299 lines
10 KiB
C++
299 lines
10 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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NGRAPH_SUPPRESS_DEPRECATED_START
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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}, create_dynamic_backend)
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{
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auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
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ASSERT_NE(backend, nullptr);
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ASSERT_TRUE(backend->supports_dynamic_tensors());
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}
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NGRAPH_TEST(${BACKEND_NAME}, create_dynamic_tensor)
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{
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auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
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auto t = backend->create_dynamic_tensor(element::f32, PartialShape{2, Dimension::dynamic(), 3});
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ASSERT_TRUE(t->get_partial_shape().same_scheme(PartialShape{2, Dimension::dynamic(), 3}));
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}
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NGRAPH_TEST(${BACKEND_NAME}, dynamic_abc)
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{
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//
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// Create a graph for f(a,b,c) = (a+b)*c, where a, b, c all have shape {2,?,3}.
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//
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auto a = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3});
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auto b = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3});
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auto c = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3});
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auto a_plus_b_times_c = (a + b) * c;
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auto f = make_shared<Function>(NodeVector{a_plus_b_times_c}, ParameterVector{a, b, c});
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//
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// Get a backend with dynamic support, and compile f.
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//
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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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//
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// Create a dynamic output tensor with shape {2,?,3}.
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//
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auto t_r =
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backend->create_dynamic_tensor(element::f32, PartialShape{2, Dimension::dynamic(), 3});
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//
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// For each of n=[0,...,5), run the compiled executable against a test vector of shape
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// {2,n,3}, and check the results.
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//
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for (size_t middle_dim = 0; middle_dim < 5; middle_dim++)
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{
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// Fill in some test input values, which we'll use for a, b, and c.
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vector<float> inputs(2 * middle_dim * 3);
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for (size_t i = 0; i < 2 * middle_dim * 3; i++)
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{
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inputs[i] = i;
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}
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// Create static tensors for the inputs and copy data.
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auto t_a = backend->create_tensor(element::f32, Shape{2, middle_dim, 3});
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auto t_b = backend->create_tensor(element::f32, Shape{2, middle_dim, 3});
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auto t_c = backend->create_tensor(element::f32, Shape{2, middle_dim, 3});
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copy_data(t_a, inputs);
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copy_data(t_b, inputs);
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copy_data(t_c, inputs);
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// Call ex, writing result into t_r (note we're using the same t_r from outside the loop.)
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ex->call_with_validate({t_r}, {t_a, t_b, t_c});
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// After call, t_r should have a shape of {2,n,3}.
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ASSERT_EQ(t_r->get_shape(), (Shape{2, middle_dim, 3}));
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// Read out the results, and compare them against expected values.
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auto results = read_vector<float>(t_r);
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vector<float> expected_values(2 * middle_dim * 3);
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for (size_t i = 0; i < 2 * middle_dim * 3; i++)
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{
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expected_values[i] = (i + i) * i;
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}
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EXPECT_TRUE(test::all_close_f(results, expected_values));
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}
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}
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static void axpy_test(const PartialShape& input_pshape, const std::vector<Shape>& input_shapes)
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{
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auto a = make_shared<op::Parameter>(element::f32, input_pshape);
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auto x = make_shared<op::Parameter>(element::f32, input_pshape);
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auto y = make_shared<op::Parameter>(element::f32, input_pshape);
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auto axpy = a * x + y;
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auto f = make_shared<Function>(NodeVector{axpy}, ParameterVector{a, x, y});
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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, input_pshape);
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for (auto& shape : input_shapes)
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{
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vector<float> inputs(shape_size(shape));
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for (size_t i = 0; i < shape_size(shape); i++)
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{
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inputs[i] = i;
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}
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auto t_a = backend->create_tensor(element::f32, shape);
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auto t_x = backend->create_tensor(element::f32, shape);
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auto t_y = backend->create_tensor(element::f32, shape);
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copy_data(t_a, inputs);
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copy_data(t_x, inputs);
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copy_data(t_y, inputs);
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ex->call_with_validate({t_r}, {t_a, t_x, t_y});
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ASSERT_EQ(t_r->get_shape(), shape);
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auto results = read_vector<float>(t_r);
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vector<float> expected_values(shape_size(shape));
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for (size_t i = 0; i < shape_size(shape); i++)
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{
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expected_values[i] = (i * i) + i;
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}
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EXPECT_TRUE(test::all_close_f(results, expected_values));
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}
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}
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NGRAPH_TEST(${BACKEND_NAME}, dynamic_axpy)
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{
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// Test with shape {?, 3, 3}.
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axpy_test(PartialShape{Dimension::dynamic(), 3, 3}, {Shape{2, 3, 3}, Shape{5, 3, 3}});
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// Test with shape {?, ?, ?}.
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axpy_test(PartialShape::dynamic(3),
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{Shape{2, 3, 3}, Shape{5, 3, 3}, Shape{2, 5, 2}, Shape{8, 1, 8}});
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// Test with shape ?. (Rank unknown.)
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axpy_test(PartialShape::dynamic(),
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{Shape{2, 3, 3},
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Shape{5, 3, 3},
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Shape{2, 5, 2},
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Shape{8, 1, 8},
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Shape{5},
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Shape{8, 2},
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Shape{8, 2, 8, 2},
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Shape{2, 3, 4, 5, 2}});
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}
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static void to_vector_test(const PartialShape& input_pshape, const std::vector<Shape>& input_shapes)
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{
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auto x = make_shared<op::Parameter>(element::f32, input_pshape);
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shared_ptr<Node> x_new_shape = make_shared<op::v0::ShapeOf>(x);
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x_new_shape = make_shared<op::Product>(x_new_shape, AxisSet{0});
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x_new_shape = make_shared<op::Reshape>(x_new_shape, AxisVector{}, Shape{1});
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auto x_reshaped = make_shared<op::v1::Reshape>(x, x_new_shape, true);
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auto f = make_shared<Function>(NodeVector{x_reshaped}, ParameterVector{x});
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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(1));
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for (auto& shape : input_shapes)
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{
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vector<float> inputs(shape_size(shape));
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for (size_t i = 0; i < shape_size(shape); i++)
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{
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inputs[i] = i;
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}
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auto t_x = backend->create_tensor(element::f32, shape);
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copy_data(t_x, inputs);
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ex->call_with_validate({t_r}, {t_x});
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ASSERT_EQ(t_r->get_shape(), (Shape{shape_size(shape)}));
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auto results = read_vector<float>(t_r);
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EXPECT_TRUE(test::all_close_f(results, inputs));
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}
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}
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NGRAPH_TEST(${BACKEND_NAME}, dynamic_to_vector)
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{
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// Test with shape {?, 3, 3}.
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to_vector_test(PartialShape{Dimension::dynamic(), 3, 3}, {Shape{2, 3, 3}, Shape{5, 3, 3}});
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// Test with shape {?, ?, ?}.
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to_vector_test(PartialShape::dynamic(3),
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{Shape{2, 3, 3}, Shape{5, 3, 3}, Shape{2, 5, 2}, Shape{8, 1, 8}});
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// Test with shape ?. (Rank unknown.)
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to_vector_test(PartialShape::dynamic(),
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{Shape{2, 3, 3},
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Shape{5, 3, 3},
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Shape{2, 5, 2},
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Shape{8, 1, 8},
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Shape{5},
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Shape{8, 2},
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Shape{8, 2, 8, 2},
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Shape{2, 3, 4, 5, 2}});
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}
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static void reverse_shape_test(const PartialShape& input_pshape,
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const std::vector<Shape>& input_shapes)
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{
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auto x = make_shared<op::Parameter>(element::f32, input_pshape);
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shared_ptr<Node> x_new_shape = make_shared<op::v0::ShapeOf>(x);
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x_new_shape = make_shared<op::Reverse>(x_new_shape, AxisSet{0});
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auto x_reshaped = make_shared<op::v1::Reshape>(x, x_new_shape, true);
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auto f = make_shared<Function>(NodeVector{x_reshaped}, ParameterVector{x});
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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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for (auto& shape : input_shapes)
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{
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vector<float> inputs(shape_size(shape));
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for (size_t i = 0; i < shape_size(shape); i++)
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{
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inputs[i] = i;
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}
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auto t_x = backend->create_tensor(element::f32, shape);
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copy_data(t_x, inputs);
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ex->call_with_validate({t_r}, {t_x});
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Shape expected_shape = shape;
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std::reverse(expected_shape.begin(), expected_shape.end());
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ASSERT_EQ(t_r->get_shape(), expected_shape);
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auto results = read_vector<float>(t_r);
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EXPECT_TRUE(test::all_close_f(results, inputs));
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}
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}
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NGRAPH_TEST(${BACKEND_NAME}, dynamic_reverse_shape)
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{
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// Test with shape {?, 3, 3}.
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reverse_shape_test(PartialShape{Dimension::dynamic(), 3, 3}, {Shape{2, 3, 3}, Shape{5, 3, 3}});
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// Test with shape {?, ?, ?}.
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reverse_shape_test(PartialShape::dynamic(3),
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{Shape{2, 3, 3}, Shape{5, 3, 3}, Shape{2, 5, 2}, Shape{8, 1, 8}});
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// Test with shape ?. (Rank unknown.)
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reverse_shape_test(PartialShape::dynamic(),
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{Shape{2, 3, 3},
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Shape{5, 3, 3},
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Shape{2, 5, 2},
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Shape{8, 1, 8},
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Shape{5},
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Shape{8, 2},
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Shape{8, 2, 8, 2},
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Shape{2, 3, 4, 5, 2}});
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}
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