| import argparse
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| import os
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| from util import util
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| import torch
|
|
|
| class BaseOptions():
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| def __init__(self):
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| self.parser = argparse.ArgumentParser()
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| self.initialized = False
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|
|
| def initialize(self):
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|
|
| self.parser.add_argument('--name', type=str, default='flow', help='name of the experiment. It decides where to store samples and models')
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| 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')
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| self.parser.add_argument('--num_gpus', type=int, default=1, help='the number of gpus')
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| self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
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| self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
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| self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
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| self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
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| self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')
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|
|
|
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| self.parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
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| self.parser.add_argument('--loadSize', type=int, default=512, help='scale images to this size')
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| self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size')
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| self.parser.add_argument('--label_nc', type=int, default=20, help='# of input label channels')
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| self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
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| self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
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|
|
|
|
| self.parser.add_argument('--dataroot', type=str,default='dataset/VITON_traindata/')
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| 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]')
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| self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
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| self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation')
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| self.parser.add_argument('--nThreads', default=1, type=int, help='# threads for loading data')
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| 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.')
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|
|
|
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| self.parser.add_argument('--display_winsize', type=int, default=512, help='display window size')
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| self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
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|
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| self.parser.add_argument('--netG', type=str, default='global', help='selects model to use for netG')
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| self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
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| self.parser.add_argument('--n_downsample_global', type=int, default=4, help='number of downsampling layers in netG')
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| self.parser.add_argument('--n_blocks_global', type=int, default=4, help='number of residual blocks in the global generator network')
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| self.parser.add_argument('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network')
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| self.parser.add_argument('--n_local_enhancers', type=int, default=1, help='number of local enhancers to use')
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| self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer')
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| self.parser.add_argument('--tv_weight', type=float, default=0.1, help='weight for TV loss')
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|
|
| self.initialized = True
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|
|
| def parse(self, save=True):
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| if not self.initialized:
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| self.initialize()
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| self.opt = self.parser.parse_args()
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| self.opt.isTrain = self.isTrain
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|
|
| str_ids = self.opt.gpu_ids.split(',')
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| self.opt.gpu_ids = []
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| for str_id in str_ids:
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| id = int(str_id)
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| if id >= 0:
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| self.opt.gpu_ids.append(id)
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|
|
|
|
| if len(self.opt.gpu_ids) > 0:
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| torch.cuda.set_device(self.opt.gpu_ids[0])
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|
|
| args = vars(self.opt)
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|
|
| print('------------ Options -------------')
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| for k, v in sorted(args.items()):
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| print('%s: %s' % (str(k), str(v)))
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| print('-------------- End ----------------')
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|
|
|
|
| expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
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| util.mkdirs(expr_dir)
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| if save and not self.opt.continue_train:
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| file_name = os.path.join(expr_dir, 'opt.txt')
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| with open(file_name, 'wt') as opt_file:
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| opt_file.write('------------ Options -------------\n')
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| for k, v in sorted(args.items()):
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| opt_file.write('%s: %s\n' % (str(k), str(v)))
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| opt_file.write('-------------- End ----------------\n')
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| return self.opt
|
|
|
