[Fix]: Refine simmim-224-ft config (#473)

configs/benchmarks/classification/imagenet/swin-base_ft-8xb256-coslr-100e_in1k-224.py

@@ -5,44 +5,32 @@ model = dict(
     backbone=dict(
         img_size=224, stage_cfgs=dict(block_cfgs=dict(window_size=7))))
 
-# dataset
-custom_imports = dict(imports='mmcls.datasets', allow_failed_imports=False)
-preprocess_cfg = dict(
-    pixel_mean=[123.675, 116.28, 103.53],
-    pixel_std=[58.395, 57.12, 57.375],
-    to_rgb=True,
-)
-
-bgr_mean = preprocess_cfg['pixel_mean'][::-1]
-bgr_std = preprocess_cfg['pixel_std'][::-1]
-
 # train pipeline
 train_pipeline = [
     dict(type='LoadImageFromFile'),
     dict(
-        type='mmcls.RandomResizedCrop',
+        type='RandomResizedCrop',
         scale=224,
         backend='pillow',
         interpolation='bicubic'),
     dict(type='RandomFlip', prob=0.5, direction='horizontal'),
     dict(
-        type='mmcls.RandAugment',
-        policies={{_base_.rand_increasing_policies}},
+        type='RandAugment',
+        policies='timm_increasing',
         num_policies=2,
         total_level=10,
         magnitude_level=9,
         magnitude_std=0.5,
-        hparams=dict(
-            pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
+        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
     dict(
-        type='mmcls.RandomErasing',
+        type='RandomErasing',
         erase_prob=0.25,
         mode='rand',
         min_area_ratio=0.02,
-        max_area_ratio=1 / 3,
-        fill_color=bgr_mean,
-        fill_std=bgr_std),
-    dict(type='PackSelfSupInputs', algorithm_keys=['gt_label']),
+        max_area_ratio=0.3333333333333333,
+        fill_color=[103.53, 116.28, 123.675],
+        fill_std=[57.375, 57.12, 58.395]),
+    dict(type='PackClsInputs')
 ]
 
 # test pipeline
@@ -55,12 +43,8 @@ test_pipeline = [
         backend='pillow',
         interpolation='bicubic'),
     dict(type='CenterCrop', crop_size=224),
-    dict(type='PackSelfSupInputs', algorithm_keys=['gt_label']),
+    dict(type='PackClsInputs')
 ]
 
-data = dict(
-    samples_per_gpu=256,
-    drop_last=False,
-    workers_per_gpu=32,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline))
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))