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