65 lines
2.3 KiB
Plaintext
65 lines
2.3 KiB
Plaintext
_base_ = [
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'../_base_/models/vars_file.alg_base_dir',
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'../_base_/datasets/vars_file.dataset_file_name', #换成自己定义的数据集
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'../_base_/default_runtime.py',
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'../_base_/schedules/schedule_vars_file.schedule_k_timesk.py'
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]
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crop_size = (vars_file.crop_size_w, vars_file.crop_size_h)
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data_preprocessor = dict(size=crop_size)
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model = dict(
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pretrained='open-mmlab://vars_file.pretrained_model',
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backbone=dict(depth=vars_file.pretrained_depth),
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data_preprocessor=data_preprocessor,
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decode_head=dict(
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num_classes=vars_file.class_num, # TODO 设置不同分类种类
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loss_decode=dict(type='DiceLoss', use_sigmoid=False, loss_weight=1.0), # TODO 设置不同分类种类，它根据预测结果和真实标签的重叠区域来度量相似性
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# align_corners=True,
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# align_corners=False, # 在不用slide时
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),
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auxiliary_head=dict(
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num_classes=vars_file.class_num, # TODO 设置不同分类种类
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loss_decode=dict(type='DiceLoss', use_sigmoid=False, loss_weight=1.0), # TODO 设置不同分类种类，它根据预测结果和真实标签的重叠区域来度量相似性
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# align_corners=True,
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# align_corners=False, # 在不用slide时
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),
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# test_cfg=dict(mode='slide', crop_size=(vars_file.crop_size_w, vars_file.crop_size_h), stride=(vars_file.crop_size_w, vars_file.crop_size_w))
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)
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# optimizer（优化器设计）TODO
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optim_wrapper = dict(
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type='OptimWrapper',
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_delete_=True,
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optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0005),
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clip_grad=dict(max_norm=1, norm_type=2))
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param_scheduler = [
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dict(
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type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
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dict(
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type='PolyLR',
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power=1.0,
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begin=1500,
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end=160000,
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eta_min=0.0,
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by_epoch=False,
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)
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]
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# optim_wrapper = dict(
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# _delete_=True,
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# type='OptimWrapper',
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# optimizer=dict(type='AdamW', lr=0.0005, weight_decay=0.05),
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# clip_grad=dict(max_norm=1, norm_type=2))
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# # learning policy
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# param_scheduler = [
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# dict(
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# type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
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# end=1000),
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# dict(
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# type='MultiStepLR',
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# begin=1000,
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# end=80000,
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# by_epoch=False,
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# milestones=[60000, 72000],
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# )
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# ] |