format code

model.py

@@ -403,8 +403,10 @@ class StaticGraphAdapter(object):
         metrics = []
         with fluid.program_guard(prog, self._startup_prog):
             if isinstance(self.model._inputs, dict):
-                ins = [self.model._inputs[n]
-                       for n in extract_args(self.model.forward) if n != 'self']
+                ins = [
+                    self.model._inputs[n]
+                    for n in extract_args(self.model.forward) if n != 'self'
+                ]
             else:
                 ins = self.model._inputs
             lbls = self.model._labels if self.model._labels else []
@@ -470,8 +472,8 @@ class StaticGraphAdapter(object):
         if self._executor is None:
             if self._nranks > 1 and device.lower() == 'gpu':
                 gpu_id = int(ParallelEnv().dev_id)
-                place = fluid.CUDAPlace(
-                    gpu_id) if device.lower() == 'gpu' else fluid.CPUPlace()
+                place = fluid.CUDAPlace(gpu_id) if device.lower(
+                ) == 'gpu' else fluid.CPUPlace()
             else:
                 place = places[0]
             self._executor = fluid.Executor(place)
@@ -521,8 +523,8 @@ class DynamicGraphAdapter(object):
             stradegy.local_rank = ParallelEnv().local_rank
             stradegy.trainer_endpoints = ParallelEnv().trainer_endpoints
             stradegy.current_endpoint = ParallelEnv().current_endpoint
-            self.ddp_model = fluid.dygraph.parallel.DataParallel(
-                self.model, stradegy)
+            self.ddp_model = fluid.dygraph.parallel.DataParallel(self.model,
+                                                                 stradegy)
 
     @property
     def mode(self):
@@ -1017,7 +1019,8 @@ class Model(fluid.dygraph.Layer):
                     logs[k] = v
 
                 logs['step'] = step
-                if mode == 'train' or self._adapter._merge_count.get(mode + '_batch', 0) <= 0:
+                if mode == 'train' or self._adapter._merge_count.get(
+                        mode + '_batch', 0) <= 0:
                     logs['batch_size'] = batch_size * ParallelEnv().nranks
                 else:
                     logs['batch_size'] = self._adapter._merge_count[mode +