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| import json | |
| import sys | |
| from pathlib import Path | |
| import torch | |
| import yaml | |
| from tqdm import tqdm | |
| sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path | |
| from utils.datasets import LoadImagesAndLabels | |
| from utils.datasets import img2label_paths | |
| from utils.general import colorstr, xywh2xyxy, check_dataset | |
| try: | |
| import wandb | |
| from wandb import init, finish | |
| except ImportError: | |
| wandb = None | |
| 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 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 | |
| model_artifact_name = 'run_' + run_id + '_model' | |
| return run_id, project, model_artifact_name | |
| def check_wandb_resume(opt): | |
| process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None | |
| if isinstance(opt.resume, str): | |
| if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): | |
| if opt.global_rank not in [-1, 0]: # For resuming DDP runs | |
| run_id, project, model_artifact_name = get_run_info(opt.resume) | |
| api = wandb.Api() | |
| artifact = api.artifact(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(opt.data) as f: | |
| data_dict = yaml.load(f, Loader=yaml.SafeLoader) # 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.dump(data_dict, f) | |
| opt.data = ddp_data_path | |
| class WandbLogger(): | |
| def __init__(self, opt, name, run_id, data_dict, job_type='Training'): | |
| # Pre-training routine -- | |
| self.job_type = job_type | |
| self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict | |
| # 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): | |
| run_id, project, 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, resume='allow') | |
| opt.resume = model_artifact_name | |
| elif self.wandb: | |
| self.wandb_run = wandb.init(config=opt, | |
| resume="allow", | |
| project='YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem, | |
| name=name, | |
| job_type=job_type, | |
| id=run_id) if not wandb.run else wandb.run | |
| if self.wandb_run: | |
| if self.job_type == 'Training': | |
| if not opt.resume: | |
| wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict | |
| # Info useful for resuming from artifacts | |
| self.wandb_run.config.opt = vars(opt) | |
| self.wandb_run.config.data_dict = wandb_data_dict | |
| self.data_dict = self.setup_training(opt, data_dict) | |
| if self.job_type == 'Dataset Creation': | |
| self.data_dict = self.check_and_upload_dataset(opt) | |
| else: | |
| prefix = colorstr('wandb: ') | |
| print(f"{prefix}Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)") | |
| def check_and_upload_dataset(self, opt): | |
| assert wandb, 'Install wandb to upload dataset' | |
| check_dataset(self.data_dict) | |
| config_path = self.log_dataset_artifact(opt.data, | |
| opt.single_cls, | |
| 'YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem) | |
| print("Created dataset config file ", config_path) | |
| with open(config_path) as f: | |
| wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) | |
| return wandb_data_dict | |
| def setup_training(self, opt, data_dict): | |
| self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants | |
| 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 = str( | |
| self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ | |
| config.opt['hyp'] | |
| data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume | |
| if 'val_artifact' not in self.__dict__: # 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) | |
| self.result_artifact, self.result_table, self.val_table, self.weights = None, 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.val_artifact_path is not None: | |
| val_path = Path(self.val_artifact_path) / 'data/images/' | |
| data_dict['val'] = str(val_path) | |
| self.val_table = self.val_artifact.get("val") | |
| self.map_val_table_path() | |
| if self.val_artifact is not None: | |
| self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") | |
| self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) | |
| if opt.bbox_interval == -1: | |
| self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 | |
| return data_dict | |
| def download_dataset_artifact(self, path, alias): | |
| if isinstance(path, str) and 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() | |
| return datadir, dataset_artifact | |
| return None, None | |
| def download_model_artifact(self, opt): | |
| 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') | |
| assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( | |
| total_epochs) | |
| return modeldir, model_artifact | |
| return None, None | |
| def log_model(self, path, opt, epoch, fitness_score, best_model=False): | |
| 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', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) | |
| print("Saving model artifact on epoch ", epoch + 1) | |
| def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): | |
| with open(data_file) as f: | |
| data = yaml.load(f, Loader=yaml.SafeLoader) # 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 | |
| self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( | |
| data['train']), names, name='train') if data.get('train') else None | |
| self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( | |
| data['val']), names, name='val') if data.get('val') else None | |
| if data.get('train'): | |
| data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') | |
| if data.get('val'): | |
| data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') | |
| path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path | |
| data.pop('download', None) | |
| with open(path, 'w') as f: | |
| yaml.dump(data, f) | |
| if self.job_type == 'Training': # builds correct artifact pipeline graph | |
| self.wandb_run.use_artifact(self.val_artifact) | |
| self.wandb_run.use_artifact(self.train_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): | |
| self.val_table_map = {} | |
| print("Mapping dataset") | |
| for i, data in enumerate(tqdm(self.val_table.data)): | |
| self.val_table_map[data[3]] = data[0] | |
| def create_dataset_table(self, dataset, class_to_id, name='dataset'): | |
| # 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.img_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)): | |
| height, width = shapes[0] | |
| labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * 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), | |
| Path(paths).name) | |
| artifact.add(table, name) | |
| return artifact | |
| def log_training_progress(self, predn, path, names): | |
| if self.val_table and self.result_table: | |
| class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) | |
| box_data = [] | |
| total_conf = 0 | |
| for *xyxy, conf, cls in predn.tolist(): | |
| if conf >= 0.25: | |
| box_data.append( | |
| {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, | |
| "class_id": int(cls), | |
| "box_caption": "%s %.3f" % (names[cls], conf), | |
| "scores": {"class_score": conf}, | |
| "domain": "pixel"}) | |
| total_conf = total_conf + conf | |
| boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space | |
| id = self.val_table_map[Path(path).name] | |
| self.result_table.add_data(self.current_epoch, | |
| id, | |
| wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), | |
| total_conf / max(1, len(box_data)) | |
| ) | |
| 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, best_result=False): | |
| if self.wandb_run: | |
| wandb.log(self.log_dict) | |
| self.log_dict = {} | |
| if self.result_artifact: | |
| train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") | |
| self.result_artifact.add(train_results, 'result') | |
| wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), | |
| ('best' if best_result else '')]) | |
| self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) | |
| self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") | |
| def finish_run(self): | |
| if self.wandb_run: | |
| if self.log_dict: | |
| wandb.log(self.log_dict) | |
| wandb.run.finish() | |