Method ResizeInputTensor

tensorflow/lite/core/subgraph.cc:640–672

}

TfLiteStatus Subgraph::ResizeInputTensor(int tensor_index,
                                         const std::vector<int>& dims) {
  const bool delegates_applied = !pre_delegation_execution_plan_.empty();
  const bool graph_is_immutable = state_ == kStateInvokableAndImmutable;
  if (graph_is_immutable && !delegates_applied) {
    ReportError("ResizeInputTensor is disallowed when graph is immutable.");
    return kTfLiteError;
  }

  // TODO(aselle): All bounds checks can be implemented as one-sided bounds
  // checks by casting to unsigned for efficiency. Profile before doing this.
  TF_LITE_ENSURE(&context_,
                 tensor_index < context_.tensors_size && tensor_index >= 0);
  TfLiteTensor* tensor = &context_.tensors[tensor_index];

  // Short-circuit the state change if the dimensions don't change, avoiding
  // unnecessary (re)allocations.
  //
  // Note that it's required to check `tensor->data.raw != nullptr`. Otherwise
  // the subgraph won't allocate memory for a dynamic tensor when its size
  // is equal to the original tensor size.
  if (tensor->data.raw != nullptr &&
      EqualArrayAndTfLiteIntArray(tensor->dims, dims.size(), dims.data())) {
    return kTfLiteOk;
  }

  if (graph_is_immutable) {
    // Undo delegation if it resulted in the graph being immutable.
    TF_LITE_ENSURE_STATUS(UndoAllDelegates());
  }
  state_ = kStateUninvokable;
  return ResizeTensorImpl(tensor, ConvertVectorToTfLiteIntArray(dims));
}

TfLiteStatus Subgraph::OpPrepare(const TfLiteRegistration& op_reg,
                                 TfLiteNode* node) {