Revert "tf_upgrade_v2 on resnet and utils folders. (tensorflow#6154)" (tensorflow#6162)

This reverts commit d6b2b83.

Author: goldiegadde

official/resnet/cifar10_download_and_extract.py

@@ -60,4 +60,4 @@ def _progress(count, block_size, total_size):
 
 if __name__ == '__main__':
   FLAGS, unparsed = parser.parse_known_args()
-  tf.compat.v1.app.run(argv=[sys.argv[0]] + unparsed)
+  tf.app.run(argv=[sys.argv[0]] + unparsed)

official/resnet/cifar10_main.py

@@ -52,7 +52,7 @@
 ###############################################################################
 def get_filenames(is_training, data_dir):
   """Returns a list of filenames."""
-  assert tf.io.gfile.exists(data_dir), (
+  assert tf.gfile.Exists(data_dir), (
       'Run cifar10_download_and_extract.py first to download and extract the '
       'CIFAR-10 data.')
 
@@ -68,7 +68,7 @@ def get_filenames(is_training, data_dir):
 def parse_record(raw_record, is_training, dtype):
   """Parse CIFAR-10 image and label from a raw record."""
   # Convert bytes to a vector of uint8 that is record_bytes long.
-  record_vector = tf.io.decode_raw(raw_record, tf.uint8)
+  record_vector = tf.decode_raw(raw_record, tf.uint8)
 
   # The first byte represents the label, which we convert from uint8 to int32
   # and then to one-hot.
@@ -81,7 +81,7 @@ def parse_record(raw_record, is_training, dtype):
 
   # Convert from [depth, height, width] to [height, width, depth], and cast as
   # float32.
-  image = tf.cast(tf.transpose(a=depth_major, perm=[1, 2, 0]), tf.float32)
+  image = tf.cast(tf.transpose(depth_major, [1, 2, 0]), tf.float32)
 
   image = preprocess_image(image, is_training)
   image = tf.cast(image, dtype)
@@ -97,7 +97,7 @@ def preprocess_image(image, is_training):
         image, HEIGHT + 8, WIDTH + 8)
 
     # Randomly crop a [HEIGHT, WIDTH] section of the image.
-    image = tf.image.random_crop(image, [HEIGHT, WIDTH, NUM_CHANNELS])
+    image = tf.random_crop(image, [HEIGHT, WIDTH, NUM_CHANNELS])
 
     # Randomly flip the image horizontally.
     image = tf.image.random_flip_left_right(image)
@@ -253,9 +253,8 @@ def run_cifar(flags_obj):
     Dictionary of results. Including final accuracy.
   """
   if flags_obj.image_bytes_as_serving_input:
-    tf.compat.v1.logging.fatal(
-        '--image_bytes_as_serving_input cannot be set to True for CIFAR. '
-        'This flag is only applicable to ImageNet.')
+    tf.logging.fatal('--image_bytes_as_serving_input cannot be set to True '
+                     'for CIFAR. This flag is only applicable to ImageNet.')
     return
 
   input_function = (flags_obj.use_synthetic_data and
@@ -274,6 +273,6 @@ def main(_):
 
 
 if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
+  tf.logging.set_verbosity(tf.logging.INFO)
   define_cifar_flags()
   absl_app.run(main)

official/resnet/cifar10_test.py

@@ -25,7 +25,7 @@
 from official.resnet import cifar10_main
 from official.utils.testing import integration
 
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+tf.logging.set_verbosity(tf.logging.ERROR)
 
 _BATCH_SIZE = 128
 _HEIGHT = 32
@@ -44,7 +44,7 @@ def setUpClass(cls):  # pylint: disable=invalid-name
 
   def tearDown(self):
     super(BaseTest, self).tearDown()
-    tf.io.gfile.rmtree(self.get_temp_dir())
+    tf.gfile.DeleteRecursively(self.get_temp_dir())
 
   def test_dataset_input_fn(self):
     fake_data = bytearray()
@@ -62,8 +62,7 @@ def test_dataset_input_fn(self):
         filename, cifar10_main._RECORD_BYTES)  # pylint: disable=protected-access
     fake_dataset = fake_dataset.map(
         lambda val: cifar10_main.parse_record(val, False, tf.float32))
-    image, label = tf.compat.v1.data.make_one_shot_iterator(
-        fake_dataset).get_next()
+    image, label = fake_dataset.make_one_shot_iterator().get_next()
 
     self.assertAllEqual(label.shape, ())
     self.assertAllEqual(image.shape, (_HEIGHT, _WIDTH, _NUM_CHANNELS))
@@ -80,7 +79,7 @@ def test_dataset_input_fn(self):
   def cifar10_model_fn_helper(self, mode, resnet_version, dtype):
     input_fn = cifar10_main.get_synth_input_fn(dtype)
     dataset = input_fn(True, '', _BATCH_SIZE)
-    iterator = tf.compat.v1.data.make_initializable_iterator(dataset)
+    iterator = dataset.make_initializable_iterator()
     features, labels = iterator.get_next()
     spec = cifar10_main.cifar10_model_fn(
         features, labels, mode, {
@@ -143,7 +142,7 @@ def _test_cifar10model_shape(self, resnet_version):
     model = cifar10_main.Cifar10Model(32, data_format='channels_last',
                                       num_classes=num_classes,
                                       resnet_version=resnet_version)
-    fake_input = tf.random.uniform([batch_size, _HEIGHT, _WIDTH, _NUM_CHANNELS])
+    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/estimator_cifar_benchmark.py

@@ -144,7 +144,7 @@ def _get_model_dir(self, folder_name):
     return os.path.join(self.output_dir, folder_name)
 
   def _setup(self):
-    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
+    tf.logging.set_verbosity(tf.logging.DEBUG)
     if EstimatorCifar10BenchmarkTests.local_flags is None:
       cifar_main.define_cifar_flags()
       # Loads flags to get defaults to then override.

official/resnet/imagenet_main.py

@@ -95,23 +95,22 @@ def _parse_example_proto(example_serialized):
   """
   # Dense features in Example proto.
   feature_map = {
-      'image/encoded': tf.io.FixedLenFeature([], dtype=tf.string,
+      'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
+                                          default_value=''),
+      'image/class/label': tf.FixedLenFeature([], dtype=tf.int64,
+                                              default_value=-1),
+      'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
                                              default_value=''),
-      'image/class/label': tf.io.FixedLenFeature([], dtype=tf.int64,
-                                                 default_value=-1),
-      'image/class/text': tf.io.FixedLenFeature([], dtype=tf.string,
-                                                default_value=''),
   }
-  sparse_float32 = tf.io.VarLenFeature(dtype=tf.float32)
+  sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
   # Sparse features in Example proto.
   feature_map.update(
       {k: sparse_float32 for k in ['image/object/bbox/xmin',
                                    'image/object/bbox/ymin',
                                    'image/object/bbox/xmax',
                                    'image/object/bbox/ymax']})
 
-  features = tf.io.parse_single_example(serialized=example_serialized,
-                                        features=feature_map)
+  features = tf.parse_single_example(example_serialized, feature_map)
   label = tf.cast(features['image/class/label'], dtype=tf.int32)
 
   xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)
@@ -125,7 +124,7 @@ def _parse_example_proto(example_serialized):
   # Force the variable number of bounding boxes into the shape
   # [1, num_boxes, coords].
   bbox = tf.expand_dims(bbox, 0)
-  bbox = tf.transpose(a=bbox, perm=[0, 2, 1])
+  bbox = tf.transpose(bbox, [0, 2, 1])
 
   return features['image/encoded'], label, bbox
 
@@ -189,7 +188,7 @@ def input_fn(is_training, data_dir, batch_size, num_epochs=1,
   # This number is low enough to not cause too much contention on small systems
   # but high enough to provide the benefits of parallelization. You may want
   # to increase this number if you have a large number of CPU cores.
-  dataset = dataset.apply(tf.data.experimental.parallel_interleave(
+  dataset = dataset.apply(tf.contrib.data.parallel_interleave(
       tf.data.TFRecordDataset, cycle_length=10))
 
   return resnet_run_loop.process_record_dataset(
@@ -353,6 +352,6 @@ def main(_):
 
 
 if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
+  tf.logging.set_verbosity(tf.logging.INFO)
   define_imagenet_flags()
   absl_app.run(main)

official/resnet/imagenet_preprocessing.py

@@ -108,7 +108,7 @@ def _central_crop(image, crop_height, crop_width):
   Returns:
     3-D tensor with cropped image.
   """
-  shape = tf.shape(input=image)
+  shape = tf.shape(image)
   height, width = shape[0], shape[1]
 
   amount_to_be_cropped_h = (height - crop_height)
@@ -195,7 +195,7 @@ def _aspect_preserving_resize(image, resize_min):
   Returns:
     resized_image: A 3-D tensor containing the resized image.
   """
-  shape = tf.shape(input=image)
+  shape = tf.shape(image)
   height, width = shape[0], shape[1]
 
   new_height, new_width = _smallest_size_at_least(height, width, resize_min)
@@ -218,7 +218,7 @@ def _resize_image(image, height, width):
     resized_image: A 3-D tensor containing the resized image. The first two
       dimensions have the shape [height, width].
   """
-  return tf.image.resize(
+  return tf.image.resize_images(
       image, [height, width], method=tf.image.ResizeMethod.BILINEAR,
       align_corners=False)
 

official/resnet/imagenet_test.py

@@ -24,7 +24,7 @@
 from official.resnet import imagenet_main
 from official.utils.testing import integration
 
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+tf.logging.set_verbosity(tf.logging.ERROR)
 
 _BATCH_SIZE = 32
 _LABEL_CLASSES = 1001
@@ -39,7 +39,7 @@ def setUpClass(cls):  # pylint: disable=invalid-name
 
   def tearDown(self):
     super(BaseTest, self).tearDown()
-    tf.io.gfile.rmtree(self.get_temp_dir())
+    tf.gfile.DeleteRecursively(self.get_temp_dir())
 
   def _tensor_shapes_helper(self, resnet_size, resnet_version, dtype, with_gpu):
     """Checks the tensor shapes after each phase of the ResNet model."""
@@ -62,7 +62,7 @@ def reshape(shape):
           resnet_version=resnet_version,
           dtype=dtype
       )
-      inputs = tf.random.uniform([1, 224, 224, 3])
+      inputs = tf.random_uniform([1, 224, 224, 3])
       output = model(inputs, training=True)
 
       initial_conv = graph.get_tensor_by_name('resnet_model/initial_conv:0')
@@ -189,11 +189,11 @@ def test_tensor_shapes_resnet_200_with_gpu_v2(self):
 
   def resnet_model_fn_helper(self, mode, resnet_version, dtype):
     """Tests that the EstimatorSpec is given the appropriate arguments."""
-    tf.compat.v1.train.create_global_step()
+    tf.train.create_global_step()
 
     input_fn = imagenet_main.get_synth_input_fn(dtype)
     dataset = input_fn(True, '', _BATCH_SIZE)
-    iterator = tf.compat.v1.data.make_initializable_iterator(dataset)
+    iterator = dataset.make_initializable_iterator()
     features, labels = iterator.get_next()
     spec = imagenet_main.imagenet_model_fn(
         features, labels, mode, {
@@ -257,7 +257,7 @@ def _test_imagenetmodel_shape(self, resnet_version):
         50, data_format='channels_last', num_classes=num_classes,
         resnet_version=resnet_version)
 
-    fake_input = tf.random.uniform([batch_size, 224, 224, 3])
+    fake_input = tf.random_uniform([batch_size, 224, 224, 3])
     output = model(fake_input, training=True)
 
     self.assertAllEqual(output.shape, (batch_size, num_classes))

official/resnet/keras/keras_benchmark.py

@@ -43,7 +43,7 @@ def _get_model_dir(self, folder_name):
 
   def _setup(self):
     """Sets up and resets flags before each test."""
-    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
+    tf.logging.set_verbosity(tf.logging.DEBUG)
     if KerasBenchmark.local_flags is None:
       for flag_method in self.flag_methods:
         flag_method()

official/resnet/keras/keras_cifar_main.py

@@ -81,7 +81,7 @@ def parse_record_keras(raw_record, is_training, dtype):
     Tuple with processed image tensor and one-hot-encoded label tensor.
   """
   image, label = cifar_main.parse_record(raw_record, is_training, dtype)
-  label = tf.compat.v1.sparse_to_dense(label, (cifar_main.NUM_CLASSES,), 1)
+  label = tf.sparse_to_dense(label, (cifar_main.NUM_CLASSES,), 1)
   return image, label
 
 
@@ -98,7 +98,7 @@ def run(flags_obj):
     Dictionary of training and eval stats.
   """
   if flags_obj.enable_eager:
-    tf.compat.v1.enable_eager_execution()
+    tf.enable_eager_execution()
 
   dtype = flags_core.get_tf_dtype(flags_obj)
   if dtype == 'fp16':
@@ -194,7 +194,7 @@ def main(_):
 
 
 if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
+  tf.logging.set_verbosity(tf.logging.INFO)
   cifar_main.define_cifar_flags()
   keras_common.define_keras_flags()
   absl_app.run(main)

official/resnet/keras/keras_common.py

@@ -80,10 +80,9 @@ def on_batch_end(self, batch, logs=None):
       if batch != 0:
         self.record_batch = True
         self.timestamp_log.append(BatchTimestamp(batch, timestamp))
-        tf.compat.v1.logging.info(
-            "BenchmarkMetric: {'num_batches':%d, 'time_taken': %f,"
-            "'images_per_second': %f}" %
-            (batch, elapsed_time, examples_per_second))
+        tf.logging.info("BenchmarkMetric: {'num_batches':%d, 'time_taken': %f,"
+                        "'images_per_second': %f}" %
+                        (batch, elapsed_time, examples_per_second))
 
 
 class LearningRateBatchScheduler(tf.keras.callbacks.Callback):
@@ -121,9 +120,8 @@ def on_batch_begin(self, batch, logs=None):
     if lr != self.prev_lr:
       self.model.optimizer.learning_rate = lr  # lr should be a float here
       self.prev_lr = lr
-      tf.compat.v1.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():
@@ -228,20 +226,22 @@ def get_synth_input_fn(height, width, num_channels, num_classes,
   def input_fn(is_training, data_dir, batch_size, *args, **kwargs):
     """Returns dataset filled with random data."""
     # Synthetic input should be within [0, 255].
-    inputs = tf.random.truncated_normal([height, width, num_channels],
-                                        dtype=dtype,
-                                        mean=127,
-                                        stddev=60,
-                                        name='synthetic_inputs')
-
-    labels = tf.random.uniform([1],
-                               minval=0,
-                               maxval=num_classes - 1,
-                               dtype=tf.int32,
-                               name='synthetic_labels')
+    inputs = tf.truncated_normal(
+        [height, width, num_channels],
+        dtype=dtype,
+        mean=127,
+        stddev=60,
+        name='synthetic_inputs')
+
+    labels = tf.random_uniform(
+        [1],
+        minval=0,
+        maxval=num_classes - 1,
+        dtype=tf.int32,
+        name='synthetic_labels')
     data = tf.data.Dataset.from_tensors((inputs, labels)).repeat()
     data = data.batch(batch_size)
-    data = data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
+    data = data.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
     return data
 
   return input_fn