1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
//*****************************************************************************
// Copyright 2017-2020 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}, transpose)
{
//
// Create a graph for f(x,perm) = Transpose(x,Convert<i64>(perm)). We'll do the permutation in
// i32 and cast it to i64, just for fun (and to mirror the TensorFlow test I am porting here).
//
auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
auto perm = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
auto perm_i64 = make_shared<op::Convert>(perm, element::i64);
auto x_transpose = make_shared<op::Transpose>(x, perm_i64);
auto f = make_shared<Function>(NodeVector{x_transpose}, ParameterVector{x, perm});
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{2, 3}, Shape{2, 3}, Shape{2, 2, 3}};
std::vector<std::vector<int32_t>> perms{{0, 1}, {1, 0}, {2, 1, 0}};
std::vector<std::vector<float>> inputs{
{1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}};
std::vector<Shape> expected_result_shapes{Shape{2, 3}, Shape{3, 2}, {3, 2, 2}};
// Generated with numpy, so don't worry. :)
std::vector<std::vector<float>> expected_results{
{1, 2, 3, 4, 5, 6}, {1, 4, 2, 5, 3, 6}, {1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12}};
for (size_t i = 0; i < x_shapes.size(); i++)
{
auto t_x = backend->create_tensor(element::f32, x_shapes[i]);
auto t_perm = backend->create_tensor(element::i32, Shape{perms[i].size()});
copy_data(t_x, inputs[i]);
copy_data(t_perm, perms[i]);
ex->call_with_validate({t_r}, {t_x, t_perm});
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));
}
}