Function FeedExample

tensorflow/lite/testing/parse_testdata.cc:151–184

}

TfLiteStatus FeedExample(tflite::Interpreter* interpreter,
                         const Example& example) {
  // Resize inputs to match example & allocate.
  for (size_t i = 0; i < interpreter->inputs().size(); i++) {
    int input_index = interpreter->inputs()[i];

    TF_LITE_ENSURE_STATUS(
        interpreter->ResizeInputTensor(input_index, example.inputs[i].shape));
  }
  TF_LITE_ENSURE_STATUS(interpreter->AllocateTensors());
  // Copy data into tensors.
  for (size_t i = 0; i < interpreter->inputs().size(); i++) {
    int input_index = interpreter->inputs()[i];
    if (float* data = interpreter->typed_tensor<float>(input_index)) {
      for (size_t idx = 0; idx < example.inputs[i].flat_data.size(); idx++) {
        data[idx] = example.inputs[i].flat_data[idx];
      }
    } else if (int32_t* data =
                   interpreter->typed_tensor<int32_t>(input_index)) {
      for (size_t idx = 0; idx < example.inputs[i].flat_data.size(); idx++) {
        data[idx] = example.inputs[i].flat_data[idx];
      }
    } else if (int64_t* data =
                   interpreter->typed_tensor<int64_t>(input_index)) {
      for (size_t idx = 0; idx < example.inputs[i].flat_data.size(); idx++) {
        data[idx] = example.inputs[i].flat_data[idx];
      }
    } else {
      fprintf(stderr, "input[%zu] was not float or int data\n", i);
      return kTfLiteError;
    }
  }
  return kTfLiteOk;
}

TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter,
                          const Example& example) {