Introducing LiteRT: Google's high-performance runtime for on-device AI, formerly known as TensorFlow Lite. Learn more

tf.lite.Interpreter

View source on GitHub

Interpreter interface for running TensorFlow Lite models.

tf.lite.Interpreter(
    model_path=None,
    model_content=None,
    experimental_delegates=None,
    num_threads=None,
    experimental_op_resolver_type=tf.lite.experimental.OpResolverType.AUTO,
    experimental_preserve_all_tensors=False,
    experimental_disable_delegate_clustering=False,
    experimental_default_delegate_latest_features=False
)

Used in the notebooks

Used in the guide	Used in the tutorials
Signatures in TensorFlow Lite Cluster preserving quantization aware training (CQAT) Keras example Pruning preserving quantization aware training (PQAT) Keras example Weight clustering in Keras example Sparsity and cluster preserving quantization aware training (PCQAT) Keras example	Post-training integer quantization Jax Model Conversion For TFLite On-Device Training with TensorFlow Lite Artistic Style Transfer with TensorFlow Lite Post-training float16 quantization

Models obtained from TfLiteConverter can be run in Python with Interpreter.

As an example, let's generate a simple Keras model and convert it to TFLite (TfLiteConverter also supports other input formats with from_saved_model and from_concrete_function)

x = np.array([[1.], [2.]])
y = np.array([[2.], [4.]])
model = tf.keras.models.Sequential([
          tf.keras.layers.Dropout(0.2),
          tf.keras.layers.Dense(units=1, input_shape=[1])
        ])
model.compile(optimizer='sgd', loss='mean_squared_error')
model.fit(x, y, epochs=1)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

tflite_model can be saved to a file and loaded later, or directly into the Interpreter. Since TensorFlow Lite pre-plans tensor allocations to optimize inference, the user needs to call allocate_tensors() before any inference.

interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()  # Needed before execution!

Sample execution:

output = interpreter.get_output_details()[0]  # Model has single output.
input = interpreter.get_input_details()[0]  # Model has single input.
input_data = tf.constant(1., shape=[1, 1])
interpreter.set_tensor(input['index'], input_data)
interpreter.invoke()
interpreter.get_tensor(output['index']).shape
(1, 1)

Use get_signature_runner() for a more user-friendly inference API.

Args
`model_path`	Path to TF-Lite Flatbuffer file.
`model_content`	Content of model.
`experimental_delegates`	Experimental. Subject to change. List of TfLiteDelegate objects returned by lite.load_delegate().
`num_threads`	Sets the number of threads used by the interpreter and available to CPU kernels. If not set, the interpreter will use an implementation-dependent default number of threads. Currently, only a subset of kernels, such as conv, support multi-threading. 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.
`experimental_op_resolver_type`	The op resolver used by the interpreter. It must be an instance of OpResolverType. By default, we use the built-in op resolver which corresponds to tflite::ops::builtin::BuiltinOpResolver in C++.
`experimental_preserve_all_tensors`	If true, then intermediate tensors used during computation are preserved for inspection, and if the passed op resolver type is AUTO or BUILTIN, the type will be changed to BUILTIN_WITHOUT_DEFAULT_DELEGATES so that no Tensorflow Lite default delegates are applied. If false, getting intermediate tensors could result in undefined values or None, especially when the graph is successfully modified by the Tensorflow Lite default delegate.
`experimental_disable_delegate_clustering`	If true, don't perform delegate clustering during delegate graph partitioning phase. Disabling delegate clustering will make the execution order of ops respect the explicitly-inserted control dependencies in the graph (inserted via `with tf.control_dependencies()`) since the TF Lite converter will drop control dependencies by default. Most users shouldn't turn this flag to True if they don't insert explicit control dependencies or the graph execution order is expected. For automatically inserted control dependencies (with `tf.Variable`, `tf.Print` etc), the user doesn't need to turn this flag to True since they are respected by default. Note that this flag is currently experimental, and it might be removed/updated if the TF Lite converter doesn't drop such control dependencies in the model. Default is False.
`experimental_default_delegate_latest_features`	If true, default delegates may enable all flag protected features. Default is False;

Raises
`ValueError`	If the interpreter was unable to create.

Methods

`allocate_tensors`

View source

allocate_tensors()

`get_input_details`

View source

get_input_details()

Gets model input tensor details.

Returns

Returns
A list in which each item is a dictionary with details about an input tensor. Each dictionary contains the following fields that describe the tensor: `name`: The tensor name. `index`: The tensor index in the interpreter. `shape`: The shape of the tensor. `shape_signature`: Same as `shape` for models with known/fixed shapes. If any dimension sizes are unknown, they are indicated with `-1`. `dtype`: The numpy data type (such as `np.int32` or `np.uint8`). `quantization`: Deprecated, use `quantization_parameters`. This field only works for per-tensor quantization, whereas `quantization_parameters` works in all cases. `quantization_parameters`: A dictionary of parameters used to quantize the tensor: ~ `scales`: List of scales (one if per-tensor quantization). ~ `zero_points`: List of zero_points (one if per-tensor quantization). ~ `quantized_dimension`: Specifies the dimension of per-axis quantization, in the case of multiple scales/zero_points. `sparsity_parameters`: A dictionary of parameters used to encode a sparse tensor. This is empty if the tensor is dense.

A list in which each item is a dictionary with details about an input tensor. Each dictionary contains the following fields that describe the tensor:

name: The tensor name.
index: The tensor index in the interpreter.
shape: The shape of the tensor.
shape_signature: Same as shape for models with known/fixed shapes. If any dimension sizes are unknown, they are indicated with -1.
dtype: The numpy data type (such as np.int32 or np.uint8).
quantization: Deprecated, use quantization_parameters. This field only works for per-tensor quantization, whereas quantization_parameters works in all cases.
quantization_parameters: A dictionary of parameters used to quantize the tensor: ~ scales: List of scales (one if per-tensor quantization). ~ zero_points: List of zero_points (one if per-tensor quantization). ~ quantized_dimension: Specifies the dimension of per-axis quantization, in the case of multiple scales/zero_points.
sparsity_parameters: A dictionary of parameters used to encode a sparse tensor. This is empty if the tensor is dense.

`get_output_details`

View source

get_output_details()

Gets model output tensor details.

Returns
A list in which each item is a dictionary with details about an output tensor. The dictionary contains the same fields as described for `get_input_details()`.

`get_signature_list`

View source

get_signature_list()

Gets the list of SignatureDefs in the model.

Example,

signatures = interpreter.get_signature_list()
print(signatures)

# {
#   'add': {'inputs': ['x', 'y'], 'outputs': ['output_0']}
# }

Then using the names in the signature list you can get a callable from
get_signature_runner().

Returns
A list of SignatureDef details in a dictionary structure. It is keyed on the SignatureDef method name, and the value holds a dictionary of inputs and outputs.

`get_signature_runner`

View source

get_signature_runner(
    signature_key=None
)

Gets callable for inference of specific SignatureDef.

Example usage,

interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
fn = interpreter.get_signature_runner('div_with_remainder')
output = fn(x=np.array([3]), y=np.array([2]))
print(output)
# {
#   'quotient': array([1.], dtype=float32)
#   'remainder': array([1.], dtype=float32)
# }

None can be passed for signature_key if the model has a single Signature only.

All names used are these specific SignatureDef names.

Args
`signature_key`	Signature key for the SignatureDef, it can be None if and only if the model has a single SignatureDef. The Default value is None.

Returns
This returns a callable that can run inference for SignatureDef defined by argument 'signature_key'. The callable will take key arguments corresponding to the arguments of the SignatureDef, that should have numpy values. The callable will return dictionary that maps from output names to numpy values of the computed results.

Raises
`ValueError`	If passed signature_key is invalid.

`get_tensor`

View source

get_tensor(
    tensor_index, subgraph_index=0
)

Gets the value of the output tensor (get a copy).

If you wish to avoid the copy, use tensor(). This function cannot be used to read intermediate results.

Args
`tensor_index`	Tensor index of tensor to get. This value can be gotten from the 'index' field in get_output_details.
`subgraph_index`	Index of the subgraph to fetch the tensor. Default value is 0, which means to fetch from the primary subgraph.

Returns
a numpy array.

`get_tensor_details`

View source

get_tensor_details()

Gets tensor details for every tensor with valid tensor details.

Tensors where required information about the tensor is not found are not added to the list. This includes temporary tensors without a name.

Returns
A list of dictionaries containing tensor information.

`invoke`

View source

invoke()

Invoke the interpreter.

Be sure to set the input sizes, allocate tensors and fill values before calling this. Also, note that this function releases the GIL so heavy computation can be done in the background while the Python interpreter continues. No other function on this object should be called while the invoke() call has not finished.

Raises
`ValueError`	When the underlying interpreter fails raise ValueError.

`reset_all_variables`

View source

reset_all_variables()

`resize_tensor_input`

View source

resize_tensor_input(
    input_index, tensor_size, strict=False
)

Resizes an input tensor.

Args
`input_index`	Tensor index of input to set. This value can be gotten from the 'index' field in get_input_details.
`tensor_size`	The tensor_shape to resize the input to.
`strict`	Only unknown dimensions can be resized when `strict` is True. Unknown dimensions are indicated as `-1` in the `shape_signature` attribute of a given tensor. (default False)

Raises
`ValueError`	If the interpreter could not resize the input tensor.

Usage:

interpreter = Interpreter(model_content=tflite_model)
interpreter.resize_tensor_input(0, [num_test_images, 224, 224, 3])
interpreter.allocate_tensors()
interpreter.set_tensor(0, test_images)
interpreter.invoke()

`set_tensor`

View source

set_tensor(
    tensor_index, value
)

Sets the value of the input tensor.

Note this copies data in value.

If you want to avoid copying, you can use the tensor() function to get a numpy buffer pointing to the input buffer in the tflite interpreter.

Args
`tensor_index`	Tensor index of tensor to set. This value can be gotten from the 'index' field in get_input_details.
`value`	Value of tensor to set.

Raises
`ValueError`	If the interpreter could not set the tensor.

`tensor`

View source

tensor(
    tensor_index
)

Returns function that gives a numpy view of the current tensor buffer.

This allows reading and writing to these tensors w/o copies. This more closely mirrors the C++ Interpreter class interface's tensor() member, hence the name. Be careful not to hold these output references through calls to allocate_tensors() and invoke(). This function cannot be used to read intermediate results.

Usage:

interpreter.allocate_tensors()
input = interpreter.tensor(interpreter.get_input_details()[0]["index"])
output = interpreter.tensor(interpreter.get_output_details()[0]["index"])
for i in range(10):
  input().fill(3.)
  interpreter.invoke()
  print("inference %s" % output())

Notice how this function avoids making a numpy array directly. This is because it is important to not hold actual numpy views to the data longer than necessary. If you do, then the interpreter can no longer be invoked, because it is possible the interpreter would resize and invalidate the referenced tensors. The NumPy API doesn't allow any mutability of the underlying buffers.

WRONG:

input = interpreter.tensor(interpreter.get_input_details()[0]["index"])()
output = interpreter.tensor(interpreter.get_output_details()[0]["index

tf.lite.Interpreter

Used in the notebooks

Sample execution:

Args

Raises

Methods

allocate_tensors

get_input_details

get_output_details

get_signature_list

get_signature_runner

get_tensor

get_tensor_details

invoke

reset_all_variables

resize_tensor_input

Usage:

set_tensor

tensor

Usage:

WRONG:

`allocate_tensors`

`get_input_details`

`get_output_details`

`get_signature_list`

`get_signature_runner`

`get_tensor`

`get_tensor_details`

`invoke`

`reset_all_variables`

`resize_tensor_input`

`set_tensor`

`tensor`