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parse_args.py
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parse_args.py
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import os
import argparse
import torch
import utils
import pickle
from os import listdir, path, mkdir
import numpy as np
def collect_args_main():
parser = argparse.ArgumentParser()
parser.add_argument('--experiment',
choices=[
'baseline',
'model',
'model_inv',
'fake_only',
])
parser.add_argument('--experiment_name', type=str, default='_')
parser.add_argument('--real_data_dir', type=str, default='data/celeba')
parser.add_argument('--fake_data_dir_orig', type=str, default='data/fake_images/AllGenImages/')
parser.add_argument('--fake_data_dir_new', type=str, default='_')
parser.add_argument('--fake_scores_target', type=str, default='_')
parser.add_argument('--fake_scores_protected', type=str, default='_')
parser.add_argument('--no_cuda', dest='cuda', action='store_false')
parser.add_argument('--random_seed', type=int, default=0)
parser.add_argument('--attribute', type=int, default=31)
parser.add_argument('--protected_attribute', type=int, default=20)
parser.add_argument('--test_mode', type=bool, default=False)
parser.add_argument('--num_train', type=int, default=160000)
parser.add_argument('--number', type=int, default=0)
parser.set_defaults(cuda=True)
opt = vars(parser.parse_args())
opt = create_experiment_setting(opt)
return opt
def create_experiment_setting(opt):
# Uncomment if deterministic run required.
#torch.backends.cudnn.deterministic = True
#torch.backends.cudnn.benchmark = False
#torch.manual_seed(opt['random_seed'])
#np.random.seed(opt['random_seed'])
attr_list = utils.get_all_attr()
attr_name = attr_list[opt['attribute']]
opt['device'] = torch.device('cuda' if opt['cuda'] else 'cpu')
opt['dtype'] = torch.float32
opt['print_freq'] = 100
opt['total_epochs'] = 20
orig_save = 'record/'
if opt['protected_attribute']!=20:
orig_save+='protected'+attr_list[opt['protected_attribute']]+'/'
utils.make_dir('record')
utils.make_dir(orig_save)
if opt['experiment_name']=='_':
opt['save_folder'] = os.path.join(orig_save +opt['experiment'],
attr_name)
utils.make_dir(orig_save+opt['experiment'])
utils.make_dir(opt['save_folder'])
else:
opt['save_folder'] = orig_save+opt['experiment_name']+'/'+attr_name
utils.make_dir(orig_save+opt['experiment_name'])
utils.make_dir(opt['save_folder'])
optimizer_setting = {
'optimizer': torch.optim.Adam,
'lr': 1e-4,
'weight_decay': 0,
}
opt['optimizer_setting'] = optimizer_setting
opt['dropout'] = 0.5
if opt['experiment']=='baseline':
params_real_train = {'batch_size': 32,
'shuffle': True,
'num_workers': 0}
params_real_val = {'batch_size': 64,
'shuffle': False,
'num_workers': 0}
data_setting = {
'path': opt['real_data_dir'],
'params_real_train': params_real_train,
'params_real_val': params_real_val,
'protected_attribute': opt['protected_attribute'],
'attribute': opt['attribute'],
'augment': True
}
opt['data_setting'] = data_setting
elif opt['experiment'] == 'model' or opt['experiment']=='fake_only':
if opt['fake_data_dir_new']=='_':
if opt['protected_attribute']!=20:
input_path_new = 'data/fake_images/protected'+attr_list[opt['protected_attribute']]+'/'+attr_name+'/'
else:
input_path_new = 'data/fake_images/{}/'.format(attr_name)
else:
input_path_new = opt['fake_data_dir_new']
input_path_orig = opt['fake_data_dir_orig']
#scores = 'data/fake_images/' + attr_name+'_scores.pkl'
if opt['fake_scores_target']=='_':
scores = 'data/fake_images/{}_scores.pkl'.format(attr_name)
else:
scores = opt['fake_scores_target']
if opt['fake_scores_protected']=='_':
domain = 'data/fake_images/all_' + attr_list[opt['protected_attribute']]+'_scores.pkl'
else:
domain = opt['fake_scores_protected']
params_train = {'batch_size': 32,
'shuffle': True,
'num_workers': 0}
params_val = {'batch_size': 64,
'shuffle': False,
'num_workers': 0}
real_params = {
'path': opt['real_data_dir'],
'attribute': opt['attribute'],
'protected_attribute': opt['protected_attribute'],
'number': 0
}
fake_params = {
'path_new': input_path_new,
'path_orig': input_path_orig,
'attr_path': scores,
'dom_path': domain,
'range_orig_image': (15000, 175000),
'range_orig_label': ( 160000, 320000),
'range_new': (0, 160000),
}
data_setting = {
'real_params': real_params,
'fake_params': fake_params,
'augment': True,
'params_train': params_train,
'params_val': params_val
}
opt['data_setting'] = data_setting
return opt
def collect_args_generate():
parser = argparse.ArgumentParser()
parser.add_argument('--experiment',
choices=[
'orig',
'pair',
], default='orig')
parser.add_argument('--attribute', type=int, default=31)
parser.add_argument('--save_dir', type=str, default='_')
parser.add_argument('--latent_file', type=str, default='_')
parser.add_argument('--random_seed', type=int, default=0)
parser.add_argument('--num_images', type=int, default=175000)
parser.add_argument('--number', type=int, default=0)
parser.add_argument('--protected_attribute', type=int, default=20)
parser.add_argument('--protected_val', type=int, default=0)
parser.add_argument('--attr_val', type=int, default=0)
parser.set_defaults(cuda=True)
opt = vars(parser.parse_args())
attr_list = utils.get_all_attr()
opt['attr_name'] = attr_list[opt['attribute']]
opt['prot_attr_name'] = attr_list[opt['protected_attribute']]
opt['device'] = torch.device('cuda' if opt['cuda'] else 'cpu')
opt['dtype'] = torch.float32
if opt['experiment']=='pair' and opt['save_dir']=='_':
opt['save_dir']='data/fake_images/{}/'.format(opt['attr_name'])
if opt['experiment']=='pair' and opt['latent_file']=='_':
opt['latent_file']='record/GAN_model/latent_vectors_{}.pkl'.format(opt['attr_name'])
return opt
def collect_args_scores():
parser = argparse.ArgumentParser()
parser.add_argument('--attribute', type=int, default=31)
parser.add_argument('--model_dir', type=str, default='record/baseline')
parser.add_argument('--out_file', type=str, default='_')
parser.add_argument('--random_seed', type=int, default=0)
parser.add_argument('--num_images', type=int, default=175000)
parser.add_argument('--number', type=int, default=0)
#parser.set_defaults(cuda=True)
opt = vars(parser.parse_args())
attr_list = utils.get_all_attr()
opt['attr_name'] = attr_list[opt['attribute']]
if torch.cuda.is_available():
opt['device'] = torch.device('cuda')
else:
opt['device'] = torch.device('cpu')
opt['dtype'] = torch.float32
if opt['out_file']=='_':
opt['out_file']='data/fake_images/all_{}_scores.pkl'.format(opt['attr_name'])
return opt
def collect_args_linear():
parser = argparse.ArgumentParser()
parser.add_argument('--attribute', type=int, default=31)
parser.add_argument('--protected_attribute', type=int, default=20)
parser.add_argument('--random_seed', type=int, default=0)
parser.add_argument('--attr_val', type=int, default=0)
parser.add_argument('--protected_val', type=int, default=0)
parser.add_argument('--number', type=int, default=0)
parser.set_defaults(cuda=True)
opt = vars(parser.parse_args())
attr_list = utils.get_all_attr()
opt['attr_name'] = attr_list[opt['attribute']]
opt['prot_attr_name'] = attr_list[opt['protected_attribute']]
if torch.cuda.is_available():
opt['device'] = torch.device('cuda')
else:
opt['device'] = torch.device('cpu')
opt['dtype'] = torch.float32
return opt
def collect_args_full_skew():
parser = argparse.ArgumentParser()
parser.add_argument('--attribute1', type=int, default=31)
parser.add_argument('--attribute2', type=int, default=20)
parser.add_argument('--real_data_dir', type=str, default='data/celeba')
parser.add_argument('--random_seed', type=int, default=0)
parser.add_argument('--test_mode', type=bool, default=False)
parser.add_argument('--opp', type=bool, default=False)
parser.set_defaults(cuda=True)
opt = vars(parser.parse_args())
attr_list = utils.get_all_attr()
opt['attr_name1'] = attr_list[opt['attribute1']]
opt['attr_name2'] = attr_list[opt['attribute2']]
if torch.cuda.is_available():
opt['device'] = torch.device('cuda')
else:
opt['device'] = torch.device('cpu')
opt['dtype'] = torch.float32
opt['total_epochs']=20
params_real_train = {'batch_size': 32,
'shuffle': True,
'num_workers': 0}
params_real_val = {'batch_size': 64,
'shuffle': False,
'num_workers': 0}
data_setting = {
'path': opt['real_data_dir'],
'params_real_train': params_real_train,
'params_real_val': params_real_val,
'attribute1': opt['attribute1'],
'attribute2': opt['attribute2'],
'augment': True
}
opt['data_setting'] = data_setting
if opt['opp']:
opt['save_folder'] = 'record/full_skew/attr_{}_{}_opp/'.format(opt['attribute1'],opt['attribute2'])
else:
opt['save_folder'] = 'record/full_skew/attr_{}_{}/'.format(opt['attribute1'],opt['attribute2'])
utils.make_dir('record/full_skew')
utils.make_dir(opt['save_folder'])
return opt