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# Copyright (c) Facebook, Inc. and its affiliates.
import json
import math
import numpy as np
import unittest
import torch
from detectron2.structures import Boxes, BoxMode, pairwise_ioa, pairwise_iou
from detectron2.utils.testing import reload_script_model
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_convert_int_mode(self):
BoxMode.convert([1, 2, 3, 4], 0, 1)
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 create_boxes(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],
]
)
return boxes1, boxes2
def test_pairwise_iou(self):
boxes1, boxes2 = self.create_boxes()
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))
def test_pairwise_ioa(self):
boxes1, boxes2 = self.create_boxes()
expected_ioas = torch.tensor(
[[1.0, 1.0, 1.0, 1.0, 1.0, 0.25], [1.0, 1.0, 1.0, 1.0, 1.0, 0.25]]
)
ioas = pairwise_ioa(Boxes(boxes1), Boxes(boxes2))
self.assertTrue(torch.allclose(ioas, expected_ioas))
class TestBoxes(unittest.TestCase):
def test_empty_cat(self):
x = Boxes.cat([])
self.assertTrue(x.tensor.shape, (0, 4))
def test_to(self):
x = Boxes(torch.rand(3, 4))
self.assertEqual(x.to(device="cpu").tensor.device.type, "cpu")
def test_scriptability(self):
def func(x):
boxes = Boxes(x)
test = boxes.to(torch.device("cpu")).tensor
return boxes.area(), test
f = torch.jit.script(func)
f = reload_script_model(f)
f(torch.rand((3, 4)))
data = torch.rand((3, 4))
def func_cat(x: torch.Tensor):
boxes1 = Boxes(x)
boxes2 = Boxes(x)
# boxes3 = Boxes.cat([boxes1, boxes2]) # this is not supported by torchsript for now.
boxes3 = boxes1.cat([boxes1, boxes2])
return boxes3
f = torch.jit.script(func_cat)
script_box = f(data)
self.assertTrue(torch.equal(torch.cat([data, data]), script_box.tensor))
if __name__ == "__main__":
unittest.main()
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