def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, no_xyz=False, sample_mode="FPRC", sample_rate=0.1, kernal=103):
first_iter = 0
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians)
NN_Comp = UNet(3, 3).cuda()
with open('NN_Comp/NN_comp.yaml', 'r') as config:
nn_comp_config = yaml.safe_load(config)
# NN_Comp = SUNet_model(nn_comp_config).cuda()
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, no_xyz=False, sample_mode="FPRC", sample_rate=0.1, kernal=103):
first_iter = 0
tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians)
NN_Comp = UNet(3, 3).cuda()
with open('NN_Comp/NN_comp.yaml', 'r') as config:
nn_comp_config = yaml.safe_load(config)