98 lines
1.6 KiB
Python
98 lines
1.6 KiB
Python
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net = dict(
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type='RESANet',
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)
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# backbone = dict(
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# type='ResNetWrapper',
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# resnet='resnet50',
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# pretrained=True,
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# replace_stride_with_dilation=[False, True, True],
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# out_conv=True,
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# fea_stride=8,
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# )
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backbone = dict(
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type='ResNetWrapper',
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resnet='resnet34',
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pretrained=True,
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replace_stride_with_dilation=[False, False, False],
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out_conv=False,
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fea_stride=8,
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)
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resa = dict(
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type='RESA',
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alpha=2.0,
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iter=4,
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input_channel=128,
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conv_stride=9,
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)
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#decoder = 'PlainDecoder'
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decoder = 'BUSD'
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trainer = dict(
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type='RESA'
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)
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evaluator = dict(
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type='CULane',
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)
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optimizer = dict(
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type='sgd',
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lr=0.025,
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weight_decay=1e-4,
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momentum=0.9
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)
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epochs = 20
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batch_size = 8
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total_iter = (88880 // batch_size) * epochs
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import math
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scheduler = dict(
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type = 'LambdaLR',
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lr_lambda = lambda _iter : math.pow(1 - _iter/total_iter, 0.9)
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)
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loss_type = 'dice_loss'
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seg_loss_weight = 2.
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eval_ep = 1
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save_ep = epochs
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bg_weight = 0.4
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img_norm = dict(
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mean=[103.939, 116.779, 123.68],
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std=[1., 1., 1.]
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)
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img_height = 288
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img_width = 800
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cut_height = 240
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dataset_path = './data/CULane'
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dataset = dict(
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train=dict(
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type='CULane',
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img_path=dataset_path,
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data_list='train_gt.txt',
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),
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val=dict(
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type='CULane',
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img_path=dataset_path,
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data_list='test.txt',
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),
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test=dict(
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type='CULane',
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img_path=dataset_path,
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data_list='test.txt',
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)
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)
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workers = 12
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num_classes = 4 + 1
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ignore_label = 255
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log_interval = 500
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