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Running
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A10G
| """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 <modify_commandline_options> functions in both dataset class and model class. | |
| """ | |
| def __init__(self, cmd_line=None): | |
| """Reset the class; indicates the class hasn't been initailized""" | |
| 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 <modify_commandline_options> 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 | |