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Documentation

The following documentation of Intel Open Image Denoise can also be found as a pdf document.

Open Image Denoise API

Open Image Denoise provides a C99 API (also compatible with C++) and a C++11 wrapper API as well. For simplicity, this document mostly refers to the C99 version of the API.

The API is designed in an object-oriented manner, e.g. it contains device objects (OIDNDevice type), buffer objects (OIDNBuffer type), and filter objects (OIDNFilter type). All objects are reference-counted, and handles can be released by calling the appropriate release function (e.g. oidnReleaseDevice) or retained by incrementing the reference count (e.g. oidnRetainDevice).

An important aspect of objects is that setting their parameters do not have an immediate effect (with a few exceptions). Instead, objects with updated parameters are in an unusable state until the parameters get explicitly committed to a given object. The commit semantic allows for batching up multiple small changes, and specifies exactly when changes to objects will occur.

All API calls are thread-safe, but operations that use the same device will be serialized, so the amount of API calls from different threads should be minimized.

Examples

To have a quick overview of the C99 and C++11 APIs, see the following simple example code snippets.

Basic Denoising (C99 API)

#include <OpenImageDenoise/oidn.h>
...

// Create an Open Image Denoise device
OIDNDevice device = oidnNewDevice(OIDN_DEVICE_TYPE_DEFAULT); // CPU or GPU if available
// OIDNDevice device = oidnNewDevice(OIDN_DEVICE_TYPE_CPU);
oidnCommitDevice(device);

// Create buffers for input/output images accessible by both host (CPU) and device (CPU/GPU)
OIDNBuffer colorBuf  = oidnNewBuffer(device, width * height * 3 * sizeof(float));
OIDNBuffer albedoBuf = ...

// Create a filter for denoising a beauty (color) image using optional auxiliary images too
// This can be an expensive operation, so try not to create a new filter for every image!
OIDNFilter filter = oidnNewFilter(device, "RT"); // generic ray tracing filter
oidnSetFilterImage(filter, "color",  colorBuf,
                   OIDN_FORMAT_FLOAT3, width, height, 0, 0, 0); // beauty
oidnSetFilterImage(filter, "albedo", albedoBuf,
                   OIDN_FORMAT_FLOAT3, width, height, 0, 0, 0); // auxiliary
oidnSetFilterImage(filter, "normal", normalBuf,
                   OIDN_FORMAT_FLOAT3, width, height, 0, 0, 0); // auxiliary
oidnSetFilterImage(filter, "output", colorBuf,
                   OIDN_FORMAT_FLOAT3, width, height, 0, 0, 0); // denoised beauty
oidnSetFilterBool(filter, "hdr", true); // beauty image is HDR
oidnCommitFilter(filter);

// Fill the input image buffers
float* colorPtr = (float*)oidnGetBufferData(colorBuf);
...

// Filter the beauty image
oidnExecuteFilter(filter);

// Check for errors
const char* errorMessage;
if (oidnGetDeviceError(device, &errorMessage) != OIDN_ERROR_NONE)
  printf("Error: %s\n", errorMessage);

// Cleanup
oidnReleaseBuffer(colorBuf);
...
oidnReleaseFilter(filter);
oidnReleaseDevice(device);

Basic Denoising (C++11 API)

#include <OpenImageDenoise/oidn.hpp>
...

// Create an Open Image Denoise device
oidn::DeviceRef device = oidn::newDevice(); // CPU or GPU if available
// oidn::DeviceRef device = oidn::newDevice(oidn::DeviceType::CPU);
device.commit();

// Create buffers for input/output images accessible by both host (CPU) and device (CPU/GPU)
oidn::BufferRef colorBuf  = device.newBuffer(width * height * 3 * sizeof(float));
oidn::BufferRef albedoBuf = ...

// Create a filter for denoising a beauty (color) image using optional auxiliary images too
// This can be an expensive operation, so try no to create a new filter for every image!
oidn::FilterRef filter = device.newFilter("RT"); // generic ray tracing filter
filter.setImage("color",  colorBuf,  oidn::Format::Float3, width, height); // beauty
filter.setImage("albedo", albedoBuf, oidn::Format::Float3, width, height); // auxiliary
filter.setImage("normal", normalBuf, oidn::Format::Float3, width, height); // auxiliary
filter.setImage("output", colorBuf,  oidn::Format::Float3, width, height); // denoised beauty
filter.set("hdr", true); // beauty image is HDR
filter.commit();

// Fill the input image buffers
float* colorPtr = (float*)colorBuf.getData();
...

// Filter the beauty image
filter.execute();

// Check for errors
const char* errorMessage;
if (device.getError(errorMessage) != oidn::Error::None)
  std::cout << "Error: " << errorMessage << std::endl;

Denoising with Prefiltering (C++11 API)

// Create a filter for denoising a beauty (color) image using prefiltered auxiliary images too
oidn::FilterRef filter = device.newFilter("RT"); // generic ray tracing filter
filter.setImage("color",  colorBuf,  oidn::Format::Float3, width, height); // beauty
filter.setImage("albedo", albedoBuf, oidn::Format::Float3, width, height); // auxiliary
filter.setImage("normal", normalBuf, oidn::Format::Float3, width, height); // auxiliary
filter.setImage("output", outputBuf, oidn::Format::Float3, width, height); // denoised beauty
filter.set("hdr", true); // beauty image is HDR
filter.set("cleanAux", true); // auxiliary images will be prefiltered
filter.commit();

// Create a separate filter for denoising an auxiliary albedo image (in-place)
oidn::FilterRef albedoFilter = device.newFilter("RT"); // same filter type as for beauty
albedoFilter.setImage("albedo", albedoBuf, oidn::Format::Float3, width, height);
albedoFilter.setImage("output", albedoBuf, oidn::Format::Float3, width, height);
albedoFilter.commit();

// Create a separate filter for denoising an auxiliary normal image (in-place)
oidn::FilterRef normalFilter = device.newFilter("RT"); // same filter type as for beauty
normalFilter.setImage("normal", normalBuf, oidn::Format::Float3, width, height);
normalFilter.setImage("output", normalBuf, oidn::Format::Float3, width, height);
normalFilter.commit();

// Prefilter the auxiliary images
albedoFilter.execute();
normalFilter.execute();

// Filter the beauty image
filter.execute();

Upgrading from Open Image Denoise 1.x

Open Image Denoise 2 introduces GPU support, which requires implementing some minor changes in applications. There are also small API changes, additions and improvements in this new version. In this section we summarize the necessary code modifications and also briefly mention the new features that users might find useful when upgrading to version 2.x. For a full description of the changes and new functionality, please see the API reference.

Buffers

The most important required change is related to how data is passed to Open Image Denoise. If the application is explicitly using only the CPU (by specifying OIDN_DEVICE_TYPE_CPU), no changes should be necessary. But if it wants to support GPUs as well, passing pointers to memory allocated with the system allocator (e.g. malloc) would raise an error because GPUs cannot access such memory in almost all cases.

To ensure compatibility with any kind of device, including GPUs, the application should use OIDNBuffer objects to store all image data passed to the library. Memory allocated using buffers is by default accessible by both the host (CPU) and the device (CPU or GPU).

Ideally, the application should directly read and write image data to/from such buffers to avoid redundant and inefficient data copying. If this cannot be implemented, the application should try to minimize the overhead of copying as much as possible:

• Data should be copied to/from buffers only if the data in system memory indeed cannot be accessed by the device. This can be determined by simply querying the systemMemorySupported device parameter. If system memory is accessible by the device, no buffers are necessary and filter image parameters can be set with oidnSetSharedFilterImage.

• If the image data cannot be accessed by the device, buffers must be created and the data must be copied to/from these buffers. These buffers should be directly passed to filters as image parameters instead of the original pointers using oidnSetFilterImage.

• Data should be copied asynchronously using using the new oidnReadBufferAsync and oidnWriteBufferAsync functions, which may achieve higher performance than plain memcpy.

• If image data must be copied, using the default buffer allocation may not be the most efficient method. If the device memory is not physically shared with the host memory (e.g. for dedicated GPUs), higher performance may be achieved by creating the buffers with device storage (OIDN_STORAGE_DEVICE) using the new oidnNewBufferWithStorage function. This way, the buffer data cannot be directly accessed by the host anymore but this should not matter because the data must be copied from some other memory location anyway. However, this ensures that the data is stored only in high-performance device memory, and the user has full control over when and how the data is transferred between host and device.

The oidnMapBuffer and oidnUnmapBuffer functions have been removed from the API due to these not being supported by any of the device backends. Please use oidnReadBuffer(Async) and oidnWriteBuffer(Async) instead.

Interop with Compute (SYCL, CUDA, HIP) and Graphics (DX, Vulkan, Metal) APIs

If the application is explicitly using a particular device type which supports unified memory allocations, e.g. SYCL or CUDA, it may directly pass pointers allocated using the native allocator of the respective compute API (e.g. sycl::malloc_device, cudaMalloc) instead of using buffers. This way, it is the responsibility of the user to correctly allocate the memory for the device.

In such cases, it often necessary to have more control over the device creation as well, to ensure that filtering is running on the intended device and command queues or streams from the application can be shared to improve performance. If the application is using the same compute or graphics API as the Open Image Denoise device, this can be achieved by creating devices with oidnNewSYCLDevice, oidnNewCUDADevice, etc. For some APIs there are additional interoperability functions as well, e.g. oidnExecuteSYCLFilterAsync.

If the application is using a graphics API which does not support unified memory allocations, e.g. DX12 or Vulkan, it may be still possible to share memory between the application and Open Image Denoise using buffers, avoiding expensive copying through host memory. External buffers can be imported from graphics APIs with the new oidnNewSharedBufferFromFD and oidnNewSharedBufferFromWin32Handle functions. To use this feature, buffers must be exported in the graphics API and must be imported in Open Image Denoise using the same kind of handle. Care must be taken to select an external memory handle type which is supported by both APIs. The external memory types supported by an Open Image Denoise device can be queried using the externalMemoryTypes device parameter. Note that some devices do not support importing external memory at all (e.g. CPUs, and on GPUs it primarily depends on the installed drivers), so the application should always implement a fallback too, which copies the data through the host if there is no other supported way. Metal buffers can be used directly with the oidnNewSharedBufferFromMetal function.

Sharing textures is currently not supported natively but it is still possible avoid copying texture data by using a linear texture layout (e.g. VK_IMAGE_TILING_LINEAR in Vulkan) and sharing the buffer that backs this data. In this case, you should ensure that the row stride of the linear texture data is correctly set.

Importing external synchronization primitives (e.g. semaphores) from graphics APIs is not yet supported either but it is planned for a future release. Meanwhile, synchronizing access to shared memory should be done on the host using oidnSyncDevice and the used graphics API.

When importing external memory, the application also needs to make sure that the Open Image Denoise device is running on the same physical device as the graphics API. This can be easily achieved by using the new physical device feature, described in the next section.

Physical Devices

Although it is possible to explicitly create devices of a particular type (with, e.g., OIDN_DEVICE_TYPE_SYCL), this is often insufficient, especially if the system has multiple devices of the same type, and with GPU support it is very common that there are multiple different types of supported devices in the system (e.g. a CPU and one or more GPUs).

Open Image Denoise 2 introduces a simple physical device API, which enables the application to query the list of supported physical devices in the system, including their name, type, UUID, LUID, PCI address, etc. (see oidnGetNumPhysicalDevices, oidnGetPhysicalDeviceString, etc.). New logical device (i.e. OIDNDevice) creation functions for have been also introduced, which enable creating a logical device on a specific physical device: oidnNewDeviceByID, oidnNewDeviceByUUID, etc.

Creating a logical device on a physical device having a particular UUID, LUID or PCI address is particularly important when importing external memory from graphics APIs. However, not all device types support all types of IDs, and some graphics drivers may even report mismatching UUIDs or LUIDs for the same physical device, so applications should try to implement multiple identification methods, or at least assume that identification might fail.

Asynchronous Execution

It is now possible to execute some operations asynchronously, most importantly filtering (oidnExecuteFilterAsync, oidnExecuteSYCLFilterAsync) and copying data (the already mentioned oidnReadBufferAsync and oidnWriteBufferAsync).

When using any asynchronous function it is the responsibility of the application to handle correct synchronization using oidnSyncDevice.

Filter Quality

Open Image Denoise still delivers the same high image quality on all device types as before, including on GPUs. But often filtering performance is more important than having the highest possible image quality, so it is now possible to switch between multiple filter quality modes. Filters have a new parameter called quality, which defaults to the existing high image quality (OIDN_QUALITY_HIGH) but balanced (OIDN_QUALITY_BALANCED) and fast (OIDN_QUALITY_FAST) quality modes have been added as well for even higher performance. We recommend using balanced or fast quality for interactive and real-time use cases.

Small API Changes

A few existing API functions have been renamed to improve clarity (e.g. oidnSetFilter1i to oidnSetFilterInt) but the old function names are still available as deprecated functions. When compiling legacy code, warnings will be emitted for these deprecated functions. To upgrade to the new API, please simply follow the instructions in the warnings.

Some filter parameters have been also renamed (alignment to tileAlignment, overlap to tileOverlap). When using the old names, warnings will be emitted at runtime.

Building as a Static Library

The support to build Open Image Denoise as a static library (OIDN_STATIC_LIB CMake option) has been limited to CPU-only builds due to switching to a modular library design that was necessary for adding multi-vendor GPU support. If the library is built with GPU support as well, the OIDN_STATIC_LIB option is still available but enabling it results in a hybrid static/shared library.

If the main reason for building as a static library would be is the ability to use multiple versions of Open Image Denoise in the same process, please use the existing OIDN_API_NAMESPACE CMake option instead. With this feature all symbols of the library will be put into a custom namespace, which can prevent symbol clashes.

Physical Devices

Systems often have multiple different types of devices supported by Open Image Denoise (CPUs and GPUs). The application can get the list of supported physical devices and select which of these to use for denoising.

The number of supported physical devices can be queried with

int oidnGetNumPhysicalDevices();

The physical devices can be identified using IDs between 0 and (oidnGetNumPhysicalDevices() − 1), and are ordered approximately from fastest to slowest (e.g., ID of 0 corresponds to the likely fastest physical device). Note that the reported number and order of physical devices may change between application runs, so no assumptions should be made about this list.

Parameters of these physical devices can be queried using

bool         oidnGetPhysicalDeviceBool  (int physicalDeviceID, const char* name);
int          oidnGetPhysicalDeviceInt   (int physicalDeviceID, const char* name);
unsigned int oidnGetPhysicalDeviceUInt  (int physicalDeviceID, const char* name);
const char*  oidnGetPhysicalDeviceString(int physicalDeviceID, const char* name);
const void*  oidnGetPhysicalDeviceData  (int physicalDeviceID, const char* name,
                                         size_t* byteSize);

where name is the name of the parameter, and byteSize is the number of returned bytes for data parameters. The following parameters can be queried:

It is also possible to directly query whether a physical device of a particular type is supported, without iterating over all supported physical devices:

bool oidnIsCPUDeviceSupported();
bool oidnIsSYCLDeviceSupported(const sycl::device* device);
bool oidnIsCUDADeviceSupported(int deviceID);
bool oidnIsHIPDeviceSupported(int deviceID);
bool oidnIsMetalDeviceSupported(MTLDevice_id device);

Devices

Open Image Denoise has a logical device concept as well, or simply referred to as device, which allows different components of the application to use the Open Image Denoise API without interfering with each other. Each physical device may be associated with one ore more logical devices. A basic way to create a device is by calling

OIDNDevice oidnNewDevice(OIDNDeviceType type);

where the type enumeration maps to a specific device implementation, which can be one of the following:

If there are multiple supported devices of the specified type, an implementation-dependent default will be selected.

A device can be created by specifying a physical device ID as well using

OIDNDevice oidnNewDeviceByID(int physicalDeviceID);

Applications can manually iterate over the list of physical devices and select from them based on their properties but there are also some built-in helper functions as well, which make creating a device by a particular physical device property easier:

OIDNDevice oidnNewDeviceByUUID(const void* uuid);
OIDNDevice oidnNewDeviceByLUID(const void* luid);
OIDNDevice oidnNewDeviceByPCIAddress(int pciDomain, int pciBus, int pciDevice,
                                     int pciFunction);

These functions are particularly useful when the application needs interoperability with a graphics API (e.g. DX12, Vulkan). However, not all of these properties may be supported by the intended physical device (or drivers might even report inconsistent identifiers), so it is recommended to select by more than one property, if possible.

If the application requires interoperability with a particular compute or graphics API (SYCL, CUDA, HIP, Metal), it is recommended to use one of the following dedicated functions instead:

OIDNDevice oidnNewSYCLDevice(const sycl::queue* queues, int numQueues);
OIDNDevice oidnNewCUDADevice(const int* deviceIDs, const cudaStream_t* streams,
                             int numPairs);
OIDNDevice oidnNewHIPDevice(const int* deviceIDs, const hipStream_t* streams,
                            int numPairs);
OIDNDevice oidnNewMetalDevice(const MTLCommandQueue_id* commandQueues,
                              int numQueues);

For SYCL, it is possible to pass one or more SYCL queues which will be used by Open Image Denoise for all device operations. This is useful when the application wants to use the same queues for both denoising and its own operations (e.g. rendering). Passing multiple queues is not intended to be used for different physical devices but just for a single SYCL root-device which consists of multiple sub-devices (e.g. Intel® Data Center GPU Max Series having multiple Xe-Stacks/tiles). The only supported SYCL backend is oneAPI Level Zero.

For CUDA and HIP, pairs of CUDA/HIP device IDs and corresponding streams can be specified but the current implementation supports only one pair. A NULL stream corresponds to the default stream on the corresponding device. Open Image Denoise automatically sets and restores the current CUDA/HIP device/context on the calling thread when necessary, thus the current device does not have to be changed manually by the application.

For Metal, a single command queue is supported.

Once a device is created, you can call

bool oidnGetDeviceBool(OIDNDevice device, const char* name);
void oidnSetDeviceBool(OIDNDevice device, const char* name, bool value);
int  oidnGetDeviceInt (OIDNDevice device, const char* name);
void oidnSetDeviceInt (OIDNDevice device, const char* name, int  value);
int  oidnGetDeviceUInt(OIDNDevice device, const char* name);
void oidnSetDeviceUInt(OIDNDevice device, const char* name, unsigned int value);

to set and get parameter values on the device. Note that some parameters are constants, thus trying to set them is an error. See the tables below for the parameters supported by devices.

Note that the CPU device heavily relies on setting the thread affinities to achieve optimal performance, so it is highly recommended to leave this option enabled. However, this may interfere with the application if that also sets the thread affinities, potentially causing performance degradation. In such cases, the recommended solution is to either disable setting the affinities in the application or in Open Image Denoise, or to always set/reset the affinities before/after each parallel region in the application (e.g., if using TBB, with tbb::task_arena and tbb::task_scheduler_observer).

Once parameters are set on the created device, the device must be committed with

void oidnCommitDevice(OIDNDevice device);

This device can then be used to construct further objects, such as buffers and filters. Note that a device can be committed only once during its lifetime.

Some functions may execute asynchronously with respect to the host. The names of these functions are suffixed with Async. Asynchronous operations are executed in order on the device but may not block on the host. Eventually, it is necessary to wait for all asynchronous operations to complete, which can be done by calling

void oidnSyncDevice(OIDNDevice device);

Before the application exits, it should release all devices by invoking

void oidnReleaseDevice(OIDNDevice device);

Note that Open Image Denoise uses reference counting for all object types, so this function decreases the reference count of the device, and if the count reaches 0 the device will automatically get deleted. It is also possible to increase the reference count by calling

void oidnRetainDevice(OIDNDevice device);

An application should typically create only a single device object per physical device (one for all CPUs or one per GPU) as creation can be very expensive and additional device objects may incur a significant memory overhead. If required differently, it should only use a small number of device objects at any given time.

Error Handling

Each user thread has its own error code per device. If an error occurs when calling an API function, this error code is set to the occurred error if it stores no previous error. The currently stored error can be queried by the application via

OIDNError oidnGetDeviceError(OIDNDevice device, const char** outMessage);

where outMessage can be a pointer to a C string which will be set to a more descriptive error message, or it can be NULL. This function also clears the error code, which assures that the returned error code is always the first error occurred since the last invocation of oidnGetDeviceError on the current thread. Note that the optionally returned error message string is valid only until the next invocation of the function.

Alternatively, the application can also register a callback function of type

typedef void (*OIDNErrorFunction)(void* userPtr, OIDNError code, const char* message);

via

void oidnSetDeviceErrorFunction(OIDNDevice device, OIDNErrorFunction func, void* userPtr);

to get notified when errors occur. Only a single callback function can be registered per device, and further invocations overwrite the previously set callback function, which do not require also calling the oidnCommitDevice function. Passing NULL as function pointer disables the registered callback function. When the registered callback function is invoked, it gets passed the user-defined payload (userPtr argument as specified at registration time), the error code (code argument) of the occurred error, as well as a string (message argument) that further describes the error. The error code is always set even if an error callback function is registered. It is recommended to always set a error callback function, to detect all errors.

When the device construction fails, oidnNewDevice returns NULL as device. To detect the error code of a such failed device construction, pass NULL as device to the oidnGetDeviceError function. For all other invocations of oidnGetDeviceError, a proper device handle must be specified.

The following errors are currently used by Open Image Denoise:

Environment Variables

Open Image Denoise supports environment variables for overriding certain settings at runtime, which can be useful for debugging and development:

Buffers

Image data can be passed to Open Image Denoise either via pointers to memory allocated and managed by the user or by creating buffer objects. Regardless of which method is used, the data must be allocated in a way that it is accessible by the device (either CPU or GPU). Using buffers is typically the preferred approach because this ensures that the allocation requirements are fulfilled regardless of device type. To create a new data buffer with memory allocated and owned by the device, use

OIDNBuffer oidnNewBuffer(OIDNDevice device, size_t byteSize);

The created buffer is bound to the specified device (device argument). The specified number of bytes (byteSize) are allocated at buffer construction time and deallocated when the buffer is destroyed. The memory is by default allocated as managed memory automatically migrated between host and device, if supported, or as pinned host memory otherwise.

If this default buffer allocation is not suitable, a buffer can be created with a manually specified storage mode as well:

OIDNBuffer oidnNewBufferWithStorage(OIDNDevice device, size_t byteSize, OIDNStorage storage);

The supported storage modes are the following:

Note that the host and device storage modes are supported by all devices but managed storage is an optional feature. Before using managed storage, the managedMemorySupported device parameter should be queried.

It is also possible to create a “shared” data buffer with memory allocated and managed by the user with

OIDNBuffer oidnNewSharedBuffer(OIDNDevice device, void* devPtr, size_t byteSize);

where devPtr points to user-managed device-accessible memory and byteSize is its size in bytes. At buffer construction time no buffer data is allocated, but the buffer data provided by the user is used. The buffer data must remain valid for as long as the buffer may be used, and the user is responsible to free the buffer data when no longer required. The user must also ensure that the memory is accessible by the device by using allocation functions supported by the device (e.g. sycl::malloc_device, cudaMalloc, hipMalloc).

Buffers can be also imported from graphics APIs as external memory, to avoid expensive copying of data through host memory. Different types of external memory can be imported from either POSIX file descriptors or Win32 handles using

OIDNBuffer oidnNewSharedBufferFromFD(OIDNDevice device,
                                     OIDNExternalMemoryTypeFlag fdType,
                                     int fd, size_t byteSize);

OIDNBuffer oidnNewSharedBufferFromWin32Handle(OIDNDevice device,
                                              OIDNExternalMemoryTypeFlag handleType,
                                              void* handle, const void* name, size_t byteSize);

Before exporting memory from the graphics API, the application should find a handle type which is supported by both the Open Image Denoise device (see externalMemoryTypes device parameter) and the graphics API. Note that different GPU vendors may support different handle types. To ensure compatibility with all device types, applications should support at least OIDN_EXTERNAL_MEMORY_TYPE_FLAG_OPAQUE_FD on Windows and both OIDN_EXTERNAL_MEMORY_TYPE_FLAG_OPAQUE_FD and OIDN_EXTERNAL_MEMORY_TYPE_FLAG_DMA_BUF on Linux. All possible external memory types are listed in the table below.

Metal buffers can be imported directly with

OIDNBuffer oidnNewSharedBufferFromMetal(OIDNDevice device, MTLBuffer_id buffer);

Note that if a buffer with an MTLStorageModeManaged storage mode is imported, it is the responsibility of the user to synchronize the contents of the buffer between the host and the device.

Similar to device objects, buffer objects are also reference-counted and can be retained and released by calling the following functions:

void oidnRetainBuffer (OIDNBuffer buffer);
void oidnReleaseBuffer(OIDNBuffer buffer);

The size of in bytes and storage mode of the buffer can be queried using

size_t      oidnGetBufferSize   (OIDNBuffer buffer);
OIDNStorage oidnGetBufferStorage(OIDNBuffer buffer);

It is possible to get a pointer directly to the buffer data, which is usually the preferred way to access the data stored in the buffer:

void* oidnGetBufferData(OIDNBuffer buffer);

Accessing the data on the host through this pointer is possible only if the buffer was created with OIDN_STORAGE_HOST or OIDN_STORAGE_MANAGED. Note that a NULL pointer may be returned if the buffer is empty.

In some cases better performance can be achieved by using device storage for buffers. Such data can be accessed on the host by copying to/from host memory (including pageable system memory) using the following functions:

void oidnReadBuffer(OIDNBuffer buffer,
                    size_t byteOffset, size_t byteSize, void* dstHostPtr);

void oidnWriteBuffer(OIDNBuffer buffer,
                     size_t byteOffset, size_t byteSize, const void* srcHostPtr);

These functions will always block until the read/write operation has been completed, which is often suboptimal. The following functions execute these operations asynchronously:

void oidnReadBufferAsync(OIDNBuffer buffer,
                         size_t byteOffset, size_t byteSize, void* dstHostPtr);

void oidnWriteBufferAsync(OIDNBuffer buffer,
                          size_t byteOffset, size_t byteSize, const void* srcHostPtr);

When copying asynchronously, the user must ensure correct synchronization with the device by calling oidnSyncDevice before accessing the copied data or releasing the buffer. Failure to do so will result in undefined behavior.

Data Format

Buffers store opaque data and thus have no information about the type and format of the data. Other objects, e.g. filters, typically require specifying the format of the data stored in buffers or shared via pointers. This can be done using the OIDNFormat enumeration type:

Filters

Filters are the main objects in Open Image Denoise that are responsible for the actual denoising. The library ships with a collection of filters which are optimized for different types of images and use cases. To create a filter object, call

OIDNFilter oidnNewFilter(OIDNDevice device, const char* type);

where type is the name of the filter type to create. The supported filter types are documented later in this section.

Creating filter objects can be very expensive, therefore it is strongly recommended to reuse the same filter for denoising as many images as possible, as long as the these images have the same same size, format, and features (i.e., only the memory locations and pixel values may be different). Otherwise (e.g. for images with different resolutions), reusing the same filter would not have any benefits.

Once created, filter objects can be retained and released with

void oidnRetainFilter (OIDNFilter filter);
void oidnReleaseFilter(OIDNFilter filter);

After creating a filter, it needs to be set up by specifying the input and output images, and potentially setting other parameter values as well.

To set image parameters of a filter, you can use one of the following functions:

void oidnSetFilterImage(OIDNFilter filter, const char* name,
                        OIDNBuffer buffer, OIDNFormat format,
                        size_t width, size_t height,
                        size_t byteOffset,
                        size_t pixelByteStride, size_t rowByteStride);

void oidnSetSharedFilterImage(OIDNFilter filter, const char* name,
                              void* devPtr, OIDNFormat format,
                              size_t width, size_t height,
                              size_t byteOffset,
                              size_t pixelByteStride, size_t rowByteStride);

It is possible to specify either a data buffer object (buffer argument) with the oidnSetFilterImage function, or directly a pointer to user-managed device-accessible data (devPtr argument) with the oidnSetSharedFilterImage function. Regardless of whether a buffer or a pointer is specified, the data must be accessible to the device. The easiest way to guarantee this regardless of the device type (CPU or GPU) is using buffer objects.

In both cases, you must also specify the name of the image parameter to set (name argument, e.g. "color", "output"), the pixel format (format argument), the width and height of the image in number of pixels (width and height arguments), the starting offset of the image data (byteOffset argument), the pixel stride (pixelByteStride argument) and the row stride (rowByteStride argument), in number of bytes.

If the pixels and/or rows are stored contiguously (tightly packed without any gaps), you can set pixelByteStride and/or rowByteStride to 0 to let the library compute the actual strides automatically, as a convenience.

Images support only FLOAT and HALF pixel formats with up to 3 channels. Custom image layouts with extra channels (e.g. alpha channel) or other data are supported as well by specifying a non-zero pixel stride. This way, expensive image layout conversion and copying can be avoided but the extra channels will be ignored by the filter. If these channels also need to be denoised, separate filters can be used.

To unset a previously set image parameter, returning it to a state as if it had not been set, call

void oidnRemoveFilterImage(OIDNFilter filter, const char* name);

Some special data used by filters are opaque/untyped (e.g. trained model weights blobs), which can be specified with the oidnSetSharedFilterData function:

void oidnSetSharedFilterData(OIDNFilter filter, const char* name,
                             void* hostPtr, size_t byteSize);

This data (hostPtr) must be accessible to the host, therefore system memory allocation is suitable (i.e., there is no reason to use buffer objects for allocation).

Modifying the contents of an opaque data parameter after setting it as a filter parameter is allowed but the filter needs to be notified that the data has been updated by calling

void oidnUpdateFilterData(OIDNFilter filter, const char* name);

Unsetting an opaque data parameter can be performed with

void oidnRemoveFilterData(OIDNFilter filter, const char* name);

Filters may have parameters other than buffers as well, which you can set and get using the following functions:

bool  oidnGetFilterBool (OIDNFilter filter, const char* name);
void  oidnSetFilterBool (OIDNFilter filter, const char* name, bool  value);
int   oidnGetFilterInt  (OIDNFilter filter, const char* name);
void  oidnSetFilterInt  (OIDNFilter filter, const char* name, int   value);
float oidnGetFilterFloat(OIDNFilter filter, const char* name);
void  oidnSetFilterFloat(OIDNFilter filter, const char* name, float value);

Filters support a progress monitor callback mechanism that can be used to report progress of filter operations and to cancel them as well. Calling oidnSetFilterProgressMonitorFunction registers a progress monitor callback function (func argument) with payload (userPtr argument) for the specified filter (filter argument):

typedef bool (*OIDNProgressMonitorFunction)(void* userPtr, double n);

void oidnSetFilterProgressMonitorFunction(OIDNFilter filter,
                                          OIDNProgressMonitorFunction func,
                                          void* userPtr);

Only a single callback function can be registered per filter, and further invocations overwrite the previously set callback function. Passing NULL as function pointer disables the registered callback function. Once registered, Open Image Denoise will invoke the callback function multiple times during filter operations, by passing the payload as set at registration time (userPtr argument), and a double in the range [0, 1] which estimates the progress of the operation (n argument). When returning true from the callback function, Open Image Denoise will continue the filter operation normally. When returning false, the library will attempt to cancel the filter operation as soon as possible, and if that is fulfilled, it will raise an OIDN_ERROR_CANCELLED error.

Please note that using a progress monitor callback function introduces some overhead, which may be significant on GPU devices, hurting performance. Therefore we recommend progress monitoring only for offline denoising, when denoising an image is expected to take several seconds.

After setting all necessary parameters for the filter, the changes must be committed by calling

void oidnCommitFilter(OIDNFilter filter);

The parameters can be updated after committing the filter, but it must be re-committed for any new changes to take effect. Committing major changes to the filter (e.g. setting new image parameters, changing the image resolution) can be expensive, and thus should not be done frequently (e.g. per frame).

Finally, an image can be filtered by executing the filter with

void oidnExecuteFilter(OIDNFilter filter);

which will read the input image data from the specified buffers and produce the denoised output image.

This function will always block until the filtering operation has been completed. The following function executes the operation asynchronously:

void oidnExecuteFilterAsync(OIDNFilter filter);

For filters created on a SYCL device it is also possible to specify dependent SYCL events (depEvents and numDepEvents arguments, may be NULL/0) and get a completion event as well (doneEvent argument, may be NULL):

void oidnExecuteSYCLFilterAsync(OIDNFilter filter,
                                const sycl::event* depEvents, int numDepEvents,
                                sycl::event* doneEvent);

When filtering asynchronously, the user must ensure correct synchronization with the device by calling oidnSyncDevice before accessing the output image data or releasing the filter. Failure to do so will result in undefined behavior.

In the following we describe the different filters that are currently implemented in Open Image Denoise.

RT

The RT (ray tracing) filter is a generic ray tracing denoising filter which is suitable for denoising images rendered with Monte Carlo ray tracing methods like unidirectional and bidirectional path tracing. It supports depth of field and motion blur as well, but it is not temporally stable. The filter is based on a convolutional neural network (CNN) and comes with a set of pre-trained models that work well with a wide range of ray tracing based renderers and noise levels.

For denoising beauty images, it accepts either a low dynamic range (LDR) or high dynamic range (HDR) image (color) as the main input image. In addition to this, it also accepts auxiliary feature images, albedo and normal, which are optional inputs that usually improve the denoising quality significantly, preserving more details.

It is possible to denoise auxiliary images as well, in which case only the respective auxiliary image has to be specified as input, instead of the beauty image. This can be done as a prefiltering step to further improve the quality of the denoised beauty image.

The RT filter has certain limitations regarding the supported input images. Most notably, it cannot denoise images that were not rendered with ray tracing. Another important limitation is related to anti-aliasing filters. Most renderers use a high-quality pixel reconstruction filter instead of a trivial box filter to minimize aliasing artifacts (e.g. Gaussian, Blackman-Harris). The RT filter does support such pixel filters but only if implemented with importance sampling. Weighted pixel sampling (sometimes called splatting) introduces correlation between neighboring pixels, which causes the denoising to fail (the noise will not be filtered), thus it is not supported.

The filter can be created by passing "RT" to the oidnNewFilter function as the filter type. The filter supports the parameters listed in the table below. All specified images must have the same dimensions. The output image can be one of the input images (i.e. in-place denoising is supported). See section Examples for simple code snippets that demonstrate the usage of the filter.

Using auxiliary feature images like albedo and normal helps preserving fine details and textures in the image thus can significantly improve denoising quality. These images should typically contain feature values for the first hit (i.e. the surface which is directly visible) per pixel. This works well for most surfaces but does not provide any benefits for reflections and objects visible through transparent surfaces (compared to just using the color as input). However, this issue can be usually fixed by storing feature values for a subsequent hit (i.e. the reflection and/or refraction) instead of the first hit. For example, it usually works well to follow perfect specular (delta) paths and store features for the first diffuse or glossy surface hit instead (e.g. for perfect specular dielectrics and mirrors). This can greatly improve the quality of reflections and transmission. We will describe this approach in more detail in the following subsections.

The auxiliary feature images should be as noise-free as possible. It is not a strict requirement but too much noise in the feature images may cause residual noise in the output. Ideally, these should be completely noise-free. If this is the case, this should be hinted to the filter using the cleanAux parameter to ensure the highest possible image quality. But this parameter should be used with care: if enabled, any noise present in the auxiliary images will end up in the denoised image as well, as residual noise. Thus, cleanAux should be enabled only if the auxiliary images are guaranteed to be noise-free.

Usually it is difficult to provide clean feature images, and some residual noise might be present in the output even with cleanAux being disabled. To eliminate this noise and to even improve the sharpness of texture details, the auxiliary images should be first denoised in a prefiltering step, as mentioned earlier. Then, these denoised auxiliary images could be used for denoising the beauty image. Since these are now noise-free, the cleanAux parameter should be enabled. See section Denoising with prefiltering (C++11 API) for a simple code example. Prefiltering makes denoising much more expensive but if there are multiple color AOVs to denoise, the prefiltered auxiliary images can be reused for denoising multiple AOVs, amortizing the cost of the prefiltering step.

Thus, for final-frame denoising, where the best possible image quality is required, it is recommended to prefilter the auxiliary features if they are noisy and enable the cleanAux parameter. Denoising with noisy auxiliary features should be reserved for previews and interactive rendering.

All auxiliary images should use the same pixel reconstruction filter as the beauty image. Using a properly anti-aliased beauty image but aliased albedo or normal images will likely introduce artifacts around edges.

Albedos

The albedo image is the feature image that usually provides the biggest quality improvement. It should contain the approximate color of the surfaces independent of illumination and viewing angle.

For simple matte surfaces this means using the diffuse color/texture as the albedo. For other, more complex surfaces it is not always obvious what is the best way to compute the albedo, but the denoising filter is flexible to a certain extent and works well with differently computed albedos. Thus it is not necessary to compute the strict, exact albedo values but must be always between 0 and 1.

For metallic surfaces the albedo should be either the reflectivity at normal incidence (e.g. from the artist friendly metallic Fresnel model) or the average reflectivity; or if these are constant (not textured) or unknown, the albedo can be simply 1 as well.

The albedo for dielectric surfaces (e.g. glass) should be either 1 or, if the surface is perfect specular (i.e. has a delta BSDF), the Fresnel blend of the reflected and transmitted albedos. The latter usually works better but only if it does not introduce too much noise or the albedo is prefiltered. If noise is an issue, we recommend to split the path into a reflected and a transmitted path at the first hit, and perhaps fall back to an albedo of 1 for subsequent dielectric hits. The reflected albedo in itself can be used for mirror-like surfaces as well.

The albedo for layered surfaces can be computed as the weighted sum of the albedos of the individual layers. Non-absorbing clear coat layers can be simply ignored (or the albedo of the perfect specular reflection can be used as well) but absorption should be taken into account.

Normals

The normal image should contain the shading normals of the surfaces either in world-space or view-space. It is recommended to include normal maps to preserve as much detail as possible.

Just like any other input image, the normal image should be anti-aliased (i.e. by accumulating the normalized normals per pixel). The final accumulated normals do not have to be normalized but must be in the [-1, 1] range (i.e. normals mapped to [0, 1] are not acceptable and must be remapped to [−1, 1]).

Similar to the albedo, the normal can be stored for either the first or a subsequent hit (if the first hit has a perfect specular/delta BSDF).

Quality

The filter supports setting an image quality mode, which determines whether to favor quality, performance, or have a balanced solution between the two. The supported quality modes are listed in the following table.

By default, filtering is performed in high quality mode, which is recommended for final-frame rendering. Using this setting the results have the same high quality regardless of what kind of device (CPU or GPU) is used. However, due to significant hardware architecture differences between devices, there might be small numerical differences between the produced outputs.

The balanced quality mode may provide somewhat lower image quality but higher performance and lower default memory usage, and is thus recommended for interactive and real-time rendering. For even higher performance and lower memory usage, a fast quality mode is also available but has noticeably lower image quality, making it suitable mainly for fast previews. Note that in the balanced and fast quality modes larger numerical differences should be expected across devices compared to the high quality mode.

The difference in quality and performance between quality modes depends on the combination of input features, parameters (e.g. cleanAux), and the device architecture. In some cases the difference may be small or even none.

Weights

Instead of using the built-in trained models for filtering, it is also possible to specify user-trained models at runtime. This can be achieved by passing the model weights blob corresponding to the specified set of features and other filter parameters, produced by the included training tool. See Section Training for details.

RTLightmap

The RTLightmap filter is a variant of the RT filter optimized for denoising HDR and normalized directional (e.g. spherical harmonics) lightmaps. It does not support LDR images.

The filter can be created by passing "RTLightmap" to the oidnNewFilter function as the filter type. The filter supports the following parameters:

Examples

Intel Open Image Denoise ships with a couple of simple example applications.

oidnDenoise

oidnDenoise is a minimal working example demonstrating how to use Intel Open Image Denoise, which can be found at apps/oidnDenoise.cpp. It uses the C++11 convenience wrappers of the C99 API.

This example is a simple command-line application that denoises the provided image, which can optionally have auxiliary feature images as well (e.g. albedo and normal). By default the images must be stored in the Portable FloatMap (PFM) format, and the color values must be encoded in little-endian format. To enable other image formats (e.g. OpenEXR, PNG) as well, the project has to be rebuilt with OpenImageIO support enabled.

Running oidnDenoise without any arguments or the -h argument will bring up a list of command-line options.

oidnBenchmark

oidnBenchmark is a basic command-line benchmarking application for measuring denoising speed, which can be found at apps/oidnBenchmark.cpp.

Running oidnBenchmark with the -h argument will bring up a list of command-line options.

Training

The Intel Open Image Denoise source distribution includes a Python-based neural network training toolkit (located in the training directory), which can be used to train the denoising filter models with image datasets provided by the user. This is an advanced feature of the library which usage requires some background knowledge of machine learning and basic familiarity with deep learning frameworks and toolkits (e.g. PyTorch or TensorFlow, TensorBoard).

The training toolkit consists of the following command-line scripts:

• preprocess.py: Preprocesses training and validation datasets.

• train.py: Trains a model using preprocessed datasets.

• infer.py: Performs inference on a dataset using the specified training result.

• export.py: Exports a training result to the runtime model weights format.

• find_lr.py: Tool for finding the optimal minimum and maximum learning rates.

• visualize.py: Invokes TensorBoard for visualizing statistics of a training result.

• split_exr.py: Splits a multi-channel EXR image into multiple feature images.

• convert_image.py: Converts a feature image to a different image format.

• compare_image.py: Compares two feature images using the specified quality metrics.

Prerequisites

Before you can run the training toolkit you need the following prerequisites:

• Linux (other operating systems are currently not supported)

• Python 3.7 or later

• PyTorch 2.3 or later

• NumPy 1.19 or later

• OpenImageIO 2.1 or later

• TensorBoard 2.4 or later

Devices

Most scripts in the training toolkit support selecting what kind of device (e.g. CPU, GPU) to use for the computations (--device or -d option). If multiple devices of the same kind are available (e.g. multiple GPUs), the user can specify which one of these to use (--device_id or -k option). Additionally, some scripts, like train.py, support data-parallel execution on multiple devices for faster performance (--num_devices or -n option).

Datasets

A dataset should consist of a collection of noisy and corresponding noise-free reference images. It is possible to have more than one noisy version of the same image in the dataset, e.g. rendered at different samples per pixel and/or using different seeds.

The training toolkit expects to have all datasets (e.g. training, validation) in the same parent directory (e.g. data). Each dataset is stored in its own subdirectory (e.g. train, valid), which can have an arbitrary name.

The images must be stored in OpenEXR format (.exr files), and the filenames must have a specific format but the files can be stored in an arbitrary directory structure inside the dataset directory. The only restriction is that all versions of an image (noisy images and the reference image) must be located in the same subdirectory. Each feature of an image (e.g. color, albedo) must be stored in a separate image file, i.e. multi-channel EXR image files are not supported. If you have multi-channel EXRs, you can split them into separate images per feature using the included split_exr.py tool.

An image filename must consist of a base name, a suffix with the number of samples per pixel or whether it is the reference image (e.g. _0128spp, _ref), the feature type extension (e.g. .hdr, .alb), and the image format extension (.exr). The exact filename format as a regular expression is the following:

.+_([0-9]+(spp)?|ref|reference|gt|target)\.(hdr|ldr|sh1[xyz]|alb|nrm)\.exr

The number of samples per pixel should be padded with leading zeros to have a fixed number of digits. If the reference image is not explicitly named as such (i.e. has the number of samples instead), the image with the most samples per pixel will be considered the reference.

The following image features are supported:

The following directory tree demonstrates an example root dataset directory (data) containing one dataset (rt_train) with HDR color and albedo feature images:

data
`-- rt_train
    |-- scene1
    |   |-- view1_0001.alb.exr
    |   |-- view1_0001.hdr.exr
    |   |-- view1_0004.alb.exr
    |   |-- view1_0004.hdr.exr
    |   |-- view1_8192.alb.exr
    |   |-- view1_8192.hdr.exr
    |   |-- view2_0001.alb.exr
    |   |-- view2_0001.hdr.exr
    |   |-- view2_8192.alb.exr
    |   `-- view2_8192.hdr.exr
    |-- scene2_000008spp.alb.exr
    |-- scene2_000008spp.hdr.exr
    |-- scene2_000064spp.alb.exr
    |-- scene2_000064spp.hdr.exr
    |-- scene2_reference.alb.exr
    `-- scene2_reference.hdr.exr

Preprocessing (preprocess.py)

Training and validation datasets can be used only after preprocessing them using the preprocess.py script. This will convert the specified training (--train_data or -t option) and validation datasets (--valid_data or -v option) located in the root dataset directory (--data_dir or -D option) to a format that can be loaded more efficiently during training. All preprocessed datasets will be stored in a root preprocessed dataset directory (--preproc_dir or -P option).

The preprocessing script requires the set of image features to include in the preprocessed dataset as command-line arguments. Only these specified features will be available for training but it is not required to use all of them at the same time. Thus, a single preprocessed dataset can be reused for training multiple models with different combinations of the preprocessed features.

By default, all input features are assumed to be noisy, including the auxiliary features (e.g. albedo, normal), each having versions at different samples per pixel. It is also possible to train with noise-free auxiliary features, in which case the reference auxiliary features are used instead of the various noisy ones (--clean_aux option). This improves quality significantly if the auxiliary features used for inference will be either originally noise-free or prefiltered with separately trained auxiliary feature denoising models. If inference will be done only with prefiltered features, even higher quality can be achieved by training with prefiltered features instead of the reference onces. This can be achieved by first training the auxiliary feature models and then specifying the list of these results when preprocessing the dataset for the main feature (--aux_results or -a option).

Preprocessing also depends on the filter that will be trained (e.g. determines which HDR/LDR transfer function has to be used), which should be also specified (--filter or -f option). The alternative is to manually specify the transfer function (--transfer or -x option) and other filter-specific parameters, which could be useful for training custom filters.

For example, to preprocess the training and validation datasets (rt_train and rt_valid) with HDR color, albedo, and normal image features, for training the RT filter, the following command can be used:

./preprocess.py hdr alb nrm --filter RT --train_data rt_train --valid_data rt_valid

It is possible to preprocess the same dataset multiple times, with possibly different combinations of features and options. The training script will use the most suitable and most recent preprocessed version depending on the training parameters.

For more details about using the preprocessing script, including other options, please have a look at the help message:

./preprocess.py -h

Training (train.py)

The filters require separate trained models for each supported combination of input features. Thus, depending on which combinations of features the user wants to support for a particular filter, one or more models have to be trained.

After preprocessing the datasets, it is possible to start training a model using the train.py script. Similar to the preprocessing script, the input features must be specified (could be a subset of the preprocessed features), and the dataset names, directory paths, and the filter can be also passed. If the --clean_aux or --aux_results options were specified for preprocessing, these must be passed identically to the training script as well.

Open Image Denoise uses models of different sizes for different quality modes (high, balanced, fast). Specifying the quality mode (--quality or -q option) will cause the model to be implicitly selected, or the model can be specified explicitly as well (--model or -m option).

The tool will produce a training result, the name of which can be either specified (--result or -r option) or automatically generated (by default). Each result is stored in its own subdirectory, and these are located in a common parent directory (--results_dir or -R option). If a training result already exists, the tool will resume training that result from the latest checkpoint.

The default training hyperparameters should work reasonably well in general, but some adjustments might be necessary for certain datasets to attain optimal performance, most importantly: the number of epochs (--num_epochs or -e option), the global mini-batch size (--batch_size or -b option), and the learning rate. The training tool uses a one-cycle learning rate schedule with cosine annealing, which can be configured by setting the base learning rate (--learning_rate or --lr option), the maximum learning rate (--max_learning_rate or --max_lr option), and the percentage of the cycle spent increasing the learning rate (--learning_rate_warmup or --lr_warmup option).

Example usage:

./train.py hdr alb --filter RT --train_data rt_train --valid_data rt_valid --result rt_hdr_alb

For finding the optimal learning rate range, we recommend using the included find_lr.py script, which trains one epoch using an increasing learning rate and logs the resulting losses in a comma-separated values (CSV) file. Plotting the loss curve can show when the model starts to learn (the base learning rate) and when it starts to diverge (the maximum learning rate).

The model is evaluated with the validation dataset at regular intervals (--num_valid_epochs option), and checkpoints are also regularly created (--num_save_epochs option) to save training progress. Also, some statistics are logged (e.g. training and validation losses, learning rate) per epoch, which can be later visualized with TensorBoard by running the visualize.py script, e.g.:

./visualize.py --result rt_hdr_alb

Training is performed with mixed precision (FP16 and FP32) by default, if it supported by the hardware, which makes training faster and use less memory. However, in some rare cases this might cause some convergence issues. The training precision can be manually set to FP32 if necessary (--precision or -p option).

Inference (infer.py)

A training result can be tested by performing inference on an image dataset (--input_data or -i option) using the infer.py script. The dataset does not have to be preprocessed. In addition to the result to use, it is possible to specify which checkpoint to load as well (-e or --num_epochs option). By default the latest checkpoint is loaded.

The tool saves the output images in a separate directory (--output_dir or -O option) in the requested formats (--format or -F option). It also evaluates a set of image quality metrics (--metric or -M option), e.g. PSNR, SSIM, for images that have reference images available. All metrics are computed in tonemapped non-linear sRGB space. Thus, HDR images are first tonemapped (with Naughty Dog’s Filmic Tonemapper from John Hable’s Uncharted 2: HDR Lighting presentation) and converted to sRGB before evaluating the metrics.

Example usage:

./infer.py --result rt_hdr_alb --input_data rt_test --format exr png --metric ssim

The inference tool supports prefiltering of auxiliary features as well, which can be performed by specifying the list of training results for each feature to prefilter (--aux_results or -a option).

Exporting Results (export.py)

The training result produced by the train.py script cannot be immediately used by the main library. It has to be first exported to the runtime model weights format, a Tensor Archive (TZA) file. Running the export.py script for a training result (and optionally a checkpoint epoch) will create a binary .tza file in the directory of the result, which can be either used at runtime through the API or it can be included in the library build by replacing one of the built-in weights files.

Example usage:

./export.py --result rt_hdr_alb

Image Conversion and Comparison

In addition to the already mentioned split_exr.py script, the toolkit contains a few other image utilities as well.

convert_image.py converts a feature image to a different image format (and/or a different feature, e.g. HDR color to LDR), performing tonemapping and other transforms as well if needed. For HDR images the exposure can be adjusted by passing a linear exposure scale (--exposure or -E option). Example usage:

./convert_image.py view1_0004.hdr.exr view1_0004.png --exposure 2.5

The compare_image.py script compares two feature images (preferably having the dataset filename format to correctly detect the feature) using the specified image quality metrics, similar to the infer.py tool. Example usage:

./compare_image.py view1_0004.hdr.exr view1_8192.hdr.exr --exposure 2.5 --metric mse ssim