net = dict(
    type='RESANet',
)

# backbone = dict(
#     type='ResNetWrapper',
#     resnet='resnet50',
#     pretrained=True,
#     replace_stride_with_dilation=[False, True, True],
#     out_conv=True,
#     fea_stride=8,
# )

backbone = dict(
    type='ResNetWrapper',
    resnet='resnet34',
    pretrained=True,
    replace_stride_with_dilation=[False, False, False],
    out_conv=False,
    fea_stride=8,
)

resa = dict(
    type='RESA',
    alpha=2.0,
    iter=4,
    input_channel=128,
    conv_stride=9,
)

#decoder = 'PlainDecoder'      
decoder = 'BUSD'

trainer = dict(
    type='RESA'
)

evaluator = dict(
    type='CULane',        
)

optimizer = dict(
  type='sgd',
  lr=0.025,
  weight_decay=1e-4,
  momentum=0.9
)

epochs = 20
batch_size = 8
total_iter = (88880 // batch_size) * epochs
import math
scheduler = dict(
    type = 'LambdaLR',
    lr_lambda = lambda _iter : math.pow(1 - _iter/total_iter, 0.9)
)

loss_type = 'dice_loss'
seg_loss_weight = 2.
eval_ep = 1
save_ep = epochs

bg_weight = 0.4

img_norm = dict(
    mean=[103.939, 116.779, 123.68],
    std=[1., 1., 1.]
)

img_height = 288
img_width = 800
cut_height = 240 

dataset_path = './data/CULane'
dataset = dict(
    train=dict(
        type='CULane',
        img_path=dataset_path,
        data_list='train_gt.txt',
    ),
    val=dict(
        type='CULane',
        img_path=dataset_path,
        data_list='test.txt',
    ),
    test=dict(
        type='CULane',
        img_path=dataset_path,
        data_list='test.txt',
    )
)


workers = 12
num_classes = 4 + 1
ignore_label = 255
log_interval = 500