Using rocAL with C++ API#
This chapter explains how to create a pipeline and add augmentations using C++ APIs directly. The Python APIs also call these C++ APIs internally using the Python pybind utility as explained in the section Installing rocAL Python Package.
C++ Common APIs#
The following sections list the commonly used C++ APIs.
rocalCreate#
Use: To create the pipeline
Returns: The context for the pipeline
Arguments:
RocalProcessMode: Defines whether rocal data loading should be on the CPU or GPU.
RocalProcessMode:: ROCAL_PROCESS_GPU
RocalProcessMode::ROCAL_PROCESS_CPU
RocalTensorOutputType: Defines whether the output of rocal tensor is FP32 or FP16.
RocalTensorOutputType::ROCAL_FP32
RocalTensorOutputType::ROCAL_FP16
See rocalCreate example.
extern "C" RocalContext ROCAL_API_CALL rocalCreate(size_t batch_size, RocalProcessMode affinity, int gpu_id = 0, size_t cpu_thread_count = 1, size_t prefetch_queue_depth = 3, RocalTensorOutputType output_tensor_data_type = RocalTensorOutputType::ROCAL_FP32);
rocalVerify#
Use: To verify the graph for all the inputs and outputs
Returns: A status code indicating the success or failure
See rocalVerify example.
extern "C" RocalStatus ROCAL_API_CALL rocalVerify(RocalContext context);
rocalRun#
Use: To process and run the built and verified graph
Returns: A status code indicating the success or failure
See rocalRun example.
extern "C" RocalStatus ROCAL_API_CALL rocalRun(RocalContext context);
rocalRelease#
Use: To free all the resources allocated during the graph creation process
Returns: A status code indicating the success or failure
See rocalRelease example.
extern "C" RocalStatus ROCAL_API_CALL rocalRelease(RocalContext rocal_context);
Image Augmentation Using C++ API#
The example below shows how to create a pipeline, read JPEG images, perform certain augmentations on them, and show the output using OpenCV by utilizing C++ API.
Auto handle = rocalCreate(inputBatchSize, processing_device?RocalProcessMode::ROCAL_PROCESS_GPU:RocalProcessMode::ROCAL_PROCESS_CPU, 0,1);
input1 = rocalJpegFileSource(handle, folderPath1, color_format, shard_count, false, shuffle, false, ROCAL_USE_USER_GIVEN_SIZE, decode_width, decode_height, dec_type);
image0 = rocalResize(handle, input1, resize_w, resize_h, true);
RocalImage image1 = rocalRain(handle, image0, false);
RocalImage image11 = rocalFishEye(handle, image1, false);
rocalRotate(handle, image11, true, rand_angle);
// Creating successive blur nodes to simulate a deep branch of augmentations
RocalImage image2 = rocalCropResize(handle, image0, resize_w, resize_h, false, rand_crop_area);;
for(int i = 0 ; i < aug_depth; i++)
{
image2 = rocalBlurFixed(handle, image2, 17.25, (i == (aug_depth -1)) ? true:false );
}
// Calling the API to verify and build the augmentation graph
if(rocalVerify(handle) != ROCAL_OK)
{
std::cout << "Could not verify the augmentation graph" << std::endl;
return -1;
}
while (!rocalIsEmpty(handle))
{
if(rocalRun(handle) != 0)
break;
}
To see a sample image augmentation application in C++, see Image Augmentation.