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config.py
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class ConfigSemanticKITTI:
k_n = 16 # KNN
num_layers = 4 # Number of layers
num_points = 4096 * 11 # Number of input points
num_classes = 20 # Number of valid classes
sub_grid_size = 0.06 # preprocess_parameter
sub_sampling_ratio = [4, 4, 4, 4] # sampling ratio of random sampling at each layer
d_out = [16, 64, 128, 256] # feature dimension
num_sub_points = [num_points // 4, num_points // 16, num_points // 64, num_points // 256]
class ConfigSemanticKITTI_BAF:
k_n = 16 # KNN
num_layers = 4 # Number of layers
num_points = 4096 * 11 # Number of input points
num_classes = 20 # Number of valid classes
sub_grid_size = 0.06 # preprocess_parameter
sub_sampling_ratio = [4, 4, 4, 4] # sampling ratio of random sampling at each layer
d_out = [16, 32, 64, 128] # feature dimension
num_sub_points = [num_points // 4, num_points // 16, num_points // 64, num_points // 256]
class ConfigSemanticPOSS:
k_n = 16 # KNN
num_layers = 4 # Number of layers
num_points = 4096 * 11 # Number of input points
num_classes = 11 # Number of valid classes
sub_grid_size = 0.06 # preprocess_parameter
sub_sampling_ratio = [4, 4, 4, 4] # sampling ratio of random sampling at each layer
d_out = [16, 64, 128, 256] # feature dimension
num_sub_points = [num_points // 4, num_points // 16, num_points // 64, num_points // 256]
class ConfigSemanticPOSS_BAF:
k_n = 16 # KNN
num_layers = 4 # Number of layers
num_points = 4096 * 11 # Number of input points
num_classes = 11 # Number of valid classes
sub_grid_size = 0.06 # preprocess_parameter
sub_sampling_ratio = [4, 4, 4, 4] # sampling ratio of random sampling at each layer
d_out = [16, 32, 64, 128] # feature dimension
num_sub_points = [num_points // 4, num_points // 16, num_points // 64, num_points // 256]