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__