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| import json | |
| import shutil | |
| import sys | |
| from datetime import datetime | |
| from pathlib import Path | |
| import torch | |
| sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path | |
| from utils.general import colorstr, xywh2xyxy | |
| try: | |
| import wandb | |
| except ImportError: | |
| wandb = None | |
| print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") | |
| WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' | |
| def remove_prefix(from_string, prefix): | |
| return from_string[len(prefix):] | |
| class WandbLogger(): | |
| def __init__(self, opt, name, run_id, data_dict, job_type='Training'): | |
| self.wandb = wandb | |
| self.wandb_run = wandb.init(config=opt, resume="allow", | |
| project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, | |
| name=name, | |
| job_type=job_type, | |
| id=run_id) if self.wandb else None | |
| if job_type == 'Training': | |
| self.setup_training(opt, data_dict) | |
| if opt.bbox_interval == -1: | |
| opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs | |
| if opt.save_period == -1: | |
| opt.save_period = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs | |
| def setup_training(self, opt, data_dict): | |
| self.log_dict = {} | |
| self.train_artifact_path, self.trainset_artifact = \ | |
| self.download_dataset_artifact(data_dict['train'], opt.artifact_alias) | |
| self.test_artifact_path, self.testset_artifact = \ | |
| self.download_dataset_artifact(data_dict['val'], opt.artifact_alias) | |
| self.result_artifact, self.result_table, self.weights = None, None, None | |
| 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.test_artifact_path is not None: | |
| test_path = Path(self.test_artifact_path) / 'data/images/' | |
| data_dict['val'] = str(test_path) | |
| self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") | |
| self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) | |
| if opt.resume_from_artifact: | |
| modeldir, _ = self.download_model_artifact(opt.resume_from_artifact) | |
| if modeldir: | |
| self.weights = Path(modeldir) / "best.pt" | |
| opt.weights = self.weights | |
| def download_dataset_artifact(self, path, alias): | |
| if path.startswith(WANDB_ARTIFACT_PREFIX): | |
| dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) | |
| assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" | |
| datadir = dataset_artifact.download() | |
| labels_zip = Path(datadir) / "data/labels.zip" | |
| shutil.unpack_archive(labels_zip, Path(datadir) / 'data/labels', 'zip') | |
| print("Downloaded dataset to : ", datadir) | |
| return datadir, dataset_artifact | |
| return None, None | |
| def download_model_artifact(self, name): | |
| model_artifact = wandb.use_artifact(name + ":latest") | |
| assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' | |
| modeldir = model_artifact.download() | |
| print("Downloaded model to : ", modeldir) | |
| return modeldir, model_artifact | |
| def log_model(self, path, opt, epoch): | |
| datetime_suffix = datetime.today().strftime('%Y-%m-%d-%H-%M-%S') | |
| model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ | |
| 'original_url': str(path), | |
| 'epoch': epoch + 1, | |
| 'save period': opt.save_period, | |
| 'project': opt.project, | |
| 'datetime': datetime_suffix | |
| }) | |
| model_artifact.add_file(str(path / 'last.pt'), name='last.pt') | |
| model_artifact.add_file(str(path / 'best.pt'), name='best.pt') | |
| wandb.log_artifact(model_artifact) | |
| print("Saving model artifact on epoch ", epoch + 1) | |
| def log_dataset_artifact(self, dataset, class_to_id, name='dataset'): | |
| artifact = wandb.Artifact(name=name, type="dataset") | |
| image_path = dataset.path | |
| artifact.add_dir(image_path, name='data/images') | |
| table = wandb.Table(columns=["id", "train_image", "Classes"]) | |
| class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) | |
| for si, (img, labels, paths, shapes) in enumerate(dataset): | |
| height, width = shapes[0] | |
| labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) | |
| labels[:, 2:] *= torch.Tensor([width, height, width, height]) | |
| box_data = [] | |
| img_classes = {} | |
| for cls, *xyxy in labels[:, 1:].tolist(): | |
| cls = int(cls) | |
| box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, | |
| "class_id": cls, | |
| "box_caption": "%s" % (class_to_id[cls]), | |
| "scores": {"acc": 1}, | |
| "domain": "pixel"}) | |
| 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), json.dumps(img_classes)) | |
| artifact.add(table, name) | |
| labels_path = 'labels'.join(image_path.rsplit('images', 1)) | |
| zip_path = Path(labels_path).parent / (name + '_labels.zip') | |
| if not zip_path.is_file(): # make_archive won't check if file exists | |
| shutil.make_archive(zip_path.with_suffix(''), 'zip', labels_path) | |
| artifact.add_file(str(zip_path), name='data/labels.zip') | |
| wandb.log_artifact(artifact) | |
| print("Saving data to W&B...") | |
| def log(self, log_dict): | |
| if self.wandb_run: | |
| for key, value in log_dict.items(): | |
| self.log_dict[key] = value | |
| def end_epoch(self): | |
| if self.wandb_run and self.log_dict: | |
| wandb.log(self.log_dict) | |
| self.log_dict = {} | |
| def finish_run(self): | |
| if self.wandb_run: | |
| if self.result_artifact: | |
| print("Add Training Progress Artifact") | |
| self.result_artifact.add(self.result_table, 'result') | |
| train_results = wandb.JoinedTable(self.testset_artifact.get("val"), self.result_table, "id") | |
| self.result_artifact.add(train_results, 'joined_result') | |
| wandb.log_artifact(self.result_artifact) | |
| if self.log_dict: | |
| wandb.log(self.log_dict) | |
| wandb.run.finish() | |