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055eba4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | // Copyright 2024 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// 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 "compression/distortion.h"
#include <stdio.h>
#include "compression/test_util.h"
#include "hwy/nanobenchmark.h"
#include "hwy/tests/hwy_gtest.h"
#include "hwy/tests/test_util-inl.h" // HWY_ASSERT_EQ
namespace gcpp {
namespace {
#if !HWY_TEST_STANDALONE
class DistortionTest : public testing::Test {};
#endif
TEST(DistortionTest, TestCascadedSummation) {
CascadedSummation<double> cs;
// Example from Priest92. Exact sum is 2.
const double kHuge = 9007199254740992.0 * hwy::Unpredictable1(); // 2^53
const double kNeg = -4503599627370495.0 * hwy::Unpredictable1(); // -(2^52-1)
const double kIn[6] = {kHuge, kHuge - 2.0, kNeg, kNeg, kNeg, kNeg};
for (double in : kIn) {
cs.Notify(in);
}
HWY_ASSERT_EQ(2.0, cs.Total());
}
// Number of exact and rounded-to-zero matches expectations.
TEST(DistortionTest, TestCounts) {
// Arbitrary positive/negative original, zero distorted.
DistortionStats stats;
for (size_t i = 1; i < 10; ++i) {
stats.Notify(i / 100.0f, 0.0f);
stats.Notify(i / -100.0f, 0.0f);
}
HWY_ASSERT(stats.NumExact() == 0);
HWY_ASSERT(stats.NumRoundedToZero() == 18);
// Add some exact (same):
size_t num_exact = 0;
for (float x = 0.0f; x <= 1.5f; x += 0.25f) {
stats.Notify(x, x);
stats.Notify(-x, -x);
num_exact += 2;
}
HWY_ASSERT_EQ(num_exact, stats.NumExact());
HWY_ASSERT(stats.NumRoundedToZero() == 18); // unchanged
}
// Few large differences are diluted in SNR but not WeightedAverageL1.
TEST(DistortionTest, TestDilution) {
DistortionStats stats;
for (size_t i = 0; i < 100; ++i) {
stats.Notify(0.998f, 0.999f); // small
}
HWY_ASSERT(IsInside(900.0, 1000.0, stats.GeomeanValueDivL1()));
// All-equal WeightedSum is exact.
HWY_ASSERT(IsNear(0.001, stats.WeightedAverageL1()));
// Now add a large difference:
stats.Notify(1.875f - 0.0625f, 1.875f); // max magnitude, 3-bit mantissa
// .. WeightedAverageL1 is closer to it.
HWY_ASSERT(IsInside(0.020, 0.025, stats.WeightedAverageL1()));
// Add a small and large difference:
stats.Notify((1.75f - 0.125f) / 1024, 1.75f / 1024); // small, 2-bit mantissa
stats.Notify(-1.875f + 0.0625f, -1.875f); // larger negative
// .. SNR is still barely affected.
HWY_ASSERT(IsInside(890.0, 900.0, stats.GeomeanValueDivL1()));
// .. WeightedAverageL1 is higher after another large error.
HWY_ASSERT(IsInside(0.030, 0.035, stats.WeightedAverageL1()));
// With these inputs, none are exact nor round to zero.
HWY_ASSERT(stats.NumExact() == 0);
HWY_ASSERT(stats.NumRoundedToZero() == 0);
HWY_ASSERT_EQ(0.0, stats.SumL1Rounded());
HWY_ASSERT(IsInside(0.220, 0.23, stats.SumL1()));
}
} // namespace
} // namespace gcpp
HWY_TEST_MAIN();
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