cfg.py

model = dict(
    type='PAN_PP',
    backbone=dict(
        type='resnet50',
        pretrained=True
    ),
    neck=dict(
        type='FPEM_v2',
        in_channels=(256, 512, 1024, 2048),
        out_channels=128,
        fpems=4
    ),
    detection_head=dict(
        type='PAN_PP_DetHead',
        in_channels=512,
        hidden_dim=128,
        num_classes=6,
        loss_text=dict(
            type='DiceLoss',
            loss_weight=1.0
        ),
        loss_kernel=dict(
            type='DiceLoss',
            loss_weight=0.5
        ),
        loss_emb=dict(
            type='EmbLoss_v2',
            feature_dim=4,
            loss_weight=0.25
        ),
        use_coordconv=False,
        # use_coordconv=True,
    )
)
data = dict(
    batch_size=1,
    train=dict(
        type='PAN_PP_TRAIN',
        split='train',
        is_transform=True,
        img_size=736,
        short_size=736,
        kernel_scale=0.5,
        read_type='pil',
        with_rec=False
    ),
    test=dict(
        type='PAN_PP_TEST',
        split='test',
        short_size=1024,
        read_type='cv2',
        with_rec=False,
        data='data/TestData/image/'
    )
)
train_cfg = dict(
#     lr=1e-3,
    lr=1e-2,
    schedule='polylr',
    epoch=200,
    optimizer='Adam',
    use_ex=False,
    # pretrain='',
)
test_cfg = dict(
    min_score=0.75,
    min_area=260,
    min_kernel_area=0.1,
    scale=2,
    bbox_type='rect',
    result_path='outputs',
    pretrain='./checkpoints/cfg/checkpoint.pth.tar', # tmp
)