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Diffstat (limited to 'third_party/libwebrtc/modules/audio_processing/agc2/rnn_vad/rnn_gru_unittest.cc')
| -rw-r--r-- | third_party/libwebrtc/modules/audio_processing/agc2/rnn_vad/rnn_gru_unittest.cc | 186 |
1 files changed, 186 insertions, 0 deletions
diff --git a/third_party/libwebrtc/modules/audio_processing/agc2/rnn_vad/rnn_gru_unittest.cc b/third_party/libwebrtc/modules/audio_processing/agc2/rnn_vad/rnn_gru_unittest.cc new file mode 100644 index 0000000000..88ae72803a --- /dev/null +++ b/third_party/libwebrtc/modules/audio_processing/agc2/rnn_vad/rnn_gru_unittest.cc @@ -0,0 +1,186 @@ +/* + * 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_gru.h" + +#include <array> +#include <memory> +#include <vector> + +#include "api/array_view.h" +#include "modules/audio_processing/agc2/rnn_vad/test_utils.h" +#include "modules/audio_processing/test/performance_timer.h" +#include "rtc_base/checks.h" +#include "rtc_base/logging.h" +#include "test/gtest.h" +#include "third_party/rnnoise/src/rnn_vad_weights.h" + +namespace webrtc { +namespace rnn_vad { +namespace { + +void TestGatedRecurrentLayer( + GatedRecurrentLayer& gru, + rtc::ArrayView<const float> input_sequence, + rtc::ArrayView<const float> expected_output_sequence) { + const int input_sequence_length = rtc::CheckedDivExact( + rtc::dchecked_cast<int>(input_sequence.size()), gru.input_size()); + const int output_sequence_length = rtc::CheckedDivExact( + rtc::dchecked_cast<int>(expected_output_sequence.size()), gru.size()); + ASSERT_EQ(input_sequence_length, output_sequence_length) + << "The test data length is invalid."; + // Feed the GRU layer and check the output at every step. + gru.Reset(); + for (int i = 0; i < input_sequence_length; ++i) { + SCOPED_TRACE(i); + gru.ComputeOutput( + input_sequence.subview(i * gru.input_size(), gru.input_size())); + const auto expected_output = + expected_output_sequence.subview(i * gru.size(), gru.size()); + ExpectNearAbsolute(expected_output, gru, 3e-6f); + } +} + +// Gated recurrent units layer test data. +constexpr int kGruInputSize = 5; +constexpr int kGruOutputSize = 4; +constexpr std::array<int8_t, 12> kGruBias = {96, -99, -81, -114, 49, 119, + -118, 68, -76, 91, 121, 125}; +constexpr std::array<int8_t, 60> kGruWeights = { + // Input 0. + 124, 9, 1, 116, // Update. + -66, -21, -118, -110, // Reset. + 104, 75, -23, -51, // Output. + // Input 1. + -72, -111, 47, 93, // Update. + 77, -98, 41, -8, // Reset. + 40, -23, -43, -107, // Output. + // Input 2. + 9, -73, 30, -32, // Update. + -2, 64, -26, 91, // Reset. + -48, -24, -28, -104, // Output. + // Input 3. + 74, -46, 116, 15, // Update. + 32, 52, -126, -38, // Reset. + -121, 12, -16, 110, // Output. + // Input 4. + -95, 66, -103, -35, // Update. + -38, 3, -126, -61, // Reset. + 28, 98, -117, -43 // Output. +}; +constexpr std::array<int8_t, 48> kGruRecurrentWeights = { + // Output 0. + -3, 87, 50, 51, // Update. + -22, 27, -39, 62, // Reset. + 31, -83, -52, -48, // Output. + // Output 1. + -6, 83, -19, 104, // Update. + 105, 48, 23, 68, // Reset. + 23, 40, 7, -120, // Output. + // Output 2. + 64, -62, 117, 85, // Update. + 51, -43, 54, -105, // Reset. + 120, 56, -128, -107, // Output. + // Output 3. + 39, 50, -17, -47, // Update. + -117, 14, 108, 12, // Reset. + -7, -72, 103, -87, // Output. +}; +constexpr std::array<float, 20> kGruInputSequence = { + 0.89395463f, 0.93224651f, 0.55788344f, 0.32341808f, 0.93355054f, + 0.13475326f, 0.97370994f, 0.14253306f, 0.93710381f, 0.76093364f, + 0.65780413f, 0.41657975f, 0.49403164f, 0.46843281f, 0.75138855f, + 0.24517593f, 0.47657707f, 0.57064998f, 0.435184f, 0.19319285f}; +constexpr std::array<float, 16> kGruExpectedOutputSequence = { + 0.0239123f, 0.5773077f, 0.f, 0.f, + 0.01282811f, 0.64330572f, 0.f, 0.04863098f, + 0.00781069f, 0.75267816f, 0.f, 0.02579715f, + 0.00471378f, 0.59162533f, 0.11087593f, 0.01334511f}; + +class RnnGruParametrization + : public ::testing::TestWithParam<AvailableCpuFeatures> {}; + +// Checks that the output of a GRU layer is within tolerance given test input +// data. +TEST_P(RnnGruParametrization, CheckGatedRecurrentLayer) { + GatedRecurrentLayer gru(kGruInputSize, kGruOutputSize, kGruBias, kGruWeights, + kGruRecurrentWeights, + /*cpu_features=*/GetParam(), + /*layer_name=*/"GRU"); + TestGatedRecurrentLayer(gru, kGruInputSequence, kGruExpectedOutputSequence); +} + +TEST_P(RnnGruParametrization, DISABLED_BenchmarkGatedRecurrentLayer) { + // Prefetch test data. + std::unique_ptr<FileReader> reader = CreateGruInputReader(); + std::vector<float> gru_input_sequence(reader->size()); + reader->ReadChunk(gru_input_sequence); + + using ::rnnoise::kHiddenGruBias; + using ::rnnoise::kHiddenGruRecurrentWeights; + using ::rnnoise::kHiddenGruWeights; + using ::rnnoise::kHiddenLayerOutputSize; + using ::rnnoise::kInputLayerOutputSize; + + GatedRecurrentLayer gru(kInputLayerOutputSize, kHiddenLayerOutputSize, + kHiddenGruBias, kHiddenGruWeights, + kHiddenGruRecurrentWeights, + /*cpu_features=*/GetParam(), + /*layer_name=*/"GRU"); + + rtc::ArrayView<const float> input_sequence(gru_input_sequence); + ASSERT_EQ(input_sequence.size() % kInputLayerOutputSize, + static_cast<size_t>(0)); + const int input_sequence_length = + input_sequence.size() / kInputLayerOutputSize; + + constexpr int kNumTests = 100; + ::webrtc::test::PerformanceTimer perf_timer(kNumTests); + for (int k = 0; k < kNumTests; ++k) { + perf_timer.StartTimer(); + for (int i = 0; i < input_sequence_length; ++i) { + gru.ComputeOutput( + input_sequence.subview(i * gru.input_size(), gru.input_size())); + } + perf_timer.StopTimer(); + } + RTC_LOG(LS_INFO) << (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, + RnnGruParametrization, + ::testing::ValuesIn(GetCpuFeaturesToTest()), + [](const ::testing::TestParamInfo<AvailableCpuFeatures>& info) { + return info.param.ToString(); + }); + +} // namespace +} // namespace rnn_vad +} // namespace webrtc |