| _base_ = [ | |
| '../_base_/models/setr_naive_pup.py', | |
| '../_base_/datasets/FoodSeg103_768x768.py', '../_base_/default_runtime.py', | |
| '../_base_/schedules/schedule_80k.py' | |
| ] | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| model = dict( | |
| backbone=dict( | |
| img_size=768, | |
| model_name='vit_base_patch16_224', | |
| pretrain_weights='pretrained_model/VIT_base_224_ReLeM.pth', | |
| embed_dim=768, | |
| depth=12, | |
| num_heads=12, | |
| pos_embed_interp=True, | |
| align_corners=False, | |
| num_classes=104, | |
| drop_rate=0. | |
| ), | |
| decode_head=dict( | |
| img_size=768, | |
| in_channels=768, | |
| in_index=11, | |
| channels=512, | |
| num_classes=104, | |
| embed_dim=768, | |
| align_corners=False, | |
| num_conv=2, | |
| upsampling_method='bilinear', | |
| ), | |
| auxiliary_head=[ | |
| dict( | |
| type='VisionTransformerUpHead', | |
| in_channels=768, | |
| channels=512, | |
| in_index=5, | |
| img_size=768, | |
| embed_dim=768, | |
| num_classes=104, | |
| norm_cfg=norm_cfg, | |
| num_conv=2, | |
| upsampling_method='bilinear', | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| dict( | |
| type='VisionTransformerUpHead', | |
| in_channels=768, | |
| channels=512, | |
| in_index=7, | |
| img_size=768, | |
| embed_dim=768, | |
| num_classes=104, | |
| norm_cfg=norm_cfg, | |
| num_conv=2, | |
| upsampling_method='bilinear', | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| dict( | |
| type='VisionTransformerUpHead', | |
| in_channels=768, | |
| channels=512, | |
| in_index=9, | |
| img_size=768, | |
| embed_dim=768, | |
| num_classes=104, | |
| norm_cfg=norm_cfg, | |
| num_conv=2, | |
| upsampling_method='bilinear', | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| ]) | |
| optimizer = dict(lr=0.01, 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) | |