configs/rsprompter/rsprompter_query_whu_config.py

custom_imports = dict(imports=['mmseg.datasets', 'mmseg.models'], allow_failed_imports=False)

sub_model_train = [
    'panoptic_head',
    'panoptic_fusion_head',
    'data_preprocessor'
]

sub_model_optim = {
    'panoptic_head': {'lr_mult': 1},
    'panoptic_fusion_head': {'lr_mult': 1},
}

max_epochs = 5000

optimizer = dict(
    type='AdamW',
    sub_model=sub_model_optim,
    lr=0.0005,
    weight_decay=1e-3
)

param_scheduler = [
    # warm up learning rate scheduler
    dict(
        type='LinearLR',
        start_factor=1e-4,
        by_epoch=True,
        begin=0,
        end=1,
        # update by iter
        convert_to_iter_based=True),
    # main learning rate scheduler
    dict(
        type='CosineAnnealingLR',
        T_max=max_epochs,
        by_epoch=True,
        begin=1,
        end=max_epochs,
    ),
]

param_scheduler_callback = dict(
    type='ParamSchedulerHook'
)


evaluator_ = dict(
        type='CocoPLMetric',
        metric=['bbox', 'segm'],
        proposal_nums=[1, 10, 100]
)

evaluator = dict(
    # train_evaluator=evaluator_,
    val_evaluator=evaluator_,
)


image_size = (1024, 1024)

data_preprocessor = dict(
    type='mmdet.DetDataPreprocessor',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    bgr_to_rgb=True,
    pad_size_divisor=32,
    pad_mask=True,
    mask_pad_value=0,
)

num_things_classes = 1
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
prompt_shape = (90, 4)


model_cfg = dict(
    type='SegSAMPLer',
    hyperparameters=dict(
        optimizer=optimizer,
        param_scheduler=param_scheduler,
        evaluator=evaluator,
    ),
    need_train_names=sub_model_train,
    data_preprocessor=data_preprocessor,
    backbone=dict(
        type='vit_h',
        checkpoint='pretrain/sam/sam_vit_h_4b8939.pth',
        # type='vit_b',
        # checkpoint='pretrain/sam/sam_vit_b_01ec64.pth',
    ),
    panoptic_head=dict(
        type='SAMInstanceHead',
        num_things_classes=num_things_classes,
        num_stuff_classes=num_stuff_classes,
        with_multiscale=True,
        with_sincos=True,
        prompt_neck=dict(
            type='SAMTransformerEDPromptGenNeck',
            prompt_shape=prompt_shape,
            in_channels=[1280] * 32,
            inner_channels=64,
            selected_channels=range(4, 32, 2),
            # in_channels=[768] * 8,
            num_encoders=1,
            num_decoders=4,
            out_channels=256
        ),
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=False,
            loss_weight=2.0,
            reduction='mean',
            class_weight=[1.0] * num_classes + [0.1]),
        loss_mask=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='mean',
            loss_weight=5.0),
        loss_dice=dict(
            type='mmdet.DiceLoss',
            use_sigmoid=True,
            activate=True,
            reduction='mean',
            naive_dice=True,
            eps=1.0,
            loss_weight=5.0)),
    panoptic_fusion_head=dict(
        type='mmdet.MaskFormerFusionHead',
        num_things_classes=num_things_classes,
        num_stuff_classes=num_stuff_classes,
        loss_panoptic=None,
        init_cfg=None),
    train_cfg=dict(
        num_points=12544,
        oversample_ratio=3.0,
        importance_sample_ratio=0.75,
        assigner=dict(
            type='mmdet.HungarianAssigner',
            match_costs=[
                dict(type='mmdet.ClassificationCost', weight=2.0),
                dict(
                    type='mmdet.CrossEntropyLossCost', weight=5.0, use_sigmoid=True),
                dict(type='mmdet.DiceCost', weight=5.0, pred_act=True, eps=1.0)
            ]),
        sampler=dict(type='mmdet.MaskPseudoSampler')),
    test_cfg=dict(
        panoptic_on=False,
        # For now, the dataset does not support
        # evaluating semantic segmentation metric.
        semantic_on=False,
        instance_on=True,
        # max_per_image is for instance segmentation.
        max_per_image=80,
        iou_thr=0.8,
        # In Mask2Former's panoptic postprocessing,
        # it will filter mask area where score is less than 0.5 .
        filter_low_score=True),
)

task_name = 'whu_ins'
exp_name = 'E20230603_0'
logger = dict(
    type='WandbLogger',
    project=task_name,
    group='sam',
    name=exp_name
)
# logger = None


callbacks = [
    param_scheduler_callback,
    dict(
        type='ModelCheckpoint',
        dirpath=f'results/{task_name}/{exp_name}/checkpoints',
        save_last=True,
        mode='max',
        monitor='valsegm_map_0',
        save_top_k=2,
        filename='epoch_{epoch}-map_{valsegm_map_0:.4f}'
    ),
    dict(
        type='LearningRateMonitor',
        logging_interval='step'
    )
]


trainer_cfg = dict(
    compiled_model=False,
    accelerator="auto",
    strategy="auto",
    # strategy="ddp",
    # strategy='ddp_find_unused_parameters_true',
    # precision='32',
    # precision='16-mixed',
    devices=8,
    default_root_dir=f'results/{task_name}/{exp_name}',
    # default_root_dir='results/tmp',
    max_epochs=max_epochs,
    logger=logger,
    callbacks=callbacks,
    log_every_n_steps=20,
    check_val_every_n_epoch=5,
    benchmark=True,
    # sync_batchnorm=True,
    # fast_dev_run=True,

    # limit_train_batches=1,
    # limit_val_batches=0,
    # limit_test_batches=None,
    # limit_predict_batches=None,
    # overfit_batches=0.0,

    # val_check_interval=None,
    # num_sanity_val_steps=0,
    # enable_checkpointing=None,
    # enable_progress_bar=None,
    # enable_model_summary=None,
    # accumulate_grad_batches=32,
    # gradient_clip_val=15,
    # gradient_clip_algorithm='norm',
    # deterministic=None,
    # inference_mode: bool=True,
    use_distributed_sampler=True,
    # profiler="simple",
    # detect_anomaly=False,
    # barebones=False,
    # plugins=None,
    # reload_dataloaders_every_n_epochs=0,
)


backend_args = None
train_pipeline = [
    dict(type='mmdet.LoadImageFromFile'),
    dict(type='mmdet.LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='mmdet.Resize', scale=image_size),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(type='mmdet.PackDetInputs')
]

test_pipeline = [
    dict(type='mmdet.LoadImageFromFile', backend_args=backend_args),
    dict(type='mmdet.Resize', scale=image_size),
    # If you don't have a gt annotation, delete the pipeline
    dict(type='mmdet.LoadAnnotations', with_bbox=True, with_mask=True),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]


train_batch_size_per_gpu = 3
train_num_workers = 2
test_batch_size_per_gpu = 3
test_num_workers = 2
persistent_workers = True

data_parent = '/mnt/search01/dataset/cky_data/WHU'
train_data_prefix = 'train/'
val_data_prefix = 'test/'

dataset_type = 'WHUInsSegDataset'

val_loader = dict(
        batch_size=test_batch_size_per_gpu,
        num_workers=test_num_workers,
        persistent_workers=persistent_workers,
        pin_memory=True,
        dataset=dict(
            type=dataset_type,
            data_root=data_parent,
            ann_file='annotations/WHU_building_test.json',
            data_prefix=dict(img_path=val_data_prefix + '/image', seg_path=val_data_prefix + '/label'),
            test_mode=True,
            filter_cfg=dict(filter_empty_gt=True, min_size=32),
            pipeline=test_pipeline,
            backend_args=backend_args))

datamodule_cfg = dict(
    type='PLDataModule',
    train_loader=dict(
        batch_size=train_batch_size_per_gpu,
        num_workers=train_num_workers,
        persistent_workers=persistent_workers,
        pin_memory=True,
        dataset=dict(
            type=dataset_type,
            data_root=data_parent,
            ann_file='annotations/WHU_building_train.json',
            data_prefix=dict(img_path=train_data_prefix + '/image', seg_path=train_data_prefix + '/label'),
            filter_cfg=dict(filter_empty_gt=True, min_size=32),
            pipeline=train_pipeline,
            backend_args=backend_args)
    ),
    val_loader=val_loader,
    # test_loader=val_loader
    predict_loader=val_loader
)