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valor-lite change iou caching #805

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valor-lite change iou caching #805

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8c27b2a
wip
czaloom Oct 21, 2024
d8c928d
testing iou computation change
czaloom Oct 21, 2024
9d9c642
revert iou computation change
czaloom Oct 21, 2024
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27 changes: 19 additions & 8 deletions lite/tests/object_detection/test_accuracy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,16 +3,26 @@
from valor_lite.object_detection.computation import compute_precion_recall


def test__compute_average_precision():
def test__compute_accuracy():

sorted_pairs = np.array(
[
# dt, gt, pd, iou, gl, pl, score,
[0.0, 0.0, 2.0, 0.25, 0.0, 0.0, 0.95],
[0.0, 0.0, 3.0, 0.33333, 0.0, 0.0, 0.9],
[0.0, 0.0, 4.0, 0.66667, 0.0, 0.0, 0.65],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 1.0, 0.5, 0.0, 0.0, 0.01],
# iou, score
[0.25, 0.95],
[0.33333, 0.9],
[0.66667, 0.65],
[1.0, 0.1],
[0.5, 0.01],
]
)
sorted_identifiers = np.array(
[
# dt, gt, pd, gl, pl
[0, 0, 2, 0, 0],
[0, 0, 3, 0, 0],
[0, 0, 4, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
]
)

Expand All @@ -22,6 +32,7 @@ def test__compute_average_precision():

(_, _, accuracy, _, _) = compute_precion_recall(
sorted_pairs,
sorted_identifiers,
label_metadata=label_metadata,
iou_thresholds=iou_thresholds,
score_thresholds=score_thresholds,
Expand All @@ -36,7 +47,7 @@ def test__compute_average_precision():
assert (accuracy == expected).all()


def test_ap_using_torch_metrics_example(
def test_accuracy_using_torch_metrics_example(
torchmetrics_detections: list[Detection],
):
"""
Expand Down
23 changes: 17 additions & 6 deletions lite/tests/object_detection/test_average_precision.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,12 +11,22 @@ def test__compute_average_precision():

sorted_pairs = np.array(
[
# dt, gt, pd, iou, gl, pl, score,
[0.0, 0.0, 2.0, 0.25, 0.0, 0.0, 0.95],
[0.0, 0.0, 3.0, 0.33333, 0.0, 0.0, 0.9],
[0.0, 0.0, 4.0, 0.66667, 0.0, 0.0, 0.65],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 1.0, 0.5, 0.0, 0.0, 0.01],
# iou, score,
[0.25, 0.95],
[0.33333, 0.9],
[0.66667, 0.65],
[1.0, 0.1],
[0.5, 0.01],
]
)
sorted_identifers = np.array(
[
# dt, gt, pd, gl, pl
[0, 0, 2, 0, 0],
[0, 0, 3, 0, 0],
[0, 0, 4, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
]
)

Expand All @@ -26,6 +36,7 @@ def test__compute_average_precision():

(results, _, _, _, _) = compute_precion_recall(
sorted_pairs,
sorted_identifers,
label_metadata=label_metadata,
iou_thresholds=iou_thresholds,
score_thresholds=score_thresholds,
Expand Down
26 changes: 19 additions & 7 deletions lite/tests/object_detection/test_average_recall.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,13 +11,24 @@ def test__compute_average_recall():

sorted_pairs = np.array(
[
# dt, gt, pd, iou, gl, pl, score,
[0.0, 0.0, 2.0, 0.25, 0.0, 0.0, 0.95],
[0.0, 1.0, 3.0, 0.33333, 0.0, 0.0, 0.9],
[0.0, 0.0, 4.0, 0.66667, 0.0, 0.0, 0.65],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 1.0, 0.5, 0.0, 0.0, 0.01],
[0.0, 2.0, 5.0, 0.5, 1.0, 1.0, 0.95],
# iou, score
[0.25, 0.95],
[0.33333, 0.9],
[0.66667, 0.65],
[1.0, 0.1],
[0.5, 0.01],
[0.5, 0.95],
]
)
sorted_identifiers = np.array(
[
# dt, gt, pd, gl, pl,
[0, 0, 2, 0, 0],
[0, 1, 3, 0, 0],
[0, 0, 4, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 2, 5, 1, 1],
]
)

Expand All @@ -27,6 +38,7 @@ def test__compute_average_recall():

(_, results, _, _, _,) = compute_precion_recall(
sorted_pairs,
sorted_identifiers,
label_metadata=label_metadata,
iou_thresholds=iou_thresholds,
score_thresholds=score_thresholds,
Expand Down
30 changes: 23 additions & 7 deletions lite/tests/object_detection/test_confusion_matrix.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,15 +20,27 @@ def _test_compute_confusion_matrix(
):
sorted_pairs = np.array(
[
# dt, gt, pd, iou, gl, pl, score,
[0.0, 0.0, 1.0, 0.98, 0.0, 0.0, 0.9],
[1.0, 1.0, 2.0, 0.55, 1.0, 0.0, 0.9],
[2.0, -1.0, 4.0, 0.0, -1.0, 0.0, 0.65],
[3.0, 4.0, 5.0, 1.0, 0.0, 0.0, 0.1],
[1.0, 2.0, 3.0, 0.55, 0.0, 0.0, 0.1],
[4.0, 5.0, -1.0, 0.0, 0.0, -1.0, -1.0],
# iou, score
[0.98, 0.9],
[0.55, 0.9],
[0.0, 0.65],
[1.0, 0.1],
[0.55, 0.1],
[0.0, -1.0],
]
)
sorted_identifiers = np.array(
[
# dt, gt, pd, gl, pl,
[0, 0, 1, 0, 0],
[1, 1, 2, 1, 0],
[2, -1, 4, -1, 0],
[3, 4, 5, 0, 0],
[1, 2, 3, 0, 0],
[4, 5, -1, 0, -1],
]
)

label_metadata = np.array([[3, 4], [1, 0]])
iou_thresholds = np.array([0.5])
score_thresholds = np.array([score / 100.0 for score in range(1, 101)])
Expand All @@ -39,6 +51,7 @@ def _test_compute_confusion_matrix(
missing_predictions,
) = compute_confusion_matrix(
data=sorted_pairs,
identifiers=sorted_identifiers,
label_metadata=label_metadata,
iou_thresholds=iou_thresholds,
score_thresholds=score_thresholds,
Expand Down Expand Up @@ -1164,6 +1177,9 @@ def test_confusion_matrix(
m["value"]["hallucinations"],
m["value"]["missing_predictions"],
)
import json

print(json.dumps(m, indent=4))
assert m in expected_metrics
for m in expected_metrics:
assert m in actual_metrics
Expand Down
2 changes: 1 addition & 1 deletion lite/tests/object_detection/test_evaluator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,4 +127,4 @@ def test_output_types_dont_contain_numpy(basic_detections: list[Detection]):
values = _flatten_metrics(metrics)
for value in values:
if isinstance(value, (np.generic, np.ndarray)):
raise TypeError(f"Value `{value}` has type `{type(value)}`.")
raise TypeError(f"Value '{value}' has type '{type(value)}'.")
65 changes: 54 additions & 11 deletions lite/tests/object_detection/test_filtering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,7 +83,16 @@ def test_filtering_one_detection(one_detection: list[Detection]):
evaluator._ranked_pairs
== np.array(
[
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.3],
[1.0, 0.3],
]
)
).all()

assert (
evaluator._ranked_identifiers
== np.array(
[
[0, 0, 0, 0, 0],
]
)
).all()
Expand Down Expand Up @@ -203,8 +212,18 @@ def test_filtering_two_detections(two_detections: list[Detection]):
evaluator._ranked_pairs
== np.array(
[
[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.98],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.3],
[1.0, 0.98],
[1.0, 0.3],
]
)
).all()

assert (
evaluator._ranked_identifiers
== np.array(
[
[1, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
]
)
).all()
Expand Down Expand Up @@ -333,10 +352,22 @@ def test_filtering_four_detections(four_detections: list[Detection]):
evaluator._ranked_pairs
== np.array(
[
[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.98],
[3.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.98],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.3],
[2.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.3],
[1.0, 0.98],
[1.0, 0.98],
[1.0, 0.3],
[1.0, 0.3],
]
)
).all()

assert (
evaluator._ranked_identifiers
== np.array(
[
[1, 0, 0, 0, 1],
[3, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
[2, 0, 0, 0, 0],
]
)
).all()
Expand Down Expand Up @@ -471,10 +502,22 @@ def test_filtering_all_detections(four_detections: list[Detection]):
evaluator._ranked_pairs
== np.array(
[
[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.98],
[3.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.98],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.3],
[2.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.3],
[1.0, 0.98],
[1.0, 0.98],
[1.0, 0.3],
[1.0, 0.3],
]
)
).all()

assert (
evaluator._ranked_identifiers
== np.array(
[
[1, 0, 0, 0, 1],
[3, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
[2, 0, 0, 0, 0],
]
)
).all()
Expand Down
23 changes: 17 additions & 6 deletions lite/tests/object_detection/test_pr_curve.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -11,12 +11,22 @@ def test_pr_curve_simple():

sorted_pairs = np.array(
[
# dt, gt, pd, iou, gl, pl, score,
[0.0, 0.0, 2.0, 0.25, 0.0, 0.0, 0.95],
[0.0, 0.0, 3.0, 0.33333, 0.0, 0.0, 0.9],
[0.0, 0.0, 4.0, 0.66667, 0.0, 0.0, 0.65],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.1],
[0.0, 0.0, 1.0, 0.5, 0.0, 0.0, 0.01],
# iou, score,
[0.25, 0.95],
[0.33333, 0.9],
[0.66667, 0.65],
[1.0, 0.1],
[0.5, 0.01],
]
)
sorted_identifiers = np.array(
[
# dt, gt, pd, gl, pl
[0, 0, 2, 0, 0],
[0, 0, 3, 0, 0],
[0, 0, 4, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0],
]
)

Expand All @@ -26,6 +36,7 @@ def test_pr_curve_simple():

(_, _, _, _, pr_curve) = compute_precion_recall(
sorted_pairs,
sorted_identifiers,
label_metadata=label_metadata,
iou_thresholds=iou_thresholds,
score_thresholds=score_thresholds,
Expand Down
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