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

max_epochs = 500

optimizer = dict(
    type='AdamW',
    lr=0.0005,
    weight_decay=1e-4
)

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(
    val_evaluator=evaluator_,
)


image_size = (512, 512)
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_mask=True,
        mask_pad_value=0,
        pad_size_divisor=32
)

num_things_classes = 1
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
num_queries = 100

# model settings
model = dict(
    type='mmdet.MaskRCNN',
    data_preprocessor=data_preprocessor,
    backbone=dict(
        type='mmdet.ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')
    ),
    neck=dict(
        type='mmdet.FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='mmdet.RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='mmdet.AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='mmdet.DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='mmdet.StandardRoIHead',
        bbox_roi_extractor=dict(
            type='mmdet.SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='mmdet.Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=num_classes,
            bbox_coder=dict(
                type='mmdet.DeltaXYWHBBoxCoder',
                target_means=[0., 0., 0., 0.],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='mmdet.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0)),
        mask_roi_extractor=dict(
            type='mmdet.SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        mask_head=dict(
            type='mmdet.FCNMaskHead',
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=num_classes,
            loss_mask=dict(
                type='mmdet.CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
    # model training and testing settings
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='mmdet.MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='mmdet.RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='mmdet.MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='mmdet.RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            mask_size=28,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100,
            mask_thr_binary=0.5)
    )
)


model_cfg = dict(
    type='MMDetPLer',
    hyperparameters=dict(
        optimizer=optimizer,
        param_scheduler=param_scheduler,
        evaluator=evaluator,
    ),
    whole_model=model,
)

task_name = 'ssdd_ins'
exp_name = 'E20230526_0'
logger = dict(
    type='WandbLogger',
    project=task_name,
    group='maskrcnn',
    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='valmap_0',
        save_top_k=2,
        filename='epoch_{epoch}-map_{valmap_0:.4f}'
        # mode='min',
        # monitor='train_loss',
        # save_top_k=2,
        # filename='epoch_{epoch}-trainloss_{train_loss:.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=4,
    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=10,
    check_val_every_n_epoch=10,
    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=1,
    # 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 = 8
train_num_workers = 4
test_batch_size_per_gpu = 8
test_num_workers = 4
persistent_workers = True

data_parent = '/Users/kyanchen/datasets/seg/SSDD'
# data_parent = '/mnt/search01/dataset/cky_data/SSDD'

dataset_type = 'SSDDInsSegDataset'

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/SSDD_instances_val.json',
        data_prefix=dict(img_path='imgs'),
        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/SSDD_instances_train.json',
            data_prefix=dict(img_path='imgs'),
            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
)