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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):
# experiment specifics
self.parser.add_argument('--name', type=str, default='label2city',
help='name of the experiment. It decides where to store samples and models')
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('--checkpoints_dir', type=str,
default='./checkpoints', help='models are saved here')
self.parser.add_argument(
'--model', type=str, default='pix2pixHD', help='which model to use')
self.parser.add_argument('--norm', type=str, default='instance',
help='instance normalization or batch normalization')
self.parser.add_argument(
'--use_dropout', action='store_true', help='use dropout for the generator')
self.parser.add_argument('--data_type', default=32, type=int, choices=[
8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
self.parser.add_argument(
'--verbose', action='store_true', default=False, help='toggles verbose')
# input/output sizes
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(
'--fineSize', type=int, default=512, help='then crop to this size')
self.parser.add_argument(
'--label_nc', type=int, default=20, help='# of input label channels')
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')
# for setting inputs
self.parser.add_argument(
'--dataroot', type=str, default='Data_preprocessing/')
self.parser.add_argument('--datapairs', type=str, default='test_pairs.txt',
help='train_pairs.txt/test_pairs.txt/test_pairs_same.txt etc.')
self.parser.add_argument('--resize_or_crop', type=str, default='scale_width',
help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
'''
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('--no_flip', action='store_true',
help='if specified, do not flip the images for data argumentation')
self.parser.add_argument(
'--nThreads', default=1, type=int, help='# threads for loading data')
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.')
# for displays
self.parser.add_argument(
'--display_winsize', type=int, default=512, help='display window size')
self.parser.add_argument('--tf_log', action='store_true',
help='if specified, use tensorboard logging. Requires tensorflow installed')
# for generator
self.parser.add_argument(
'--netG', type=str, default='global', help='selects model to use for netG')
self.parser.add_argument(
'--ngf', type=int, default=64, help='# of gen filters in first conv layer')
self.parser.add_argument('--n_downsample_global', type=int,
default=4, help='number of downsampling layers in netG')
self.parser.add_argument('--n_blocks_global', type=int, default=4,
help='number of residual blocks in the global generator network')
self.parser.add_argument('--n_blocks_local', type=int, default=3,
help='number of residual blocks in the local enhancer network')
self.parser.add_argument(
'--n_local_enhancers', type=int, default=1, help='number of local enhancers to use')
self.parser.add_argument('--niter_fix_global', type=int, default=0,
help='number of epochs that we only train the outmost local enhancer')
self.parser.add_argument('--continue_train', action='store_true',
help='continue training: load the latest model')
self.initialized = True
def parse(self, save=True):
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)
if save and not self.opt.continue_train:
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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