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# Copyright (c) Shanghai AI Lab. All rights reserved. |
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_base_ = [ |
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'../_base_/models/upernet_beit.py', |
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'../_base_/datasets/ade20k.py', |
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'../_base_/default_runtime.py', |
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'../_base_/schedules/schedule_160k.py' |
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] |
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crop_size = (640, 640) |
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# pretrained = 'https: |
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pretrained = 'pretrained/beit_large_patch16_224_pt22k_ft22k.pth' |
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model = dict( |
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pretrained=pretrained, |
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backbone=dict( |
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type='BEiTAdapter', |
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img_size=640, |
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patch_size=16, |
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embed_dim=1024, |
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depth=24, |
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num_heads=16, |
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mlp_ratio=4, |
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qkv_bias=True, |
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use_abs_pos_emb=False, |
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use_rel_pos_bias=True, |
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init_values=1e-6, |
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drop_path_rate=0.3, |
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conv_inplane=64, |
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n_points=4, |
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deform_num_heads=16, |
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cffn_ratio=0.25, |
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deform_ratio=0.5, |
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with_cp=True, # set with_cp=True to save memory |
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interaction_indexes=[[0, 5], [6, 11], [12, 17], [18, 23]], |
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), |
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decode_head=dict( |
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in_channels=[1024, 1024, 1024, 1024], |
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num_classes=150, |
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channels=1024, |
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), |
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auxiliary_head=dict( |
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in_channels=1024, |
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num_classes=150 |
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), |
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test_cfg = dict(mode='slide', crop_size=crop_size, stride=(426, 426)) |
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) |
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img_norm_cfg = dict( |
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations', reduce_zero_label=True), |
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dict(type='Resize', img_scale=(2048, 640), ratio_range=(0.5, 2.0)), |
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), |
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dict(type='RandomFlip', prob=0.5), |
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dict(type='PhotoMetricDistortion'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_semantic_seg']) |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(2048, 640), |
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='ResizeToMultiple', size_divisor=32), |
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dict(type='RandomFlip'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='ImageToTensor', keys=['img']), |
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dict(type='Collect', keys=['img']), |
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]) |
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] |
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optimizer = dict(_delete_=True, type='AdamW', lr=2e-5, betas=(0.9, 0.999), weight_decay=0.05, |
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constructor='LayerDecayOptimizerConstructor', |
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paramwise_cfg=dict(num_layers=24, layer_decay_rate=0.90)) |
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lr_config = dict(_delete_=True, policy='poly', |
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warmup='linear', |
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warmup_iters=1500, |
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warmup_ratio=1e-6, |
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power=1.0, min_lr=0.0, by_epoch=False) |
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data=dict(samples_per_gpu=2, |
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train=dict(pipeline=train_pipeline), |
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val=dict(pipeline=test_pipeline), |
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test=dict(pipeline=test_pipeline)) |
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runner = dict(type='IterBasedRunner') |
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checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=1) |
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evaluation = dict(interval=16000, metric='mIoU', save_best='mIoU') |
