fix paddleTensor and paddleInference

ernie/ernie_encoder.py

@@ -22,13 +22,16 @@ import argparse
 import numpy as np
 import multiprocessing
 
+import logging
 import paddle.fluid as fluid
 
 import reader.task_reader as task_reader
-from model.ernie import ErnieConfig, ErnieModel
-from utils.args import ArgumentGroup, print_arguments
+from model.ernie_v1 import ErnieConfig, ErnieModel
+from utils.args import ArgumentGroup, print_arguments, prepare_logger
 from utils.init import init_pretraining_params
 
+log = logging.getLogger()
+
 # yapf: disable
 parser = argparse.ArgumentParser(__doc__)
 model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
@@ -67,7 +70,6 @@ def create_model(args, pyreader_name, ernie_config):
         src_ids=src_ids,
         position_ids=pos_ids,
         sentence_ids=sent_ids,
-        task_ids=task_ids,
         input_mask=input_mask,
         config=ernie_config)
 
@@ -179,6 +181,7 @@ def main(args):
 
 
 if __name__ == '__main__':
+    prepare_logger(log)
     args = parser.parse_args()
     print_arguments(args)
 

ernie/finetune/classifier.py

@@ -52,7 +52,7 @@ def create_model(args,
     elif is_regression:
         labels = fluid.layers.data(name='6', shape=[-1, 1], dtype='float32')
 
-    pyreader = fluid.io.DataLoader.from_generator(feed_list=[src_ids, sent_ids, pos_ids, task_ids, input_mask, qids], 
+    pyreader = fluid.io.DataLoader.from_generator(feed_list=[src_ids, sent_ids, pos_ids, task_ids, input_mask, labels, qids], 
             capacity=70, 
             iterable=False)
 

ernie/infer_classifyer.py

@@ -130,6 +130,7 @@ def main(args):
     if not args.use_cuda:
         log.info("disable gpu")
         config.disable_gpu()
+        config.switch_ir_optim(True) 
     else:
         log.info("using gpu")
         config.enable_use_gpu(1024)
@@ -162,8 +163,7 @@ def main(args):
 
         # parse outputs
         output = outputs[0]
-        output_data = output.data.float_data()
-        batch_result  = np.array(output_data).reshape(output.shape)
+        batch_result  = output.as_ndarray()
         for single_example_probs in batch_result:
             print('\t'.join(map(str, single_example_probs.tolist())))
             index += 1
@@ -173,13 +173,7 @@ def main(args):
 def array2tensor(ndarray):
     """ convert numpy array to PaddleTensor"""
     assert isinstance(ndarray, np.ndarray), "input type must be np.ndarray"
-    if ndarray.dtype == np.float32:
-        dtype = PaddleDType.FLOAT32
-    elif ndarray.dtype == np.int64:
-        dtype = PaddleDType.INT64
-    else:
-        raise ValueError("{} type ndarray is unsupported".format(ndarray.dtype))
-    tensor = PaddleTensor(data=ndarray, name="data")
+    tensor = PaddleTensor(data=ndarray)
     return tensor
 
 if __name__ == '__main__':

propeller/service/utils.py

@@ -73,7 +73,7 @@ def slot_to_paddlearray(slot):
         raise RuntimeError('know type %s' % slot.type)
     num = len(slot.data) // struct.calcsize(type_str)
     arr = struct.unpack('%d%s' % (num, type_str), slot.data)
-    ret = core.PaddleTensor(name='1', data=np.array(arr, dtype=dtype).reshape(slot.dims))
+    ret = core.PaddleTensor(np.array(arr, dtype=dtype).reshape(slot.dims))
     return ret