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fix remaining tests
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@SkafteNicki
SkafteNicki committed Oct 10, 2024
1 parent 734abb4 commit 35083b0
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Showing 2 changed files with 57 additions and 54 deletions.
109 changes: 56 additions & 53 deletions src/torchmetrics/detection/mean_ap.py
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Original file line number Diff line number Diff line change
Expand Up @@ -531,64 +531,67 @@ def compute(self) -> dict:
for anno in coco_preds.dataset["annotations"]:
anno["area"] = anno[f"area_{i_type}"]

coco_eval = self.cocoeval(coco_target, coco_preds, iouType=i_type) # type: ignore[operator]
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

coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
stats = coco_eval.stats
result_dict.update(self._coco_stats_to_tensor_dict(stats, prefix=prefix))

summary = {}
if self.extended_summary:
summary = {
f"{prefix}ious": apply_to_collection(
coco_eval.ious, np.ndarray, lambda x: torch.tensor(x, dtype=torch.float32)
),
f"{prefix}precision": torch.tensor(coco_eval.eval["precision"]),
f"{prefix}recall": torch.tensor(coco_eval.eval["recall"]),
f"{prefix}scores": torch.tensor(coco_eval.eval["scores"]),
}
result_dict.update(summary)

# if class mode is enabled, evaluate metrics per class
if self.class_metrics:
# regardless of average method, reinitialize dataset to get rid of internal state which can
# lead to wrong results when evaluating per class
coco_preds, coco_target = self._get_coco_datasets(average="macro")
if len(coco_preds.imgs) == 0 or len(coco_target.imgs) == 0:
result_dict.update(self._coco_stats_to_tensor_dict(12 * [-1.0], prefix=prefix))
else:
coco_eval = self.cocoeval(coco_target, coco_preds, iouType=i_type) # type: ignore[operator]
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

map_per_class_list = []
mar_per_class_list = []
for class_id in self._get_classes():
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

map_per_class_list.append(torch.tensor([class_stats[0]]))
mar_per_class_list.append(torch.tensor([class_stats[8]]))

map_per_class_values = torch.tensor(map_per_class_list, dtype=torch.float32)
mar_per_class_values = torch.tensor(mar_per_class_list, dtype=torch.float32)
else:
map_per_class_values = torch.tensor([-1], dtype=torch.float32)
mar_per_class_values = torch.tensor([-1], dtype=torch.float32)
prefix = "" if len(self.iou_type) == 1 else f"{i_type}_"
result_dict.update(
{
f"{prefix}map_per_class": map_per_class_values,
f"{prefix}mar_{self.max_detection_thresholds[-1]}_per_class": mar_per_class_values,
},
)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
stats = coco_eval.stats
result_dict.update(self._coco_stats_to_tensor_dict(stats, prefix=prefix))

summary = {}
if self.extended_summary:
summary = {
f"{prefix}ious": apply_to_collection(
coco_eval.ious, np.ndarray, lambda x: torch.tensor(x, dtype=torch.float32)
),
f"{prefix}precision": torch.tensor(coco_eval.eval["precision"]),
f"{prefix}recall": torch.tensor(coco_eval.eval["recall"]),
f"{prefix}scores": torch.tensor(coco_eval.eval["scores"]),
}
result_dict.update(summary)

# if class mode is enabled, evaluate metrics per class
if self.class_metrics:
# regardless of average method, reinitialize dataset to get rid of internal state which can
# lead to wrong results when evaluating per class
coco_preds, coco_target = self._get_coco_datasets(average="macro")
coco_eval = self.cocoeval(coco_target, coco_preds, iouType=i_type) # type: ignore[operator]
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

map_per_class_list = []
mar_per_class_list = []
for class_id in self._get_classes():
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

map_per_class_list.append(torch.tensor([class_stats[0]]))
mar_per_class_list.append(torch.tensor([class_stats[8]]))

map_per_class_values = torch.tensor(map_per_class_list, dtype=torch.float32)
mar_per_class_values = torch.tensor(mar_per_class_list, dtype=torch.float32)
else:
map_per_class_values = torch.tensor([-1], dtype=torch.float32)
mar_per_class_values = torch.tensor([-1], dtype=torch.float32)
prefix = "" if len(self.iou_type) == 1 else f"{i_type}_"
result_dict.update(
{
f"{prefix}map_per_class": map_per_class_values,
f"{prefix}mar_{self.max_detection_thresholds[-1]}_per_class": mar_per_class_values,
},
)
result_dict.update({"classes": torch.tensor(self._get_classes(), dtype=torch.int32)})

return result_dict
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2 changes: 1 addition & 1 deletion tests/unittests/detection/test_map.py
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Original file line number Diff line number Diff line change
Expand Up @@ -410,7 +410,7 @@ def test_compare_both_same_time(tmpdir, backend):
},
],
[
{"boxes": Tensor([]), "scores": Tensor([]), "labels": Tensor([])},
{"boxes": Tensor([]), "scores": Tensor([]), "labels": IntTensor([])},
],
],
"target": [
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