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render/renderutils/tests/test_cubemap.py

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+# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. 
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction, 
+# disclosure or distribution of this material and related documentation 
+# without an express license agreement from NVIDIA CORPORATION or 
+# its affiliates is strictly prohibited.
+
+import torch
+
+import os
+import sys
+sys.path.insert(0, os.path.join(sys.path[0], '../..'))
+import renderutils as ru
+
+RES = 4
+DTYPE = torch.float32
+
+def relative_loss(name, ref, cuda):
+	ref = ref.float()
+	cuda = cuda.float()
+	print(name, torch.max(torch.abs(ref - cuda) / torch.abs(ref + 1e-7)).item())
+
+def test_cubemap():
+	cubemap_cuda = torch.rand(6, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
+	cubemap_ref = cubemap_cuda.clone().detach().requires_grad_(True)
+	weights = torch.rand(3, 3, 1, dtype=DTYPE, device='cuda')
+	target = torch.rand(6, RES, RES, 3, dtype=DTYPE, device='cuda')
+
+	ref = ru.filter_cubemap(cubemap_ref, weights, use_python=True)
+	ref_loss = torch.nn.MSELoss()(ref, target)
+	ref_loss.backward()
+
+	cuda = ru.filter_cubemap(cubemap_cuda, weights, use_python=False)
+	cuda_loss = torch.nn.MSELoss()(cuda, target)
+	cuda_loss.backward()
+
+	print("-------------------------------------------------------------")
+	print("    Cubemap:")
+	print("-------------------------------------------------------------")
+
+	relative_loss("flt:", ref, cuda)
+	relative_loss("cubemap:", cubemap_ref.grad, cubemap_cuda.grad)
+
+
+test_cubemap()