fix chinese_ner inference appearing linear_chain_crf_grad op

fluid/chinese_ner/infer.py

@@ -61,6 +61,21 @@ def load_reverse_dict(dict_path):
     return dict((idx, line.strip().split("\t")[0])
                 for idx, line in enumerate(open(dict_path, "r").readlines()))
 
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = fluid.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
+
+
 
 def infer(args):
     word = fluid.layers.data(name='word', shape=[1], dtype='int64', lod_level=1)
@@ -93,9 +108,13 @@ def infer(args):
                 profiler.reset_profiler()
             iters = 0
             for data in test_data():
+                word = to_lodtensor(map(lambda x: x[0], data), place)
+                mention = to_lodtensor(map(lambda x: x[1], data), place)
+
                 start = time.time()
                 crf_decode = exe.run(inference_program,
-                                     feed=feeder.feed(data),
+                                     feed={"word": word,
+                                           "mention": mention},
                                      fetch_list=fetch_targets,
                                      return_numpy=False)
                 batch_time = time.time() - start

fluid/chinese_ner/train.py

@@ -266,12 +266,13 @@ def main(args):
     with fluid.program_guard(main, startup):
         avg_cost, feature_out, word, mention, target = ner_net(
             args.word_dict_len, args.label_dict_len)
-        sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
-        sgd_optimizer.minimize(avg_cost)
 
         crf_decode = fluid.layers.crf_decoding(
             input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
+        sgd_optimizer.minimize(avg_cost)
+
         (precision, recall, f1_score, num_infer_chunks, num_label_chunks,
          num_correct_chunks) = fluid.layers.chunk_eval(
              input=crf_decode,
@@ -348,8 +349,8 @@ def main(args):
                   + str(f1))
             save_dirname = os.path.join(args.model_save_dir,
                                         "params_pass_%d" % pass_id)
-            fluid.io.save_inference_model(
-                save_dirname, ['word', 'mention', 'target'], [crf_decode], exe)
+            fluid.io.save_inference_model(save_dirname, ['word', 'mention'],
+                                          [crf_decode], exe)
 
 
 if __name__ == "__main__":