Fix lint issues in the official models (#2487)

official/mnist/convert_to_records.py

@@ -93,5 +93,5 @@ def main(unused_argv):
 
 if __name__ == '__main__':
   tf.logging.set_verbosity(tf.logging.INFO)
-  FLAGS = parser.parse_args()
-  tf.app.run()
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

official/mnist/mnist.py

@@ -19,8 +19,8 @@ from __future__ import print_function
 
 import argparse
 import os
+import sys
 
-import numpy as np
 import tensorflow as tf
 
 parser = argparse.ArgumentParser()
@@ -42,7 +42,7 @@ parser.add_argument('--steps', type=int, default=20000,
 def input_fn(mode, batch_size=1):
   """A simple input_fn using the contrib.data input pipeline."""
 
-  def parser(serialized_example):
+  def example_parser(serialized_example):
     """Parses a single tf.Example into image and label tensors."""
     features = tf.parse_single_example(
         serialized_example,
@@ -64,8 +64,9 @@ def input_fn(mode, batch_size=1):
     assert mode == tf.estimator.ModeKeys.EVAL, 'invalid mode'
     tfrecords_file = os.path.join(FLAGS.data_dir, 'test.tfrecords')
 
-  assert tf.gfile.Exists(tfrecords_file), ('Run convert_to_records.py first to '
-      'convert the MNIST data to TFRecord file format.')
+  assert tf.gfile.Exists(tfrecords_file), (
+      'Run convert_to_records.py first to convert the MNIST data to TFRecord '
+      'file format.')
 
   dataset = tf.contrib.data.TFRecordDataset([tfrecords_file])
 
@@ -73,8 +74,9 @@ def input_fn(mode, batch_size=1):
   if mode == tf.estimator.ModeKeys.TRAIN:
     dataset = dataset.repeat()
 
-  # Map the parser over dataset, and batch results by up to batch_size
-  dataset = dataset.map(parser, num_threads=1, output_buffer_size=batch_size)
+  # Map example_parser over dataset, and batch results by up to batch_size
+  dataset = dataset.map(
+      example_parser, num_threads=1, output_buffer_size=batch_size)
   dataset = dataset.batch(batch_size)
   images, labels = dataset.make_one_shot_iterator().get_next()
 
@@ -223,5 +225,5 @@ def main(unused_argv):
 
 if __name__ == '__main__':
   tf.logging.set_verbosity(tf.logging.INFO)
-  FLAGS = parser.parse_args()
-  tf.app.run()
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

official/mnist/mnist_test.py

@@ -17,7 +17,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import numpy as np
 import tensorflow as tf
 
 import mnist

official/resnet/cifar10_download_and_extract.py

@@ -46,8 +46,8 @@ def main(unused_argv):
 
   if not os.path.exists(filepath):
     def _progress(count, block_size, total_size):
-      sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,
-          100.0 * count * block_size / total_size))
+      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+          filename, 100.0 * count * block_size / total_size))
       sys.stdout.flush()
 
     filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)

official/resnet/cifar10_main.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""Runs a ResNet model on the CIFAR-10 dataset."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -19,9 +20,7 @@ from __future__ import print_function
 
 import argparse
 import os
-import sys
 
-import numpy as np
 import tensorflow as tf
 
 import resnet_model
@@ -75,12 +74,13 @@ def record_dataset(filenames):
   return tf.contrib.data.FixedLengthRecordDataset(filenames, record_bytes)
 
 
-def filenames(mode):
+def get_filenames(mode):
   """Returns a list of filenames based on 'mode'."""
   data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
 
-  assert os.path.exists(data_dir), ('Run cifar10_download_and_extract.py first '
-      'to download and extract the CIFAR-10 data.')
+  assert os.path.exists(data_dir), (
+      'Run cifar10_download_and_extract.py first to download and extract the '
+      'CIFAR-10 data.')
 
   if mode == tf.estimator.ModeKeys.TRAIN:
     return [
@@ -137,10 +137,13 @@ def input_fn(mode, batch_size):
   """Input_fn using the contrib.data input pipeline for CIFAR-10 dataset.
 
   Args:
-    mode: Standard names for model modes (tf.estimators.ModeKeys).
+    mode: Standard names for model modes from tf.estimator.ModeKeys.
     batch_size: The number of samples per batch of input requested.
+
+  Returns:
+    A tuple of images and labels.
   """
-  dataset = record_dataset(filenames(mode))
+  dataset = record_dataset(get_filenames(mode))
 
   # For training repeat forever.
   if mode == tf.estimator.ModeKeys.TRAIN:
@@ -227,7 +230,7 @@ def cifar10_model_fn(features, labels, mode):
   else:
     train_op = None
 
-  accuracy= tf.metrics.accuracy(
+  accuracy = tf.metrics.accuracy(
       tf.argmax(labels, axis=1), predictions['classes'])
   metrics = {'accuracy': accuracy}
 
@@ -250,7 +253,7 @@ def main(unused_argv):
   cifar_classifier = tf.estimator.Estimator(
       model_fn=cifar10_model_fn, model_dir=FLAGS.model_dir)
 
-  for cycle in range(FLAGS.train_steps // FLAGS.steps_per_eval):
+  for _ in range(FLAGS.train_steps // FLAGS.steps_per_eval):
     tensors_to_log = {
         'learning_rate': 'learning_rate',
         'cross_entropy': 'cross_entropy',

official/resnet/cifar10_test.py

@@ -17,9 +17,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-import sys
-from tempfile import mkdtemp
 from tempfile import mkstemp
 
 import numpy as np
@@ -36,13 +33,13 @@ class BaseTest(tf.test.TestCase):
     fake_data = bytearray()
     fake_data.append(7)
     for i in xrange(3):
-      for j in xrange(1024):
+      for _ in xrange(1024):
         fake_data.append(i)
 
     _, filename = mkstemp(dir=self.get_temp_dir())
-    file = open(filename, 'wb')
-    file.write(fake_data)
-    file.close()
+    data_file = open(filename, 'wb')
+    data_file.write(fake_data)
+    data_file.close()
 
     fake_dataset = cifar10_main.record_dataset(filename)
     fake_dataset = fake_dataset.map(cifar10_main.dataset_parser)

official/resnet/imagenet_main.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""Runs a ResNet model on the ImageNet dataset."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -163,7 +164,7 @@ def input_fn(is_training):
 
 
 def resnet_model_fn(features, labels, mode):
-  """ Our model_fn for ResNet to be used with our Estimator."""
+  """Our model_fn for ResNet to be used with our Estimator."""
   tf.summary.image('images', features, max_outputs=6)
 
   logits = network(
@@ -239,7 +240,7 @@ def main(unused_argv):
   resnet_classifier = tf.estimator.Estimator(
       model_fn=resnet_model_fn, model_dir=FLAGS.model_dir)
 
-  for cycle in range(FLAGS.train_steps // FLAGS.steps_per_eval):
+  for _ in range(FLAGS.train_steps // FLAGS.steps_per_eval):
     tensors_to_log = {
         'learning_rate': 'learning_rate',
         'cross_entropy': 'cross_entropy',

official/resnet/imagenet_test.py

@@ -34,11 +34,9 @@ class BaseTest(tf.test.TestCase):
   def tensor_shapes_helper(self, resnet_size, with_gpu=False):
     """Checks the tensor shapes after each phase of the ResNet model."""
     def reshape(shape):
-      """Returns the expected dimensions depending on if 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.
-      """
+      """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:
         return shape
       return shape[0], shape[2], shape[3], shape[1]

official/resnet/resnet_model.py

@@ -40,8 +40,8 @@ _BATCH_NORM_EPSILON = 1e-5
 
 def batch_norm_relu(inputs, is_training, data_format):
   """Performs a batch normalization followed by a ReLU."""
-  # We set fused=True for a significant performance boost.
-  # See https://www.tensorflow.org/performance/performance_guide#common_fused_ops
+  # We set fused=True for a significant performance boost. See
+  # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
   inputs = tf.layers.batch_normalization(
       inputs=inputs, axis=1 if data_format == 'channels_first' else 3,
       momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True,
@@ -78,11 +78,9 @@ def fixed_padding(inputs, kernel_size, data_format):
 
 
 def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format):
-  """Strided 2-D convolution with explicit padding.
-
-  The padding is consistent and is based only on `kernel_size`, not on the
-  dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
-  """
+  """Strided 2-D convolution with explicit padding."""
+  # The padding is consistent and is based only on `kernel_size`, not on the
+  # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
   if strides > 1:
     inputs = fixed_padding(inputs, kernel_size, data_format)
 
@@ -210,7 +208,7 @@ def block_layer(inputs, filters, block_fn, blocks, strides, is_training, name,
   inputs = block_fn(inputs, filters, is_training, projection_shortcut, strides,
                     data_format)
 
-  for i in range(1, blocks):
+  for _ in range(1, blocks):
     inputs = block_fn(inputs, filters, is_training, None, 1, data_format)
 
   return tf.identity(inputs, name)
@@ -228,6 +226,9 @@ def cifar10_resnet_v2_generator(resnet_size, num_classes, data_format=None):
   Returns:
     The model function that takes in `inputs` and `is_training` and
     returns the output tensor of the ResNet model.
+
+  Raises:
+    ValueError: If `resnet_size` is invalid.
   """
   if resnet_size % 6 != 2:
     raise ValueError('resnet_size must be 6n + 2:', resnet_size)
@@ -235,13 +236,15 @@ def cifar10_resnet_v2_generator(resnet_size, num_classes, data_format=None):
   num_blocks = (resnet_size - 2) // 6
 
   if data_format is None:
-    data_format = 'channels_first' if tf.test.is_built_with_cuda() else 'channels_last'
+    data_format = (
+        'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
 
   def model(inputs, is_training):
+    """Constructs the ResNet model given the inputs."""
     if data_format == 'channels_first':
       # Convert from channels_last (NHWC) to channels_first (NCHW). This
-      # provides a large performance boost on GPU.
-      # See https://www.tensorflow.org/performance/performance_guide#data_formats
+      # provides a large performance boost on GPU. See
+      # https://www.tensorflow.org/performance/performance_guide#data_formats
       inputs = tf.transpose(inputs, [0, 3, 1, 2])
 
     inputs = conv2d_fixed_padding(
@@ -294,9 +297,11 @@ def imagenet_resnet_v2_generator(block_fn, layers, num_classes,
     returns the output tensor of the ResNet model.
   """
   if data_format is None:
-    data_format = 'channels_first' if tf.test.is_built_with_cuda() else 'channels_last'
+    data_format = (
+        'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
 
   def model(inputs, is_training):
+    """Constructs the ResNet model given the inputs."""
     if data_format == 'channels_first':
       # Convert from channels_last (NHWC) to channels_first (NCHW). This
       # provides a large performance boost on GPU.