|
|
|
import json |
|
import math |
|
import numpy as np |
|
import unittest |
|
import torch |
|
|
|
from detectron2.structures import Boxes, BoxMode, pairwise_iou |
|
|
|
|
|
class TestBoxMode(unittest.TestCase): |
|
def _convert_xy_to_wh(self, x): |
|
return BoxMode.convert(x, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) |
|
|
|
def _convert_xywha_to_xyxy(self, x): |
|
return BoxMode.convert(x, BoxMode.XYWHA_ABS, BoxMode.XYXY_ABS) |
|
|
|
def _convert_xywh_to_xywha(self, x): |
|
return BoxMode.convert(x, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS) |
|
|
|
def test_box_convert_list(self): |
|
for tp in [list, tuple]: |
|
box = tp([5.0, 5.0, 10.0, 10.0]) |
|
output = self._convert_xy_to_wh(box) |
|
self.assertIsInstance(output, tp) |
|
self.assertIsInstance(output[0], float) |
|
self.assertEqual(output, tp([5.0, 5.0, 5.0, 5.0])) |
|
|
|
with self.assertRaises(Exception): |
|
self._convert_xy_to_wh([box]) |
|
|
|
def test_box_convert_array(self): |
|
box = np.asarray([[5, 5, 10, 10], [1, 1, 2, 3]]) |
|
output = self._convert_xy_to_wh(box) |
|
self.assertEqual(output.dtype, box.dtype) |
|
self.assertEqual(output.shape, box.shape) |
|
self.assertTrue((output[0] == [5, 5, 5, 5]).all()) |
|
self.assertTrue((output[1] == [1, 1, 1, 2]).all()) |
|
|
|
def test_box_convert_cpu_tensor(self): |
|
box = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]) |
|
output = self._convert_xy_to_wh(box) |
|
self.assertEqual(output.dtype, box.dtype) |
|
self.assertEqual(output.shape, box.shape) |
|
output = output.numpy() |
|
self.assertTrue((output[0] == [5, 5, 5, 5]).all()) |
|
self.assertTrue((output[1] == [1, 1, 1, 2]).all()) |
|
|
|
@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") |
|
def test_box_convert_cuda_tensor(self): |
|
box = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]).cuda() |
|
output = self._convert_xy_to_wh(box) |
|
self.assertEqual(output.dtype, box.dtype) |
|
self.assertEqual(output.shape, box.shape) |
|
self.assertEqual(output.device, box.device) |
|
output = output.cpu().numpy() |
|
self.assertTrue((output[0] == [5, 5, 5, 5]).all()) |
|
self.assertTrue((output[1] == [1, 1, 1, 2]).all()) |
|
|
|
def test_box_convert_xywha_to_xyxy_list(self): |
|
for tp in [list, tuple]: |
|
box = tp([50, 50, 30, 20, 0]) |
|
output = self._convert_xywha_to_xyxy(box) |
|
self.assertIsInstance(output, tp) |
|
self.assertEqual(output, tp([35, 40, 65, 60])) |
|
|
|
with self.assertRaises(Exception): |
|
self._convert_xywha_to_xyxy([box]) |
|
|
|
def test_box_convert_xywha_to_xyxy_array(self): |
|
for dtype in [np.float64, np.float32]: |
|
box = np.asarray( |
|
[ |
|
[50, 50, 30, 20, 0], |
|
[50, 50, 30, 20, 90], |
|
[1, 1, math.sqrt(2), math.sqrt(2), -45], |
|
], |
|
dtype=dtype, |
|
) |
|
output = self._convert_xywha_to_xyxy(box) |
|
self.assertEqual(output.dtype, box.dtype) |
|
expected = np.asarray([[35, 40, 65, 60], [40, 35, 60, 65], [0, 0, 2, 2]], dtype=dtype) |
|
self.assertTrue(np.allclose(output, expected, atol=1e-6), "output={}".format(output)) |
|
|
|
def test_box_convert_xywha_to_xyxy_tensor(self): |
|
for dtype in [torch.float32, torch.float64]: |
|
box = torch.tensor( |
|
[ |
|
[50, 50, 30, 20, 0], |
|
[50, 50, 30, 20, 90], |
|
[1, 1, math.sqrt(2), math.sqrt(2), -45], |
|
], |
|
dtype=dtype, |
|
) |
|
output = self._convert_xywha_to_xyxy(box) |
|
self.assertEqual(output.dtype, box.dtype) |
|
expected = torch.tensor([[35, 40, 65, 60], [40, 35, 60, 65], [0, 0, 2, 2]], dtype=dtype) |
|
|
|
self.assertTrue(torch.allclose(output, expected, atol=1e-6), "output={}".format(output)) |
|
|
|
def test_box_convert_xywh_to_xywha_list(self): |
|
for tp in [list, tuple]: |
|
box = tp([50, 50, 30, 20]) |
|
output = self._convert_xywh_to_xywha(box) |
|
self.assertIsInstance(output, tp) |
|
self.assertEqual(output, tp([65, 60, 30, 20, 0])) |
|
|
|
with self.assertRaises(Exception): |
|
self._convert_xywh_to_xywha([box]) |
|
|
|
def test_box_convert_xywh_to_xywha_array(self): |
|
for dtype in [np.float64, np.float32]: |
|
box = np.asarray([[30, 40, 70, 60], [30, 40, 60, 70], [-1, -1, 2, 2]], dtype=dtype) |
|
output = self._convert_xywh_to_xywha(box) |
|
self.assertEqual(output.dtype, box.dtype) |
|
expected = np.asarray( |
|
[[65, 70, 70, 60, 0], [60, 75, 60, 70, 0], [0, 0, 2, 2, 0]], dtype=dtype |
|
) |
|
self.assertTrue(np.allclose(output, expected, atol=1e-6), "output={}".format(output)) |
|
|
|
def test_box_convert_xywh_to_xywha_tensor(self): |
|
for dtype in [torch.float32, torch.float64]: |
|
box = torch.tensor([[30, 40, 70, 60], [30, 40, 60, 70], [-1, -1, 2, 2]], dtype=dtype) |
|
output = self._convert_xywh_to_xywha(box) |
|
self.assertEqual(output.dtype, box.dtype) |
|
expected = torch.tensor( |
|
[[65, 70, 70, 60, 0], [60, 75, 60, 70, 0], [0, 0, 2, 2, 0]], dtype=dtype |
|
) |
|
|
|
self.assertTrue(torch.allclose(output, expected, atol=1e-6), "output={}".format(output)) |
|
|
|
def test_json_serializable(self): |
|
payload = {"box_mode": BoxMode.XYWH_REL} |
|
try: |
|
json.dumps(payload) |
|
except Exception: |
|
self.fail("JSON serialization failed") |
|
|
|
def test_json_deserializable(self): |
|
payload = '{"box_mode": 2}' |
|
obj = json.loads(payload) |
|
try: |
|
obj["box_mode"] = BoxMode(obj["box_mode"]) |
|
except Exception: |
|
self.fail("JSON deserialization failed") |
|
|
|
|
|
class TestBoxIOU(unittest.TestCase): |
|
def test_pairwise_iou(self): |
|
boxes1 = torch.tensor([[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]) |
|
|
|
boxes2 = torch.tensor( |
|
[ |
|
[0.0, 0.0, 1.0, 1.0], |
|
[0.0, 0.0, 0.5, 1.0], |
|
[0.0, 0.0, 1.0, 0.5], |
|
[0.0, 0.0, 0.5, 0.5], |
|
[0.5, 0.5, 1.0, 1.0], |
|
[0.5, 0.5, 1.5, 1.5], |
|
] |
|
) |
|
|
|
expected_ious = torch.tensor( |
|
[ |
|
[1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], |
|
[1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], |
|
] |
|
) |
|
|
|
ious = pairwise_iou(Boxes(boxes1), Boxes(boxes2)) |
|
|
|
self.assertTrue(torch.allclose(ious, expected_ious)) |
|
|
|
|
|
class TestBoxes(unittest.TestCase): |
|
def test_empty_cat(self): |
|
x = Boxes.cat([]) |
|
self.assertTrue(x.tensor.shape, (0, 4)) |
|
|
|
|
|
if __name__ == "__main__": |
|
unittest.main() |
|
|
