mirror of https://github.com/open-mmlab/mmyolo.git
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
_base_ = './ppyoloe_plus_s_fast_8xb8-80e_coco.py'
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# The pretrained model is geted and converted from official PPYOLOE.
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# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
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checkpoint = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/cspresnet_s_imagenet1k_pretrained-2be81763.pth' # noqa
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train_batch_size_per_gpu = 32
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max_epochs = 300
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# Base learning rate for optim_wrapper
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base_lr = 0.01
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model = dict(
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data_preprocessor=dict(
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mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
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std=[0.229 * 255., 0.224 * 255., 0.225 * 255.]),
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backbone=dict(
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block_cfg=dict(use_alpha=False),
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init_cfg=dict(
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type='Pretrained',
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prefix='backbone.',
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checkpoint=checkpoint,
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map_location='cpu')),
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train_cfg=dict(initial_epoch=100))
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train_dataloader = dict(batch_size=train_batch_size_per_gpu)
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optim_wrapper = dict(optimizer=dict(lr=base_lr))
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default_hooks = dict(param_scheduler=dict(total_epochs=int(max_epochs * 1.2)))
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train_cfg = dict(max_epochs=max_epochs)
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# PPYOLOE plus use obj365 pretrained model, but PPYOLOE not,
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# `load_from` need to set to None.
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load_from = None
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