import glob import json import logging import os import sys from pathlib import Path logger = logging.getLogger(__name__) 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 try: import comet_ml # Project Configuration config = comet_ml.config.get_config() COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') except (ModuleNotFoundError, ImportError): comet_ml = None COMET_PROJECT_NAME = None import PIL import torch import torchvision.transforms as T import yaml from utils.dataloaders import img2label_paths from utils.general import check_dataset, scale_boxes, xywh2xyxy from utils.metrics import box_iou COMET_PREFIX = 'comet://' COMET_MODE = os.getenv('COMET_MODE', 'online') # Model Saving Settings COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') # Dataset Artifact Settings COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true' # Evaluation Settings COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true' COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true' COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100)) # Confusion Matrix Settings CONF_THRES = float(os.getenv('CONF_THRES', 0.001)) IOU_THRES = float(os.getenv('IOU_THRES', 0.6)) # Batch Logging Settings COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true' COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1) COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1) COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true' RANK = int(os.getenv('RANK', -1)) to_pil = T.ToPILImage() class CometLogger: """Log metrics, parameters, source code, models and much more with Comet """ def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None: self.job_type = job_type self.opt = opt self.hyp = hyp # Comet Flags self.comet_mode = COMET_MODE self.save_model = opt.save_period > -1 self.model_name = COMET_MODEL_NAME # Batch Logging Settings self.log_batch_metrics = COMET_LOG_BATCH_METRICS self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL # Dataset Artifact Settings self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET self.resume = self.opt.resume # Default parameters to pass to Experiment objects self.default_experiment_kwargs = { 'log_code': False, 'log_env_gpu': True, 'log_env_cpu': True, 'project_name': COMET_PROJECT_NAME,} self.default_experiment_kwargs.update(experiment_kwargs) self.experiment = self._get_experiment(self.comet_mode, run_id) self.data_dict = self.check_dataset(self.opt.data) self.class_names = self.data_dict['names'] self.num_classes = self.data_dict['nc'] self.logged_images_count = 0 self.max_images = COMET_MAX_IMAGE_UPLOADS if run_id is None: self.experiment.log_other('Created from', 'YOLOv5') if not isinstance(self.experiment, comet_ml.OfflineExperiment): workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:] self.experiment.log_other( 'Run Path', f'{workspace}/{project_name}/{experiment_id}', ) self.log_parameters(vars(opt)) self.log_parameters(self.opt.hyp) self.log_asset_data( self.opt.hyp, name='hyperparameters.json', metadata={'type': 'hyp-config-file'}, ) self.log_asset( f'{self.opt.save_dir}/opt.yaml', metadata={'type': 'opt-config-file'}, ) self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX if hasattr(self.opt, 'conf_thres'): self.conf_thres = self.opt.conf_thres else: self.conf_thres = CONF_THRES if hasattr(self.opt, 'iou_thres'): self.iou_thres = self.opt.iou_thres else: self.iou_thres = IOU_THRES self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres}) self.comet_log_predictions = COMET_LOG_PREDICTIONS if self.opt.bbox_interval == -1: self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10 else: self.comet_log_prediction_interval = self.opt.bbox_interval if self.comet_log_predictions: self.metadata_dict = {} self.logged_image_names = [] self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS self.experiment.log_others({ 'comet_mode': COMET_MODE, 'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS, 'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS, 'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS, 'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX, 'comet_model_name': COMET_MODEL_NAME,}) # Check if running the Experiment with the Comet Optimizer if hasattr(self.opt, 'comet_optimizer_id'): self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id) self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective) self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric) self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp)) def _get_experiment(self, mode, experiment_id=None): if mode == 'offline': if experiment_id is not None: return comet_ml.ExistingOfflineExperiment( previous_experiment=experiment_id, **self.default_experiment_kwargs, ) return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,) else: try: if experiment_id is not None: return comet_ml.ExistingExperiment( previous_experiment=experiment_id, **self.default_experiment_kwargs, ) return comet_ml.Experiment(**self.default_experiment_kwargs) except ValueError: logger.warning('COMET WARNING: ' 'Comet credentials have not been set. ' 'Comet will default to offline logging. ' 'Please set your credentials to enable online logging.') return self._get_experiment('offline', experiment_id) return def log_metrics(self, log_dict, **kwargs): self.experiment.log_metrics(log_dict, **kwargs) def log_parameters(self, log_dict, **kwargs): self.experiment.log_parameters(log_dict, **kwargs) def log_asset(self, asset_path, **kwargs): self.experiment.log_asset(asset_path, **kwargs) def log_asset_data(self, asset, **kwargs): self.experiment.log_asset_data(asset, **kwargs) def log_image(self, img, **kwargs): self.experiment.log_image(img, **kwargs) def log_model(self, path, opt, epoch, fitness_score, best_model=False): if not self.save_model: return model_metadata = { 'fitness_score': fitness_score[-1], 'epochs_trained': epoch + 1, 'save_period': opt.save_period, 'total_epochs': opt.epochs,} model_files = glob.glob(f'{path}/*.pt') for model_path in model_files: name = Path(model_path).name self.experiment.log_model( self.model_name, file_or_folder=model_path, file_name=name, metadata=model_metadata, overwrite=True, ) def check_dataset(self, data_file): with open(data_file) as f: data_config = yaml.safe_load(f) if data_config['path'].startswith(COMET_PREFIX): path = data_config['path'].replace(COMET_PREFIX, '') data_dict = self.download_dataset_artifact(path) return data_dict self.log_asset(self.opt.data, metadata={'type': 'data-config-file'}) return check_dataset(data_file) def log_predictions(self, image, labelsn, path, shape, predn): if self.logged_images_count >= self.max_images: return detections = predn[predn[:, 4] > self.conf_thres] iou = box_iou(labelsn[:, 1:], detections[:, :4]) mask, _ = torch.where(iou > self.iou_thres) if len(mask) == 0: return filtered_detections = detections[mask] filtered_labels = labelsn[mask] image_id = path.split('/')[-1].split('.')[0] image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}' if image_name not in self.logged_image_names: native_scale_image = PIL.Image.open(path) self.log_image(native_scale_image, name=image_name) self.logged_image_names.append(image_name) metadata = [] for cls, *xyxy in filtered_labels.tolist(): metadata.append({ 'label': f'{self.class_names[int(cls)]}-gt', 'score': 100, 'box': { 'x': xyxy[0], 'y': xyxy[1], 'x2': xyxy[2], 'y2': xyxy[3]},}) for *xyxy, conf, cls in filtered_detections.tolist(): metadata.append({ 'label': f'{self.class_names[int(cls)]}', 'score': conf * 100, 'box': { 'x': xyxy[0], 'y': xyxy[1], 'x2': xyxy[2], 'y2': xyxy[3]},}) self.metadata_dict[image_name] = metadata self.logged_images_count += 1 return def preprocess_prediction(self, image, labels, shape, pred): nl, _ = labels.shape[0], pred.shape[0] # Predictions if self.opt.single_cls: pred[:, 5] = 0 predn = pred.clone() scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) labelsn = None if nl: tbox = xywh2xyxy(labels[:, 1:5]) # target boxes scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred return predn, labelsn def add_assets_to_artifact(self, artifact, path, asset_path, split): img_paths = sorted(glob.glob(f'{asset_path}/*')) label_paths = img2label_paths(img_paths) for image_file, label_file in zip(img_paths, label_paths): image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file]) try: artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split}) artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split}) except ValueError as e: logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.') logger.error(f'COMET ERROR: {e}') continue return artifact def upload_dataset_artifact(self): dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset') path = str((ROOT / Path(self.data_dict['path'])).resolve()) metadata = self.data_dict.copy() for key in ['train', 'val', 'test']: split_path = metadata.get(key) if split_path is not None: metadata[key] = split_path.replace(path, '') artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata) for key in metadata.keys(): if key in ['train', 'val', 'test']: if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): continue asset_path = self.data_dict.get(key) if asset_path is not None: artifact = self.add_assets_to_artifact(artifact, path, asset_path, key) self.experiment.log_artifact(artifact) return def download_dataset_artifact(self, artifact_path): logged_artifact = self.experiment.get_artifact(artifact_path) artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) logged_artifact.download(artifact_save_dir) metadata = logged_artifact.metadata data_dict = metadata.copy() data_dict['path'] = artifact_save_dir metadata_names = metadata.get('names') if type(metadata_names) == dict: data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()} elif type(metadata_names) == list: data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} else: raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" data_dict = self.update_data_paths(data_dict) return data_dict def update_data_paths(self, data_dict): path = data_dict.get('path', '') for split in ['train', 'val', 'test']: if data_dict.get(split): split_path = data_dict.get(split) data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [ f'{path}/{x}' for x in split_path]) return data_dict def on_pretrain_routine_end(self, paths): if self.opt.resume: return for path in paths: self.log_asset(str(path)) if self.upload_dataset: if not self.resume: self.upload_dataset_artifact() return def on_train_start(self): self.log_parameters(self.hyp) def on_train_epoch_start(self): return def on_train_epoch_end(self, epoch): self.experiment.curr_epoch = epoch return def on_train_batch_start(self): return def on_train_batch_end(self, log_dict, step): self.experiment.curr_step = step if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): self.log_metrics(log_dict, step=step) return def on_train_end(self, files, save_dir, last, best, epoch, results): if self.comet_log_predictions: curr_epoch = self.experiment.curr_epoch self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch) for f in files: self.log_asset(f, metadata={'epoch': epoch}) self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch}) if not self.opt.evolve: model_path = str(best if best.exists() else last) name = Path(model_path).name if self.save_model: self.experiment.log_model( self.model_name, file_or_folder=model_path, file_name=name, overwrite=True, ) # Check if running Experiment with Comet Optimizer if hasattr(self.opt, 'comet_optimizer_id'): metric = results.get(self.opt.comet_optimizer_metric) self.experiment.log_other('optimizer_metric_value', metric) self.finish_run() def on_val_start(self): return def on_val_batch_start(self): return def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): return for si, pred in enumerate(outputs): if len(pred) == 0: continue image = images[si] labels = targets[targets[:, 0] == si, 1:] shape = shapes[si] path = paths[si] predn, labelsn = self.preprocess_prediction(image, labels, shape, pred) if labelsn is not None: self.log_predictions(image, labelsn, path, shape, predn) return def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): if self.comet_log_per_class_metrics: if self.num_classes > 1: for i, c in enumerate(ap_class): class_name = self.class_names[c] self.experiment.log_metrics( { 'mAP@.5': ap50[i], 'mAP@.5:.95': ap[i], 'precision': p[i], 'recall': r[i], 'f1': f1[i], 'true_positives': tp[i], 'false_positives': fp[i], 'support': nt[c]}, prefix=class_name) if self.comet_log_confusion_matrix: epoch = self.experiment.curr_epoch class_names = list(self.class_names.values()) class_names.append('background') num_classes = len(class_names) self.experiment.log_confusion_matrix( matrix=confusion_matrix.matrix, max_categories=num_classes, labels=class_names, epoch=epoch, column_label='Actual Category', row_label='Predicted Category', file_name=f'confusion-matrix-epoch-{epoch}.json', ) def on_fit_epoch_end(self, result, epoch): self.log_metrics(result, epoch=epoch) def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) def on_params_update(self, params): self.log_parameters(params) def finish_run(self): self.experiment.end()