Fix expected box format by pycoco

src/torchmetrics/detection/mean_ap.py

@@ -145,7 +145,15 @@ class MeanAveragePrecision(Metric):
 
  Args:
  box_format:
- Input format of given boxes. Supported formats are ``[`xyxy`, `xywh`, `cxcywh`]``.
+ Input format of given boxes. Supported formats are:
+
+ - 'xyxy': boxes are represented via corners, x1, y1 being top left and x2, y2 being bottom right.
+ - 'xywh' : boxes are represented via corner, width and height, x1, y2 being top left, w, h being
+ width and height. This is the default format used by pycoco and all input formats will be converted
+ to this.
+ - 'cxcywh': boxes are represented via centre, width and height, cx, cy being center of box, w, h being
+ width and height.
+
  iou_type:
  Type of input (either masks or bounding-boxes) used for computing IOU.
  Supported IOU types are ``["bbox", "segm"]``. If using ``"segm"``, masks should be provided in input.
@@ -232,7 +240,7 @@ class MeanAveragePrecision(Metric):
 
  def __init__(
  self,
- box_format: str = "xyxy",
+ box_format: Literal["xyxy", "xywh", "cxcywh"] = "xyxy",
  iou_type: Literal["bbox", "segm"] = "bbox",
  iou_thresholds: Optional[List[float]] = None,
  rec_thresholds: Optional[List[float]] = None,
@@ -345,27 +353,27 @@ def compute(self) -> dict:
  coco_target.createIndex()
  coco_preds.createIndex()
 
- coco_eval = COCOeval(coco_target, coco_preds, iouType=self.iou_type)
- coco_eval.params.iouThrs = np.array(self.iou_thresholds, dtype=np.float64)
- coco_eval.params.recThrs = np.array(self.rec_thresholds, dtype=np.float64)
- coco_eval.params.maxDets = self.max_detection_thresholds
+ self.coco_eval = COCOeval(coco_target, coco_preds, iouType=self.iou_type)
+ self.coco_eval.params.iouThrs = np.array(self.iou_thresholds, dtype=np.float64)
+ self.coco_eval.params.recThrs = np.array(self.rec_thresholds, dtype=np.float64)
+ self.coco_eval.params.maxDets = self.max_detection_thresholds
 
- coco_eval.evaluate()
- coco_eval.accumulate()
- coco_eval.summarize()
- stats = coco_eval.stats
+ self.coco_eval.evaluate()
+ self.coco_eval.accumulate()
+ self.coco_eval.summarize()
+ stats = self.coco_eval.stats
 
  # if class mode is enabled, evaluate metrics per class
  if self.class_metrics:
  map_per_class_list = []
  mar_100_per_class_list = []
  for class_id in self._get_classes():
- coco_eval.params.catIds = [class_id]
+ self.coco_eval.params.catIds = [class_id]
  with contextlib.redirect_stdout(io.StringIO()):
- coco_eval.evaluate()
- coco_eval.accumulate()
- coco_eval.summarize()
- class_stats = coco_eval.stats
+ self.coco_eval.evaluate()
+ self.coco_eval.accumulate()
+ self.coco_eval.summarize()
+ class_stats = self.coco_eval.stats
 
  map_per_class_list.append(torch.tensor([class_stats[0]]))
  mar_100_per_class_list.append(torch.tensor([class_stats[8]]))
@@ -545,7 +553,7 @@ def _get_safe_item_values(self, item: Dict[str, Any]) -> Union[Tensor, Tuple]:
  if self.iou_type == "bbox":
  boxes = _fix_empty_tensors(item["boxes"])
  if boxes.numel() > 0:
- boxes = box_convert(boxes, in_fmt=self.box_format, out_fmt="xyxy")
+ boxes = box_convert(boxes, in_fmt=self.box_format, out_fmt="xywh")
  return boxes
  if self.iou_type == "segm":
  masks = []

tests/unittests/detection/test_map.py

@@ -140,6 +140,7 @@ def test_map(self, iou_type, iou_thresholds, rec_thresholds, ddp):
  "iou_thresholds": iou_thresholds,
  "rec_thresholds": rec_thresholds,
  "class_metrics": False,
+ "box_format": "xywh",
  },
  check_batch=False,
  atol=1e-2,
@@ -154,7 +155,7 @@ def test_map_classwise(self, iou_type, ddp):
  target=target,
  metric_class=MeanAveragePrecision,
  reference_metric=partial(_compare_again_coco_fn, iou_type=iou_type, class_metrics=True),
- metric_args={"iou_type": iou_type, "class_metrics": True},
+ metric_args={"box_format": "xywh", "iou_type": iou_type, "class_metrics": True},
  check_batch=False,
  atol=1e-1,
  )
@@ -656,3 +657,29 @@ def test_device_changing():
  metric = metric.cpu()
  val = metric.compute()
  assert isinstance(val, dict)
+
+
+@pytest.mark.parametrize(
+ ("box_format", "iou_val_expected", "map_val_expected"),
+ [
+ ("xyxy", 0.25, 1),
+ ("xywh", 0.143, 0.0),
+ ("cxcywh", 0.143, 0.0),
+ ],
+)
+def test_for_box_format(box_format, iou_val_expected, map_val_expected):
+ """Test that only the correct box format lead to a score of 1.
+
+ See issue: https://github.com/Lightning-AI/torchmetrics/issues/1908.
+ """
+ predictions = [
+ {"boxes": torch.tensor([[0.5, 0.5, 1, 1]]), "scores": torch.tensor([1.0]), "labels": torch.tensor([0])}
+ ]
+
+ targets = [{"boxes": torch.tensor([[0, 0, 1, 1]]), "labels": torch.tensor([0])}]
+
+ metric = MeanAveragePrecision(box_format=box_format, iou_thresholds=[0.2])
+ metric.update(predictions, targets)
+ result = metric.compute()
+ assert result["map"].item() == map_val_expected
+ assert round(float(metric.coco_eval.ious[(0, 0)]), 3) == iou_val_expected