tflite
Summary
Memory management for TF Lite.
This provides a few C++ helpers that are useful for manipulating C structures in C++.
Main abstraction controlling the tflite interpreter. Do NOT include this file directly, instead include third_party/tensorflow/lite/interpreter.h See third_party/tensorflow/lite/c/common.h for the API for defining operations (TfLiteRegistration).
Provides functionality to construct an interpreter for a model.
WARNING: Users of TensorFlow Lite should not include this file directly, but should instead include "third_party/tensorflow/lite/interpreter_builder.h". Only the TensorFlow Lite implementation itself should include this file directly.
Deserialization infrastructure for tflite. Provides functionality to go from a serialized tflite model in flatbuffer format to an in-memory representation of the model.
WARNING: Users of TensorFlow Lite should not include this file directly, but should instead include "third_party/tensorflow/lite/model_builder.h". Only the TensorFlow Lite implementation itself should include this file directly.
Typedefs
impl::FlatBufferModel
::tflite::impl::Interpreter
impl::InterpreterBuilder
model
.Functions
GetRegistrationFromOpCode
(const OperatorCode *opcode, const OpResolver
& op_resolver, ErrorReporter
*error_reporter, const TfLiteRegistration **registration)
TfLiteStatus
Classes
A memory allocation handle. This could be a mmap or shared memory.
A functor that reports error to supporting system.
Note that not all platforms support MMAP-based allocation.
An OpResolver that is mutable, also used as the op in gen_op_registration.
Abstract interface that returns TfLiteRegistrations given op codes or custom op names.
Provides a range iterable wrapper for TfLiteIntArray* (C lists) that TfLite C api uses.
Structs
Namespaces
An RAII object that represents a read-only tflite model, copied from disk, or mmapped.
Typedefs
FlatBufferModel
impl :: FlatBufferModel FlatBufferModel
Interpreter
:: tflite :: impl :: Interpreter Interpreter
An interpreter for a graph of nodes that input and output from tensors.
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.
}
if
(interpreter->AllocateTensors()
!=
kTfLiteOk)
{
//
Return
failure.
}
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.
\warning This class is not thread-safe. The client is responsible for ensuring serialized interaction to avoid data races and undefined behavior.
InterpreterBuilder
impl :: InterpreterBuilder InterpreterBuilder
Build an interpreter capable of interpreting model
.
-
model: A model whose lifetime must be at least as long as any interpreter(s) created by the builder. In principle multiple interpreters can be made from a single model. -
op_resolver: An instance that implements theOpResolverinterface, which maps custom op names and builtin op codes to op registrations. The lifetime of the providedop_resolverobject must be at least as long as theInterpreterBuilder; unlikemodelanderror_reporter, theop_resolverdoes not need to exist for the duration of any createdInterpreterobjects. -
error_reporter: a functor that is called to report errors that handles printf var arg semantics. The lifetime of theerror_reporterobject must be greater than or equal to theInterpretercreated byoperator(). -
options_experimental: Options that can change behavior of interpreter. WARNING: this parameter is an experimental API and is subject to change.
Returns a kTfLiteOk when successful and sets interpreter to a valid Interpreter. Note: The user must ensure the lifetime of the model (and error reporter, if provided) is at least as long as interpreter's lifetime, and a single model instance may safely be used with multiple interpreters.
Functions
DefaultErrorReporter
ErrorReporter * DefaultErrorReporter()
GetRegistrationFromOpCode
TfLiteStatus GetRegistrationFromOpCode ( const OperatorCode * opcode , const OpResolver & op_resolver , ErrorReporter * error_reporter , const TfLiteRegistration ** registration )
