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| _base_ = [ | |
| '../_base_/models/san_vit-b16.py', '../_base_/datasets/coco-stuff164k.py', | |
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' | |
| ] | |
| crop_size = (640, 640) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[int(640 * x * 0.1) for x in range(5, 16)], | |
| resize_type='ResizeShortestEdge', | |
| max_size=2560), | |
| dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=1.0), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackSegInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='ResizeShortestEdge', scale=crop_size, max_size=2560), | |
| dict(type='LoadAnnotations'), | |
| dict(type='PackSegInputs') | |
| ] | |
| # By default, models are trained on 4 GPUs with 8 images per GPU | |
| train_dataloader = dict(batch_size=8, dataset=dict(pipeline=train_pipeline)) | |
| val_dataloader = dict(batch_size=1, dataset=dict(pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/san/clip_vit-base-patch16-224_3rdparty-d08f8887.pth' # noqa | |
| data_preprocessor = dict( | |
| mean=[122.7709, 116.7460, 104.0937], | |
| std=[68.5005, 66.6322, 70.3232], | |
| size_divisor=640, | |
| test_cfg=dict(size_divisor=32)) | |
| model = dict( | |
| pretrained=pretrained, | |
| text_encoder=dict(dataset_name='coco-stuff164k'), | |
| decode_head=dict(num_classes=171)) | |
| # training schedule for 60k | |
| train_cfg = dict( | |
| type='IterBasedTrainLoop', | |
| max_iters=60000, | |
| val_interval=500, | |
| val_begin=55000) | |
| default_hooks = dict( | |
| checkpoint=dict( | |
| type='CheckpointHook', | |
| by_epoch=False, | |
| interval=10000, | |
| save_best='mIoU')) | |
| # AdamW optimizer, no weight decay for position embedding & layer norm | |
| # in backbone | |
| optim_wrapper = dict( | |
| _delete_=True, | |
| type='AmpOptimWrapper', | |
| optimizer=dict( | |
| type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001), | |
| paramwise_cfg=dict( | |
| custom_keys={ | |
| 'img_encoder': dict(lr_mult=0.1, decay_mult=1.0), | |
| 'pos_embed': dict(decay_mult=0.), | |
| 'cls_token': dict(decay_mult=0.), | |
| 'norm': dict(decay_mult=0.) | |
| }), | |
| loss_scale='dynamic', | |
| clip_grad=dict(max_norm=0.01, norm_type=2)) | |
| param_scheduler = [ | |
| dict( | |
| type='PolyLR', | |
| eta_min=0.0, | |
| power=1.0, | |
| begin=0, | |
| end=60000, | |
| by_epoch=False, | |
| ) | |
| ] | |