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Fix _process_batch() docstrings (#14454)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-07-16 01:01:18 +02:00 • committed by GitHub
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4 changed files with 77 additions and 25 deletions
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15
ultralytics/models/yolo/detect/val.py
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@ -202,13 +202,18 @@ class DetectionValidator(BaseValidator):
Return correct prediction matrix.
Args:
detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
Each detection is of the format: x1, y1, x2, y2, conf, class.
labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
Each label is of the format: class, x1, y1, x2, y2.
detections (torch.Tensor): Tensor of shape (N, 6) representing detections where each detection is
(x1, y1, x2, y2, conf, class).
gt_bboxes (torch.Tensor): Tensor of shape (M, 4) representing ground-truth bounding box coordinates. Each
bounding box is of the format: (x1, y1, x2, y2).
gt_cls (torch.Tensor): Tensor of shape (M,) representing target class indices.
Returns:
(torch.Tensor): Correct prediction matrix of shape [N, 10] for 10 IoU levels.
(torch.Tensor): Correct prediction matrix of shape (N, 10) for 10 IoU levels.
Note:
The function does not return any value directly usable for metrics calculation. Instead, it provides an
intermediate representation used for evaluating predictions against ground truth.
"""
iou = box_iou(gt_bboxes, detections[:, :4])
return self.match_predictions(detections[:, 5], gt_cls, iou)

26
ultralytics/models/yolo/obb/val.py
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@ -52,17 +52,29 @@ class OBBValidator(DetectionValidator):
def _process_batch(self, detections, gt_bboxes, gt_cls):
"""
Return correct prediction matrix.
Perform computation of the correct prediction matrix for a batch of detections and ground truth bounding boxes.
Args:
detections (torch.Tensor): Tensor of shape [N, 7] representing detections.
Each detection is of the format: x1, y1, x2, y2, conf, class, angle.
gt_bboxes (torch.Tensor): Tensor of shape [M, 5] representing rotated boxes.
Each box is of the format: x1, y1, x2, y2, angle.
labels (torch.Tensor): Tensor of shape [M] representing labels.
detections (torch.Tensor): A tensor of shape (N, 7) representing the detected bounding boxes and associated
data. Each detection is represented as (x1, y1, x2, y2, conf, class, angle).
gt_bboxes (torch.Tensor): A tensor of shape (M, 5) representing the ground truth bounding boxes. Each box is
represented as (x1, y1, x2, y2, angle).
gt_cls (torch.Tensor): A tensor of shape (M,) representing class labels for the ground truth bounding boxes.
Returns:
(torch.Tensor): Correct prediction matrix of shape [N, 10] for 10 IoU levels.
(torch.Tensor): The correct prediction matrix with shape (N, 10), which includes 10 IoU (Intersection over
Union) levels for each detection, indicating the accuracy of predictions compared to the ground truth.
Example:
```python
detections = torch.rand(100, 7) # 100 sample detections
gt_bboxes = torch.rand(50, 5) # 50 sample ground truth boxes
gt_cls = torch.randint(0, 5, (50,)) # 50 ground truth class labels
correct_matrix = OBBValidator._process_batch(detections, gt_bboxes, gt_cls)
```
Note:
This method relies on `batch_probiou` to calculate IoU between detections and ground truth bounding boxes.
"""
iou = batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1))
return self.match_predictions(detections[:, 5], gt_cls, iou)

33
ultralytics/models/yolo/pose/val.py
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@ -152,19 +152,34 @@ class PoseValidator(DetectionValidator):
def _process_batch(self, detections, gt_bboxes, gt_cls, pred_kpts=None, gt_kpts=None):
"""
Return correct prediction matrix.
Return correct prediction matrix by computing Intersection over Union (IoU) between detections and ground truth.
Args:
detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
Each detection is of the format: x1, y1, x2, y2, conf, class.
labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
Each label is of the format: class, x1, y1, x2, y2.
pred_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing predicted keypoints.
51 corresponds to 17 keypoints each with 3 values.
gt_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing ground truth keypoints.
detections (torch.Tensor): Tensor with shape (N, 6) representing detection boxes and scores, where each
detection is of the format (x1, y1, x2, y2, conf, class).
gt_bboxes (torch.Tensor): Tensor with shape (M, 4) representing ground truth bounding boxes, where each
box is of the format (x1, y1, x2, y2).
gt_cls (torch.Tensor): Tensor with shape (M,) representing ground truth class indices.
pred_kpts (torch.Tensor | None): Optional tensor with shape (N, 51) representing predicted keypoints, where
51 corresponds to 17 keypoints each having 3 values.
gt_kpts (torch.Tensor | None): Optional tensor with shape (N, 51) representing ground truth keypoints.
Returns:
torch.Tensor: Correct prediction matrix of shape [N, 10] for 10 IoU levels.
torch.Tensor: A tensor with shape (N, 10) representing the correct prediction matrix for 10 IoU levels,
where N is the number of detections.
Example:
```python
detections = torch.rand(100, 6) # 100 predictions: (x1, y1, x2, y2, conf, class)
gt_bboxes = torch.rand(50, 4) # 50 ground truth boxes: (x1, y1, x2, y2)
gt_cls = torch.randint(0, 2, (50,)) # 50 ground truth class indices
pred_kpts = torch.rand(100, 51) # 100 predicted keypoints
gt_kpts = torch.rand(50, 51) # 50 ground truth keypoints
correct_preds = _process_batch(detections, gt_bboxes, gt_cls, pred_kpts, gt_kpts)
```
Note:
`0.53` scale factor used in area computation is referenced from https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384.
"""
if pred_kpts is not None and gt_kpts is not None:
# `0.53` is from https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384

28
ultralytics/models/yolo/segment/val.py
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@ -164,14 +164,34 @@ class SegmentationValidator(DetectionValidator):
def _process_batch(self, detections, gt_bboxes, gt_cls, pred_masks=None, gt_masks=None, overlap=False, masks=False):
"""
Return correct prediction matrix.
Compute correct prediction matrix for a batch based on bounding boxes and optional masks.
Args:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
detections (torch.Tensor): Tensor of shape (N, 6) representing detected bounding boxes and
associated confidence scores and class indices. Each row is of the format [x1, y1, x2, y2, conf, class].
gt_bboxes (torch.Tensor): Tensor of shape (M, 4) representing ground truth bounding box coordinates.
Each row is of the format [x1, y1, x2, y2].
gt_cls (torch.Tensor): Tensor of shape (M,) representing ground truth class indices.
pred_masks (torch.Tensor | None): Tensor representing predicted masks, if available. The shape should
match the ground truth masks.
gt_masks (torch.Tensor | None): Tensor of shape (M, H, W) representing ground truth masks, if available.
overlap (bool): Flag indicating if overlapping masks should be considered.
masks (bool): Flag indicating if the batch contains mask data.
Returns:
correct (array[N, 10]), for 10 IoU levels
(torch.Tensor): A correct prediction matrix of shape (N, 10), where 10 represents different IoU levels.
Note:
- If `masks` is True, the function computes IoU between predicted and ground truth masks.
- If `overlap` is True and `masks` is True, overlapping masks are taken into account when computing IoU.
Example:
```python
detections = torch.tensor([[25, 30, 200, 300, 0.8, 1], [50, 60, 180, 290, 0.75, 0]])
gt_bboxes = torch.tensor([[24, 29, 199, 299], [55, 65, 185, 295]])
gt_cls = torch.tensor([1, 0])
correct_preds = validator._process_batch(detections, gt_bboxes, gt_cls)
```
"""
if masks:
if overlap: