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configs/TTP/ttp_sam_large_levircd_fp16.py

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+default_scope = 'opencd'
+
+work_dir = 'work_dirs/lervicd/ttp_sam_large_levircd_fp16'
+
+custom_imports = dict(imports=['mmseg.ttp'], allow_failed_imports=False)
+
+env_cfg = dict(
+    cudnn_benchmark=True,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=10, log_metric_by_epoch=True),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=True, interval=10, save_best='cd/iou_changed', max_keep_ckpts=5, greater_keys=['cd/iou_changed'], save_last=True),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='CDVisualizationHook', interval=1,
+                       img_shape=(1024, 1024, 3))
+)
+vis_backends = [dict(type='CDLocalVisBackend'),
+                dict(type='WandbVisBackend',
+                     init_kwargs=dict(project='samcd', group='levircd', name='ttp_sam_large_levircd_fp16'))
+                ]
+
+visualizer = dict(
+    type='CDLocalVisualizer',
+    vis_backends=vis_backends, name='visualizer', alpha=1.0)
+log_processor = dict(by_epoch=True)
+
+log_level = 'INFO'
+load_from = None
+resume = False
+
+crop_size = (512, 512)
+
+data_preprocessor = dict(
+    type='DualInputSegDataPreProcessor',
+    mean=[123.675, 116.28, 103.53] * 2,
+    std=[58.395, 57.12, 57.375] * 2,
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255,
+    size_divisor=32,
+    test_cfg=dict(size_divisor=32)
+)
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+fpn_norm_cfg = dict(type='LN2d', requires_grad=True)
+
+sam_pretrain_ckpt_path = 'https://download.openmmlab.com/mmclassification/v1/vit_sam/vit-large-p16_sam-pre_3rdparty_sa1b-1024px_20230411-595feafd.pth'
+
+model = dict(
+    type='SiamEncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    backbone=dict(
+        type='MMPretrainSamVisionEncoder',
+        encoder_cfg=dict(
+            type='mmpretrain.ViTSAM',
+            arch='large',
+            img_size=crop_size[0],
+            patch_size=16,
+            out_channels=256,
+            use_abs_pos=True,
+            use_rel_pos=True,
+            window_size=14,
+            layer_cfgs=dict(type='TimeFusionTransformerEncoderLayer'),
+            init_cfg=dict(type='Pretrained', checkpoint=sam_pretrain_ckpt_path, prefix='backbone.'),
+        ),
+        peft_cfg=dict(
+            r=16,
+            target_modules=["qkv"],
+            lora_dropout=0.01,
+            bias='lora_only',
+        ),
+    ),
+    neck=dict(
+        type='SequentialNeck',
+        necks=[
+            dict(
+                type='FeatureFusionNeck',
+                policy='concat',
+                out_indices=(0,)),
+            dict(
+                type='SimpleFPN',
+                backbone_channel=512,
+                in_channels=[128, 256, 512, 512],
+                out_channels=256,
+                num_outs=5,
+                norm_cfg=fpn_norm_cfg),
+        ],
+    ),
+    decode_head=dict(
+        type='MLPSegHead',
+        out_size=(128, 128),
+        in_channels=[256]*5,
+        in_index=[0, 1, 2, 3, 4],
+        channels=256,
+        dropout_ratio=0,
+        num_classes=2,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='mmseg.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    train_cfg=dict(),
+    test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2))
+)  # yapf: disable
+
+dataset_type = 'LEVIR_CD_Dataset'
+data_root = '/mnt/levir_datasets/levir-cd'
+
+
+train_pipeline = [
+    dict(type='MultiImgLoadImageFromFile'),
+    dict(type='MultiImgLoadAnnotations'),
+    dict(type='MultiImgRandomRotate', prob=0.5, degree=180),
+    dict(type='MultiImgRandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='MultiImgRandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='MultiImgRandomFlip', prob=0.5, direction='vertical'),
+    # dict(type='MultiImgExchangeTime', prob=0.5),
+    dict(
+        type='MultiImgPhotoMetricDistortion',
+        brightness_delta=10,
+        contrast_range=(0.8, 1.2),
+        saturation_range=(0.8, 1.2),
+        hue_delta=10),
+    dict(type='MultiImgPackSegInputs')
+]
+test_pipeline = [
+    dict(type='MultiImgLoadImageFromFile'),
+    dict(type='MultiImgResize', scale=(1024, 1024), keep_ratio=True),
+    # add loading annotation after ``Resize`` because ground truth
+    # does not need to do resize data transform
+    dict(type='MultiImgLoadAnnotations'),
+    dict(type='MultiImgPackSegInputs')
+]
+
+batch_size_per_gpu = 2
+
+train_dataloader = dict(
+    batch_size=batch_size_per_gpu,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            seg_map_path='train/label',
+            img_path_from='train/A',
+            img_path_to='train/B'),
+        pipeline=train_pipeline)
+)
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            seg_map_path='test/label',
+            img_path_from='test/A',
+            img_path_to='test/B'),
+        pipeline=test_pipeline)
+)
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CDMetric',
+)
+test_evaluator = val_evaluator
+
+max_epochs = 300
+base_lr = 0.0004
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-4, by_epoch=True, begin=0, end=5, convert_to_iter_based=True),
+    dict(
+        type='CosineAnnealingLR',
+        T_max=max_epochs,
+        begin=5,
+        by_epoch=True,
+        end=max_epochs,
+        convert_to_iter_based=True
+    ),
+]
+
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=5)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+
+optim_wrapper = dict(
+    type='AmpOptimWrapper',
+    optimizer=dict(
+        type='AdamW', lr=base_lr, betas=(0.9, 0.999), weight_decay=0.05),
+    dtype='float16',
+)
+