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__