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net = dict(
type='RESANet',
)
backbone = dict(
type='ResNetWrapper',
resnet='resnet34',
pretrained=True,
replace_stride_with_dilation=[False, True, True],
out_conv=True,
fea_stride=8,
)
resa = dict(
type='RESA',
alpha=2.0,
iter=5,
input_channel=128,
conv_stride=9,
)
decoder = 'BUSD'
trainer = dict(
type='RESA'
)
evaluator = dict(
type='Tusimple',
thresh = 0.60
)
optimizer = dict(
type='sgd',
lr=0.020,
weight_decay=1e-4,
momentum=0.9
)
total_iter = 80000
import math
scheduler = dict(
type = 'LambdaLR',
lr_lambda = lambda _iter : math.pow(1 - _iter/total_iter, 0.9)
)
bg_weight = 0.4
img_norm = dict(
mean=[103.939, 116.779, 123.68],
std=[1., 1., 1.]
)
img_height = 368
img_width = 640
cut_height = 160
seg_label = "seg_label"
dataset_path = '/data/workspace/myshixun/step4/dataset/tusimple'
test_json_file = '/data/workspace/myshixun/step4/dataset/tusimple/test_label.json'
dataset = dict(
train=dict(
type='TuSimple',
img_path=dataset_path,
data_list='train_val_gt.txt',
),
val=dict(
type='TuSimple',
img_path=dataset_path,
data_list='predict_val.txt'
),
test=dict(
type='TuSimple',
img_path=dataset_path,
data_list='test_gt.txt'
)
)
loss_type = 'cross_entropy'
seg_loss_weight = 1.0
batch_size = 4
workers = 0
num_classes = 6 + 1
ignore_label = 255
epochs = 2
log_interval = 100
eval_ep = 1
save_ep = epochs
log_note = ''