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cameras_from_opencv_projection device #1021
Summary: Fix https://github.com/facebookresearch/pytorch3d/issues/1021 that cameras_from_opencv_projection always creates on CPU. Reviewed By: nikhilaravi Differential Revision: D33508211 fbshipit-source-id: fadebd45cacafd633af6a58094cf6f654529992c
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@ -55,6 +55,7 @@ def _cameras_from_opencv_projection(
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focal_length=focal_pytorch3d,
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principal_point=p0_pytorch3d,
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image_size=image_size,
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device=R.device,
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)
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@ -68,7 +68,7 @@ class TestCameraConversions(TestCaseMixin, unittest.TestCase):
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return correct projections of random 3D points. The check is done
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against a set of results precomuted using `cv2.projectPoints` function.
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"""
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device = torch.device("cuda:0")
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image_size = [[480, 640]] * 4
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R = [
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[
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@ -116,17 +116,19 @@ class TestCameraConversions(TestCaseMixin, unittest.TestCase):
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]
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principal_point, focal_length, R, tvec, image_size = [
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torch.FloatTensor(x)
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torch.tensor(x, device=device)
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for x in (principal_point, focal_length, R, tvec, image_size)
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]
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camera_matrix = eyes(dim=3, N=4)
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camera_matrix = eyes(dim=3, N=4, device=device)
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camera_matrix[:, 0, 0], camera_matrix[:, 1, 1] = (
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focal_length[:, 0],
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focal_length[:, 1],
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)
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camera_matrix[:, :2, 2] = principal_point
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pts = torch.nn.functional.normalize(torch.randn(4, 1000, 3), dim=-1)
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pts = torch.nn.functional.normalize(
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torch.randn(4, 1000, 3, device=device), dim=-1
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)
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# project the 3D points with the opencv projection function
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rvec = so3_log_map(R)
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@ -136,6 +138,7 @@ class TestCameraConversions(TestCaseMixin, unittest.TestCase):
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cameras_opencv_to_pytorch3d = cameras_from_opencv_projection(
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R, tvec, camera_matrix, image_size
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)
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self.assertEqual(cameras_opencv_to_pytorch3d.device, device)
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# project the 3D points with converted cameras to screen space.
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pts_proj_pytorch3d_screen = cameras_opencv_to_pytorch3d.transform_points_screen(
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