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import os
import json
import argparse
class BaseOptions():
def initialize(self, parser):
parser.add_argument('--nChannel', metavar='N', default=100, type=int,
help='number of channels')
parser.add_argument('--maxIter', metavar='T', default=1000, type=int,
help='number of maximum iterations')
parser.add_argument('--lr', metavar='LR', default=0.1, type=float,
help='learning rate')
parser.add_argument('--nConv', metavar='M', default=2, type=int,
help='number of convolutional layers')
parser.add_argument("--work_dir", type=str, default="./",
help='project directory')
parser.add_argument("--out_dir", type=str, default=None,
help='logging output')
parser.add_argument("--use_wandb", type=int, default=0,
help='use wandb or not')
parser.add_argument("--data_path", type=str, default="images",
help="data path")
parser.add_argument("--img_path", type=str, default=None,
help="image path")
parser.add_argument('--crop_size', type=int, default= 224,
help='crop_size')
parser.add_argument("--batch_size", type=int, default=1,
help='batch size')
parser.add_argument('--workers', type=int, default=4,
help='number of data loading workers')
parser.add_argument("--use_slic", default = 1, type=int,
help="choose to use slic or gt label")
parser.add_argument("-f", "--config_file", type=str, default='models/week0417/json/single_scale_grouping_ft.json',
help='json files including all arguments')
parser.add_argument("--log_freq", type=int, default=10,
help='frequency to print log')
parser.add_argument("--display_freq", type=int, default=100,
help='frequency to save visualization')
parser.add_argument("--pretrained_ae", type=str,
default = "/home/xli/WORKDIR/07-16/transformer/cpk.pth")
parser.add_argument("--pretrained_path", type=str, default=None,
help='pretrained reconstruction model')
parser.add_argument('--momentum', type=float, default=0.5,
help='momentum for sgd, alpha parameter for adam')
parser.add_argument('--beta', type=float, default=0.999,
help='beta parameter for adam')
parser.add_argument("--l1_loss_wt", default=1.0, type=float)
parser.add_argument("--perceptual_loss_wt", default=1.0, type=float)
parser.add_argument('--project_name', type=str, default='test_time',
help='project name')
parser.add_argument("--save_freq", type=int, default=2000,
help='frequency to save model')
parser.add_argument("--local_rank", type=int)
parser.add_argument('--lr_decay_freq', type=int, default=3000,
help='frequency to decay learning rate')
parser.add_argument('--no_ganFeat_loss', action='store_true',
help='if specified, do *not* use discriminator feature matching loss')
parser.add_argument('--sp_num', type=int, default=None,
help='superpixel number')
parser.add_argument('--add_self_loops', type=int, default=1,
help='set to 1 to add self loops in GCNs')
parser.add_argument('--test_time', type=int, default=0,
help='set to 1 to add self loops in GCNs')
parser.add_argument('--add_texture_epoch', type=int, default=1000,
help='when to add texture synthesis')
parser.add_argument('--add_clustering_epoch', type=int, default=1000,
help='when to add grouping')
parser.add_argument('--temperature', type=int, default=1,
help='temperature in SoftMax')
parser.add_argument('--gumbel', type=int, default=0,
help='if use gumbel SoftMax')
parser.add_argument('--patch_size', type=int, default=40,
help='patch size in texture synthesis')
parser.add_argument("--netE_num_downsampling_sp", default=4, type=int)
parser.add_argument('--num_classes', type=int, default=0)
parser.add_argument(
"--netG_num_base_resnet_layers",
default=2, type=int,
help="The number of resnet layers before the upsampling layers."
)
parser.add_argument("--netG_scale_capacity", default=1.0, type=float)
parser.add_argument("--netG_resnet_ch", type=int, default=256)
parser.add_argument("--spatial_code_ch", default=8, type=int)
parser.add_argument("--texture_code_ch", default=256, type=int)
parser.add_argument("--netE_scale_capacity", default=1.0, type=float)
parser.add_argument("--netE_num_downsampling_gl", default=2, type=int)
parser.add_argument("--netE_nc_steepness", default=2.0, type=float)
parser.add_argument("--spatial_code_dim", type=int, default=256, help="codebook entry dimension")
return parser
def print_options(self, opt):
"""Print and save options
It will print both current options and default values(if different).
It will save options into a text file / [checkpoints_dir] / opt.txt
"""
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 save_options(self, opt):
os.makedirs(opt.out_dir, exist_ok=True)
file_name = os.path.join(opt.out_dir, 'exp_args.txt')
with open(file_name, '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))
opt_file.close()
def gather_options(self):
parser = argparse.ArgumentParser(description='PyTorch Unsupervised Segmentation')
self.parser = self.initialize(parser)
opt = self.parser.parse_args()
opt = self.update_with_json(opt)
opt.out_dir = os.path.join(opt.work_dir, opt.exp_name)
opt.use_slic = (opt.use_slic == 1)
opt.use_wandb = (opt.use_wandb == 1)
# logging
self.print_options(opt)
self.save_options(opt)
return opt
def update_with_json(self, args):
arg_dict = vars(args)
# arguments house keeping
with open(args.config_file, 'r') as f:
arg_str = f.read()
file_args = json.loads(arg_str)
arg_dict.update(file_args)
args = argparse.Namespace(**arg_dict)
return args
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