fix bugs

download_models.py

-# -*- coding: UTF-8 -*-
-#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import paddlepalm as palm
-import sys
-import argparse
- 
-# create parser
-parser = argparse.ArgumentParser(prog='download_models.py', usage='python %(prog)s -l | -d <model_name> [-h]\n\nFor example,\n\tpython %(prog)s -d bert-en-uncased-large ',description = 'Download pretrain models for initializing params of backbones. ')
-parser1= parser.add_argument_group("required arguments")
-parser1.add_argument('-l','--list', action = 'store_true', help = 'show the list of available pretrain models', default = False)
-parser1.add_argument('-d','--download', action = 'store', help = 'download pretrain models. The available pretrain models can be listed by run "python download_models.py -l"') 
-args = parser.parse_args()
-
-if(args.list):
-  palm.downloader.ls('pretrain')
-elif(args.download):
-  print('download~~~')
-  print(args.download)
-  palm.downloader.download('pretrain', args.download)
-else:
-  print (parser.parse_args(['-h']))

examples/predict/run.py

@@ -43,7 +43,7 @@ if __name__ == '__main__':
     trainer.build_predict_forward(pred_ernie, cls_pred_head)
  
     # step 6: load pretrained model
-    pred_model = trainer.load_ckpt(pre_params)
+    pred_model = trainer.load_pretrain(pre_params)
 
     # step 7: fit prepared reader and data
     trainer.fit_reader(predict_cls_reader, phase='predict')

paddlepalm/head/cls.py

@@ -38,6 +38,7 @@ class Classify(Head):
         self._param_initializer = fluid.initializer.TruncatedNormal(
             scale=param_initializer_range)
         self._preds = []
+        self._probs = []
 
     @property
     def inputs_attrs(self):
@@ -52,7 +53,9 @@ class Classify(Head):
         if self._is_training:
             return {'loss': [[1], 'float32']}
         else:
-            return {'logits': [[-1, self.num_classes], 'float32']}
+            return {'logits': [[-1, self.num_classes], 'float32'],
+                    'probs': [[-1, self.num_classes], 'float32']}
+            
 
     def build(self, inputs, scope_name=''):
         sent_emb = inputs['backbone']['sentence_embedding']
@@ -71,11 +74,10 @@ class Classify(Head):
                 initializer=self._param_initializer),
             bias_attr=fluid.ParamAttr(
                 name=scope_name+"cls_out_b", initializer=fluid.initializer.Constant(0.)))
-
+        probs = fluid.layers.softmax(logits)
         if self._is_training:
-            inputs = fluid.layers.softmax(logits)
             loss = fluid.layers.cross_entropy(
-                input=inputs, label=label_ids)
+                input=probs, label=label_ids)
             loss = layers.mean(loss)
             return {"loss": loss}
         else:

paddlepalm/lr_sched/slanted_triangular_schedualer.py

@@ -13,7 +13,7 @@ class TriangularSchedualer(Schedualer):
             num_train_steps: the number of train steps.
 
         """
-        BaseSchedualer.__init__(self)
+        Schedualer.__init__(self)
         assert num_train_steps > warmup_steps > 0
         self.warmup_steps = warmup_steps
         self.num_train_steps = num_train_steps