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option_parser.py
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import argparse
import torch
import os
from utils import utils
class OptionParser(object):
def __init__(self):
self.parser = argparse.ArgumentParser()
self.set_arguments()
def parse_args(self):
opt = self.parser.parse_args()
str_ids = str(opt.gpu_ids)
opt.gpu_ids = []
for str_id in str_ids.split(','):
opt.gpu_ids.append(int(str_id))
if len(opt.gpu_ids) > 0:
torch.cuda.set_device(opt.gpu_ids[0])
args = vars(opt)
print('-------- [INFO] Options --------')
for k, v in sorted(args.items()):
print('%s: %s' % (str(k), str(v)))
expr_dir = os.path.join(opt.ckpt_dir, opt.model)
utils.mkdir(expr_dir)
file_name = os.path.join(expr_dir, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write(' [INFO] Options\n')
for k, v in sorted(args.items()):
opt_file.write('%s: %s\n' % (str(k), str(v)))
print('------------- END -------------')
return opt
def set_arguments(self):
# training options
self.parser.add_argument('--dataset', type=str, default='CARS', help='name of dataset. MNIST default')
self.parser.add_argument('--data_dir', type=str, default='data', help='root data dir')
self.parser.add_argument('--small_batch_size', type=int, default=60, help='small batch size')
self.parser.add_argument('--large_batch_size', type=int, default=100, help='large batch size')
self.parser.add_argument('--num_preprocess_workers', type=int, default=2, help='num preprocess workers')
self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids ex) 0,1,2')
self.parser.add_argument('--ckpt_dir', type=str, default='./ckpt/', help='checkpoint dir')
self.parser.add_argument('--model', type=str, default='inception', help='name of model')
self.parser.add_argument('--lr', type=float, default=1e-5, help='learning rate')
self.parser.add_argument('--large_batch_epoch', type=int, default=250, help='large batch epoch')
self.parser.add_argument('--width_size', type=int, default=299, help='image width size')
self.parser.add_argument('--channels', type=int, default=3, help='channels')
self.parser.add_argument('--label_size', type=int, default=98, help='label size')
self.parser.add_argument('--vector_size', type=int, default=196, help='vector size')
self.parser.add_argument('--fine_tune', type=int, default=1, help='is fine tune')
self.parser.add_argument('--eps', type=float, default=1e-8, help='eps')
self.parser.add_argument('--dropout_rate', type=float, default=0.3, help='dropout rate')
# visualize options
self.parser.add_argument('--display_winsize', type=int, default=256, help='display window size')
self.parser.add_argument('--display_id', type=int, default=1, help='window id of the web display')
self.parser.add_argument('--display_port', type=int, default=8096, help='visdom port of the web display')
class TrainingOptionParser(OptionParser):
def set_arguments(self):
super(TrainingOptionParser, self).set_arguments()
self.parser.add_argument('--is_train', type=int, default=1, help='is training')
self.parser.add_argument('--display_single_pane_ncols', type=int, default=0,
help='if positive, display all images in a single visdom web panel with certain number of images per row.')
self.parser.add_argument('--print_small_batch', type=int, default=0, help='print small batch for debugging')
self.parser.add_argument('--plot_freq', type=int, default=15000, help='iteration count per a single plot')
class TestingOptionParser(OptionParser):
def set_arguments(self):
super(TestingOptionParser, self).set_arguments()
self.parser.add_argument('--is_train', type=int, default=0, help='is training')
self.parser.add_argument('--save_as_numpy', type=int, default=1, help='save as numpy format')
self.parser.add_argument('--k', type=int, default=10, help='k nearest neighbors.')
self.parser.add_argument('--test_dir', type=str, default='./test/', help='test dir')
def parse_args(self):
opt = self.parser.parse_args()
str_ids = str(opt.gpu_ids)
opt.gpu_ids = []
for str_id in str_ids.split(','):
opt.gpu_ids.append(int(str_id))
if len(opt.gpu_ids) > 0:
torch.cuda.set_device(opt.gpu_ids[0])
args = vars(opt)
print('-------- [INFO] Options --------')
for k, v in sorted(args.items()):
print('%s: %s' % (str(k), str(v)))
expr_dir = os.path.join(opt.ckpt_dir, opt.model)
utils.mkdir(expr_dir)
test_dir = os.path.join(opt.test_dir, opt.model)
utils.mkdir(test_dir)
file_name = os.path.join(expr_dir, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write(' [INFO] Options\n')
for k, v in sorted(args.items()):
opt_file.write('%s: %s\n' % (str(k), str(v)))
print('------------- END -------------')
return opt