From 64222c9752a0f0a70d2df41a58d127a8acbad234 Mon Sep 17 00:00:00 2001 From: Ayush Chaurasia Date: Tue, 2 Feb 2021 11:08:41 +0530 Subject: [PATCH] Start setup for improved W&B integration (#1948) * Add helper functions for wandb and artifacts * cleanup * Reorganize files * Update wandb_utils.py * Update log_dataset.py We can remove this code, as the giou hyp has been deprecated for a while now. * Reorganize and update dataloader call * yaml.SafeLoader * PEP8 reformat * remove redundant checks * Add helper functions for wandb and artifacts * cleanup * Reorganize files * Update wandb_utils.py * Update log_dataset.py We can remove this code, as the giou hyp has been deprecated for a while now. * Reorganize and update dataloader call * yaml.SafeLoader * PEP8 reformat * remove redundant checks * Update util files * Update wandb_utils.py * Remove word size * Change path of labels.zip * remove unused imports * remove --rect * log_dataset.py cleanup * log_dataset.py cleanup2 * wandb_utils.py cleanup * remove redundant id_count * wandb_utils.py cleanup2 * rename cls * use pathlib for zip * rename dataloader to dataset * Change import order * Remove redundant code * remove unused import * remove unused imports Co-authored-by: Glenn Jocher --- utils/datasets.py | 3 +- utils/wandb_logging/__init__.py | 0 utils/wandb_logging/log_dataset.py | 39 ++++++++ utils/wandb_logging/wandb_utils.py | 145 +++++++++++++++++++++++++++++ 4 files changed, 186 insertions(+), 1 deletion(-) create mode 100644 utils/wandb_logging/__init__.py create mode 100644 utils/wandb_logging/log_dataset.py create mode 100644 utils/wandb_logging/wandb_utils.py diff --git a/utils/datasets.py b/utils/datasets.py index 360d24c18874..1e23934b63cc 100755 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -348,7 +348,8 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) self.mosaic_border = [-img_size // 2, -img_size // 2] self.stride = stride - + self.path = path + try: f = [] # image files for p in path if isinstance(path, list) else [path]: diff --git a/utils/wandb_logging/__init__.py b/utils/wandb_logging/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/utils/wandb_logging/log_dataset.py b/utils/wandb_logging/log_dataset.py new file mode 100644 index 000000000000..d790a9ce721e --- /dev/null +++ b/utils/wandb_logging/log_dataset.py @@ -0,0 +1,39 @@ +import argparse +from pathlib import Path + +import yaml + +from wandb_utils import WandbLogger +from utils.datasets import LoadImagesAndLabels + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def create_dataset_artifact(opt): + with open(opt.data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + logger = WandbLogger(opt, '', None, data, job_type='create_dataset') + nc, names = (1, ['item']) if opt.single_cls else (int(data['nc']), data['names']) + names = {k: v for k, v in enumerate(names)} # to index dictionary + logger.log_dataset_artifact(LoadImagesAndLabels(data['train']), names, name='train') # trainset + logger.log_dataset_artifact(LoadImagesAndLabels(data['val']), names, name='val') # valset + + # Update data.yaml with artifact links + data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(opt.project) / 'train') + data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(opt.project) / 'val') + path = opt.data if opt.overwrite_config else opt.data.replace('.', '_wandb.') # updated data.yaml path + data.pop('download', None) # download via artifact instead of predefined field 'download:' + with open(path, 'w') as f: + yaml.dump(data, f) + print("New Config file => ", path) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') + parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') + parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') + parser.add_argument('--overwrite_config', action='store_true', help='overwrite data.yaml') + opt = parser.parse_args() + + create_dataset_artifact(opt) diff --git a/utils/wandb_logging/wandb_utils.py b/utils/wandb_logging/wandb_utils.py new file mode 100644 index 000000000000..264cd4840e3c --- /dev/null +++ b/utils/wandb_logging/wandb_utils.py @@ -0,0 +1,145 @@ +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()