219 lines
8.7 KiB
Python
219 lines
8.7 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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import torch
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from ultralytics.data import YOLODataset
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from ultralytics.data.augment import Compose, Format, v8_transforms
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.utils import colorstr, ops
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__all__ = ("RTDETRValidator",) # tuple or list
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class RTDETRDataset(YOLODataset):
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"""
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Real-Time DEtection and TRacking (RT-DETR) dataset class extending the base YOLODataset class.
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This specialized dataset class is designed for use with the RT-DETR object detection model and is optimized for
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real-time detection and tracking tasks.
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Attributes:
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augment (bool): Whether to apply data augmentation.
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rect (bool): Whether to use rectangular training.
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use_segments (bool): Whether to use segmentation masks.
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use_keypoints (bool): Whether to use keypoint annotations.
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imgsz (int): Target image size for training.
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Methods:
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load_image: Load one image from dataset index.
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build_transforms: Build transformation pipeline for the dataset.
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Examples:
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Initialize an RT-DETR dataset
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>>> dataset = RTDETRDataset(img_path="path/to/images", imgsz=640)
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>>> image, hw = dataset.load_image(0)
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"""
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def __init__(self, *args, data=None, **kwargs):
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"""
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Initialize the RTDETRDataset class by inheriting from the YOLODataset class.
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This constructor sets up a dataset specifically optimized for the RT-DETR (Real-Time DEtection and TRacking)
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model, building upon the base YOLODataset functionality.
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Args:
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*args (Any): Variable length argument list passed to the parent YOLODataset class.
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data (dict | None): Dictionary containing dataset information. If None, default values will be used.
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**kwargs (Any): Additional keyword arguments passed to the parent YOLODataset class.
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"""
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super().__init__(*args, data=data, **kwargs)
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def load_image(self, i, rect_mode=False):
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"""
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Load one image from dataset index 'i'.
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Args:
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i (int): Index of the image to load.
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rect_mode (bool, optional): Whether to use rectangular mode for batch inference.
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Returns:
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im (torch.Tensor): The loaded image.
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resized_hw (tuple): Height and width of the resized image with shape (2,).
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Examples:
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Load an image from the dataset
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>>> dataset = RTDETRDataset(img_path="path/to/images")
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>>> image, hw = dataset.load_image(0)
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"""
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return super().load_image(i=i, rect_mode=rect_mode)
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def build_transforms(self, hyp=None):
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"""
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Build transformation pipeline for the dataset.
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Args:
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hyp (dict, optional): Hyperparameters for transformations.
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Returns:
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(Compose): Composition of transformation functions.
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"""
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if self.augment:
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hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
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hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
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hyp.cutmix = hyp.cutmix if self.augment and not self.rect else 0.0
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transforms = v8_transforms(self, self.imgsz, hyp, stretch=True)
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else:
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# transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), auto=False, scale_fill=True)])
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transforms = Compose([])
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transforms.append(
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Format(
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bbox_format="xywh",
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normalize=True,
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return_mask=self.use_segments,
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return_keypoint=self.use_keypoints,
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batch_idx=True,
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mask_ratio=hyp.mask_ratio,
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mask_overlap=hyp.overlap_mask,
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)
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)
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return transforms
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class RTDETRValidator(DetectionValidator):
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"""
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RTDETRValidator extends the DetectionValidator class to provide validation capabilities specifically tailored for
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the RT-DETR (Real-Time DETR) object detection model.
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The class allows building of an RTDETR-specific dataset for validation, applies Non-maximum suppression for
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post-processing, and updates evaluation metrics accordingly.
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Attributes:
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args (Namespace): Configuration arguments for validation.
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data (dict): Dataset configuration dictionary.
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Methods:
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build_dataset: Build an RTDETR Dataset for validation.
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postprocess: Apply Non-maximum suppression to prediction outputs.
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Examples:
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Initialize and run RT-DETR validation
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>>> from ultralytics.models.rtdetr import RTDETRValidator
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>>> args = dict(model="rtdetr-l.pt", data="coco8.yaml")
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>>> validator = RTDETRValidator(args=args)
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>>> validator()
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Notes:
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For further details on the attributes and methods, refer to the parent DetectionValidator class.
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"""
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def build_dataset(self, img_path, mode="val", batch=None):
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"""
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Build an RTDETR Dataset.
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Args:
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img_path (str): Path to the folder containing images.
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mode (str, optional): `train` mode or `val` mode, users are able to customize different augmentations for
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each mode.
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batch (int, optional): Size of batches, this is for `rect`.
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Returns:
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(RTDETRDataset): Dataset configured for RT-DETR validation.
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"""
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return RTDETRDataset(
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img_path=img_path,
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imgsz=self.args.imgsz,
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batch_size=batch,
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augment=False, # no augmentation
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hyp=self.args,
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rect=False, # no rect
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cache=self.args.cache or None,
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prefix=colorstr(f"{mode}: "),
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data=self.data,
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)
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def postprocess(
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self, preds: torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor]
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) -> list[dict[str, torch.Tensor]]:
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"""
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Apply Non-maximum suppression to prediction outputs.
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Args:
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preds (torch.Tensor | list | tuple): Raw predictions from the model. If tensor, should have shape
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(batch_size, num_predictions, num_classes + 4) where last dimension contains bbox coords and class scores.
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Returns:
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(list[dict[str, torch.Tensor]]): List of dictionaries for each image, each containing:
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- 'bboxes': Tensor of shape (N, 4) with bounding box coordinates
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- 'conf': Tensor of shape (N,) with confidence scores
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- 'cls': Tensor of shape (N,) with class indices
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"""
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if not isinstance(preds, (list, tuple)): # list for PyTorch inference but list[0] Tensor for export inference
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preds = [preds, None]
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bs, _, nd = preds[0].shape
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bboxes, scores = preds[0].split((4, nd - 4), dim=-1)
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bboxes *= self.args.imgsz
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outputs = [torch.zeros((0, 6), device=bboxes.device)] * bs
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for i, bbox in enumerate(bboxes): # (300, 4)
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bbox = ops.xywh2xyxy(bbox)
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score, cls = scores[i].max(-1) # (300, )
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pred = torch.cat([bbox, score[..., None], cls[..., None]], dim=-1) # filter
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# Sort by confidence to correctly get internal metrics
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pred = pred[score.argsort(descending=True)]
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outputs[i] = pred[score > self.args.conf]
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return [{"bboxes": x[:, :4], "conf": x[:, 4], "cls": x[:, 5]} for x in outputs]
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def pred_to_json(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> None:
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"""
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Serialize YOLO predictions to COCO json format.
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Args:
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predn (dict[str, torch.Tensor]): Predictions dictionary containing 'bboxes', 'conf', and 'cls' keys
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with bounding box coordinates, confidence scores, and class predictions.
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pbatch (dict[str, Any]): Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'.
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"""
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path = Path(pbatch["im_file"])
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stem = path.stem
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image_id = int(stem) if stem.isnumeric() else stem
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box = predn["bboxes"].clone()
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box[..., [0, 2]] *= pbatch["ori_shape"][1] / self.args.imgsz # native-space pred
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box[..., [1, 3]] *= pbatch["ori_shape"][0] / self.args.imgsz # native-space pred
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box = ops.xyxy2xywh(box) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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for b, s, c in zip(box.tolist(), predn["conf"].tolist(), predn["cls"].tolist()):
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self.jdict.append(
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{
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"image_id": image_id,
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"file_name": path.name,
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"category_id": self.class_map[int(c)],
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"bbox": [round(x, 3) for x in b],
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"score": round(s, 5),
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}
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)
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