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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import logging | |
| import unittest | |
| import torch | |
| from detectron2.modeling.box_regression import ( | |
| Box2BoxTransform, | |
| Box2BoxTransformLinear, | |
| Box2BoxTransformRotated, | |
| ) | |
| from detectron2.utils.testing import random_boxes | |
| logger = logging.getLogger(__name__) | |
| class TestBox2BoxTransform(unittest.TestCase): | |
| def test_reconstruction(self): | |
| weights = (5, 5, 10, 10) | |
| b2b_tfm = Box2BoxTransform(weights=weights) | |
| src_boxes = random_boxes(10) | |
| dst_boxes = random_boxes(10) | |
| devices = [torch.device("cpu")] | |
| if torch.cuda.is_available(): | |
| devices.append(torch.device("cuda")) | |
| for device in devices: | |
| src_boxes = src_boxes.to(device=device) | |
| dst_boxes = dst_boxes.to(device=device) | |
| deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes) | |
| dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes) | |
| self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed)) | |
| def test_apply_deltas_tracing(self): | |
| weights = (5, 5, 10, 10) | |
| b2b_tfm = Box2BoxTransform(weights=weights) | |
| with torch.no_grad(): | |
| func = torch.jit.trace(b2b_tfm.apply_deltas, (torch.randn(10, 20), torch.randn(10, 4))) | |
| o = func(torch.randn(10, 20), torch.randn(10, 4)) | |
| self.assertEqual(o.shape, (10, 20)) | |
| o = func(torch.randn(5, 20), torch.randn(5, 4)) | |
| self.assertEqual(o.shape, (5, 20)) | |
| def random_rotated_boxes(mean_box, std_length, std_angle, N): | |
| return torch.cat( | |
| [torch.rand(N, 4) * std_length, torch.rand(N, 1) * std_angle], dim=1 | |
| ) + torch.tensor(mean_box, dtype=torch.float) | |
| class TestBox2BoxTransformRotated(unittest.TestCase): | |
| def test_reconstruction(self): | |
| weights = (5, 5, 10, 10, 1) | |
| b2b_transform = Box2BoxTransformRotated(weights=weights) | |
| src_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) | |
| dst_boxes = random_rotated_boxes([10, 10, 20, 20, -30], 5, 60.0, 10) | |
| devices = [torch.device("cpu")] | |
| if torch.cuda.is_available(): | |
| devices.append(torch.device("cuda")) | |
| for device in devices: | |
| src_boxes = src_boxes.to(device=device) | |
| dst_boxes = dst_boxes.to(device=device) | |
| deltas = b2b_transform.get_deltas(src_boxes, dst_boxes) | |
| dst_boxes_reconstructed = b2b_transform.apply_deltas(deltas, src_boxes) | |
| assert torch.allclose(dst_boxes[:, :4], dst_boxes_reconstructed[:, :4], atol=1e-5) | |
| # angle difference has to be normalized | |
| assert torch.allclose( | |
| (dst_boxes[:, 4] - dst_boxes_reconstructed[:, 4] + 180.0) % 360.0 - 180.0, | |
| torch.zeros_like(dst_boxes[:, 4]), | |
| atol=1e-4, | |
| ) | |
| class TestBox2BoxTransformLinear(unittest.TestCase): | |
| def test_reconstruction(self): | |
| b2b_tfm = Box2BoxTransformLinear() | |
| src_boxes = random_boxes(10) | |
| dst_boxes = torch.tensor([0, 0, 101, 101] * 10).reshape(10, 4).float() | |
| devices = [torch.device("cpu")] | |
| if torch.cuda.is_available(): | |
| devices.append(torch.device("cuda")) | |
| for device in devices: | |
| src_boxes = src_boxes.to(device=device) | |
| dst_boxes = dst_boxes.to(device=device) | |
| deltas = b2b_tfm.get_deltas(src_boxes, dst_boxes) | |
| dst_boxes_reconstructed = b2b_tfm.apply_deltas(deltas, src_boxes) | |
| self.assertTrue(torch.allclose(dst_boxes, dst_boxes_reconstructed, atol=1e-3)) | |
| if __name__ == "__main__": | |
| unittest.main() | |