python3 ok

paddlepalm/backbone/utils/transformer.py

@@ -24,6 +24,7 @@ import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 
 from paddle.fluid.layer_helper import LayerHelper as LayerHelper
+from functools import reduce # py3
 def layer_norm(x, begin_norm_axis=1, epsilon=1e-6, param_attr=None, bias_attr=None):
     helper = LayerHelper('layer_norm', **locals())
     mean = layers.reduce_mean(x, dim=begin_norm_axis, keep_dim=True)

paddlepalm/mtl_controller.py

@@ -95,7 +95,7 @@ def _try_float(s):
 def _check_conf(conf, checklist=None):
     assert isinstance(conf, dict), "{} is not a dict.".format(conf)
     ret = {}
-    for k,v in conf.items():
+    for k,v in list(conf.items()):
         if isinstance(v, str):
             v = _try_float(v)
         ret[k] = v

paddlepalm/task_instance.py

@@ -92,7 +92,7 @@ class TaskInstance(object):
         output_vars = self._task_layer[phase].build(net_inputs, scope_name=scope)
         if phase == 'pred':
             if output_vars is not None:
-                self._pred_fetch_name_list, self._pred_fetch_var_list = zip(*output_vars.items())
+                self._pred_fetch_name_list, self._pred_fetch_var_list = list(zip(*list(output_vars.items())))
             else:
                 self._pred_fetch_name_list = []
                 self._pred_fetch_var_list = []
@@ -113,7 +113,7 @@ class TaskInstance(object):
         fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, prog)
 
         conf = {}
-        for k, strv in self._save_protocol.items():
+        for k, strv in list(self._save_protocol.items()): # py3
             exec('v={}'.format(strv))
             conf[k] = v
         with open(os.path.join(dirpath, '__conf__'), 'w') as writer:
@@ -123,7 +123,7 @@ class TaskInstance(object):
     def load(self, infer_model_path=None):
         if infer_model_path is None:
             infer_model_path = self._save_infermodel_path
-        for k,v in json.load(open(os.path.join(infer_model_path, '__conf__'))).items():
+        or k,v in list(json.load(open(os.path.join(infer_model_path, '__conf__'))).items()): # py3
             strv = self._save_protocol[k]
             exec('{}=v'.format(strv))
         pred_prog, self._pred_input_varname_list, self._pred_fetch_var_list = \
@@ -167,13 +167,13 @@ class TaskInstance(object):
 
     @property
     def pred_input(self):
-        return zip(*[self._pred_input_name_list, self._pred_input_varname_list])
+        return list(zip(*[self._pred_input_name_list, self._pred_input_varname_list])) # py3
 
     @pred_input.setter
     def pred_input(self, val):
         assert isinstance(val, dict)
         self._pred_input_name_list, self._pred_input_varname_list = \
-            zip(*[[k, v.name] for k,v in val.items()])
+            list(zip(*[[k, v.name] for k,v in list(val.items())])) # py3s
 
     @property
     def pred_fetch_list(self):

paddlepalm/tokenizer/bert_tokenizer.py

@@ -111,7 +111,7 @@ class FullTokenizer(object):
 
     def __init__(self, vocab_file, do_lower_case=True):
         self.vocab = load_vocab(vocab_file)
-        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.inv_vocab = {v: k for k, v in list(self.vocab.items())}
         self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
 
@@ -135,7 +135,7 @@ class CharTokenizer(object):
 
     def __init__(self, vocab_file, do_lower_case=True):
         self.vocab = load_vocab(vocab_file)
-        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.inv_vocab = {v: k for k, v in list(self.vocab.items())}
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
 
     def tokenize(self, text):

paddlepalm/tokenizer/ernie_tokenizer.py

@@ -115,7 +115,7 @@ class FullTokenizer(object):
 
     def __init__(self, vocab_file, do_lower_case=True):
         self.vocab = load_vocab(vocab_file)
-        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.inv_vocab = {v: k for k, v in list(self.vocab.items())}
         self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
 
@@ -139,7 +139,7 @@ class CharTokenizer(object):
 
     def __init__(self, vocab_file, do_lower_case=True):
         self.vocab = load_vocab(vocab_file)
-        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.inv_vocab = {v: k for k, v in list(self.vocab.items())}
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
 
     def tokenize(self, text):

paddlepalm/utils/reader_helper.py

@@ -87,7 +87,7 @@ def create_iterator_fn(iterator, iterator_prefix, shape_and_dtypes, outname_to_p
 
             outputs = next(iterator) # dict type
             prefix = iterator_prefixe
-            for outname, val in outputs.items():
+            for outname, val in list(outputs.items()):
                 task_outname = prefix + '/' + outname
 
                 if outname in outname_to_pos:
@@ -121,7 +121,7 @@ def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtype
         outputs = next(iterators[id]) # dict type
         outbuf[id] = outputs
         prefix = iterator_prefixes[id]
-        for outname, val in outputs.items():
+        for outname, val in list(outputs.items()):
             task_outname = prefix + '/' + outname
 
             if outname in outname_to_pos:
@@ -176,7 +176,7 @@ def create_joint_iterator_fn(iterators, iterator_prefixes, joint_shape_and_dtype
                         has_show_warn = True
 
                 prefix = iterator_prefixes[id]
-                for outname, val in outputs.items():
+                for outname, val in list(outputs.items()):
                     if v > 0:
                         print('reader generate: '+outname)
                     task_outname = prefix + '/' + outname