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Add a test case for ml_commmon.h (#1947)

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Changming Sun 2019-09-27 20:14:27 -07:00 committed by GitHub
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commit e4b8494eaa
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onnxruntime/test/providers/cpu/ml/write_scores_test.cc Normal file
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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include "gtest/gtest.h"
#include "core/framework/tensor.h"
#include "core/providers/cpu/ml/ml_common.h"
#include "test/framework/dummy_allocator.h"
#include <random>
using namespace onnxruntime;
using namespace onnxruntime::ml;
class WriteScores : public ::testing::Test {
protected:
std::random_device r;
std::default_random_engine rd{r()};
};
TEST_F(WriteScores, multiple_scores_transform_none) {
POST_EVAL_TRANSFORM trans[] = {POST_EVAL_TRANSFORM::NONE,
POST_EVAL_TRANSFORM::LOGISTIC,
POST_EVAL_TRANSFORM::SOFTMAX,
POST_EVAL_TRANSFORM::SOFTMAX_ZERO,
POST_EVAL_TRANSFORM::PROBIT};
for (POST_EVAL_TRANSFORM tran : trans) {
std::vector<float> v1(100);
std::uniform_real_distribution<float> uniform_dist(-1, 1);
std::generate_n(v1.data(), v1.size(), [&]() -> float { return uniform_dist(rd); });
std::vector<float> v2 = v1;
auto alloc = std::make_shared<test::DummyAllocator>();
Tensor t(DataTypeImpl::GetType<float>(), {static_cast<int64_t>(v1.size())}, alloc);
if (tran == POST_EVAL_TRANSFORM::SOFTMAX_ZERO) {
//random set one number as zero
v1[3] = 0;
}
write_scores<float>(v1, tran, 0, &t, -1);
const float* output_data = t.Data<float>();
//verify the result
if (tran == POST_EVAL_TRANSFORM::NONE) {
for (size_t i = 0; i != v2.size(); ++i) {
EXPECT_FLOAT_EQ(v1[i], output_data[i]);
}
} else if (tran == POST_EVAL_TRANSFORM::SOFTMAX || tran == POST_EVAL_TRANSFORM::SOFTMAX_ZERO) {
//sum of the output values should be near to 1
double sum = 0;
for (size_t i = 0; i != v1.size(); ++i) {
sum += output_data[i];
}
EXPECT_NEAR(sum, 1, 1e-6);
if (tran == POST_EVAL_TRANSFORM::SOFTMAX_ZERO) {
EXPECT_FLOAT_EQ(output_data[3], 0);
}
}
}
}
TEST_F(WriteScores, single_score_transform_none) {
std::vector<float> v1;
std::uniform_real_distribution<float> uniform_dist(-100, 100);
v1.push_back(uniform_dist(rd));
std::vector<float> v2 = v1;
auto alloc = std::make_shared<test::DummyAllocator>();
Tensor t(DataTypeImpl::GetType<float>(), {static_cast<int64_t>(v1.size())}, alloc);
write_scores<float>(v1, POST_EVAL_TRANSFORM::NONE, 0, &t, -1);
const float* output_data = t.Data<float>();
for (size_t i = 0; i != v2.size(); ++i) {
EXPECT_FLOAT_EQ(v1[i], output_data[i]);
}
}
TEST_F(WriteScores, single_score_transform_none_add_second_class) {
for (int i = 0; i != 4; ++i) {
std::vector<float> v1;
std::uniform_real_distribution<float> uniform_dist(-100, 100);
v1.push_back(uniform_dist(rd));
std::vector<float> v2 = v1;
auto alloc = std::make_shared<test::DummyAllocator>();
Tensor t(DataTypeImpl::GetType<float>(), {2}, alloc);
write_scores<float>(v1, POST_EVAL_TRANSFORM::NONE, 0, &t, i);
const float* output_data = t.Data<float>();
EXPECT_NEAR(output_data[1] + output_data[0], i == 0 || i == 1 ? 1 : 0, 1e-6);
if (i == 0 || i == 1) {
EXPECT_FLOAT_EQ(v2[0], output_data[1]);
EXPECT_FLOAT_EQ(1 - v2[0], output_data[0]);
}
}
}