Function ImportShape

tensorflow/lite/toco/import_tensorflow.cc:197–236

}

tensorflow::Status ImportShape(
    const TFLITE_PROTO_NS::RepeatedPtrField<tensorflow::TensorShapeProto_Dim>&
        input_dims,
    int* input_flat_size, Shape* shape) {
  std::vector<int> input_dims_only_sizes;
  bool zero_sized_shape = false;
  for (auto& d : input_dims) {
    // TensorFlow's shapes use int64s, while TOCO uses ints.
    if (d.size() > std::numeric_limits<int>::max()) {
      return tensorflow::errors::InvalidArgument("Shape element overflows");
    }
    if (d.size() == 0) {
      zero_sized_shape = true;
    }
    input_dims_only_sizes.push_back(d.size());
  }

  // Note that up to this point we were OK with the input shape containing
  // elements valued -1 or 0, which are perfectly legal in tensorflow. However
  // our CheckValidShapeDimensions() insists on them being >= 1, with the
  // exception of the "scalar" shape [0]. The main issue with zero-values shape
  // elements is that the corresponding arrays don't contain any data and the
  // allocation code gets a bit confused. It seems that the code expects an
  // empty shape for zero-sized shapes, so we will do just that, except for the
  // [0] case.
  // TODO(b/119325030): In order to correctly import the "scalar" shapes the
  // following test must include "&& input_dims_only_sizes.size() > 1", but
  // that seems to slow everything down a lot.
  if (zero_sized_shape) {
    shape->mutable_dims()->clear();
    if (input_flat_size != nullptr) *input_flat_size = 0;
    return tensorflow::Status::OK();
  }

  *shape->mutable_dims() = input_dims_only_sizes;

  if (input_flat_size == nullptr) return tensorflow::Status::OK();

  return NumElements(input_dims_only_sizes, input_flat_size);
}

// Define ways to retrieve data from tensors of different types.
// TODO(b/80208043): simply use tensorflow::Tensor::FromProto() instead.