format all file names

mmpretrain/configs/_base_/datasets/imagenet_bs64_swin_384 copy.py

@@ -1,55 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# dataset settings
-dataset_type = 'ImageNet'
-data_preprocessor = dict(
-    num_classes=1000,
-    # RGB format normalization parameters
-    mean=[123.675, 116.28, 103.53],
-    std=[58.395, 57.12, 57.375],
-    # convert image from BGR to RGB
-    to_rgb=True,
-)
-
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='RandomResizedCrop',
-        scale=384,
-        backend='pillow',
-        interpolation='bicubic'),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='PackInputs'),
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='Resize', scale=384, backend='pillow', interpolation='bicubic'),
-    dict(type='PackInputs'),
-]
-
-train_dataloader = dict(
-    batch_size=64,
-    num_workers=5,
-    dataset=dict(
-        type=dataset_type,
-        data_root='data/imagenet',
-        split='train',
-        pipeline=train_pipeline),
-    sampler=dict(type='DefaultSampler', shuffle=True),
-)
-
-val_dataloader = dict(
-    batch_size=64,
-    num_workers=5,
-    dataset=dict(
-        type=dataset_type,
-        data_root='data/imagenet',
-        split='val',
-        pipeline=test_pipeline),
-    sampler=dict(type='DefaultSampler', shuffle=False),
-)
-val_evaluator = dict(type='Accuracy', topk=(1, 5))
-
-# If you want standard test, please manually configure the test dataset
-test_dataloader = val_dataloader
-test_evaluator = val_evaluator