pass args.single to CombinedDataGenerator

Author: hyoo

Pilot1/Uno/uno_baseline_keras2.py

@@ -426,8 +426,8 @@ def warmup_scheduler(epoch):
             train_gen = DataFeeder(filename=args.use_exported_data, batch_size=args.batch_size, shuffle=args.shuffle, single=args.single, agg_dose=args.agg_dose)
             val_gen = DataFeeder(partition='val', filename=args.use_exported_data, batch_size=args.batch_size, shuffle=args.shuffle, single=args.single, agg_dose=args.agg_dose)
         else:
-            train_gen = CombinedDataGenerator(loader, fold=fold, batch_size=args.batch_size, shuffle=args.shuffle)
-            val_gen = CombinedDataGenerator(loader, partition='val', fold=fold, batch_size=args.batch_size, shuffle=args.shuffle)
+            train_gen = CombinedDataGenerator(loader, fold=fold, batch_size=args.batch_size, shuffle=args.shuffle, single=args.single)
+            val_gen = CombinedDataGenerator(loader, partition='val', fold=fold, batch_size=args.batch_size, shuffle=args.shuffle, single=args.single)
 
         df_val = val_gen.get_response(copy=True)
         y_val = df_val[target].values

Pilot1/Uno/uno_data.py

@@ -997,10 +997,11 @@ def close(self):
 class CombinedDataGenerator(keras.utils.Sequence):
     """Generate training, validation or testing batches from loaded data
     """
-    def __init__(self, data, partition='train', fold=0, source=None, batch_size=32, shuffle=True, rank=0, total_ranks=1):
+    def __init__(self, data, partition='train', fold=0, source=None, batch_size=32, shuffle=True, single=False, rank=0, total_ranks=1):
         self.data = data
         self.partition = partition
         self.batch_size = batch_size
+        self.single = single
 
         if partition == 'train':
             index = data.train_indexes[fold]
@@ -1031,7 +1032,7 @@ def __len__(self):
 
     def __getitem__(self, idx):
         shard = self.index[idx * self.batch_size:(idx + 1) * self.batch_size]
-        x_list, y = self.get_slice(self.batch_size, partial_index=shard)
+        x_list, y = self.get_slice(self.batch_size, single=self.single, partial_index=shard)
         return x_list, y
 
     def reset(self):