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94 lines
1.5 KiB
94 lines
1.5 KiB
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='resnet34',
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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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resa = dict(
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type='RESA',
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alpha=2.0,
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iter=5,
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input_channel=128,
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conv_stride=9,
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)
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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='Tusimple',
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thresh = 0.60
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)
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optimizer = dict(
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type='sgd',
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lr=0.020,
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weight_decay=1e-4,
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momentum=0.9
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)
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total_iter = 80000
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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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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 = 368
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img_width = 640
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cut_height = 160
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seg_label = "seg_label"
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dataset_path = '/data/workspace/myshixun/step4/dataset/tusimple'
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test_json_file = '/data/workspace/myshixun/step4/dataset/tusimple/test_label.json'
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dataset = dict(
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train=dict(
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type='TuSimple',
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img_path=dataset_path,
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data_list='train_val_gt.txt',
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),
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val=dict(
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type='TuSimple',
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img_path=dataset_path,
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data_list='predict_val.txt'
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),
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test=dict(
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type='TuSimple',
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img_path=dataset_path,
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data_list='test_gt.txt'
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)
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)
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loss_type = 'cross_entropy'
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seg_loss_weight = 1.0
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batch_size = 4
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workers = 0
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num_classes = 6 + 1
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ignore_label = 255
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epochs = 2
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log_interval = 100
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eval_ep = 1
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save_ep = epochs
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log_note = ''
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