TfliteModel

class TfliteModel

Class to access a .tflite model flatbuffer’s layers and tensors

Refer to schema_v3.fbs for more details on the .tflite flatbuffer schema

Example Usage

from yzlite.core import TfliteModel

# Load you .tflite model file
model = TfliteModel.load_flatbuffer_file('some/path/my_model.tflite')

# Print a summary of the model
print(tflite_model.summary())

# Iterate through each layer of the model
for layer in tflite_model.layers:
    # See TfliteLayer for additional info
    print(layer)

# Update the model's description
# This updates the .tflite's "description" field (which will be displayed in GUIs like https://netron.app)
tflite_model.description = "My awesome model"
print(f'New model description: {tflite_model.description}')

# Save a new .tflite with the updated description
tflite_model.save('some/path/my_new_model.tflite')

# Add some metadata to the .tflite
metadata = 'this is metadata'.encode('utf-8')
tflite_model.add_metadata('my_metadata', metadata)

# Retrieve all the metadata in the .tflite
all_metadata = tflite_model.get_all_metadata()
for key, data in all_metadata.items():
    print(f'{key}: length={len(data)} bytes')

# Save a new .tflite with the updated metadata
tflite_model.save('some/path/my_new_model.tflite')

# You must have Tensorflow instance to perform this step
# This will run inference with the given buffer and return
# the results. The input_buffer can be:
# - a single sample as a numpy array
# - a numpy array of 1 or more samples
# - A Python generator that returns (batch_x, batch_y)
# inference_results = tflite_model.predict(..)

Properties

description	Get/set model description
filename	File name of associated .tflite model file Return None if not path is set
flatbuffer_data	Flatbuffer binary data
flatbuffer_model	Flatbuffer schema Model object
flatbuffer_size	Size of the model flatbuffer in bytes
flatbuffer_subgraph	Flatbuffer schema model subgraph
inputs	List of all input tensors
layers	List of all model layers for the current subgraph
n_inputs	Return the number of model inputs
n_outputs	Return the number of model outputs
n_subgraphs	Return the number of model subgraphs
name	The name of the model which is the filename without the .tflite extension or "my_model" if no path is set
outputs	List of all output tensors
path	Path to .tflite file Returns None if no path was specified.
selected_model_subgraph	The index of the selected model subgraph.
tensors	List of all model tensors for the current subgraph

Methods

__init__
add_metadata	Set or add metadata to model
dequantize_output_to_float32	De-quantize the model output to float32 (if necessary)
get_all_metadata	Return all model metadata as a dictionary
get_flatbuffer_subgraph	Flatbuffer schema model subgraph at the given index
get_input_data	Return a model input as a np.ndarray
get_input_tensor	Return a model input tensor as a TfliteTensor
get_metadata	Return model metadata with specified tag
get_output_data	Return a model output tensor as a np.ndarray
get_output_tensor	Return a model output tensor as a TfliteTensor
get_tensor	Return a specific model tensor as a TfliteTensor
get_tensor_data	Return a specific model tensor as a np.ndarray
load_flatbuffer_file	Load a .tflite flatbuffer file
predict	Invoke the TfLite interpreter with the given input sample and return the results
quantize_to_input_dtype	Quantize the input sample(s) to the model's input dtype (if necessary)
regenerate_flatbuffer	Re-generate the underlying flatbuffer based on the information cached in the local ModelT instance
remove_metadata	Remove model metadata with specified tag
save	Save flatbuffer data to file If output_path is specified then write to new file, otherwise overwrite existing file
summary	Generate a summary of the model

static load_flatbuffer_file(path, cwd=None)

Load a .tflite flatbuffer file

Return type:

TfliteModel

Parameters:

path (str) –

__init__(flatbuffer_data, path=None)

Parameters:

• flatbuffer_data (bytes) –

• path (Optional[str]) –

property path: str

Path to .tflite file Returns None if no path was specified. The path is normalized and backslashes are converted to forward slash

Return type:

str

property filename: str

File name of associated .tflite model file Return None if not path is set

Return type:

str

property name: str

The name of the model which is the filename without the .tflite extension or “my_model” if no path is set

Return type:

str

property description: str

Get/set model description

Note

save() must be called for changes to persist

Return type:

str

property flatbuffer_data: bytes

Flatbuffer binary data

Return type:

bytes

property flatbuffer_size: int

Size of the model flatbuffer in bytes

Return type:

int

property flatbuffer_model: ModelT

Flatbuffer schema Model object

Return type:

ModelT

property flatbuffer_subgraph: SubGraphT

Flatbuffer schema model subgraph

Return type:

SubGraphT

property selected_model_subgraph: int

The index of the selected model subgraph. Other properties and APIs will return layers/tensors from the selected subgraph

Return type:

int

property n_subgraphs: int

Return the number of model subgraphs

Return type:

int

property n_inputs: int

Return the number of model inputs

Return type:

int

property inputs: List[TfliteTensor]

List of all input tensors

Return type:

List[TfliteTensor]

property n_outputs: int

Return the number of model outputs

Return type:

int

property outputs: List[TfliteTensor]

List of all output tensors

Return type:

List[TfliteTensor]

property layers: List[TfliteLayer]

List of all model layers for the current subgraph

Return type:

List[TfliteLayer]

property tensors: List[TfliteTensor]

List of all model tensors for the current subgraph

Return type:

List[TfliteTensor]

summary()

Generate a summary of the model

Return type:

str

get_flatbuffer_subgraph(index=None)

Flatbuffer schema model subgraph at the given index

If no index is given, then use the selected_model_subgraph

Return type:

SubGraphT

Parameters:

index (Optional[int]) –

get_tensor(index)

Return a specific model tensor as a TfliteTensor

Return type:

TfliteTensor

Parameters:

index (int) –

get_tensor_data(index)

Return a specific model tensor as a np.ndarray

Return type:

ndarray

Parameters:

index (int) –

get_input_tensor(index=0)

Return a model input tensor as a TfliteTensor

Return type:

TfliteTensor

Parameters:

index (int) –

get_input_data(index=0)

Return a model input as a np.ndarray

Return type:

ndarray

Parameters:

index (int) –

get_output_tensor(index=0)

Return a model output tensor as a TfliteTensor

Return type:

TfliteTensor

Parameters:

index (int) –

get_output_data(index=0)

Return a model output tensor as a np.ndarray

Return type:

ndarray

Parameters:

index (int) –

get_all_metadata()

Return all model metadata as a dictionary

Return type:

Dict[str, bytes]

get_metadata(tag)

Return model metadata with specified tag

Return type:

bytes

Parameters:

tag (str) –

add_metadata(tag, value)

Set or add metadata to model

Note

save() must be called for changes to persist

Parameters:

• tag (str) – The key to use to lookup the metadata

• value (bytes) – The metadata value as a binary blob to add to the .tflite

remove_metadata(tag)

Remove model metadata with specified tag

Note

save() must be called for changes to persist

Parameters:

tag (str) – The key to use to lookup the metadata

Return type:

bool

Returns:

True if the metadata was found and removed, False else

save(output_path=None)

Save flatbuffer data to file If output_path is specified then write to new file, otherwise overwrite existing file

Parameters:

output_path (Optional[str]) –

regenerate_flatbuffer()

Re-generate the underlying flatbuffer based on the information cached in the local ModelT instance

Note

save() must be called for changes to persist

predict(x, y_dtype=None, **kwargs)

Invoke the TfLite interpreter with the given input sample and return the results

Note

This API only supports models with a single input & output

Parameters:

• x (Union[ndarray, Iterator]) – The input samples(s) as a numpy array or data generator. If x is a numpy array then it must have the same shape as the model input or it must be a vector (i.e. batch) of samples having the same shape as the model input. The data type must either be the same as the model input’s OR it must be a float32, in which case the input sample will automatically be quantized using the model input’s quantizing scaler/zeropoint. If x is a generator, then each iteration must return a tuple: batch_x, batch_y batch_x must be a vector (i.e. batch) of samples having the same shape as the model input batch_y is ignored.

• y_dtype – The return value’s data type. By default, data type is None in which case the model output is directly returned. If y_dtype=np.float32 then the model output is de-quantized to float32 using the model’s output quantization scaler/zeropoint (if necessary)

Return type:

ndarray

Returns:

Output of model inference, y. If x was a single sample, then y is a single result. Otherwise y is a vector (i.e. batch) of model results. If y_dtype is given, the y if automatically converted/de-quantized to the given dtype.

quantize_to_input_dtype(x)

Quantize the input sample(s) to the model’s input dtype (if necessary)

dequantize_output_to_float32(y)

De-quantize the model output to float32 (if necessary)