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 <glenn.jocher@ultralytics.com>

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]:

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)

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()