- #PERMUTE APP MERGE FOR FREE#
- #PERMUTE APP MERGE DRIVER#
- #PERMUTE APP MERGE FULL#
- #PERMUTE APP MERGE WINDOWS#
The editor offers many handy tools for flexible video editing. Filmed several episodes from the holiday and want to combine them into a single film? VideoVinci will allow you to do this beautifully and neatly.
#PERMUTE APP MERGE FOR FREE#
Free Video Editor will help you merge videos online for free and without registration. Now there are many services for this task. Users often want to merge videos online for free. If there is anything else I can do to help clarify the problem, please don't hesitate to ask.What is video merging and why you might need it Loading model (path = C:#\permute_net.onnx).īinding (device = CPU, iteration = 1, inputBinding = CPU, inputDataType = Tensor, deviceCreationLocation = WinML).Įvaluating (device = CPU, iteration = 1, inputBinding = CPU, inputDataType = Tensor, deviceCreationLocation = WinML).Ĭreating Session with GPU: NVIDIA TITAN VĮxception during initialization: onecoreuap\windows\windowsai\winml\dll\mloperatorauthorimpl.cpp(1260)\Windows.AI.MachineLearning.dll!00007FF8FBE380A4: (caller: 00007FF8FBE44EBE) Exception(3) tid(2c64) 80070057 The parameter is incorrect. When running the permute net on the new version of the WinMLRunner tool, I receive this similar result:Ĭ:#\Downloads\WinMLRunner\圆4_release>WinMLRunner.exe -model C:#\source\repos\permute_net.onnx
#PERMUTE APP MERGE FULL#
When the full network is run on alternative onnx backends, the network acts as expected.
Instead, the permute acts improperly and causes an output that looks like the second picture. Supposing that the top image was tiled 4 times, the permutation followed by a reshape should result in a large version of the image. When run through the CPU, instead of outputting the expected image composited of r^2 subimages, it mixes them in a non-expected manner, indicating that the permutation is not acting as it is supposed to. The false result is related to a subpixel convolution layer. CoreML, Scikit-learn, …), Torch, but the network is untrained.
#PERMUTE APP MERGE DRIVER#
Graphics Driver version: 419.67 on Titan V. OS Version (Server, IoT Core, Desktop, etc): Desktop
#PERMUTE APP MERGE WINDOWS#
EnvironmentĪpp min and target version: Universal Windows, Windows 10, Version 1809. I have also run these networks through the actual WinML process in both a C++ and a C# app as described on the windows webpage, with similar failing results.
Here is the message received on a GPU run of permute_net.onnx:Įxception during initialization: onecoreuap\windows\windowsai\winml\dll\mloperatorauthorimpl.cpp(1260)\Windows.AI.MachineLearning.dll!00007FFAF52E80A4: (caller: 00007FFAF52F4EBE) Exception(3) tid(3234) 80070057 The parameter is incorrect. Even the success results in faulty output. Simply run each network in the WinML dashboard and observe that the transpose one runs fine on CPU and GPU, while the permute_net file "Succeeds" on cpu and fails outright on GPU. I have attached two onnx files, one titled permute_net.onnx and one titled transpose_net.onnx. Minimal reproduction of the problem with instructions The expected behavior would be that WinML is able to perform permutations as described in the ONNX guidelines. Specifically, permutations with only one axis swap (akin to a simple transpose) work fine, while permutations with more than one axis swap fail outright. When attempting to perform a Permutation that is allowable under the ONNX guidelines, WindowsML returns a false result on CPU and outright fails to run on GPU. Bug report (I searched for similar issues and did not find one) Current behavior
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