Class InferenceProcessorOp
Defined in File inference_processor.hpp
Base Type
public holoscan::Operator
(Class Operator)
-
class InferenceProcessorOp : public holoscan::Operator
Inference Processor Operator class to perform operations per input tensor.
==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 processing operations specified inprocess_map
.
==Named Outputs==
transmitter :
nvidia::gxf::Tensor
(s)A message containing tensors corresponding to the processed results from operations will be emitted. The names of the tensors transmitted correspond to those in
out_tensor_names
.
==Parameters==
allocator: Memory allocator to use for the output.
process_operations: Operations (
DataVecMap
) in sequence on tensors.processed_map: Input-output tensor mapping (
DataVecMap
)in_tensor_names: Names of input tensors (
std::vector<std::string>
) in the order to be fed into the operator. Optional.out_tensor_names: Names of output tensors (
std::vector<std::string>
) in the order to be fed into the operator. Optional.input_on_cuda: Whether the input buffer is on the GPU. Optional (default:
false
).output_on_cuda: Whether the output buffer is on the GPU. Optional (default:
false
).transmit_on_cuda: Whether to transmit the message on the GPU. Optional (default:
false
).cuda_stream_pool:
holoscan::CudaStreamPool
instance to allocate CUDA streams. Optional (default:nullptr
).config_path: File path to the config file. Optional (default:
""
).disable_transmitter: If
true
, disable the transmitter output port of the operator. Optional (default:false
).
==Device Memory Requirements==
When using this operator with a
BlockMemoryPool
,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 (InferenceProcessorOp) InferenceProcessorOp()=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.
-
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