try to fix imperative orc unitest error; test=develop

python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py

@@ -29,19 +29,19 @@ class Config(object):
     config for training
     '''
     # encoder rnn hidden_size
-    encoder_size = 16
+    encoder_size = 8
     # decoder size for decoder stage
-    decoder_size = 16
+    decoder_size = 8
     # size for word embedding
-    word_vector_dim = 16
+    word_vector_dim = 8
     # max length for label padding
-    max_length = 5
+    max_length = 3
     # optimizer setting
     LR = 1.0
     learning_rate_decay = None
 
     # batch size to train
-    batch_size = 8
+    batch_size = 2
     # class number to classify
     num_classes = 64
 
@@ -55,7 +55,7 @@ class Config(object):
     TRAIN_LIST_FILE_NAME = "train.list"
 
     # data shape for input image
-    DATA_SHAPE = [1, 48, 384]
+    DATA_SHAPE = [1, 16, 64]
 
 
 class ConvBNPool(fluid.dygraph.Layer):
@@ -124,13 +124,13 @@ class OCRConv(fluid.dygraph.Layer):
     def __init__(self, is_test=False, use_cudnn=True):
         super(OCRConv, self).__init__()
         self.conv_bn_pool_1 = ConvBNPool(
-            2, [16, 16], [1, 16], is_test=is_test, use_cudnn=use_cudnn)
+            2, [8, 8], [1, 8], is_test=is_test, use_cudnn=use_cudnn)
         self.conv_bn_pool_2 = ConvBNPool(
-            2, [32, 32], [16, 32], is_test=is_test, use_cudnn=use_cudnn)
+            2, [8, 8], [8, 8], is_test=is_test, use_cudnn=use_cudnn)
         self.conv_bn_pool_3 = ConvBNPool(
-            2, [64, 64], [32, 64], is_test=is_test, use_cudnn=use_cudnn)
+            2, [8, 8], [8, 8], is_test=is_test, use_cudnn=use_cudnn)
         self.conv_bn_pool_4 = ConvBNPool(
-            2, [128, 128], [64, 128],
+            2, [16, 16], [8, 16],
             is_test=is_test,
             pool=False,
             use_cudnn=use_cudnn)
@@ -212,9 +212,9 @@ class EncoderNet(fluid.dygraph.Layer):
         self.ocr_convs = OCRConv(is_test=is_test, use_cudnn=use_cudnn)
 
         self.fc_1_layer = Linear(
-            768, rnn_hidden_size * 3, param_attr=para_attr, bias_attr=False)
+            32, rnn_hidden_size * 3, param_attr=para_attr, bias_attr=False)
         self.fc_2_layer = Linear(
-            768, rnn_hidden_size * 3, param_attr=para_attr, bias_attr=False)
+            32, rnn_hidden_size * 3, param_attr=para_attr, bias_attr=False)
         self.gru_forward_layer = DynamicGRU(
             size=rnn_hidden_size,
             h_0=h_0,
@@ -241,10 +241,9 @@ class EncoderNet(fluid.dygraph.Layer):
 
         transpose_conv_features = fluid.layers.transpose(
             conv_features, perm=[0, 3, 1, 2])
-
         sliced_feature = fluid.layers.reshape(
             transpose_conv_features, [
-                -1, 48, transpose_conv_features.shape[2] *
+                -1, 8, transpose_conv_features.shape[2] *
                 transpose_conv_features.shape[3]
             ],
             inplace=False)
@@ -376,9 +375,9 @@ class TestDygraphOCRAttention(unittest.TestCase):
         seed = 90
         epoch_num = 1
         if core.is_compiled_with_cuda():
-            batch_num = 6
+            batch_num = 3
         else:
-            batch_num = 4
+            batch_num = 2
         np.random.seed = seed
         image_np = np.random.randn(Config.batch_size, Config.DATA_SHAPE[0],
                                    Config.DATA_SHAPE[1],
@@ -536,8 +535,9 @@ class TestDygraphOCRAttention(unittest.TestCase):
             self.assertTrue(np.array_equal(value, dy_param_init_value[key]))
 
         for key, value in six.iteritems(static_param_value):
-            self.assertTrue(np.allclose(value, dy_param_value[key]))
+            self.assertTrue(np.allclose(value, dy_param_value[key], rtol=1e-05))
 
 
 if __name__ == '__main__':
+    paddle.enable_static()
     unittest.main()