Failed to create swapchain since recent commit

Wine 7.20
Nvidia GTX1050TI
Driver 525.116.04
Since https://github.com/doitsujin/dxvk/commit/5a1ebfa4ee014ca896adebeb0244ad357ed449c0, every game will crash certainly.
Log

info:  Game: iw6sp64_ship.exe
info:  DXVK: v2.2-89-ga287566c
info:  Found config file: dxvk.conf
info:  Effective configuration:
info:    d3d11.maxTessFactor = 8
info:    dxgi.maxFrameRate = 60
info:    d3d11.samplerAnisotropy = 16
info:    d3d9.samplerAnisotropy = 16
info:  Vulkan: Found vkGetInstanceProcAddr in winevulkan.dll @ 0x65dc4120
info:  Built-in extension providers:
info:    Win32 WSI
info:    OpenVR
info:    OpenXR
info:  OpenVR: could not open registry key, status 2
info:  OpenVR: Failed to locate module
info:  Enabled instance extensions:
info:    VK_KHR_get_surface_capabilities2
info:    VK_KHR_surface
info:    VK_KHR_win32_surface
[obs-vkcapture] Init Vulkan 1.4.0
warn:  Skipping CPU adapter: llvmpipe (LLVM 15.0.6, 256 bits)
info:  NVIDIA GeForce GTX 1050 Ti:
info:    Driver : NVIDIA 525.116.4
info:    Memory Heap[0]:
info:      Size: 4096 MiB
info:      Flags: 0x1
info:      Memory Type[7]: Property Flags = 0x1
info:    Memory Heap[1]:
info:      Size: 11916 MiB
info:      Flags: 0x0
info:      Memory Type[0]: Property Flags = 0x0
info:      Memory Type[1]: Property Flags = 0x0
info:      Memory Type[2]: Property Flags = 0x0
info:      Memory Type[3]: Property Flags = 0x0
info:      Memory Type[4]: Property Flags = 0x0
info:      Memory Type[5]: Property Flags = 0x0
info:      Memory Type[6]: Property Flags = 0x0
info:      Memory Type[8]: Property Flags = 0x6
info:      Memory Type[9]: Property Flags = 0xe
info:    Memory Heap[2]:
info:      Size: 246 MiB
info:      Flags: 0x1
info:      Memory Type[10]: Property Flags = 0x7
info:  Intel(R) HD Graphics 630 (KBL GT2):
info:    Driver : Intel open-source Mesa driver 22.3.5
info:    Memory Heap[0]:
info:      Size: 11916 MiB
info:      Flags: 0x1
info:      Memory Type[0]: Property Flags = 0xf
013c:fixme:system:NtUserSystemParametersInfo Unimplemented action: 59 (SPI_SETSTICKYKEYS)
013c:fixme:system:NtUserSystemParametersInfo Unimplemented action: 53 (SPI_SETTOGGLEKEYS)
013c:fixme:system:NtUserSystemParametersInfo Unimplemented action: 51 (SPI_SETFILTERKEYS)
info:  Game: iw6sp64_ship.exe
info:  DXVK: v2.2-89-ga287566c
info:  Found config file: dxvk.conf
info:  Effective configuration:
info:    d3d11.maxTessFactor = 8
info:    dxgi.maxFrameRate = 60
info:    d3d11.samplerAnisotropy = 16
info:    d3d9.samplerAnisotropy = 16
info:  Vulkan: Found vkGetInstanceProcAddr in winevulkan.dll @ 0x65dc4120
info:  Built-in extension providers:
info:    Win32 WSI
info:    OpenVR
info:    OpenXR
info:  OpenVR: could not open registry key, status 2
info:  OpenVR: Failed to locate module
info:  Enabled instance extensions:
info:    VK_KHR_get_surface_capabilities2
info:    VK_KHR_surface
info:    VK_KHR_win32_surface
[obs-vkcapture] Init Vulkan 1.4.0
warn:  Skipping CPU adapter: llvmpipe (LLVM 15.0.6, 256 bits)
info:  NVIDIA GeForce GTX 1050 Ti:
info:    Driver : NVIDIA 525.116.4
info:    Memory Heap[0]:
info:      Size: 4096 MiB
info:      Flags: 0x1
info:      Memory Type[7]: Property Flags = 0x1
info:    Memory Heap[1]:
info:      Size: 11916 MiB
info:      Flags: 0x0
info:      Memory Type[0]: Property Flags = 0x0
info:      Memory Type[1]: Property Flags = 0x0
info:      Memory Type[2]: Property Flags = 0x0
info:      Memory Type[3]: Property Flags = 0x0
info:      Memory Type[4]: Property Flags = 0x0
info:      Memory Type[5]: Property Flags = 0x0
info:      Memory Type[6]: Property Flags = 0x0
info:      Memory Type[8]: Property Flags = 0x6
info:      Memory Type[9]: Property Flags = 0xe
info:    Memory Heap[2]:
info:      Size: 246 MiB
info:      Flags: 0x1
info:      Memory Type[10]: Property Flags = 0x7
info:  Intel(R) HD Graphics 630 (KBL GT2):
info:    Driver : Intel open-source Mesa driver 22.3.5
info:    Memory Heap[0]:
info:      Size: 11916 MiB
info:      Flags: 0x1
info:      Memory Type[0]: Property Flags = 0xf
013c:fixme:system:DisplayConfigGetDeviceInfo DISPLAYCONFIG_DEVICE_INFO_GET_TARGET_NAME: stub
err:   getMonitorDevicePath: DisplayConfigGetDeviceInfo with DISPLAYCONFIG_DEVICE_INFO_GET_TARGET_NAME failed. ret: 50 LastError: 0
err:   getMonitorDevicePath: Failed to find a link from source -> target.
err:   getMonitorEdid: Failed to get monitor device path.
err:   DXGI: Failed to parse display metadata + colorimetry info, using blank.
info:  NVIDIA GeForce GTX 1050 Ti:
info:    Driver : NVIDIA 525.116.4
info:    Memory Heap[0]:
info:      Size: 4096 MiB
info:      Flags: 0x1
info:      Memory Type[7]: Property Flags = 0x1
info:    Memory Heap[1]:
info:      Size: 11916 MiB
info:      Flags: 0x0
info:      Memory Type[0]: Property Flags = 0x0
info:      Memory Type[1]: Property Flags = 0x0
info:      Memory Type[2]: Property Flags = 0x0
info:      Memory Type[3]: Property Flags = 0x0
info:      Memory Type[4]: Property Flags = 0x0
info:      Memory Type[5]: Property Flags = 0x0
info:      Memory Type[6]: Property Flags = 0x0
info:      Memory Type[8]: Property Flags = 0x6
info:      Memory Type[9]: Property Flags = 0xe
info:    Memory Heap[2]:
info:      Size: 246 MiB
info:      Flags: 0x1
info:      Memory Type[10]: Property Flags = 0x7
info:  Intel(R) HD Graphics 630 (KBL GT2):
info:    Driver : Intel open-source Mesa driver 22.3.5
info:    Memory Heap[0]:
info:      Size: 11916 MiB
info:      Flags: 0x1
info:      Memory Type[0]: Property Flags = 0xf
warn:  D3D11DeviceFeatures: External memory features not supported
info:  D3D11CoreCreateDevice: Maximum supported feature level: D3D_FEATURE_LEVEL_12_1
info:  D3D11CoreCreateDevice: Using feature level D3D_FEATURE_LEVEL_11_0
info:  Device properties:
info:    Device : NVIDIA GeForce GTX 1050 Ti
info:    Driver : NVIDIA 525.116.4
info:  Enabled device extensions:
info:    VK_EXT_attachment_feedback_loop_layout
info:    VK_EXT_conservative_rasterization
info:    VK_EXT_custom_border_color
info:    VK_EXT_depth_clip_enable
info:    VK_EXT_fragment_shader_interlock
info:    VK_EXT_graphics_pipeline_library
info:    VK_EXT_non_seamless_cube_map
info:    VK_EXT_robustness2
info:    VK_EXT_shader_module_identifier
info:    VK_EXT_transform_feedback
info:    VK_EXT_vertex_attribute_divisor
info:    VK_KHR_pipeline_library
info:    VK_KHR_present_id
info:    VK_KHR_present_wait
info:    VK_KHR_swapchain
info:  Device features:
info:    robustBufferAccess                     : 1
info:    fullDrawIndexUint32                    : 1
info:    imageCubeArray                         : 1
info:    independentBlend                       : 1
info:    geometryShader                         : 1
info:    tessellationShader                     : 1
info:    sampleRateShading                      : 1
info:    dualSrcBlend                           : 1
info:    logicOp                                : 1
info:    multiDrawIndirect                      : 1
info:    drawIndirectFirstInstance              : 1
info:    depthClamp                             : 1
info:    depthBiasClamp                         : 1
info:    fillModeNonSolid                       : 1
info:    depthBounds                            : 1
info:    multiViewport                          : 1
info:    samplerAnisotropy                      : 1
info:    textureCompressionBC                   : 1
info:    occlusionQueryPrecise                  : 1
info:    pipelineStatisticsQuery                : 1
info:    vertexPipelineStoresAndAtomics         : 1
info:    fragmentStoresAndAtomics               : 1
info:    shaderImageGatherExtended              : 1
info:    shaderClipDistance                     : 1
info:    shaderCullDistance                     : 1
info:    shaderFloat64                          : 1
info:    shaderInt64                            : 1
info:    variableMultisampleRate                : 1
info:    shaderResourceResidency                : 1
info:    shaderResourceMinLod                   : 1
info:    sparseBinding                          : 1
info:    sparseResidencyBuffer                  : 1
info:    sparseResidencyImage2D                 : 1
info:    sparseResidencyImage3D                 : 1
info:    sparseResidency2Samples                : 1
info:    sparseResidency4Samples                : 1
info:    sparseResidency8Samples                : 1
info:    sparseResidency16Samples               : 1
info:    sparseResidencyAliased                 : 1
info:  Vulkan 1.1
info:    shaderDrawParameters                   : 1
info:  Vulkan 1.2
info:    samplerMirrorClampToEdge               : 1
info:    drawIndirectCount                      : 1
info:    samplerFilterMinmax                    : 1
info:    hostQueryReset                         : 1
info:    timelineSemaphore                      : 1
info:    bufferDeviceAddress                    : 0
info:    shaderOutputViewportIndex              : 1
info:    shaderOutputLayer                      : 1
info:    vulkanMemoryModel                      : 1
info:  Vulkan 1.3
info:    robustImageAccess                      : 0
info:    pipelineCreationCacheControl           : 1
info:    shaderDemoteToHelperInvocation         : 1
info:    shaderZeroInitializeWorkgroupMemory    : 0
info:    synchronization2                       : 1
info:    dynamicRendering                       : 1
info:  VK_AMD_shader_fragment_mask
info:    extension supported                    : 0
info:  VK_EXT_attachment_feedback_loop_layout
info:    attachmentFeedbackLoopLayout           : 0
info:  VK_EXT_conservative_rasterization
info:    extension supported                    : 1
info:  VK_EXT_custom_border_color
info:    customBorderColors                     : 1
info:    customBorderColorWithoutFormat         : 1
info:  VK_EXT_depth_clip_enable
info:    depthClipEnable                        : 1
info:  VK_EXT_depth_bias_control
info:    depthBiasControl                       : 0
info:    leastRepresentableValueForceUnormRepresentation : 0
info:    floatRepresentation                    : 0
info:    depthBiasExact                         : 0
info:  VK_EXT_extended_dynamic_state3
info:    extDynamicState3AlphaToCoverageEnable  : 0
info:    extDynamicState3DepthClipEnable        : 0
info:    extDynamicState3RasterizationSamples   : 0
info:    extDynamicState3SampleMask             : 0
info:  VK_EXT_fragment_shader_interlock
info:    fragmentShaderSampleInterlock          : 1
info:    fragmentShaderPixelInterlock           : 1
info:  VK_EXT_full_screen_exclusive
info:    extension supported                    : 0
info:  VK_EXT_graphics_pipeline_library
info:    graphicsPipelineLibrary                : 1
info:  VK_EXT_memory_budget
info:    extension supported                    : 1
info:  VK_EXT_memory_priority
info:    memoryPriority                         : 0
info:  VK_EXT_non_seamless_cube_map
info:    nonSeamlessCubeMap                     : 0
info:  VK_EXT_robustness2
info:    robustBufferAccess2                    : 1
info:    robustImageAccess2                     : 1
info:    nullDescriptor                         : 1
info:  VK_EXT_shader_module_identifier
info:    shaderModuleIdentifier                 : 1
info:  VK_EXT_shader_stencil_export
info:    extension supported                    : 0
info:  VK_EXT_swapchain_colorspace
info:    extension supported                    : 0
info:  VK_EXT_swapchain_maintenance1
info:    swapchainMaintenance1                  : 0
info:  VK_EXT_hdr_metadata
info:    extension supported                    : 0
info:  VK_EXT_transform_feedback
info:    transformFeedback                      : 1
info:    geometryStreams                        : 1
info:  VK_EXT_vertex_attribute_divisor
info:    vertexAttributeInstanceRateDivisor     : 1
info:    vertexAttributeInstanceRateZeroDivisor : 1
info:  VK_KHR_external_memory_win32
info:    extension supported                    : 0
info:  VK_KHR_external_semaphore_win32
info:    extension supported                    : 0
info:  VK_KHR_present_id
info:    presentId                              : 0
info:  VK_KHR_present_wait
info:    presentWait                            : 0
info:  VK_NVX_binary_import
info:    extension supported                    : 0
info:  VK_NVX_image_view_handle
info:    extension supported                    : 0
info:  Queue families:
info:    Graphics : 0
info:    Transfer : 1
info:    Sparse   : 0
info:  Memory type mask for sparse resources: 0x80
info:  DXVK: Read 6682 valid state cache entries
info:  DXVK: Graphics pipeline libraries supported
013c:fixme:system:DisplayConfigGetDeviceInfo DISPLAYCONFIG_DEVICE_INFO_GET_TARGET_NAME: stub
err:   getMonitorDevicePath: DisplayConfigGetDeviceInfo with DISPLAYCONFIG_DEVICE_INFO_GET_TARGET_NAME failed. ret: 50 LastError: 0
err:   getMonitorDevicePath: Failed to find a link from source -> target.
err:   getMonitorEdid: Failed to get monitor device path.
err:   DXGI: Failed to parse display metadata + colorimetry info, using blank.
info:  D3D11CoreCreateDevice: Maximum supported feature level: D3D_FEATURE_LEVEL_12_1
info:  D3D11CoreCreateDevice: Using feature level D3D_FEATURE_LEVEL_11_0
info:  Device properties:
info:    Device : NVIDIA GeForce GTX 1050 Ti
info:    Driver : NVIDIA 525.116.4
info:  Enabled device extensions:
info:    VK_EXT_attachment_feedback_loop_layout
info:    VK_EXT_conservative_rasterization
info:    VK_EXT_custom_border_color
info:    VK_EXT_depth_clip_enable
info:    VK_EXT_fragment_shader_interlock
info:    VK_EXT_graphics_pipeline_library
info:    VK_EXT_non_seamless_cube_map
info:    VK_EXT_robustness2
info:    VK_EXT_shader_module_identifier
info:    VK_EXT_transform_feedback
info:    VK_EXT_vertex_attribute_divisor
info:    VK_KHR_pipeline_library
info:    VK_KHR_present_id
info:    VK_KHR_present_wait
info:    VK_KHR_swapchain
info:  Device features:
info:    robustBufferAccess                     : 1
info:    fullDrawIndexUint32                    : 1
info:    imageCubeArray                         : 1
info:    independentBlend                       : 1
info:    geometryShader                         : 1
info:    tessellationShader                     : 1
info:    sampleRateShading                      : 1
info:    dualSrcBlend                           : 1
info:    logicOp                                : 1
info:    multiDrawIndirect                      : 1
info:    drawIndirectFirstInstance              : 1
info:    depthClamp                             : 1
info:    depthBiasClamp                         : 1
info:    fillModeNonSolid                       : 1
info:    depthBounds                            : 1
info:    multiViewport                          : 1
info:    samplerAnisotropy                      : 1
info:    textureCompressionBC                   : 1
info:    occlusionQueryPrecise                  : 1
info:    pipelineStatisticsQuery                : 1
info:    vertexPipelineStoresAndAtomics         : 1
info:    fragmentStoresAndAtomics               : 1
info:    shaderImageGatherExtended              : 1
info:    shaderClipDistance                     : 1
info:    shaderCullDistance                     : 1
info:    shaderFloat64                          : 1
info:    shaderInt64                            : 1
info:    variableMultisampleRate                : 1
info:    shaderResourceResidency                : 1
info:    shaderResourceMinLod                   : 1
info:    sparseBinding                          : 1
info:    sparseResidencyBuffer                  : 1
info:    sparseResidencyImage2D                 : 1
info:    sparseResidencyImage3D                 : 1
info:    sparseResidency2Samples                : 1
info:    sparseResidency4Samples                : 1
info:    sparseResidency8Samples                : 1
info:    sparseResidency16Samples               : 1
info:    sparseResidencyAliased                 : 1
info:  Vulkan 1.1
info:    shaderDrawParameters                   : 1
info:  Vulkan 1.2
info:    samplerMirrorClampToEdge               : 1
info:    drawIndirectCount                      : 1
info:    samplerFilterMinmax                    : 1
info:    hostQueryReset                         : 1
info:    timelineSemaphore                      : 1
info:    bufferDeviceAddress                    : 0
info:    shaderOutputViewportIndex              : 1
info:    shaderOutputLayer                      : 1
info:    vulkanMemoryModel                      : 1
info:  Vulkan 1.3
info:    robustImageAccess                      : 0
info:    pipelineCreationCacheControl           : 1
info:    shaderDemoteToHelperInvocation         : 1
info:    shaderZeroInitializeWorkgroupMemory    : 0
info:    synchronization2                       : 1
info:    dynamicRendering                       : 1
info:  VK_AMD_shader_fragment_mask
info:    extension supported                    : 0
info:  VK_EXT_attachment_feedback_loop_layout
info:    attachmentFeedbackLoopLayout           : 0
info:  VK_EXT_conservative_rasterization
info:    extension supported                    : 1
info:  VK_EXT_custom_border_color
info:    customBorderColors                     : 1
info:    customBorderColorWithoutFormat         : 1
info:  VK_EXT_depth_clip_enable
info:    depthClipEnable                        : 1
info:  VK_EXT_depth_bias_control
info:    depthBiasControl                       : 0
info:    leastRepresentableValueForceUnormRepresentation : 0
info:    floatRepresentation                    : 0
info:    depthBiasExact                         : 0
info:  VK_EXT_extended_dynamic_state3
info:    extDynamicState3AlphaToCoverageEnable  : 0
info:    extDynamicState3DepthClipEnable        : 0
info:    extDynamicState3RasterizationSamples   : 0
info:    extDynamicState3SampleMask             : 0
info:  VK_EXT_fragment_shader_interlock
info:    fragmentShaderSampleInterlock          : 1
info:    fragmentShaderPixelInterlock           : 1
info:  VK_EXT_full_screen_exclusive
info:    extension supported                    : 0
info:  VK_EXT_graphics_pipeline_library
info:    graphicsPipelineLibrary                : 1
info:  VK_EXT_memory_budget
info:    extension supported                    : 1
info:  VK_EXT_memory_priority
info:    memoryPriority                         : 0
info:  VK_EXT_non_seamless_cube_map
info:    nonSeamlessCubeMap                     : 0
info:  VK_EXT_robustness2
info:    robustBufferAccess2                    : 1
info:    robustImageAccess2                     : 1
info:    nullDescriptor                         : 1
info:  VK_EXT_shader_module_identifier
info:    shaderModuleIdentifier                 : 1
info:  VK_EXT_shader_stencil_export
info:    extension supported                    : 0
info:  VK_EXT_swapchain_colorspace
info:    extension supported                    : 0
info:  VK_EXT_swapchain_maintenance1
info:    swapchainMaintenance1                  : 0
info:  VK_EXT_hdr_metadata
info:    extension supported                    : 0
info:  VK_EXT_transform_feedback
info:    transformFeedback                      : 1
info:    geometryStreams                        : 1
info:  VK_EXT_vertex_attribute_divisor
info:    vertexAttributeInstanceRateDivisor     : 1
info:    vertexAttributeInstanceRateZeroDivisor : 1
info:  VK_KHR_external_memory_win32
info:    extension supported                    : 0
info:  VK_KHR_external_semaphore_win32
info:    extension supported                    : 0
info:  VK_KHR_present_id
info:    presentId                              : 0
info:  VK_KHR_present_wait
info:    presentWait                            : 0
info:  VK_NVX_binary_import
info:    extension supported                    : 0
info:  VK_NVX_image_view_handle
info:    extension supported                    : 0
info:  Queue families:
info:    Graphics : 0
info:    Transfer : 1
info:    Sparse   : 0
info:  Memory type mask for sparse resources: 0x80
info:  DXVK: Read 6682 valid state cache entries
info:  DXVK: Graphics pipeline libraries supported
warn:  D3D11DXGIDevice::QueryInterface: Unknown interface query
warn:  88399375-734f-4892-a95f-70dd42ce7cdd
info:  Presenter: Actual swap chain properties:
info:    Format:       VK_FORMAT_B8G8R8A8_UNORM
info:    Color space:  VK_COLOR_SPACE_SRGB_NONLINEAR_KHR
info:    Present mode: VK_PRESENT_MODE_IMMEDIATE_KHR (dynamic: no)
info:    Buffer size:  1280x720
info:    Image count:  3
info:    Exclusive FS: 1
err:   D3D11SwapChain: Failed to recreate swap chain: VK_ERROR_INITIALIZATION_FAILED
err:   DXGI: CreateSwapChainForHwnd: Failed to create swap chain, hr -2147467259
warn:  DXGI: MakeWindowAssociation: Ignoring flags
wine: Unhandled page fault on read access to<PHONE_NUMBER>000000 at address<PHONE_NUMBER>537E18 (thread 013c), starting debugger...

That commit only touches d3d9 but i gather this log is from Call of Duty: Ghosts a d3d11 game?

That commit only touches d3d9 but i gather this log is from Call of Duty: Ghosts a d3d11 game?

Yup

And you say the commit before makes everything work?

Exactly

The commit in question just reverts a under 2 weeks old commit. Does this build work? https://github.com/doitsujin/dxvk/actions/runs/5229786037

[obs-vkcapture] Init Vulkan 1.4.0
is it possible for a test to disable third party software that could potentially cause incompatibility issues?
(because sometimes in such cases it can help just rebuilding them in the same build environment as dxvk or something like that)

The commit in question just reverts a under 2 weeks old commit. Does this build work? https://github.com/doitsujin/dxvk/actions/runs/5229786037

I'm really sorry, I broke my english while writing this issue...
I wanted to say that this is the last commit that works, sorry sorry sorry everyone @Blisto91 @Iglu47

Well, that is definitely a driver bug then, because the next commit enables VK_KHR_present_id and VK_KHR_present_wait, which are known to be broken on nvidia drivers older than the 535 series.

Well, that is definitely a driver bug then, because the next commit enables VK_KHR_present_id and VK_KHR_present_wait, which are known to be broken on nvidia drivers older than the 535 series.

That checks my "internal" theory... I shall wait until the update officially hits Pop!_Os, so I can finally update DXVK.
Would you kindly tell me what's the benefits of those commits?
I've read something about frame presentation, something "even more immediate" or similar things.

I'm really sorry, I broke my english while writing this issue...
I wanted to say that this is the last commit that works, sorry sorry sorry everyone
Well, that is definitely a driver bug then, because the next commit enables VK_KHR_present_id and VK_KHR_present_wait, which are known to be broken on nvidia drivers older than the 535 series.

info: DXVK:v2.2-89-ga287566c
ok, @gabriele2000 can you confirm that the problem is not present on this build?
https://github.com/doitsujin/dxvk/actions/runs/5348944229

Well, that is definitely a driver bug then, because the next commit enables VK_KHR_present_id and VK_KHR_present_wait, which are known to be broken on nvidia drivers older than the 535 series.

They don't get enabled on Nvidia drivers older than 535. I'm still on 530 and everything still works.

I'm really sorry, I broke my english while writing this issue...
I wanted to say that this is the last commit that works, sorry sorry sorry everyone @Blisto91 @Iglu47

No worries

They don't get enabled on Nvidia drivers older than 535. I'm still on 530 and everything still works.

Apparently 525 is the black sheep then

Apparently 525 is the black sheep then

No, at least not "every game will crash certainly".
(for example Overwatch works fine.)
Software information
Overwatch, <IP_ADDRESS> - 113652 game build
System information

GPU: NVIDIA GeForce GTX 1080 Ti (Pascal)
Driver: 525.116.04
Wine version: WineStaging 7.15 (with some TkG-patches)
DXVK version: https://github.com/doitsujin/dxvk/commit/8704ed7af665751e0a5c38b9b5b8355b1714809f

Terminal output

Overwatch_dxvk.log

I still haven't gotten any feedback on the previous questions, but if it's not related to them - my last guess if it's not related to third party apps - it's unlikely but it might have something to do with Wayland (not sure what Pop!_Os provides for Nvidia) it might be worth testing to switch to x11 session, try using gamescope or maybe something else. Unfortunately, I can't run such tests on Cinnamon.

@gabriele2000 probably my local Wine version is very old. Сould you check https://github.com/doitsujin/dxvk/actions/runs/5363935969 ?

Basically installing driver 535 fixed everything by the way

@gabriele2000 probably my local Wine version is very old. Сould you check https://github.com/doitsujin/dxvk/actions/runs/5363935969 ?

Same issue
warn:  D3D11DXGIDevice::QueryInterface: Unknown interface query
warn:  88399375-734f-4892-a95f-70dd42ce7cdd
info:  Presenter: Actual swap chain properties:
info:    Format:       VK_FORMAT_B8G8R8A8_UNORM
info:    Color space:  VK_COLOR_SPACE_SRGB_NONLINEAR_KHR
info:    Present mode: VK_PRESENT_MODE_IMMEDIATE_KHR (dynamic: no)
info:    Buffer size:  1280x720
info:    Image count:  3
info:    Exclusive FS: 2
err:   D3D11SwapChain: Failed to recreate swap chain: VK_ERROR_INITIALIZATION_FAILED
err:   DXGI: CreateSwapChainForHwnd: Failed to create swap chain, hr -2147467259


Update for typescript-eslint v6

Public-Facing Changes

Update package version to 1.0.0 since the package has been stable for quite a while.

Breaking changes:

Updated peer and dev dependencies to v6 of typescript-eslint packages
Updated plugin:@foxglove/typescript config to be based on new strict-type-checked and stylistic-type-checked configs from upstream

This adds several new rules that may require disabling or fixing.

Deleted no-private-identifier rule (see #46)
Enabled prefer-hash-private by default

Description
See typescript-eslint v6 release notes: https://typescript-eslint.io/blog/announcing-typescript-eslint-v6/

checkCompoundAssignments: true is now the default for restrict-plus-operands
Switched from @typescript-eslint/recommended + @typescript-eslint/recommended-requiring-type-checking to  @typescript-eslint/strict-type-checked + @typescript-eslint/stylistic-type-checked

prefer-nullish-coalescing and prefer-optional-chain are now on by default

Switched to @typescript-eslint/rule-tester for RuleTester

Currently looking for feedback on these changes and whether the following newly enabled rules (which cause a lot of errors in Studio) should be left up to the downstream user to decide whether they want them on or off?
"@typescript-eslint/consistent-type-definitions": off
"@typescript-eslint/consistent-indexed-object-style": off
"@typescript-eslint/consistent-generic-constructors": off
"@typescript-eslint/no-confusing-void-expression": off
"@typescript-eslint/dot-notation": off
"@typescript-eslint/prefer-for-of": off
"@typescript-eslint/no-dynamic-delete": off
"@typescript-eslint/array-type": off
"@typescript-eslint/no-invalid-void-type": off
"@typescript-eslint/no-unsafe-enum-comparison": off
"@typescript-eslint/unified-signatures": off
@typescript-eslint/no-unnecessary-type-arguments
@typescript-eslint/prefer-function-type
@typescript-eslint/no-redundant-type-constituents
@typescript-eslint/no-base-to-string
@typescript-eslint/prefer-nullish-coalescing
@typescript-eslint/prefer-optional-chain (think we already had this but it catches more cases now)
@typescript-eslint/prefer-reduce-type-parameter
@typescript-eslint/no-throw-literal
@typescript-eslint/prefer-string-starts-ends-with
@typescript-eslint/non-nullable-type-assertion-style

consistent-type-definitions
consistent-indexed-object-style
consistent-generic-constructors
no-confusing-void-expression
dot-notation
prefer-for-of
no-dynamic-delete
array-type
no-invalid-void-type
no-unsafe-enum-comparison
unified-signatures
no-unnecessary-type-arguments
prefer-function-type
no-redundant-type-constituents
no-base-to-string
prefer-nullish-coalescing
prefer-optional-chain
prefer-reduce-type-parameter
no-throw-literal
prefer-string-starts-ends-with
non-nullable-type-assertion-style
Rules for reference

no-confusing-void-expression
no-invalid-void-type
These two seem possibly useful. The rest not so much.


how to draw this result directly? is there any script?

https://github.com/mit-han-lab/smoothquant/blob/main/figures/migrate.jpg

@Guangxuan-Xiao


Microsoft

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ID: edace175-268c-7428-dad5-95cd974422c3
Version Independent ID: 707aed84-b636-088a-bf57-1cc5e4f69631
Content: What is ML.NET and how does it work? - ML.NET
Content Source: docs/machine-learning/how-does-mldotnet-work.md
Product: dotnet-ml
GitHub Login: @luisquintanilla
Microsoft Alias: luquinta

There isn't anything actionable here, so closing.


account name is not shown without mouse over

What is the problem?
The current alias is not being shown, however, if I put the mouse over the button, I can see the alias.
It seems to be the same issue described in https://github.com/xhiroga/aws-peacock-management-console/issues/76
The colors are working
Reproduction Steps
Setup iam identity center and the configuration of aws-peacock-management plugin
Browser Name
Chrome
Browser Version
Version 126.0.6478.116 (Official Build) (64-bit)
Other information

@filipemarques87 Hi, thank you for reporting your issue!
I guess its spec of AWS management console. It just shows title attribute of span.

I think this spec is rational, but if you feel any problem, please share it :)

Hi @filipemarques87,
I have fixed this bug in version 2.12. Could you please try it out and let us know if it works as expected😀?

Hi @xhiroga
Yes, it is working. Thanks a lot :)


How to evaluate the results?

How to evaluate the results?

I used the following evaluation protocol to create table 1.

For unseen cameras, I temporally sample the video frames at the rate of 30. For example, for subject 387, I evaluated the model at frames 0, 30, 60, .., and 510 for all unseen 22 cameras.

I evaluate the model on the whole image with a black background.

Normalize the image values to 0-1. Use the code below to compute PSNR

def psnr_metric(img_pred, img_gt):
    mse = np.mean((img_pred - img_gt)**2)
    psnr = -10 * np.log(mse) / np.log(10)
    return psnr

Normalize the image values to 0-1. Compute SSIM via the structural_similarity function provided by scikit-image package.

Normalize the image values to -1~1. Compute LPIPS where VGG is used as the backbone model.

I hope this makes it clear to evaluate the trained model.

If possible, would you mind releasing the evaluation code? I will understand if it is not possible.

Does anyone duplicate the evaluation code? It would be thankful if anyone could share here (duplicate / original version). Thanks!

Thanks for the detail explanation.
If possible, could you please share the evaluation code for comparison? Thank you very much!
It would also be thankful if anyone could share the reproduced evaluation code. Thank you!

Actually I wrote an evaluation script by myself, and got reasonable results compared with the paper. Feel free to check it and let me know if anything is wrong. The only problem is that you need to add skip=30 to pick every 30th frame, and change view in the yaml file.
import skimage
import os

import torch
import numpy as np
from tqdm import tqdm
from PIL import Image

from core.data import create_dataloader
from core.nets import create_network
from core.utils.train_util import cpu_data_to_gpu
from core.utils.image_util import ImageWriter, to_8b_image, to_8b3ch_image

from configs import cfg, args

from third_parties.lpips import LPIPS

EXCLUDE_KEYS_TO_GPU = ['frame_name',
                       'img_width', 'img_height']

def load_network():
    model = create_network()
    ckpt_path = os.path.join(cfg.logdir, f'{cfg.load_net}.tar')
    ckpt = torch.load(ckpt_path, map_location='cuda:0')
    model.load_state_dict(ckpt['network'], strict=False)
    print('load network from ', ckpt_path)
    return model.cuda().deploy_mlps_to_secondary_gpus()

def unpack_alpha_map(alpha_vals, ray_mask, width, height):
    alpha_map = np.zeros((height * width), dtype='float32')
    alpha_map[ray_mask] = alpha_vals
    return alpha_map.reshape((height, width))

def unpack_to_image(width, height, ray_mask, bgcolor,
                    rgb, alpha, truth=None):

    rgb_image = np.full((height * width, 3), bgcolor, dtype='float32')
    truth_image = np.full((height * width, 3), bgcolor, dtype='float32')

    rgb_image[ray_mask] = rgb
    rgb_image = to_8b_image(rgb_image.reshape((height, width, 3)))

    if truth is not None:
        truth_image[ray_mask] = truth
        truth_image = to_8b_image(truth_image.reshape((height, width, 3)))

    alpha_map = unpack_alpha_map(alpha, ray_mask, width, height)
    alpha_image  = to_8b3ch_image(alpha_map)

    return rgb_image, alpha_image, truth_image

def psnr_metric(img_pred, img_gt):
    mse = np.mean((img_pred - img_gt)**2)
    psnr = -10 * np.log(mse) / np.log(10)
    return psnr

def scale_for_lpips(image_tensor):
    return image_tensor * 2. - 1.

def get_loss(rgb, target):
    lpips = LPIPS(net='vgg')#.cuda()
    lpips_loss = lpips(scale_for_lpips(rgb.permute(0, 3, 1, 2)),
                       scale_for_lpips(target.permute(0, 3, 1, 2)))
    return torch.mean(lpips_loss).cpu().detach().numpy()

def evaluate():
    cfg.perturb = 0.

    psnr_l = []
    ssim_l = []
    lpips_l = []

    model = load_network()
    test_loader = create_dataloader('movement')

    model.eval()
    for idx, batch in enumerate(tqdm(test_loader)):
        for k, v in batch.items():
            batch[k] = v[0]

        data = cpu_data_to_gpu(
                    batch,
                    exclude_keys=EXCLUDE_KEYS_TO_GPU + ['target_rgbs'])

        with torch.no_grad():
            net_output = model(**data, iter_val=cfg.eval_iter)

        rgb = net_output['rgb']
        alpha = net_output['alpha']

        width = batch['img_width']
        height = batch['img_height']
        ray_mask = batch['ray_mask']

        pred_img, alpha_img, gt_img = \
            unpack_to_image(
                width, height, ray_mask, np.array(cfg.bgcolor)/255.,
                rgb.data.cpu().numpy(),
                alpha.data.cpu().numpy(),
                batch['target_rgbs'])

        pred_img_norm = pred_img / pred_img.max()
        gt_img_norm = gt_img / gt_img.max()

        # get evaluation
        psnr_l.append(psnr_metric(pred_img_norm, gt_img_norm))
        ssim_l.append(skimage.metrics.structural_similarity(pred_img_norm, gt_img_norm, multichannel=True))
        lpips_loss = get_loss(rgb=torch.from_numpy(pred_img_norm).float().unsqueeze(0), target=torch.from_numpy(gt_img_norm).float().unsqueeze(0))
        lpips_l.append(lpips_loss)

    return psnr_l, ssim_l, lpips_l

psnr_l, ssim_l, lpips_l = evaluate()
print (np.array(psnr_l).mean())
print (np.array(ssim_l).mean())
print (np.array(lpips_l).mean())

Actually I wrote an evaluation script by myself, and got reasonable results compared with the paper. Feel free to check it and let me know if anything is wrong. The only problem is that you need to add skip=30 to pick every 30th frame, and change view in the yaml file.
import skimage
import os

import torch
import numpy as np
from tqdm import tqdm
from PIL import Image

from core.data import create_dataloader
from core.nets import create_network
from core.utils.train_util import cpu_data_to_gpu
from core.utils.image_util import ImageWriter, to_8b_image, to_8b3ch_image

from configs import cfg, args

from third_parties.lpips import LPIPS

EXCLUDE_KEYS_TO_GPU = ['frame_name',
                       'img_width', 'img_height']

def load_network():
    model = create_network()
    ckpt_path = os.path.join(cfg.logdir, f'{cfg.load_net}.tar')
    ckpt = torch.load(ckpt_path, map_location='cuda:0')
    model.load_state_dict(ckpt['network'], strict=False)
    print('load network from ', ckpt_path)
    return model.cuda().deploy_mlps_to_secondary_gpus()

def unpack_alpha_map(alpha_vals, ray_mask, width, height):
    alpha_map = np.zeros((height * width), dtype='float32')
    alpha_map[ray_mask] = alpha_vals
    return alpha_map.reshape((height, width))

def unpack_to_image(width, height, ray_mask, bgcolor,
                    rgb, alpha, truth=None):

    rgb_image = np.full((height * width, 3), bgcolor, dtype='float32')
    truth_image = np.full((height * width, 3), bgcolor, dtype='float32')

    rgb_image[ray_mask] = rgb
    rgb_image = to_8b_image(rgb_image.reshape((height, width, 3)))

    if truth is not None:
        truth_image[ray_mask] = truth
        truth_image = to_8b_image(truth_image.reshape((height, width, 3)))

    alpha_map = unpack_alpha_map(alpha, ray_mask, width, height)
    alpha_image  = to_8b3ch_image(alpha_map)

    return rgb_image, alpha_image, truth_image

def psnr_metric(img_pred, img_gt):
    mse = np.mean((img_pred - img_gt)**2)
    psnr = -10 * np.log(mse) / np.log(10)
    return psnr

def scale_for_lpips(image_tensor):
    return image_tensor * 2. - 1.

def get_loss(rgb, target):
    lpips = LPIPS(net='vgg')#.cuda()
    lpips_loss = lpips(scale_for_lpips(rgb.permute(0, 3, 1, 2)),
                       scale_for_lpips(target.permute(0, 3, 1, 2)))
    return torch.mean(lpips_loss).cpu().detach().numpy()

def evaluate():
    cfg.perturb = 0.

    psnr_l = []
    ssim_l = []
    lpips_l = []

    model = load_network()
    test_loader = create_dataloader('movement')

    model.eval()
    for idx, batch in enumerate(tqdm(test_loader)):
        for k, v in batch.items():
            batch[k] = v[0]

        data = cpu_data_to_gpu(
                    batch,
                    exclude_keys=EXCLUDE_KEYS_TO_GPU + ['target_rgbs'])

        with torch.no_grad():
            net_output = model(**data, iter_val=cfg.eval_iter)

        rgb = net_output['rgb']
        alpha = net_output['alpha']

        width = batch['img_width']
        height = batch['img_height']
        ray_mask = batch['ray_mask']

        pred_img, alpha_img, gt_img = \
            unpack_to_image(
                width, height, ray_mask, np.array(cfg.bgcolor)/255.,
                rgb.data.cpu().numpy(),
                alpha.data.cpu().numpy(),
                batch['target_rgbs'])

        pred_img_norm = pred_img / pred_img.max()
        gt_img_norm = gt_img / gt_img.max()

        # get evaluation
        psnr_l.append(psnr_metric(pred_img_norm, gt_img_norm))
        ssim_l.append(skimage.metrics.structural_similarity(pred_img_norm, gt_img_norm, multichannel=True))
        lpips_loss = get_loss(rgb=torch.from_numpy(pred_img_norm).float().unsqueeze(0), target=torch.from_numpy(gt_img_norm).float().unsqueeze(0))
        lpips_l.append(lpips_loss)

    return psnr_l, ssim_l, lpips_l

psnr_l, ssim_l, lpips_l = evaluate()
print (np.array(psnr_l).mean())
print (np.array(ssim_l).mean())
print (np.array(lpips_l).mean())

Hi, Did you get the same result in the paper report using the pretrained model? I got some small difference in my evaluation code, still didn't figure out the reasons.

Actually I wrote an evaluation script by myself, and got reasonable results compared with the paper. Feel free to check it and let me know if anything is wrong. The only problem is that you need to add skip=30 to pick every 30th frame, and change view in the yaml file.
import skimage
import os

import torch
import numpy as np
from tqdm import tqdm
from PIL import Image

from core.data import create_dataloader
from core.nets import create_network
from core.utils.train_util import cpu_data_to_gpu
from core.utils.image_util import ImageWriter, to_8b_image, to_8b3ch_image

from configs import cfg, args

from third_parties.lpips import LPIPS

EXCLUDE_KEYS_TO_GPU = ['frame_name',
                       'img_width', 'img_height']

def load_network():
    model = create_network()
    ckpt_path = os.path.join(cfg.logdir, f'{cfg.load_net}.tar')
    ckpt = torch.load(ckpt_path, map_location='cuda:0')
    model.load_state_dict(ckpt['network'], strict=False)
    print('load network from ', ckpt_path)
    return model.cuda().deploy_mlps_to_secondary_gpus()

def unpack_alpha_map(alpha_vals, ray_mask, width, height):
    alpha_map = np.zeros((height * width), dtype='float32')
    alpha_map[ray_mask] = alpha_vals
    return alpha_map.reshape((height, width))

def unpack_to_image(width, height, ray_mask, bgcolor,
                    rgb, alpha, truth=None):

    rgb_image = np.full((height * width, 3), bgcolor, dtype='float32')
    truth_image = np.full((height * width, 3), bgcolor, dtype='float32')

    rgb_image[ray_mask] = rgb
    rgb_image = to_8b_image(rgb_image.reshape((height, width, 3)))

    if truth is not None:
        truth_image[ray_mask] = truth
        truth_image = to_8b_image(truth_image.reshape((height, width, 3)))

    alpha_map = unpack_alpha_map(alpha, ray_mask, width, height)
    alpha_image  = to_8b3ch_image(alpha_map)

    return rgb_image, alpha_image, truth_image

def psnr_metric(img_pred, img_gt):
    mse = np.mean((img_pred - img_gt)**2)
    psnr = -10 * np.log(mse) / np.log(10)
    return psnr

def scale_for_lpips(image_tensor):
    return image_tensor * 2. - 1.

def get_loss(rgb, target):
    lpips = LPIPS(net='vgg')#.cuda()
    lpips_loss = lpips(scale_for_lpips(rgb.permute(0, 3, 1, 2)),
                       scale_for_lpips(target.permute(0, 3, 1, 2)))
    return torch.mean(lpips_loss).cpu().detach().numpy()

def evaluate():
    cfg.perturb = 0.

    psnr_l = []
    ssim_l = []
    lpips_l = []

    model = load_network()
    test_loader = create_dataloader('movement')

    model.eval()
    for idx, batch in enumerate(tqdm(test_loader)):
        for k, v in batch.items():
            batch[k] = v[0]

        data = cpu_data_to_gpu(
                    batch,
                    exclude_keys=EXCLUDE_KEYS_TO_GPU + ['target_rgbs'])

        with torch.no_grad():
            net_output = model(**data, iter_val=cfg.eval_iter)

        rgb = net_output['rgb']
        alpha = net_output['alpha']

        width = batch['img_width']
        height = batch['img_height']
        ray_mask = batch['ray_mask']

        pred_img, alpha_img, gt_img = \
            unpack_to_image(
                width, height, ray_mask, np.array(cfg.bgcolor)/255.,
                rgb.data.cpu().numpy(),
                alpha.data.cpu().numpy(),
                batch['target_rgbs'])

        pred_img_norm = pred_img / pred_img.max()
        gt_img_norm = gt_img / gt_img.max()

        # get evaluation
        psnr_l.append(psnr_metric(pred_img_norm, gt_img_norm))
        ssim_l.append(skimage.metrics.structural_similarity(pred_img_norm, gt_img_norm, multichannel=True))
        lpips_loss = get_loss(rgb=torch.from_numpy(pred_img_norm).float().unsqueeze(0), target=torch.from_numpy(gt_img_norm).float().unsqueeze(0))
        lpips_l.append(lpips_loss)

    return psnr_l, ssim_l, lpips_l

psnr_l, ssim_l, lpips_l = evaluate()
print (np.array(psnr_l).mean())
print (np.array(ssim_l).mean())
print (np.array(lpips_l).mean())

Hi, Did you get the same result in the paper report using the pretrained model? I got some small difference in my evaluation code, still didn't figure out the reasons.

I have a small difference, which seems acceptable. Here are my evaluation results on the downloaded model: 28.04, 0.9628, 35.65 for 387.

28.04, 0.9628, 35.65 for 387.

Thanks for your quick reply, my psnr evaluation results on the downloaded model is 27.61 for 387, maybe there are some difference in my code, thanks!

Using the script provided above and evaluating on the pre-trained datasets, I get reasonable results from the PSNR and SSIM. However, my LPIPS results seem to be far off, they are about ~9.0.
Does anyone have an idea what the problem could be?

@chungyiweng
Are the results published in the paper computed from the provided models?
And why is the evaluation code still not published?
Being able to compare the results quantitatively using the exact same protocol is a necessity for any subsequent work.

@yuchenrao Hi, I run your evaluate code but the running time is so long :

is it normal?

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper.
Code address: https://github.com/escapefreeg/humannerf-eval

If there are errors or improvements in the code, please feel free to contact me!

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

@chungyiweng
I retrained the model with the original Human-NeRF code and then ran the validation with this code and the results I got here were beter than the one in paper. I would like to ask if the experimental results in the paper are the ones with floating.

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

Does the results in table1 tested on the training data(training view)?

No, I'm evaluating on test data (camera index is 1-22, and image is taken every 30 frames)

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

Does the results in table1 tested on the training data(training view)?

No, The results are evaluated on test data (camera index is 1-22, and image is taken every 30 frames)

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

Does the results in table1 tested on the training data(training view)?

No, The results are evaluated on test data (camera index is 1-22, and image is taken every 30 frames)

I've also download the pretrained model p387 and tested on the 22 unseen cameras, and the result is close to the paper.  But I download the pretained model p377 and tested on the 22 unseen views, the result is far to the paper, have you tested on p377?

sorry, I find the bug, it's my fault.

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

Does the results in table1 tested on the training data(training view)?

No, The results are evaluated on test data (camera index is 1-22, and image is taken every 30 frames)

I've also download the pretrained model p387 and tested on the 22 unseen cameras, and the result is close to the paper.  But I download the pretained model p377 and tested on the 22 unseen views, the result is far to the paper, have you tested on p377?

@escapefreeg sorry, I find the bug, it's my fault.

It's fine. glad you found the problem.

Do you test the model in 100k, 200k ,300k iters? I tested these models but the result in RSNR, SSIM, LPIPS are not much difference. Dose the model reach convergence before 100k iter?

其实我自己写了一个评估脚本，与论文相比得到了合理的结果。请随时检查它，如果有任何问题，请告诉我。唯一的问题是你需要skip=30每 30 帧添加一次拾取，并view在 yaml 文件中进行更改。
import skimage
import os

import torch
import numpy as np
from tqdm import tqdm
from PIL import Image

from core.data import create_dataloader
from core.nets import create_network
from core.utils.train_util import cpu_data_to_gpu
from core.utils.image_util import ImageWriter, to_8b_image, to_8b3ch_image

from configs import cfg, args

from third_parties.lpips import LPIPS

EXCLUDE_KEYS_TO_GPU = ['frame_name',
                       'img_width', 'img_height']

def load_network():
    model = create_network()
    ckpt_path = os.path.join(cfg.logdir, f'{cfg.load_net}.tar')
    ckpt = torch.load(ckpt_path, map_location='cuda:0')
    model.load_state_dict(ckpt['network'], strict=False)
    print('load network from ', ckpt_path)
    return model.cuda().deploy_mlps_to_secondary_gpus()

def unpack_alpha_map(alpha_vals, ray_mask, width, height):
    alpha_map = np.zeros((height * width), dtype='float32')
    alpha_map[ray_mask] = alpha_vals
    return alpha_map.reshape((height, width))

def unpack_to_image(width, height, ray_mask, bgcolor,
                    rgb, alpha, truth=None):

    rgb_image = np.full((height * width, 3), bgcolor, dtype='float32')
    truth_image = np.full((height * width, 3), bgcolor, dtype='float32')

    rgb_image[ray_mask] = rgb
    rgb_image = to_8b_image(rgb_image.reshape((height, width, 3)))

    if truth is not None:
        truth_image[ray_mask] = truth
        truth_image = to_8b_image(truth_image.reshape((height, width, 3)))

    alpha_map = unpack_alpha_map(alpha, ray_mask, width, height)
    alpha_image  = to_8b3ch_image(alpha_map)

    return rgb_image, alpha_image, truth_image

def psnr_metric(img_pred, img_gt):
    mse = np.mean((img_pred - img_gt)**2)
    psnr = -10 * np.log(mse) / np.log(10)
    return psnr

def scale_for_lpips(image_tensor):
    return image_tensor * 2. - 1.

def get_loss(rgb, target):
    lpips = LPIPS(net='vgg')#.cuda()
    lpips_loss = lpips(scale_for_lpips(rgb.permute(0, 3, 1, 2)),
                       scale_for_lpips(target.permute(0, 3, 1, 2)))
    return torch.mean(lpips_loss).cpu().detach().numpy()

def evaluate():
    cfg.perturb = 0.

    psnr_l = []
    ssim_l = []
    lpips_l = []

    model = load_network()
    test_loader = create_dataloader('movement')

    model.eval()
    for idx, batch in enumerate(tqdm(test_loader)):
        for k, v in batch.items():
            batch[k] = v[0]

        data = cpu_data_to_gpu(
                    batch,
                    exclude_keys=EXCLUDE_KEYS_TO_GPU + ['target_rgbs'])

        with torch.no_grad():
            net_output = model(**data, iter_val=cfg.eval_iter)

        rgb = net_output['rgb']
        alpha = net_output['alpha']

        width = batch['img_width']
        height = batch['img_height']
        ray_mask = batch['ray_mask']

        pred_img, alpha_img, gt_img = \
            unpack_to_image(
                width, height, ray_mask, np.array(cfg.bgcolor)/255.,
                rgb.data.cpu().numpy(),
                alpha.data.cpu().numpy(),
                batch['target_rgbs'])

        pred_img_norm = pred_img / pred_img.max()
        gt_img_norm = gt_img / gt_img.max()

        # get evaluation
        psnr_l.append(psnr_metric(pred_img_norm, gt_img_norm))
        ssim_l.append(skimage.metrics.structural_similarity(pred_img_norm, gt_img_norm, multichannel=True))
        lpips_loss = get_loss(rgb=torch.from_numpy(pred_img_norm).float().unsqueeze(0), target=torch.from_numpy(gt_img_norm).float().unsqueeze(0))
        lpips_l.append(lpips_loss)

    return psnr_l, ssim_l, lpips_l

psnr_l, ssim_l, lpips_l = evaluate()
print (np.array(psnr_l).mean())
print (np.array(ssim_l).mean())
print (np.array(lpips_l).mean())

Hello, where do I need to set the view and skip=30 options?

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

Does the results in table1 tested on the training data(training view)?

No, The results are evaluated on test data (camera index is 1-22, and image is taken every 30 frames)

I've also download the pretrained model p387 and tested on the 22 unseen cameras, and the result is close to the paper.  But I download the pretained model p377 and tested on the 22 unseen views, the result is far to the paper, have you tested on p377?

@escapefreeg sorry, I find the bug, it's my fault.

Hello, I have encountered the same error, but I don't know where the problem is. What is the reason for this?

I also wrote an evaluation code and obtained the exact same evaluation indicators in the paper. Code address: https://github.com/escapefreeg/humannerf-eval   If there are errors or improvements in the code, please feel free to contact me!

@escapefreeg Thanks for your code. There is still some difference in my result. I run the code on a single RTX 3080, but it is useful for my next step experiment.

You‘re welcome, glad that I could help.

Does the results in table1 tested on the training data(training view)?

No, The results are evaluated on test data (camera index is 1-22, and image is taken every 30 frames)

I've also download the pretrained model p387 and tested on the 22 unseen cameras, and the result is close to the paper.  But I download the pretained model p377 and tested on the 22 unseen views, the result is far to the paper, have you tested on p377?

@escapefreeg sorry, I find the bug, it's my fault.

Hello, I have encountered the same error, but I don't know where the problem is. What is the reason for this?

 When I use black as my bgcolor, the result gets better.


Multi-arch builds

We are running a multi arch build in a github workflow using docker buildx build, and are wondering how to manage the balena deploy so all architecture images are merged in a single release.
https://github.com/kerberos-io/agent/blob/master/.github/workflows/docker.yml#L63-L67

Hi @cedricve, thanks for your question. I was also looking into this myself. Unfortunately the limitation here is that the balena CLI, which this action uses under the hood, does not yet support pushing multiple images for the same release to our registry, and it doesn't push the multi-arch manifest either. Multi-arch support is on our roadmap but we don't have an ETA for that feature yet or the implementation details for it.
That being said, I am looking into adding a useDeploy feature, that could use image artifacts from a previous step in order to do a balena deploy instead of a balena push
The action would look in its local image store for one or more <projectName>_<serviceName>:<architecture> or  <projectName>_<serviceName>:latest images (loaded by a previous step of the same job), and then deploy the images matching the target fleet architecture.
What do you think about that as an interface?

Thanks @pipex  that would already make more sense. Where I think a lot of confusion starts is the difference between the "direct" deployment on Balena Hub.

When browsing to the Balena Hub, opening the Kerberos Agent project, and pressing the Get Started button, it will list all the available supported architectures. However under the hood the fleet was pointed to the arm7 architecture, so only that image is available in the balena registry. If you select an ARM64 device for example Raspberry Pi 4 from the list, it will not boot. Which is not expected and can been seen by the users as malfunctioning of Balena.

On the other hand when you "fork" the fleet to your own fleet. You can select all (even non-supported) architectures from the list. This helps to get the other "available" architectures which are supported, as after creation it will make a new release using the right base image. However, here you can select a non-supported architecture from the list, and then it will not work.

Bottom line is that it's really not straight forward to a users what is working and what is not.

Thanks for explaining, I didn't know that v8 was backwards compatible. However that wouldn't solve the amd64 boards right, as the target board on your fleet is determining the specific architecture build.

Understood! So we’ll need to wait for the multi architecture capability. I think it would be nice and intuitive you can just select the right board (and under the hood the correct base image is selected arm or amd).

On 27 Sep 2022, at 15:59, Felipe Lalanne @.***> wrote:
If your fleet is raspberry pi3 (armv7), you should be able to add raspberry pi 4 (armv8) devices to it, the other way around is not possible: a raspberry pi3 (arvm7) will fail if deployed into an armv8 fleet.
—
Reply to this email directly, view it on GitHub https://github.com/balena-io/deploy-to-balena-action/issues/200#issuecomment-1259553503, or unsubscribe https://github.com/notifications/unsubscribe-auth/AALZUGZJDZBITTH2ETIST5DWAL4VNANCNFSM6AAAAAAQSMJXRE.
You are receiving this because you were mentioned.

I'm hoping to have more to share on multi-arch apps soon!

exciting !

Hey @avihaibd, thanks for your interest in this feature! Here is the related roadmap item that you can follow for updates!
https://roadmap.balena.io/posts/28/mixed-architecture-fleets-and-apps
In short, there are a lot of backend changes required to our API and database to support mixed device fleets, and multi-arch applications, but it is a priority project that we are excited to progress!

Meanwhile, this is what I am doing
FROM arm64v8/nginx:1.25 as nginx-aarch64
FROM arm32v7/nginx:1.25 as nginx-armv7hf
FROM nginx:1.25 as nginx-amd64

FROM nginx-%%BALENA_ARCH%%


Initial landing home screen is blank , fix it by prompting a message to user to create ticket.

Please assign me this issue.
My approach:
I shall add a card with similar style as of ticket which will display, "No tickets yet, Create now" with a button to create a ticket. This will display only when no ticket exists.

Kindly review the PR @himani-r5 mam.

Hi @himani-r5 mam, this issue was resolved 4 days ago but in the leaderboard I have not received points for this issue in the leaderboard. Kindly see to it there may be some problem in the leaderboard code or something manual error or anything else.
Thanks

Hi
There is some issue with the leaderboard currently , so thats why it isnt
updated . Dont worry , we will fix it soon . You can continue working on
PRs and issues , we will take care of rest :))
On Tue, 31 Dec 2024 at 10:21 PM, Madhav Dhall @.***>
wrote:

Hi @himani-r5 https://github.com/himani-r5 mam, this issue was resolved
4 days ago but in the leaderboard I have not received points for this issue
in the leaderboard. Kindly see to it there may be some problem in the
leaderboard code or something manual error or anything else.
Thanks
—
Reply to this email directly, view it on GitHub
https://github.com/IMGIITRoorkee/Ticketify/issues/14#issuecomment-2566588177,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/BFJUWYBHSEX26I6R4VJKTFL2ILDRLAVCNFSM6AAAAABTVQQFCKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKNRWGU4DQMJXG4
.
You are receiving this because you were mentioned.Message ID:
@.***>


The test thinks that this file descriptor is leaking. The solution is pretty simple, it needs to ignore a file descriptor that is pidfd.
This is a hacky way to ignore file descriptors internal to the runtime, there is a proposal open at https://github.com/golang/go/issues/67639 that should provide a better way to detect leaking fds.
Steps to reproduce the issue
$ go install golang.org/dl/gotip@latest
$ gotip download
$ gotip test -v ./cmd/containerd-shim-runc-v2/process -run=TestNewBinaryIOCleanup

Describe the results you received and expected
Test fails. It should pass and continue to work on 1.21 and 1.22 as well.
What version of containerd are you using?
tip
Any other relevant information
No response
Show configuration if it is related to CRI plugin.
No response

Wow, I could not have envisioned that pidfd functionality to break a test case this way 😄


fix: Fix active pods count in node pending status with pod deleted.

Signed-off-by: Saravanan Balasubramanian<EMAIL_ADDRESS>Checklist:

[ ] My organization is added to USERS.md.

Tips:

Your PR needs to pass the required checks before it can be approved. If the check is not required (e.g. E2E tests) it does not need to pass
Sign-off your commits to pass the DCO check: git commit --signoff.
Run make pre-commit -B to fix codegen or lint problems.
Say how how you tested your changes. If you changed the UI, attach screenshots.

must fix this line:
https://github.com/argoproj/argo-workflows/blob/02071057c082cf295ab8da68f1b2027ff8762b5a/workflow/controller/operator.go#L982


docs(example): add example: git-server

Coverage remained the same at 21.268% when pulling 24d214bf24cb136102e8218fe2f67a7c69c3bc41 on example-git-server into e95702a537eb523c9a37eec64c328d693bd9aa13 on master.


UseQuotationMarks = true // wrong behaviour

I have a problem with generate sql.
if we set in DapperRepository constructor :
new SqlGeneratorConfig()
{
SqlConnector = ESqlConnector.PostgreSQL,
UseQuotationMarks = true
}
and then execute :
await base.InsertAsync(dbEntity).ConfigureAwait(false)
we will have sql like this :
INSERT INTO "Blogs"("Name") VALUES  (@Name)RETURNING Id
but expected result :
INSERT INTO "Blogs"("Name") VALUES  (@Name)RETURNING

"Id"

the same behaviour and for MsSql :
INSERT INTO [Blogs]([Name]) VALUES  (@Name)SELECT SCOPE_IDENTITY() AS Id
but expected result :
INSERT INTO [Blogs]([Name]) VALUES  (@Name)SELECT SCOPE_IDENTITY() AS

[Id]

PS^ This important because  pgsql not case sensitive and UseQuotationMarks = true, very helpful. In another case we need add ColumnAttribute for example : [Column("\"Id\"")] to all property, with corresponding names, in class to achieve correct behaviour and turn off UseQuotationMarks.

Fixed in 1.7.1


Change to the same format as other

Many other articles(*1) do not list Mandatory or Optional in the Description.
It is expressed by the item called Required.
Therefore, I made this article the same table format.
*1:

articles/azure-resource-manager/resource-group-template-functions-array.md
articles/azure-resource-manager/resource-group-template-functions-comparison.md
articles/azure-resource-manager/resource-group-template-functions-deployment.md
articles/azure-resource-manager/resource-group-template-functions-logical.md
articles/azure-resource-manager/resource-group-template-functions-numeric.md

@changeworld : Thanks for your contribution! The author(s) have been notified to review your proposed change.

@changeworld : Thanks for your contribution! The author(s) have been notified to review your proposed change.

@divyaswarnkar, these edits look OK. Please sign off? Thanks!

@divyaswarnkar, sign off please? Thanks!

#sign-off


beware of using an older version of protoc-gen-go for now

newer versions of protoc-gen-go adds additional fields with dynamically changed values to generated structs. This brokes service.Hash() computation. When we complete #337 and create a Service type by hand, this will be fixed.

this is not needed anymore. we can use the latest versions


Make generated claims available after signing

Currently it’s a bit tricky to retrieve generated claims after signing a token since the claims fields only includes those that are not generated.
This proposed solution changes Signer.sign/2 to move any generated claims into claims and clear out the claims_generation map after signing. This way it’s easy to just use Joken.get_claims/1 to fetch the claims that were signed.
Please let me know if you have any suggestions related to this. Thanks!

This looks good to me!
Em 27/02/2016 11:17 AM, "Leo Nikkilä"<EMAIL_ADDRESS>escreveu:

Currently it’s a bit tricky to retrieve generated claims after signing a
token since the claims fields only includes those that are not generated.
This proposed solution changes Signer.sign/2 to move any generated claims
into claims and clear out the claims_generation map after signing. This
way it’s easy to just use Joken.get_claims/1 to fetch the claims that
were signed.
Please let me know if you have any suggestions or questions related to
this. Thanks!
You can view, comment on, or merge this pull request online at:
https://github.com/bryanjos/joken/pull/115
Commit Summary

Make generated claims available after signing

File Changes

M lib/joken.ex
https://github.com/bryanjos/joken/pull/115/files#diff-0 (10)
M lib/joken/signer.ex
https://github.com/bryanjos/joken/pull/115/files#diff-1 (2)
M test/joken_test.exs
https://github.com/bryanjos/joken/pull/115/files#diff-2 (16)

Patch Links:

https://github.com/bryanjos/joken/pull/115.patch
https://github.com/bryanjos/joken/pull/115.diff

—
Reply to this email directly or view it on GitHub
https://github.com/bryanjos/joken/pull/115.

:+1: Thanks @lnikkila!

Great, thanks for your time!


[Bug]: DNS errors when cloning with GitHub API key

Error Message and Logs
The main error is fatal: unable to access 'https://github.com/myorg/repo.git/': Could not resolve host: github.com
2024-Nov-16 11:49:01.340379
docker run -d --network coolify --name jgs0g0kwk4wc84gckoccw4gk --rm -v /var/run/docker.sock:/var/run/docker.sock ghcr.io/coollabsio/coolify-helper:1.0.4
2024-Nov-16 11:49:01.340379
d8b0bf22d96ae4718a2f969414c0fdbee04a5224c7799dd994ebe7b72d4b3805
2024-Nov-16 11:49:06.162130
docker exec jgs0g0kwk4wc84gckoccw4gk bash -c 'GIT_SSH_COMMAND="ssh -o ConnectTimeout=30 -p 22 -o Port=22 -o LogLevel=ERROR -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null" git ls-remote<EMAIL_ADDRESS>trunk'
2024-Nov-16 11:49:06.162130
d94960e8c4ca17e5ca599fa2894751b50be49465	refs/heads/trunk
2024-Nov-16 11:49:06.534257
----------------------------------------
2024-Nov-16 11:49:06.542906
Importing myorg/repo:trunk (commit sha HEAD) to /artifacts/jgs0g0kwk4wc84gckoccw4gk.
2024-Nov-16 11:49:06.884216
docker exec jgs0g0kwk4wc84gckoccw4gk bash -c 'git clone -b "trunk"<EMAIL_ADDRESS>/artifacts/jgs0g0kwk4wc84gckoccw4gk && cd /artifacts/jgs0g0kwk4wc84gckoccw4gk && GIT_SSH_COMMAND="ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null" git submodule update --init --recursive && cd /artifacts/jgs0g0kwk4wc84gckoccw4gk && GIT_SSH_COMMAND="ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null" git lfs pull'
2024-Nov-16 11:49:06.884216
Cloning into '/artifacts/jgs0g0kwk4wc84gckoccw4gk'...
2024-Nov-16 11:49:18.966696
fatal: unable to access 'https://github.com/myorg/repo.git/': Could not resolve host: github.com
2024-Nov-16 11:49:19.097716
Oops something is not okay, are you okay? 😢
2024-Nov-16 11:49:19.108455
Cloning into '/artifacts/jgs0g0kwk4wc84gckoccw4gk'...
2024-Nov-16 11:49:19.108455
fatal: unable to access 'https://github.com/myorg/repo.git/': Could not resolve host: github.com
2024-Nov-16 11:49:19.118653
Deployment failed. Removing the new version of your application.

Steps to Reproduce

Deploy an app from GitHub via "Private Repository (with GitHub app)" setting.

Example Repository URL
No response
Coolify Version
v4.0.0-beta.370
Are you using Coolify Cloud?
No (self-hosted)
Operating System and Version (self-hosted)
Ubuntu 22.04
Additional Information
I'd presume it's something to do with #2568.
I have verified by reproducing manually the error is occurring specifically with the git clone. I can connect to github.com via HTTPS from both the host machine and containers running on it with curl

@jarhodes314
if you run nslookup github.com what server is it using for name lookup
the build is running inside a container and can have a different dns resolver, which im guessing isnt working properly
also can you check
journalctl -u docker.service -S "15 minutes ago"

its possible to set a dns resolver in /etc/docker/daemon.json
you can add
{
    dns: ["<IP_ADDRESS>","<IP_ADDRESS>"],
   ...
}

also can you check
journalctl -u docker.service -S "15 minutes ago"

Nov 16 15:10:39 orangepi5 dockerd[1486]: time="2024-11-16T15:10:39.954399833Z" level=warning msg="failed to close stdin: NotFound: task 8944193b3f9b2dbf2dc0ec5cad69e49f77bb366f825f61c83a1054538f46cf5d not found: not found"
Nov 16 15:11:07 orangepi5 dockerd[1486]: time="2024-11-16T15:11:07.211745017Z" level=info msg="ignoring event" container=5e31d0c5beeb160840c8e0b817949f234245b73e74e239309f5c24a2a6cc025d module=libcontainerd namespace=moby topic=/tasks/delete type="*events.TaskDelete"
Nov 16 15:12:11 orangepi5 dockerd[1486]: time="2024-11-16T15:12:11.496145327Z" level=info msg="ignoring event" container=5e31d0c5beeb160840c8e0b817949f234245b73e74e239309f5c24a2a6cc025d module=libcontainerd namespace=moby topic=/tasks/delete type="*events.TaskDelete"
Nov 16 15:13:02 orangepi5 dockerd[1486]: time="2024-11-16T15:13:02.702974845Z" level=info msg="ignoring event" container=b4f99d7bd9134541fc3c1bb31bff31ad2140b1d108d3e985d8fa62b7d8c5d6c7 module=libcontainerd namespace=moby topic=/tasks/delete type="*events.TaskDelete"
Nov 16 15:13:03 orangepi5 dockerd[1486]: time="2024-11-16T15:13:03.003601954Z" level=error msg="error removing container" container=b4f99d7bd9134541fc3c1bb31bff31ad2140b1d108d3e985d8fa62b7d8c5d6c7 error="removal of container b4f99d7bd9134541fc3c1bb31bff31ad2140b1d108d3e985d8fa62b7d8c5d6c7 is already in progress"
Nov 16 15:13:15 orangepi5 dockerd[1486]: time="2024-11-16T15:13:15.709254554Z" level=info msg="ignoring event" container=5e31d0c5beeb160840c8e0b817949f234245b73e74e239309f5c24a2a6cc025d module=libcontainerd namespace=moby topic=/tasks/delete type="*events.TaskDelete"
Nov 16 15:13:16 orangepi5 dockerd[1486]: time="2024-11-16T15:13:16.676568166Z" level=info msg="ignoring event" container=26140dc0aeae419a2f2b20d9db802604dc6b23d552889ddbf8fe823a80be8e27 module=libcontainerd namespace=moby topic=/tasks/delete type="*events.TaskDelete"
Nov 16 15:13:16 orangepi5 dockerd[1486]: time="2024-11-16T15:13:16.716571496Z" level=warning msg="failed to close stdin: NotFound: task 26140dc0aeae419a2f2b20d9db802604dc6b23d552889ddbf8fe823a80be8e27 not found: not found"

From another container, including within the coolify network, I can copy the git command that coolify is running and it works as expected (i.e. it fails with an authentication error because I haven't supplied the token).
Additionally:
docker exec jgs0g0kwk4wc84gckoccw4gk bash -c 'GIT_SSH_COMMAND="ssh -o ConnectTimeout=30 -p 22 -o Port=22 -o LogLevel=ERROR -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null" git ls-remote<EMAIL_ADDRESS>trunk'
2024-Nov-16 11:49:06.162130
d94960e8c4ca17e5ca599fa2894751b50be49465	refs/heads/trunk
2024-Nov-16 11:49:06.534257

from the start of the logs indicates that the container is able to connect to github as it's retrieving the correct git hash. I am unable to reproduce the issue outside of the coolify build process.


accounts balance happend but dont refresh on ganache or metamask

Hello i have unboxing Quintor on this enviroment:
Windows 10
Truffle v5.0.0 (core: 5.0.0)
Solidity v0.5.0 (solc-js)
Node v10.14.2
Angular :  (7.1.0)
Visual Code Studio like IDE
Ganache
I have unboxing, compile and migration well.
I can see the web app but the first thing that I see extrange is in real balance in ganache or metamask first acount have 9999.98 ETH and in web i see You have 10000 META
The address of the first account is the same that I have in ganache or metamask 0xF6E59Ed94346E1e2e7e232A92b640761e4851E83. If i selected other account in metamask this address change.  So I try to send 100 to  my second address ( I have 5) 0xA49E8fDD3033Ed17cf732dbB94B0E15eC9d3339A and when I send MetaCoin appears this:

But the receiver address is always this: 0xBF5FaBf41260908c7A5505D0204a79da3a36DF77
I push on Confirm  say transaction done , on app i see 9990 but on ganache or metamask nothing have been changed.

I have reset the migration with truffle migration --reset
And now the receiver address is 0xb178A84028d0931300AC5c66a53eEd54f69dd7d0
In ganache ui i see the transaction but dont send the metacoin:

This is a common issue when restarting ganache. You can reset metamask by going to Settings -> reset account

I'm assuming this is resolved, if you have more issues, feel free to reopen this issue or open a new one:)


chore: fix custom build with zwave-js

Fixes #3352

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

19 of 20 (95.0%)  changed or added relevant lines in 2 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.1%) to 21.515%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

vite.config.js
0
1
0.0%

Totals

Change from base Build<PHONE_NUMBER>:
0.1%

Covered Lines:
3700

Relevant Lines:
18318

💛  - Coveralls


Cleaned and updated modules for projectGenerator

This pull request add OpenAL sound for all supported platforms (Mac, Linux and Windows), OpenGL is added by default on Windows platform while generating Visual Studio solution along with libGLEW as another project dependences
Also it include some cleaning of not-used and some other retardness what I found inside projectGenerator
<3 Dušan Jocić

This PR has redundant changes from #1522. Perhaps should close it and instead use this PR only?

Thanks Dušan for you help!
I just wait for @Areloch because maybe he prefer to merge template PR before accepting new PRs that make changes on data (scripts).


Update iOS build signing error message

Description
Updated fixWithDevelopmentTeamInstruction to match the most recent version of Xcode which moved the singing configurations from General tab to Signing & Capabilities tab.
Related Issues
Issue #45821
Tests
Checklist
Before you create this PR confirm that it meets all requirements listed below by checking the relevant checkboxes ([x]). This will ensure a smooth and quick review process.

[x] I read the Contributor Guide and followed the process outlined there for submitting PRs.
[x] I signed the CLA.
[x] I read and followed the Flutter Style Guide, including Features we expect every widget to implement.
[x] I updated/added relevant documentation (doc comments with ///).
[x] All existing and new tests are passing.
[x] The analyzer (flutter analyze --flutter-repo) does not report any problems on my PR.
[x] I am willing to follow-up on review comments in a timely manner.

Breaking Change
Does your PR require Flutter developers to manually update their apps to accommodate your change?

[ ] Yes, this is a breaking change (Please read Handling breaking changes). Replace this with a link to the e-mail where you asked for input on this proposed change.
[x] No, this is not a breaking change.

@Hixie Is it possible to get an exemption from the test rule for this change. I don't think it's needed in this instance.

Matches https://flutter.dev/docs/get-started/install/macos#deploy-to-ios-devices, LGTM

Well... kind of matches.  I leave this up to @christopherfujino

For Xcode 9 & 10, look under General > Signing > Team.
For Xcode 11 and newer, look under Signing & Capabilities > Team.

Not a problem at all. I will make this change ny tomorrow :)
On Thu, 5 Dec 2019, 9:17 pm Christopher Fujino<EMAIL_ADDRESS>wrote:

@christopherfujino requested changes on this pull request.
Thanks for making this PR @amrfarid140 https://github.com/amrfarid140!
To be safe, can we be consistent with the website, and offer both
instructions, since we support XCode 10.2 and later. Can you make it
something to the effect of:
3- Make sure a 'Development Team' is selected in either the 'Signing & Capabilities' tab (for Xcode 11 and newer) or the 'General' tab (for Xcode 10).
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@amrfarid140 thank you! Note, I just opened this PR to remove the reference to Xcode 9 on the website, so you don't need to reference version 9.

@christopherfujino @jmagman I've updated the message to match https://flutter.dev/docs/get-started/install/macos#deploy-to-ios-devices

This should be tested — there should be a test that verifies that the output is what we expect when this condition is met.


Infinite redirect with internationalization

Hugo version
Hugo Static Site Generator v0.58.3/extended darwin/amd64

Latest netlify version (?)

config.yaml

assetDir: static
publishDir: generated

DefaultContentLanguage: en
languages:
    en:
        languageName: English
        weight: 1
    nl:
        languageName: Nederlands
        weight: 2

disableHugoGeneratorInject: true
#disableLiveReload: true

enableRobotsTXT: true

permalinks:
  posts: /:year/:month/:title/

outputFormats:
    - html:
        minify:true

minificationOptions:
    KeepConditionalComments: true
    KeepEndTags: false

Issue
When visiting the default language on /en/, the site keeps infinitely redirecting.
This is because of response:
<!DOCTYPE html><title></title><link rel="canonical" href=""/><meta name="robots" content="noindex"><meta charset="utf-8" /><meta http-equiv="refresh" content="0; url=" />

I guess this is because of a missing baseURL. We should probably fail the build when that is blank.

@bep I'm not quire sure why /en/ does not simply redirect to /.

See my comment.

Can confirm it works with baseURL: /.
Can we default to /?

Can we default to /

No, "/" is not a valid baseURL. A valid baseURL is https://example.com. As I said, we do not validate these at the moment, which we probably should. At least make sure it's not empty.

Can we make this mandatory with i18n?

Reopen

Reopen

Reopen


Add "bevy_input_focus" crate.

Objective
Define a framework for handling keyboard focus and bubbled keyboard events, as discussed in #15374.
Solution
Introduces a new crate, bevy_input_focus. This crate provides:

A resource for tracking which entity has keyboard focus.
Methods for getting and setting keyboard focus.
Event definitions for triggering bubble-able keyboard input events to the focused entity.
A system for dispatching keyboard input events to the focused entity.

This crate does not provide any integration with UI widgets, or provide functions for
tab navigation or gamepad-based focus navigation, as those are typically application-specific.
Testing
Most of the code has been copied from a different project, one that has been well tested. However, most of what's in this module consists of type definitions, with relatively small amounts of executable code. That being said, I expect that there will be substantial bikeshedding on the design, and I would prefer to hold off writing tests until after things have settled.
I think that an example would be appropriate, however I'm waiting on a few other pending changes to Bevy before doing so. In particular, I can see a simple example with four buttons, with focus navigation between them, and which can be triggered by the keyboard.
@alice-i-cecile

I really want this, and I like the fundamental design. At the start of the 0.16 cycle I'm going to come back to this, evaluate scope and then decide how to proceed. We may want a working group for all of the follow-up.

One thing that has not been established is the relationship between this and bevy_a11y. I had originally assumed that this would depend on bevy_a11y, but it sounds like you were thinking that it would be the other way around.

Yeah, my expectation is that bevy_a11y is a consumer of the focus data produced here from user input, and so should depend on this crate.

I've got a library waiting in the wings that needs keyboard focus before moving forward. I'll be happy to attempt integrating this as a means to exercise and review it.

There are two immediate follow-ups that need to happen:

Integration with bevy_a11y
@NthTensor's proposed change to allow bubbled events to propagate to the window. This is important because it allows "global" key handlers to work regardless of whether focus is Some or None:

If focus is None, keyboard events are dispatched to the window directly
If focus is Some, keyboard events are first processed by the focus widget and its ancestors, and if none of them call propagate(false) then the window gets the event.

I'll have time to publish that work tomorrow.

I've updated this so that if the current KeyboardFocus entity is None, then keyboard events are dispatched to the PrimaryWindow instead of all windows. If there's no primary window, then the event is dropped.
One open issue is where this crate belongs in the dependency order. Right now it's last in the list, but the plan is for bevy_a11y to depend on it, so it will likely need to come before that. (The actual dependency will be added in a follow-up PR.)

@cart and I have approved this approach in principle here, so we should nail down the preliminary details via the standard review process and get this merged.

I've updated the PR as follows:

Rebased to latest Bevy main
Re-ordered the new crate to come after both bevy_input and bevy_a11y, but not at the end of the list.
Added KeyboardFocusVisible resource based on our discussion on Discord.
Added trait extensions for World and DeferredWorld for testing whether an element, or its descendants, have keyboard focus.

Does this still need bubble-to-window? I have an open PR if so.

Does this still need bubble-to-window? I have an open PR if so.

Yes, this is assuming that bubble-to-window is available.

@NthTensor Consider the following scenario: Let's say we have a blender-like app which has a lot of hotkeys. Say the user presses 'q' for 'quit'. There are three cases:

No widget has focus (focus = None) - the keyboard event gets sent directly to the primary window, where it is handled and processed through the global hotkey map.
A button has focus - the keyboard event is dispatched to the button entity, but since button doesn't recognize 'q' as a shortcut, it bubbles all the way to the top, eventually reaching the window, where it has handled as in the previous case.
A text input field has focus - in this case, the key is sent to the text input entity, which does recognize 'q' - so it calls propagate(false), and inserts the letter 'q' into the text buffer. The keyboard even never reaches the window.

The code in this PR handles the 'no-focus' case by detecting focus = None and sending the event directly to the primary window. But I still need your PR to handle the second case, where the event is sent to the focus entity, but bubbles to the top of the hierarchy.

I was thinking in local multiplayer with the impression that it might need something more complex. But I think this PR design is also OK for local multiplayer. These are game specific considerations:

in the menus, the game can decide that the focus can be changed by any player (input device)… or it could be locked to "Player 1" (or whoever opened the pause menu)
a character selection screen is purely game code

@doup

I was thinking in local multiplayer with the impression that it might need something more complex. But I think this PR design is also OK for local multiplayer.

I deliberately decided to punt on the issue of multiple game controllers. First, I don't know if there are any games which use multiple controllers which also have the concept of "navigable focus". Second, even if there were such games, I'm not sure that they would conform to our expectations - that is, we could easily end up building something that didn't match the needs of those games. So it seems like the best solution is for games like that to build their own solutions.

@viridia, imo the approach of this PR is correct. In https://github.com/bevyengine/bevy/pull/15611#issuecomment-2511020639 I meant that multi-focus is NOT something Bevy should solve.
I checked Super Mario Wonder which has local multiplayer:

Player1 controls the "focus" in general menus
While playing if any of the players pauses the game they have control of the "focus" on the pause menu.
The only instance where multiple focus is used in the player select screen. This is game code, not Bevy's responsibility.

I don't have any other local multiplayer game, but I suspect is similar in any another game. Although, it would be nice if someone could check this in a fighting game or in Mario Kart. :-)

@viridia ping me when CI is green and I'll give this a final review before merging.

@mockersf One of the reasons why the CI is failing is because I wrote the dependencies as "0.15.0", however Bevy mainline in still has its version numbers as "0.15.0-dev".

I think main always uses the -dev suffix.

@alice-i-cecile I fixed the problem with the deps. I don't know what the other CI failures are, or whether they have anything to do with this PR.

All prs that touch the Cargo.toml are currently failing on that check-ban. It's flagging a newly unmaintained crate deep in our dependency tree (which incidentally happens to owned by a colleague). Don't think we can fix it unless the crate we depend on updates. Maybe there's a way to ignore it?


Interrupting the program yields panic: send on closed channel

What happened:
Interrupting the program yields panic: send on closed channel
What you expected to happen:
Interrupt silently
How to reproduce it (as minimally and precisely as possible):
Run a build with docforge and hit ctrl+C (or the Mac analogue) before it ends

/priority low

This issue does not present anymore
/close


WIP: fix rotated inertia

This PR fixes a bug when applying a rotation to inertia matrices defined in the URDF file.
WIP on correcting the floating-base case for the ant leg.

@googlebot I signed it!

Thanks a lot Eric. It looks like the new tests don't pass in CI?

I forgot to commit my changes to test_rbdl so that a random base transform is only set for the tests of floating-base and not fixed-base systems.

Thanks for the contribution, LGTM.


[windows] Update the released_suites list

Since the windows wrt suites have been merged,  need to add the suites to this release list
Since the webapi-appsecurityapi-external-tests suite is ready, need to add the suite to cordova and windows release list

LGTM


Display of projects without Activity Score

According to the docs the calculation of an Activity Score is optional.
However when creating a repos.json without _InnerSourceMetadata.score then the UI shows false instead of the score. (see screenshot)
I am wondering if that is the expected behavior?

I further notices that in the project popup a missing score leads to a display of undefined. See screenshot.

fixed with #9

fixed with #9


Create Your SAP Fiori Launchpad Project and its Module

Tutorial URL: https://developers.sap.com/tutorials/cp-workflow-2-create-module-cf.html
Please specify the step you are referring to 4.2
Copy the URL to your browser.
If the URL does not work, append /cp.portal to it.
This gives an internal server error as shown below-
https://4ccf4faftrial-dev-workflowtilesapprouter.cfapps.eu10.hana.ondemand.com/cp.portal/

Regards,
Saritha K

Hi,
We are currently working on this tutorial. The content has to be adapted to use the SAP Business Application Studio before you can execute it.
Best regards,
Astrid

@AstridHerbst Any updates?

Hi,
The new tutorial for Trial is available at: https://developers.sap.com/group.cp-workflow-cf.html
Best regards, Astrid

Thank you Astrid.
Will try it out.
Regards,
Saritha
From: Astrid Herbst<EMAIL_ADDRESS>Sent: 05 October 2020 11:48
To: SAPDocuments/Tutorials<EMAIL_ADDRESS>Cc: Saritha Koroth<EMAIL_ADDRESS>Author<EMAIL_ADDRESS>Subject: Re: [SAPDocuments/Tutorials] Create Your SAP Fiori Launchpad Project and its Module (#5474)
Hi,
The new tutorial for Trial is available at: https://developers.sap.com/group.cp-workflow-cf.htmlhttps://apc01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdevelopers.sap.com%2Fgroup.cp-workflow-cf.html&data=01|01|saritha.koroth%40invenio-solutions.com|5be04e1e80d340ddb07d08d868f66a74|e83d39e6f19d49ca826107ae0659a01c|0&sdata=7R3TwIwfARSCOuSeP8gBIGs4pahdoPlSKRNFA2cpvkg%3D&reserved=0
Best regards, Astrid
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IP address contention in CI

Integration and BATS tests run in the same subnet and contend for IP addresses. This can lead to flakes. Each test type should have its own subnet.

Resolved by specifying non-overlapping blocks in bosh manifests.


Feat/autocomplete

Functional but not optimal, its very trick to set the initial value on this component using data from api request, specially if you are using it as a select input with pair label/value because the initial value must be in the first api request page if you have many options and the requests are paginated.

I have added examples for single and multiple options.
To do:

Check a bug with multiple and required prop when form initialData is set, the submit wont recognize that input is filled with data but it's received by the formData submit

ts <Autocomplete multiple required />

Create more tests

@allandiego Do have interest to update your pull request to cover all tests case for the autocomplete resource?


Correction for raw affiliation INRA, LEPSE, UMR 759, INRA/AGRO-M, Montpellier, France#TAB#

Correction needed for raw affiliation INRA, LEPSE, UMR 759, INRA/AGRO-M, Montpellier, France#TAB#
raw_affiliation_name: INRA, LEPSE, UMR 759, INRA/AGRO-M, Montpellier, France#TAB#
new_rors: 05y503v71;03fte3n86
previous_rors: 05y503v71
works_examples: W290374981
contact: b044a6a04a4fd108884eaba5a533bf77:83e01b994f887d03ea37cf2150beccd7154d @ inrae.fr

This issue was accepted and ingested by the OpenAlex team on 2024-10-09. The new affiliations should be visible within the next 7 days.


Fix GroupBy limit push down descending sorting on numeric columns

This PR fixes a bug in GroupBy where results are incorrectly merged due to a comparator bug when using numeric dimensions with numeric descending sort order.

👍


Support CNAME for API gateway

I want to create a custom DNS name (api.hsl.fi), point it to umbrella.digipalvelutehdas.fi and configure it in APIKA as an alias for umbrella.digipalvelutehdas.fi/hsl

@tuukka Is this needed if APIKA supports #23? In other words, proxy can be deployed directly at api.hsl.fi? There is an upstream issue in API Umbrella about CNAME. See https://github.com/NREL/api-umbrella/issues/213


Change Double Scenes to be saved (and with correct scripts).

Currently when you double a scene, it is only stored in the variable and not saved. This was a half-measure because when saving it, it would use the wrong scripts.

Save the scene
Make sure the scripts are correct

It seems this was already achieved at some point.  Closing until further notice.

My bad. I was reading the wrong part of the code base. Currently we handling all of the instancing inside the scene data script itself (this is because we lose the reference to meta values if we save then instance).
We could create a scene/script data resource to be saved or move script double instancing to script-data to be consistent.

This should have been solved a while ago.


Symfony dependencies using old versions preventing use of other libraries

Hi team. This module is necessary for any Silverstripe install. While the Symfony finder and yaml components being set at version constraint "^3.4 || ^4.0" are technically still supported by a 4.4 version, that's quite aged (and 3.4 being well beyond eol). Current version is 6.x with 5.x tracks for each.
This is preventing use of other libraries through the broader PHP ecosystem in a Silverstripe project, as these libs are using 6.x or at least at a minimum 5.x.
Can this please be reviewed. I'm unfamiliar with the inners of these modules and what effort is involved in at least support 5.x, and can appreciate there might be some work involved. But given the team's recent push to keep up to date / drop support / stay in sync with broader ecosystem EOLs I'm hoping that you're giving equal attention to lifting support as these libraries lift their versions too. Not immediately - but hopefully surely after major is bumped twice.
Example library: https://github.com/PHLAK/Splat
Cheers.

Thanks @dizzystuff, there’s some discussion about this happening in https://github.com/silverstripe/silverstripe-framework/issues/10220.


Allow slice for FixedLengthByteArray decimals to improve performance

At the moment for fixed length byte array decimals the array is never addressable so it calls reflect.New for each value. There is some code to check the addressability of the array but my understanding is that it will never be addressable requiring this allocation each time. Using a slice of the correct size allows skipping this allocation.
For pointer fields currently the schema functions will show the kind as reflect.Pointer and cause it to throw an invalid field tag error. Added some code to check if the type is a pointer and get the underlying value with Elem.

reflect.Pointer was introduced in Go 1.18, it used to be named reflect.Ptr which causes a build failure with Go 1.17:
# github.com/segmentio/parquet-go [github.com/segmentio/parquet-go.test]
Error: ./schema.go:441:22: undefined: reflect.Pointer

I had to make more changes as doing f.Type == f.Type.Elem caused other parts of the code to break since they do this check as well. Not sure if this is really the best way to do it but does scope the type to this single function.


Specificy specific trials

Right now the code only takes a maximum number of trails. However, sometimes we want to run trials 1,2,3 of an experiment and then trials 4,5,6.

Resolved by https://github.com/logic-and-learning-lab/ilp-experiments/pull/8


Add HoangVu personal page

Description
This PR is to add my personal page (HoangVu)
Additional Information

[x] I have read the contributing docs (if this is your first contribution)
[x] This is not a duplicate of any existing pull request

Related Issues
Your ENS/address: 0xDbD3834299D363B4Fb84Aef9309d3068E52CFc0C

@carletex Please help me review this PR

Looks clean and responsive J !! Just added a small comment, could you please update it ?

Looks clean and responsive J !! Just added a small comment, could you please update it ?
I updated the code, please help me review my PR again