tflite:: impl:: Interpreter

    Summary

    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::impl::InterpreterBuilder
    friend class

    Public functions

    AddProfiler (Profiler *profiler)
    void
    \warning This is an experimental API and subject to change.
    AddProfiler (std::unique_ptr< Profiler > profiler)
    void
    \warning This is an experimental API and subject to change.
    AllocateTensors ()
    TfLiteStatus
    Update allocations for all tensors.
    ApplyOptions (InterpreterOptions *options)
    TfLiteStatus
    \warning This is an experimental API and subject to change.
    Cancel ()
    TfLiteStatus
    \warning This is an experimental API and subject to change.
    EnsureTensorDataIsReadable (int tensor_index)
    TfLiteStatus
    \warning This is an experimental API and subject to change.
    GetAllowFp16PrecisionForFp32 () const
    bool
    \warning Experimental interface, subject to change.
    GetAsyncSignatureRunner (const char *signature_key)
    async::AsyncSignatureRunner *
    \warning Experimental interface, subject to change.
    GetBufferHandle (int tensor_index, TfLiteBufferHandle *buffer_handle, TfLiteDelegate **delegate)
    TfLiteStatus
    \warning This is an experimental API and subject to change.
    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 *
    \warning This is an experimental API and subject to change.
    GetSignatureRunner (const char *signature_key)
    SignatureRunner *
    Returns a pointer to the SignatureRunner instance to run the part of the graph identified by a SignatureDef.
    GetSubgraphIndexFromSignature (const char *signature_key) const
    int
    \warning Experimental interface, subject to change.
    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 (TfLiteOpaqueDelegateStruct *delegate)
    TfLiteStatus
    ModifyGraphWithDelegate (std::unique_ptr< Delegate, Deleter > delegate)
    TfLiteStatus
    \warning This is an experimental API and subject to change.
    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
    \warning This is an experimental API and subject to change.
    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
    Change the dimensionality of a given tensor.
    SetAllowBufferHandleOutput (bool allow_buffer_handle_output)
    void
    \warning This is an experimental API and subject to change.
    SetAllowFp16PrecisionForFp32 (bool allow)
    void
    Allow float16 precision for FP32 calculation when possible.
    SetBufferHandle (int tensor_index, TfLiteBufferHandle buffer_handle, TfLiteDelegate *delegate)
    TfLiteStatus
    \warning This is an experimental API and subject to change.
    SetBufferHandle (TfLiteTensor *tensor, TfLiteBufferHandle buffer_handle, TfLiteDelegate *delegate)
    TfLiteStatus
    \warning This is an experimental API and subject to change.
    SetCancellationFunction (void *data, bool(*)(void *) check_cancelled_func)
    void
    \warning This is an experimental API and subject to change.
    SetCustomAllocationForTensor (int tensor_index, const TfLiteCustomAllocation & allocation, int64_t flags)
    TfLiteStatus
    Assigns (or reassigns) a custom memory allocation for the given tensor.
    SetExternalContext (TfLiteExternalContextType type, TfLiteExternalContext *ctx)
    void
    SetNumThreads (int num_threads)
    TfLiteStatus
    Set the number of threads available to the interpreter.
    SetProfiler (Profiler *profiler)
    void
    \warning This is an experimental API and subject to change.
    SetProfiler (std::unique_ptr< Profiler > profiler)
    void
    \warning This is an experimental API and subject to change.
    error_reporter () const
    \warning Experimental interface, subject to change.
    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 pointer to the given input tensor.
    input_tensor_by_signature (const char *signature_input_name, const char *signature_key)
    TfLiteTensor *
    Returns the input tensor identified by 'signature_input_name' in the signature identified by 'signature_key'.
    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 > *
    Returns a pointer to an operation and registration data structure if in bounds from the primary subgraph(subgraph_[0]).
    node_and_registration (int subgraph_index, int node_index) const
    const std::pair< TfLiteNode, TfLiteRegistration > *
    Returns 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
    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.
    output_tensor_by_signature (const char *signature_output_name, const char *signature_key) const
    const TfLiteTensor *
    Returns the output tensor identified by 'signature_output_name' in the signature identified by 'signature_key'.
    outputs () const
    const std::vector< int > &
    Read only access to list of outputs.
    signature_inputs (const char *signature_key) const
    const std::map< std::string, uint32_t > &
    Returns the mapping of inputs to tensor index in the signature specified through 'signature_key'.
    signature_keys () const
    std::vector< const std::string * >
    Returns list of all keys of different method signatures defined in the model.
    signature_outputs (const char *signature_key) const
    const std::map< std::string, uint32_t > &
    Returns the mapping of outputs to tensor index in the signature specified through 'signature_key'.
    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::impl::InterpreterBuilder

     friend 
  
 class 
  
 tflite 
 :: 
 impl 
 :: 
 InterpreterBuilder

    Public functions

    AddProfiler

    void AddProfiler(
  Profiler *profiler
)

    \warning This is an experimental API and subject to change.

    \n Adds the profiler to tracing execution. The caller retains ownership of the profiler and must ensure its validity. nullptr profiler will be ignored.

    AddProfiler

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

    \warning This is an experimental API and subject to change.

    \n Adds the profiler to tracing execution. Transfers ownership of the profiler to the interpreter. nullptr profiler will be ignored.

    AllocateTensors

    TfLiteStatus AllocateTensors()

    Update allocations for all tensors.

    This will redim dependent tensors using the input tensor dimensionality as given. This is relatively expensive. This must be called after the interpreter has been created and before running inference (and accessing tensor buffers), and must be called again if (and only if) an input tensor is resized. Returns status of success or failure. Will fail if any of the ops in the model (other than those which were rewritten by delegates, if any) are not supported by the Interpreter's OpResolver .

    ApplyOptions

    TfLiteStatus ApplyOptions(
  InterpreterOptions *options
)

    \warning This is an experimental API and subject to change.

    \n Apply InterpreterOptions which tunes behavior of the interpreter.

    Cancel

    TfLiteStatus Cancel()

    \warning This is an experimental API and subject to change.

    \n Attempts to cancel in flight invocation if any. This will not affect Invoke s that happends after the cancellation. Non blocking. Thread safe. Returns kTfLiteError if cancellation is not enabled, otherwise returns kTfLiteOk.

    EnsureTensorDataIsReadable

    TfLiteStatus EnsureTensorDataIsReadable(
  int tensor_index
)

    \warning This is an experimental API and subject to change.

    \n Ensure the data in tensor.data is readable. If a delegate has been used, and SetAllowBufferHandleOutput(true) has been called, tensor outputs may be stored as delegate buffer handles whose data is not directly readable until this method has been called. In such cases, this method will copy the data from the delegate buffer handle to CPU memory.

    GetAllowFp16PrecisionForFp32

     bool 
  
 GetAllowFp16PrecisionForFp32 
 () 
  
 const

    \warning Experimental interface, subject to change.

    \n Get the half precision flag.

    GetAsyncSignatureRunner

     async 
 :: 
 AsyncSignatureRunner 
  
 * 
  
 GetAsyncSignatureRunner 
 ( 
  
 const 
  
 char 
  
 * 
 signature_key 
 )

    \warning Experimental interface, subject to change.

    \n Returns a pointer to the AsyncSignatureRunner instance to run the part of the graph identified by a SignatureDef. The nullptr is returned if the given signature key is not valid. if the model does not have signature def, pass nullptr to signature_key and AsyncSignatureRunner will be created using primary subgraph (0). The async delegate should be applied before calling this function.

    GetBufferHandle

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

    \warning This is an experimental API and subject to change.

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

    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()

    \warning This is an experimental API and subject to change.

    \n Gets the profiler used for op tracing.

    GetSignatureRunner

     SignatureRunner 
  
 * 
  
 GetSignatureRunner 
 ( 
  
 const 
  
 char 
  
 * 
 signature_key 
 )

    Returns a pointer to the SignatureRunner instance to run the part of the graph identified by a SignatureDef.

    The nullptr is returned if the given signature key is not valid. If you need to specify delegates, you have to do that before calling this function. This function will additionally apply default delegates. Thus, applying delegates after that might lead to undesirable behaviors. Note, the pointed instance has lifetime same as the Interpreter object and the SignatureRunner class is not thread-safe.

    GetSubgraphIndexFromSignature

     int 
  
 GetSubgraphIndexFromSignature 
 ( 
  
 const 
  
 char 
  
 * 
 signature_key 
 ) 
  
 const

    \warning Experimental interface, subject to change.

    \n Return the subgraph index that corresponds to a SignatureDef, defined by 'signature_key'. If invalid name passed, -1 will be returned.

    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 status codes:

    1. kTfLiteOk: Success.
    2. kTfLiteDelegateError: Delegation failed due to an error in the delegate, or the delegate parameter was null. The Interpreter has been restored to its pre-delegation state. NOTE: This undoes all delegates previously applied to the Interpreter.
    3. 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.
    4. kTfLiteUnresolvedOps: Delegation failed because the model has an operator that cannot be resolved. This can happen when the op is not registered or built with the TF Lite framework.
    5. kTfLiteError: Unexpected/runtime failure. \n \warning This is an experimental API and subject to change. \n

    ModifyGraphWithDelegate

    TfLiteStatus ModifyGraphWithDelegate(
  TfLiteOpaqueDelegateStruct *delegate
)

    ModifyGraphWithDelegate

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

    \warning This is an experimental API and subject to change.

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

    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.

    \n This releases memory held by non-persistent tensors. It does NOT re-perform memory planning. AllocateTensors needs to be called before next invocation.

    ResetVariableTensors

    TfLiteStatus ResetVariableTensors()

    \warning This is an experimental API and subject to change.

    \n 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.

    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 
 )

    Change the dimensionality of a given tensor.

    This is only acceptable for tensor indices that are inputs or variables. Only unknown dimensions can be resized with this function. Unknown dimensions are indicated as -1 in the dims_signature attribute of a TfLiteTensor . 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.

    SetAllowBufferHandleOutput

    void SetAllowBufferHandleOutput(
  bool allow_buffer_handle_output
)

    \warning This is an experimental API and subject to change.

    \n 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 true, so Interpreter won't copy the data from buffer handle to CPU memory.

    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
)

    \warning This is an experimental API and subject to change.

    \n Set the delegate buffer handle to a tensor. It can be called in the following cases:

    1. 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.
    2. 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.

    SetBufferHandle

    TfLiteStatus SetBufferHandle(
  TfLiteTensor *tensor,
  TfLiteBufferHandle buffer_handle,
  TfLiteDelegate *delegate
)

    \warning This is an experimental API and subject to change.

    \n Set the delegate buffer handle to the given tensor.

    SetCancellationFunction

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

    \warning This is an experimental API and subject to change.

    \n 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.

    SetCustomAllocationForTensor

     TfLiteStatus 
  
 SetCustomAllocationForTensor 
 ( 
  
 int 
  
 tensor_index 
 , 
  
 const 
  
 TfLiteCustomAllocation 
 & 
 allocation 
 , 
  
 int64_t 
  
 flags 
 )

    Assigns (or reassigns) a custom memory allocation for the given tensor.

    flags is a bitmask, see TfLiteCustomAllocationFlags. The runtime does NOT take ownership of the underlying memory.

    NOTE: User needs to call AllocateTensors() after this. Invalid/insufficient buffers will cause an error during AllocateTensors or Invoke (in case of dynamic shapes in the graph).

    Parameters should satisfy the following conditions:

    1. tensor->allocation_type == kTfLiteArenaRw or kTfLiteArenaRwPersistent In general, this is true for I/O tensors & variable tensors.
    2. allocation->data has the appropriate permissions for runtime access (Read-only for inputs, Read-Write for others), and outlives Interpreter.
    3. allocation->bytes >= tensor->bytes. This condition is checked again if any tensors are resized.
    4. allocation->data should be aligned to kDefaultTensorAlignment defined in lite/util.h. (Currently 64 bytes) This check is skipped if kTfLiteCustomAllocationFlagsSkipAlignCheck is set through flags . \warning This is an experimental API and subject to change. \n

    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. Setting num_threads to 0 has the effect to disable multithreading, which is equivalent to setting num_threads to 1. If set to the value -1, the number of threads used will be implementation-defined and platform-dependent.

    As TfLite interpreter could internally apply a TfLite delegate by default (i.e. XNNPACK), the number of threads that are available to the default delegate should be set via InterpreterBuilder APIs as follows:

    std::unique_ptr  
interpreter;
tflite::InterpreterBuilder  
builder(tflite  
model,  
op  
resolver);
builder.SetNumThreads(...)
ASSERT_EQ(builder(&interpreter),  
kTfLiteOk);

    WARNING: This API is deprecated: prefer using InterpreterBuilder::SetNumThreads , as documented above.

    SetProfiler

    void SetProfiler(
  Profiler *profiler
)

    \warning This is an experimental API and subject to change.

    \n Sets the profiler to tracing execution. The caller retains ownership of the profiler and must ensure its validity. Previously registered profilers will be unregistered. If profiler is nullptr, all previously installed profilers will be removed.

    SetProfiler

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

    \warning This is an experimental API and subject to change.

    \n Same as SetProfiler except this interpreter takes ownership of the provided profiler. Previously registered profilers will be unregistered. If profiler is nullptr, all previously installed profilers will be removed.

    error_reporter

      ErrorReporter 
 
  
 * 
  
 error_reporter 
 () 
  
 const

    \warning Experimental interface, subject to change.

    \n Get the error reporter associated with this interpreter.

    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 pointer to the given input tensor.

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

    input_tensor_by_signature

     TfLiteTensor 
  
 * 
  
 input_tensor_by_signature 
 ( 
  
 const 
  
 char 
  
 * 
 signature_input_name 
 , 
  
 const 
  
 char 
  
 * 
 signature_key 
 )

    Returns the input tensor identified by 'signature_input_name' in the signature identified by 'signature_key'.

    Returns nullptr if not found.

    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

    Returns a pointer to an operation and registration data structure if in bounds from the primary subgraph(subgraph_[0]).

    node_and_registration

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

    Returns 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().

    output_tensor_by_signature

     const 
  
 TfLiteTensor 
  
 * 
  
 output_tensor_by_signature 
 ( 
  
 const 
  
 char 
  
 * 
 signature_output_name 
 , 
  
 const 
  
 char 
  
 * 
 signature_key 
 ) 
  
 const

    Returns the output tensor identified by 'signature_output_name' in the signature identified by 'signature_key'.

    Returns nullptr if not found.

    outputs

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

    Read only access to list of outputs.

    signature_inputs

     const 
  
 std 
 :: 
 map 
<  
 std 
 :: 
 string 
 , 
  
 uint32_t 
 > & 
 signature_inputs 
 ( 
  
 const 
  
 char 
  
 * 
 signature_key 
 ) 
  
 const

    Returns the mapping of inputs to tensor index in the signature specified through 'signature_key'.

    If invalid name passed, an empty list will be returned.

    signature_keys

     std 
 :: 
 vector 
<  
 const 
  
 std 
 :: 
 string 
  
 * 
 > 
 signature_keys 
 () 
  
 const

    Returns list of all keys of different method signatures defined in the model.

    Note, pointers returned have lifetime same as the Interpreter object.

    signature_outputs

     const 
  
 std 
 :: 
 map 
<  
 std 
 :: 
 string 
 , 
  
 uint32_t 
 > & 
 signature_outputs 
 ( 
  
 const 
  
 char 
  
 * 
 signature_key 
 ) 
  
 const

    Returns the mapping of outputs to tensor index in the signature specified through 'signature_key'.

    If invalid name passed, an empty list will be returned.

    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()
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