[Enhance] Add extra dataloader settings in configs.

mmcls/apis/train.py

@@ -80,20 +80,27 @@ def train_model(model,
     # prepare data loaders
     dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
 
-    sampler_cfg = cfg.data.get('sampler', None)
-
-    data_loaders = [
-        build_dataloader(
-            ds,
-            cfg.data.samples_per_gpu,
-            cfg.data.workers_per_gpu,
-            # cfg.gpus will be ignored if distributed
-            num_gpus=len(cfg.gpu_ids),
-            dist=distributed,
-            round_up=True,
-            seed=cfg.seed,
-            sampler_cfg=sampler_cfg) for ds in dataset
-    ]
+    # The default loader config
+    loader_cfg = dict(
+        # cfg.gpus will be ignored if distributed
+        num_gpus=len(cfg.gpu_ids),
+        dist=distributed,
+        round_up=True,
+        seed=cfg.get('seed'),
+        sampler_cfg=cfg.get('sampler', None),
+    )
+    # The overall dataloader settings
+    loader_cfg.update({
+        k: v
+        for k, v in cfg.data.items() if k not in [
+            'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
+            'test_dataloader'
+        ]
+    })
+    # The specific dataloader settings
+    train_loader_cfg = {**loader_cfg, **cfg.data.get('train_dataloader', {})}
+
+    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
 
     # put model on gpus
     if distributed:
@@ -169,13 +176,14 @@ def train_model(model,
     # register eval hooks
     if validate:
         val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-        val_dataloader = build_dataloader(
-            val_dataset,
-            samples_per_gpu=cfg.data.samples_per_gpu,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False,
-            round_up=True)
+        # The specific dataloader settings
+        val_loader_cfg = {
+            **loader_cfg,
+            'shuffle': False,  # Not shuffle by default
+            'sampler_cfg': None,  # Not use sampler by default
+            **cfg.data.get('val_dataloader', {}),
+        }
+        val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
         eval_cfg = cfg.get('evaluation', {})
         eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
         eval_hook = DistEvalHook if distributed else EvalHook

tools/test.py

@@ -135,16 +135,32 @@ def main():
         distributed = True
         init_dist(args.launcher, **cfg.dist_params)
 
-    # build the dataloader
     dataset = build_dataset(cfg.data.test, default_args=dict(test_mode=True))
-    # the extra round_up data will be removed during gpu/cpu collect
-    data_loader = build_dataloader(
-        dataset,
-        samples_per_gpu=cfg.data.samples_per_gpu,
-        workers_per_gpu=cfg.data.workers_per_gpu,
+
+    # build the dataloader
+    # The default loader config
+    loader_cfg = dict(
+        # cfg.gpus will be ignored if distributed
+        num_gpus=len(cfg.gpu_ids),
         dist=distributed,
-        shuffle=False,
-        round_up=True)
+        round_up=True,
+    )
+    # The overall dataloader settings
+    loader_cfg.update({
+        k: v
+        for k, v in cfg.data.items() if k not in [
+            'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
+            'test_dataloader'
+        ]
+    })
+    test_loader_cfg = {
+        **loader_cfg,
+        'shuffle': False,  # Not shuffle by default
+        'sampler_cfg': None,  # Not use sampler by default
+        **cfg.data.get('test_dataloader', {}),
+    }
+    # the extra round_up data will be removed during gpu/cpu collect
+    data_loader = build_dataloader(dataset, **test_loader_cfg)
 
     # build the model and load checkpoint
     model = build_classifier(cfg.model)