Fix atention model. (#2059)

PaddleCV/ocr_recognition/attention_model.py

@@ -339,7 +339,7 @@ def attention_infer(images, num_classes, use_cudnn=True):
     return ids
 
 
-def attention_eval(data_shape, num_classes):
+def attention_eval(data_shape, num_classes, use_cudnn=True):
     images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
     label_in = fluid.layers.data(
         name='label_in', shape=[1], dtype='int32', lod_level=1)
@@ -349,7 +349,7 @@ def attention_eval(data_shape, num_classes):
     label_in = fluid.layers.cast(x=label_in, dtype='int64')
 
     gru_backward, encoded_vector, encoded_proj = encoder_net(
-        images, is_test=True)
+        images, is_test=True, use_cudnn=use_cudnn)
 
     backward_first = fluid.layers.sequence_pool(
         input=gru_backward, pool_type='first')

PaddleCV/ocr_recognition/crnn_ctc_model.py

@@ -213,12 +213,12 @@ def ctc_train_net(args, data_shape, num_classes):
     return sum_cost, error_evaluator, inference_program, model_average
 
 
-def ctc_infer(images, num_classes, use_cudnn):
+def ctc_infer(images, num_classes, use_cudnn=True):
     fc_out = encoder_net(images, num_classes, is_test=True, use_cudnn=use_cudnn)
     return fluid.layers.ctc_greedy_decoder(input=fc_out, blank=num_classes)
 
 
-def ctc_eval(data_shape, num_classes, use_cudnn):
+def ctc_eval(data_shape, num_classes, use_cudnn=True):
     images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
     label = fluid.layers.data(
         name='label', shape=[1], dtype='int32', lod_level=1)