# YOLOv6n model
model = dict(
    type='YOLOv6n',
    pretrained='./assets/v6s_n.pt',
    scales='./assets/v6n_v2_scale_last.pt',
    depth_multiple=0.33,
    width_multiple=0.25,
    backbone=dict(
        type='EfficientRep',
        num_repeats=[1, 6, 12, 18, 6],
        out_channels=[64, 128, 256, 512, 1024],
        ),
    neck=dict(
        type='RepPANNeck',
        num_repeats=[12, 12, 12, 12],
        out_channels=[256, 128, 128, 256, 256, 512],
        ),
    head=dict(
        type='EffiDeHead',
        in_channels=[128, 256, 512],
        num_layers=3,
        begin_indices=24,
        anchors=1,
        out_indices=[17, 20, 23],
        strides=[8, 16, 32],
        atss_warmup_epoch=0,
        iou_type='siou',
        use_dfl=False,
        reg_max=0, #if use_dfl is False, please set reg_max to 0
        distill_weight={
            'class': 1.0,
            'dfl': 1.0,
        },
    )
)

solver = dict(
    optim='SGD',
    lr_scheduler='Cosine',
    lr0=0.00001, #0.01 # 0.02
    lrf=0.001,
    momentum=0.937,
    weight_decay=0.00005,
    warmup_epochs=3.0,
    warmup_momentum=0.8,
    warmup_bias_lr=0.1
)

data_aug = dict(
    hsv_h=0.015,
    hsv_s=0.7,
    hsv_v=0.4,
    degrees=0.0,
    translate=0.1,
    scale=0.5,
    shear=0.0,
    flipud=0.0,
    fliplr=0.5,
    mosaic=1.0,
    mixup=0.0,
)

ptq = dict(
    num_bits = 8,
    calib_batches = 4,
    # 'max', 'histogram'
    calib_method = 'max',
    # 'entropy', 'percentile', 'mse'
    histogram_amax_method='entropy',
    histogram_amax_percentile=99.99,
    calib_output_path='./',
    sensitive_layers_skip=False,
    sensitive_layers_list=[],
)

qat = dict(
    calib_pt = './assets/v6s_n_calib_max.pt',
    sensitive_layers_skip = False,
    sensitive_layers_list=[],
)
# Choose Rep-block by the training Mode, choices=["repvgg", "hyper-search", "repopt"]
training_mode='repopt'