27 lines
879 B
Python
27 lines
879 B
Python
_base_ = [
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'../_base_/models/repmlp-base_224.py',
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'../_base_/datasets/imagenet_bs64_pil_resize.py',
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'../_base_/schedules/imagenet_bs1024_adamw_swin.py',
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'../_base_/default_runtime.py'
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]
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# dataset settings
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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# resizing to (256, 256) here, different from resizing shorter edge to 256
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dict(type='Resize', scale=(256, 256), backend='pillow'),
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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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val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
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# schedule settings
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optim_wrapper = dict(clip_grad=dict(max_norm=5.0))
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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# base_batch_size = (8 GPUs) x (64 samples per GPU)
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auto_scale_lr = dict(base_batch_size=512)
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