_base_ = [ '../_base_/models/mask-rcnn_r50_fpn.py', '../_base_/datasets/coco_instance.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa model = dict( type='MaskRCNN', backbone=dict( _delete_=True, type='SwinTransformer', embed_dims=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4, qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.2, patch_norm=True, out_indices=(0, 1, 2, 3), with_cp=False, convert_weights=True, init_cfg=dict(type='Pretrained', checkpoint=pretrained)), neck=dict(in_channels=[96, 192, 384, 768])) max_epochs = 12 train_cfg = dict(max_epochs=max_epochs) # learning rate param_scheduler = [ dict( type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=1000), dict( type='MultiStepLR', begin=0, end=max_epochs, by_epoch=True, milestones=[8, 11], gamma=0.1) ] # optimizer optim_wrapper = dict( type='OptimWrapper', paramwise_cfg=dict( custom_keys={ 'absolute_pos_embed': dict(decay_mult=0.), 'relative_position_bias_table': dict(decay_mult=0.), 'norm': dict(decay_mult=0.) }), optimizer=dict( _delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05))
标签:swin,p4,decay,1x,py,base,dict,type,True From: https://www.cnblogs.com/wenluderen/p/17835648.html