update autofinetune and run command

paddlehub/autofinetune/evaluator.py

@@ -136,7 +136,17 @@ class FullTrailEvaluator(BaseEvaluator):
             os.system(run_cmd)
             with open(log_file, "r") as f:
                 lines = f.readlines()
-                eval_result = float(lines[-1])
+                eval_result = []
+                for line in lines:
+                    line = line.strip()
+                    if line.startswith("AutoFinetuneEval"):
+                        data = line.split("\t")
+                        eval_result = float(data[-1])
+                if eval_result == []:
+                    print(
+                        "WARNING: Program which was ran with hyperparameters as %s was crashed!"
+                        % param_str.replace("--", ""))
+                    eval_result = 0.0
         except:
             print(
                 "WARNING: Program which was ran with hyperparameters as %s was crashed!"
@@ -190,7 +200,17 @@ class ModelBasedEvaluator(BaseEvaluator):
             os.system(run_cmd)
             with open(log_file, "r") as f:
                 lines = f.readlines()
-                eval_result = float(lines[-1])
+                eval_result = []
+                for line in lines:
+                    line = line.strip()
+                    if line.startswith("AutoFinetuneEval"):
+                        data = line.split("\t")
+                        eval_result = float(data[-1])
+                if eval_result == []:
+                    print(
+                        "WARNING: Program which was ran with hyperparameters as %s was crashed!"
+                        % param_str.replace("--", ""))
+                    eval_result = 0.0
         except:
             print(
                 "WARNING: Program which was ran with hyperparameters as %s was crashed!"

paddlehub/commands/run.py

@@ -108,6 +108,13 @@ class RunCommand(BaseCommand):
             type=str,
             default=None,
             help="file contain input data")
+        self.arg_input_group.add_argument(
+            '--use_strip',
+            type=ast.literal_eval,
+            default=True,
+            help=
+            "whether need to strip whitespace characters from the beginning and the end of the line in the file or not."
+        )
         if len(expect_data_format) == 1:
             if module_type.startswith("cv"):
                 self.arg_input_group.add_argument(
@@ -147,7 +154,8 @@ class RunCommand(BaseCommand):
         if len(expect_data_format) == 1:
             key = list(expect_data_format.keys())[0]
             if self.args.input_file:
-                input_data[key] = txt_parser.parse(self.args.input_file)
+                input_data[key] = txt_parser.parse(self.args.input_file,
+                                                   self.args.use_strip)
             else:
                 if module_type.startswith("cv"):
                     input_data[key] = [self.args.input_path]

paddlehub/io/parser.py

@@ -71,11 +71,12 @@ class TextFileParser(object):
     def _check(self):
         pass
 
-    def parse(self, txt_file):
+    def parse(self, txt_file, use_strip=True):
         with codecs.open(txt_file, "r", sys_stdin_encoding()) as file:
             contents = []
             for line in file:
-                line = line.strip()
+                if use_strip:
+                    line = line.strip()
                 if line:
                     contents.append(line)
         return contents

tutorial/autofinetune.md

@@ -163,7 +163,7 @@ if __name__ == '__main__':
         shutil.copytree(config.checkpoint_dir+"/best_model/", args.saved_params_dir)
         shutil.rmtree(config.checkpoint_dir)
 
-    print(eval_avg_score["acc"], end="")
+    print("AutoFinetuneEval"+"\t"+str(float(eval_avg_score["acc"])))
 ```
 **Note**:以上是finetunee.py的写法。
 > finetunee.py必须可以接收待优化超参数选项参数, 并且待搜素超参数选项名字和yaml文件中的超参数名字保持一致.
@@ -174,7 +174,7 @@ if __name__ == '__main__':
 
 > PaddleHub Auto Fine-tune优化超参策略选择hazero时，必须提供两个以上的待优化超参。
 
-> finetunee.py的最后一个输出必须是模型在数据集dev上的评价效果，同时以“”结束，如print(eval_avg_score["acc"], end="").
+> finetunee.py必须输出模型在数据集dev上的评价效果，同时以“AutoFinetuneEval"开始，和评价效果之间以“\t”分开，如print("AutoFinetuneEval"+"\t"+str(float(eval_avg_score["acc"]))).