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import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self):
self.parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
self.parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
self.parser.add_argument('--fineSize', type=int, default=256, help='then crop 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='# of gen filters in first conv layer')
self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
self.parser.add_argument('--which_model_netD', type=str, default='basic', help='selects model to use for netD')
self.parser.add_argument('--which_model_netG', type=str, default='resnet_9blocks', help='selects model to use for netG')
self.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
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('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
self.parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single]')
self.parser.add_argument('--model', type=str, default='cycle_gan',
help='chooses which model to use. cycle_gan, pix2pix, test')
self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA')
self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')
self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
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=8097, help='visdom port of the web display')
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('--identity', type=float, default=0.0, help='use identity mapping. Setting identity other than 1 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set optidentity = 0.1')
self.parser.add_argument('--no_dropout', action='store_true', 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('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation')
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.name)
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
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