Add return stats after run for cifar

- kears_cifar_main returns dict of stats after run.
- unit tests added for keras_common.
- lint issues fixed in all 4 files.

official/resnet/keras/keras_cifar_benchmark.py

@@ -66,9 +66,8 @@ class KerasCifar10BenchmarkTests(object):
 
   def _fill_report_object(self, stats):
     if self.oss_report_object:
-      self.oss_report_object.top_1 = stats['accuracy_top_1'].item()
-      self.oss_report_object.add_other_quality(stats['training_accuracy_top_1']
-                                               .item(),
+      self.oss_report_object.top_1 = stats['accuracy_top_1']
+      self.oss_report_object.add_other_quality(stats['training_accuracy_top_1'],
                                                'top_1_train_accuracy')
     else:
       raise ValueError('oss_report_object has not been set.')

official/resnet/keras/keras_cifar_main.py

@@ -23,7 +23,6 @@ from absl import flags
 import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.resnet import cifar10_main as cifar_main
-from official.resnet import resnet_run_loop
 from official.resnet.keras import keras_common
 from official.resnet.keras import resnet_cifar_model
 from official.utils.flags import core as flags_core
@@ -36,15 +35,20 @@ LR_SCHEDULE = [  # (multiplier, epoch to start) tuples
 ]
 
 
-def learning_rate_schedule(current_epoch, current_batch, batches_per_epoch, batch_size):
+def learning_rate_schedule(current_epoch,
+                           current_batch,
+                           batches_per_epoch,
+                           batch_size):
   """Handles linear scaling rule, gradual warmup, and LR decay.
 
-  Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the provided scaling
-  factor.
+  Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the
+  provided scaling factor.
 
   Args:
     current_epoch: integer, current epoch indexed from 0.
     current_batch: integer, current batch in the current epoch, indexed from 0.
+    batches_per_epoch: integer, number of steps in an epoch.
+    batch_size: integer, total batch sized.
 
   Returns:
     Adjusted learning rate.
@@ -89,6 +93,9 @@ def run(flags_obj):
 
   Raises:
     ValueError: If fp16 is passed as it is not currently supported.
+
+  Returns:
+    Dictionary of training and eval stats.
   """
   if flags_obj.enable_eager:
     tf.enable_eager_execution()
@@ -166,11 +173,13 @@ def run(flags_obj):
                       validation_steps=num_eval_steps,
                       validation_data=validation_data,
                       verbose=1)
-
+  eval_output = None
   if not flags_obj.skip_eval:
     eval_output = model.evaluate(eval_input_dataset,
                                  steps=num_eval_steps,
                                  verbose=1)
+  stats = keras_common.build_stats(history, eval_output)
+  return stats
 
 
 def main(_):

official/resnet/keras/keras_common.py

@@ -29,6 +29,8 @@ from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_de
 
 FLAGS = flags.FLAGS
 BASE_LEARNING_RATE = 0.1  # This matches Jing's version.
+TRAIN_TOP_1 = 'training_accuracy_top_1'
+
 
 class TimeHistory(tf.keras.callbacks.Callback):
   """Callback for Keras models."""
@@ -49,7 +51,7 @@ class TimeHistory(tf.keras.callbacks.Callback):
 
   def on_batch_begin(self, batch, logs=None):
     if self.record_batch:
-      self.start_time= time.time()
+      self.start_time = time.time()
       self.record_batch = False
 
   def on_batch_end(self, batch, logs=None):
@@ -63,6 +65,7 @@ class TimeHistory(tf.keras.callbacks.Callback):
                         "'images_per_second': %f}" %
                         (batch, elapsed_time, examples_per_second))
 
+
 class LearningRateBatchScheduler(tf.keras.callbacks.Callback):
   """Callback to update learning rate on every batch (not epoch boundaries).
 
@@ -88,22 +91,27 @@ class LearningRateBatchScheduler(tf.keras.callbacks.Callback):
     self.epochs += 1
 
   def on_batch_begin(self, batch, logs=None):
-    lr = self.schedule(self.epochs, batch, self.batches_per_epoch, self.batch_size)
+    lr = self.schedule(self.epochs,
+                       batch,
+                       self.batches_per_epoch,
+                       self.batch_size)
     if not isinstance(lr, (float, np.float32, np.float64)):
       raise ValueError('The output of the "schedule" function should be float.')
     if lr != self.prev_lr:
       self.model.optimizer.learning_rate = lr  # lr should be a float here
       self.prev_lr = lr
-      tf.logging.debug('Epoch %05d Batch %05d: LearningRateBatchScheduler change '
-                   'learning rate to %s.', self.epochs, batch, lr)
+      tf.logging.debug('Epoch %05d Batch %05d: LearningRateBatchScheduler '
+                       'change learning rate to %s.', self.epochs, batch, lr)
+
 
 def get_optimizer():
-  # The learning rate set here is a placeholder and not use. It will be overwritten
-  # at the beginning of each batch by callback
+  """Returns optimizer to use."""
+  # The learning_rate is overwritten at the beginning of each step by callback.
   return gradient_descent_v2.SGD(learning_rate=0.1, momentum=0.9)
 
 
 def get_callbacks(learning_rate_schedule_fn, num_images):
+  """Returns common callbacks."""
   time_callback = TimeHistory(FLAGS.batch_size)
 
   tensorboard_callback = tf.keras.callbacks.TensorBoard(
@@ -117,14 +125,43 @@ def get_callbacks(learning_rate_schedule_fn, num_images):
   return time_callback, tensorboard_callback, lr_callback
 
 
+def build_stats(history, eval_output):
+  """Normalizes and returns dictionary of stats.
+
+  Args:
+    history: Results of the training step. Supports both categorical_accuracy
+      and sparse_categorical_accuracy.
+    eval_output: Output of the eval step. Assumes first value is eval_loss and
+      second value is accuracy_top_1.
+
+  Returns:
+    Dictionary of normalized results.
+  """
+  stats = {}
+  if eval_output:
+    stats['accuracy_top_1'] = eval_output[1].item()
+    stats['eval_loss'] = eval_output[0].item()
+  if history and history.history:
+    train_hist = history.history
+    # Gets final loss from training.
+    stats['loss'] = train_hist['loss'][-1].item()
+    # Gets top_1 training accuracy.
+    if 'categorical_accuracy' in train_hist:
+      stats[TRAIN_TOP_1] = train_hist['categorical_accuracy'][-1].item()
+    elif 'sparse_categorical_accuracy' in train_hist:
+      stats[TRAIN_TOP_1] = train_hist['sparse_categorical_accuracy'][-1].item()
+
+  return stats
+
+
 def define_keras_flags():
   flags.DEFINE_boolean(name='enable_eager', default=False, help='Enable eager?')
   flags.DEFINE_boolean(name='skip_eval', default=False, help='Skip evaluation?')
   flags.DEFINE_integer(
-      name="train_steps", default=None,
-      help="The number of steps to run for training. If it is larger than "
-      "# batches per epoch, then use # bathes per epoch. When this flag is "
-      "set, only one epoch is going to run for training.")
+      name='train_steps', default=None,
+      help='The number of steps to run for training. If it is larger than '
+      '# batches per epoch, then use # bathes per epoch. When this flag is '
+      'set, only one epoch is going to run for training.')
 
 
 def get_synth_input_fn(height, width, num_channels, num_classes,

official/resnet/keras/keras_common_test.py

+# Copyright 2018 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.
+# ==============================================================================
+"""Tests for the keras_common module."""
+from __future__ import absolute_import
+from __future__ import print_function
+
+from mock import Mock
+import numpy as np
+import tensorflow as tf  # pylint: disable=g-bad-import-order
+
+from official.resnet.keras import keras_common
+
+tf.logging.set_verbosity(tf.logging.ERROR)
+
+
+class KerasCommonTests(tf.test.TestCase):
+  """Tests for keras_common."""
+
+  @classmethod
+  def setUpClass(cls):  # pylint: disable=invalid-name
+    super(KerasCommonTests, cls).setUpClass()
+
+  def test_build_stats(self):
+
+    history = self._build_history(1.145, cat_accuracy=.99988)
+    eval_output = self._build_eval_output(.56432111, 5.990)
+    stats = keras_common.build_stats(history, eval_output)
+
+    self.assertEqual(1.145, stats['loss'])
+    self.assertEqual(.99988, stats['training_accuracy_top_1'])
+
+    self.assertEqual(.56432111, stats['accuracy_top_1'])
+    self.assertEqual(5.990, stats['eval_loss'])
+
+  def test_build_stats_sparse(self):
+
+    history = self._build_history(1.145, cat_accuracy_sparse=.99988)
+    eval_output = self._build_eval_output(.928, 1.9844)
+    stats = keras_common.build_stats(history, eval_output)
+
+    self.assertEqual(1.145, stats['loss'])
+    self.assertEqual(.99988, stats['training_accuracy_top_1'])
+
+    self.assertEqual(.928, stats['accuracy_top_1'])
+    self.assertEqual(1.9844, stats['eval_loss'])
+
+  def _build_history(self, loss, cat_accuracy=None,
+                     cat_accuracy_sparse=None):
+    history_p = Mock()
+    history = {}
+    history_p.history = history
+    history['loss'] = [np.float64(loss)]
+    if cat_accuracy:
+      history['categorical_accuracy'] = [np.float64(cat_accuracy)]
+    if cat_accuracy_sparse:
+      history['sparse_categorical_accuracy'] = [np.float64(cat_accuracy_sparse)]
+
+    return history_p
+
+  def _build_eval_output(self, top_1, eval_loss):
+    eval_output = [np.float64(eval_loss), np.float64(top_1)]
+    return eval_output
+