89 lines
1.4 KiB
Python
89 lines
1.4 KiB
Python
|
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,
|
||
|
|
)
|
||
|
|
|
||
|
|
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 = 12
|
||
|
|
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 = 6
|
||
|
|
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
|