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| # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | |
| from ultralytics.data import YOLOConcatDataset, build_grounding, build_yolo_dataset | |
| from ultralytics.data.utils import check_det_dataset | |
| from ultralytics.models.yolo.world import WorldTrainer | |
| from ultralytics.utils import DEFAULT_CFG | |
| from ultralytics.utils.torch_utils import de_parallel | |
| class WorldTrainerFromScratch(WorldTrainer): | |
| """ | |
| A class extending the WorldTrainer class for training a world model from scratch on open-set dataset. | |
| Example: | |
| ```python | |
| from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch | |
| from ultralytics import YOLOWorld | |
| data = dict( | |
| train=dict( | |
| yolo_data=["Objects365.yaml"], | |
| grounding_data=[ | |
| dict( | |
| img_path="../datasets/flickr30k/images", | |
| json_file="../datasets/flickr30k/final_flickr_separateGT_train.json", | |
| ), | |
| dict( | |
| img_path="../datasets/GQA/images", | |
| json_file="../datasets/GQA/final_mixed_train_no_coco.json", | |
| ), | |
| ], | |
| ), | |
| val=dict(yolo_data=["lvis.yaml"]), | |
| ) | |
| model = YOLOWorld("yolov8s-worldv2.yaml") | |
| model.train(data=data, trainer=WorldTrainerFromScratch) | |
| ``` | |
| """ | |
| def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): | |
| """Initialize a WorldTrainer object with given arguments.""" | |
| if overrides is None: | |
| overrides = {} | |
| super().__init__(cfg, overrides, _callbacks) | |
| def build_dataset(self, img_path, mode="train", batch=None): | |
| """ | |
| Build YOLO Dataset. | |
| Args: | |
| img_path (List[str] | str): Path to the folder containing images. | |
| mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode. | |
| batch (int, optional): Size of batches, this is for `rect`. Defaults to None. | |
| """ | |
| gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32) | |
| if mode != "train": | |
| return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs) | |
| dataset = [ | |
| build_yolo_dataset(self.args, im_path, batch, self.data, stride=gs, multi_modal=True) | |
| if isinstance(im_path, str) | |
| else build_grounding(self.args, im_path["img_path"], im_path["json_file"], batch, stride=gs) | |
| for im_path in img_path | |
| ] | |
| return YOLOConcatDataset(dataset) if len(dataset) > 1 else dataset[0] | |
| def get_dataset(self): | |
| """ | |
| Get train, val path from data dict if it exists. | |
| Returns None if data format is not recognized. | |
| """ | |
| final_data = {} | |
| data_yaml = self.args.data | |
| assert data_yaml.get("train", False), "train dataset not found" # object365.yaml | |
| assert data_yaml.get("val", False), "validation dataset not found" # lvis.yaml | |
| data = {k: [check_det_dataset(d) for d in v.get("yolo_data", [])] for k, v in data_yaml.items()} | |
| assert len(data["val"]) == 1, f"Only support validating on 1 dataset for now, but got {len(data['val'])}." | |
| val_split = "minival" if "lvis" in data["val"][0]["val"] else "val" | |
| for d in data["val"]: | |
| if d.get("minival") is None: # for lvis dataset | |
| continue | |
| d["minival"] = str(d["path"] / d["minival"]) | |
| for s in ["train", "val"]: | |
| final_data[s] = [d["train" if s == "train" else val_split] for d in data[s]] | |
| # save grounding data if there's one | |
| grounding_data = data_yaml[s].get("grounding_data") | |
| if grounding_data is None: | |
| continue | |
| grounding_data = grounding_data if isinstance(grounding_data, list) else [grounding_data] | |
| for g in grounding_data: | |
| assert isinstance(g, dict), f"Grounding data should be provided in dict format, but got {type(g)}" | |
| final_data[s] += grounding_data | |
| # NOTE: to make training work properly, set `nc` and `names` | |
| final_data["nc"] = data["val"][0]["nc"] | |
| final_data["names"] = data["val"][0]["names"] | |
| self.data = final_data | |
| return final_data["train"], final_data["val"][0] | |
| def plot_training_labels(self): | |
| """DO NOT plot labels.""" | |
| pass | |
| def final_eval(self): | |
| """Performs final evaluation and validation for object detection YOLO-World model.""" | |
| val = self.args.data["val"]["yolo_data"][0] | |
| self.validator.args.data = val | |
| self.validator.args.split = "minival" if isinstance(val, str) and "lvis" in val else "val" | |
| return super().final_eval() | |