Fix for resnet data_dir name in copybara.

PiperOrigin-RevId: 270079001

official/benchmark/keras_imagenet_benchmark.py

@@ -46,7 +46,8 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
 
     flag_methods = [resnet_imagenet_main.define_imagenet_keras_flags]
 
-    self.data_dir = os.path.join(root_data_dir, 'imagenet')
+    self.data_dir = ("/readahead/200M/placer/prod/home/distbelief/"
+                     "imagenet-tensorflow/imagenet-2012-tfrecord")
     super(Resnet50KerasAccuracy, self).__init__(
         output_dir=output_dir, flag_methods=flag_methods)
 
@@ -837,7 +838,8 @@ class Resnet50KerasBenchmarkReal(Resnet50KerasBenchmarkBase):
     def_flags = {}
     def_flags['skip_eval'] = True
     def_flags['report_accuracy_metrics'] = False
-    def_flags['data_dir'] = os.path.join(root_data_dir, 'imagenet')
+    def_flags['data_dir'] = ("/readahead/200M/placer/prod/home/distbelief/"
+                     "imagenet-tensorflow/imagenet-2012-tfrecord")
     def_flags['train_steps'] = 110
     def_flags['log_steps'] = 10
 
@@ -857,7 +859,8 @@ class TrivialKerasBenchmarkReal(keras_benchmark.KerasBenchmark):
     def_flags['report_accuracy_metrics'] = False
     def_flags['use_tensor_lr'] = True
     def_flags['dtype'] = 'fp16'
-    def_flags['data_dir'] = os.path.join(root_data_dir, 'imagenet')
+    def_flags['data_dir'] = ("/readahead/200M/placer/prod/home/distbelief/"
+                     "imagenet-tensorflow/imagenet-2012-tfrecord")
     def_flags['train_steps'] = 600
     def_flags['log_steps'] = 100
     def_flags['distribution_strategy'] = 'default'
@@ -971,7 +974,8 @@ class Resnet50MultiWorkerKerasAccuracy(keras_benchmark.KerasBenchmark):
 
   def __init__(self, output_dir=None, root_data_dir=None, **kwargs):
     flag_methods = [resnet_imagenet_main.define_imagenet_keras_flags]
-    self.data_dir = os.path.join(root_data_dir, 'imagenet')
+    self.data_dir = ("/readahead/200M/placer/prod/home/distbelief/"
+                     "imagenet-tensorflow/imagenet-2012-tfrecord")
     super(Resnet50MultiWorkerKerasAccuracy, self).__init__(
         output_dir=output_dir, flag_methods=flag_methods)
 
@@ -1149,7 +1153,8 @@ class Resnet50MultiWorkerKerasBenchmarkReal(Resnet50MultiWorkerKerasBenchmark):
     def_flags = {}
     def_flags['skip_eval'] = True
     def_flags['report_accuracy_metrics'] = False
-    def_flags['data_dir'] = os.path.join(root_data_dir, 'imagenet')
+    def_flags['data_dir'] = ("/readahead/200M/placer/prod/home/distbelief/"
+                     "imagenet-tensorflow/imagenet-2012-tfrecord")
     def_flags['train_steps'] = 110
     def_flags['log_steps'] = 10
 

official/resnet/ctl/ctl_imagenet_benchmark.py

@@ -123,8 +123,7 @@ class Resnet50CtlAccuracy(CtlBenchmark):
 
     flag_methods = [ctl_common.define_ctl_flags, common.define_keras_flags]
 
-    self.data_dir = ('/readahead/200M/placer/prod/home/distbelief/'
-                     'imagenet-tensorflow/imagenet-2012-tfrecord')
+    self.data_dir = os.path.join(root_data_dir, 'imagenet')
     super(Resnet50CtlAccuracy, self).__init__(
         output_dir=output_dir, flag_methods=flag_methods)