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119 lines
7.3 KiB
119 lines
7.3 KiB
### Copyright (C) 2020 Roy Or-El. All rights reserved.
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### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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import argparse
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import os
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from util import util
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import torch
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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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# experiment specifics
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self.parser.add_argument('--name', type=str, default='males_model', help='name of the experiment. It decides where to store samples and agingModels')
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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('--checkpoints_dir', type=str, default='./checkpoints', help='agingModels are saved here')
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# input/output sizes
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self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
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self.parser.add_argument('--loadSize', type=int, default=256, help='scale images to this size')
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self.parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
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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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# for setting inputs
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self.parser.add_argument('--dataroot', type=str, default='./static/uploads/')
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self.parser.add_argument('--sort_classes', type=bool, default=True, help='a flag that indicates whether to sort the classes')
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self.parser.add_argument('--sort_order', type=str, default='0-2,3-6,7-9,15-19,30-39,50-69', help='a specific order to sort the classes, must contain all classes, only works when sort_classes is true')
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self.parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', 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=4, 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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self.parser.add_argument('--display_single_pane_ncols', type=int, default=6, help='if positive, display all images in a single visdom web panel with certain number of images per row.')
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# for displays
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self.parser.add_argument('--display_winsize', type=int, default=256, help='display window size')
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self.parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')
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self.parser.add_argument('--display_id', type=int, default=0, help='window id of the web display')
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# for generator
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self.parser.add_argument('--use_modulated_conv', type=bool, default = True, help='if specified, use modulated conv layers in the decoder like in StyleGAN2')
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self.parser.add_argument('--conv_weight_norm', type=bool, default = True, help='if specified, use weight normalization in conv and linear layers like in progrssive growing of GANs')
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self.parser.add_argument('--id_enc_norm', type=str, default='pixel', help='instance, pixel normalization')
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self.parser.add_argument('--decoder_norm',type=str, default='pixel', choices=['pixel','none'], help='type of upsampling layers normalization')
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self.parser.add_argument('--n_adaptive_blocks', type=int, default=4, help='# of adaptive normalization blocks')
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self.parser.add_argument('--activation',type=str, default='lrelu', choices=['relu','lrelu'], help='type of generator activation layer')
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self.parser.add_argument('--normalize_mlp', type=bool, default = True, help='if specified, normalize the generator MLP inputs and outputs')
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self.parser.add_argument('--no_moving_avg', action='store_true', help='if specified, do not use moving average network')
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self.parser.add_argument('--use_resblk_pixel_norm', action='store_true', help='if specified, apply pixel norm on the resnet block outputs')
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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('--no_cond_noise', action='store_true', help='remove gaussian noise from latent age code')
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self.parser.add_argument('--gen_dim_per_style', type=int, default=50, help='per class dimension of adain generator style latent code')
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self.parser.add_argument('--n_downsample', type=int, default=2, help='number of downsampling layers in generator')
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self.parser.add_argument('--verbose', action='store_true', default = True, help='toggles verbose')
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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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try:
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self.opt = self.parser.parse_args()
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except: # solves argparse error in google colab
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self.opt = self.parser.parse_args(args=[])
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self.opt.isTrain = self.isTrain # train or test
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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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# set gpu ids
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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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# set class specific sort order
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if self.opt.sort_order is not None:
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order = self.opt.sort_order.split(',')
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self.opt.sort_order = []
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for currName in order:
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self.opt.sort_order += [currName]
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# set decay schedule
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if self.isTrain and self.opt.decay_epochs is not None:
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decay_epochs = self.opt.decay_epochs.split(',')
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self.opt.decay_epochs = []
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for curr_epoch in decay_epochs:
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self.opt.decay_epochs += [int(curr_epoch)]
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# create full image paths in traverse/deploy mode
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if (not self.isTrain) and (self.opt.traverse or self.opt.deploy):
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with open(self.opt.image_path_file,'r') as f:
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# temp_paths = f.read().splitlines()
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self.opt.image_path_list = f.read().splitlines()
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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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# save to the disk
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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
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