Merged commit includes the following changes: (#7368)

* Merged commit includes the following changes:
261380794  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

261374439  by haoyuzhang<haoyuzhang@google.com>:

    Change Keras CTL dependencies from r1 ResNet to Keras ResNet.

--

PiperOrigin-RevId: 261380794

* Revert unintentional change

* Revert unintentional change

official/resnet/ctl/ctl_common.py

@@ -21,19 +21,9 @@ from __future__ import print_function
 from absl import flags
 
 
-FLAGS = flags.FLAGS
-
-
 def define_ctl_flags():
   """Define flags for CTL."""
 
   flags.DEFINE_boolean(name='use_tf_function', default=True,
                        help='Wrap the train and test step inside a '
                        'tf.function.')
-  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 # batches per epoch. When this flag is '
-      'set, only one epoch is going to run for training.')
-  
\ No newline at end of file

official/resnet/ctl/ctl_imagenet_benchmark.py

@@ -22,7 +22,7 @@ import time
 from absl import flags
 import tensorflow as tf
 
-from official.resnet import imagenet_main
+from official.resnet.keras import keras_common
 from official.resnet.ctl import ctl_imagenet_main
 from official.resnet.ctl import ctl_common
 from official.utils.testing.perfzero_benchmark import PerfZeroBenchmark
@@ -118,7 +118,7 @@ class Resnet50CtlAccuracy(CtlBenchmark):
 
     flag_methods = [
         ctl_common.define_ctl_flags,
-        lambda: imagenet_main.define_imagenet_flags()
+        keras_common.define_keras_flags
     ]
 
     self.data_dir = os.path.join(root_data_dir, 'imagenet')
@@ -162,7 +162,7 @@ class Resnet50CtlBenchmarkBase(CtlBenchmark):
   def __init__(self, output_dir=None, default_flags=None):
     flag_methods = [
         ctl_common.define_ctl_flags,
-        lambda: imagenet_main.define_imagenet_flags()
+        keras_common.define_keras_flags
     ]
 
     super(Resnet50CtlBenchmarkBase, self).__init__(

official/resnet/ctl/ctl_imagenet_main.py

@@ -18,39 +18,21 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-import tempfile
-import datetime
-import time
-import numpy as np
-
 from absl import app as absl_app
 from absl import flags
 from absl import logging
-
 import tensorflow as tf
 
-from official.resnet import imagenet_main
+from official.resnet.ctl import ctl_common
+from official.resnet.keras import imagenet_preprocessing
 from official.resnet.keras import keras_common
 from official.resnet.keras import keras_imagenet_main
 from official.resnet.keras import resnet_model
 from official.utils.flags import core as flags_core
 from official.utils.logs import logger
 from official.utils.misc import distribution_utils
-from official.utils.misc import model_helpers
-from official.resnet.ctl import ctl_common
 from official.utils.misc import keras_utils
-
-
-def parse_record_keras(raw_record, is_training, dtype):
-  """Adjust the shape of label."""
-  image, label = imagenet_main.parse_record(raw_record, is_training, dtype)
-
-  # Subtract one so that labels are in [0, 1000), and cast to float32 for
-  # Keras model.
-  label = tf.cast(tf.cast(tf.reshape(label, shape=[1]), dtype=tf.int32) - 1,
-                  dtype=tf.float32)
-  return image, label
+from official.utils.misc import model_helpers
 
 
 def build_stats(train_result, eval_result, time_callback):
@@ -68,18 +50,18 @@ def build_stats(train_result, eval_result, time_callback):
   stats = {}
 
   if eval_result:
-    stats["eval_loss"] = eval_result[0]
-    stats["eval_acc"] = eval_result[1]
+    stats['eval_loss'] = eval_result[0]
+    stats['eval_acc'] = eval_result[1]
 
     stats['train_loss'] = train_result[0]
     stats['train_acc'] = train_result[1]
 
   if time_callback:
     timestamp_log = time_callback.timestamp_log
-    stats["step_timestamp_log"] = timestamp_log
-    stats["train_finish_time"] = time_callback.train_finish_time
+    stats['step_timestamp_log'] = timestamp_log
+    stats['train_finish_time'] = time_callback.train_finish_time
     if len(timestamp_log) > 1:
-      stats["avg_exp_per_second"] = (
+      stats['avg_exp_per_second'] = (
           time_callback.batch_size * time_callback.log_steps *
           (len(time_callback.timestamp_log) - 1) /
           (timestamp_log[-1].timestamp - timestamp_log[0].timestamp))
@@ -92,20 +74,20 @@ def get_input_dataset(flags_obj, strategy):
   dtype = flags_core.get_tf_dtype(flags_obj)
   if flags_obj.use_synthetic_data:
     input_fn = keras_common.get_synth_input_fn(
-        height=imagenet_main.DEFAULT_IMAGE_SIZE,
-        width=imagenet_main.DEFAULT_IMAGE_SIZE,
-        num_channels=imagenet_main.NUM_CHANNELS,
-        num_classes=imagenet_main.NUM_CLASSES,
+        height=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
+        width=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
+        num_channels=imagenet_preprocessing.NUM_CHANNELS,
+        num_classes=imagenet_preprocessing.NUM_CLASSES,
         dtype=dtype,
         drop_remainder=True)
   else:
-    input_fn = imagenet_main.input_fn
+    input_fn = imagenet_preprocessing.input_fn
 
   train_ds = input_fn(
       is_training=True,
       data_dir=flags_obj.data_dir,
       batch_size=flags_obj.batch_size,
-      parse_record_fn=parse_record_keras,
+      parse_record_fn=imagenet_preprocessing.parse_record,
       datasets_num_private_threads=flags_obj.datasets_num_private_threads,
       dtype=dtype)
 
@@ -118,7 +100,7 @@ def get_input_dataset(flags_obj, strategy):
         is_training=False,
         data_dir=flags_obj.data_dir,
         batch_size=flags_obj.batch_size,
-        parse_record_fn=parse_record_keras,
+        parse_record_fn=imagenet_preprocessing.parse_record,
         dtype=dtype)
 
     if strategy:
@@ -129,14 +111,16 @@ def get_input_dataset(flags_obj, strategy):
 
 def get_num_train_iterations(flags_obj):
   """Returns the number of training stesps, train and test epochs."""
-  train_steps = imagenet_main.NUM_IMAGES['train'] // flags_obj.batch_size
+  train_steps = (
+      imagenet_preprocessing.NUM_IMAGES['train'] // flags_obj.batch_size)
   train_epochs = flags_obj.train_epochs
 
   if flags_obj.train_steps:
     train_steps = min(flags_obj.train_steps, train_steps)
     train_epochs = 1
 
-  eval_steps = imagenet_main.NUM_IMAGES['validation'] // flags_obj.batch_size
+  eval_steps = (
+      imagenet_preprocessing.NUM_IMAGES['validation'] // flags_obj.batch_size)
 
   return train_steps, train_epochs, eval_steps
 
@@ -177,8 +161,9 @@ def run(flags_obj):
 
   strategy_scope = distribution_utils.get_strategy_scope(strategy)
   with strategy_scope:
-    model = resnet_model.resnet50(num_classes=imagenet_main.NUM_CLASSES,
-                                  dtype=dtype, batch_size=flags_obj.batch_size)
+    model = resnet_model.resnet50(
+        num_classes=imagenet_preprocessing.NUM_CLASSES,
+        dtype=dtype, batch_size=flags_obj.batch_size)
 
     optimizer = tf.keras.optimizers.SGD(
         learning_rate=keras_common.BASE_LEARNING_RATE, momentum=0.9,
@@ -296,6 +281,6 @@ def main(_):
 
 if __name__ == '__main__':
   logging.set_verbosity(logging.INFO)
-  imagenet_main.define_imagenet_flags()
+  keras_common.define_keras_flags()
   ctl_common.define_ctl_flags()
   absl_app.run(main)

official/resnet/ctl/ctl_imagenet_test.py

@@ -21,14 +21,14 @@ from __future__ import print_function
 from tempfile import mkdtemp
 import tensorflow as tf
 
-from official.resnet import imagenet_main
-from official.resnet.ctl import ctl_imagenet_main
+from tensorflow.python.eager import context
+from tensorflow.python.platform import googletest
 from official.resnet.ctl import ctl_common
+from official.resnet.ctl import ctl_imagenet_main
+from official.resnet.keras import imagenet_preprocessing
+from official.resnet.keras import keras_common
 from official.utils.misc import keras_utils
 from official.utils.testing import integration
-# pylint: disable=ungrouped-imports
-from tensorflow.python.eager import context
-from tensorflow.python.platform import googletest
 
 
 class CtlImagenetTest(googletest.TestCase):
@@ -49,14 +49,14 @@ class CtlImagenetTest(googletest.TestCase):
   @classmethod
   def setUpClass(cls):  # pylint: disable=invalid-name
     super(CtlImagenetTest, cls).setUpClass()
-    imagenet_main.define_imagenet_flags()
+    keras_common.define_keras_flags()
     ctl_common.define_ctl_flags()
 
   def setUp(self):
     super(CtlImagenetTest, self).setUp()
     if not keras_utils.is_v2_0():
       tf.compat.v1.enable_v2_behavior()
-    imagenet_main.NUM_IMAGES['validation'] = 4
+    imagenet_preprocessing.NUM_IMAGES['validation'] = 4
 
   def tearDown(self):
     super(CtlImagenetTest, self).tearDown()