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()