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render/renderutils/tests/test_loss.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 = 8
+DTYPE = torch.float32
+
+def tonemap_srgb(f):
+    return torch.where(f > 0.0031308, torch.pow(torch.clamp(f, min=0.0031308), 1.0/2.4)*1.055 - 0.055, 12.92*f)
+
+def l1(output, target):
+    x = torch.clamp(output, min=0, max=65535)
+    r = torch.clamp(target, min=0, max=65535)
+    x = tonemap_srgb(torch.log(x + 1))
+    r = tonemap_srgb(torch.log(r + 1))
+    return torch.nn.functional.l1_loss(x,r)
+
+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_loss(loss, tonemapper):
+	img_cuda = torch.rand(1, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
+	img_ref = img_cuda.clone().detach().requires_grad_(True)
+	target_cuda = torch.rand(1, RES, RES, 3, dtype=DTYPE, device='cuda', requires_grad=True)
+	target_ref = target_cuda.clone().detach().requires_grad_(True)
+
+	ref_loss = ru.image_loss(img_ref, target_ref, loss=loss, tonemapper=tonemapper, use_python=True)
+	ref_loss.backward()
+
+	cuda_loss = ru.image_loss(img_cuda, target_cuda, loss=loss, tonemapper=tonemapper)
+	cuda_loss.backward()
+
+	print("-------------------------------------------------------------")
+	print("    Loss: %s, %s" % (loss, tonemapper))
+	print("-------------------------------------------------------------")
+
+	relative_loss("res:", ref_loss, cuda_loss)
+	relative_loss("img:", img_ref.grad, img_cuda.grad)
+	relative_loss("target:", target_ref.grad, target_cuda.grad)
+
+
+test_loss('l1', 'none')
+test_loss('l1', 'log_srgb')
+test_loss('mse', 'log_srgb')
+test_loss('smape', 'none')
+test_loss('relmse', 'none')
+test_loss('mse', 'none')