Adapt to the async data reader

fluid/DeepASR/infer_from_ckpt.py → fluid/DeepASR/infer_by_ckpt.py

@@ -12,7 +12,7 @@ import paddle.fluid as fluid
 import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
 import data_utils.augmentor.trans_add_delta as trans_add_delta
 import data_utils.augmentor.trans_splice as trans_splice
-import data_utils.data_reader as reader
+import data_utils.async_data_reader as reader
 from data_utils.util import lodtensor_to_ndarray
 from model_utils.model import stacked_lstmp_model
 
@@ -127,8 +127,8 @@ def infer_from_ckpt(args):
     label_t = fluid.LoDTensor()
 
     # infer data reader
-    infer_data_reader = reader.DataReader(args.infer_feature_lst,
-                                          args.infer_label_lst)
+    infer_data_reader = reader.AsyncDataReader(args.infer_feature_lst,
+                                               args.infer_label_lst)
     infer_data_reader.set_transformers(ltrans)
     infer_costs, infer_accs = [], []
     for batch_id, batch_data in enumerate(
@@ -136,10 +136,12 @@ def infer_from_ckpt(args):
                                              args.minimum_batch_size)):
         # load_data
         (features, labels, lod) = batch_data
-        feature_t.set(features, place)
-        feature_t.set_lod([lod])
-        label_t.set(labels, place)
-        label_t.set_lod([lod])
+        feature_t.set(features.ndarray, place)
+        feature_t.set_lod([lod.ndarray])
+        label_t.set(labels.ndarray, place)
+        label_t.set_lod([lod.ndarray])
+
+        infer_data_reader.recycle(features, labels, lod)
 
         cost, acc = exe.run(infer_program,
                             feed={"feature": feature_t,