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Merged
vfdev-5 merged 1 commit into from
May 7, 2025
Merged

available device in test_object_detection_map.py #3409

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61 changes: 34 additions & 27 deletions tests/ignite/metrics/vision/test_object_detection_map.py
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Original file line number Diff line number Diff line change
Expand Up @@ -623,12 +623,13 @@ def test_wrong_input():
m.update(([{"bbox": None, "scores": None, "labels": None}], [{"labels": None}]))


def test_empty_data():
def test_empty_data(available_device):
"""
Note that PyCOCO returns -1 when threre's no ground truth data.
"""

metric = ObjectDetectionAvgPrecisionRecall()
metric = ObjectDetectionAvgPrecisionRecall(device=available_device)
assert metric._device == torch.device(available_device)
metric.update(
(
[{"bbox": torch.zeros((0, 4)), "scores": torch.zeros((0,)), "labels": torch.zeros((0), dtype=torch.long)}],
Expand All @@ -652,7 +653,8 @@ def test_empty_data():
)
assert metric.compute() == (-1, -1)

metric = ObjectDetectionAvgPrecisionRecall()
metric = ObjectDetectionAvgPrecisionRecall(device=available_device)
assert metric._device == torch.device(available_device)
metric.update(
(
[{"bbox": torch.zeros((0, 4)), "scores": torch.zeros((0,)), "labels": torch.zeros((0), dtype=torch.long)}],
Expand All @@ -670,7 +672,8 @@ def test_empty_data():
assert metric._y_true_count[1] == 1
assert metric.compute() == (0, 0)

metric = ObjectDetectionAvgPrecisionRecall()
metric = ObjectDetectionAvgPrecisionRecall(device=available_device)
assert metric._device == torch.device(available_device)
pred = {
"bbox": torch.tensor([[0.0, 0.0, 100.0, 100.0]]),
"scores": torch.tensor([0.9]),
Expand All @@ -689,8 +692,9 @@ def test_no_torchvision():
ObjectDetectionAvgPrecisionRecall()


def test_iou(sample):
m = ObjectDetectionAvgPrecisionRecall(num_classes=91)
def test_iou(sample, available_device):
m = ObjectDetectionAvgPrecisionRecall(num_classes=91, device=available_device)
assert m._device == torch.device(available_device)
from pycocotools.mask import iou as pycoco_iou

for pred, tgt in zip(*sample.data):
Expand All @@ -710,8 +714,9 @@ def test_iou(sample):
assert equal.all()


def test_iou_thresholding():
metric = ObjectDetectionAvgPrecisionRecall(iou_thresholds=[0.0, 0.3, 0.5, 0.75])
def test_iou_thresholding(available_device):
metric = ObjectDetectionAvgPrecisionRecall(iou_thresholds=[0.0, 0.3, 0.5, 0.75], device=available_device)
assert metric._device == torch.device(available_device)

pred = {
"bbox": torch.tensor([[0.0, 0.0, 100.0, 100.0]]),
Expand All @@ -720,7 +725,7 @@ def test_iou_thresholding():
}
gt = {"bbox": torch.tensor([[0.0, 0.0, 50.0, 100.0]]), "iscrowd": torch.zeros((1,)), "labels": torch.tensor([1])}
metric.update(([pred], [gt]))
assert (metric._tps[0] == torch.tensor([[True], [True], [True], [False]])).all()
assert (metric._tps[0] == torch.tensor([[True], [True], [True], [False]], device=available_device)).all()

pred = {
"bbox": torch.tensor([[0.0, 0.0, 100.0, 100.0]]),
Expand All @@ -729,10 +734,10 @@ def test_iou_thresholding():
}
gt = {"bbox": torch.tensor([[100.0, 0.0, 200.0, 100.0]]), "iscrowd": torch.zeros((1,)), "labels": torch.tensor([1])}
metric.update(([pred], [gt]))
assert (metric._tps[1] == torch.tensor([[True], [False], [False], [False]])).all()
assert (metric._tps[1] == torch.tensor([[True], [False], [False], [False]], device=available_device)).all()


def test_matching():
def test_matching(available_device):
"""
PyCOCO matching rules:
1. The higher confidence in a prediction, the sooner decision is made for it.
Expand All @@ -750,7 +755,8 @@ def test_matching():
7. Non-ignored ground truths are given priority over the ignored ones when matching with a prediction
even if their IOU is lower.
"""
metric = ObjectDetectionAvgPrecisionRecall(iou_thresholds=[0.2])
metric = ObjectDetectionAvgPrecisionRecall(iou_thresholds=[0.2], device=available_device)
assert metric._device == torch.device(available_device)

pred = {
"bbox": torch.tensor([[0.0, 0.0, 100.0, 100.0], [0.0, 0.0, 100.0, 100.0]]),
Expand All @@ -764,27 +770,27 @@ def test_matching():
}
metric.update(([pred], [gt]))
# Preds are sorted by their scores internally
assert (metric._tps[0] == torch.tensor([[True, False]])).all()
assert (metric._fps[0] == torch.tensor([[False, True]])).all()
assert (metric._scores[0] == torch.tensor([[0.9, 0.8]])).all()
assert (metric._tps[0] == torch.tensor([[True, False]], device=available_device)).all()
assert (metric._fps[0] == torch.tensor([[False, True]], device=available_device)).all()
assert (metric._scores[0] == torch.tensor([[0.9, 0.8]], device=available_device)).all()

pred["scores"] = torch.tensor([0.9, 0.9])
metric.update(([pred], [gt]))
assert (metric._tps[1] == torch.tensor([[True, False]])).all()
assert (metric._fps[1] == torch.tensor([[False, True]])).all()
assert (metric._tps[1] == torch.tensor([[True, False]], device=available_device)).all()
assert (metric._fps[1] == torch.tensor([[False, True]], device=available_device)).all()

gt["iscrowd"] = torch.tensor([1])
metric.update(([pred], [gt]))
assert (metric._tps[2] == torch.tensor([[False, False]])).all()
assert (metric._fps[2] == torch.tensor([[False, False]])).all()
assert (metric._tps[2] == torch.tensor([[False, False]], device=available_device)).all()
assert (metric._fps[2] == torch.tensor([[False, False]], device=available_device)).all()

pred["bbox"] = torch.tensor([[0.0, 0.0, 100.0, 100.0], [100.0, 0.0, 200.0, 100.0]])
gt["bbox"] = torch.tensor([[0.0, 0.0, 25.0, 50.0], [50.0, 0.0, 150.0, 100.0]])
gt["iscrowd"] = torch.zeros((2,))
gt["labels"] = torch.tensor([1, 1])
metric.update(([pred], [gt]))
assert (metric._tps[3] == torch.tensor([[True, False]])).all()
assert (metric._fps[3] == torch.tensor([[False, True]])).all()
assert (metric._tps[3] == torch.tensor([[True, False]], device=available_device)).all()
assert (metric._fps[3] == torch.tensor([[False, True]], device=available_device)).all()

metric._area_range = "small"
pred["bbox"] = torch.tensor(
Expand All @@ -794,14 +800,14 @@ def test_matching():
pred["labels"] = torch.tensor([1, 1, 1, 1])
gt["bbox"] = torch.tensor([[0.0, 0.0, 100.0, 11.0], [0.0, 0.0, 100.0, 5.0]])
metric.update(([pred], [gt]))
assert (metric._tps[4] == torch.tensor([[True, False, False, False]])).all()
assert (metric._fps[4] == torch.tensor([[False, False, False, True]])).all()
assert (metric._tps[4] == torch.tensor([[True, False, False, False]], device=available_device)).all()
assert (metric._fps[4] == torch.tensor([[False, False, False, True]], device=available_device)).all()

pred["scores"] = torch.tensor([0.9, 1.0, 0.9, 0.9])
metric._max_detections_per_image_per_class = 1
metric.update(([pred], [gt]))
assert (metric._tps[5] == torch.tensor([[True]])).all()
assert (metric._fps[5] == torch.tensor([[False]])).all()
assert (metric._tps[5] == torch.tensor([[True]], device=available_device)).all()
assert (metric._fps[5] == torch.tensor([[False]], device=available_device)).all()


def sklearn_precision_recall_curve_allowing_multiple_recalls_at_single_threshold(y_true, y_score):
Expand All @@ -825,11 +831,12 @@ def sklearn_precision_recall_curve_allowing_multiple_recalls_at_single_threshold
return np.hstack((precision[sl], 1)), np.hstack((recall[sl], 0)), None


def test__compute_recall_and_precision():
def test__compute_recall_and_precision(available_device):
# The case in which detector detects all gt objects but also produces some wrong predictions.
scores = torch.rand((50,))
y_true = torch.randint(0, 2, (50,))
m = ObjectDetectionAvgPrecisionRecall()
m = ObjectDetectionAvgPrecisionRecall(device=available_device)
assert m._device == torch.device(available_device)

ignite_recall, ignite_precision = m._compute_recall_and_precision(
y_true.bool(), ~(y_true.bool()), scores, y_true.sum()
Expand Down
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