Upload config.py

lib/common/config.py

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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: [email protected]
+
+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)