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48 lines
2.0 KiB
48 lines
2.0 KiB
train{
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folderprefix = ""
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input_path = ../data/input_data/
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fileprefix_list = [
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broken_bullet_50k, # more input models can be added here
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]
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d_in = 3 # 输入数据的维度。在3D点云数据中，通常为3（x、y、z坐标）
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plot_frequency = 5000 # 每5000次迭代绘制一次点云
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checkpoint_frequency = 5000
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status_frequency = 100
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weight_decay = 0
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learning_rate_schedule = [{
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"Type" : "Step", # 学习率调度类型。"Step"表示在指定迭代次数后将学习率乘以因子
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"Initial" : 0.005,
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"Interval" : 2000,
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"Factor" : 0.5
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}]
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network_class = model.network.NHRepNet # 网络类型。NHRepNet是neural halfspace representation network的缩写
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}
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plot{
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resolution = 128 # 体素网格的分辨率。128表示体素网格的每个维度有128个单元格
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mc_value = 0.0 # 体素网格的体素值。0.0表示体素网格的体素值为0
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is_uniform_grid = True
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verbose = False
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save_html = False
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save_ply = True
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overwrite = True
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}
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network{
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inputs{
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dims_sdf = [256, 256, 256] # 体素网格的维度。[256, 256, 256]表示体素网格的每个维度有256个单元格
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skip_in = [] # 跳过输入的索引。[]表示不跳过任何输入
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geometric_init= True # 几何初始化。True表示使用几何初始化
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radius_init = 1 # 半径初始化。1表示半径初始化为1
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beta=100 # beta值。100表示beta值为100
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}
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sampler{
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sampler_type = NormalPerPoint # 采样器类型。NormalPerPoint表示每个点都使用正态分布采样
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properties{
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global_sigma = 1.8 # 全局sigma值。1.8表示全局sigma值为1.8
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}
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}
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loss{
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lambda = 1 # 损失函数中的lambda值。1表示lambda值为1
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normals_lambda = 1 # 损失函数中的normals_lambda值。1表示normals_lambda值为1
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}
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}
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