runInference method

void runInference(List<Object> inputs)

Just run inference

Implementation

void runInference(List<Object> inputs) {
  if (inputs.isEmpty) {
    throw ArgumentError('Input error: Inputs should not be null or empty.');
  }

  final inputCount = _inputTensorsCount ??=
      tfliteBinding.TfLiteInterpreterGetInputTensorCount(_interpreter);
  if (inputs.length > inputCount) {
    throw RangeError.range(inputs.length - 1, 0, inputCount - 1, 'inputs');
  }

  // Steady-state fast path: allocated and no resize needed. One pass with
  // a fresh pointer per index, no wrapper list churn. Any resize or
  // missing allocation falls through to the two-pass path below, which
  // re-reads every pointer because resize/allocate relocates tensors.
  var deferred = !_allocated;
  for (int i = 0; i < inputs.length && !deferred; i++) {
    final tensor = Tensor(
      tfliteBinding.TfLiteInterpreterGetInputTensor(_interpreter, i),
    );
    final newShape = tensor.getInputShapeIfDifferent(inputs[i]);
    if (newShape != null) {
      resizeInputTensor(i, newShape);
      deferred = true;
    } else {
      tensor.setTo(inputs[i]);
    }
  }

  if (deferred) {
    for (int i = 0; i < inputs.length; i++) {
      final tensor = Tensor(
        tfliteBinding.TfLiteInterpreterGetInputTensor(_interpreter, i),
      );
      final newShape = tensor.getInputShapeIfDifferent(inputs[i]);
      if (newShape != null) {
        resizeInputTensor(i, newShape);
      }
    }

    if (!_allocated) {
      allocateTensors();
    }

    for (int i = 0; i < inputs.length; i++) {
      Tensor(
        tfliteBinding.TfLiteInterpreterGetInputTensor(_interpreter, i),
      ).setTo(inputs[i]);
    }
  }

  _inferenceStopwatch
    ..reset()
    ..start();
  invoke();
  _inferenceStopwatch.stop();
  _lastInferenceDurationMicroseconds =
      _inferenceStopwatch.elapsedMicroseconds;
}