fix mixup & cutmix in multiprocessing (#1320)

ppdet/data/reader.py

@@ -183,6 +183,7 @@ class Reader(object):
         inputs_def (dict): network input definition use to get input fields,
             which is used to determine the order of returned data.
         devices_num (int): number of devices.
+        num_trainers (int): number of trainers. Default 1.
     """
 
     def __init__(self,
@@ -203,7 +204,8 @@ class Reader(object):
                  bufsize=-1,
                  memsize='3G',
                  inputs_def=None,
-                 devices_num=1):
+                 devices_num=1,
+                 num_trainers=1):
         self._dataset = dataset
         self._roidbs = self._dataset.get_roidb()
         self._fields = copy.deepcopy(inputs_def[
@@ -244,8 +246,8 @@ class Reader(object):
         self._drop_empty = drop_empty
 
         # sampling
-        self._mixup_epoch = mixup_epoch
-        self._cutmix_epoch = cutmix_epoch
+        self._mixup_epoch = mixup_epoch // num_trainers
+        self._cutmix_epoch = cutmix_epoch // num_trainers
         self._class_aware_sampling = class_aware_sampling
 
         self._load_img = False
@@ -415,7 +417,11 @@ class Reader(object):
             self._parallel.stop()
 
 
-def create_reader(cfg, max_iter=0, global_cfg=None, devices_num=1):
+def create_reader(cfg,
+                  max_iter=0,
+                  global_cfg=None,
+                  devices_num=1,
+                  num_trainers=1):
     """
     Return iterable data reader.
 
@@ -431,6 +437,7 @@ def create_reader(cfg, max_iter=0, global_cfg=None, devices_num=1):
                                                'use_fine_grained_loss', False)
         cfg['num_classes'] = getattr(global_cfg, 'num_classes', 80)
     cfg['devices_num'] = devices_num
+    cfg['num_trainers'] = num_trainers
     reader = Reader(**cfg)()
 
     def _reader():

tools/train.py

@@ -56,6 +56,7 @@ def main():
     FLAGS.dist = 'PADDLE_TRAINER_ID' in env \
                     and 'PADDLE_TRAINERS_NUM' in env \
                     and int(env['PADDLE_TRAINERS_NUM']) > 1
+    num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 1))
     if FLAGS.dist:
         trainer_id = int(env['PADDLE_TRAINER_ID'])
         local_seed = (99 + trainer_id)
@@ -203,7 +204,8 @@ def main():
     train_reader = create_reader(
         cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num,
         cfg,
-        devices_num=devices_num)
+        devices_num=devices_num,
+        num_trainers=num_trainers)
     train_loader.set_sample_list_generator(train_reader, place)
 
     # whether output bbox is normalized in model output layer