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/*
* Copyright (c) 2020 The WebRTC project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "modules/audio_processing/agc2/rnn_vad/rnn_fc.h"
#include <array>
#include <vector>
#include "api/array_view.h"
#include "modules/audio_processing/agc2/cpu_features.h"
#include "modules/audio_processing/agc2/rnn_vad/test_utils.h"
#include "modules/audio_processing/test/performance_timer.h"
#include "rtc_base/logging.h"
#include "rtc_base/system/arch.h"
#include "test/gtest.h"
#include "third_party/rnnoise/src/rnn_vad_weights.h"
namespace webrtc {
namespace rnn_vad {
namespace {
using ::rnnoise::kInputDenseBias;
using ::rnnoise::kInputDenseWeights;
using ::rnnoise::kInputLayerInputSize;
using ::rnnoise::kInputLayerOutputSize;
// Fully connected layer test data.
constexpr std::array<float, 42> kFullyConnectedInputVector = {
-1.00131f, -0.627069f, -7.81097f, 7.86285f, -2.87145f, 3.32365f,
-0.653161f, 0.529839f, -0.425307f, 0.25583f, 0.235094f, 0.230527f,
-0.144687f, 0.182785f, 0.57102f, 0.125039f, 0.479482f, -0.0255439f,
-0.0073141f, -0.147346f, -0.217106f, -0.0846906f, -8.34943f, 3.09065f,
1.42628f, -0.85235f, -0.220207f, -0.811163f, 2.09032f, -2.01425f,
-0.690268f, -0.925327f, -0.541354f, 0.58455f, -0.606726f, -0.0372358f,
0.565991f, 0.435854f, 0.420812f, 0.162198f, -2.13f, 10.0089f};
constexpr std::array<float, 24> kFullyConnectedExpectedOutput = {
-0.623293f, -0.988299f, 0.999378f, 0.967168f, 0.103087f, -0.978545f,
-0.856347f, 0.346675f, 1.f, -0.717442f, -0.544176f, 0.960363f,
0.983443f, 0.999991f, -0.824335f, 0.984742f, 0.990208f, 0.938179f,
0.875092f, 0.999846f, 0.997707f, -0.999382f, 0.973153f, -0.966605f};
class RnnFcParametrization
: public ::testing::TestWithParam<AvailableCpuFeatures> {};
// Checks that the output of a fully connected layer is within tolerance given
// test input data.
TEST_P(RnnFcParametrization, CheckFullyConnectedLayerOutput) {
FullyConnectedLayer fc(kInputLayerInputSize, kInputLayerOutputSize,
kInputDenseBias, kInputDenseWeights,
ActivationFunction::kTansigApproximated,
/*cpu_features=*/GetParam(),
/*layer_name=*/"FC");
fc.ComputeOutput(kFullyConnectedInputVector);
ExpectNearAbsolute(kFullyConnectedExpectedOutput, fc, 1e-5f);
}
TEST_P(RnnFcParametrization, DISABLED_BenchmarkFullyConnectedLayer) {
const AvailableCpuFeatures cpu_features = GetParam();
FullyConnectedLayer fc(kInputLayerInputSize, kInputLayerOutputSize,
kInputDenseBias, kInputDenseWeights,
ActivationFunction::kTansigApproximated, cpu_features,
/*layer_name=*/"FC");
constexpr int kNumTests = 10000;
::webrtc::test::PerformanceTimer perf_timer(kNumTests);
for (int k = 0; k < kNumTests; ++k) {
perf_timer.StartTimer();
fc.ComputeOutput(kFullyConnectedInputVector);
perf_timer.StopTimer();
}
RTC_LOG(LS_INFO) << "CPU features: " << cpu_features.ToString() << " | "
<< (perf_timer.GetDurationAverage() / 1000) << " +/- "
<< (perf_timer.GetDurationStandardDeviation() / 1000)
<< " ms";
}
// Finds the relevant CPU features combinations to test.
std::vector<AvailableCpuFeatures> GetCpuFeaturesToTest() {
std::vector<AvailableCpuFeatures> v;
v.push_back(NoAvailableCpuFeatures());
AvailableCpuFeatures available = GetAvailableCpuFeatures();
if (available.sse2) {
v.push_back({/*sse2=*/true, /*avx2=*/false, /*neon=*/false});
}
if (available.avx2) {
v.push_back({/*sse2=*/false, /*avx2=*/true, /*neon=*/false});
}
if (available.neon) {
v.push_back({/*sse2=*/false, /*avx2=*/false, /*neon=*/true});
}
return v;
}
INSTANTIATE_TEST_SUITE_P(
RnnVadTest,
RnnFcParametrization,
::testing::ValuesIn(GetCpuFeaturesToTest()),
[](const ::testing::TestParamInfo<AvailableCpuFeatures>& info) {
return info.param.ToString();
});
} // namespace
} // namespace rnn_vad
} // namespace webrtc