//***************************************************************************** // 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}, quantized_convolution) { Shape shape_a{1, 1, 3, 4}; Shape shape_b{1, 1, 3, 3}; Shape shape_r{1, 1, 3, 4}; vector<uint8_t> a_data = {1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4}; vector<int8_t> b_data = {1, 2, 3, 4, 5, 0, 0, 1, 2}; auto A = make_shared<op::Parameter>(element::u8, shape_a); auto B = make_shared<op::Parameter>(element::i8, shape_b); auto C = make_shared<op::Parameter>(element::f32, Shape{}); auto D = make_shared<op::Parameter>(element::f32, Shape{}); auto E = make_shared<op::Parameter>(element::f32, Shape{}); auto F = make_shared<op::Parameter>(element::f32, Shape{}); auto G = make_shared<op::Parameter>(element::f32, Shape{}); auto H = make_shared<op::Parameter>(element::f32, Shape{}); auto CV = ngraph::builder::QuantizedConvolutionBuilder(A, B, Strides{1, 1}, Strides{1, 1}, CoordinateDiff{1, 1}, CoordinateDiff{1, 1}, Strides{1, 1}, C, D, E, F, G, H, element::i8); auto f = make_shared<Function>(NodeVector{CV}, ParameterVector{A, B, C, D, E, F, G, H}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::u8, shape_a); copy_data(a, a_data); auto b = backend->create_tensor(element::i8, shape_b); copy_data(b, b_data); auto d = backend->create_tensor(element::f32, Shape{}); copy_data(d, vector<float>{0.0f}); auto e = backend->create_tensor(element::f32, Shape{}); copy_data(e, vector<float>{255.0f}); auto e_a = backend->create_tensor(element::f32, Shape{}); copy_data(e_a, vector<float>{-127.0f}); auto g = backend->create_tensor(element::f32, Shape{}); copy_data(g, vector<float>{127.0f}); auto h = backend->create_tensor(element::f32, Shape{}); copy_data(h, vector<float>{22.0f}); auto i = backend->create_tensor(element::f32, Shape{}); copy_data(i, vector<float>{90.0f}); auto result = backend->create_tensor(element::i8, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a, b, d, e, e_a, g, h, i}); EXPECT_EQ((vector<int8_t>{31, 48, 42, 45, 54, 102, 127, 61, 47, 73, 61, 55}), read_vector<int8_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, quantized_conv_int32_output) { Shape shape_a{1, 1, 3, 4}; Shape shape_b{1, 1, 3, 3}; Shape shape_r{1, 1, 3, 4}; vector<uint8_t> a_data = {1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4}; vector<uint8_t> b_data = {1, 2, 3, 4, 5, 0, 0, 1, 2}; auto A = make_shared<op::Parameter>(element::u8, shape_a); auto B = make_shared<op::Parameter>(element::u8, shape_b); auto C = make_shared<op::Parameter>(element::f32, Shape{}); auto D = op::Constant::create(element::u8, Shape{}, {0}); auto E = make_shared<op::Parameter>(element::f32, Shape{}); auto F = op::Constant::create(element::u8, Shape{}, {0}); auto G = make_shared<op::Parameter>(element::f32, Shape{}); auto H = op::Constant::create(element::i32, Shape{}, {0}); auto CV = make_shared<op::QuantizedConvolution>(A, B, Strides{1, 1}, Strides{1, 1}, CoordinateDiff{1, 1}, CoordinateDiff{1, 1}, Strides{1, 1}, C, D, E, F, G, H, element::i32); auto f = make_shared<Function>(NodeVector{CV}, ParameterVector{A, B, C, E, G}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::u8, shape_a); copy_data(a, a_data); auto b = backend->create_tensor(element::u8, shape_b); copy_data(b, b_data); auto c = backend->create_tensor(element::f32, Shape{}); copy_data(c, vector<float>{1.0f}); auto d = backend->create_tensor(element::f32, Shape{}); copy_data(d, vector<float>{1.0f}); auto e = backend->create_tensor(element::f32, Shape{}); copy_data(e, vector<float>{1.0f}); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a, b, c, d, e}); EXPECT_EQ((vector<int32_t>{22, 34, 30, 32, 38, 72, 90, 43, 33, 52, 43, 39}), read_vector<int32_t>(result)); }