76 lines
3.0 KiB
C++
76 lines
3.0 KiB
C++
//*****************************************************************************
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// Copyright 2017-2020 Intel Corporation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//*****************************************************************************
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#include "gtest/gtest.h"
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#include "ngraph/ngraph.hpp"
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#include "ngraph/runtime/tensor.hpp"
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#include "runtime/backend.hpp"
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#include "util/all_close_f.hpp"
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#include "util/test_control.hpp"
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#include "util/test_tools.hpp"
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using namespace std;
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using namespace ngraph;
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static string s_manifest = "${MANIFEST}";
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NGRAPH_TEST(${BACKEND_NAME}, transpose)
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{
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//
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// Create a graph for f(x,perm) = Transpose(x,Convert<i64>(perm)). We'll do the permutation in
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// i32 and cast it to i64, just for fun (and to mirror the TensorFlow test I am porting here).
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//
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auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
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auto perm = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
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auto perm_i64 = make_shared<op::Convert>(perm, element::i64);
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auto x_transpose = make_shared<op::Transpose>(x, perm_i64);
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auto f = make_shared<Function>(NodeVector{x_transpose}, ParameterVector{x, perm});
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auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
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auto ex = backend->compile(f);
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auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());
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std::vector<Shape> x_shapes{Shape{2, 3}, Shape{2, 3}, Shape{2, 2, 3}};
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std::vector<std::vector<int32_t>> perms{{0, 1}, {1, 0}, {2, 1, 0}};
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std::vector<std::vector<float>> inputs{
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{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}};
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std::vector<Shape> expected_result_shapes{Shape{2, 3}, Shape{3, 2}, {3, 2, 2}};
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// Generated with numpy, so don't worry. :)
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std::vector<std::vector<float>> expected_results{
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{1, 2, 3, 4, 5, 6}, {1, 4, 2, 5, 3, 6}, {1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12}};
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for (size_t i = 0; i < x_shapes.size(); i++)
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{
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auto t_x = backend->create_tensor(element::f32, x_shapes[i]);
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auto t_perm = backend->create_tensor(element::i32, Shape{perms[i].size()});
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copy_data(t_x, inputs[i]);
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copy_data(t_perm, perms[i]);
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ex->call_with_validate({t_r}, {t_x, t_perm});
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ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]);
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auto results = read_vector<float>(t_r);
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ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS));
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}
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}
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