//***************************************************************************** // Copyright 2017-2019 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. //***************************************************************************** #include "gtest/gtest.h" #include "ngraph/ngraph.hpp" #include "util/all_close_f.hpp" #include "util/test_control.hpp" #include "util/test_tools.hpp" using namespace std; using namespace ngraph; static string s_manifest = "${MANIFEST}"; NGRAPH_TEST(${BACKEND_NAME}, dyn_broadcast) { // Create a graph for // f(x,shape:i32,axes:32) = Broadcast(x,Convert<i64>(shape),Convert<i64>(axes)). auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic()); auto shape = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()}); auto axes = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()}); auto shape_i64 = make_shared<op::Convert>(shape, element::i64); auto axes_i64 = make_shared<op::Convert>(axes, element::i64); auto bc = make_shared<op::DynBroadcast>(x, shape_i64, axes_i64); auto f = make_shared<Function>(NodeVector{bc}, ParameterVector{x, shape, axes}); auto backend = runtime::Backend::create("${BACKEND_NAME}", true); auto ex = backend->compile(f); auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic()); std::vector<Shape> x_shapes{Shape{}, Shape{}, Shape{2}, Shape{2}}; std::vector<std::vector<int32_t>> shapes{{2, 2}, {2, 2, 2}, {3, 2}, {2, 3}}; std::vector<std::vector<int32_t>> axeses{{0, 1}, {0, 1, 2}, {0}, {1}}; std::vector<std::vector<float>> inputs{{6}, {7}, {10, 11}, {10, 11}}; std::vector<Shape> expected_result_shapes{ Shape{2, 2}, Shape{2, 2, 2}, Shape{3, 2}, Shape{2, 3}}; std::vector<std::vector<float>> expected_results{ {6, 6, 6, 6}, {7, 7, 7, 7, 7, 7, 7, 7}, {10, 11, 10, 11, 10, 11}, {10, 10, 10, 11, 11, 11}}; for (size_t i = 0; i < x_shapes.size(); i++) { auto t_x = backend->create_tensor(element::f32, x_shapes[i]); auto t_shape = backend->create_tensor(element::i32, Shape{shapes[i].size()}); auto t_axes = backend->create_tensor(element::i32, Shape{axeses[i].size()}); copy_data(t_x, inputs[i]); copy_data(t_shape, shapes[i]); copy_data(t_axes, axeses[i]); ex->call_with_validate({t_r}, {t_x, t_shape, t_axes}); ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]); auto results = read_vector<float>(t_r); ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS)); } }