//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <cmath>
#include <cstdint>
#include <fstream>
#include <iterator>
#include <limits>
#include <sstream>
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "ngraph/frontend/onnx_import/onnx.hpp"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
static std::string s_manifest = "${MANIFEST}";
using Inputs = std::vector<std::vector<float>>;
using Outputs = std::vector<std::vector<float>>;
TEST(onnx_${BACKEND_NAME}, model_output_names_check)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_default.onnx"));
std::size_t size = function->get_output_size();
for (std::size_t i{0}; i < size; ++i)
{
std::shared_ptr<Node> node = function->get_output_op(i);
EXPECT_EQ(node->get_friendly_name(), "output_" + std::to_string(i + 1));
}
}
TEST(onnx_${BACKEND_NAME}, model_add_abc)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/add_abc.onnx"));
Inputs inputs{{1}, {2}, {3}};
Outputs expected_outputs{{6}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_add_abc_initializers)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/add_abc_initializers.onnx"));
Inputs inputs{{1, 2, 3, 4}};
Outputs expected_outputs{{3, 6, 9, 12}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_addmul_abc)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/addmul_abc.onnx"));
std::vector<std::vector<float>> inputs;
Shape shape{1, 2, 2};
inputs.emplace_back(test::NDArray<float, 3>({{{9, 10}}, {{11, 12}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{5, 6}}, {{7, 8}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{1, 2}}, {{3, 4}}}).get_vector());
auto expected_output = test::NDArray<float, 3>({{{46, 62}}, {{80, 100}}}).get_vector();
auto result_vectors = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
}
TEST(onnx_${BACKEND_NAME}, model_argmin_no_keepdims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/argmin_no_keepdims.onnx"));
Inputs inputs{test::NDArray<float, 2>{{2, 1}, {3, 10}}.get_vector()};
std::vector<std::vector<int64_t>> expected_output{{1, 0}};
std::vector<std::vector<int64_t>> result{
execute<float, int64_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, result);
}
TEST(onnx_${BACKEND_NAME}, model_split_equal_parts_default)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_default.onnx"));
Inputs inputs{{1, 2, 3, 4, 5, 6}};
Outputs expected_outputs{{1, 2}, {3, 4}, {5, 6}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
}
TEST(onnx_${BACKEND_NAME}, model_split_equal_parts_2d)
{
// Split into 2 equal parts along axis=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_equal_parts_2d.onnx"));
Inputs inputs{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}};
Outputs expected_outputs{{0, 1, 2, 6, 7, 8}, {3, 4, 5, 9, 10, 11}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
}
TEST(onnx_${BACKEND_NAME}, model_split_variable_parts_2d)
{
// Split into variable parts {2, 4} along axis=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/split_variable_parts_2d.onnx"));
Inputs inputs{{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}};
Outputs expected_outputs{{0, 1, 6, 7}, {2, 3, 4, 5, 8, 9, 10, 11}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(outputs.size(), expected_outputs.size());
for (std::size_t i = 0; i < expected_outputs.size(); ++i)
{
EXPECT_EQ(outputs[i].size(), expected_outputs[i].size());
EXPECT_TRUE(test::all_close_f(outputs[i], expected_outputs[i]));
}
}
namespace
{
std::vector<std::vector<float>> conv2d_execute(const std::shared_ptr<Function>& function)
{
std::vector<std::vector<float>> args;
// data (1, 1, 7, 5) input tensor
args.emplace_back(test::NDArray<float, 4>{{{{{0.f, 1.f, 2.f, 3.f, 4.f},
{5.f, 6.f, 7.f, 8.f, 9.f},
{10.f, 11.f, 12.f, 13.f, 14.f},
{15.f, 16.f, 17.f, 18.f, 19.f},
{20.f, 21.f, 22.f, 23.f, 24.f},
{25.f, 26.f, 27.f, 28.f, 29.f},
{30.f, 31.f, 32.f, 33.f, 34.f}}}}}
.get_vector());
// filters (1, 1, 3, 3) aka convolution weights
args.emplace_back(
test::NDArray<float, 4>{{{{{1.f, 1.f, 1.f}, {1.f, 1.f, 1.f}, {1.f, 1.f, 1.f}}}}}
.get_vector());
return execute(function, args, "${BACKEND_NAME}");
}
} // namespace
TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_padding)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_with_strides_padding.onnx"));
// (1, 1, 4, 3)
auto expected_output = test::NDArray<float, 4>({{{{12.f, 27.f, 24.f},
{63.f, 108.f, 81.f},
{123.f, 198.f, 141.f},
{112.f, 177.f, 124.f}}}})
.get_vector();
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
}
TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_no_padding)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_with_strides_no_padding.onnx"));
// (1, 1, 3, 2)
auto expected_output =
test::NDArray<float, 4>({{{{54.f, 72.f}, {144.f, 162.f}, {234.f, 252.f}}}}).get_vector();
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
}
TEST(onnx_${BACKEND_NAME}, model_conv2d_strides_assymetric_padding)
{
// Convolution with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_with_strides_and_asymmetric_padding.onnx"));
// (1, 1, 4, 2)
auto expected_output =
test::NDArray<float, 4>({{{{21.f, 33.f}, {99.f, 117.f}, {189.f, 207.f}, {171.f, 183.f}}}})
.get_vector();
auto result = conv2d_execute(function);
EXPECT_EQ(expected_output, result.front());
}
TEST(onnx_${BACKEND_NAME}, model_average_pool_2d)
{
// Pooling with strides=2 and no padding
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/average_pool_2d.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
// (1, 1, 2, 2)
auto expected_output = test::NDArray<float, 4>({{{{2.5f, 4.5f}, {10.5f, 12.5f}}}}).get_vector();
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, outputs.front());
}
TEST(onnx_${BACKEND_NAME}, model_average_pool_2d_pads)
{
// Pooling with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/average_pool_2d_pads.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
// (1, 1, 3, 3)
auto expected_output =
test::NDArray<float, 4>({{{{0.f, 1.5f, 3.f}, {6.f, 7.5f, 9.f}, {12.f, 13.5f, 15.f}}}})
.get_vector();
Outputs outputs = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_EQ(expected_output, outputs.front());
}
TEST(onnx_${BACKEND_NAME}, model_max_pool_2d_pads)
{
// Pooling with strides=2 and padding=1
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/max_pool_2d_pads.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs;
inputs.push_back(test::NDArray<float, 4>({{{{0.f, 1.f, 2.f, 3.f},
{4.f, 5.f, 6.f, 7.f},
{8.f, 9.f, 10.f, 11.f},
{12.f, 13.f, 14.f, 15.f}}}})
.get_vector());
// (1, 1, 3, 3)
auto expected_output =
test::NDArray<float, 4>({{{{0.f, 2.f, 3.f}, {8.f, 10.f, 11.f}, {12.f, 14.f, 15.f}}}})
.get_vector();
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_EQ(expected_output, outputs.front());
}
TEST(onnx_${BACKEND_NAME}, model_batchnorm_default)
{
// Batch Normalization with default parameters
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/batchnorm_default.onnx"));
Inputs inputs;
// input data shape (1, 2, 1, 3)
inputs.push_back(
test::NDArray<float, 4>({{{{-1.f, 0.f, 1.f}}, {{2.f, 3.f, 4.f}}}}).get_vector());
// scale (3)
inputs.emplace_back(std::vector<float>{1.f, 1.5f});
// bias (3)
inputs.emplace_back(std::vector<float>{0.f, 1.f});
// mean (3)
inputs.emplace_back(std::vector<float>{0.f, 3.f});
// var (3)
inputs.emplace_back(std::vector<float>{1.f, 1.5f});
// shape (1, 2, 1, 3)
Outputs expected_outputs{test::NDArray<float, 4>{
{{{{-0.999995f, 0.f, 0.999995f}}, {{-0.22474074f, 1.f, 2.2247407f}}}}}
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_relu)
{
// Simple ReLU test
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/relu.onnx"));
Inputs inputs{{-1, -2, 0, 1, 2, 3}};
Outputs expected_outputs{{0, 0, 0, 1, 2, 3}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_sum)
{
// Simple Sum test
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sum.onnx"));
// input data shape (3, )
Inputs inputs;
inputs.emplace_back(std::vector<float>{3.f, 0.f, 2.f});
inputs.emplace_back(std::vector<float>{1.f, 3.f, 4.f});
inputs.emplace_back(std::vector<float>{2.f, 6.f, 6.f});
Outputs expected_outputs{{6.f, 9.f, 12.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_sum_one_input)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/sum_one_input.onnx"));
// input data shape (3, )
Inputs inputs{{3.f, 0.f, 2.f}};
Outputs expected_outputs{{3.f, 0.f, 2.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_min_two_inputs)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/min_two_inputs.onnx"));
// input data shape (3, )
Inputs inputs;
inputs.emplace_back(std::vector<float>{1.f, 2.f, 1.f});
inputs.emplace_back(std::vector<float>{1.f, 4.f, 4.f});
Outputs expected_outputs{{1.f, 2.f, 1.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_max)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/max.onnx"));
// input data shape (3, )
Inputs inputs;
inputs.emplace_back(std::vector<float>{3.f, 2.f, 1.f});
inputs.emplace_back(std::vector<float>{1.f, 4.f, 4.f});
inputs.emplace_back(std::vector<float>{2.f, 5.f, 3.f});
Outputs expected_outputs{{3.f, 5.f, 4.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_mean)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/mean.onnx"));
// input data shape (3, )
Inputs inputs;
inputs.emplace_back(std::vector<float>{3.f, 0.f, 2.f});
inputs.emplace_back(std::vector<float>{1.f, 3.f, 4.f});
inputs.emplace_back(std::vector<float>{2.f, 6.f, 6.f});
Outputs expected_outputs{{2.f, 3.f, 4.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_gemm_abc)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/gemm_abc.onnx"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 2>(
{{1, 2, 3, 4, 5, 6}, {7, 8, 9, 10, 11, 12}, {13, 14, 15, 16, 17, 18}})
.get_vector());
inputs.emplace_back(test::NDArray<float, 2>({{19, 20, 21, 22},
{23, 24, 25, 26},
{27, 28, 29, 30},
{31, 32, 33, 34},
{35, 36, 37, 38},
{39, 40, 41, 42}})
.get_vector());
inputs.emplace_back(
test::NDArray<float, 2>({{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}).get_vector());
Outputs expected_outputs{
test::NDArray<float, 2>(
{{340, 350.5, 361, 371.5}, {862, 890.5, 919, 947.5}, {1384, 1430.5, 1477, 1523.5}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_matmul)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/matmul.onnx"));
std::vector<std::vector<float>> inputs;
inputs.emplace_back(
test::NDArray<float, 2>({{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}}).get_vector());
inputs.emplace_back(
test::NDArray<float, 2>({{13, 14, 15}, {16, 17, 18}, {19, 20, 21}, {22, 23, 24}})
.get_vector());
Outputs expected_outputs{
test::NDArray<float, 2>({{190, 200, 210}, {470, 496, 522}, {750, 792, 834}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_softmax)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/softmax.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}, {16, 17, 18, 19, 20}},
{{21, 22, 23, 24, 25},
{26, 27, 28, 29, 30},
{31, 32, 33, 34, 35},
{36, 37, 38, 39, 40}},
{{41, 42, 43, 44, 45},
{46, 47, 48, 49, 50},
{51, 52, 53, 54, 55},
{56, 57, 58, 59, 60}}})
.get_vector());
auto expected_output =
test::NDArray<float, 3>(
{{{1.50461533e-26f, 4.08996852e-26f, 1.11176871e-25f, 3.02210068e-25f, 8.21492137e-25f},
{2.23304715e-24f, 6.07005148e-24f, 1.65001106e-23f, 4.48519509e-23f, 1.21920243e-22f},
{3.31413582e-22f, 9.00875516e-22f, 2.44883355e-21f, 6.65661973e-21f, 1.80945684e-20f},
{4.91861366e-20f,
1.33701781e-19f,
3.63439123e-19f,
9.87929963e-19f,
2.68547207e-18f}},
{{7.29986992e-18f, 1.98431037e-17f, 5.39391483e-17f, 1.46621807e-16f, 3.98559393e-16f},
{1.08339676e-15f, 2.94497771e-15f, 8.00527940e-15f, 2.17606055e-14f, 5.91514586e-14f},
{1.60790335e-13f, 4.37073446e-13f, 1.18808881e-12f, 3.22956021e-12f, 8.77885484e-12f},
{2.38634016e-11f,
6.48674509e-11f,
1.76328013e-10f,
4.79309234e-10f,
1.30289758e-09f}},
{{3.54164282e-09f, 9.62718331e-09f, 2.61693974e-08f, 7.11357975e-08f, 1.93367146e-07f},
{5.25626399e-07f, 1.42880069e-06f, 3.88388295e-06f, 1.05574884e-05f, 2.86982290e-05f},
{7.80098743e-05f, 2.12052824e-04f, 5.76419338e-04f, 1.56687021e-03f, 4.25919482e-03f},
{1.15776919e-02f,
3.14714295e-02f,
8.55482149e-02f,
2.32544158e-01f,
6.32120559e-01f}}})
.get_vector();
auto result_vectors = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
}
TEST(onnx_${BACKEND_NAME}, model_concat)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/concat.onnx"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 1>({1, 2}).get_vector());
inputs.emplace_back(test::NDArray<float, 1>({3, 4}).get_vector());
Outputs expected_outputs{test::NDArray<float, 1>({1, 2, 3, 4}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_flatten)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/flatten.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 4>({{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}}).get_vector());
Outputs expected_outputs{test::NDArray<float, 3>({{{1, 2, 3, 4}, {5, 6, 7, 8}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_sub)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sub.onnx"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>({{{1, 2, 3}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{4, 5, 7}}}).get_vector());
auto expected_output = test::NDArray<float, 3>({{{-3, -3, -4}}}).get_vector();
auto result_vectors = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
}
TEST(onnx_${BACKEND_NAME}, model_unsqueeze)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/unsqueeze.onnx"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>(
{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}})
.get_vector());
Outputs expected_output{
test::NDArray<float, 4>(
{{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_squeeze)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/squeeze_duplicate_axes.onnx"));
// {1, 4, 1, 1, 2}
Inputs inputs{test::NDArray<float, 5>(
{{{{{1.0f, 2.0f}}}, {{{3.0f, 4.0f}}}, {{{5.0f, 6.0f}}}, {{{7.0f, 8.0f}}}}})
.get_vector()};
// {4, 2}
Outputs expected_output{
test::NDArray<float, 2>({{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}, {7.0f, 8.0f}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_div)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/div.onnx"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>({{{1, 2, 3}}}).get_vector());
inputs.emplace_back(test::NDArray<float, 3>({{{1, 4, 12}}}).get_vector());
auto expected_output = test::NDArray<float, 3>({{{1, 0.5, 0.25}}}).get_vector();
auto result_vectors = execute(function, inputs, "${BACKEND_NAME}");
EXPECT_TRUE(test::all_close_f(expected_output, result_vectors.front()));
}
TEST(onnx_${BACKEND_NAME}, model_add_bcast)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/add_bcast.onnx"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>(
{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}})
.get_vector());
inputs.emplace_back(test::NDArray<float, 1>({1, 2, 3, 4, 5}).get_vector());
Outputs expected_output{
test::NDArray<float, 4>(
{{{{2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}},
{{2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}},
{{2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}, {2, 3, 4, 5, 6}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reshape_reduced_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_reduced_dims.onnx"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 12)
Outputs expected_outputs{
test::NDArray<float, 2>({{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
{12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reshape_reordered_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_reordered_dims.onnx"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (4, 2, 3)
Outputs expected_outputs{test::NDArray<float, 3>({{{0, 1, 2}, {3, 4, 5}},
{{6, 7, 8}, {9, 10, 11}},
{{12, 13, 14}, {15, 16, 17}},
{{18, 19, 20}, {21, 22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reshape_extended_dims)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_extended_dims.onnx"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (3, 2, 2, 2)
Outputs expected_outputs{test::NDArray<float, 4>({{{{0, 1}, {2, 3}}, {{4, 5}, {6, 7}}},
{{{8, 9}, {10, 11}}, {{12, 13}, {14, 15}}},
{{{16, 17}, {18, 19}}, {{20, 21}, {22, 23}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reshape_single_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_single_dim.onnx"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (24, )
Outputs expected_outputs{
test::NDArray<float, 1>(
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reshape_negative_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_negative_dim.onnx"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (6, 2, 2)
Outputs expected_outputs{test::NDArray<float, 3>({{{0, 1}, {2, 3}},
{{4, 5}, {6, 7}},
{{8, 9}, {10, 11}},
{{12, 13}, {14, 15}},
{{16, 17}, {18, 19}},
{{20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reshape_negative_with_zero_dim)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_negative_with_zero_dims.onnx"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 6, 2)
Outputs expected_outputs{
test::NDArray<float, 3>({{{0, 1}, {2, 3}, {4, 5}, {6, 7}, {8, 9}, {10, 11}},
{{12, 13}, {14, 15}, {16, 17}, {18, 19}, {20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reshape_output_shape_as_input)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reshape_output_shape_as_input.onnx"));
// input data shape (2, 3, 4)
Inputs inputs{test::NDArray<float, 3>({{{0, 1, 2, 3}, {4, 5, 6, 7}, {8, 9, 10, 11}},
{{12, 13, 14, 15}, {16, 17, 18, 19}, {20, 21, 22, 23}}})
.get_vector()};
// output data shape (2, 6, 2)
Outputs expected_outputs{
test::NDArray<float, 3>({{{0, 1}, {2, 3}, {4, 5}, {6, 7}, {8, 9}, {10, 11}},
{{12, 13}, {14, 15}, {16, 17}, {18, 19}, {20, 21}, {22, 23}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_log_sum)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_log_sum.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{2.77258872f}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_log_sum_exp)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_log_sum_exp.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{3.77258872f}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_l1)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_l1.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{16}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_l2)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_l2.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{4}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_max)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_max.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}, {13, 14, 15, 16}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{16}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_mean)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_mean.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{1}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_min)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_min.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 10, 11, 12}, {13, 14, 15, 16}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{1}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_prod)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_prod.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{1}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_sum)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_sum.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{16}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_reduce_sum_square)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/reduce_sum_square.onnx"));
// input data shape (1, 1, 4, 4)
Inputs inputs{
test::NDArray<float, 4>({{{{1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}, {1, 1, 1, 1}}}})
.get_vector()};
// output data shape (1,)
Outputs expected_outputs{test::NDArray<float, 4>({{{{16}}}}).get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_shape)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/shape.onnx"));
Inputs inputs;
inputs.emplace_back(test::NDArray<float, 3>(
{{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}},
{{1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}, {1, 1, 1, 1, 1}}})
.get_vector());
std::vector<std::vector<int64_t>> expected_output{{3, 4, 5}};
std::vector<std::vector<int64_t>> outputs =
execute<float, int64_t>(function, inputs, "${BACKEND_NAME}");
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_elu)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/elu.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{-9, -8, -7, -6, -5}, {-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector());
Outputs expected_output{test::NDArray<float, 3>({{{-1.999753180391830f,
-1.999329074744190f,
-1.998176236068890f,
-1.995042495646670f,
-1.986524106001830f},
{-1.963368722222530f,
-1.900425863264270f,
-1.729329433526770f,
-1.264241117657120f,
0},
{1, 2, 3, 4, 5},
{6, 7, 8, 9, 10}},
{{-1.963368722222530f,
-1.900425863264270f,
-1.729329433526770f,
-1.264241117657120f,
0},
{1, 2, 3, 4, 5},
{6, 7, 8, 9, 10},
{11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1},
{-1.264241117657120f,
-1.264241117657120f,
-1.264241117657120f,
-1.264241117657120f,
-1.264241117657120f},
{0, 0, 0, 0, 0},
{2, 2, 2, 2, 2}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_leaky_relu)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/leaky_relu.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{-9, -8, -7, -6, -5}, {-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector());
Outputs expected_output{test::NDArray<float, 3>({{{-0.9f, -0.8f, -0.7f, -0.6f, -0.5f},
{-0.4f, -0.3f, -0.2f, -0.1f, 0},
{1, 2, 3, 4, 5},
{6, 7, 8, 9, 10}},
{{-0.4f, -0.3f, -0.2f, -0.1f, 0},
{1, 2, 3, 4, 5},
{6, 7, 8, 9, 10},
{11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1},
{-0.1f, -0.1f, -0.1f, -0.1f, -0.1f},
{0, 0, 0, 0, 0},
{2, 2, 2, 2, 2}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, prelu)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/prelu.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{-9, -8, -7, -6, -5}, {-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector());
inputs.emplace_back(test::NDArray<float, 3>(
{{{1, 0, 1, 0, 1}, {0, 1, 0, 1, 0}, {1, 0, 1, 0, 1}, {0, 1, 0, 1, 0}},
{{0, 1, 0, 1, 0}, {1, 0, 1, 0, 1}, {0, 1, 0, 1, 0}, {1, 0, 1, 0, 1}},
{{1, 0, 1, 0, 1}, {0, 1, 0, 1, 0}, {1, 0, 1, 0, 1}, {0, 1, 0, 1, 0}}})
.get_vector());
Outputs expected_output{
test::NDArray<float, 3>(
{{{-9, 0, -7, 0, -5}, {0, -3, 0, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{0, -3, 0, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {0, -1, 0, -1, 0}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_selu)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/selu.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{-9, -8, -7, -6, -5}, {-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector());
Outputs expected_output{
test::NDArray<float, 3>(
{{{-5.99925954117548f,
-5.99798722423258f,
-5.99452870820667f,
-5.98512748694000f,
-5.95957231800549f},
{-5.89010616666759f, -5.70127758979282f, -5.18798830058032f, -3.79272335297135f, 0},
{3, 6, 9, 12, 15},
{18, 21, 24, 27, 30}},
{{-5.89010616666759f, -5.70127758979282f, -5.18798830058032f, -3.79272335297135f, 0},
{3, 6, 9, 12, 15},
{18, 21, 24, 27, 30},
{33, 36, 39, 42, 45}},
{{3, 3, 3, 3, 3},
{-3.79272335297135f,
-3.79272335297135f,
-3.79272335297135f,
-3.79272335297135f,
-3.79272335297135f},
{0, 0, 0, 0, 0},
{6, 6, 6, 6, 6}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_sigmoid)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sigmoid.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{-9, -8, -7, -6, -5}, {-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector());
Outputs expected_output{test::NDArray<float, 3>({{{0.00012339457598623f,
0.00033535013046648f,
0.00091105119440065f,
0.00247262315663477f,
0.00669285092428486f},
{0.01798620996209160f,
0.04742587317756680f,
0.119202922022118f,
0.268941421369995f,
0.5f},
{0.731058578630005f,
0.880797077977882f,
0.952574126822433f,
0.982013790037908f,
0.993307149075715f},
{0.997527376843365f,
0.999088948805599f,
0.999664649869534f,
0.999876605424014f,
0.999954602131298f}},
{{0.01798620996209160f,
0.04742587317756680f,
0.119202922022118f,
0.268941421369995f,
0.5f},
{0.731058578630005f,
0.880797077977882f,
0.952574126822433f,
0.982013790037908f,
0.993307149075715f},
{0.997527376843365f,
0.999088948805599f,
0.999664649869534f,
0.999876605424014f,
0.999954602131298f},
{0.999983298578152f,
0.999993855825398f,
0.999997739675702f,
0.999999168471972f,
0.999999694097773f}},
{{0.731058578630005f,
0.731058578630005f,
0.731058578630005f,
0.731058578630005f,
0.731058578630005f},
{0.268941421369995f,
0.268941421369995f,
0.268941421369995f,
0.268941421369995f,
0.268941421369995f},
{0.5f, 0.5f, 0.5f, 0.5f, 0.5f},
{0.880797077977882f,
0.880797077977882f,
0.880797077977882f,
0.880797077977882f,
0.880797077977882f}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_tanh)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/tanh.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{-9, -8, -7, -6, -5}, {-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector());
Outputs expected_output{test::NDArray<float, 3>({{{-0.999999969540041f,
-0.999999774929676f,
-0.999998336943945f,
-0.999987711650796f,
-0.999909204262595f},
{-0.999329299739067f,
-0.995054753686731f,
-0.964027580075817f,
-0.761594155955765f,
0},
{0.761594155955765f,
0.964027580075817f,
0.995054753686731f,
0.999329299739067f,
0.999909204262595f},
{0.999987711650796f,
0.999998336943945f,
0.999999774929676f,
0.999999969540041f,
0.999999995877693f}},
{{-0.999329299739067f,
-0.995054753686731f,
-0.964027580075817f,
-0.761594155955765f,
0},
{0.761594155955765f,
0.964027580075817f,
0.995054753686731f,
0.999329299739067f,
0.999909204262595f},
{0.999987711650796f,
0.999998336943945f,
0.999999774929676f,
0.999999969540041f,
0.999999995877693f},
{0.999999999442106f,
0.999999999924497f,
0.999999999989782f,
0.999999999998617f,
0.999999999999813f}},
{{0.761594155955765f,
0.761594155955765f,
0.761594155955765f,
0.761594155955765f,
0.761594155955765f},
{-0.761594155955765f,
-0.761594155955765f,
-0.761594155955765f,
-0.761594155955765f,
-0.761594155955765f},
{0, 0, 0, 0, 0},
{0.964027580075817f,
0.964027580075817f,
0.964027580075817f,
0.964027580075817f,
0.964027580075817f}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_thresholded_relu)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/thresholded_relu.onnx"));
Inputs inputs;
inputs.emplace_back(
test::NDArray<float, 3>(
{{{-9, -8, -7, -6, -5}, {-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{-4, -3, -2, -1, 0}, {1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{1, 1, 1, 1, 1}, {-1, -1, -1, -1, -1}, {0, 0, 0, 0, 0}, {2, 2, 2, 2, 2}}})
.get_vector());
Outputs expected_output{
test::NDArray<float, 3>(
{{{0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {0, 0, 3, 4, 5}, {6, 7, 8, 9, 10}},
{{0, 0, 0, 0, 0}, {0, 0, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15}},
{{0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}, {0, 0, 0, 0, 0}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_unsupported_op)
{
try
{
onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/unsupported_op.onnx"));
FAIL() << "Expected ngraph::ngraph_error";
}
catch (ngraph::ngraph_error const& err)
{
std::string what{err.what()};
EXPECT_NE(what.find("unknown operations"), std::string::npos);
EXPECT_NE(what.find("FakeOpName"), std::string::npos);
EXPECT_NE(what.find("AnotherFakeOpName"), std::string::npos);
}
catch (...)
{
FAIL() << "Expected ngraph::ngraph_error";
}
}
TEST(onnx_${BACKEND_NAME}, model_custom_op)
{
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/custom_operator.onnx"));
Inputs inputs{{1, 2, 3, 4}};
Outputs expected_outputs{{3, 6, 9, 12}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_custom_op_default_domain)
{
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/custom_operator_default_domain.onnx"));
Inputs inputs{{1, 2, 3, 4}};
Outputs expected_outputs{{3, 6, 9, 12}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_conv2d_dilation_assymetric_pads_strides)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv2d_dilation_assym_pads_strides.onnx"));
// "", // auto_pad
// vector<int64_t>{1, 1}, // dilations
// 1, // group
// vector<int64_t>{3, 3}, // kernel_shape
// vector<int64_t>{1, 1, 1, 2}, // pads
// vector<int64_t>{3, 1} // strides
Inputs inputs;
// {2, 1, 1, 1}
inputs.emplace_back(
test::NDArray<float, 4>({{{{-0.09103918075561523f}}}, {{{-0.32513630390167236f}}}})
.get_vector());
// {2, 1, 3, 3}
inputs.emplace_back(
test::NDArray<float, 4>(
{{{{0.4312484860420227f, -0.12559029459953308f, 0.44889551401138306f},
{-0.3100617825984955f, 0.13522827625274658f, -0.06791308522224426f},
{0.22671669721603394f, -0.17391827702522278f, -0.31299442052841187f}}},
{{{-0.31545522809028625f, 0.06560015678405762f, 0.2656586766242981f},
{0.41363757848739624f, 0.31231558322906494f, -0.376018226146698f},
{-0.005708813667297363f, 0.34922850131988525f, 0.45095211267471313f}}}})
.get_vector());
// {2, 2, 1, 2}
Outputs expected_output{
test::NDArray<float, 4>({{{{-0.012311071157455444f, 0.02822777070105076f}},
{{-0.028432954102754593f, -0.037657227367162704f}}},
{{{-0.04396762326359749f, 0.10081233829259872f}},
{{-0.10154513269662857f, -0.13448859751224518f}}}})
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_conv3d_bias)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv3d_bias.onnx"));
// "", // auto_pad
// vector<int64_t>{2, 2, 2}, // dilations
// 1, // group
// vector<int64_t>{2, 2, 2}, // kernel_shape
// vector<int64_t>{2, 2, 2, 2, 2, 2}, // pads
// vector<int64_t>{2, 2, 2} // strides
Inputs inputs;
// X: {2, 1, 4, 4, 4}
inputs.emplace_back(
std::vector<float>{0.46796226501464844f, -0.4613912105560303f, 0.33512794971466064f,
-0.4010460674762726f, 0.41722816228866577f, -0.048133403062820435f,
0.20415884256362915f, 0.03189706802368164f, -0.04779183864593506f,
-0.0795503556728363f, 0.4987630844116211f, 0.3506373167037964f,
0.48065757751464844f, 0.269855260848999f, -0.2463444471359253f,
0.19044137001037598f, -0.11830493807792664f, -0.2576887905597687f,
-0.33940935134887695f, -0.257951021194458f, -0.08279827237129211f,
0.3513314127922058f, -0.29122066497802734f, -0.43358397483825684f,
-0.13429927825927734f, 0.44032156467437744f, 0.05308258533477783f,
-0.3499870300292969f, -0.28474611043930054f, -0.44209951162338257f,
-0.07418054342269897f, -0.10919415950775146f, 0.2845439314842224f,
0.3498746156692505f, -0.19313520193099976f, 0.32609254121780396f,
0.4880145788192749f, 0.05574071407318115f, -0.46457427740097046f,
-0.02524462342262268f, -0.18780940771102905f, -0.14720159769058228f,
0.207585871219635f, 0.47157740592956543f, -0.05567386746406555f,
-0.49871665239334106f, 0.2274145483970642f, 0.4589425325393677f,
-0.4725189805030823f, -0.4358765780925751f, 0.2841453552246094f,
-0.27037882804870605f, 0.34227508306503296f, 0.33575427532196045f,
-0.19485199451446533f, -0.27679920196533203f, -0.4238079786300659f,
-0.4385119676589966f, 0.43724071979522705f, 0.3065117597579956f,
0.45696544647216797f, 0.05291992425918579f, -0.023618370294570923f,
-0.1860884726047516f, 0.08669537305831909f, 0.32541000843048096f,
0.1846179962158203f, -0.1984834372997284f, -0.2754465937614441f,
0.32004624605178833f, -0.34846532344818115f, 0.0999596118927002f,
-0.11374691128730774f, 0.21225297451019287f, -0.02315312623977661f,
0.1671370267868042f, 0.22319108247756958f, 0.03609824180603027f,
-0.1587022840976715f, 0.059984564781188965f, -0.03951650857925415f,
-0.4841443598270416f, 0.32919085025787354f, -0.23115816712379456f,
0.39441078901290894f, -0.3554944396018982f, -0.17022761702537537f,
-0.055081307888031006f, 0.15856128931045532f, -0.4183449149131775f,
-0.2474445104598999f, 0.03603637218475342f, -0.2836887538433075f,
0.4602506160736084f, 0.29092925786972046f, -0.199321448802948f,
0.380856454372406f, -0.13847029209136963f, -0.238397479057312f,
-0.1907123327255249f, -0.11061936616897583f, -0.08717870712280273f,
0.24449139833450317f, -0.14727482199668884f, 0.1437196135520935f,
0.3955056071281433f, -0.12538021802902222f, 0.11590522527694702f,
0.4598066806793213f, -0.30005723237991333f, -0.46578651666641235f,
-0.33955082297325134f, -0.2671887278556824f, 0.3611910939216614f,
-0.11423084139823914f, -0.08382436633110046f, -0.31819307804107666f,
0.14515334367752075f, 0.3157258629798889f, 0.33179205656051636f,
-0.2558857202529907f, 0.11888682842254639f, 0.12824326753616333f,
-0.33106181025505066f, 0.2549159526824951f, -0.46760573983192444f,
-0.11983257532119751f, 0.1834418773651123f});
// W: {2, 1, 2, 2, 2}
inputs.emplace_back(std::vector<float>{0.388077974319458f,
-0.16366064548492432f,
-0.42871910333633423f,
0.4276432394981384f,
0.21517693996429443f,
0.007908165454864502f,
0.33897721767425537f,
0.21843165159225464f,
0.34095364809036255f,
-0.17043980956077576f,
-0.013571739196777344f,
-0.26793742179870605f,
-0.34863436222076416f,
-0.2672275900840759f,
-0.36691007018089294f,
0.37296557426452637f});
// B: {2}
inputs.emplace_back(std::vector<float>{0.4310183525085449f, -0.4564093053340912f});
// {2, 2, 3, 3, 3}
Outputs expected_output{std::vector<float>{
0.5332361459732056f, 0.6628494262695312f, 0.544619083404541f, 0.4242798388004303f,
0.6271085739135742f, 0.6721994876861572f, 0.43064039945602417f, 0.4246789515018463f,
0.53834068775177f, 0.6932926177978516f, 0.42797625064849854f, 0.2218741625547409f,
0.29522019624710083f, 0.8329390287399292f, 0.37605351209640503f, 0.43735477328300476f,
0.2920728623867035f, 0.6692450046539307f, 0.5527016520500183f, 0.22643595933914185f,
0.5138190984725952f, 0.3041342794895172f, 0.7423423528671265f, 0.26707080006599426f,
0.4617553651332855f, 0.32416003942489624f, 0.511577844619751f, -0.28187549114227295f,
-0.5031181573867798f, -0.5793710947036743f, -0.5992864370346069f, -0.5055556893348694f,
-0.7562476396560669f, -0.44363799691200256f, -0.5730307102203369f, -0.6302952766418457f,
-0.4756688177585602f, -0.728988528251648f, -0.3900943398475647f, -0.6694478988647461f,
-0.38822290301322937f, -0.35774707794189453f, -0.39807581901550293f, -0.547709047794342f,
-0.35872578620910645f, -0.5326492786407471f, -0.40852290391921997f, -0.4537881314754486f,
-0.4545857608318329f, -0.379546195268631f, -0.5250767469406128f, -0.42439910769462585f,
-0.5558245182037354f, -0.38563215732574463f, 0.44995537400245667f, 0.5007325410842896f,
0.49359965324401855f, 0.40685802698135376f, 0.407518208026886f, 0.4628955125808716f,
0.4301188290119171f, 0.40635955333709717f, 0.4260363280773163f, 0.55128413438797f,
0.5498291254043579f, 0.27105778455734253f, 0.40259143710136414f, 0.5747092962265015f,
0.4187920391559601f, 0.4507707953453064f, 0.420598566532135f, 0.3950541913509369f,
0.593889057636261f, 0.16578882932662964f, 0.5332239270210266f, 0.43014785647392273f,
0.50260329246521f, 0.39225444197654724f, 0.4074971079826355f, 0.5073125958442688f,
0.3823610544204712f, -0.4240749180316925f, -0.41936254501342773f, -0.5241475105285645f,
-0.5220003724098206f, -0.502869725227356f, -0.5122783780097961f, -0.4260129928588867f,
-0.4105660617351532f, -0.4483373165130615f, -0.33759188652038574f, -0.735706090927124f,
-0.3714444637298584f, -0.4888814687728882f, -0.6191370487213135f, -0.2640320658683777f,
-0.47542816400527954f, -0.5078460574150085f, -0.4205915927886963f, -0.5584549903869629f,
-0.39770257472991943f, -0.45317384600639343f, -0.5598302483558655f, -0.2542789578437805f,
-0.5359901785850525f, -0.48090484738349915f, -0.38603779673576355f, -0.4991581439971924f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_matmul_vec_ten3d)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/matmul_vec_ten3d.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f});
inputs.emplace_back(
test::NDArray<float, 3>{{{0.f}, {1.f}}, {{2.f}, {3.f}}, {{4.f}, {5.f}}}.get_vector());
Outputs expected_output{test::NDArray<float, 2>{{1.f}, {3.f}, {5.f}}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_softplus)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/softplus.onnx"));
// -1.0f, 0, 1.0f, 10.f, normal input values for activation
// 100.0f, -100.0f, 1000.0f, -1000.0f, input values that leads to exp() overflow
// FLT_MIN, FLT_MIN / 16, -FLT_MIN / 16, min, denorm, -denorm
// FLT_MAX, -FLT_MAX, max, -max;
Inputs inputs{std::vector<float>{-1.0f,
0,
1.0f,
10.f,
100.0f,
-100.0f,
1000.0f,
-1000.0f,
FLT_MIN,
FLT_MIN / 16,
-FLT_MIN / 16,
FLT_MAX,
-FLT_MAX}};
std::vector<float>& input = inputs.back();
std::vector<float> output;
auto softplus_impl = [](float x) -> float {
if (x > 0)
{
return x + std::log(std::exp(-x) + 1);
}
else
{
return std::log(std::exp(x) + 1);
}
};
std::transform(std::begin(input), std::end(input), std::back_inserter(output), softplus_impl);
Outputs expected_output{output};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_softplus_infinity)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/softplus.onnx"));
Inputs inputs{std::vector<float>{std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity(),
std::numeric_limits<float>::infinity()}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
for (float v : outputs.front())
{
EXPECT_TRUE(std::isinf(v));
}
}
TEST(onnx_${BACKEND_NAME}, model_sum_opset8)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/sum_opset8.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{1.0f, 2.0f, 3.0f});
inputs.emplace_back(test::NDArray<float, 2>{{10.0f}, {20.0f}, {30.0f}}.get_vector());
inputs.emplace_back(test::NDArray<float, 3>{{{100.0f}}, {{200.0f}}, {{300.0f}}}.get_vector());
Outputs expected_output{test::NDArray<float, 3>{
{{111.0f, 112.0f, 113.0f}, {121.0f, 122.0f, 123.0f}, {131.0f, 132.0f, 133.0f}},
{{211.0f, 212.0f, 213.0f}, {221.0f, 222.0f, 223.0f}, {231.0f, 232.0f, 233.0f}},
{{311.0f, 312.0f, 313.0f}, {321.0f, 322.0f, 323.0f}, {331.0f, 332.0f, 333.0f}}}
.get_vector()};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_conv_transpose_w_groups)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/conv_transpose_w_groups.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.0f});
Outputs expected_output{
std::vector<float>{28.f, 34.f, 252.f, 274.f, 732.f, 770.f, 1468.f, 1522.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_argmax_int32)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/argmax_int32.onnx"));
std::vector<std::vector<std::int32_t>> inputs{
std::vector<std::int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}};
std::vector<std::vector<std::int64_t>> expected_output{
std::vector<std::int64_t>{1, 1, 1, 1, 1, 1}};
std::vector<std::vector<std::int64_t>> outputs{
execute<std::int32_t, std::int64_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_argmin_int32)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/argmin_int32.onnx"));
std::vector<std::vector<std::int32_t>> inputs{
std::vector<std::int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}};
std::vector<std::vector<std::int64_t>> expected_output{std::vector<std::int64_t>{0, 0, 0, 0}};
std::vector<std::vector<std::int64_t>> outputs{
execute<std::int32_t, std::int64_t>(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, is_op_supported)
{
// Simple case
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 1, "ai.onnx"));
// With fallback
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 100, "ai.onnx"));
// Different opset versions
EXPECT_TRUE(onnx_import::is_operator_supported("Add", 1, "ai.onnx"));
EXPECT_TRUE(onnx_import::is_operator_supported("Add", 7, "ai.onnx"));
// Default domain name
EXPECT_TRUE(onnx_import::is_operator_supported("Sum", 1));
// Unregistered operator
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 1));
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 1, "ai.onnx"));
EXPECT_FALSE(onnx_import::is_operator_supported("DummyOp", 10, "ai.onnx"));
// Operator with bad domain name
EXPECT_FALSE(onnx_import::is_operator_supported("Sum", 1, "bad.domain"));
// Registered custom operator
onnx_import::register_operator(
"AddQ", 1, "com.intel.ai", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
EXPECT_TRUE(onnx_import::is_operator_supported("AddQ", 1, "com.intel.ai"));
}
TEST(onnx_${BACKEND_NAME}, model_depth_to_space)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/depth_to_space.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
Outputs expected_output{std::vector<float>{
0.f, 4.f, 1.f, 5.f, 8.f, 12.f, 9.f, 13.f, 2.f, 6.f, 3.f, 7.f, 10.f, 14.f, 11.f, 15.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_depth_to_space_chw)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/depth_to_space_chw.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{
0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f});
Outputs expected_output{std::vector<float>{
0.f, 4.f, 1.f, 5.f, 8.f, 12.f, 9.f, 13.f, 2.f, 6.f, 3.f, 7.f, 10.f, 14.f, 11.f, 15.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_depth_to_space_bad_blocksize)
{
// This model fails to import since the depth channel length must be a multiple of the
// `blocksize` attribute value.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/depth_to_space_bad_blocksize.onnx")),
std::runtime_error);
}
TEST(onnx_${BACKEND_NAME}, model_depth_to_space_no_blocksize)
{
// This model fails to import since it lacks of required parameter `blocksize`.
EXPECT_THROW(onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/depth_to_space_no_blocksize.onnx")),
std::runtime_error);
}
TEST(onnx_${BACKEND_NAME}, model_space_to_depth)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/space_to_depth.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
Outputs expected_output{std::vector<float>{
0.f, 2.f, 8.f, 10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f, 11.f, 17.f, 19.f, 25.f, 27.f,
4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f,
}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_space_to_depth_chw)
{
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/space_to_depth_chw.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f, 12.f, 13.f, 14.f, 15.f,
16.f, 17.f, 18.f, 19.f, 20.f, 21.f, 22.f, 23.f,
24.f, 25.f, 26.f, 27.f, 28.f, 29.f, 30.f, 31.f});
Outputs expected_output{std::vector<float>{
0.f, 2.f, 8.f, 10.f, 16.f, 18.f, 24.f, 26.f, 1.f, 3.f, 9.f, 11.f, 17.f, 19.f, 25.f, 27.f,
4.f, 6.f, 12.f, 14.f, 20.f, 22.f, 28.f, 30.f, 5.f, 7.f, 13.f, 15.f, 21.f, 23.f, 29.f, 31.f,
}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_space_to_depth_bad_blocksize)
{
// This model fails to import since the depth channel length must be a multiple of the
// `blocksize` attribute value.
EXPECT_THROW(onnx_import::import_onnx_model(file_util::path_join(
SERIALIZED_ZOO, "onnx/space_to_depth_bad_blocksize.onnx")),
std::runtime_error);
}
TEST(onnx_${BACKEND_NAME}, model_space_to_depth_no_blocksize)
{
// This model fails to import since it lacks of required `blocksize` attribute.
EXPECT_THROW(onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/space_to_depth_no_blocksize.onnx")),
std::runtime_error);
}
TEST(onnx_${BACKEND_NAME}, model_missing_op_domain)
{
onnx_import::register_operator(
"CustomAdd", 1, "custom.op", [](const onnx_import::Node& node) -> NodeVector {
NodeVector ng_inputs{node.get_ng_inputs()};
return {std::make_shared<ngraph::op::Add>(ng_inputs.at(0), ng_inputs.at(1))};
});
EXPECT_TRUE(onnx_import::is_operator_supported("CustomAdd", 1, "custom.op"));
auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/missing_op_domain.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
inputs.emplace_back(std::vector<float>{0.f, 1.f, 2.f, 3.f});
Outputs expected_output{std::vector<float>{0.f, 2.f, 4.f, 6.f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_output.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_top_k)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/top_k.onnx"));
Inputs inputs;
inputs.emplace_back(std::vector<float>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11});
std::vector<float> expected_values_output{3, 2, 1, 7, 6, 5, 11, 10, 9};
std::vector<std::int64_t> expected_indices_output{3, 2, 1, 3, 2, 1, 3, 2, 1};
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> result_tensors =
prepare_and_run(function, inputs, "${BACKEND_NAME}");
std::vector<float> values_output = read_vector<float>(result_tensors.at(0));
std::vector<std::int64_t> indices_output = read_vector<std::int64_t>(result_tensors.at(1));
EXPECT_TRUE(test::all_close_f(expected_values_output, values_output));
EXPECT_TRUE(test::all_close(expected_indices_output, indices_output));
}
TEST(onnx_${BACKEND_NAME}, model_sinh)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sinh.onnx"));
Inputs inputs{std::vector<float>{-1.0f, 0.0f, 1.0f}};
Outputs expected_outputs{std::vector<float>{-1.1752012f, 0.f, 1.1752012f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}
TEST(onnx_${BACKEND_NAME}, model_cosh)
{
auto function =
onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/cosh.onnx"));
Inputs inputs{std::vector<float>{-1.0f, 0.0f, 1.0f}};
Outputs expected_outputs{std::vector<float>{1.54308069f, 1.f, 1.54308069f}};
Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
}