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118
ML_tests/Object_detection_tests/iou_test.py Normal file
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import sys
import unittest
import torch
sys.path.append("ML/Pytorch/object_detection/metrics/")
from iou import intersection_over_union
class TestIntersectionOverUnion(unittest.TestCase):
def setUp(self):
# test cases we want to run
self.t1_box1 = torch.tensor([0.8, 0.1, 0.2, 0.2])
self.t1_box2 = torch.tensor([0.9, 0.2, 0.2, 0.2])
self.t1_correct_iou = 1 / 7
self.t2_box1 = torch.tensor([0.95, 0.6, 0.5, 0.2])
self.t2_box2 = torch.tensor([0.95, 0.7, 0.3, 0.2])
self.t2_correct_iou = 3 / 13
self.t3_box1 = torch.tensor([0.25, 0.15, 0.3, 0.1])
self.t3_box2 = torch.tensor([0.25, 0.35, 0.3, 0.1])
self.t3_correct_iou = 0
self.t4_box1 = torch.tensor([0.7, 0.95, 0.6, 0.1])
self.t4_box2 = torch.tensor([0.5, 1.15, 0.4, 0.7])
self.t4_correct_iou = 3 / 31
self.t5_box1 = torch.tensor([0.5, 0.5, 0.2, 0.2])
self.t5_box2 = torch.tensor([0.5, 0.5, 0.2, 0.2])
self.t5_correct_iou = 1
# (x1,y1,x2,y2) format
self.t6_box1 = torch.tensor([2, 2, 6, 6])
self.t6_box2 = torch.tensor([4, 4, 7, 8])
self.t6_correct_iou = 4 / 24
self.t7_box1 = torch.tensor([0, 0, 2, 2])
self.t7_box2 = torch.tensor([3, 0, 5, 2])
self.t7_correct_iou = 0
self.t8_box1 = torch.tensor([0, 0, 2, 2])
self.t8_box2 = torch.tensor([0, 3, 2, 5])
self.t8_correct_iou = 0
self.t9_box1 = torch.tensor([0, 0, 2, 2])
self.t9_box2 = torch.tensor([2, 0, 5, 2])
self.t9_correct_iou = 0
self.t10_box1 = torch.tensor([0, 0, 2, 2])
self.t10_box2 = torch.tensor([1, 1, 3, 3])
self.t10_correct_iou = 1 / 7
self.t11_box1 = torch.tensor([0, 0, 3, 2])
self.t11_box2 = torch.tensor([1, 1, 3, 3])
self.t11_correct_iou = 0.25
self.t12_bboxes1 = torch.tensor(
[
[0, 0, 2, 2],
[0, 0, 2, 2],
[0, 0, 2, 2],
[0, 0, 2, 2],
[0, 0, 2, 2],
[0, 0, 3, 2],
]
)
self.t12_bboxes2 = torch.tensor(
[
[3, 0, 5, 2],
[3, 0, 5, 2],
[0, 3, 2, 5],
[2, 0, 5, 2],
[1, 1, 3, 3],
[1, 1, 3, 3],
]
)
self.t12_correct_ious = torch.tensor([0, 0, 0, 0, 1 / 7, 0.25])
# Accept if the difference in iou is small
self.epsilon = 0.001
def test_both_inside_cell_shares_area(self):
iou = intersection_over_union(self.t1_box1, self.t1_box2, box_format="midpoint")
self.assertTrue((torch.abs(iou - self.t1_correct_iou) < self.epsilon))
def test_partially_outside_cell_shares_area(self):
iou = intersection_over_union(self.t2_box1, self.t2_box2, box_format="midpoint")
self.assertTrue((torch.abs(iou - self.t2_correct_iou) < self.epsilon))
def test_both_inside_cell_shares_no_area(self):
iou = intersection_over_union(self.t3_box1, self.t3_box2, box_format="midpoint")
self.assertTrue((torch.abs(iou - self.t3_correct_iou) < self.epsilon))
def test_midpoint_outside_cell_shares_area(self):
iou = intersection_over_union(self.t4_box1, self.t4_box2, box_format="midpoint")
self.assertTrue((torch.abs(iou - self.t4_correct_iou) < self.epsilon))
def test_both_inside_cell_shares_entire_area(self):
iou = intersection_over_union(self.t5_box1, self.t5_box2, box_format="midpoint")
self.assertTrue((torch.abs(iou - self.t5_correct_iou) < self.epsilon))
def test_box_format_x1_y1_x2_y2(self):
iou = intersection_over_union(self.t6_box1, self.t6_box2, box_format="corners")
self.assertTrue((torch.abs(iou - self.t6_correct_iou) < self.epsilon))
def test_additional_and_batch(self):
ious = intersection_over_union(
self.t12_bboxes1, self.t12_bboxes2, box_format="corners"
)
all_true = torch.all(
torch.abs(self.t12_correct_ious - ious.squeeze(1)) < self.epsilon
)
self.assertTrue(all_true)
if __name__ == "__main__":
print("Running Intersection Over Union Tests:")
unittest.main()

115
ML_tests/Object_detection_tests/map_test.py Executable file
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import sys
import unittest
import torch
sys.path.append("ML/Pytorch/object_detection/metrics/")
from mean_avg_precision import mean_average_precision
class TestMeanAveragePrecision(unittest.TestCase):
def setUp(self):
# test cases we want to run
self.t1_preds = [
[0, 0, 0.9, 0.55, 0.2, 0.3, 0.2],
[0, 0, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 0, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t1_targets = [
[0, 0, 0.9, 0.55, 0.2, 0.3, 0.2],
[0, 0, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 0, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t1_correct_mAP = 1
self.t2_preds = [
[1, 0, 0.9, 0.55, 0.2, 0.3, 0.2],
[0, 0, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 0, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t2_targets = [
[1, 0, 0.9, 0.55, 0.2, 0.3, 0.2],
[0, 0, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 0, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t2_correct_mAP = 1
self.t3_preds = [
[0, 1, 0.9, 0.55, 0.2, 0.3, 0.2],
[0, 1, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 1, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t3_targets = [
[0, 0, 0.9, 0.55, 0.2, 0.3, 0.2],
[0, 0, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 0, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t3_correct_mAP = 0
self.t4_preds = [
[0, 0, 0.9, 0.15, 0.25, 0.1, 0.1],
[0, 0, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 0, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t4_targets = [
[0, 0, 0.9, 0.55, 0.2, 0.3, 0.2],
[0, 0, 0.8, 0.35, 0.6, 0.3, 0.2],
[0, 0, 0.7, 0.8, 0.7, 0.2, 0.2],
]
self.t4_correct_mAP = 5 / 18
self.epsilon = 1e-4
def test_all_correct_one_class(self):
mean_avg_prec = mean_average_precision(
self.t1_preds,
self.t1_targets,
iou_threshold=0.5,
box_format="midpoint",
num_classes=1,
)
self.assertTrue(abs(self.t1_correct_mAP - mean_avg_prec) < self.epsilon)
def test_all_correct_batch(self):
mean_avg_prec = mean_average_precision(
self.t2_preds,
self.t2_targets,
iou_threshold=0.5,
box_format="midpoint",
num_classes=1,
)
self.assertTrue(abs(self.t2_correct_mAP - mean_avg_prec) < self.epsilon)
def test_all_wrong_class(self):
mean_avg_prec = mean_average_precision(
self.t3_preds,
self.t3_targets,
iou_threshold=0.5,
box_format="midpoint",
num_classes=2,
)
self.assertTrue(abs(self.t3_correct_mAP - mean_avg_prec) < self.epsilon)
def test_one_inaccurate_box(self):
mean_avg_prec = mean_average_precision(
self.t4_preds,
self.t4_targets,
iou_threshold=0.5,
box_format="midpoint",
num_classes=1,
)
self.assertTrue(abs(self.t4_correct_mAP - mean_avg_prec) < self.epsilon)
def test_all_wrong_class(self):
mean_avg_prec = mean_average_precision(
self.t3_preds,
self.t3_targets,
iou_threshold=0.5,
box_format="midpoint",
num_classes=2,
)
self.assertTrue(abs(self.t3_correct_mAP - mean_avg_prec) < self.epsilon)
if __name__ == "__main__":
print("Running Mean Average Precisions Tests:")
unittest.main()

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ML_tests/Object_detection_tests/nms_test.py Executable file
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import sys
import unittest
import torch
sys.path.append("ML/Pytorch/object_detection/metrics/")
from nms import nms
class TestNonMaxSuppression(unittest.TestCase):
def setUp(self):
# test cases we want to run
self.t1_boxes = [
[1, 1, 0.5, 0.45, 0.4, 0.5],
[1, 0.8, 0.5, 0.5, 0.2, 0.4],
[1, 0.7, 0.25, 0.35, 0.3, 0.1],
[1, 0.05, 0.1, 0.1, 0.1, 0.1],
]
self.c1_boxes = [[1, 1, 0.5, 0.45, 0.4, 0.5], [1, 0.7, 0.25, 0.35, 0.3, 0.1]]
self.t2_boxes = [
[1, 1, 0.5, 0.45, 0.4, 0.5],
[2, 0.9, 0.5, 0.5, 0.2, 0.4],
[1, 0.8, 0.25, 0.35, 0.3, 0.1],
[1, 0.05, 0.1, 0.1, 0.1, 0.1],
]
self.c2_boxes = [
[1, 1, 0.5, 0.45, 0.4, 0.5],
[2, 0.9, 0.5, 0.5, 0.2, 0.4],
[1, 0.8, 0.25, 0.35, 0.3, 0.1],
]
self.t3_boxes = [
[1, 0.9, 0.5, 0.45, 0.4, 0.5],
[1, 1, 0.5, 0.5, 0.2, 0.4],
[2, 0.8, 0.25, 0.35, 0.3, 0.1],
[1, 0.05, 0.1, 0.1, 0.1, 0.1],
]
self.c3_boxes = [[1, 1, 0.5, 0.5, 0.2, 0.4], [2, 0.8, 0.25, 0.35, 0.3, 0.1]]
self.t4_boxes = [
[1, 0.9, 0.5, 0.45, 0.4, 0.5],
[1, 1, 0.5, 0.5, 0.2, 0.4],
[1, 0.8, 0.25, 0.35, 0.3, 0.1],
[1, 0.05, 0.1, 0.1, 0.1, 0.1],
]
self.c4_boxes = [
[1, 0.9, 0.5, 0.45, 0.4, 0.5],
[1, 1, 0.5, 0.5, 0.2, 0.4],
[1, 0.8, 0.25, 0.35, 0.3, 0.1],
]
def test_remove_on_iou(self):
bboxes = nms(
self.t1_boxes,
threshold=0.2,
iou_threshold=7 / 20,
box_format="midpoint",
)
self.assertTrue(sorted(bboxes) == sorted(self.c1_boxes))
def test_keep_on_class(self):
bboxes = nms(
self.t2_boxes,
threshold=0.2,
iou_threshold=7 / 20,
box_format="midpoint",
)
self.assertTrue(sorted(bboxes) == sorted(self.c2_boxes))
def test_remove_on_iou_and_class(self):
bboxes = nms(
self.t3_boxes,
threshold=0.2,
iou_threshold=7 / 20,
box_format="midpoint",
)
self.assertTrue(sorted(bboxes) == sorted(self.c3_boxes))
def test_keep_on_iou(self):
bboxes = nms(
self.t4_boxes,
threshold=0.2,
iou_threshold=9 / 20,
box_format="midpoint",
)
self.assertTrue(sorted(bboxes) == sorted(self.c4_boxes))
if __name__ == "__main__":
print("Running Non Max Suppression Tests:")
unittest.main()