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