fixed lint issues.

official/resnet/keras/keras_imagenet_main.py

@@ -23,8 +23,6 @@ from absl import flags
 import tensorflow as tf  # pylint: disable=g-bad-import-order
 
 from official.resnet import imagenet_main
-from official.resnet import imagenet_preprocessing
-from official.resnet import resnet_run_loop
 from official.resnet.keras import keras_common
 from official.resnet.keras import resnet_model
 from official.utils.flags import core as flags_core
@@ -37,28 +35,33 @@ 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.
   """
-  initial_learning_rate = keras_common.BASE_LEARNING_RATE * batch_size / 256
+  initial_lr = keras_common.BASE_LEARNING_RATE * batch_size / 256
   epoch = current_epoch + float(current_batch) / batches_per_epoch
   warmup_lr_multiplier, warmup_end_epoch = LR_SCHEDULE[0]
   if epoch < warmup_end_epoch:
     # Learning rate increases linearly per step.
-    return initial_learning_rate * warmup_lr_multiplier * epoch / warmup_end_epoch
+    return initial_lr * warmup_lr_multiplier * epoch / warmup_end_epoch
   for mult, start_epoch in LR_SCHEDULE:
     if epoch >= start_epoch:
-      learning_rate = initial_learning_rate * mult
+      learning_rate = initial_lr * mult
     else:
       break
   return learning_rate
@@ -71,7 +74,7 @@ def parse_record_keras(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)
+                  dtype=tf.float32)
   return image, label
 
 
@@ -106,23 +109,21 @@ def run(flags_obj):
   else:
     input_fn = imagenet_main.input_fn
 
-  train_input_dataset = input_fn(
-        is_training=True,
-        data_dir=flags_obj.data_dir,
-        batch_size=per_device_batch_size,
-        num_epochs=flags_obj.train_epochs,
-        parse_record_fn=parse_record_keras)
+  train_input_dataset = input_fn(is_training=True,
+                                 data_dir=flags_obj.data_dir,
+                                 batch_size=per_device_batch_size,
+                                 num_epochs=flags_obj.train_epochs,
+                                 parse_record_fn=parse_record_keras)
 
-  eval_input_dataset = input_fn(
-        is_training=False,
-        data_dir=flags_obj.data_dir,
-        batch_size=per_device_batch_size,
-        num_epochs=flags_obj.train_epochs,
-        parse_record_fn=parse_record_keras)
+  eval_input_dataset = input_fn(is_training=False,
+                                data_dir=flags_obj.data_dir,
+                                batch_size=per_device_batch_size,
+                                num_epochs=flags_obj.train_epochs,
+                                parse_record_fn=parse_record_keras)
 
   optimizer = keras_common.get_optimizer()
   strategy = distribution_utils.get_distribution_strategy(
-    flags_obj.num_gpus, flags_obj.turn_off_distribution_strategy)
+      flags_obj.num_gpus, flags_obj.turn_off_distribution_strategy)
 
   model = resnet_model.resnet50(num_classes=imagenet_main.NUM_CLASSES)
 
@@ -142,29 +143,29 @@ def run(flags_obj):
     train_epochs = 1
 
   num_eval_steps = (imagenet_main.NUM_IMAGES['validation'] //
-                  flags_obj.batch_size)
+                    flags_obj.batch_size)
 
   validation_data = eval_input_dataset
   if flags_obj.skip_eval:
     num_eval_steps = None
     validation_data = None
 
-  history = model.fit(train_input_dataset,
-      epochs=train_epochs,
-      steps_per_epoch=train_steps,
-      callbacks=[
-        time_callback,
-        lr_callback,
-        tensorboard_callback
-        ],
-      validation_steps=num_eval_steps,
-      validation_data=validation_data,
-      verbose=1)
+  model.fit(train_input_dataset,
+            epochs=train_epochs,
+            steps_per_epoch=train_steps,
+            callbacks=[
+                time_callback,
+                lr_callback,
+                tensorboard_callback
+                ],
+            validation_steps=num_eval_steps,
+            validation_data=validation_data,
+            verbose=1)
 
   if not flags_obj.skip_eval:
-    eval_output = model.evaluate(eval_input_dataset,
-                                 steps=num_eval_steps,
-                                 verbose=1)
+    model.evaluate(eval_input_dataset,
+                   steps=num_eval_steps,
+                   verbose=1)
 
 
 def main(_):