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_base_ = [ |
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'../_base_/models/setr_naive_pup.py', |
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'../_base_/datasets/FoodSeg103_768x768.py', '../_base_/default_runtime.py', |
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'../_base_/schedules/schedule_80k.py' |
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] |
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norm_cfg = dict(type='SyncBN', requires_grad=True) |
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model = dict( |
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backbone=dict( |
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img_size=768, |
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model_name='vit_base_patch16_224', |
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embed_dim=768, |
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depth=12, |
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num_heads=12, |
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pos_embed_interp=True, |
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align_corners=False, |
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num_classes=104, |
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drop_rate=0. |
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), |
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decode_head=dict( |
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img_size=768, |
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in_channels=768, |
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in_index=11, |
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channels=512, |
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num_classes=104, |
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embed_dim=768, |
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align_corners=False, |
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num_conv=2, |
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upsampling_method='bilinear', |
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), |
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auxiliary_head=[ |
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dict( |
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type='VisionTransformerUpHead', |
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in_channels=768, |
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channels=512, |
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in_index=5, |
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img_size=768, |
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embed_dim=768, |
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num_classes=104, |
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norm_cfg=norm_cfg, |
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num_conv=2, |
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upsampling_method='bilinear', |
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align_corners=False, |
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loss_decode=dict( |
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), |
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dict( |
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type='VisionTransformerUpHead', |
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in_channels=768, |
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channels=512, |
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in_index=7, |
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img_size=768, |
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embed_dim=768, |
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num_classes=104, |
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norm_cfg=norm_cfg, |
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num_conv=2, |
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upsampling_method='bilinear', |
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align_corners=False, |
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loss_decode=dict( |
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), |
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dict( |
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type='VisionTransformerUpHead', |
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in_channels=768, |
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channels=512, |
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in_index=9, |
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img_size=768, |
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embed_dim=768, |
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num_classes=104, |
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norm_cfg=norm_cfg, |
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num_conv=2, |
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upsampling_method='bilinear', |
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align_corners=False, |
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loss_decode=dict( |
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), |
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]) |
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|
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optimizer = dict(lr=0.01, weight_decay=0.0, paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)})) |
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|
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crop_size = (768, 768) |
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test_cfg = dict(mode='slide', crop_size=crop_size, stride=(512, 512)) |
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find_unused_parameters = True |
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data = dict(samples_per_gpu=1) |
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|