Revert "mmocr-style cfg (open-mmlab#926)" (open-mmlab#927)

This reverts commit 7fdaebb.

Author: ly015

mmpose/apis/train.py

@@ -43,33 +43,19 @@ def train_model(model,
 
     # prepare data loaders
     dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    # step 1: give default values and override (if exist) from cfg.data
-    loader_cfg = {
-        **dict(
-            seed=cfg.get('seed'),
-            drop_last=False,
-            dist=distributed,
-            num_gpus=len(cfg.gpu_ids)),
-        **({} if torch.__version__ != 'parrots' else dict(
-               prefetch_num=2,
-               pin_memory=False,
-           )),
-        **dict((k, cfg.data[k]) for k in [
-                   'samples_per_gpu',
-                   'workers_per_gpu',
-                   'shuffle',
-                   'seed',
-                   'drop_last',
-                   'prefetch_num',
-                   'pin_memory',
-                   'persistent_workers',
-               ] if k in cfg.data)
-    }
-
-    # step 2: cfg.data.train_dataloader has highest priority
-    train_loader_cfg = dict(loader_cfg, **cfg.data.get('train_dataloader', {}))
-
-    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
+    dataloader_setting = dict(
+        samples_per_gpu=cfg.data.get('samples_per_gpu', {}),
+        workers_per_gpu=cfg.data.get('workers_per_gpu', {}),
+        # cfg.gpus will be ignored if distributed
+        num_gpus=len(cfg.gpu_ids),
+        dist=distributed,
+        seed=cfg.seed)
+    dataloader_setting = dict(dataloader_setting,
+                              **cfg.data.get('train_dataloader', {}))
+
+    data_loaders = [
+        build_dataloader(ds, **dataloader_setting) for ds in dataset
+    ]
 
     # determine wether use adversarial training precess or not
     use_adverserial_train = cfg.get('use_adversarial_train', False)

tools/test.py

@@ -111,29 +111,15 @@ def main():
 
     # build the dataloader
     dataset = build_dataset(cfg.data.test, dict(test_mode=True))
-    # step 1: give default values and override (if exist) from cfg.data
-    loader_cfg = {
-        **dict(seed=cfg.get('seed'), drop_last=False, dist=distributed),
-        **({} if torch.__version__ != 'parrots' else dict(
-               prefetch_num=2,
-               pin_memory=False,
-           )),
-        **dict((k, cfg.data[k]) for k in [
-                   'seed',
-                   'prefetch_num',
-                   'pin_memory',
-                   'persistent_workers',
-               ] if k in cfg.data)
-    }
-    # step2: cfg.data.test_dataloader has higher priority
-    test_loader_cfg = {
-        **loader_cfg,
-        **dict(shuffle=False, drop_last=False),
-        **cfg.data.get('workers_per_gpu', 1),
-        **cfg.data.get('test_dataloader', {}),
-        **dict(samples_per_gpu=1)
-    }
-    data_loader = build_dataloader(dataset, **test_loader_cfg)
+    dataloader_setting = dict(
+        samples_per_gpu=1,
+        workers_per_gpu=cfg.data.get('workers_per_gpu', 1),
+        dist=distributed,
+        shuffle=False,
+        drop_last=False)
+    dataloader_setting = dict(dataloader_setting,
+                              **cfg.data.get('test_dataloader', {}))
+    data_loader = build_dataloader(dataset, **dataloader_setting)
 
     # build the model and load checkpoint
     model = build_posenet(cfg.model)