//***************************************************************************** // 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.hpp" #include "util/all_close_f.hpp" #include "util/known_element_types.hpp" #include "util/ndarray.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}, computation_reuse) { Shape shape_a{1, 16, 2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_b{32, 16, 1, 1}; auto B = make_shared<op::Parameter>(element::f32, shape_b, true); Shape shape_r{1, 32, 2, 2}; auto conv = make_shared<op::Convolution>(A, B, Strides{1, 1}, Strides{1, 1}, CoordinateDiff{0, 0}, CoordinateDiff{0, 0}, Strides{1, 1}); Shape pool_shape{1, 1}; auto pool = make_shared<op::AvgPool>(conv, pool_shape); auto bias = make_shared<op::Broadcast>( op::Constant::create(element::f32, Shape{}, {2.14}), shape_r, AxisSet{0, 1, 2, 3}); auto result_op = make_shared<op::Result>(pool + bias); auto f = make_shared<Function>(ResultVector{result_op}, ParameterVector{A, B}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); vector<float> input(64, 1.0f); vector<float> weights(512, 0.5f); vector<float> rv(128); auto a = backend->create_tensor(element::f32, shape_a); auto b = backend->create_tensor(element::f32, shape_b); auto result = backend->create_tensor(element::f32, shape_r); copy_data(a, input); copy_data(b, weights); auto exec = backend->compile(f); exec->call_with_validate({result}, {a, b}); vector<float> rv_saved(read_vector<float>(result)); b->set_stale(false); exec->call_with_validate({result}, {a, b}); EXPECT_TRUE(test::all_close_f(rv_saved, read_vector<float>(result))); }