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
@@ -106,15 +109,13 @@ def run(flags_obj):
   else:
     input_fn = imagenet_main.input_fn
 
-  train_input_dataset = input_fn(
-        is_training=True,
+  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,
+  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,
@@ -149,7 +150,7 @@ def run(flags_obj):
     num_eval_steps = None
     validation_data = None
 
-  history = model.fit(train_input_dataset,
+  model.fit(train_input_dataset,
             epochs=train_epochs,
             steps_per_epoch=train_steps,
             callbacks=[
@@ -162,7 +163,7 @@ def run(flags_obj):
             verbose=1)
 
   if not flags_obj.skip_eval:
-    eval_output = model.evaluate(eval_input_dataset,
+    model.evaluate(eval_input_dataset,
                    steps=num_eval_steps,
                    verbose=1)