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"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import os
import sys
import torch
import pickle
import argparse
import sstan_models
import utils as util
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
# experiment specifics
parser.add_argument('--name', type=str, default='multimodal_artworks', help='name of the experiment. It decides where to store samples and sstan_models')
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
parser.add_argument('--checkpoints_dir', type=str, default='./seg2art/checkpoints', help='sstan_models are saved here')
parser.add_argument('--model', type=str, default='pix2pix', help='which model to use')
parser.add_argument('--norm_G', type=str, default='spectralinstance', help='instance normalization or batch normalization')
parser.add_argument('--norm_D', type=str, default='spectralinstance', help='instance normalization or batch normalization')
parser.add_argument('--norm_E', type=str, default='spectralinstance', help='instance normalization or batch normalization')
parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
# input/output sizes
parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
parser.add_argument('--preprocess_mode', type=str, default='scale_width_and_crop', help='scaling and cropping of images at load time.', choices=("resize_and_crop", "crop", "scale_width", "scale_width_and_crop", "scale_shortside", "scale_shortside_and_crop", "fixed", "none"))
parser.add_argument('--load_size', type=int, default=512, help='Scale images to this size. The final image will be cropped to --crop_size.')
parser.add_argument('--crop_size', type=int, default=512, help='Crop to the width of crop_size (after initially scaling the images to load_size.)')
parser.add_argument('--aspect_ratio', type=float, default=1.0, help='The ratio width/height. The final height of the load image will be crop_size/aspect_ratio')
parser.add_argument('--label_nc', type=int, default=16, help='# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.')
parser.add_argument('--contain_dontcare_label', action='store_true', help='if the label map contains dontcare label (dontcare=255)')
parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
# for setting inputs
parser.add_argument('--dataroot', type=str, default='./datasets/cityscapes/')
parser.add_argument('--dataset_mode', type=str, default='custom')
parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation')
parser.add_argument('--nThreads', default=0, type=int, help='# threads for loading data')
parser.add_argument('--max_dataset_size', type=int, default=sys.maxsize, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
parser.add_argument('--load_from_opt_file', action='store_true', help='load the options from checkpoints and use that as default')
parser.add_argument('--cache_filelist_write', action='store_true', help='saves the current filelist into a text file, so that it loads faster')
parser.add_argument('--cache_filelist_read', action='store_true', help='reads from the file list cache')
# for displays
parser.add_argument('--display_winsize', type=int, default=400, help='display window size')
# for generator
parser.add_argument('--netG', type=str, default='spade', help='selects model to use for netG (pix2pixhd | spade)')
parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]')
parser.add_argument('--init_variance', type=float, default=0.02, help='variance of the initialization distribution')
parser.add_argument('--z_dim', type=int, default=256,
help="dimension of the latent z vector")
# for instance-wise features
parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')
parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')
parser.add_argument('--use_vae', action='store_true', help='enable training with an image encoder.')
self.initialized = True
return parser
def gather_options(self):
# initialize parser with basic options
if not self.initialized:
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
# get the basic options
opt, unknown = parser.parse_known_args()
# modify model-related parser options
model_name = opt.model
model_option_setter = sstan_models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
# # modify dataset-related parser options
# dataset_mode = opt.dataset_mode
# dataset_option_setter = data.get_option_setter(dataset_mode)
# parser = dataset_option_setter(parser, self.isTrain)
# opt, unknown = parser.parse_known_args()
# # if there is opt_file, load it.
# # The previous default options will be overwritten
# if opt.load_from_opt_file:
# parser = self.update_options_from_file(parser, opt)
opt = parser.parse_args()
opt.contain_dontcare_label = False
opt.no_instance = True
opt.use_vae = False
self.parser = parser
return opt
def print_options(self, opt):
message = ''
message += '----------------- Options ---------------\n'
for k, v in sorted(vars(opt).items()):
comment = ''
default = self.parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
message += '----------------- End -------------------'
print(message)
def option_file_path(self, opt, makedir=False):
expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
if makedir:
util.mkdirs(expr_dir)
file_name = os.path.join(expr_dir, 'opt')
return file_name
def save_options(self, opt):
file_name = self.option_file_path(opt, makedir=True)
with open(file_name + '.txt', 'wt') as opt_file:
for k, v in sorted(vars(opt).items()):
comment = ''
default = self.parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
opt_file.write('{:>25}: {:<30}{}\n'.format(str(k), str(v), comment))
with open(file_name + '.pkl', 'wb') as opt_file:
pickle.dump(opt, opt_file)
def update_options_from_file(self, parser, opt):
new_opt = self.load_options(opt)
for k, v in sorted(vars(opt).items()):
if hasattr(new_opt, k) and v != getattr(new_opt, k):
new_val = getattr(new_opt, k)
parser.set_defaults(**{k: new_val})
return parser
def load_options(self, opt):
file_name = self.option_file_path(opt, makedir=False)
new_opt = pickle.load(open(file_name + '.pkl', 'rb'))
return new_opt
def parse(self, save=False):
opt = self.gather_options()
opt.isTrain = self.isTrain # train or test
#self.print_options(opt)
if opt.isTrain:
self.save_options(opt)
# Set semantic_nc based on the option.
# This will be convenient in many places
opt.semantic_nc = opt.label_nc + \
(1 if opt.contain_dontcare_label else 0) + \
(0 if opt.no_instance else 1)
# set gpu ids
str_ids = opt.gpu_ids.split(',')
opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
opt.gpu_ids.append(id)
opt.gpu_ids = [] if torch.cuda.device_count() == 0 else opt.gpu_ids
if len(opt.gpu_ids) > 0:
torch.cuda.set_device(opt.gpu_ids[0])
assert len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0, \
"Batch size %d is wrong. It must be a multiple of # GPUs %d." \
% (opt.batchSize, len(opt.gpu_ids))
self.opt = opt
return self.opt
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