""" 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