Class InferenceOp
Defined in File inference.hpp
Base Type
public holoscan::Operator
(Class Operator)
-
class InferenceOp : public holoscan::Operator
Inference Operator class to perform single/multi model inference.
==Named Inputs==
receivers : multi-receiver accepting
nvidia::gxf::Tensor
(s)Any number of upstream ports may be connected to this
receivers
port. The operator will search across all messages for tensors matching those specified inin_tensor_names
. These are the set of input tensors used by the models ininference_map
.
==Named Outputs==
transmitter :
nvidia::gxf::Tensor
(s)A message containing tensors corresponding to the inference results from all models will be emitted. The names of the tensors transmitted correspond to those in
out_tensor_names
.
==Parameters==
For more details on
<a class="reference internal" href="#classholoscan_1_1ops_1_1InferenceOp" target="_self">InferenceOp</a>
parameters, see Customizing the Inference Operator or refer to Inference.backend: Backend to use for inference. Set
"trt"
for TensorRT,"torch"
for LibTorch and"onnxrt"
for the ONNX runtime.allocator: Memory allocator to use for the output.
inference_map: Tensor to model map.
model_path_map: Path to the ONNX model to be loaded.
pre_processor_map: Pre processed data to model map.
device_map: Mapping of model (
<a class="reference internal" href="structholoscan_1_1ops_1_1InferenceOp_1_1DataMap.html#structholoscan_1_1ops_1_1InferenceOp_1_1DataMap" target="_self">DataMap</a>
) to GPU ID for inference. Optional.backend_map: Mapping of model (
<a class="reference internal" href="structholoscan_1_1ops_1_1InferenceOp_1_1DataMap.html#structholoscan_1_1ops_1_1InferenceOp_1_1DataMap" target="_self">DataMap</a>
) to backend type for inference. Backend options:"trt"
or"torch"
. Optional.temporal_map: Mapping of model (
<a class="reference internal" href="structholoscan_1_1ops_1_1InferenceOp_1_1DataMap.html#structholoscan_1_1ops_1_1InferenceOp_1_1DataMap" target="_self">DataMap</a>
) to a frame delay for model inference. Optional.activation_map: Mapping of model (
<a class="reference internal" href="structholoscan_1_1ops_1_1InferenceOp_1_1DataMap.html#structholoscan_1_1ops_1_1InferenceOp_1_1DataMap" target="_self">DataMap</a>
) to a activation state for model inference. Optional.in_tensor_names: Input tensors (
std::vector<std::string>
). Optional.out_tensor_names: Output tensors (
std::vector<std::string>
). Optional.infer_on_cpu: Whether to run the computation on the CPU instead of GPU. Optional (default:
false
).parallel_inference: Whether to enable parallel execution. Optional (default:
true
).input_on_cuda: Whether the input buffer is on the GPU. Optional (default:
true
).output_on_cuda: Whether the output buffer is on the GPU. Optional (default:
true
).transmit_on_cuda: Whether to transmit the message on the GPU. Optional (default:
true
).enable_fp16: Use 16-bit floating point computations. Optional (default:
false
).is_engine_path: Whether the input model path mapping is for trt engine files. Optional (default:
false
).cuda_stream_pool:
<a class="reference internal" href="classholoscan_1_1CudaStreamPool.html#classholoscan_1_1CudaStreamPool" target="_self">holoscan::CudaStreamPool</a>
instance to allocate CUDA streams. Optional (default:nullptr
).
==Device Memory Requirements==
When using this operator with a
<a class="reference internal" href="classholoscan_1_1BlockMemoryPool.html#classholoscan_1_1BlockMemoryPool" target="_self">BlockMemoryPool</a>
,num_blocks
must be greater than or equal to the number of output tensors that will be produced. Theblock_size
in bytes must be greater than or equal to the largest output tensor (in bytes). Ifoutput_on_cuda
is true, the blocks should be in device memory (storage_type
=1), otherwise they should be CUDA pinned host memory (storage_type
=0).Public Functions
- HOLOSCAN_OPERATOR_FORWARD_ARGS (InferenceOp) InferenceOp()=default
-
virtual void setup(OperatorSpec &spec) override
Define the operator specification.
- Parameters
spec – The reference to the operator specification.
-
virtual void initialize() override
Initialize the operator.
This function is called when the fragment is initialized by Executor::initialize_fragment().
-
virtual void start() override
Implement the startup logic of the operator.
This method is called multiple times over the lifecycle of the operator according to the order defined in the lifecycle, and used for heavy initialization tasks such as allocating memory resources.
-
virtual void compute(InputContext &op_input, OutputContext &op_output, ExecutionContext &context) override
Implement the compute method.
This method is called by the runtime multiple times. The runtime calls this method until the operator is stopped.
- Parameters
op_input – The input context of the operator.
op_output – The output context of the operator.
context – The execution context of the operator.
-
virtual void stop() override
Implement the shutdown logic of the operator.
This method is called multiple times over the lifecycle of the operator according to the order defined in the lifecycle, and used for heavy deinitialization tasks such as deallocation of all resources previously assigned in start.
-
struct DataMap
DataMap specification
Public Functions
-
DataMap() = default
-
inline explicit operator bool() const noexcept
-
inline void insert(const std::string &key, const std::string &value)
-
inline std::map<std::string, std::string> get_map() const
Public Members
-
std::map<std::string, std::string> mappings_
-
DataMap() = default
-
struct DataVecMap
DataVecMap specification
Public Functions
-
DataVecMap() = default
-
inline explicit operator bool() const noexcept
-
inline void insert(const std::string &key, const std::vector<std::string> &value)
-
inline std::map<std::string, std::vector<std::string>> get_map() const
Public Members
-
std::map<std::string, std::vector<std::string>> mappings_
-
DataVecMap() = default