diff --git a/model_zoo/official/cv/crnn_seq2seq_ocr/eval.py b/model_zoo/official/cv/crnn_seq2seq_ocr/eval.py
index 6019c7c57fe..32cb86a5eb9 100755
--- a/model_zoo/official/cv/crnn_seq2seq_ocr/eval.py
+++ b/model_zoo/official/cv/crnn_seq2seq_ocr/eval.py
@@ -21,12 +21,11 @@ import os
 import codecs
 import numpy as np
 
-import mindspore.ops.operations as P
 import mindspore.common.dtype as mstype
-
 from mindspore.common import set_seed
 from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.model import Model
 
 from src.utils import initialize_vocabulary
 from src.dataset import create_ocr_val_dataset
@@ -103,6 +102,8 @@ def run_eval():
     network.set_train(False)
     print("Checkpoint loading Done!")
 
+    model = Model(network)
+
     vocab_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), config.vocab_path)
     _, rev_vocab = initialize_vocabulary(vocab_path)
     eos_id = config.characters_dictionary.eos_id
@@ -120,23 +121,18 @@ def run_eval():
             codecs.open(incorrect_file, 'w', encoding='utf-8') as fp_output_incorrect:
 
         for data in data_loader:
-            images = Tensor(data["image"])
-            decoder_inputs = Tensor(data["decoder_input"])
+            images = Tensor(data["image"]).astype(np.float32)
             # decoder_targets = Tensor(data["decoder_target"])
 
             decoder_hidden = Tensor(np.zeros((1, config.eval_batch_size, config.decoder_hidden_size),
                                              dtype=np.float16), mstype.float16)
             decoder_input = Tensor((np.ones((config.eval_batch_size, 1)) * sos_id).astype(np.int32))
-            encoder_outputs = network.encoder(images)
-            batch_decoded_label = []
 
-            for _ in range(decoder_inputs.shape[1]):
-                decoder_output, decoder_hidden, _ = network.decoder(decoder_input, decoder_hidden, encoder_outputs)
-                topi = P.Argmax()(decoder_output)
-                ni = P.ExpandDims()(topi, 1)
-                decoder_input = ni
-                topi_id = topi.asnumpy()
-                batch_decoded_label.append(topi_id)
+            result_batch_decoded_label = model.predict(images, decoder_input, decoder_hidden)
+
+            batch_decoded_label = []
+            for ele in result_batch_decoded_label:
+                batch_decoded_label.append(ele.asnumpy())
 
             for b in range(config.eval_batch_size):
                 text = data["annotation"][b].decode("utf8")
diff --git a/model_zoo/official/cv/crnn_seq2seq_ocr/src/attention_ocr.py b/model_zoo/official/cv/crnn_seq2seq_ocr/src/attention_ocr.py
index 3e0b257e876..c789cd3fce1 100755
--- a/model_zoo/official/cv/crnn_seq2seq_ocr/src/attention_ocr.py
+++ b/model_zoo/official/cv/crnn_seq2seq_ocr/src/attention_ocr.py
@@ -60,14 +60,23 @@ class AttentionOCRInfer(nn.Cell):
                                max_length=max_length,
                                dropout_p=dropout_p)
 
+        self.max_length = max_length
+
     def construct(self, img, decoder_input, decoder_hidden):
         '''
         get token output
         '''
         encoder_outputs = self.encoder(img)
-        decoder_output, decoder_hidden, decoder_attention = self.decoder(
-            decoder_input, decoder_hidden, encoder_outputs)
-        return decoder_output, decoder_hidden, decoder_attention
+        batch_decoded_label = []
+
+        for _ in range(self.max_length):
+            decoder_output, decoder_hidden, _ = self.decoder(decoder_input, decoder_hidden, encoder_outputs)
+            topi = P.Argmax()(decoder_output)
+            ni = P.ExpandDims()(topi, 1)
+            decoder_input = ni
+            batch_decoded_label.append(topi)
+
+        return batch_decoded_label
 
 
 class AttentionOCR(nn.Cell):