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, _ = parser.parse_known_args()
# modify model-related parser options
model_name = opt.model
model_option_setter = models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
opt, _ = parser.parse_known_args() # parse again with the new defaults
# modify dataset-related parser options
dataset_name = opt.dataset_mode
dataset_option_setter = data.get_option_setter(dataset_name)
parser = dataset_option_setter(parser, self.isTrain)
self.parser = parser
return parser.parse_args()
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
"""
if not self.initialized:
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
opt, _ = parser.parse_known_args()
model_name = opt.model
model_option_setter = models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
opt, _ = parser.parse_known_args()
dataset_name = opt.dataset_mode
dataset_option_setter = data.get_option_setter(dataset_name)
parser = dataset_option_setter(parser, self.isTrain)
self.parser = parser
return parser.parse_args()
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, _ = parser.parse_known_args()
# modify model-related parser options
model_name = opt.model
model_option_setter = models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
opt, _ = parser.parse_known_args() # parse again with the new defaults
# modify dataset-related parser options
dataset_name = opt.dataset_mode
dataset_option_setter = data.get_option_setter(dataset_name)
parser = dataset_option_setter(parser, self.isTrain)
self.parser = parser
return parser.parse_args()
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 = 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()
self.parser = parser
return opt
def print_options(self, opt):
message = ''
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
"""
if not self.initialized: # check if it has been initialized
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
# get the basic options
opt, _ = parser.parse_known_args()
#Phase Specific option:
method_name = "phase_" + opt.phase + "_options"
module = importlib.import_module('options.phase_specific_options')
method = getattr(module, method_name)
parser = method(parser)
opt, _ = parser.parse_known_args()
self.isTrain = opt.phase == 'train'
# print('so far', opt)
# print('str_options_from_file',str_options_from_file)
dict_options, isLoading = self.get_all_specific_options_from_file(opt)
# print('so far 2', dict_options)
# parser_phase_sepcific = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.load_base_option(parser)
# if isLoading:
# print('\n\nis loading is true: {}'.format(dict_options))
opt_spec, _ = parser.parse_known_args(dict_options)
# print('so far 3', opt_spec)
# print('\n\nso far so good')
managers_option_setter = managers.get_option_setter(opt_spec.model)
parser = managers_option_setter(parser, self.isTrain)
# opt, _ = parser.parse_known_args() # parse again with new defaults
model_option_setter = models.get_option_setter(opt_spec.model)
parser = model_option_setter(parser, self.isTrain)
# opt, _ = parser.parse_known_args() # parse again with new defaults
# modify dataset-related parser options
dataset_option_setter = data.get_option_setter(opt_spec.dataset)
# print(dataset_option_setter)
parser = dataset_option_setter(parser, self.isTrain)
# opt, _ = parser.parse_known_args()
# parser_all = method(parser_phase_sepcific)
# parser_all = self.initialize(parser_all)
self.parser = parser
# print('so far so good - last')
options = parser.parse_args()
# print(options)
# print('so far so good - last last')
# print('\n\n\n',options)
# print('\n\n\n',options2)
# print('so far so good')
# todelete = []
# for idx,elem in enumerate(dict_options):
# kk = elem.replace('--','').split('=')[0]
# if not hasattr(options2, kk):
# todelete.append(idx)
# print('deleting elements: {}'.format(todelete))
# for i in todelete:
# del dict_options[i]
if isLoading:
options2 = parser.parse_known_args(dict_options)
# print('so far so good - last last last')
for kk, vv in vars(options2[0]).items():
if hasattr(options, kk) and getattr(options, kk) != vv:
old = getattr(options, kk)
setattr(options, kk, vv)
print('updating option: {} from {} to {}'.format(kk,old, vv))
return options
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
"""
if not self.initialized: # check if it has been initialized
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
# get the basic options
opt, _ = parser.parse_known_args()
# modify model-related parser options
# modify model-related parser options
model_name = opt.model
model_option_setter = models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
opt, _ = parser.parse_known_args() # parse again with new defaults
# modify dataset-related parser options
dataset_name = opt.dataset_mode
dataset_option_setter = data.get_option_setter(dataset_name)
parser = dataset_option_setter(parser, self.isTrain)
# save and return the parser
self.parser = parser
output_opt, _ = parser.parse_known_args()
# determine alphabet and dataroot according to dataname:
output_opt.dataroot = dataset_catalog.datasets[output_opt.dataname]
if self.isTrain:
if output_opt.unlabeled_dataname is not None:
output_opt.unlabeled_dataroot = dataset_catalog.datasets[
output_opt.unlabeled_dataname]
alphabet_dict = dataset_catalog.alphabet_dict
lex_dict = dataset_catalog.lex_dict
for name in alphabet_dict.keys():
if name in opt.dataname:
output_opt.alphabet = getattr(alphabets, alphabet_dict[name])
if output_opt.lex == '':
output_opt.lex = lex_dict[name]
lex_str = ''
else:
lex_str = '_lex_' + os.path.splitext(
output_opt.lex.split('/')[-1])[0]
# save to the disk
if output_opt.name == '':
# name is constructed from the following:
# 1. path to the datasets - with underscore instead of / beginning from main dataset name which is taken from the list IAM/RIMES/CVL/gw
# 2. lexicon if it's not the default one for this dataset
# 3. resolution
# 4. batch size
# if changed from default:
# 5. dim_z
# 6. no Hierarchial Z
# 7. one_hot_k
# 8. if different OCR from default is used, the structure of the OCR (TPS, feature extractor and prediction layer)
# 9. if an rnn is used, '_useRNN' is added
# 10. noGB or GB alpha different from 1
# 11. semi supervised parameters
# 12. Single writer parameters
# 13. Not optimizing G
# 14. Use reconstruction loss instead of adversarial loss and which one
# 15. Use only rec onstruction loss (alpha=Inf)
output_opt.name += output_opt.name_prefix + '_' + output_opt.dataname + lex_str + output_opt.capitalize * '_CapitalizeLex' + '_GANres%s' % output_opt.resolution + '_bs%s' % output_opt.batch_size
if output_opt.dim_z != 128:
output_opt.name += '_dimZ%s' % output_opt.dim_z
if output_opt.no_hier:
output_opt.name += '_noHier'
if output_opt.one_hot_k > 1:
output_opt.name += '_oneHot%s' % output_opt.one_hot_k
if output_opt.use_rnn:
output_opt.name += '_useRNN'
if self.isTrain:
if output_opt.no_grad_balance:
output_opt.name += '_noGB'
elif output_opt.gb_alpha != 1:
output_opt.name += '_GB%s' % output_opt.gb_alpha
if output_opt.unlabeled_dataname is not None:
output_opt.name += '_SemiSupervised_' + output_opt.unlabeled_dataname
if output_opt.disjoint:
output_opt.name += '_disjoint'
if output_opt.single_writer:
output_opt.name += '_SingleWriter'
if output_opt.optimize_z:
output_opt.name += 'OptimizeZ'
if output_opt.not_optimize_G:
output_opt.name += '_NotOptimizeG'
if output_opt.onlyOCR:
output_opt.name += '_OnlyOCRLoss'
output_opt.len_vocab = len(output_opt.alphabet)
return output_opt
parser.add_argument('--dataset_mode', type=str, default='aligned',
choices=['aligned', 'single', 'cityscapes', 'coco'],
help='chooses how datasets are loaded.')
parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
parser.add_argument('--load_size', type=int, default=256, help='scale images to this size')
parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')
parser.add_argument('--preprocess', type=str, default='none', help='scaling and cropping of images at load time '
'[resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
parser.add_argument('--phase', type=str, default='val', help='train, val, test, etc')
parser.add_argument('--output_path', type=str, required=True,
help='the path to save the statistical information.')
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
opt, _ = parser.parse_known_args()
dataset_name = opt.dataset_mode
dataset_option_setter = data.get_option_setter(dataset_name)
parser = dataset_option_setter(parser, False)
opt = parser.parse_args()
opt.num_threads = 0
opt.batch_size = 1
opt.serial_batches = True
opt.no_flip = True
opt.max_dataset_size = -1
opt.load_in_memory = False
opt.isTrain = False
if opt.dataset_mode == 'single' and opt.direction == 'AtoB':
warnings.warn('Dataset mode [single] only supports direction BtoA. '
'We will change the direction to BtoA.!')
opt.direction = 'BtoA'
