fix bugs

paddlepalm/trainer.py

@@ -168,10 +168,10 @@ class Trainer(object):
 
         if not self._lock_prog:
             with fluid.program_guard(train_prog, train_init_prog):
-                net_inputs = reader_helper.create_net_inputs(input_attrs, async=False)
+                net_inputs = reader_helper.create_net_inputs(input_attrs, is_async=False)
                 bb_output_vars = backbone.build(net_inputs)
         else:
-            net_inputs = reader_helper.create_net_inputs(input_attrs, async=False)
+            net_inputs = reader_helper.create_net_inputs(input_attrs, is_async=False)
             bb_output_vars = backbone.build(net_inputs)
         self._net_inputs = net_inputs
         assert sorted(bb_output_vars.keys()) == sorted(backbone.outputs_attr.keys())

paddlepalm/utils/reader_helper.py

@@ -117,7 +117,7 @@ def _zero_batch_x(attrs, batch_size):
     return [np.zeros(shape=shape, dtype=dtype) for shape, dtype in pos_attrs]
 
 
-def create_net_inputs(input_attrs, async=False, iterator_fn=None, dev_count=1, n_prefetch=1):
+def create_net_inputs(input_attrs, is_async=False, iterator_fn=None, dev_count=1, n_prefetch=1):
     inputs = []
     ret = {}
     for name, shape, dtype in input_attrs:
@@ -125,7 +125,7 @@ def create_net_inputs(input_attrs, async=False, iterator_fn=None, dev_count=1, n
         ret[name] = p
         inputs.append(p)
 
-    if async:
+    if is_async:
         assert iterator_fn is not None, "iterator_fn is needed for building async input layer."
         reader = fluid.io.PyReader(inputs, capacity=dev_count, iterable=False)
         reader.decorate_batch_generator(iterator_fn)