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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| import itertools | |
| from ultralytics.data import build_yolo_dataset | |
| from ultralytics.models import yolo | |
| from ultralytics.nn.tasks import WorldModel | |
| from ultralytics.utils import DEFAULT_CFG, RANK, checks | |
| from ultralytics.utils.torch_utils import de_parallel | |
| def on_pretrain_routine_end(trainer): | |
| """Callback.""" | |
| if RANK in {-1, 0}: | |
| # NOTE: for evaluation | |
| names = [name.split("/")[0] for name in list(trainer.test_loader.dataset.data["names"].values())] | |
| de_parallel(trainer.ema.ema).set_classes(names, cache_clip_model=False) | |
| device = next(trainer.model.parameters()).device | |
| trainer.text_model, _ = trainer.clip.load("ViT-B/32", device=device) | |
| for p in trainer.text_model.parameters(): | |
| p.requires_grad_(False) | |
| class WorldTrainer(yolo.detect.DetectionTrainer): | |
| """ | |
| A class to fine-tune a world model on a close-set dataset. | |
| Example: | |
| ```python | |
| from ultralytics.models.yolo.world import WorldModel | |
| args = dict(model='yolov8s-world.pt', data='coco8.yaml', epochs=3) | |
| trainer = WorldTrainer(overrides=args) | |
| trainer.train() | |
| ``` | |
| """ | |
| 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) | |
| # Import and assign clip | |
| try: | |
| import clip | |
| except ImportError: | |
| checks.check_requirements("git+https://github.com/ultralytics/CLIP.git") | |
| import clip | |
| self.clip = clip | |
| def get_model(self, cfg=None, weights=None, verbose=True): | |
| """Return WorldModel initialized with specified config and weights.""" | |
| # NOTE: This `nc` here is the max number of different text samples in one image, rather than the actual `nc`. | |
| # NOTE: Following the official config, nc hard-coded to 80 for now. | |
| model = WorldModel( | |
| cfg["yaml_file"] if isinstance(cfg, dict) else cfg, | |
| ch=3, | |
| nc=min(self.data["nc"], 80), | |
| verbose=verbose and RANK == -1, | |
| ) | |
| if weights: | |
| model.load(weights) | |
| self.add_callback("on_pretrain_routine_end", on_pretrain_routine_end) | |
| return model | |
| def build_dataset(self, img_path, mode="train", batch=None): | |
| """ | |
| Build YOLO Dataset. | |
| Args: | |
| img_path (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) | |
| return build_yolo_dataset( | |
| self.args, img_path, batch, self.data, mode=mode, rect=mode == "val", stride=gs, multi_modal=mode == "train" | |
| ) | |
| def preprocess_batch(self, batch): | |
| """Preprocesses a batch of images for YOLOWorld training, adjusting formatting and dimensions as needed.""" | |
| batch = super().preprocess_batch(batch) | |
| # NOTE: add text features | |
| texts = list(itertools.chain(*batch["texts"])) | |
| text_token = self.clip.tokenize(texts).to(batch["img"].device) | |
| txt_feats = self.text_model.encode_text(text_token).to(dtype=batch["img"].dtype) # torch.float32 | |
| txt_feats = txt_feats / txt_feats.norm(p=2, dim=-1, keepdim=True) | |
| batch["txt_feats"] = txt_feats.reshape(len(batch["texts"]), -1, txt_feats.shape[-1]) | |
| return batch | |