47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
_base_ = '../_base_/default_runtime.py'
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model = dict(
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type='ImageClassifier',
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backbone=dict(
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type='SwinTransformer',
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arch='base',
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img_size=192,
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out_indices=-1,
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drop_path_rate=0.1,
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stage_cfgs=dict(block_cfgs=dict(window_size=6))),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearClsHead',
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num_classes=1000,
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in_channels=1024,
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init_cfg=None, # suppress the default init_cfg of LinearClsHead.
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loss=dict(
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type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
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cal_acc=False))
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dataset_type = 'ImageNet'
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data_root = 'data/imagenet/'
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data_preprocessor = dict(
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num_classes=1000,
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True,
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)
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='ResizeEdge', scale=256, edge='short'),
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dict(type='CenterCrop', crop_size=224),
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dict(type='PackInputs'),
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]
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test_dataloader = dict(
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batch_size=8,
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num_workers=4,
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dataset=dict(
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type=dataset_type,
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data_root='data/imagenet',
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ann_file='meta/val.txt',
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data_prefix='val',
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pipeline=test_pipeline),
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sampler=dict(type='DefaultSampler', shuffle=False),
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)
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