import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
self.initialized = False
def initialize(self):
self.parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, trainC, etc)')
self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
self.parser.add_argument('--loadSize', type=int, default=512, help='scale images to this size')
self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
self.parser.add_argument('--ngf', type=int, default=64, help='the basic number for channels in the network')
self.parser.add_argument('--which_model_netG', type=str, default='reflrmnetwork', help='selects model to use for netG')
self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')
self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints_removal', help='models are saved here')
self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
self.parser.add_argument('--name', type=str, default='reflection_removal', help='name of the experiment. It decides where to store samples and models')
self.parser.add_argument('--serial_batches', action='store_false', help='if true, takes images in order to make batches, otherwise takes them randomly')
self.parser.add_argument('--no_dropout', action='store_false', help='no dropout for the generator')
self.parser.add_argument('--max_dataset_size', type=int, default=float("inf"),
help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
self.parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]')
self.parser.add_argument('--which_dataset', type=str, default='syn', help='choose synthetic dataset')
self.parser.add_argument('--which_type', type=str, default='focused', help='choose dataset type for our dataset [focused|defocused|ghosting]')
self.initialized = True
def parse(self):
if not self.initialized:
self.initialize()
self.opt = self.parser.parse_args()
self.opt.isTrain = self.isTrain # train or test
str_ids = self.opt.gpu_ids.split(',')
self.opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
self.opt.gpu_ids.append(id)
# set gpu ids
if len(self.opt.gpu_ids) > 0:
torch.cuda.set_device(self.opt.gpu_ids[0])
args = vars(self.opt)
print('------------ Options -------------')
for k, v in sorted(args.items()):
print('%s: %s' % (str(k), str(v)))
print('-------------- End ----------------')
# save to the disk
expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.which_type)
util.mkdirs(expr_dir)
file_name = os.path.join(expr_dir, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write('------------ Options -------------\n')
for k, v in sorted(args.items()):
opt_file.write('%s: %s\n' % (str(k), str(v)))
opt_file.write('-------------- End ----------------\n')
return self.opt
