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| """Utilities and tools for tracking runs with Weights & Biases.""" | |
| import logging | |
| import os | |
| import sys | |
| from contextlib import contextmanager | |
| from pathlib import Path | |
| from typing import Dict | |
| import yaml | |
| from tqdm import tqdm | |
| 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 | |
| from utils.dataloaders import LoadImagesAndLabels, img2label_paths | |
| from utils.general import LOGGER, check_dataset, check_file | |
| try: | |
| import wandb | |
| assert hasattr(wandb, '__version__') # verify package import not local dir | |
| except (ImportError, AssertionError): | |
| wandb = None | |
| RANK = int(os.getenv('RANK', -1)) | |
| WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' | |
| def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): | |
| return from_string[len(prefix):] | |
| def check_wandb_config_file(data_config_file): | |
| wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path | |
| if Path(wandb_config).is_file(): | |
| return wandb_config | |
| return data_config_file | |
| def check_wandb_dataset(data_file): | |
| is_trainset_wandb_artifact = False | |
| is_valset_wandb_artifact = False | |
| if isinstance(data_file, dict): | |
| # In that case another dataset manager has already processed it and we don't have to | |
| return data_file | |
| if check_file(data_file) and data_file.endswith('.yaml'): | |
| with open(data_file, errors='ignore') as f: | |
| data_dict = yaml.safe_load(f) | |
| is_trainset_wandb_artifact = isinstance(data_dict['train'], | |
| str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX) | |
| is_valset_wandb_artifact = isinstance(data_dict['val'], | |
| str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX) | |
| if is_trainset_wandb_artifact or is_valset_wandb_artifact: | |
| return data_dict | |
| else: | |
| return check_dataset(data_file) | |
| def get_run_info(run_path): | |
| run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) | |
| run_id = run_path.stem | |
| project = run_path.parent.stem | |
| entity = run_path.parent.parent.stem | |
| model_artifact_name = 'run_' + run_id + '_model' | |
| return entity, project, run_id, model_artifact_name | |
| def check_wandb_resume(opt): | |
| process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None | |
| if isinstance(opt.resume, str): | |
| if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): | |
| if RANK not in [-1, 0]: # For resuming DDP runs | |
| entity, project, run_id, model_artifact_name = get_run_info(opt.resume) | |
| api = wandb.Api() | |
| artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest') | |
| modeldir = artifact.download() | |
| opt.weights = str(Path(modeldir) / "last.pt") | |
| return True | |
| return None | |
| def process_wandb_config_ddp_mode(opt): | |
| with open(check_file(opt.data), errors='ignore') as f: | |
| data_dict = yaml.safe_load(f) # data dict | |
| train_dir, val_dir = None, None | |
| if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): | |
| api = wandb.Api() | |
| train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) | |
| train_dir = train_artifact.download() | |
| train_path = Path(train_dir) / 'data/images/' | |
| data_dict['train'] = str(train_path) | |
| if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): | |
| api = wandb.Api() | |
| val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) | |
| val_dir = val_artifact.download() | |
| val_path = Path(val_dir) / 'data/images/' | |
| data_dict['val'] = str(val_path) | |
| if train_dir or val_dir: | |
| ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') | |
| with open(ddp_data_path, 'w') as f: | |
| yaml.safe_dump(data_dict, f) | |
| opt.data = ddp_data_path | |
| 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 | |
| """ | |
| # Temporary-fix | |
| if opt.upload_dataset: | |
| opt.upload_dataset = False | |
| # LOGGER.info("Uploading Dataset functionality is not being supported temporarily due to a bug.") | |
| # Pre-training routine -- | |
| self.job_type = job_type | |
| self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run | |
| 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.bbox_media_panel_images = [] | |
| self.val_table_path_map = None | |
| self.max_imgs_to_log = 16 | |
| self.wandb_artifact_data_dict = None | |
| self.data_dict = None | |
| # It's more elegant to stick to 1 wandb.init call, | |
| # but useful config data is overwritten in the WandbLogger's wandb.init call | |
| if isinstance(opt.resume, str): # checks resume from artifact | |
| if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): | |
| entity, project, run_id, model_artifact_name = get_run_info(opt.resume) | |
| model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name | |
| assert wandb, 'install wandb to resume wandb runs' | |
| # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config | |
| self.wandb_run = wandb.init(id=run_id, | |
| project=project, | |
| entity=entity, | |
| resume='allow', | |
| allow_val_change=True) | |
| opt.resume = model_artifact_name | |
| elif 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 opt.upload_dataset: | |
| if not opt.resume: | |
| self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt) | |
| 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 | |
| elif opt.resume: | |
| # resume from artifact | |
| if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX): | |
| self.data_dict = dict(self.wandb_run.config.data_dict) | |
| else: # local resume | |
| self.data_dict = check_wandb_dataset(opt.data) | |
| else: | |
| self.data_dict = check_wandb_dataset(opt.data) | |
| self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict | |
| # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming. | |
| self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict}, allow_val_change=True) | |
| self.setup_training(opt) | |
| if self.job_type == 'Dataset Creation': | |
| self.wandb_run.config.update({"upload_dataset": True}) | |
| self.data_dict = self.check_and_upload_dataset(opt) | |
| def check_and_upload_dataset(self, opt): | |
| """ | |
| Check if the dataset format is compatible and upload it as W&B artifact | |
| arguments: | |
| opt (namespace)-- Commandline arguments for current run | |
| returns: | |
| Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links. | |
| """ | |
| assert wandb, 'Install wandb to upload dataset' | |
| config_path = self.log_dataset_artifact(opt.data, opt.single_cls, | |
| 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) | |
| with open(config_path, errors='ignore') as f: | |
| wandb_data_dict = yaml.safe_load(f) | |
| return wandb_data_dict | |
| 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): | |
| modeldir, _ = self.download_model_artifact(opt) | |
| if modeldir: | |
| self.weights = Path(modeldir) / "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 | |
| data_dict = self.data_dict | |
| if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download | |
| self.train_artifact_path, self.train_artifact = self.download_dataset_artifact( | |
| data_dict.get('train'), opt.artifact_alias) | |
| self.val_artifact_path, self.val_artifact = self.download_dataset_artifact( | |
| data_dict.get('val'), opt.artifact_alias) | |
| if self.train_artifact_path is not None: | |
| train_path = Path(self.train_artifact_path) / 'data/images/' | |
| data_dict['train'] = str(train_path) | |
| if self.val_artifact_path is not None: | |
| val_path = Path(self.val_artifact_path) / 'data/images/' | |
| data_dict['val'] = str(val_path) | |
| if self.val_artifact is not None: | |
| self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") | |
| columns = ["epoch", "id", "ground truth", "prediction"] | |
| columns.extend(self.data_dict['names']) | |
| self.result_table = wandb.Table(columns) | |
| self.val_table = self.val_artifact.get("val") | |
| if self.val_table_path_map is None: | |
| self.map_val_table_path() | |
| 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 | |
| train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None | |
| # Update the the data_dict to point to local artifacts dir | |
| if train_from_artifact: | |
| self.data_dict = data_dict | |
| def download_dataset_artifact(self, path, alias): | |
| """ | |
| download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX | |
| arguments: | |
| path -- path of the dataset to be used for training | |
| alias (str)-- alias of the artifact to be download/used for training | |
| returns: | |
| (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset | |
| is found otherwise returns (None, None) | |
| """ | |
| if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): | |
| artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) | |
| dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/")) | |
| assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" | |
| datadir = dataset_artifact.download() | |
| return datadir, dataset_artifact | |
| return None, None | |
| def download_model_artifact(self, opt): | |
| """ | |
| download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX | |
| arguments: | |
| opt (namespace) -- Commandline arguments for this run | |
| """ | |
| if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): | |
| model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") | |
| assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' | |
| modeldir = model_artifact.download() | |
| # epochs_trained = model_artifact.metadata.get('epochs_trained') | |
| total_epochs = model_artifact.metadata.get('total_epochs') | |
| is_finished = total_epochs is None | |
| assert not is_finished, 'training is finished, can only resume incomplete runs.' | |
| return modeldir, model_artifact | |
| return None, None | |
| 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 log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): | |
| """ | |
| Log the dataset as W&B artifact and return the new data file with W&B links | |
| arguments: | |
| data_file (str) -- the .yaml file with information about the dataset like - path, classes etc. | |
| single_class (boolean) -- train multi-class data as single-class | |
| project (str) -- project name. Used to construct the artifact path | |
| overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new | |
| file with _wandb postfix. Eg -> data_wandb.yaml | |
| returns: | |
| the new .yaml file with artifact links. it can be used to start training directly from artifacts | |
| """ | |
| upload_dataset = self.wandb_run.config.upload_dataset | |
| log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val' | |
| self.data_dict = check_dataset(data_file) # parse and check | |
| data = dict(self.data_dict) | |
| nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) | |
| names = {k: v for k, v in enumerate(names)} # to index dictionary | |
| # log train set | |
| if not log_val_only: | |
| self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(data['train'], rect=True, batch_size=1), | |
| names, | |
| name='train') if data.get('train') else None | |
| if data.get('train'): | |
| data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') | |
| self.val_artifact = self.create_dataset_table( | |
| LoadImagesAndLabels(data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None | |
| if data.get('val'): | |
| data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') | |
| path = Path(data_file) | |
| # create a _wandb.yaml file with artifacts links if both train and test set are logged | |
| if not log_val_only: | |
| path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml' # updated data.yaml path | |
| path = ROOT / 'data' / path | |
| data.pop('download', None) | |
| data.pop('path', None) | |
| with open(path, 'w') as f: | |
| yaml.safe_dump(data, f) | |
| LOGGER.info(f"Created dataset config file {path}") | |
| if self.job_type == 'Training': # builds correct artifact pipeline graph | |
| if not log_val_only: | |
| self.wandb_run.log_artifact( | |
| self.train_artifact) # calling use_artifact downloads the dataset. NOT NEEDED! | |
| self.wandb_run.use_artifact(self.val_artifact) | |
| self.val_artifact.wait() | |
| self.val_table = self.val_artifact.get('val') | |
| self.map_val_table_path() | |
| else: | |
| self.wandb_run.log_artifact(self.train_artifact) | |
| self.wandb_run.log_artifact(self.val_artifact) | |
| return path | |
| def map_val_table_path(self): | |
| """ | |
| Map the validation dataset Table like name of file -> it's id in the W&B Table. | |
| Useful for - referencing artifacts for evaluation. | |
| """ | |
| self.val_table_path_map = {} | |
| LOGGER.info("Mapping dataset") | |
| for i, data in enumerate(tqdm(self.val_table.data)): | |
| self.val_table_path_map[data[3]] = data[0] | |
| def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'): | |
| """ | |
| Create and return W&B artifact containing W&B Table of the dataset. | |
| arguments: | |
| dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table | |
| class_to_id -- hash map that maps class ids to labels | |
| name -- name of the artifact | |
| returns: | |
| dataset artifact to be logged or used | |
| """ | |
| # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging | |
| artifact = wandb.Artifact(name=name, type="dataset") | |
| img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None | |
| img_files = tqdm(dataset.im_files) if not img_files else img_files | |
| for img_file in img_files: | |
| if Path(img_file).is_dir(): | |
| artifact.add_dir(img_file, name='data/images') | |
| labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) | |
| artifact.add_dir(labels_path, name='data/labels') | |
| else: | |
| artifact.add_file(img_file, name='data/images/' + Path(img_file).name) | |
| label_file = Path(img2label_paths([img_file])[0]) | |
| artifact.add_file(str(label_file), name='data/labels/' + | |
| label_file.name) if label_file.exists() else None | |
| table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) | |
| class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) | |
| for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): | |
| box_data, img_classes = [], {} | |
| for cls, *xywh in labels[:, 1:].tolist(): | |
| cls = int(cls) | |
| box_data.append({ | |
| "position": { | |
| "middle": [xywh[0], xywh[1]], | |
| "width": xywh[2], | |
| "height": xywh[3]}, | |
| "class_id": cls, | |
| "box_caption": "%s" % (class_to_id[cls])}) | |
| img_classes[cls] = class_to_id[cls] | |
| boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space | |
| table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()), | |
| Path(paths).name) | |
| artifact.add(table, name) | |
| return artifact | |
| def log_training_progress(self, predn, path, names): | |
| """ | |
| Build evaluation Table. Uses reference from validation dataset table. | |
| arguments: | |
| predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class] | |
| path (str): local path of the current evaluation image | |
| names (dict(int, str)): hash map that maps class ids to labels | |
| """ | |
| class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) | |
| box_data = [] | |
| avg_conf_per_class = [0] * len(self.data_dict['names']) | |
| pred_class_count = {} | |
| for *xyxy, conf, cls in predn.tolist(): | |
| if conf >= 0.25: | |
| cls = int(cls) | |
| box_data.append({ | |
| "position": { | |
| "minX": xyxy[0], | |
| "minY": xyxy[1], | |
| "maxX": xyxy[2], | |
| "maxY": xyxy[3]}, | |
| "class_id": cls, | |
| "box_caption": f"{names[cls]} {conf:.3f}", | |
| "scores": { | |
| "class_score": conf}, | |
| "domain": "pixel"}) | |
| avg_conf_per_class[cls] += conf | |
| if cls in pred_class_count: | |
| pred_class_count[cls] += 1 | |
| else: | |
| pred_class_count[cls] = 1 | |
| for pred_class in pred_class_count.keys(): | |
| avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class] | |
| boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space | |
| id = self.val_table_path_map[Path(path).name] | |
| self.result_table.add_data(self.current_epoch, id, self.val_table.data[id][1], | |
| wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), | |
| *avg_conf_per_class) | |
| def val_one_image(self, pred, predn, path, names, im): | |
| """ | |
| Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel | |
| arguments: | |
| pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] | |
| predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class] | |
| path (str): local path of the current evaluation image | |
| """ | |
| if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact | |
| self.log_training_progress(predn, path, names) | |
| if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0: | |
| if self.current_epoch % self.bbox_interval == 0: | |
| box_data = [{ | |
| "position": { | |
| "minX": xyxy[0], | |
| "minY": xyxy[1], | |
| "maxX": xyxy[2], | |
| "maxY": xyxy[3]}, | |
| "class_id": int(cls), | |
| "box_caption": f"{names[int(cls)]} {conf:.3f}", | |
| "scores": { | |
| "class_score": conf}, | |
| "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] | |
| boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space | |
| self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name)) | |
| 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, best_result=False): | |
| """ | |
| 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(): | |
| if self.bbox_media_panel_images: | |
| self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images | |
| 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 = {} | |
| self.bbox_media_panel_images = [] | |
| if self.result_artifact: | |
| self.result_artifact.add(self.result_table, 'result') | |
| wandb.log_artifact(self.result_artifact, | |
| aliases=[ | |
| 'latest', 'last', 'epoch ' + str(self.current_epoch), | |
| ('best' if best_result else '')]) | |
| wandb.log({"evaluation": self.result_table}) | |
| columns = ["epoch", "id", "ground truth", "prediction"] | |
| columns.extend(self.data_dict['names']) | |
| self.result_table = wandb.Table(columns) | |
| self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") | |
| 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() | |
| 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) | |