pool.in.cpp 65.3 KB
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//*****************************************************************************
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// Copyright 2017-2020 Intel Corporation
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//
// 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 <algorithm>
#include <cinttypes>
#include <cmath>
#include <cstdlib>
#include <random>
#include <string>

#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
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#include "util/float_util.hpp"
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#include "util/ndarray.hpp"
#include "util/random.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"

using namespace std;
using namespace ngraph;

static string s_manifest = "${MANIFEST}";

NGRAPH_TEST(${BACKEND_NAME}, max_pool_1d_1channel_1image)
{
    Shape shape_a{1, 1, 14};
    Shape window_shape{3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 12};
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    auto f = make_shared<Function>(make_shared<op::MaxPool>(A, window_shape), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 3>{{{0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0}}}.get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(
        (test::NDArray<float, 3>({{{1, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 0}}}).get_vector()),
        read_vector<float>(result),
        MIN_FLOAT_TOLERANCE_BITS));
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}

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NGRAPH_TEST(${BACKEND_NAME}, max_pool_uint8)
{
    vector<uint8_t> a_data = {0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0, 1};
    Shape shape_a{1, 1, 3, 5};
    Shape window_shape{2, 3};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 0};
    Shape padding_above{0, 0};
    Shape shape_r{1, 1, 2, 3};
    auto A = make_shared<op::Parameter>(element::u8, shape_a);
    auto QMP = make_shared<ngraph::op::MaxPool>(
        A, window_shape, window_movement_strides, padding_below, padding_above);
    auto f = make_shared<Function>(NodeVector{QMP}, ParameterVector{A});
    auto backend = runtime::Backend::create("${BACKEND_NAME}");
    // Create some tensors for input/output
    auto a = backend->create_tensor(element::u8, shape_a);
    copy_data(a, a_data);
    auto result = backend->create_tensor(element::u8, shape_r);
    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a});
    EXPECT_EQ((vector<uint8_t>{3, 3, 2, 3, 3, 2}), read_vector<uint8_t>(result));
}

NGRAPH_TEST(${BACKEND_NAME}, max_pool_int8)
{
    vector<int8_t> a_data = {0, 1, 0, -2, 1, 0, -3, 2, 0, 0, 2, 0, 0, 0, 1};
    Shape shape_a{1, 1, 3, 5};
    Shape window_shape{2, 3};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 0};
    Shape padding_above{0, 0};
    Shape shape_r{1, 1, 2, 3};
    auto A = make_shared<op::Parameter>(element::i8, shape_a);
    auto QMP = make_shared<ngraph::op::MaxPool>(
        A, window_shape, window_movement_strides, padding_below, padding_above);
    auto f = make_shared<Function>(NodeVector{QMP}, ParameterVector{A});
    auto backend = runtime::Backend::create("${BACKEND_NAME}");
    // Create some tensors for input/output
    auto a = backend->create_tensor(element::i8, shape_a);
    copy_data(a, a_data);
    auto result = backend->create_tensor(element::i8, shape_r);
    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a});
    EXPECT_EQ((vector<int8_t>{2, 2, 2, 2, 2, 2}), read_vector<int8_t>(result));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_uint8)
{
    vector<uint8_t> a_data = {0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0, 1};
    Shape shape_a{1, 1, 3, 5};
    Shape window_shape{2, 3};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 0};
    Shape padding_above{0, 0};
    Shape shape_r{1, 1, 2, 3};
    auto A = make_shared<op::Parameter>(element::u8, shape_a);
    auto QAP = make_shared<ngraph::op::AvgPool>(
        A, window_shape, window_movement_strides, padding_below, padding_above);
    auto f = make_shared<Function>(NodeVector{QAP}, ParameterVector{A});
    auto backend = runtime::Backend::create("${BACKEND_NAME}");
    // Create some tensors for input/output
    auto a = backend->create_tensor(element::u8, shape_a);
    copy_data(a, a_data);
    auto result = backend->create_tensor(element::u8, shape_r);
    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a});
    EXPECT_EQ((vector<uint8_t>{1, 1, 1, 1, 1, 0}), read_vector<uint8_t>(result));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_int8)
{
    vector<int8_t> a_data = {10, 1, 0, -2, 1, 0, -3, 4, 0, 0, 2, 0, 0, 0, 1};
    Shape shape_a{1, 1, 3, 5};
    Shape window_shape{2, 3};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 0};
    Shape padding_above{0, 0};
    Shape shape_r{1, 1, 2, 3};
    auto A = make_shared<op::Parameter>(element::i8, shape_a);
    auto QAP = make_shared<ngraph::op::AvgPool>(
        A, window_shape, window_movement_strides, padding_below, padding_above);
    auto f = make_shared<Function>(NodeVector{QAP}, ParameterVector{A});
    auto backend = runtime::Backend::create("${BACKEND_NAME}");
    // Create some tensors for input/output
    auto a = backend->create_tensor(element::i8, shape_a);
    copy_data(a, a_data);
    auto result = backend->create_tensor(element::i8, shape_r);
    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a});
    EXPECT_EQ((vector<int8_t>{2, 0, 0, 0, 0, 1}), read_vector<int8_t>(result));
}

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NGRAPH_TEST(${BACKEND_NAME}, max_pool_1d_1channel_2image)
{
    Shape shape_a{2, 1, 14};
    Shape window_shape{3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 12};
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    auto f = make_shared<Function>(make_shared<op::MaxPool>(A, window_shape), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 3>({{{0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0}},
                                       {{0, 2, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 2}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f((test::NDArray<float, 3>({{{1, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 0}},
                                                            {{2, 2, 1, 1, 0, 2, 2, 2, 1, 1, 1, 2}}})
                                       .get_vector()),
                                  read_vector<float>(result),
                                  MIN_FLOAT_TOLERANCE_BITS));
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}

NGRAPH_TEST(${BACKEND_NAME}, max_pool_1d_2channel_2image)
{
    Shape shape_a{2, 2, 14};
    Shape window_shape{3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 2, 12};
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    auto f = make_shared<Function>(make_shared<op::MaxPool>(A, window_shape), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 3>({{{0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0},
                                        {0, 0, 0, 2, 0, 0, 2, 3, 0, 1, 2, 0, 1, 0}},

                                       {{0, 2, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 2},
                                        {2, 1, 0, 0, 1, 0, 2, 0, 0, 0, 1, 1, 2, 0}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(
        (test::NDArray<float, 3>(
             {{{1, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 0}, {0, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 1}},
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              {{2, 2, 1, 1, 0, 2, 2, 2, 1, 1, 1, 2}, {2, 1, 1, 1, 2, 2, 2, 0, 1, 1, 2, 2}}})
             .get_vector()),
        read_vector<float>(result),
        MIN_FLOAT_TOLERANCE_BITS));
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}

NGRAPH_TEST(${BACKEND_NAME}, max_pool_2d_2channel_2image)
{
    Shape shape_a{2, 2, 5, 5};
    Shape window_shape{2, 3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 2, 4, 3};
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    auto f = make_shared<Function>(make_shared<op::MaxPool>(A, window_shape), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>({{{{0, 1, 0, 2, 1}, // img 0 chan 0
                                         {0, 3, 2, 0, 0},
                                         {2, 0, 0, 0, 1},
                                         {2, 0, 1, 1, 2},
                                         {0, 2, 1, 0, 0}},

                                        {{0, 0, 0, 2, 0}, // img 0 chan 1
                                         {0, 2, 3, 0, 1},
                                         {2, 0, 1, 0, 2},
                                         {3, 1, 0, 0, 0},
                                         {2, 0, 0, 0, 0}}},

                                       {{{0, 2, 1, 1, 0}, // img 1 chan 0
                                         {0, 0, 2, 0, 1},
                                         {0, 0, 1, 2, 3},
                                         {2, 0, 0, 3, 0},
                                         {0, 0, 0, 0, 0}},

                                        {{2, 1, 0, 0, 1}, // img 1 chan 1
                                         {0, 2, 0, 0, 0},
                                         {1, 1, 2, 0, 2},
                                         {1, 1, 1, 0, 1},
                                         {1, 0, 0, 0, 2}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f((test::NDArray<float, 4>({{{{3, 3, 2}, // img 0 chan 0
                                                              {3, 3, 2},
                                                              {2, 1, 2},
                                                              {2, 2, 2}},

                                                             {{3, 3, 3}, // img 0 chan 1
                                                              {3, 3, 3},
                                                              {3, 1, 2},
                                                              {3, 1, 0}}},

                                                            {{{2, 2, 2}, // img 1 chan 0
                                                              {2, 2, 3},
                                                              {2, 3, 3},
                                                              {2, 3, 3}},

                                                             {{2, 2, 1}, // img 1 chan 1
                                                              {2, 2, 2},
                                                              {2, 2, 2},
                                                              {1, 1, 2}}}})
                                       .get_vector()),
                                  read_vector<float>(result),
                                  MIN_FLOAT_TOLERANCE_BITS));
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}

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// this test cover the case with multiple image and with asymetric pad
// one bug been found on GPU side is covered by this test
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NGRAPH_TEST(${BACKEND_NAME}, max_pool_2d_2channel_2image_asym_pad)
{
    Shape shape_a{2, 2, 4, 4};
    Shape window_shape{3, 3};
    auto window_movement_strides = Strides{2, 2};
    Shape padding_below{0, 0};
    Shape padding_above{1, 1};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 2, 2, 2};
    auto f = make_shared<Function>(
        make_shared<op::MaxPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above),
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        ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>({{{{0, 1, 0, 2}, // img 0 chan 0
                                         {0, 3, 2, 0},
                                         {2, 0, 0, 0},
                                         {0, 2, 1, 0}},

                                        {{0, 0, 0, 2}, // img 0 chan 1
                                         {0, 2, 3, 0},
                                         {2, 0, 1, 0},
                                         {2, 0, 0, 0}}},

                                       {{{0, 2, 1, 1}, // img 1 chan 0
                                         {0, 0, 2, 0},
                                         {0, 0, 1, 2},
                                         {0, 0, 0, 0}},

                                        {{2, 1, 0, 0}, // img 1 chan 1
                                         {0, 2, 0, 0},
                                         {1, 1, 2, 0},
                                         {1, 0, 0, 0}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f((test::NDArray<float, 4>({{{{3, 2}, // img 0 chan 0
                                                              {2, 1}},
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                                                             {{3, 3}, // img 0 chan 1
                                                              {2, 1}}},
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                                                            {{{2, 2}, // img 1 chan 0
                                                              {1, 2}},
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                                                             {{2, 2}, // img 1 chan 1
                                                              {2, 2}}}})
                                       .get_vector()),
                                  read_vector<float>(result),
                                  MIN_FLOAT_TOLERANCE_BITS));
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}

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// MaxPool2D1ChannelTests test fixture for test setup reuse
class MaxPool2D1ChannelTests : public testing::Test
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{
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public:
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    Shape shape_a{1, 1, 5, 5};
    Shape window_shape{2, 3};
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    Strides window_movement_strides{1, 1};

protected:
    virtual void SetUp() override {}
};

NGRAPH_TEST_F(${BACKEND_NAME}, MaxPool2D1ChannelTests, max_pool_2d_1channel_1image_overpadded)
{
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    Shape padding_below{2, 0};
    Shape padding_above{1, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 7, 5};
    auto f = make_shared<Function>(
        make_shared<op::MaxPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above),
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        ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>({{{{0, 1, 0, 2, 1},
                                         {0, 3, 2, 0, 0},
                                         {2, 0, 0, 0, 1},
                                         {2, 0, 1, 1, 2},
                                         {0, 2, 1, 0, 0}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    auto min = std::numeric_limits<float>::lowest();
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    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 4>({{{{min, min, min, min, min},
                                                             {1, 2, 2, 2, 1},
                                                             {3, 3, 2, 2, 1},
                                                             {3, 3, 2, 1, 1},
                                                             {2, 1, 2, 2, 2},
                                                             {2, 2, 2, 2, 2},
                                                             {2, 2, 1, 0, 0}}}})
                                      .get_vector(),
                                  read_vector<float>(result),
                                  MIN_FLOAT_TOLERANCE_BITS));
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}

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NGRAPH_TEST_F(${BACKEND_NAME}, MaxPool2D1ChannelTests, max_pool_2d_1channel_1image_padded)
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{
    Shape padding_below{1, 0};
    Shape padding_above{1, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 6, 5};
    auto f = make_shared<Function>(
        make_shared<op::MaxPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above),
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        ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>({{{{0, 1, 0, 2, 1},
                                         {0, 3, 2, 0, 0},
                                         {2, 0, 0, 0, 1},
                                         {2, 0, 1, 1, 2},
                                         {0, 2, 1, 0, 0}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f((test::NDArray<float, 4>({{{{1, 2, 2, 2, 1},
                                                              {3, 3, 2, 2, 1},
                                                              {3, 3, 2, 1, 1},
                                                              {2, 1, 2, 2, 2},
                                                              {2, 2, 2, 2, 2},
                                                              {2, 2, 1, 0, 0}}}})
                                       .get_vector()),
                                  read_vector<float>(result),
                                  MIN_FLOAT_TOLERANCE_BITS));
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}

// Test to make sure that negative elements and padding are handled properly. Added this because
// mkldnn calls its padding "zero padding" but apparently that is not technically true (negative
// values still "win" versus out-of-bounds values), which is good.
NGRAPH_TEST(${BACKEND_NAME}, max_pool_2d_1channel_1image_padded_negative_values)
{
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    auto shape_a = Shape{1, 1, 1, 14}; // 1 image, 1 channel, 1 row, 14 columns (if it's 1D we don't
                                       // get mkldnn as of this writing)
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    Shape window_shape{1, 3};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 1};
    Shape padding_above{0, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 1, 15};
    auto f = make_shared<Function>(
        make_shared<op::MaxPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above),
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        ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>{{{{-1, -2, -3, -3, -2, -1, -3, -2, -2, -2, -2, -3, -4, -5}}}}
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(
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        (test::NDArray<float, 4>({{{{-1, -1, -2, -2, -1, -1, -1, -2, -2, -2, -2, -2, -3, -4, -5}}}})
             .get_vector()),
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        read_vector<float>(result),
        MIN_FLOAT_TOLERANCE_BITS));
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}

NGRAPH_TEST(${BACKEND_NAME}, max_pool_2d_1channel_1image_strided)
{
    Shape shape_a{1, 1, 8, 8};
    Shape window_shape{2, 3};
    auto window_movement_strides = Strides{3, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 3, 3};
    auto f = make_shared<Function>(
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        make_shared<op::MaxPool>(A, window_shape, window_movement_strides), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>({{{{0, 1, 0, 2, 1, 2, 0, 0},
                                         {0, 3, 2, 0, 0, 0, 1, 0},
                                         {2, 0, 0, 0, 1, 0, 0, 0},
                                         {2, 0, 1, 1, 2, 2, 3, 0},
                                         {0, 2, 1, 0, 0, 0, 1, 0},
                                         {2, 0, 3, 1, 0, 0, 0, 0},
                                         {1, 2, 0, 0, 0, 1, 2, 0},
                                         {1, 0, 2, 0, 0, 0, 1, 0}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

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    auto handle = backend->compile(f);
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    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(
        (test::NDArray<float, 4>({{{{3, 2, 2}, {2, 2, 3}, {2, 2, 2}}}}).get_vector()),
        read_vector<float>(result),
        MIN_FLOAT_TOLERANCE_BITS));
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}

NGRAPH_TEST(${BACKEND_NAME}, max_pool_3d)
{
    Shape shape_a{64, 3, 7, 8, 10};
    Shape window_shape{2, 3, 2};
    auto move_strides = Strides{2, 3, 4};
    Shape padding_below{5, 6, 4};
    Shape padding_above{6, 4, 5};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    auto B = make_shared<op::Parameter>(element::f32, shape_a);

    auto cpu_f = make_shared<Function>(
        make_shared<op::MaxPool>(A, window_shape, move_strides, padding_below, padding_above),
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        ParameterVector{A});
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    auto int_f = make_shared<Function>(
        make_shared<op::MaxPool>(B, window_shape, move_strides, padding_below, padding_above),
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        ParameterVector{B});
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    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "${BACKEND_NAME}");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
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        EXPECT_TRUE(
            test::all_close_f(cpu_results.at(i), int_results.at(i), MIN_FLOAT_TOLERANCE_BITS));
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    }
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_1d_1channel_1image)
{
    Shape shape_a{1, 1, 14};
    Shape window_shape{3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 12};
539
    auto f = make_shared<Function>(make_shared<op::AvgPool>(A, window_shape), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 3>{{{0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0}}}.get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

    float denom = 3.0;

551
    auto handle = backend->compile(f);
552
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 3>({{{1 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             4 / denom,
                                                             5 / denom,
                                                             5 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             0 / denom}}})
                                      .get_vector(),
                                  read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_1d_1channel_2image)
{
    Shape shape_a{2, 1, 14};
    Shape window_shape{3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 12};
575
    auto f = make_shared<Function>(make_shared<op::AvgPool>(A, window_shape), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 3>({{{0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0}},
                                       {{0, 2, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 2}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

    float denom = 3.0;

589
    auto handle = backend->compile(f);
590
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 3>({{{1 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             4 / denom,
                                                             5 / denom,
                                                             5 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             0 / denom}},
                                                           {{3 / denom,
                                                             4 / denom,
                                                             2 / denom,
                                                             1 / denom,
                                                             0 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             3 / denom,
                                                             1 / denom,
                                                             1 / denom,
                                                             1 / denom,
                                                             3 / denom}}})
                                      .get_vector(),
                                  read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_1d_2channel_2image)
{
    Shape shape_a{2, 2, 14};
    Shape window_shape{3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 2, 12};
625
    auto f = make_shared<Function>(make_shared<op::AvgPool>(A, window_shape), ParameterVector{A});
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    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 3>({{{0, 1, 0, 2, 1, 0, 3, 2, 0, 0, 2, 0, 0, 0},
                                        {0, 0, 0, 2, 0, 0, 2, 3, 0, 1, 2, 0, 1, 0}},

                                       {{0, 2, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 2},
                                        {2, 1, 0, 0, 1, 0, 2, 0, 0, 0, 1, 1, 2, 0}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

    float denom = 3.0;

642
    auto handle = backend->compile(f);
643
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 3>({{{1 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             4 / denom,
                                                             5 / denom,
                                                             5 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             0 / denom},
                                                            {0 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             5 / denom,
                                                             5 / denom,
                                                             4 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             3 / denom,
                                                             1 / denom}},

                                                           {{3 / denom,
                                                             4 / denom,
                                                             2 / denom,
                                                             1 / denom,
                                                             0 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             3 / denom,
                                                             1 / denom,
                                                             1 / denom,
                                                             1 / denom,
                                                             3 / denom},
                                                            {3 / denom,
                                                             1 / denom,
                                                             1 / denom,
                                                             1 / denom,
                                                             3 / denom,
                                                             2 / denom,
                                                             2 / denom,
                                                             0 / denom,
                                                             1 / denom,
                                                             2 / denom,
                                                             4 / denom,
                                                             3 / denom}}})
                                      .get_vector(),
                                  read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image)
{
    Shape shape_a{2, 2, 5, 5};
    Shape window_shape{2, 3};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 2, 4, 3};
703
    auto f = make_shared<Function>(make_shared<op::AvgPool>(A, window_shape), ParameterVector{A});
704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>({{{{0, 1, 0, 2, 1}, // img 0 chan 0
                                         {0, 3, 2, 0, 0},
                                         {2, 0, 0, 0, 1},
                                         {2, 0, 1, 1, 2},
                                         {0, 2, 1, 0, 0}},

                                        {{0, 0, 0, 2, 0}, // img 0 chan 1
                                         {0, 2, 3, 0, 1},
                                         {2, 0, 1, 0, 2},
                                         {3, 1, 0, 0, 0},
                                         {2, 0, 0, 0, 0}}},

                                       {{{0, 2, 1, 1, 0}, // img 1 chan 0
                                         {0, 0, 2, 0, 1},
                                         {0, 0, 1, 2, 3},
                                         {2, 0, 0, 3, 0},
                                         {0, 0, 0, 0, 0}},

                                        {{2, 1, 0, 0, 1}, // img 1 chan 1
                                         {0, 2, 0, 0, 0},
                                         {1, 1, 2, 0, 2},
                                         {1, 1, 1, 0, 1},
                                         {1, 0, 0, 0, 2}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

    float denom = 2 * 3;

738
    auto handle = backend->compile(f);
739
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(
        test::NDArray<float, 4>({{{{6 / denom, 8 / denom, 5 / denom}, // img 0 chan 0
                                   {7 / denom, 5 / denom, 3 / denom},
                                   {5 / denom, 2 / denom, 5 / denom},
                                   {6 / denom, 5 / denom, 5 / denom}},

                                  {{5 / denom, 7 / denom, 6 / denom}, // img 0 chan 1
                                   {8 / denom, 6 / denom, 7 / denom},
                                   {7 / denom, 2 / denom, 3 / denom},
                                   {6 / denom, 1 / denom, 0 / denom}}},

                                 {{{5 / denom, 6 / denom, 5 / denom}, // img 1 chan 0
                                   {3 / denom, 5 / denom, 9 / denom},
                                   {3 / denom, 6 / denom, 9 / denom},
                                   {2 / denom, 3 / denom, 3 / denom}},

                                  {{5 / denom, 3 / denom, 1 / denom}, // img 1 chan 1
                                   {6 / denom, 5 / denom, 4 / denom},
                                   {7 / denom, 5 / denom, 6 / denom},
                                   {4 / denom, 2 / denom, 4 / denom}}}})
            .get_vector(),
        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_1channel_1image_strided)
{
    Shape shape_a{1, 1, 8, 8};
    Shape window_shape{2, 3};
    auto window_movement_strides = Strides{3, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 3, 3};
    auto f = make_shared<Function>(
773
        make_shared<op::AvgPool>(A, window_shape, window_movement_strides), ParameterVector{A});
774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>({{{{0, 1, 0, 2, 1, 2, 0, 0},
                                         {0, 3, 2, 0, 0, 0, 1, 0},
                                         {2, 0, 0, 0, 1, 0, 0, 0},
                                         {2, 0, 1, 1, 2, 2, 3, 0},
                                         {0, 2, 1, 0, 0, 0, 1, 0},
                                         {2, 0, 3, 1, 0, 0, 0, 0},
                                         {1, 2, 0, 0, 0, 1, 2, 0},
                                         {1, 0, 2, 0, 0, 0, 1, 0}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

    float denom = 2 * 3;

793
    auto handle = backend->compile(f);
794
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 4>({{{{6 / denom, 5 / denom, 4 / denom},
                                                             {6 / denom, 5 / denom, 8 / denom},
                                                             {6 / denom, 2 / denom, 4 / denom}}}})
                                      .get_vector(),
                                  read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_1channel_1image_padded_do_not_include_in_computation)
{
    Shape shape_a{1, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{1, 1};
    Shape padding_above{1, 1};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 4, 4};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, false),
814
        ParameterVector{A});
815 816 817 818 819 820 821 822

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a, test::NDArray<float, 4>({{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}}}).get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

823
    auto handle = backend->compile(f);
824
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(
        test::all_close(test::NDArray<float, 4>({{{{0.0f / 1, 1.0f / 2, 1.0f / 2, 0.0f / 1},
                                                   {0.0f / 2, 4.0f / 4, 6.0f / 4, 2.0f / 2},
                                                   {2.0f / 2, 5.0f / 4, 5.0f / 4, 2.0f / 2},
                                                   {2.0f / 1, 2.0f / 2, 0.0f / 2, 0.0f / 1}}}})
                            .get_vector(),
                        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_1channel_1image_padded_include_in_computation)
{
    Shape shape_a{1, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{1, 1};
    Shape padding_above{1, 1};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{1, 1, 4, 4};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, true),
846
        ParameterVector{A});
847 848 849 850 851 852 853 854

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a, test::NDArray<float, 4>({{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}}}).get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

855
    auto handle = backend->compile(f);
856
    handle->call_with_validate({result}, {a});
857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877
    EXPECT_TRUE(
        test::all_close(test::NDArray<float, 4>({{{{0.0f / 4, 1.0f / 4, 1.0f / 4, 0.0f / 4},
                                                   {0.0f / 4, 4.0f / 4, 6.0f / 4, 2.0f / 4},
                                                   {2.0f / 4, 5.0f / 4, 5.0f / 4, 2.0f / 4},
                                                   {2.0f / 4, 2.0f / 4, 0.0f / 4, 0.0f / 4}}}})
                            .get_vector(),
                        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image_padded_do_not_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{1, 1};
    Shape padding_above{1, 1};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 4, 4};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, false),
878
        ParameterVector{A});
879 880 881 882 883 884 885 886 887 888 889

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

890
    auto handle = backend->compile(f);
891
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(
        test::all_close(test::NDArray<float, 4>({{{{0.0f / 1, 1.0f / 2, 1.0f / 2, 0.0f / 1},
                                                   {0.0f / 2, 4.0f / 4, 6.0f / 4, 2.0f / 2},
                                                   {2.0f / 2, 5.0f / 4, 5.0f / 4, 2.0f / 2},
                                                   {2.0f / 1, 2.0f / 2, 0.0f / 2, 0.0f / 1}},
                                                  {{3.0f / 1, 8.0f / 2, 7.0f / 2, 2.0f / 1},
                                                   {5.0f / 2, 10.0f / 4, 16.0f / 4, 11.0f / 2},
                                                   {5.0f / 2, 11.0f / 4, 20.0f / 4, 14.0f / 2},
                                                   {3.0f / 1, 9.0f / 2, 11.0f / 2, 5.0f / 1}}}})
                            .get_vector(),
                        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image_padded_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{1, 1};
    Shape padding_above{1, 1};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 4, 4};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, true),
917
        ParameterVector{A});
918 919 920 921 922 923 924 925 926 927 928

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

929
    auto handle = backend->compile(f);
930
    handle->call_with_validate({result}, {a});
931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956
    EXPECT_TRUE(
        test::all_close(test::NDArray<float, 4>({{{{0.0f / 4, 1.0f / 4, 1.0f / 4, 0.0f / 4},
                                                   {0.0f / 4, 4.0f / 4, 6.0f / 4, 2.0f / 4},
                                                   {2.0f / 4, 5.0f / 4, 5.0f / 4, 2.0f / 4},
                                                   {2.0f / 4, 2.0f / 4, 0.0f / 4, 0.0f / 4}},
                                                  {{3.0f / 4, 8.0f / 4, 7.0f / 4, 2.0f / 4},
                                                   {5.0f / 4, 10.0f / 4, 16.0f / 4, 11.0f / 4},
                                                   {5.0f / 4, 11.0f / 4, 20.0f / 4, 14.0f / 4},
                                                   {3.0f / 4, 9.0f / 4, 11.0f / 4, 5.0f / 4}}}})
                            .get_vector(),
                        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME},
            avg_pool_2d_2channel_2image_padded_only_below_do_not_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{1, 1};
    Shape padding_above{0, 0};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 3};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, false),
957
        ParameterVector{A});
958 959 960 961 962 963 964 965 966 967 968

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

969
    auto handle = backend->compile(f);
970
    handle->call_with_validate({result}, {a});
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    EXPECT_TRUE(test::all_close(test::NDArray<float, 4>({{{{0.0f / 1, 1.0f / 2, 1.0f / 2},
                                                           {0.0f / 2, 4.0f / 4, 6.0f / 4},
                                                           {2.0f / 2, 5.0f / 4, 5.0f / 4}},
                                                          {{3.0f / 1, 8.0f / 2, 7.0f / 2},
                                                           {5.0f / 2, 10.0f / 4, 16.0f / 4},
                                                           {5.0f / 2, 11.0f / 4, 20.0f / 4}}}})
                                    .get_vector(),
                                read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image_padded_only_below_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{1, 1};
    Shape padding_above{0, 0};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 3};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, true),
993
        ParameterVector{A});
994 995 996 997 998 999 1000 1001 1002 1003 1004

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1005
    auto handle = backend->compile(f);
1006
    handle->call_with_validate({result}, {a});
1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029
    EXPECT_TRUE(test::all_close(test::NDArray<float, 4>({{{{0.0f / 4, 1.0f / 4, 1.0f / 4},
                                                           {0.0f / 4, 4.0f / 4, 6.0f / 4},
                                                           {2.0f / 4, 5.0f / 4, 5.0f / 4}},
                                                          {{3.0f / 4, 8.0f / 4, 7.0f / 4},
                                                           {5.0f / 4, 10.0f / 4, 16.0f / 4},
                                                           {5.0f / 4, 11.0f / 4, 20.0f / 4}}}})
                                    .get_vector(),
                                read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME},
            avg_pool_2d_2channel_2image_padded_only_above_do_not_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 0};
    Shape padding_above{1, 1};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 3};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, false),
1030
        ParameterVector{A});
1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1042
    auto handle = backend->compile(f);
1043
    handle->call_with_validate({result}, {a});
1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065
    EXPECT_TRUE(test::all_close(test::NDArray<float, 4>({{{{4.0f / 4, 6.0f / 4, 2.0f / 2},
                                                           {5.0f / 4, 5.0f / 4, 2.0f / 2},
                                                           {2.0f / 2, 0.0f / 2, 0.0f / 1}},
                                                          {{10.0f / 4, 16.0f / 4, 11.0f / 2},
                                                           {11.0f / 4, 20.0f / 4, 14.0f / 2},
                                                           {9.0f / 2, 11.0f / 2, 5.0f / 1}}}})
                                    .get_vector(),
                                read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image_padded_only_above_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 0};
    Shape padding_above{1, 1};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 3};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, true),
1066
        ParameterVector{A});
1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1078
    auto handle = backend->compile(f);
1079
    handle->call_with_validate({result}, {a});
1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101
    EXPECT_TRUE(test::all_close(test::NDArray<float, 4>({{{{4.0f / 4, 6.0f / 4, 2.0f / 4},
                                                           {5.0f / 4, 5.0f / 4, 2.0f / 4},
                                                           {2.0f / 4, 0.0f / 4, 0.0f / 4}},
                                                          {{10.0f / 4, 16.0f / 4, 11.0f / 4},
                                                           {11.0f / 4, 20.0f / 4, 14.0f / 4},
                                                           {9.0f / 4, 11.0f / 4, 5.0f / 4}}}})
                                    .get_vector(),
                                read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image_3x3_padded_do_not_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{3, 3};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{2, 2};
    Shape padding_above{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 5, 5};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, false),
1102
        ParameterVector{A});
1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1114
    auto handle = backend->compile(f);
1115
    handle->call_with_validate({result}, {a});
1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142
    EXPECT_TRUE(test::all_close_f(
        test::NDArray<float, 4>({{{{0.0f / 1, 1.0f / 2, 1.0f / 3, 1.0f / 2, 0.0f / 1},
                                   {0.0f / 2, 4.0f / 4, 6.0f / 6, 6.0f / 4, 2.0f / 2},
                                   {2.0f / 3, 6.0f / 6, 8.0f / 9, 6.0f / 6, 2.0f / 3},
                                   {2.0f / 2, 5.0f / 4, 7.0f / 6, 5.0f / 4, 2.0f / 2},
                                   {2.0f / 1, 2.0f / 2, 2.0f / 3, 0.0f / 2, 0.0f / 1}},
                                  {{3.0f / 1, 8.0f / 2, 10.0f / 3, 7.0f / 2, 2.0f / 1},
                                   {5.0f / 2, 10.0f / 4, 21.0f / 6, 16.0f / 4, 11.0f / 2},
                                   {8.0f / 3, 19.0f / 6, 35.0f / 9, 27.0f / 6, 16.0f / 3},
                                   {5.0f / 2, 11.0f / 4, 25.0f / 6, 20.0f / 4, 14.0f / 2},
                                   {3.0f / 1, 9.0f / 2, 14.0f / 3, 11.0f / 2, 5.0f / 1}}}})
            .get_vector(),
        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image_3x3_padded_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{3, 3};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{2, 2};
    Shape padding_above{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 5, 5};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, true),
1143
        ParameterVector{A});
1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1155
    auto handle = backend->compile(f);
1156
    handle->call_with_validate({result}, {a});
1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
    EXPECT_TRUE(test::all_close_f(
        test::NDArray<float, 4>({{{{0.0f / 9, 1.0f / 9, 1.0f / 9, 1.0f / 9, 0.0f / 9},
                                   {0.0f / 9, 4.0f / 9, 6.0f / 9, 6.0f / 9, 2.0f / 9},
                                   {2.0f / 9, 6.0f / 9, 8.0f / 9, 6.0f / 9, 2.0f / 9},
                                   {2.0f / 9, 5.0f / 9, 7.0f / 9, 5.0f / 9, 2.0f / 9},
                                   {2.0f / 9, 2.0f / 9, 2.0f / 9, 0.0f / 9, 0.0f / 9}},
                                  {{3.0f / 9, 8.0f / 9, 10.0f / 9, 7.0f / 9, 2.0f / 9},
                                   {5.0f / 9, 10.0f / 9, 21.0f / 9, 16.0f / 9, 11.0f / 9},
                                   {8.0f / 9, 19.0f / 9, 35.0f / 9, 27.0f / 9, 16.0f / 9},
                                   {5.0f / 9, 11.0f / 9, 25.0f / 9, 20.0f / 9, 14.0f / 9},
                                   {3.0f / 9, 9.0f / 9, 14.0f / 9, 11.0f / 9, 5.0f / 9}}}})
            .get_vector(),
        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME},
            avg_pool_2d_2channel_2image_3x3_strided_padded_do_not_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{3, 3};
    auto window_movement_strides = Strides{2, 2};
    Shape padding_below{2, 2};
    Shape padding_above{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 3};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, false),
1185
        ParameterVector{A});
1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1197
    auto handle = backend->compile(f);
1198
    handle->call_with_validate({result}, {a});
1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220
    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 4>({{{{0.0f / 1, 1.0f / 3, 0.0f / 1},
                                                             {2.0f / 3, 8.0f / 9, 2.0f / 3},
                                                             {2.0f / 1, 2.0f / 3, 0.0f / 1}},
                                                            {{3.0f / 1, 10.0f / 3, 2.0f / 1},
                                                             {8.0f / 3, 35.0f / 9, 16.0f / 3},
                                                             {3.0f / 1, 14.0f / 3, 5.0f / 1}}}})
                                      .get_vector(),
                                  read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME}, avg_pool_2d_2channel_2image_3x3_strided_padded_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{3, 3};
    auto window_movement_strides = Strides{2, 2};
    Shape padding_below{2, 2};
    Shape padding_above{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 3};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, true),
1221
        ParameterVector{A});
1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1233
    auto handle = backend->compile(f);
1234
    handle->call_with_validate({result}, {a});
1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257
    EXPECT_TRUE(test::all_close_f(test::NDArray<float, 4>({{{{0.0f / 9, 1.0f / 9, 0.0f / 9},
                                                             {2.0f / 9, 8.0f / 9, 2.0f / 9},
                                                             {2.0f / 9, 2.0f / 9, 0.0f / 9}},
                                                            {{3.0f / 9, 10.0f / 9, 2.0f / 9},
                                                             {8.0f / 9, 35.0f / 9, 16.0f / 9},
                                                             {3.0f / 9, 14.0f / 9, 5.0f / 9}}}})
                                      .get_vector(),
                                  read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME},
            avg_pool_2d_2channel_2image_3x3_strided_uneven_padded_do_not_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{3, 3};
    auto window_movement_strides = Strides{2, 3};
    Shape padding_below{2, 2};
    Shape padding_above{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 2};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, false),
1258
        ParameterVector{A});
1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1270
    auto handle = backend->compile(f);
1271
    handle->call_with_validate({result}, {a});
1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292
    EXPECT_TRUE(test::all_close_f(
        test::NDArray<float, 4>(
            {{{{0.0f / 1, 1.0f / 2}, {2.0f / 3, 6.0f / 6}, {2.0f / 1, 0.0f / 2}},
              {{3.0f / 1, 7.0f / 2}, {8.0f / 3, 27.0f / 6}, {3.0f / 1, 11.0f / 2}}}})
            .get_vector(),
        read_vector<float>(result)));
}

NGRAPH_TEST(${BACKEND_NAME},
            avg_pool_2d_2channel_2image_3x3_strided_uneven_padded_include_in_computation)
{
    Shape shape_a{2, 1, 3, 3};
    Shape window_shape{3, 3};
    auto window_movement_strides = Strides{2, 3};
    Shape padding_below{2, 2};
    Shape padding_above{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_r{2, 1, 3, 2};
    auto f = make_shared<Function>(
        make_shared<op::AvgPool>(
            A, window_shape, window_movement_strides, padding_below, padding_above, true),
1293
        ParameterVector{A});
1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304

    auto backend = runtime::Backend::create("${BACKEND_NAME}");

    // Create some tensors for input/output
    auto a = backend->create_tensor(element::f32, shape_a);
    copy_data(a,
              test::NDArray<float, 4>(
                  {{{{0, 1, 0}, {0, 3, 2}, {2, 0, 0}}, {{3, 5, 2}, {2, 0, 9}, {3, 6, 5}}}})
                  .get_vector());
    auto result = backend->create_tensor(element::f32, shape_r);

1305
    auto handle = backend->compile(f);
1306
    handle->call_with_validate({result}, {a});
1307 1308 1309 1310 1311 1312 1313 1314
    EXPECT_TRUE(test::all_close_f(
        test::NDArray<float, 4>(
            {{{{0.0f / 9, 1.0f / 9}, {2.0f / 9, 6.0f / 9}, {2.0f / 9, 0.0f / 9}},
              {{3.0f / 9, 7.0f / 9}, {8.0f / 9, 27.0f / 9}, {3.0f / 9, 11.0f / 9}}}})
            .get_vector(),
        read_vector<float>(result)));
}

1315 1316 1317 1318 1319 1320 1321 1322 1323 1324
// Params to drive avg_pool_3d testing variations
class avg_pool_3d_params : public ::testing::TestWithParam<bool>
{
protected:
    avg_pool_3d_params() { include_pad = GetParam(); }
    bool include_pad;
};

// avg_pool_3d test code using params
NGRAPH_TEST_P(${BACKEND_NAME}, avg_pool_3d_params, avg_pool_3d_uneven_strided_padded)
1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335
{
    Shape shape_a{64, 3, 12, 13, 15};
    Shape window_shape{4, 5, 4};
    auto move_strides = Strides{2, 3, 4};
    Shape padding_below{2, 3, 1};
    Shape padding_above{3, 1, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    auto B = make_shared<op::Parameter>(element::f32, shape_a);

    auto cpu_f = make_shared<Function>(
        make_shared<op::AvgPool>(
1336
            A, window_shape, move_strides, padding_below, padding_above, include_pad),
1337
        ParameterVector{A});
1338 1339
    auto int_f = make_shared<Function>(
        make_shared<op::AvgPool>(
1340
            B, window_shape, move_strides, padding_below, padding_above, include_pad),
1341
        ParameterVector{B});
1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354
    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto backend_results = execute(cpu_f, args, "${BACKEND_NAME}");
    for (size_t i = 0; i < backend_results.size(); i++)
    {
1355 1356
        EXPECT_TRUE(test::all_close_f(
            backend_results.at(i), int_results.at(i), DEFAULT_FLOAT_TOLERANCE_BITS + 1));
1357 1358 1359
    }
}

1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
NGRAPH_TEST(${BACKEND_NAME}, avg_pool_bprop_2d_2channel_2image_dyn_shape)
{
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    Shape padding_below{0, 0};
    Shape padding_above{0, 0};
    Shape shape_d{2, 2, 2, 2};
    auto delta = make_shared<op::Parameter>(element::f32, shape_d);
    auto forward_arg_shape =
        make_shared<op::Parameter>(element::i64, PartialShape{Dimension::dynamic()});

    auto avg_pool_bprop = make_shared<op::v1::AvgPoolBackprop>(delta,
                                                               forward_arg_shape,
                                                               window_movement_strides,
                                                               padding_below,
                                                               padding_above,
                                                               window_shape,
1377
                                                               true);
1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434

    auto f = make_shared<Function>(NodeVector{avg_pool_bprop},
                                   ParameterVector{delta, forward_arg_shape});

    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);

    auto ex = backend->compile(f);

    auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());

    vector<int64_t> shapes = {2, 2, 3, 3};

    // Create some tensors for input/output
    auto deltas = backend->create_tensor(element::f32, shape_d);
    copy_data(deltas,
              test::NDArray<float, 4>({{{{0.3, 0.3}, // img 0 chan 0
                                         {0.3, 0.3}},

                                        {{0.2, 0.2}, // img 0 chan 1
                                         {0.2, 0.2}}},

                                       {{{0.1, 0.1}, // img 1 chan 0
                                         {0.1, 0.1}},

                                        {{0.4, 0.4}, // img 1 chan 1
                                         {0.4, 0.4}}}})
                  .get_vector());

    auto forward_shape = backend->create_tensor(element::i64, Shape{shapes.size()});
    copy_data(forward_shape, shapes);

    float denom = 2 * 2;

    ex->call_with_validate({t_r}, {deltas, forward_shape});

    ASSERT_EQ(t_r->get_shape(), (Shape{2, 2, 3, 3}));
    EXPECT_TRUE(test::all_close_f(
        (test::NDArray<float, 4>({{{{0.3f / denom, 0.6f / denom, 0.3f / denom}, // img 0 chan 0
                                    {0.6f / denom, 1.2f / denom, 0.6f / denom},
                                    {0.3f / denom, 0.6f / denom, 0.3f / denom}},

                                   {{0.2f / denom, 0.4f / denom, 0.2f / denom}, // img 0 chan 1
                                    {0.4f / denom, 0.8f / denom, 0.4f / denom},
                                    {0.2f / denom, 0.4f / denom, 0.2f / denom}}},

                                  {{{0.1f / denom, 0.2f / denom, 0.1f / denom}, // img 1 chan 0
                                    {0.2f / denom, 0.4f / denom, 0.2f / denom},
                                    {0.1f / denom, 0.2f / denom, 0.1f / denom}},

                                   {{0.4f / denom, 0.8f / denom, 0.4f / denom}, // img 1 chan 1
                                    {0.8f / denom, 1.6f / denom, 0.8f / denom},
                                    {0.4f / denom, 0.8f / denom, 0.4f / denom}}}})
             .get_vector()),
        read_vector<float>(t_r),
        MIN_FLOAT_TOLERANCE_BITS));
}

1435 1436
// avg_pool_3d case generation
NGRAPH_INSTANTIATE_TEST_CASE_P(${BACKEND_NAME}, include_pad, avg_pool_3d_params, testing::Bool());