fix the dataset flag

official/resnet/keras/keras_main.py

@@ -181,7 +181,7 @@ def parse_record_keras(raw_record, is_training, dtype):
   Returns:
     Tuple with processed image tensor and one-hot-encoded label tensor.
   """
-  if shining.dataset == IMAGENET_DATASET:
+  if flags_obj.dataset == IMAGENET_DATASET:
     image_buffer, label, bbox = imagenet_main._parse_example_proto(raw_record)
 
     image = imagenet_preprocessing.preprocess_image(
@@ -194,11 +194,11 @@ def parse_record_keras(raw_record, is_training, dtype):
     image = tf.cast(image, dtype)
     label = tf.sparse_to_dense(label, (imagenet_main._NUM_CLASSES,), 1)
 
-  elif shining.dataset == CIFAR_DATASET:
+  elif flags_obj.dataset == CIFAR_DATASET:
     image, label = cifar_main.parse_record(raw_record, is_training, dtype)
     label = tf.sparse_to_dense(label, (cifar_main._NUM_CLASSES,), 1)
   else:
-    raise ValueError("Unknown dataset: {%s}".format(shining.dataset))
+    raise ValueError("Unknown dataset: {%s}".format(flags_obj.dataset))
 
   return image, label
 
@@ -223,7 +223,7 @@ def run_imagenet_with_keras(flags_obj):
       flags_obj.batch_size, flags_core.get_num_gpus(flags_obj))
 
   train_input_dataset, eval_input_dataset = get_data(
-      shining.dataset, flags_obj.use_synthetic_data)
+      flags_obj.dataset, flags_obj.use_synthetic_data)
 
   # Use Keras ResNet50 applications model and native keras APIs
   # initialize RMSprop optimizer
@@ -240,7 +240,7 @@ def run_imagenet_with_keras(flags_obj):
   strategy = distribution_utils.get_distribution_strategy(
       num_gpus=flags_obj.num_gpus)
 
-  if shining.dataset == IMAGENET_DATASET:
+  if flags_obj.dataset == IMAGENET_DATASET:
     model = resnet_model_tpu.ResNet50(num_classes=imagenet_main._NUM_CLASSES)
     steps_per_epoch = imagenet_main._NUM_IMAGES['train'] // flags_obj.batch_size
 
@@ -251,7 +251,7 @@ def run_imagenet_with_keras(flags_obj):
 
     num_eval_steps = (imagenet_main._NUM_IMAGES['validation'] //
                     flags_obj.batch_size)
-  elif shining.dataset = CIFAR_DATASET:
+  elif flags_obj.dataset = CIFAR_DATASET:
     model = keras_resnet_model.ResNet56(input_shape=(32, 32, 3),
                                         include_top=True,
                                         classes=cifar_main._NUM_CLASSES,
@@ -267,7 +267,7 @@ def run_imagenet_with_keras(flags_obj):
     num_eval_steps = (cifar_main._NUM_IMAGES['validation'] //
                       flags_obj.batch_size)
   else:
-    raise ValueError("Unknown dataset: {%s}".format(shining.dataset))
+    raise ValueError("Unknown dataset: {%s}".format(flags_obj.dataset))
 
   loss = 'categorical_crossentropy'
   accuracy = 'categorical_accuracy'
@@ -418,9 +418,9 @@ if __name__ == '__main__':
   tf.logging.set_verbosity(tf.logging.INFO)
   define_keras_flags()
 
-  if shining.dataset == IMAGENET_DATASET:
+  if flags_obj.dataset == IMAGENET_DATASET:
     imagenet_main.define_imagenet_flags()
-  elif shining.dataset == CIFAR_DATASET:
+  elif flags_obj.dataset == CIFAR_DATASET:
     cifar_main.define_cifar_flags()
 
   absl_app.run(main)