| // Copyright 2018 The Chromium OS Authors. All rights reserved. |
| // Use of this source code is governed by a BSD-style license that can be |
| // found in the LICENSE file. |
| |
| #include <memory> |
| #include <string> |
| #include <unordered_map> |
| #include <utility> |
| #include <vector> |
| |
| #include <base/bind.h> |
| #include <base/files/file_util.h> |
| #include <base/macros.h> |
| #include <base/run_loop.h> |
| #include <gmock/gmock.h> |
| #include <gtest/gtest.h> |
| #include <mojo/public/cpp/bindings/binding.h> |
| #include <mojo/public/cpp/bindings/interface_request.h> |
| |
| #include "ml/machine_learning_service_impl.h" |
| #include "ml/mojom/graph_executor.mojom.h" |
| #include "ml/mojom/machine_learning_service.mojom.h" |
| #include "ml/mojom/model.mojom.h" |
| #include "ml/tensor_view.h" |
| #include "ml/test_utils.h" |
| |
| namespace ml { |
| namespace { |
| |
| constexpr double kSearchRanker20190923TestInput[] = { |
| 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, |
| 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, |
| }; |
| |
| constexpr double kSmartDim20181115TestInput[] = { |
| 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, |
| 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| }; |
| |
| constexpr double kSmartDim20190221TestInput[] = { |
| 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| }; |
| |
| constexpr double kSmartDim20190521TestInput[] = { |
| 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, |
| 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, |
| }; |
| |
| constexpr double kTopCat20190722TestInput[] = { |
| 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 |
| }; |
| |
| using ::chromeos::machine_learning::mojom::BuiltinModelId; |
| using ::chromeos::machine_learning::mojom::BuiltinModelSpec; |
| using ::chromeos::machine_learning::mojom::BuiltinModelSpecPtr; |
| using ::chromeos::machine_learning::mojom::CreateGraphExecutorResult; |
| using ::chromeos::machine_learning::mojom::ExecuteResult; |
| using ::chromeos::machine_learning::mojom::FlatBufferModelSpec; |
| using ::chromeos::machine_learning::mojom::FlatBufferModelSpecPtr; |
| using ::chromeos::machine_learning::mojom::GraphExecutorPtr; |
| using ::chromeos::machine_learning::mojom::LoadModelResult; |
| using ::chromeos::machine_learning::mojom::MachineLearningServicePtr; |
| using ::chromeos::machine_learning::mojom::Model; |
| using ::chromeos::machine_learning::mojom::ModelPtr; |
| using ::chromeos::machine_learning::mojom::ModelRequest; |
| using ::chromeos::machine_learning::mojom::TensorPtr; |
| using ::testing::DoubleEq; |
| using ::testing::DoubleNear; |
| using ::testing::ElementsAre; |
| |
| // A version of MachineLearningServiceImpl that loads from the testing model |
| // directory. |
| class MachineLearningServiceImplForTesting : public MachineLearningServiceImpl { |
| public: |
| // Pass a dummy callback and use the testing model directory. |
| explicit MachineLearningServiceImplForTesting( |
| mojo::ScopedMessagePipeHandle pipe) |
| : MachineLearningServiceImpl( |
| std::move(pipe), base::Closure(), GetTestModelDir()) {} |
| }; |
| |
| TEST(MachineLearningServiceImplTest, TestBadModel) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Set up model spec to specify an invalid model. |
| BuiltinModelSpecPtr spec = BuiltinModelSpec::New(); |
| spec->id = BuiltinModelId::UNSUPPORTED_UNKNOWN; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadBuiltinModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::MODEL_SPEC_ERROR); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| } |
| |
| TEST(ModelLoadAndInferenceTest, TestModel) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Set up model spec. |
| BuiltinModelSpecPtr spec = BuiltinModelSpec::New(); |
| spec->id = BuiltinModelId::TEST_MODEL; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadBuiltinModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::OK); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| ASSERT_TRUE(model.is_bound()); |
| |
| // Get graph executor. |
| GraphExecutorPtr graph_executor; |
| bool ge_callback_done = false; |
| model->CreateGraphExecutor( |
| mojo::MakeRequest(&graph_executor), |
| base::Bind( |
| [](bool* ge_callback_done, const CreateGraphExecutorResult result) { |
| EXPECT_EQ(result, CreateGraphExecutorResult::OK); |
| *ge_callback_done = true; |
| }, |
| &ge_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(ge_callback_done); |
| ASSERT_TRUE(graph_executor.is_bound()); |
| |
| // Construct input. |
| std::unordered_map<std::string, TensorPtr> inputs; |
| inputs.emplace("x", NewTensor<double>({1}, {0.5})); |
| inputs.emplace("y", NewTensor<double>({1}, {0.25})); |
| std::vector<std::string> outputs({"z"}); |
| |
| // Perform inference. |
| bool infer_callback_done = false; |
| graph_executor->Execute( |
| std::move(inputs), std::move(outputs), |
| base::Bind( |
| [](bool* infer_callback_done, const ExecuteResult result, |
| base::Optional<std::vector<TensorPtr>> outputs) { |
| // Check that the inference succeeded and gives the expected number |
| // of outputs. |
| EXPECT_EQ(result, ExecuteResult::OK); |
| ASSERT_TRUE(outputs.has_value()); |
| ASSERT_EQ(outputs->size(), 1); |
| |
| // Check that the output tensor has the right type and format. |
| const TensorView<double> out_tensor((*outputs)[0]); |
| EXPECT_TRUE(out_tensor.IsValidType()); |
| EXPECT_TRUE(out_tensor.IsValidFormat()); |
| |
| // Check the output tensor has the expected shape and values. |
| EXPECT_THAT(out_tensor.GetShape(), ElementsAre(1)); |
| EXPECT_THAT(out_tensor.GetValues(), ElementsAre(DoubleEq(0.75))); |
| |
| *infer_callback_done = true; |
| }, |
| &infer_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(infer_callback_done); |
| } |
| |
| // Tests that the Smart Dim (20181115) model file loads correctly and produces |
| // the expected inference result. |
| TEST(ModelLoadAndInferenceTest, SmartDim20181115) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Set up model spec. |
| BuiltinModelSpecPtr spec = BuiltinModelSpec::New(); |
| spec->id = BuiltinModelId::SMART_DIM_20181115; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadBuiltinModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::OK); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| ASSERT_TRUE(model.is_bound()); |
| |
| // Get graph executor. |
| GraphExecutorPtr graph_executor; |
| bool ge_callback_done = false; |
| model->CreateGraphExecutor( |
| mojo::MakeRequest(&graph_executor), |
| base::Bind( |
| [](bool* ge_callback_done, const CreateGraphExecutorResult result) { |
| EXPECT_EQ(result, CreateGraphExecutorResult::OK); |
| *ge_callback_done = true; |
| }, |
| &ge_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(ge_callback_done); |
| ASSERT_TRUE(graph_executor.is_bound()); |
| |
| // Construct input. |
| std::unordered_map<std::string, TensorPtr> inputs; |
| inputs.emplace( |
| "input", NewTensor<double>( |
| {1, arraysize(kSmartDim20181115TestInput)}, |
| std::vector<double>(std::begin(kSmartDim20181115TestInput), |
| std::end(kSmartDim20181115TestInput)))); |
| std::vector<std::string> outputs({"output"}); |
| |
| // Perform inference. |
| bool infer_callback_done = false; |
| graph_executor->Execute( |
| std::move(inputs), std::move(outputs), |
| base::Bind( |
| [](bool* infer_callback_done, const ExecuteResult result, |
| base::Optional<std::vector<TensorPtr>> outputs) { |
| // Check that the inference succeeded and gives the expected number |
| // of outputs. |
| EXPECT_EQ(result, ExecuteResult::OK); |
| ASSERT_TRUE(outputs.has_value()); |
| ASSERT_EQ(outputs->size(), 1); |
| |
| // Check that the output tensor has the right type and format. |
| const TensorView<double> out_tensor((*outputs)[0]); |
| EXPECT_TRUE(out_tensor.IsValidType()); |
| EXPECT_TRUE(out_tensor.IsValidFormat()); |
| |
| // Check the output tensor has the expected shape and values. |
| EXPECT_THAT(out_tensor.GetShape(), ElementsAre(1, 1)); |
| EXPECT_THAT(out_tensor.GetValues(), |
| ElementsAre(DoubleNear(-3.36311, 0.1))); |
| |
| *infer_callback_done = true; |
| }, |
| &infer_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(infer_callback_done); |
| } |
| |
| // Tests that the Smart Dim (20190221) model file loads correctly and produces |
| // the expected inference result. |
| TEST(ModelLoadAndInferenceTest, SmartDim20190221) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Set up model spec. |
| BuiltinModelSpecPtr spec = BuiltinModelSpec::New(); |
| spec->id = BuiltinModelId::SMART_DIM_20190221; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadBuiltinModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::OK); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| ASSERT_TRUE(model.is_bound()); |
| |
| // Get graph executor. |
| GraphExecutorPtr graph_executor; |
| bool ge_callback_done = false; |
| model->CreateGraphExecutor( |
| mojo::MakeRequest(&graph_executor), |
| base::Bind( |
| [](bool* ge_callback_done, const CreateGraphExecutorResult result) { |
| EXPECT_EQ(result, CreateGraphExecutorResult::OK); |
| *ge_callback_done = true; |
| }, |
| &ge_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(ge_callback_done); |
| ASSERT_TRUE(graph_executor.is_bound()); |
| |
| // Construct input. |
| std::unordered_map<std::string, TensorPtr> inputs; |
| inputs.emplace( |
| "input", NewTensor<double>( |
| {1, arraysize(kSmartDim20190221TestInput)}, |
| std::vector<double>(std::begin(kSmartDim20190221TestInput), |
| std::end(kSmartDim20190221TestInput)))); |
| std::vector<std::string> outputs({"output"}); |
| |
| // Perform inference. |
| bool infer_callback_done = false; |
| graph_executor->Execute( |
| std::move(inputs), std::move(outputs), |
| base::Bind( |
| [](bool* infer_callback_done, const ExecuteResult result, |
| base::Optional<std::vector<TensorPtr>> outputs) { |
| // Check that the inference succeeded and gives the expected number |
| // of outputs. |
| EXPECT_EQ(result, ExecuteResult::OK); |
| ASSERT_TRUE(outputs.has_value()); |
| ASSERT_EQ(outputs->size(), 1); |
| |
| // Check that the output tensor has the right type and format. |
| const TensorView<double> out_tensor((*outputs)[0]); |
| EXPECT_TRUE(out_tensor.IsValidType()); |
| EXPECT_TRUE(out_tensor.IsValidFormat()); |
| |
| // Check the output tensor has the expected shape and values. |
| EXPECT_THAT(out_tensor.GetShape(), ElementsAre(1, 1)); |
| EXPECT_THAT(out_tensor.GetValues(), |
| ElementsAre(DoubleNear(-0.900591, 0.1))); |
| |
| *infer_callback_done = true; |
| }, |
| &infer_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(infer_callback_done); |
| } |
| |
| // Tests that the Smart Dim (20190521) model file loads correctly and produces |
| // the expected inference result. |
| TEST(ModelLoadAndInferenceTest, SmartDim20190521) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Set up model spec. |
| BuiltinModelSpecPtr spec = BuiltinModelSpec::New(); |
| spec->id = BuiltinModelId::SMART_DIM_20190521; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadBuiltinModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::OK); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| ASSERT_TRUE(model.is_bound()); |
| |
| // Get graph executor. |
| GraphExecutorPtr graph_executor; |
| bool ge_callback_done = false; |
| model->CreateGraphExecutor( |
| mojo::MakeRequest(&graph_executor), |
| base::Bind( |
| [](bool* ge_callback_done, const CreateGraphExecutorResult result) { |
| EXPECT_EQ(result, CreateGraphExecutorResult::OK); |
| *ge_callback_done = true; |
| }, |
| &ge_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(ge_callback_done); |
| ASSERT_TRUE(graph_executor.is_bound()); |
| |
| // Construct input. |
| std::unordered_map<std::string, TensorPtr> inputs; |
| inputs.emplace( |
| "input", NewTensor<double>( |
| {1, arraysize(kSmartDim20190521TestInput)}, |
| std::vector<double>(std::begin(kSmartDim20190521TestInput), |
| std::end(kSmartDim20190521TestInput)))); |
| std::vector<std::string> outputs({"output"}); |
| |
| // Perform inference. |
| bool infer_callback_done = false; |
| graph_executor->Execute( |
| std::move(inputs), std::move(outputs), |
| base::Bind( |
| [](bool* infer_callback_done, const ExecuteResult result, |
| base::Optional<std::vector<TensorPtr>> outputs) { |
| // Check that the inference succeeded and gives the expected number |
| // of outputs. |
| EXPECT_EQ(result, ExecuteResult::OK); |
| ASSERT_TRUE(outputs.has_value()); |
| ASSERT_EQ(outputs->size(), 1); |
| |
| // Check that the output tensor has the right type and format. |
| const TensorView<double> out_tensor((*outputs)[0]); |
| EXPECT_TRUE(out_tensor.IsValidType()); |
| EXPECT_TRUE(out_tensor.IsValidFormat()); |
| |
| // Check the output tensor has the expected shape and values. |
| EXPECT_THAT(out_tensor.GetShape(), ElementsAre(1, 1)); |
| EXPECT_THAT(out_tensor.GetValues(), |
| ElementsAre(DoubleNear(0.66962254, 0.1))); |
| |
| *infer_callback_done = true; |
| }, |
| &infer_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(infer_callback_done); |
| } |
| |
| // Tests that the Top Cat (20190722) model file loads correctly and produces |
| // the expected inference result. |
| TEST(ModelLoadAndInferenceTest, TopCat20190722) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Set up model spec. |
| BuiltinModelSpecPtr spec = BuiltinModelSpec::New(); |
| spec->id = BuiltinModelId::TOP_CAT_20190722; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadBuiltinModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::OK); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| ASSERT_TRUE(model.is_bound()); |
| |
| // Get graph executor. |
| GraphExecutorPtr graph_executor; |
| bool ge_callback_done = false; |
| model->CreateGraphExecutor( |
| mojo::MakeRequest(&graph_executor), |
| base::Bind( |
| [](bool* ge_callback_done, const CreateGraphExecutorResult result) { |
| EXPECT_EQ(result, CreateGraphExecutorResult::OK); |
| *ge_callback_done = true; |
| }, |
| &ge_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(ge_callback_done); |
| ASSERT_TRUE(graph_executor.is_bound()); |
| |
| // Construct input. |
| std::unordered_map<std::string, TensorPtr> inputs; |
| inputs.emplace( |
| "input", NewTensor<double>( |
| {1, arraysize(kTopCat20190722TestInput)}, |
| std::vector<double>(std::begin(kTopCat20190722TestInput), |
| std::end(kTopCat20190722TestInput)))); |
| std::vector<std::string> outputs({"output"}); |
| |
| // Perform inference. |
| bool infer_callback_done = false; |
| graph_executor->Execute( |
| std::move(inputs), std::move(outputs), |
| base::Bind( |
| [](bool* infer_callback_done, const ExecuteResult result, |
| base::Optional<std::vector<TensorPtr>> outputs) { |
| // Check that the inference succeeded and gives the expected number |
| // of outputs. |
| EXPECT_EQ(result, ExecuteResult::OK); |
| ASSERT_TRUE(outputs.has_value()); |
| ASSERT_EQ(outputs->size(), 1); |
| |
| // Check that the output tensor has the right type and format. |
| const TensorView<double> out_tensor((*outputs)[0]); |
| EXPECT_TRUE(out_tensor.IsValidType()); |
| EXPECT_TRUE(out_tensor.IsValidFormat()); |
| |
| // Check the output tensor has the expected shape and values. |
| EXPECT_THAT(out_tensor.GetShape(), ElementsAre(1, 1)); |
| EXPECT_THAT(out_tensor.GetValues(), |
| ElementsAre(DoubleNear(-3.02972, 0.1))); |
| |
| *infer_callback_done = true; |
| }, |
| &infer_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(infer_callback_done); |
| } |
| |
| // Tests that the Search Ranker (20190923) model file loads correctly and |
| // produces the expected inference result. |
| TEST(ModelLoadAndInferenceTest, SearchRanker20190923) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Set up model spec. |
| BuiltinModelSpecPtr spec = BuiltinModelSpec::New(); |
| spec->id = BuiltinModelId::SEARCH_RANKER_20190923; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadBuiltinModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::OK); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| ASSERT_TRUE(model.is_bound()); |
| |
| // Get graph executor. |
| GraphExecutorPtr graph_executor; |
| bool ge_callback_done = false; |
| model->CreateGraphExecutor( |
| mojo::MakeRequest(&graph_executor), |
| base::Bind( |
| [](bool* ge_callback_done, const CreateGraphExecutorResult result) { |
| EXPECT_EQ(result, CreateGraphExecutorResult::OK); |
| *ge_callback_done = true; |
| }, |
| &ge_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(ge_callback_done); |
| ASSERT_TRUE(graph_executor.is_bound()); |
| |
| // Construct input. |
| std::unordered_map<std::string, TensorPtr> inputs; |
| inputs.emplace("input", NewTensor<double>( |
| {1, arraysize(kSearchRanker20190923TestInput)}, |
| std::vector<double>( |
| std::begin(kSearchRanker20190923TestInput), |
| std::end(kSearchRanker20190923TestInput)))); |
| std::vector<std::string> outputs({"output"}); |
| |
| // Perform inference. |
| bool infer_callback_done = false; |
| graph_executor->Execute( |
| std::move(inputs), std::move(outputs), |
| base::Bind( |
| [](bool* infer_callback_done, const ExecuteResult result, |
| base::Optional<std::vector<TensorPtr>> outputs) { |
| // Check that the inference succeeded and gives the expected number |
| // of outputs. |
| EXPECT_EQ(result, ExecuteResult::OK); |
| ASSERT_TRUE(outputs.has_value()); |
| ASSERT_EQ(outputs->size(), 1); |
| |
| // Check that the output tensor has the right type and format. |
| const TensorView<double> out_tensor((*outputs)[0]); |
| EXPECT_TRUE(out_tensor.IsValidType()); |
| EXPECT_TRUE(out_tensor.IsValidFormat()); |
| |
| // Check the output tensor has the expected shape and values. |
| EXPECT_THAT(out_tensor.GetShape(), ElementsAre(1)); |
| EXPECT_THAT(out_tensor.GetValues(), |
| ElementsAre(DoubleNear(0.658488, 0.01))); |
| |
| *infer_callback_done = true; |
| }, |
| &infer_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(infer_callback_done); |
| } |
| |
| // Tests loading an empty model througth the downloaded model api. |
| TEST(LoadModelStringTest, EmptyModelString) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| FlatBufferModelSpecPtr spec = FlatBufferModelSpec::New(); |
| spec->model_string = ""; |
| spec->inputs["x"] = 1; |
| spec->inputs["y"] = 2; |
| spec->outputs["z"] = 0; |
| spec->metrics_model_name = "TestModel"; |
| |
| // Load model from an empty model string. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadFlatBufferModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::LOAD_MODEL_ERROR); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| } |
| |
| // Tests loading a bad model string througth the downloaded model api. |
| TEST(LoadModelStringTest, BadModelString) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| FlatBufferModelSpecPtr spec = FlatBufferModelSpec::New(); |
| spec->model_string = "bad model string"; |
| spec->inputs["x"] = 1; |
| spec->inputs["y"] = 2; |
| spec->outputs["z"] = 0; |
| spec->metrics_model_name = "TestModel"; |
| |
| // Load model from an empty model string. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadFlatBufferModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::LOAD_MODEL_ERROR); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| } |
| |
| // Tests loading Smart Dim model througth the downloaded model api. |
| TEST(LoadModelStringAndInferenceTest, SmartDim20181115ModelString) { |
| MachineLearningServicePtr ml_service; |
| const MachineLearningServiceImplForTesting ml_service_impl( |
| mojo::MakeRequest(&ml_service).PassMessagePipe()); |
| |
| // Load SmartDim model into string. |
| std::string model_string; |
| ASSERT_TRUE(base::ReadFileToString( |
| base::FilePath(GetTestModelDir() + |
| "mlservice-model-smart_dim-20181115.tflite"), |
| &model_string)); |
| |
| FlatBufferModelSpecPtr spec = FlatBufferModelSpec::New(); |
| spec->model_string = std::move(model_string); |
| spec->inputs["input"] = 3; |
| spec->outputs["output"] = 4; |
| spec->metrics_model_name = "SmartDimModel"; |
| |
| // Load model. |
| ModelPtr model; |
| bool model_callback_done = false; |
| ml_service->LoadFlatBufferModel( |
| std::move(spec), mojo::MakeRequest(&model), |
| base::Bind( |
| [](bool* model_callback_done, const LoadModelResult result) { |
| EXPECT_EQ(result, LoadModelResult::OK); |
| *model_callback_done = true; |
| }, |
| &model_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(model_callback_done); |
| ASSERT_NE(model, nullptr); |
| |
| // Get graph executor. |
| GraphExecutorPtr graph_executor; |
| bool ge_callback_done = false; |
| model->CreateGraphExecutor( |
| mojo::MakeRequest(&graph_executor), |
| base::Bind( |
| [](bool* ge_callback_done, const CreateGraphExecutorResult result) { |
| EXPECT_EQ(result, CreateGraphExecutorResult::OK); |
| *ge_callback_done = true; |
| }, |
| &ge_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(ge_callback_done); |
| ASSERT_TRUE(graph_executor.is_bound()); |
| |
| // Construct input. |
| std::unordered_map<std::string, TensorPtr> inputs; |
| inputs.emplace( |
| "input", NewTensor<double>( |
| {1, arraysize(kSmartDim20181115TestInput)}, |
| std::vector<double>(std::begin(kSmartDim20181115TestInput), |
| std::end(kSmartDim20181115TestInput)))); |
| std::vector<std::string> outputs({"output"}); |
| |
| // Perform inference. |
| bool infer_callback_done = false; |
| graph_executor->Execute( |
| std::move(inputs), std::move(outputs), |
| base::Bind( |
| [](bool* infer_callback_done, const ExecuteResult result, |
| base::Optional<std::vector<TensorPtr>> outputs) { |
| // Check that the inference succeeded and gives the expected number |
| // of outputs. |
| EXPECT_EQ(result, ExecuteResult::OK); |
| ASSERT_TRUE(outputs.has_value()); |
| ASSERT_EQ(outputs->size(), 1); |
| |
| // Check that the output tensor has the right type and format. |
| const TensorView<double> out_tensor((*outputs)[0]); |
| EXPECT_TRUE(out_tensor.IsValidType()); |
| EXPECT_TRUE(out_tensor.IsValidFormat()); |
| |
| // Check the output tensor has the expected shape and values. |
| EXPECT_THAT(out_tensor.GetShape(), ElementsAre(1, 1)); |
| EXPECT_THAT(out_tensor.GetValues(), |
| ElementsAre(DoubleNear(-3.36311, 0.1))); |
| *infer_callback_done = true; |
| }, |
| &infer_callback_done)); |
| base::RunLoop().RunUntilIdle(); |
| ASSERT_TRUE(infer_callback_done); |
| } |
| |
| } // namespace |
| } // namespace ml |
