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Running
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Zero
| # 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()) | |
| 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() | |