in lib/options.py [0:0]
def initialize(self, parser):
# Datasets related
g_data = parser.add_argument_group('Data')
g_data.add_argument('--dataset', type=str, default='renderppl', help='dataset name')
g_data.add_argument('--dataroot', type=str, default='./data',
help='path to images (data folder)')
g_data.add_argument('--loadSize', type=int, default=512, help='load size of input image')
# Experiment related
g_exp = parser.add_argument_group('Experiment')
g_exp.add_argument('--name', type=str, default='',
help='name of the experiment. It decides where to store samples and models')
g_exp.add_argument('--debug', action='store_true', help='debug mode or not')
g_exp.add_argument('--mode', type=str, default='inout', help='inout || color')
# Training related
g_train = parser.add_argument_group('Training')
g_train.add_argument('--tmp_id', type=int, default=0, help='tmp_id')
g_train.add_argument('--gpu_id', type=int, default=0, help='gpu id for cuda')
g_train.add_argument('--batch_size', type=int, default=32, help='input batch size')
g_train.add_argument('--num_threads', default=1, type=int, help='# sthreads for loading data')
g_train.add_argument('--serial_batches', action='store_true',
help='if true, takes images in order to make batches, otherwise takes them randomly')
g_train.add_argument('--pin_memory', action='store_true', help='pin_memory')
g_train.add_argument('--learning_rate', type=float, default=1e-3, help='adam learning rate')
g_train.add_argument('--num_iter', type=int, default=30000, help='num iterations to train')
g_train.add_argument('--freq_plot', type=int, default=100, help='freqency of the error plot')
g_train.add_argument('--freq_mesh', type=int, default=20000, help='freqency of the save_checkpoints')
g_train.add_argument('--freq_eval', type=int, default=5000, help='freqency of the save_checkpoints')
g_train.add_argument('--freq_save_ply', type=int, default=5000, help='freqency of the save ply')
g_train.add_argument('--freq_save_image', type=int, default=100, help='freqency of the save input image')
g_train.add_argument('--resume_epoch', type=int, default=-1, help='epoch resuming the training')
g_train.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
g_train.add_argument('--finetune', action='store_true', help='fine tuning netG in training C')
# Testing related
g_test = parser.add_argument_group('Testing')
g_test.add_argument('--resolution', type=int, default=512, help='# of grid in mesh reconstruction')
g_test.add_argument('--no_numel_eval', action='store_true', help='no numerical evaluation')
g_test.add_argument('--no_mesh_recon', action='store_true', help='no mesh reconstruction')
# Sampling related
g_sample = parser.add_argument_group('Sampling')
g_sample.add_argument('--num_sample_inout', type=int, default=6000, help='# of sampling points')
g_sample.add_argument('--num_sample_surface', type=int, default=0, help='# of sampling points')
g_sample.add_argument('--num_sample_normal', type=int, default=0, help='# of sampling points')
g_sample.add_argument('--num_sample_color', type=int, default=0, help='# of sampling points')
g_sample.add_argument('--num_pts_dic', type=int, default=1, help='# of pts dic you load')
g_sample.add_argument('--crop_type', type=str, default='fullbody', help='Sampling file name.')
g_sample.add_argument('--uniform_ratio', type=float, default=0.1, help='maximum sigma for sampling')
g_sample.add_argument('--mask_ratio', type=float, default=0.5, help='maximum sigma for sampling')
g_sample.add_argument('--sampling_parts', action='store_true', help='Sampling on the fly')
g_sample.add_argument('--sampling_otf', action='store_true', help='Sampling on the fly')
g_sample.add_argument('--sampling_mode', type=str, default='sigma_uniform', help='Sampling file name.')
g_sample.add_argument('--linear_anneal_sigma', action='store_true', help='linear annealing of sigma')
g_sample.add_argument('--sigma_max', type=float, default=0.0, help='maximum sigma for sampling')
g_sample.add_argument('--sigma_min', type=float, default=0.0, help='minimum sigma for sampling')
g_sample.add_argument('--sigma', type=float, default=1.0, help='sigma for sampling')
g_sample.add_argument('--sigma_surface', type=float, default=1.0, help='sigma for sampling')
g_sample.add_argument('--z_size', type=float, default=200.0, help='z normalization factor')
# Model related
g_model = parser.add_argument_group('Model')
# General
g_model.add_argument('--norm', type=str, default='batch',
help='instance normalization or batch normalization or group normalization')
# Image filter General
g_model.add_argument('--netG', type=str, default='hgpifu', help='piximp | fanimp | hghpifu')
g_model.add_argument('--netC', type=str, default='resblkpifu', help='resblkpifu | resblkhpifu')
# hgimp specific
g_model.add_argument('--num_stack', type=int, default=4, help='# of hourglass')
g_model.add_argument('--hg_depth', type=int, default=2, help='# of stacked layer of hourglass')
g_model.add_argument('--hg_down', type=str, default='ave_pool', help='ave pool || conv64 || conv128')
g_model.add_argument('--hg_dim', type=int, default=256, help='256 | 512')
# Classification General
g_model.add_argument('--mlp_norm', type=str, default='group', help='normalization for volume branch')
g_model.add_argument('--mlp_dim', nargs='+', default=[257, 1024, 512, 256, 128, 1], type=int,
help='# of dimensions of mlp. no need to put the first channel')
g_model.add_argument('--mlp_dim_color', nargs='+', default=[1024, 512, 256, 128, 3], type=int,
help='# of dimensions of mlp. no need to put the first channel')
g_model.add_argument('--mlp_res_layers', nargs='+', default=[2,3,4], type=int,
help='leyers that has skip connection. use 0 for no residual pass')
g_model.add_argument('--merge_layer', type=int, default=-1)
# for train
parser.add_argument('--random_body_chop', action='store_true', help='if random flip')
parser.add_argument('--random_flip', action='store_true', help='if random flip')
parser.add_argument('--random_trans', action='store_true', help='if random flip')
parser.add_argument('--random_scale', action='store_true', help='if random flip')
parser.add_argument('--random_rotate', action='store_true', help='if random flip')
parser.add_argument('--random_bg', action='store_true', help='using random background')
parser.add_argument('--schedule', type=int, nargs='+', default=[10, 15],
help='Decrease learning rate at these epochs.')
parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.')
parser.add_argument('--lambda_nml', type=float, default=0.0, help='weight of normal loss')
parser.add_argument('--lambda_cmp_l1', type=float, default=0.0, help='weight of normal loss')
parser.add_argument('--occ_loss_type', type=str, default='mse', help='bce | brock_bce | mse')
parser.add_argument('--clr_loss_type', type=str, default='mse', help='mse | l1')
parser.add_argument('--nml_loss_type', type=str, default='mse', help='mse | l1')
parser.add_argument('--occ_gamma', type=float, default=None, help='weighting term')
parser.add_argument('--no_finetune', action='store_true', help='fine tuning netG in training C')
# for eval
parser.add_argument('--val_test_error', action='store_true', help='validate errors of test data')
parser.add_argument('--val_train_error', action='store_true', help='validate errors of train data')
parser.add_argument('--gen_test_mesh', action='store_true', help='generate test mesh')
parser.add_argument('--gen_train_mesh', action='store_true', help='generate train mesh')
parser.add_argument('--all_mesh', action='store_true', help='generate meshs from all hourglass output')
parser.add_argument('--num_gen_mesh_test', type=int, default=4,
help='how many meshes to generate during testing')
# path
parser.add_argument('--load_netG_checkpoint_path', type=str, help='path to save checkpoints')
parser.add_argument('--load_netC_checkpoint_path', type=str, help='path to save checkpoints')
parser.add_argument('--checkpoints_path', type=str, default='./checkpoints', help='path to save checkpoints')
parser.add_argument('--results_path', type=str, default='./results', help='path to save results ply')
parser.add_argument('--load_checkpoint_path', type=str, help='path to save results ply')
parser.add_argument('--single', type=str, default='', help='single data for training')
# for single image reconstruction
parser.add_argument('--mask_path', type=str, help='path for input mask')
parser.add_argument('--img_path', type=str, help='path for input image')
# for multi resolution
parser.add_argument('--load_netMR_checkpoint_path', type=str, help='path to save checkpoints')
parser.add_argument('--loadSizeBig', type=int, default=1024, help='load size of input image')
parser.add_argument('--loadSizeLocal', type=int, default=512, help='load size of input image')
parser.add_argument('--train_full_pifu', action='store_true', help='enable end-to-end training')
parser.add_argument('--num_local', type=int, default=1, help='number of local cropping')
# for normal condition
parser.add_argument('--load_netFB_checkpoint_path', type=str, help='path to save checkpoints')
parser.add_argument('--load_netF_checkpoint_path', type=str, help='path to save checkpoints')
parser.add_argument('--load_netB_checkpoint_path', type=str, help='path to save checkpoints')
parser.add_argument('--use_aio_normal', action='store_true')
parser.add_argument('--use_front_normal', action='store_true')
parser.add_argument('--use_back_normal', action='store_true')
parser.add_argument('--no_intermediate_loss', action='store_true')
# aug
group_aug = parser.add_argument_group('aug')
group_aug.add_argument('--aug_alstd', type=float, default=0.0, help='augmentation pca lighting alpha std')
group_aug.add_argument('--aug_bri', type=float, default=0.2, help='augmentation brightness')
group_aug.add_argument('--aug_con', type=float, default=0.2, help='augmentation contrast')
group_aug.add_argument('--aug_sat', type=float, default=0.05, help='augmentation saturation')
group_aug.add_argument('--aug_hue', type=float, default=0.05, help='augmentation hue')
group_aug.add_argument('--aug_gry', type=float, default=0.1, help='augmentation gray scale')
group_aug.add_argument('--aug_blur', type=float, default=0.0, help='augmentation blur')
# for reconstruction
parser.add_argument('--start_id', type=int, default=-1, help='load size of input image')
parser.add_argument('--end_id', type=int, default=-1, help='load size of input image')
# special tasks
self.initialized = True
return parser