add reader debug mode

core/trainers/transpiler_trainer.py

@@ -42,7 +42,7 @@ class TranspileTrainer(Trainer):
             namespace = "train.reader"
             class_name = "TrainReader"
         else:
-            dataloader = self.model._infer_data_loader
+            readerdataloader = self.model._infer_data_loader
             namespace = "evaluate.reader"
             class_name = "EvaluateReader"
 
@@ -52,22 +52,22 @@ class TranspileTrainer(Trainer):
         reader = dataloader_instance.dataloader(
             reader_class, state, self._config_yaml)
 
-        debug_mode = envs.get_global_env("debug_mode", False, namespace)
+        reader_class = envs.lazy_instance_by_fliename(reader_class, class_name)
+        reader_ins = reader_class(self._config_yaml)
+        if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
+            dataloader.set_sample_list_generator(reader)
+        else:
+            dataloader.set_sample_generator(reader, batch_size)
+
+        debug_mode = envs.get_global_env("reader_debug_mode", False, namespace)
         if debug_mode:
             print("--- DataLoader Debug Mode Begin , show pre 10 data ---")
-            for idx, line in enumerate(reader):
+            for idx, line in enumerate(reader()):
                 print(line)
                 if idx >= 9:
                     break
             print("--- DataLoader Debug Mode End , show pre 10 data ---")
             exit(0)
-
-        reader_class = envs.lazy_instance_by_fliename(reader_class, class_name)
-        reader_ins = reader_class(self._config_yaml)
-        if hasattr(reader_ins, 'generate_batch_from_trainfiles'):
-            dataloader.set_sample_list_generator(reader)
-        else:
-            dataloader.set_sample_generator(reader, batch_size)
         return dataloader
 
     def _get_dataset(self, state="TRAIN"):
@@ -109,7 +109,7 @@ class TranspileTrainer(Trainer):
 
         dataset.set_filelist(file_list)
 
-        debug_mode = envs.get_global_env("debug_mode", False, namespace)
+        debug_mode = envs.get_global_env("reader_debug_mode", False, namespace)
         if debug_mode:
             print(
                 "--- Dataset Debug Mode Begin , show pre 10 data of {}---".format(file_list[0]))

models/rank/dnn/config.yaml

@@ -24,7 +24,7 @@ train:
     batch_size: 2
     class: "{workspace}/../criteo_reader.py"
     train_data_path: "{workspace}/data/train"
-    debug_mode: False
+    reader_debug_mode: False
 
   model:
     models: "{workspace}/model.py"

models/recall/tdm/config.yaml

@@ -25,6 +25,7 @@ train:
     class: "{workspace}/tdm_reader.py"
     train_data_path: "{workspace}/data/train"
     test_data_path: "{workspace}/data/test"
+    reader_debug_mode: False
 
   model:
     models: "{workspace}/model.py"

models/recall/tdm/tdm_evaluate_reader.py

@@ -33,7 +33,8 @@ class EvaluateReader(Reader):
             This function needs to be implemented by the user, based on data format
             """
             features = (line.strip('\n')).split('\t')
-            input_emb = map(float, features[0].split(' '))
+            input_emb = features[0].split(' ')
+            input_emb = [float(i) for i in input_emb]
 
             feature_name = ["input_emb"]
             yield zip(feature_name, [input_emb])

models/recall/tdm/tdm_reader.py

@@ -33,7 +33,8 @@ class TrainReader(Reader):
             This function needs to be implemented by the user, based on data format
             """
             features = (line.strip('\n')).split('\t')
-            input_emb = map(float, features[0].split(' '))
+            input_emb = features[0].split(' ')
+            input_emb = [float(i) for i in input_emb]
             item_label = [int(features[1])]
 
             feature_name = ["input_emb", "item_label"]