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| # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | |
| # WARNING ⚠️ wandb is deprecated and will be removed in future release. | |
| # See supported integrations at https://github.com/ultralytics/yolov5#integrations | |
| import logging | |
| import os | |
| import sys | |
| from contextlib import contextmanager | |
| from pathlib import Path | |
| from utils.general import LOGGER, colorstr | |
| FILE = Path(__file__).resolve() | |
| ROOT = FILE.parents[3] # YOLOv5 root directory | |
| if str(ROOT) not in sys.path: | |
| sys.path.append(str(ROOT)) # add ROOT to PATH | |
| RANK = int(os.getenv('RANK', -1)) | |
| DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \ | |
| f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.' | |
| try: | |
| import wandb | |
| assert hasattr(wandb, '__version__') # verify package import not local dir | |
| LOGGER.warning(DEPRECATION_WARNING) | |
| except (ImportError, AssertionError): | |
| wandb = None | |
| class WandbLogger(): | |
| """Log training runs, datasets, models, and predictions to Weights & Biases. | |
| This logger sends information to W&B at wandb.ai. By default, this information | |
| includes hyperparameters, system configuration and metrics, model metrics, | |
| and basic data metrics and analyses. | |
| By providing additional command line arguments to train.py, datasets, | |
| models and predictions can also be logged. | |
| For more on how this logger is used, see the Weights & Biases documentation: | |
| https://docs.wandb.com/guides/integrations/yolov5 | |
| """ | |
| def __init__(self, opt, run_id=None, job_type='Training'): | |
| """ | |
| - Initialize WandbLogger instance | |
| - Upload dataset if opt.upload_dataset is True | |
| - Setup training processes if job_type is 'Training' | |
| arguments: | |
| opt (namespace) -- Commandline arguments for this run | |
| run_id (str) -- Run ID of W&B run to be resumed | |
| job_type (str) -- To set the job_type for this run | |
| """ | |
| # Pre-training routine -- | |
| self.job_type = job_type | |
| self.wandb, self.wandb_run = wandb, wandb.run if wandb else None | |
| self.val_artifact, self.train_artifact = None, None | |
| self.train_artifact_path, self.val_artifact_path = None, None | |
| self.result_artifact = None | |
| self.val_table, self.result_table = None, None | |
| self.max_imgs_to_log = 16 | |
| self.data_dict = None | |
| if self.wandb: | |
| self.wandb_run = wandb.init(config=opt, | |
| resume='allow', | |
| project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, | |
| entity=opt.entity, | |
| name=opt.name if opt.name != 'exp' else None, | |
| job_type=job_type, | |
| id=run_id, | |
| allow_val_change=True) if not wandb.run else wandb.run | |
| if self.wandb_run: | |
| if self.job_type == 'Training': | |
| if isinstance(opt.data, dict): | |
| # This means another dataset manager has already processed the dataset info (e.g. ClearML) | |
| # and they will have stored the already processed dict in opt.data | |
| self.data_dict = opt.data | |
| self.setup_training(opt) | |
| def setup_training(self, opt): | |
| """ | |
| Setup the necessary processes for training YOLO models: | |
| - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX | |
| - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded | |
| - Setup log_dict, initialize bbox_interval | |
| arguments: | |
| opt (namespace) -- commandline arguments for this run | |
| """ | |
| self.log_dict, self.current_epoch = {}, 0 | |
| self.bbox_interval = opt.bbox_interval | |
| if isinstance(opt.resume, str): | |
| model_dir, _ = self.download_model_artifact(opt) | |
| if model_dir: | |
| self.weights = Path(model_dir) / 'last.pt' | |
| config = self.wandb_run.config | |
| opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str( | |
| self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \ | |
| config.hyp, config.imgsz | |
| if opt.bbox_interval == -1: | |
| self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 | |
| if opt.evolve or opt.noplots: | |
| self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval | |
| def log_model(self, path, opt, epoch, fitness_score, best_model=False): | |
| """ | |
| Log the model checkpoint as W&B artifact | |
| arguments: | |
| path (Path) -- Path of directory containing the checkpoints | |
| opt (namespace) -- Command line arguments for this run | |
| epoch (int) -- Current epoch number | |
| fitness_score (float) -- fitness score for current epoch | |
| best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. | |
| """ | |
| model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', | |
| type='model', | |
| metadata={ | |
| 'original_url': str(path), | |
| 'epochs_trained': epoch + 1, | |
| 'save period': opt.save_period, | |
| 'project': opt.project, | |
| 'total_epochs': opt.epochs, | |
| 'fitness_score': fitness_score}) | |
| model_artifact.add_file(str(path / 'last.pt'), name='last.pt') | |
| wandb.log_artifact(model_artifact, | |
| aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) | |
| LOGGER.info(f'Saving model artifact on epoch {epoch + 1}') | |
| def val_one_image(self, pred, predn, path, names, im): | |
| pass | |
| def log(self, log_dict): | |
| """ | |
| save the metrics to the logging dictionary | |
| arguments: | |
| log_dict (Dict) -- metrics/media to be logged in current step | |
| """ | |
| if self.wandb_run: | |
| for key, value in log_dict.items(): | |
| self.log_dict[key] = value | |
| def end_epoch(self): | |
| """ | |
| commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. | |
| arguments: | |
| best_result (boolean): Boolean representing if the result of this evaluation is best or not | |
| """ | |
| if self.wandb_run: | |
| with all_logging_disabled(): | |
| try: | |
| wandb.log(self.log_dict) | |
| except BaseException as e: | |
| LOGGER.info( | |
| f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}' | |
| ) | |
| self.wandb_run.finish() | |
| self.wandb_run = None | |
| self.log_dict = {} | |
| def finish_run(self): | |
| """ | |
| Log metrics if any and finish the current W&B run | |
| """ | |
| if self.wandb_run: | |
| if self.log_dict: | |
| with all_logging_disabled(): | |
| wandb.log(self.log_dict) | |
| wandb.run.finish() | |
| LOGGER.warning(DEPRECATION_WARNING) | |
| def all_logging_disabled(highest_level=logging.CRITICAL): | |
| """ source - https://gist.github.com/simon-weber/7853144 | |
| A context manager that will prevent any logging messages triggered during the body from being processed. | |
| :param highest_level: the maximum logging level in use. | |
| This would only need to be changed if a custom level greater than CRITICAL is defined. | |
| """ | |
| previous_level = logging.root.manager.disable | |
| logging.disable(highest_level) | |
| try: | |
| yield | |
| finally: | |
| logging.disable(previous_level) | |