_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
    backbone=dict(
        embed_dims=64,
        num_layers=[3, 6, 40, 3],
        mlp_ratios=(4, 4, 4, 4),
        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
                      'releases/download/v2/pvt_v2_b5.pth')),
    neck=dict(in_channels=[64, 128, 320, 512]))
# optimizer
optim_wrapper = dict(
    optimizer=dict(
        _delete_=True, type='AdamW', lr=0.0001 / 1.4, weight_decay=0.0001))

# dataset settings
train_dataloader = dict(batch_size=1, num_workers=1)

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)

_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
    backbone=dict(
        embed_dims=64,
        num_layers=[3, 6, 40, 3],
        mlp_ratios=(4, 4, 4, 4),
        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
                      'releases/download/v2/pvt_v2_b5.pth')),
    neck=dict(in_channels=[64, 128, 320, 512]))
# optimizer
optimizer = dict(
    _delete_=True, type='AdamW', lr=0.0001 / 1.4, weight_decay=0.0001)
# dataset settings
data = dict(samples_per_gpu=1, workers_per_gpu=1)

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)
