_parallel_calls,
       examples_per_epoch=num_images, multi_gpu=multi_gpu)
 
 
 def get_synth_input_fn():
-  return resnet_run_loop.get_synth_input_fn(_HEIGHT, _WIDTH, _NUM_CHANNELS, _NUM_CLASSES)
+  return resnet_run_loop.get_synth_input_fn(
+      _HEIGHT, _WIDTH, _NUM_CHANNELS, _NUM_CLASSES)
 
 
 ###############################################################################
 # Running the model
 ###############################################################################
 class Cifar10Model(resnet_model.Model):
+  """Model class with appropriate defaults for CIFAR-10 data."""
 
   def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
                version=resnet_model.DEFAULT_VERSION):
@@ -153,6 +156,9 @@ class Cifar10Model(resnet_model.Model):
         enables users to extend the same model to their own datasets.
       version: Integer representing which version of the ResNet network to use.
         See README for details. Valid values: [1, 2]
+
+    Raises:
+      ValueError: if invalid resnet_size is chosen
     """
     if resnet_size % 6 != 2:
       raise ValueError('resnet_size must be 6n + 2:', resnet_size)
@@ -195,7 +201,7 @@ def cifar10_model_fn(features, labels, mode, params):
   # for the CIFAR-10 dataset, perhaps because the regularization prevents
   # overfitting on the small data set. We therefore include all vars when
   # regularizing and computing loss during training.
-  def loss_filter_fn(name):
+  def loss_filter_fn(_):
     return True
 
   return resnet_run_loop.resnet_model_fn(features, labels, mode, Cifar10Model,

official/resnet/cifar10_test.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 from tempfile import mkstemp
 
 import numpy as np
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.resnet import cifar10_main
 from official.utils.testing import integration
@@ -34,6 +34,8 @@ _NUM_CHANNELS = 3
 
 
 class BaseTest(tf.test.TestCase):
+  """Tests for the Cifar10 version of Resnet.
+  """
 
   def tearDown(self):
     super(BaseTest, self).tearDown()
@@ -52,7 +54,7 @@ class BaseTest(tf.test.TestCase):
     data_file.close()
 
     fake_dataset = tf.data.FixedLengthRecordDataset(
-        filename, cifar10_main._RECORD_BYTES)
+        filename, cifar10_main._RECORD_BYTES)  # pylint: disable=protected-access
     fake_dataset = fake_dataset.map(
         lambda val: cifar10_main.parse_record(val, False))
     image, label = fake_dataset.make_one_shot_iterator().get_next()
@@ -133,9 +135,11 @@ class BaseTest(tf.test.TestCase):
     num_classes = 246
 
     for version in (1, 2):
-      model = cifar10_main.Cifar10Model(32, data_format='channels_last',
-                                     num_classes=num_classes, version=version)
-      fake_input = tf.random_uniform([batch_size, _HEIGHT, _WIDTH, _NUM_CHANNELS])
+      model = cifar10_main.Cifar10Model(
+          32, data_format='channels_last', num_classes=num_classes,
+          version=version)
+      fake_input = tf.random_uniform(
+          [batch_size, _HEIGHT, _WIDTH, _NUM_CHANNELS])
       output = model(fake_input, training=True)
 
       self.assertAllEqual(output.shape, (batch_size, num_classes))

official/resnet/imagenet_main.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import os
 import sys
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.resnet import imagenet_preprocessing
 from official.resnet import resnet_model
@@ -157,6 +157,7 @@ def parse_record(raw_record, is_training):
 def input_fn(is_training, data_dir, batch_size, num_epochs=1,
              num_parallel_calls=1, multi_gpu=False):
   """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.
@@ -199,6 +200,7 @@ def get_synth_input_fn():
 # 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,
                version=resnet_model.DEFAULT_VERSION):
@@ -241,9 +243,20 @@ class ImagenetModel(resnet_model.Model):
 
 
 def _get_block_sizes(resnet_size):
-  """The number of block layers used for the Resnet model varies according
+  """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],

official/resnet/imagenet_preprocessing.py

@@ -204,8 +204,10 @@ def _aspect_preserving_resize(image, resize_min):
 
 
 def _resize_image(image, height, width):
-  """Simple wrapper around tf.resize_images to make sure we use the same
-  `method` and other details each time.
+  """Simple wrapper around tf.resize_images.
+
+  This is primarily to make sure we use the same `ResizeMethod` and other
+  details each time.
 
   Args:
     image: A 3-D image `Tensor`.
@@ -220,6 +222,7 @@ def _resize_image(image, height, width):
       image, [height, width], method=tf.image.ResizeMethod.BILINEAR,
       align_corners=False)
 
+
 def preprocess_image(image_buffer, bbox, output_height, output_width,
                      num_channels, is_training=False):
   """Preprocesses the given image.

official/resnet/imagenet_test.py

@@ -19,7 +19,7 @@ from __future__ import print_function
 
 import unittest
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.resnet import imagenet_main
 from official.utils.testing import integration
@@ -39,9 +39,8 @@ class BaseTest(tf.test.TestCase):
   def tensor_shapes_helper(self, resnet_size, version, with_gpu=False):
     """Checks the tensor shapes after each phase of the ResNet model."""
     def reshape(shape):
-      """Returns the expected dimensions depending on if a
-      GPU is being used.
-      """
+      """Returns the expected dimensions depending on if a GPU is being used."""
+
       # If a GPU is used for the test, the shape is returned (already in NCHW
       # form). When GPU is not used, the shape is converted to NHWC.
       if with_gpu:
@@ -240,8 +239,9 @@ class BaseTest(tf.test.TestCase):
     num_classes = 246
 
     for version in (1, 2):
-      model = imagenet_main.ImagenetModel(50, data_format='channels_last',
-                                      num_classes=num_classes, version=version)
+      model = imagenet_main.ImagenetModel(
+          50, data_format='channels_last', num_classes=num_classes,
+          version=version)
       fake_input = tf.random_uniform([batch_size, 224, 224, 3])
       output = model(fake_input, training=True)
 
@@ -285,4 +285,3 @@ class BaseTest(tf.test.TestCase):
 
 if __name__ == '__main__':
   tf.test.main()
-

official/resnet/resnet_model.py

@@ -99,7 +99,8 @@ def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
 ################################################################################
 def _building_block_v1(inputs, filters, training, projection_shortcut, strides,
                        data_format):
-  """
+  """A single block for ResNet v1, without a bottleneck.
+
   Convolution then batch normalization then ReLU as described by:
     Deep Residual Learning for Image Recognition
     https://arxiv.org/pdf/1512.03385.pdf
@@ -118,7 +119,7 @@ def _building_block_v1(inputs, filters, training, projection_shortcut, strides,
     data_format: The input format ('channels_last' or 'channels_first').
 
   Returns:
-    The output tensor of the block.
+    The output tensor of the block; shape should match inputs.
   """
   shortcut = inputs
 
@@ -145,7 +146,8 @@ def _building_block_v1(inputs, filters, training, projection_shortcut, strides,
 
 def _building_block_v2(inputs, filters, training, projection_shortcut, strides,
                        data_format):
-  """
+  """A single block for ResNet v2, without a bottleneck.
+
   Batch normalization then ReLu then convolution as described by:
     Identity Mappings in Deep Residual Networks
     https://arxiv.org/pdf/1603.05027.pdf
@@ -164,7 +166,7 @@ def _building_block_v2(inputs, filters, training, projection_shortcut, strides,
     data_format: The input format ('channels_last' or 'channels_first').
 
   Returns:
-    The output tensor of the block.
+    The output tensor of the block; shape should match inputs.
   """
   shortcut = inputs
   inputs = batch_norm(inputs, training, data_format)
@@ -190,13 +192,29 @@ def _building_block_v2(inputs, filters, training, projection_shortcut, strides,
 
 def _bottleneck_block_v1(inputs, filters, training, projection_shortcut,
                          strides, data_format):
-  """
+  """A single block for ResNet v1, with a bottleneck.
+
   Similar to _building_block_v1(), except using the "bottleneck" blocks
   described in:
     Convolution then batch normalization then ReLU as described by:
       Deep Residual Learning for Image Recognition
       https://arxiv.org/pdf/1512.03385.pdf
       by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
+
+  Args:
+    inputs: A tensor of size [batch, channels, height_in, width_in] or
+      [batch, height_in, width_in, channels] depending on data_format.
+    filters: The number of filters for the convolutions.
+    training: A Boolean for whether the model is in training or inference
+      mode. Needed for batch normalization.
+    projection_shortcut: The function to use for projection shortcuts
+      (typically a 1x1 convolution when downsampling the input).
+    strides: The block's stride. If greater than 1, this block will ultimately
+      downsample the input.
+    data_format: The input format ('channels_last' or 'channels_first').
+
+  Returns:
+    The output tensor of the block; shape should match inputs.
   """
   shortcut = inputs
 
@@ -229,7 +247,8 @@ def _bottleneck_block_v1(inputs, filters, training, projection_shortcut,
 
 def _bottleneck_block_v2(inputs, filters, training, projection_shortcut,
                          strides, data_format):
-  """
+  """A single block for ResNet v2, without a bottleneck.
+
   Similar to _building_block_v2(), except using the "bottleneck" blocks
   described in:
     Convolution then batch normalization then ReLU as described by:
@@ -237,11 +256,26 @@ def _bottleneck_block_v2(inputs, filters, training, projection_shortcut,
       https://arxiv.org/pdf/1512.03385.pdf
       by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Dec 2015.
 
-  adapted to the ordering conventions of:
+  Adapted to the ordering conventions of:
     Batch normalization then ReLu then convolution as described by:
       Identity Mappings in Deep Residual Networks
       https://arxiv.org/pdf/1603.05027.pdf
       by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, Jul 2016.
+
+  Args:
+    inputs: A tensor of size [batch, channels, height_in, width_in] or
+      [batch, height_in, width_in, channels] depending on data_format.
+    filters: The number of filters for the convolutions.
+    training: A Boolean for whether the model is in training or inference
+      mode. Needed for batch normalization.
+    projection_shortcut: The function to use for projection shortcuts
+      (typically a 1x1 convolution when downsampling the input).
+    strides: The block's stride. If greater than 1, this block will ultimately
+      downsample the input.
+    data_format: The input format ('channels_last' or 'channels_first').
+
+  Returns:
+    The output tensor of the block; shape should match inputs.
   """
   shortcut = inputs
   inputs = batch_norm(inputs, training, data_format)
@@ -313,8 +347,7 @@ def block_layer(inputs, filters, bottleneck, block_fn, blocks, strides,
 
 
 class Model(object):
-  """Base class for building the Resnet Model.
-  """
+  """Base class for building the Resnet Model."""
 
   def __init__(self, resnet_size, bottleneck, num_classes, num_filters,
                kernel_size,
@@ -348,6 +381,9 @@ class Model(object):
         See README for details. Valid values: [1, 2]
       data_format: Input format ('channels_last', 'channels_first', or None).
         If set to None, the format is dependent on whether a GPU is available.
+
+    Raises:
+      ValueError: if invalid version is selected.
     """
     self.resnet_size = resnet_size
 
@@ -358,7 +394,7 @@ class Model(object):
     self.resnet_version = version
     if version not in (1, 2):
       raise ValueError(
-          "Resnet version should be 1 or 2. See README for citations.")
+          'Resnet version should be 1 or 2. See README for citations.')
 
     self.bottleneck = bottleneck
     if bottleneck:
@@ -435,4 +471,3 @@ class Model(object):
     inputs = tf.layers.dense(inputs=inputs, units=self.num_classes)
     inputs = tf.identity(inputs, 'final_dense')
     return inputs
-

official/resnet/resnet_run_loop.py

@@ -26,11 +26,11 @@ from __future__ import print_function
 import argparse
 import os
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
-from official.utils.arg_parsers import parsers  # pylint: disable=g-bad-import-order
-from official.utils.logging import hooks_helper
 from official.resnet import resnet_model
+from official.utils.arg_parsers import parsers
+from official.utils.logging import hooks_helper
 
 
 ################################################################################
@@ -39,8 +39,7 @@ from official.resnet import resnet_model
 def process_record_dataset(dataset, is_training, batch_size, shuffle_buffer,
                            parse_record_fn, num_epochs=1, num_parallel_calls=1,
                            examples_per_epoch=0, multi_gpu=False):
-  """Given a Dataset with raw records, parse each record into images and labels,
-  and return an iterator over the records.
+  """Given a Dataset with raw records, return an iterator over the records.
 
   Args:
     dataset: A Dataset representing raw records
@@ -121,7 +120,7 @@ def get_synth_input_fn(height, width, num_channels, num_classes):
     An input_fn that can be used in place of a real one to return a dataset
     that can be used for iteration.
   """
-  def input_fn(is_training, data_dir, batch_size, *args):
+  def input_fn(is_training, data_dir, batch_size, *args):  # pylint: disable=unused-argument
     images = tf.zeros((batch_size, height, width, num_channels), tf.float32)
     labels = tf.zeros((batch_size, num_classes), tf.int32)
     return tf.data.Dataset.from_tensors((images, labels)).repeat()
@@ -231,9 +230,9 @@ def resnet_model_fn(features, labels, mode, model_class,
 
   # If no loss_filter_fn is passed, assume we want the default behavior,
   # which is that batch_normalization variables are excluded from loss.
-  if not loss_filter_fn:
-    def loss_filter_fn(name):
+  def exclude_batch_norm(name):
     return 'batch_normalization' not in name
+  loss_filter_fn = loss_filter_fn or exclude_batch_norm
 
   # Add weight decay to the loss.
   loss = cross_entropy + weight_decay * tf.add_n(
@@ -279,14 +278,19 @@ def resnet_model_fn(features, labels, mode, model_class,
 
 
 def validate_batch_size_for_multi_gpu(batch_size):
-  """For multi-gpu, batch-size must be a multiple of the number of
-  available GPUs.
+  """For multi-gpu, batch-size must be a multiple of the number of GPUs.
 
   Note that this should eventually be handled by replicate_model_fn
   directly. Multi-GPU support is currently experimental, however,
   so doing the work here until that feature is in place.
+
+  Args:
+    batch_size: the number of examples processed in each training batch.
+
+  Raises:
+    ValueError: if no GPUs are found, or selected batch_size is invalid.
   """
-  from tensorflow.python.client import device_lib
+  from tensorflow.python.client import device_lib  # pylint: disable=g-import-not-at-top
 
   local_device_protos = device_lib.list_local_devices()
   num_gpus = sum([1 for d in local_device_protos if d.device_type == 'GPU'])
@@ -304,6 +308,8 @@ def validate_batch_size_for_multi_gpu(batch_size):
 
 
 def resnet_main(flags, model_function, input_function):
+  """Shared main loop for ResNet Models."""
+
   # Using the Winograd non-fused algorithms provides a small performance boost.
   os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
 
@@ -340,8 +346,8 @@ def resnet_main(flags, model_function, input_function):
       })
 
   for _ in range(flags.train_epochs // flags.epochs_between_evals):
-    train_hooks = hooks_helper.get_train_hooks(flags.hooks,
-                                               batch_size=flags.batch_size)
+    train_hooks = hooks_helper.get_train_hooks(
+        flags.hooks, batch_size=flags.batch_size)
 
     print('Starting a training cycle.')
 
@@ -384,7 +390,7 @@ class ResnetArgParser(argparse.ArgumentParser):
     self.add_argument(
         '--version', '-v', type=int, choices=[1, 2],
         default=resnet_model.DEFAULT_VERSION,
-        help="Version of ResNet. (1 or 2) See README.md for details."
+        help='Version of ResNet. (1 or 2) See README.md for details.'
     )
 
     self.add_argument(

official/utils/__init__.py

-# 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.
-# ==============================================================================
\ No newline at end of file

official/utils/arg_parsers/parsers.py

@@ -46,11 +46,11 @@ Notes about add_argument():
     The metavar variable determines how the flag will appear in help text. If
   not specified, the convention is to use name.upper(). Thus rather than:
 
-    --application_specific_arg APPLICATION_SPECIFIC_ARG, -asa APPLICATION_SPECIFIC_ARG
+    --app_specific_arg APP_SPECIFIC_ARG, -asa APP_SPECIFIC_ARG
 
   if metavar="<ASA>" is set, the user sees:
 
-    --application_specific_arg <ASA>, -asa <ASA>
+    --app_specific_arg <ASA>, -asa <ASA>
 
 """
 
@@ -216,7 +216,7 @@ class ImageModelParser(argparse.ArgumentParser):
       self.add_argument(
           "--data_format", "-df",
           default=None,
-          choices=['channels_first', 'channels_last'],
+          choices=["channels_first", "channels_last"],
           help="A flag to override the data format used in the model. "
                "channels_first provides a performance boost on GPU but is not "
                "always compatible with CPU. If left unspecified, the data "

official/utils/logging/hooks_helper.py

@@ -24,7 +24,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.utils.logging import hooks
 
@@ -40,7 +40,7 @@ def get_train_hooks(name_list, **kwargs):
     name_list: a list of strings to name desired hook classes. Allowed:
       LoggingTensorHook, ProfilerHook, ExamplesPerSecondHook, which are defined
       as keys in HOOKS
-    kwargs: a dictionary of arguments to the hooks.
+    **kwargs: a dictionary of arguments to the hooks.
 
   Returns:
     list of instantiated hooks, ready to be used in a classifier.train call.
@@ -71,7 +71,7 @@ def get_logging_tensor_hook(every_n_iter=100, tensors_to_log=None, **kwargs):  #
       steps taken on the current worker.
     tensors_to_log: List of tensor names or dictionary mapping labels to tensor
       names. If not set, log _TENSORS_TO_LOG by default.
-    kwargs: a dictionary of arguments to LoggingTensorHook.
+    **kwargs: a dictionary of arguments to LoggingTensorHook.
 
   Returns:
     Returns a LoggingTensorHook with a standard set of tensors that will be
@@ -90,7 +90,7 @@ def get_profiler_hook(save_steps=1000, **kwargs):  # pylint: disable=unused-argu
 
   Args:
     save_steps: `int`, print profile traces every N steps.
-    kwargs: a dictionary of arguments to ProfilerHook.
+    **kwargs: a dictionary of arguments to ProfilerHook.
 
   Returns:
     Returns a ProfilerHook that writes out timelines that can be loaded into
@@ -111,7 +111,7 @@ def get_examples_per_second_hook(every_n_steps=100,
     batch_size: `int`, total batch size used to calculate examples/second from
       global time.
     warm_steps: skip this number of steps before logging and running average.
-    kwargs: a dictionary of arguments to ExamplesPerSecondHook.
+    **kwargs: a dictionary of arguments to ExamplesPerSecondHook.
 
   Returns:
     Returns a ProfilerHook that writes out timelines that can be loaded into
@@ -128,4 +128,3 @@ HOOKS = {
     'profilerhook': get_profiler_hook,
     'examplespersecondhook': get_examples_per_second_hook,
 }
-

official/utils/logging/hooks_helper_test.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 
 import unittest
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.utils.logging import hooks_helper
 

official/utils/logging/hooks_test.py

@@ -21,9 +21,9 @@ from __future__ import print_function
 
 import time
 
-import tensorflow as tf
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+from tensorflow.python.training import monitored_session  # pylint: disable=g-bad-import-order
 
-from tensorflow.python.training import monitored_session
 from official.utils.logging import hooks
 
 
@@ -31,6 +31,7 @@ tf.logging.set_verbosity(tf.logging.ERROR)
 
 
 class ExamplesPerSecondHookTest(tf.test.TestCase):
+  """Tests for the ExamplesPerSecondHook."""
 
   def setUp(self):
     """Mock out logging calls to verify if correct info is being monitored."""
@@ -71,7 +72,7 @@ class ExamplesPerSecondHookTest(tf.test.TestCase):
         every_n_steps=every_n_steps,
         warm_steps=warm_steps)
     hook.begin()
-    mon_sess = monitored_session._HookedSession(sess, [hook])
+    mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
     sess.run(tf.global_variables_initializer())
 
     self.logged_message = ''
@@ -120,7 +121,7 @@ class ExamplesPerSecondHookTest(tf.test.TestCase):
         every_n_steps=None,
         every_n_secs=every_n_secs)
     hook.begin()
-    mon_sess = monitored_session._HookedSession(sess, [hook])
+    mon_sess = monitored_session._HookedSession(sess, [hook])  # pylint: disable=protected-access
     sess.run(tf.global_variables_initializer())
 
     self.logged_message = ''