def _set_input_tensors(self, interpreter: tf.lite.Interpreter,
tensor_data: Sequence[np.ndarray],
initialize: bool) -> None:
"""Sets input tensors into TFLite model Interpreter.
Args:
interpreter: a tf.lite.Interpreter object with allocated tensors.
tensor_data: a list of Numpy array data.
initialize: set to true when input is first set for the interpreter, to
set input shapes and allocate tensors.
Raises:
ValueError: when inputs can't be set, or size of provided inputs does not
match size of model inputs.
"""
input_details = interpreter.get_input_details()
if len(input_details) != len(tensor_data):
raise ValueError(
'Number of inputs provided ({}) does not match number of inputs to '
'the model ({})'.format(len(tensor_data), len(input_details)))
if initialize:
for input_detail, tensor in zip(input_details, tensor_data):
interpreter.resize_tensor_input(input_detail['index'],
tensor.shape)
interpreter.allocate_tensors()
for input_detail, tensor in zip(input_details, tensor_data):
if tensor.dtype == np.float32 and input_detail['dtype'] == np.int8:
quant_params = _get_quant_params(input_detail)
if quant_params:
scale, zero_point = quant_params
tensor = np.round((tensor / scale) + zero_point).astype(
np.int8)
interpreter.set_tensor(input_detail['index'], tensor)
def _set_input_tensors(
self,
interpreter: tf.lite.Interpreter,
tensor_data: Sequence[np.ndarray],
initialize: bool,
) -> None:
"""Sets input tensors into TFLite model Interpreter.
Args:
interpreter: a tf.lite.Interpreter object with allocated tensors.
tensor_data: a list of Numpy array data.
initialize: set to true when input is first set for the interpreter, to
set input shapes and allocate tensors.
Raises:
ValueError: when inputs can't be set, or size of provided inputs does not
match size of model inputs.
"""
input_indices = [
detail['index'] for detail in interpreter.get_input_details()
]
if len(input_indices) != len(tensor_data):
raise ValueError(
'Number of inputs provided ({}) does not match number of inputs to '
'the model ({})'.format(len(tensor_data), len(input_indices)))
if initialize:
for input_idx, tensor in zip(input_indices, tensor_data):
interpreter.resize_tensor_input(input_idx, tensor.shape)
interpreter.allocate_tensors()
for input_idx, tensor in zip(input_indices, tensor_data):
interpreter.set_tensor(input_idx, tensor)
