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| # -*- coding: utf-8 -*- | |
| # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is | |
| # holder of all proprietary rights on this computer program. | |
| # You can only use this computer program if you have closed | |
| # a license agreement with MPG or you get the right to use the computer | |
| # program from someone who is authorized to grant you that right. | |
| # Any use of the computer program without a valid license is prohibited and | |
| # liable to prosecution. | |
| # | |
| # Copyright©2019 Max-Planck-Gesellschaft zur Förderung | |
| # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute | |
| # for Intelligent Systems. All rights reserved. | |
| # | |
| # Contact: ps-license@tuebingen.mpg.de | |
| from yacs.config import CfgNode as CN | |
| import os | |
| _C = CN(new_allowed=True) | |
| # needed by trainer | |
| _C.name = 'default' | |
| _C.gpus = [0] | |
| _C.test_gpus = [1] | |
| _C.root = "./data/" | |
| _C.ckpt_dir = './data/ckpt/' | |
| _C.resume_path = '' | |
| _C.normal_path = '' | |
| _C.corr_path = '' | |
| _C.results_path = './data/results/' | |
| _C.projection_mode = 'orthogonal' | |
| _C.num_views = 1 | |
| _C.sdf = False | |
| _C.sdf_clip = 5.0 | |
| _C.lr_G = 1e-3 | |
| _C.lr_C = 1e-3 | |
| _C.lr_N = 2e-4 | |
| _C.weight_decay = 0.0 | |
| _C.momentum = 0.0 | |
| _C.optim = 'RMSprop' | |
| _C.schedule = [5, 10, 15] | |
| _C.gamma = 0.1 | |
| _C.overfit = False | |
| _C.resume = False | |
| _C.test_mode = False | |
| _C.test_uv = False | |
| _C.draw_geo_thres = 0.60 | |
| _C.num_sanity_val_steps = 2 | |
| _C.fast_dev = 0 | |
| _C.get_fit = False | |
| _C.agora = False | |
| _C.optim_cloth = False | |
| _C.optim_body = False | |
| _C.mcube_res = 256 | |
| _C.clean_mesh = True | |
| _C.remesh = False | |
| _C.batch_size = 4 | |
| _C.num_threads = 8 | |
| _C.num_epoch = 10 | |
| _C.freq_plot = 0.01 | |
| _C.freq_show_train = 0.1 | |
| _C.freq_show_val = 0.2 | |
| _C.freq_eval = 0.5 | |
| _C.accu_grad_batch = 4 | |
| _C.test_items = ['sv', 'mv', 'mv-fusion', 'hybrid', 'dc-pred', 'gt'] | |
| _C.net = CN() | |
| _C.net.gtype = 'HGPIFuNet' | |
| _C.net.ctype = 'resnet18' | |
| _C.net.classifierIMF = 'MultiSegClassifier' | |
| _C.net.netIMF = 'resnet18' | |
| _C.net.norm = 'group' | |
| _C.net.norm_mlp = 'group' | |
| _C.net.norm_color = 'group' | |
| _C.net.hg_down = 'ave_pool' | |
| _C.net.num_views = 1 | |
| # kernel_size, stride, dilation, padding | |
| _C.net.conv1 = [7, 2, 1, 3] | |
| _C.net.conv3x3 = [3, 1, 1, 1] | |
| _C.net.num_stack = 4 | |
| _C.net.num_hourglass = 2 | |
| _C.net.hourglass_dim = 256 | |
| _C.net.voxel_dim = 32 | |
| _C.net.resnet_dim = 120 | |
| _C.net.mlp_dim = [320, 1024, 512, 256, 128, 1] | |
| _C.net.mlp_dim_knn = [320, 1024, 512, 256, 128, 3] | |
| _C.net.mlp_dim_color = [513, 1024, 512, 256, 128, 3] | |
| _C.net.mlp_dim_multiseg = [1088, 2048, 1024, 500] | |
| _C.net.res_layers = [2, 3, 4] | |
| _C.net.filter_dim = 256 | |
| _C.net.smpl_dim = 3 | |
| _C.net.cly_dim = 3 | |
| _C.net.soft_dim = 64 | |
| _C.net.z_size = 200.0 | |
| _C.net.N_freqs = 10 | |
| _C.net.geo_w = 0.1 | |
| _C.net.norm_w = 0.1 | |
| _C.net.dc_w = 0.1 | |
| _C.net.C_cat_to_G = False | |
| _C.net.skip_hourglass = True | |
| _C.net.use_tanh = True | |
| _C.net.soft_onehot = True | |
| _C.net.no_residual = True | |
| _C.net.use_attention = False | |
| _C.net.prior_type = "sdf" | |
| _C.net.smpl_feats = ['sdf', 'cmap', 'norm', 'vis'] | |
| _C.net.use_filter = True | |
| _C.net.use_cc = False | |
| _C.net.use_PE = False | |
| _C.net.use_IGR = False | |
| _C.net.in_geo = () | |
| _C.net.in_nml = () | |
| _C.dataset = CN() | |
| _C.dataset.root = '' | |
| _C.dataset.set_splits = [0.95, 0.04] | |
| _C.dataset.types = [ | |
| "3dpeople", "axyz", "renderpeople", "renderpeople_p27", "humanalloy" | |
| ] | |
| _C.dataset.scales = [1.0, 100.0, 1.0, 1.0, 100.0 / 39.37] | |
| _C.dataset.rp_type = "pifu900" | |
| _C.dataset.th_type = 'train' | |
| _C.dataset.input_size = 512 | |
| _C.dataset.rotation_num = 3 | |
| _C.dataset.num_precomp = 10 # Number of segmentation classifiers | |
| _C.dataset.num_multiseg = 500 # Number of categories per classifier | |
| _C.dataset.num_knn = 10 # for loss/error | |
| _C.dataset.num_knn_dis = 20 # for accuracy | |
| _C.dataset.num_verts_max = 20000 | |
| _C.dataset.zray_type = False | |
| _C.dataset.online_smpl = False | |
| _C.dataset.noise_type = ['z-trans', 'pose', 'beta'] | |
| _C.dataset.noise_scale = [0.0, 0.0, 0.0] | |
| _C.dataset.num_sample_geo = 10000 | |
| _C.dataset.num_sample_color = 0 | |
| _C.dataset.num_sample_seg = 0 | |
| _C.dataset.num_sample_knn = 10000 | |
| _C.dataset.sigma_geo = 5.0 | |
| _C.dataset.sigma_color = 0.10 | |
| _C.dataset.sigma_seg = 0.10 | |
| _C.dataset.thickness_threshold = 20.0 | |
| _C.dataset.ray_sample_num = 2 | |
| _C.dataset.semantic_p = False | |
| _C.dataset.remove_outlier = False | |
| _C.dataset.train_bsize = 1.0 | |
| _C.dataset.val_bsize = 1.0 | |
| _C.dataset.test_bsize = 1.0 | |
| def get_cfg_defaults(): | |
| """Get a yacs CfgNode object with default values for my_project.""" | |
| # Return a clone so that the defaults will not be altered | |
| # This is for the "local variable" use pattern | |
| return _C.clone() | |
| # Alternatively, provide a way to import the defaults as | |
| # a global singleton: | |
| cfg = _C # users can `from config import cfg` | |
| # cfg = get_cfg_defaults() | |
| # cfg.merge_from_file('./configs/example.yaml') | |
| # # Now override from a list (opts could come from the command line) | |
| # opts = ['dataset.root', './data/XXXX', 'learning_rate', '1e-2'] | |
| # cfg.merge_from_list(opts) | |
| def update_cfg(cfg_file): | |
| # cfg = get_cfg_defaults() | |
| _C.merge_from_file(cfg_file) | |
| # return cfg.clone() | |
| return _C | |
| def parse_args(args): | |
| cfg_file = args.cfg_file | |
| if args.cfg_file is not None: | |
| cfg = update_cfg(args.cfg_file) | |
| else: | |
| cfg = get_cfg_defaults() | |
| # if args.misc is not None: | |
| # cfg.merge_from_list(args.misc) | |
| return cfg | |
| def parse_args_extend(args): | |
| if args.resume: | |
| if not os.path.exists(args.log_dir): | |
| raise ValueError( | |
| 'Experiment are set to resume mode, but log directory does not exist.' | |
| ) | |
| # load log's cfg | |
| cfg_file = os.path.join(args.log_dir, 'cfg.yaml') | |
| cfg = update_cfg(cfg_file) | |
| if args.misc is not None: | |
| cfg.merge_from_list(args.misc) | |
| else: | |
| parse_args(args) | |