| _base_ = [ | |
| '../_base_/models/setr_mla.py', | |
| '../_base_/datasets/FoodSeg103_768x768.py', '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_80k.py' | |
| ] | |
| model = dict( | |
| backbone=dict( | |
| img_size=768, | |
| pos_embed_interp=True, | |
| drop_rate=0., | |
| mla_channels=256, | |
| mla_index=(5,11,17,23) | |
| ), | |
| decode_head=dict(img_size=768,mla_channels=256,mlahead_channels=128,num_classes=104), | |
| auxiliary_head=[ | |
| dict( | |
| type='VIT_MLA_AUXIHead', | |
| in_channels=256, | |
| channels=512, | |
| in_index=0, | |
| img_size=768, | |
| num_classes=104, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| dict( | |
| type='VIT_MLA_AUXIHead', | |
| in_channels=256, | |
| channels=512, | |
| in_index=1, | |
| img_size=768, | |
| num_classes=104, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| dict( | |
| type='VIT_MLA_AUXIHead', | |
| in_channels=256, | |
| channels=512, | |
| in_index=2, | |
| img_size=768, | |
| num_classes=104, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| dict( | |
| type='VIT_MLA_AUXIHead', | |
| in_channels=256, | |
| channels=512, | |
| in_index=3, | |
| img_size=768, | |
| num_classes=104, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| ]) | |
| optimizer = dict(lr=0.002, weight_decay=0.0, | |
| paramwise_cfg = dict(custom_keys={'head': dict(lr_mult=10.)}) | |
| ) | |
| crop_size = (768, 768) | |
| test_cfg = dict(mode='slide', crop_size=crop_size, stride=(512, 512)) | |
| find_unused_parameters = True | |
| data = dict(samples_per_gpu=1) | |
| checkpoint_config = dict(by_epoch=False, interval=4000) | |
| evaluation = dict(interval=80000, metric='mIoU') | |