tflite:: Interpreter


   #include <interpreter.h>

An interpreter for a graph of nodes that input and output from tensors.

Summary

Each node of the graph processes a set of input tensors and produces a set of output Tensors. All inputs/output tensors are referenced by index.

Usage:


// Create model from file. Note that the model instance must outlive the
// interpreter instance.
auto model = tflite::FlatBufferModel::BuildFromFile(...);
if (model == nullptr) {
  // Return error.
}
// Create an Interpreter with an InterpreterBuilder.
std::unique_ptr interpreter;
tflite::ops::builtin::BuiltinOpResolver resolver;
if (InterpreterBuilder(*model, resolver)(&interpreter) != kTfLiteOk) {
  // Return failure.
}
interpreter->AllocateTensors();

auto input = interpreter->typed_tensor(0);
for (int i = 0; i < input_size; i++) {
  input[i] = ...;  interpreter.Invoke();

Note: for nearly all practical use cases, one should not directly construct an Interpreter object, but rather use the InterpreterBuilder .

Constructors and Destructors
`Interpreter ( ErrorReporter *error_reporter)`
`Interpreter (const Interpreter &)`
`~Interpreter ()`

Public types
`TfLiteDelegatePtr`	using `std::unique_ptr< TfLiteDelegate, void()(TfLiteDelegate )>`

Public static attributes
`kTensorsCapacityHeadroom = 16`	`constexpr int` The capacity headroom of `tensors_` vector before calling ops' `prepare` and `invoke` function.
`kTensorsReservedCapacity = 128`	`constexpr int`

Friend classes
`tflite::InterpreterTest`	`friend class`
`tflite::TestDelegate`	`friend class`
`tflite::delegates::InterpreterUtils`	`friend class`

Public functions
`AllocateTensors ()`	`TfLiteStatus`
`EnsureTensorDataIsReadable (int tensor_index)`	`TfLiteStatus` Ensure the data in `tensor.data` is readable.
`GetAllowFp16PrecisionForFp32 () const`	`bool` Get the half precision flag.
`GetBufferHandle (int tensor_index, TfLiteBufferHandle buffer_handle, TfLiteDelegate *delegate)`	`TfLiteStatus` Get the delegate buffer handle, and the delegate which can process the buffer handle.
`GetInputName (int index) const`	`const char *` Return the name of a given input.
`GetOutputName (int index) const`	`const char *` Return the name of a given output.
`GetProfiler ()`	`Profiler *` Gets the profiler used for op tracing.
`Invoke ()`	`TfLiteStatus` Invoke the interpreter (run the whole graph in dependency order).
`ModifyGraphWithDelegate (TfLiteDelegate *delegate)`	`TfLiteStatus` Allow a delegate to look at the graph and modify the graph to handle parts of the graph themselves.
`ModifyGraphWithDelegate (std::unique_ptr< Delegate, Deleter > delegate)`	`TfLiteStatus` Same as ModifyGraphWithDelegate except this interpreter takes ownership of the provided delegate.
`ModifyGraphWithDelegate (std::unique_ptr< TfLiteDelegate > delegate)=delete`	`TfLiteStatus` This overload is never OK.
`OpProfilingString (const TfLiteRegistration & op_reg, const TfLiteNode *node) const`	`const char *` Retrieve an operator's description of its work, for profiling purposes.
`ReleaseNonPersistentMemory ()`	`TfLiteStatus` WARNING: Experimental interface, subject to change.
`ResetVariableTensors ()`	`TfLiteStatus` Reset all variable tensors to the default value.
`ResizeInputTensor (int tensor_index, const std::vector< int > & dims)`	`TfLiteStatus` Change the dimensionality of a given tensor.
`ResizeInputTensorStrict (int tensor_index, const std::vector< int > & dims)`	`TfLiteStatus` A call to AllocateTensors() is required to change the tensor input buffer.
`SetAllowBufferHandleOutput (bool allow_buffer_handle_output)`	`void` Set if buffer handle output is allowed.
`SetAllowFp16PrecisionForFp32 (bool allow)`	`void` Allow float16 precision for FP32 calculation when possible.
`SetBufferHandle (int tensor_index, TfLiteBufferHandle buffer_handle, TfLiteDelegate *delegate)`	`TfLiteStatus` Set the delegate buffer handle to a tensor.
`SetCancellationFunction (void data, bool()(void *) check_cancelled_func)`	`void` Sets the cancellation function pointer in order to cancel a request in the middle of a call to Invoke() .
`SetCustomAllocationForTensor (int tensor_index, const TfLiteCustomAllocation & allocation)`	`TfLiteStatus`
`SetExecutionPlan (const std::vector< int > & new_plan)`	`TfLiteStatus` WARNING: Experimental interface, subject to change Overrides execution plan.
`SetExternalContext (TfLiteExternalContextType type, TfLiteExternalContext *ctx)`	`void`
`SetNumThreads (int num_threads)`	`TfLiteStatus` Set the number of threads available to the interpreter.
`SetProfiler (Profiler *profiler)`	`void` Sets the profiler to tracing execution.
`SetProfiler (std::unique_ptr< Profiler > profiler)`	`void` Same as SetProfiler except this interpreter takes ownership of the provided profiler.
`UseNNAPI (bool enable)`	`void` Enable or disable NNAPI (true to enable).
`execution_plan () const`	`const std::vector< int > &` WARNING: Experimental interface, subject to change.
`input_tensor (size_t index)`	`TfLiteTensor *` Return a mutable pointer to the given input tensor.
`input_tensor (size_t index) const`	`const TfLiteTensor *` Return an immutable pointerto the given input tensor.
`inputs () const`	`const std::vector< int > &` Read only access to list of inputs.
`node_and_registration (int node_index) const`	`const std::pair< TfLiteNode, TfLiteRegistration > *` Get a pointer to an operation and registration data structure if in bounds.
`nodes_size () const`	`size_t` Return the number of ops in the model.
`operator= (const Interpreter &)=delete`	`Interpreter &`
`output_tensor (size_t index)`	`TfLiteTensor *` Return a mutable pointer to the given output tensor.
`output_tensor (size_t index) const`	`const TfLiteTensor *` Return an immutable pointer to the given output tensor.
`outputs () const`	`const std::vector< int > &` Read only access to list of outputs.
`tensor (int tensor_index)`	`TfLiteTensor *` Get a mutable tensor data structure.
`tensor (int tensor_index) const`	`const TfLiteTensor *` Get an immutable tensor data structure.
`tensors_size () const`	`size_t` Return the number of tensors in the model.
`typed_input_tensor (int index)`	`T *` Return a mutable pointer into the data of a given input tensor.
`typed_input_tensor (int index) const`	`const T *` Return an immutable pointer into the data of a given input tensor.
`typed_output_tensor (int index)`	`T *` Return a mutable pointer into the data of a given output tensor.
`typed_output_tensor (int index) const`	`const T *` Return an immutable pointer into the data of a given output tensor.
`typed_tensor (int tensor_index)`	`T *` Perform a checked cast to the appropriate tensor type (mutable pointer version).
`typed_tensor (int tensor_index) const`	`const T *` Perform a checked cast to the appropriate tensor type (immutable pointer version).
`variables () const`	`const std::vector< int > &` Read only access to list of variable tensors.

Public types

TfLiteDelegatePtr

std::unique_ptr< TfLiteDelegate, void(*)(TfLiteDelegate *)> TfLiteDelegatePtr

Public static attributes

kTensorsCapacityHeadroom

constexpr int kTensorsCapacityHeadroom = 16

The capacity headroom of tensors_ vector before calling ops' prepare and invoke function.

In these functions, it's guaranteed allocating up to kTensorsCapacityHeadroom more tensors won't invalidate pointers to existing tensors.

kTensorsReservedCapacity

constexpr int kTensorsReservedCapacity = 128

Friend classes

tflite::InterpreterTest

friend class tflite::InterpreterTest

tflite::TestDelegate

friend class tflite::TestDelegate

tflite::delegates::InterpreterUtils

friend class tflite::delegates::InterpreterUtils

Public functions

AllocateTensors

TfLiteStatus AllocateTensors()

EnsureTensorDataIsReadable

TfLiteStatus EnsureTensorDataIsReadable(
  int tensor_index
)

Ensure the data in tensor.data is readable.

In case delegate is used, it might require to copy the data from delegate buffer to raw memory. WARNING: This is an experimental API and subject to change.

GetAllowFp16PrecisionForFp32

bool GetAllowFp16PrecisionForFp32() const

Get the half precision flag.

WARNING: This is an experimental API and subject to change.

GetBufferHandle

TfLiteStatus GetBufferHandle(
  int tensor_index,
  TfLiteBufferHandle *buffer_handle,
  TfLiteDelegate **delegate
)

Get the delegate buffer handle, and the delegate which can process the buffer handle.

WARNING: This is an experimental API and subject to change.

GetInputName

const char * GetInputName(
  int index
) const

Return the name of a given input.

The given index must be between 0 and inputs() .size().

GetOutputName

const char * GetOutputName(
  int index
) const

Return the name of a given output.

The given index must be between 0 and outputs() .size().

GetProfiler

Profiler * GetProfiler()

Gets the profiler used for op tracing.

WARNING: This is an experimental API and subject to change.

Interpreter

 Interpreter(
  ErrorReporter *error_reporter
)

Interpreter

 Interpreter(
  const Interpreter &
)=delete

Invoke

TfLiteStatus Invoke()

Invoke the interpreter (run the whole graph in dependency order).

NOTE: It is possible that the interpreter is not in a ready state to evaluate (i.e. if a ResizeTensor() has been performed without an AllocateTensors(). Returns status of success or failure.

ModifyGraphWithDelegate

TfLiteStatus ModifyGraphWithDelegate(
  TfLiteDelegate *delegate
)

Allow a delegate to look at the graph and modify the graph to handle parts of the graph themselves.

After this is called, the graph may contain new nodes that replace 1 more nodes. 'delegate' must outlive the interpreter. Returns one of the following three status codes:

kTfLiteOk: Success.
kTfLiteDelegateError: Delegation failed due to an error in the delegate. The Interpreter has been restored to its pre-delegation state. NOTE: This undoes all delegates previously applied to the Interpreter .
kTfLiteApplicationError : Delegation failed to be applied due to the incompatibility with the TfLite runtime, e.g., the model graph is already immutable when applying the delegate. However, the interpreter could still be invoked.
kTfLiteError: Unexpected/runtime failure. WARNING: This is an experimental API and subject to change.

ModifyGraphWithDelegate

TfLiteStatus ModifyGraphWithDelegate(
  std::unique_ptr< Delegate, Deleter > delegate
)

Same as ModifyGraphWithDelegate except this interpreter takes ownership of the provided delegate.

WARNING: This is an experimental API and subject to change.

ModifyGraphWithDelegate

TfLiteStatus ModifyGraphWithDelegate(
  std::unique_ptr< TfLiteDelegate > delegate
)=delete

This overload is never OK.

TfLiteDelegate is a C structure, so it has no virtual destructor. The default deleter of the unique_ptr does not know how to delete C++ objects deriving from TfLiteDelegate.

OpProfilingString

const char * OpProfilingString(
  const TfLiteRegistration & op_reg,
  const TfLiteNode *node
) const

Retrieve an operator's description of its work, for profiling purposes.

ReleaseNonPersistentMemory

TfLiteStatus ReleaseNonPersistentMemory()

WARNING: Experimental interface, subject to change.

ResetVariableTensors

TfLiteStatus ResetVariableTensors()

Reset all variable tensors to the default value.

If a variable tensor doesn't have a buffer, reset it to zero. TODO(b/115961645): Implement - If a variable tensor has a buffer, reset it to the value of the buffer. WARNING: This is an experimental API and subject to change.

ResizeInputTensor

TfLiteStatus ResizeInputTensor(
  int tensor_index,
  const std::vector< int > & dims
)

Change the dimensionality of a given tensor.

Note, this is only acceptable for tensor indices that are inputs or variables. Returns status of failure or success. Note that this doesn't actually resize any existing buffers. A call to AllocateTensors() is required to change the tensor input buffer.

ResizeInputTensorStrict

TfLiteStatus ResizeInputTensorStrict(
  int tensor_index,
  const std::vector< int > & dims
)

A call to AllocateTensors() is required to change the tensor input buffer.

SetAllowBufferHandleOutput

void SetAllowBufferHandleOutput(
  bool allow_buffer_handle_output
)

Set if buffer handle output is allowed.

When using hardware delegation, Interpreter will make the data of output tensors available in tensor->data by default. If the application can consume the buffer handle directly (e.g. reading output from OpenGL texture), it can set this flag to false, so Interpreter won't copy the data from buffer handle to CPU memory. WARNING: This is an experimental API and subject to change.

SetAllowFp16PrecisionForFp32

void SetAllowFp16PrecisionForFp32(
  bool allow
)

Allow float16 precision for FP32 calculation when possible.

Default: not allow.

WARNING: This API is deprecated: prefer controlling this via delegate options, e.g. `tflite::StatefulNnApiDelegate::Options::allow_fp16' or TfLiteGpuDelegateOptionsV2::is_precision_loss_allowed . This method will be removed in a future release.

SetBufferHandle

TfLiteStatus SetBufferHandle(
  int tensor_index,
  TfLiteBufferHandle buffer_handle,
  TfLiteDelegate *delegate
)

Set the delegate buffer handle to a tensor.

It can be called in the following cases:

Set the buffer handle to a tensor that's not being written by a delegate. For example, feeding an OpenGL texture as the input of the inference graph.
Set the buffer handle to a tensor that uses the same delegate. For example, set an OpenGL texture as the output of inference, while the node which produces output is an OpenGL delegate node. WARNING: This is an experimental API and subject to change.

SetCancellationFunction

void SetCancellationFunction(
  void *data,
  bool(*)(void *) check_cancelled_func
)

Sets the cancellation function pointer in order to cancel a request in the middle of a call to Invoke() .

The interpreter queries this function during inference, between op invocations; when it returns true, the interpreter will abort execution and return kTfLiteError . The data parameter contains any data used by the cancellation function, and if non-null, remains owned by the caller. WARNING: This is an experimental API and subject to change.

SetCustomAllocationForTensor

TfLiteStatus SetCustomAllocationForTensor(
  int tensor_index,
  const TfLiteCustomAllocation & allocation
)

SetExecutionPlan

TfLiteStatus SetExecutionPlan(
  const std::vector< int > & new_plan
)

WARNING: Experimental interface, subject to change Overrides execution plan.

This bounds checks indices sent in.

SetExternalContext

void SetExternalContext(
  TfLiteExternalContextType type,
  TfLiteExternalContext *ctx
)

SetNumThreads

TfLiteStatus SetNumThreads(
  int num_threads
)

Set the number of threads available to the interpreter.

NOTE: num_threads should be >= -1. User may pass -1 to let the TFLite interpreter set the no of threads available to itself.

SetProfiler

void SetProfiler(
  Profiler *profiler
)

Sets the profiler to tracing execution.

The caller retains ownership of the profiler and must ensure its validity. WARNING: This is an experimental API and subject to change.

SetProfiler

void SetProfiler(
  std::unique_ptr< Profiler > profiler
)

Same as SetProfiler except this interpreter takes ownership of the provided profiler.

WARNING: This is an experimental API and subject to change.

UseNNAPI

void UseNNAPI(
  bool enable
)

Enable or disable NNAPI (true to enable).

Disabled by default.

WARNING: NNAPI cannot be disabled after the graph has been prepared (via AllocateTensors ) with NNAPI enabled.

WARNING: This API is deprecated, prefer using the NNAPI delegate directly. This method will be removed in a future release.

execution_plan

const std::vector< int > & execution_plan() const

WARNING: Experimental interface, subject to change.

input_tensor

TfLiteTensor * input_tensor(
  size_t index
)

Return a mutable pointer to the given input tensor.

The given index must be between 0 and inputs() .size().

input_tensor

const TfLiteTensor * input_tensor(
  size_t index
) const

Return an immutable pointerto the given input tensor.

The given index must be between 0 and inputs() .size().

inputs

const std::vector< int > & inputs() const

Read only access to list of inputs.

node_and_registration

const std::pair< TfLiteNode, TfLiteRegistration > * node_and_registration(
  int node_index
) const

Get a pointer to an operation and registration data structure if in bounds.

nodes_size

size_t nodes_size() const

Return the number of ops in the model.

operator=

Interpreter & operator=(
  const Interpreter &
)=delete

output_tensor

TfLiteTensor * output_tensor(
  size_t index
)

Return a mutable pointer to the given output tensor.

The given index must be between 0 and outputs() .size().

output_tensor

const TfLiteTensor * output_tensor(
  size_t index
) const

Return an immutable pointer to the given output tensor.

The given index must be between 0 and outputs() .size().

outputs

const std::vector< int > & outputs() const

Read only access to list of outputs.

tensor

TfLiteTensor * tensor(
  int tensor_index
)

Get a mutable tensor data structure.

tensor

const TfLiteTensor * tensor(
  int tensor_index
) const

Get an immutable tensor data structure.

tensors_size

size_t tensors_size() const

Return the number of tensors in the model.

typed_input_tensor

T * typed_input_tensor(
  int index
)

Return a mutable pointer into the data of a given input tensor.

The given index must be between 0 and inputs() .size().

typed_input_tensor

const T * typed_input_tensor(
  int index
) const

Return an immutable pointer into the data of a given input tensor.

The given index must be between 0 and inputs() .size().

typed_output_tensor

T * typed_output_tensor(
  int index
)

Return a mutable pointer into the data of a given output tensor.

The given index must be between 0 and outputs() .size().

typed_output_tensor

const T * typed_output_tensor(
  int index
) const

Return an immutable pointer into the data of a given output tensor.

The given index must be between 0 and outputs() .size().

typed_tensor

T * typed_tensor(
  int tensor_index
)

Perform a checked cast to the appropriate tensor type (mutable pointer version).

typed_tensor

const T * typed_tensor(
  int tensor_index
) const

Perform a checked cast to the appropriate tensor type (immutable pointer version).

variables

const std::vector< int > & variables() const

Read only access to list of variable tensors.

~Interpreter

 ~Interpreter()

tflite:: Interpreter

Summary

Constructors and Destructors

Public types

Public static attributes

Friend classes

Public functions

Public types

TfLiteDelegatePtr

Public static attributes

kTensorsCapacityHeadroom

kTensorsReservedCapacity

Friend classes

tflite::InterpreterTest

tflite::TestDelegate

tflite::delegates::InterpreterUtils

Public functions

AllocateTensors

EnsureTensorDataIsReadable

GetAllowFp16PrecisionForFp32

GetBufferHandle

GetInputName

GetOutputName

GetProfiler

Interpreter

Interpreter

Invoke

ModifyGraphWithDelegate

ModifyGraphWithDelegate

ModifyGraphWithDelegate

OpProfilingString

ReleaseNonPersistentMemory

ResetVariableTensors

ResizeInputTensor

ResizeInputTensorStrict

SetAllowBufferHandleOutput

SetAllowFp16PrecisionForFp32

SetBufferHandle

SetCancellationFunction

SetCustomAllocationForTensor

SetExecutionPlan

SetExternalContext

SetNumThreads

SetProfiler

SetProfiler

UseNNAPI

execution_plan

input_tensor

input_tensor

inputs

node_and_registration

nodes_size

operator=

output_tensor

output_tensor

outputs

tensor

tensor

tensors_size

typed_input_tensor

typed_input_tensor

typed_output_tensor

typed_output_tensor

typed_tensor

typed_tensor

variables

~Interpreter