Spaces:
Running
on
Zero
Running
on
Zero
| import uuid | |
| import logging | |
| import os.path as osp | |
| from argparse import Namespace | |
| # from tensorboardX import SummaryWriter | |
| class Base: | |
| """ | |
| Base configure file, which contains the basic training parameters and should be inherited by other attribute configure file. | |
| """ | |
| def __init__(self, config_name, ckpt_dir='./', image_dir='./', annot_dir='./'): | |
| self.type = config_name | |
| self.id = str(uuid.uuid4()) | |
| self.note = "" | |
| self.ckpt_dir = ckpt_dir | |
| self.image_dir = image_dir | |
| self.annot_dir = annot_dir | |
| self.loader_type = "alignment" | |
| self.loss_func = "STARLoss" | |
| # train | |
| self.batch_size = 128 | |
| self.val_batch_size = 1 | |
| self.test_batch_size = 32 | |
| self.channels = 3 | |
| self.width = 256 | |
| self.height = 256 | |
| # mean values in r, g, b channel. | |
| self.means = (127, 127, 127) | |
| self.scale = 0.0078125 | |
| self.display_iteration = 100 | |
| self.milestones = [50, 80] | |
| self.max_epoch = 100 | |
| self.net = "stackedHGnet_v1" | |
| self.nstack = 4 | |
| # ["adam", "sgd"] | |
| self.optimizer = "adam" | |
| self.learn_rate = 0.1 | |
| self.momentum = 0.01 # caffe: 0.99 | |
| self.weight_decay = 0.0 | |
| self.nesterov = False | |
| self.scheduler = "MultiStepLR" | |
| self.gamma = 0.1 | |
| self.loss_weights = [1.0] | |
| self.criterions = ["SoftmaxWithLoss"] | |
| self.metrics = ["Accuracy"] | |
| self.key_metric_index = 0 | |
| self.classes_num = [1000] | |
| self.label_num = len(self.classes_num) | |
| # model | |
| self.ema = False | |
| self.use_AAM = True | |
| # visualization | |
| self.writer = None | |
| # log file | |
| self.logger = None | |
| def init_instance(self): | |
| # self.writer = SummaryWriter(logdir=self.log_dir, comment=self.type) | |
| log_formatter = logging.Formatter("%(asctime)s %(levelname)-8s: %(message)s") | |
| root_logger = logging.getLogger() | |
| file_handler = logging.FileHandler(osp.join(self.log_dir, "log.txt")) | |
| file_handler.setFormatter(log_formatter) | |
| file_handler.setLevel(logging.NOTSET) | |
| root_logger.addHandler(file_handler) | |
| console_handler = logging.StreamHandler() | |
| console_handler.setFormatter(log_formatter) | |
| console_handler.setLevel(logging.NOTSET) | |
| root_logger.addHandler(console_handler) | |
| root_logger.setLevel(logging.NOTSET) | |
| self.logger = root_logger | |
| def __del__(self): | |
| # tensorboard --logdir self.log_dir | |
| if self.writer is not None: | |
| # self.writer.export_scalars_to_json(self.log_dir + "visual.json") | |
| self.writer.close() | |
| def init_from_args(self, args: Namespace): | |
| args_vars = vars(args) | |
| for key, value in args_vars.items(): | |
| if hasattr(self, key) and value is not None: | |
| setattr(self, key, value) | |