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| import argparse | |
| import os | |
| class BaseOptions(): | |
| def __init__(self): | |
| self.initialized = False | |
| argparse | |
| def initialize(self, parser): | |
| # Datasets related | |
| g_data = parser.add_argument_group('Data') | |
| 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='example', | |
| 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('--num_views', type=int, default=1, help='How many views to use for multiview network.') | |
| g_exp.add_argument('--random_multiview', action='store_true', help='Select random multiview combination.') | |
| # Training related | |
| g_train = parser.add_argument_group('Training') | |
| g_train.add_argument('--gpu_id', type=int, default=0, help='gpu id for cuda') | |
| g_train.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2, -1 for CPU mode') | |
| 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('--batch_size', type=int, default=2, help='input batch size') | |
| g_train.add_argument('--learning_rate', type=float, default=1e-3, help='adam learning rate') | |
| g_train.add_argument('--learning_rateC', type=float, default=1e-3, help='adam learning rate') | |
| g_train.add_argument('--num_epoch', type=int, default=100, help='num epoch to train') | |
| g_train.add_argument('--freq_plot', type=int, default=10, help='freqency of the error plot') | |
| g_train.add_argument('--freq_save', type=int, default=50, help='freqency of the save_checkpoints') | |
| g_train.add_argument('--freq_save_ply', type=int, default=100, help='freqency of the save ply') | |
| g_train.add_argument('--no_gen_mesh', action='store_true') | |
| g_train.add_argument('--no_num_eval', action='store_true') | |
| 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') | |
| # Testing related | |
| g_test = parser.add_argument_group('Testing') | |
| g_test.add_argument('--resolution', type=int, default=256, help='# of grid in mesh reconstruction') | |
| g_test.add_argument('--test_folder_path', type=str, default=None, help='the folder of test image') | |
| # Sampling related | |
| g_sample = parser.add_argument_group('Sampling') | |
| g_sample.add_argument('--sigma', type=float, default=5.0, help='perturbation standard deviation for positions') | |
| g_sample.add_argument('--num_sample_inout', type=int, default=5000, help='# of sampling points') | |
| g_sample.add_argument('--num_sample_color', type=int, default=0, help='# of sampling points') | |
| 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='group', | |
| help='instance normalization or batch normalization or group normalization') | |
| g_model.add_argument('--norm_color', type=str, default='instance', | |
| help='instance normalization or batch normalization or group normalization') | |
| # hg filter specify | |
| g_model.add_argument('--num_stack', type=int, default=4, help='# of hourglass') | |
| g_model.add_argument('--num_hourglass', type=int, default=2, help='# of stacked layer of hourglass') | |
| g_model.add_argument('--skip_hourglass', action='store_true', help='skip connection in hourglass') | |
| g_model.add_argument('--hg_down', type=str, default='ave_pool', help='ave pool || conv64 || conv128') | |
| g_model.add_argument('--hourglass_dim', type=int, default='256', help='256 | 512') | |
| # Classification General | |
| g_model.add_argument('--mlp_dim', nargs='+', default=[257, 1024, 512, 256, 128, 1], type=int, | |
| help='# of dimensions of mlp') | |
| g_model.add_argument('--mlp_dim_color', nargs='+', default=[513, 1024, 512, 256, 128, 3], | |
| type=int, help='# of dimensions of color mlp') | |
| g_model.add_argument('--use_tanh', action='store_true', | |
| help='using tanh after last conv of image_filter network') | |
| # for train | |
| 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('--no_residual', action='store_true', help='no skip connection in mlp') | |
| parser.add_argument('--schedule', type=int, nargs='+', default=[60, 80], | |
| 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('--color_loss_type', type=str, default='l1', help='mse | l1') | |
| # 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=1, | |
| help='how many meshes to generate during testing') | |
| # path | |
| parser.add_argument('--checkpoints_path', type=str, default='./checkpoints', help='path to save checkpoints') | |
| parser.add_argument('--load_netG_checkpoint_path', type=str, default=None, help='path to save checkpoints') | |
| parser.add_argument('--load_netC_checkpoint_path', type=str, default=None, 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') | |
| # 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=0.0, help='augmentation brightness') | |
| group_aug.add_argument('--aug_con', type=float, default=0.0, help='augmentation contrast') | |
| group_aug.add_argument('--aug_sat', type=float, default=0.0, help='augmentation saturation') | |
| group_aug.add_argument('--aug_hue', type=float, default=0.0, help='augmentation hue') | |
| group_aug.add_argument('--aug_blur', type=float, default=0.0, help='augmentation blur') | |
| # special tasks | |
| self.initialized = True | |
| return parser | |
| def gather_options(self): | |
| # initialize parser with basic options | |
| if not self.initialized: | |
| parser = argparse.ArgumentParser( | |
| formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| parser = self.initialize(parser) | |
| self.parser = parser | |
| return parser.parse_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): | |
| opt = self.gather_options() | |
| return opt | |
| def parse_to_dict(self): | |
| opt = self.gather_options() | |
| return opt.__dict__ |