# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import argparse import os class BaseOptions(): def __init__(self): self.initialized = False self.parser = None 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 def gather_options(self, args=None): # initialize parser with basic options if not self.initialized: parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser = self.initialize(parser) self.parser = parser if args is None: return self.parser.parse_args() else: return self.parser.parse_args(args) def print_options(self, opt): message = '' message += '----------------- Options ---------------\n' for k, v in sorted(vars(opt).items()): comment = '' default = self.parser.get_default(k) if v != default: comment = '\t[default: %s]' % str(default) message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) message += '----------------- End -------------------' print(message) def parse(self, args=None): opt = self.gather_options(args) opt.sigma = opt.sigma_max if len(opt.mlp_res_layers) == 1 and opt.mlp_res_layers[0] < 1: opt.mlp_res_layers = [] return opt