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# Ultralytics YOLO 🚀, AGPL-3.0 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()
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