Method create_dataset_table

utils/loggers/wandb/wandb_utils.py:403–447

Create and return W&B artifact containing W&B Table of the dataset. arguments: dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table class_to_id -- hash map that maps class ids to labels name -- name of the artifact

(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset')

            self.val_table_path_map[data[3]] = data[0]

    def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'):
        """
        Create and return W&B artifact containing W&B Table of the dataset.

        arguments:
        dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
        class_to_id -- hash map that maps class ids to labels
        name -- name of the artifact

        returns:
        dataset artifact to be logged or used
        """
        # 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.im_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)):
            box_data, img_classes = [], {}
            for cls, *xywh in labels[:, 1:].tolist():
                cls = int(cls)
                box_data.append({
                    "position": {
                        "middle": [xywh[0], xywh[1]],
                        "width": xywh[2],
                        "height": xywh[3]},
                    "class_id": cls,
                    "box_caption": "%s" % (class_to_id[cls])})
                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), list(img_classes.values()),
                           Path(paths).name)
        artifact.add(table, name)
        return artifact

    def log_training_progress(self, predn, path, names):
        """