"""This script contains base options for Deep3DFaceRecon_pytorch """ import argparse import os from util import util import numpy as np import torch import face3d.models as models import face3d.data as data class BaseOptions(): """This class defines options used during both training and test time. It also implements several helper functions such as parsing, printing, and saving the options. It also gathers additional options defined in functions in both dataset class and model class. """ def __init__(self, cmd_line=None): """Reset the class; indicates the class hasn't been initialized""" self.initialized = False self.cmd_line = None if cmd_line is not None: self.cmd_line = cmd_line.split() def initialize(self, parser): """Define the common options that are used in both training and test.""" # basic parameters parser.add_argument('--name', type=str, default='face_recon', help='name of the experiment. It decides where to store samples and 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='./checkpoints', help='models are saved here') parser.add_argument('--vis_batch_nums', type=float, default=1, help='batch nums of images for visulization') parser.add_argument('--eval_batch_nums', type=float, default=float('inf'), help='batch nums of images for evaluation') parser.add_argument('--use_ddp', type=util.str2bool, nargs='?', const=True, default=True, help='whether use distributed data parallel') parser.add_argument('--ddp_port', type=str, default='12355', help='ddp port') parser.add_argument('--display_per_batch', type=util.str2bool, nargs='?', const=True, default=True, help='whether use batch to show losses') parser.add_argument('--add_image', type=util.str2bool, nargs='?', const=True, default=True, help='whether add image to tensorboard') parser.add_argument('--world_size', type=int, default=1, help='batch nums of images for evaluation') # model parameters parser.add_argument('--model', type=str, default='facerecon', help='chooses which model to use.') # additional parameters parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}') self.initialized = True return parser 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 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 if self.cmd_line is None: opt, _ = parser.parse_known_args() else: opt, _ = parser.parse_known_args(self.cmd_line) # set cuda visible devices os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_ids # 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) if self.cmd_line is None: opt, _ = parser.parse_known_args() # parse again with new defaults else: opt, _ = parser.parse_known_args(self.cmd_line) # parse again with new defaults # modify dataset-related parser options if opt.dataset_mode: 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 if self.cmd_line is None: return parser.parse_args() else: return parser.parse_args(self.cmd_line) 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) # save to the disk expr_dir = os.path.join(opt.checkpoints_dir, opt.name) util.mkdirs(expr_dir) file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase)) try: with open(file_name, 'wt') as opt_file: opt_file.write(message) opt_file.write('\n') except PermissionError as error: print("permission error {}".format(error)) pass def parse(self): """Parse our options, create checkpoints directory suffix, and set up gpu device.""" opt = self.gather_options() opt.isTrain = self.isTrain # train or test # process opt.suffix if opt.suffix: suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else '' opt.name = opt.name + suffix # set gpu ids str_ids = opt.gpu_ids.split(',') gpu_ids = [] for str_id in str_ids: id = int(str_id) if id >= 0: gpu_ids.append(id) opt.world_size = len(gpu_ids) # if len(opt.gpu_ids) > 0: # torch.cuda.set_device(gpu_ids[0]) if opt.world_size == 1: opt.use_ddp = False if opt.phase != 'test': # set continue_train automatically if opt.pretrained_name is None: model_dir = os.path.join(opt.checkpoints_dir, opt.name) else: model_dir = os.path.join(opt.checkpoints_dir, opt.pretrained_name) if os.path.isdir(model_dir): model_pths = [i for i in os.listdir(model_dir) if i.endswith('pth')] if os.path.isdir(model_dir) and len(model_pths) != 0: opt.continue_train= True # update the latest epoch count if opt.continue_train: if opt.epoch == 'latest': epoch_counts = [int(i.split('.')[0].split('_')[-1]) for i in model_pths if 'latest' not in i] if len(epoch_counts) != 0: opt.epoch_count = max(epoch_counts) + 1 else: opt.epoch_count = int(opt.epoch) + 1 self.print_options(opt) self.opt = opt return self.opt