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RNN VAD: FC and GRU layers implicit conversion to ArrayView

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Plus a few minor code readability improvements.

Bug: webrtc:10480
Change-Id: I590d8e203b1d05959a8c15373841e37abe83237e
Reviewed-on: https://webrtc-review.googlesource.com/c/src/+/195334
Commit-Queue: Alessio Bazzica <alessiob@webrtc.org>
Reviewed-by: Karl Wiberg <kwiberg@webrtc.org>
Cr-Commit-Position: refs/heads/master@{#32764}
This commit is contained in:
Alessio Bazzica
2020-12-03 16:54:38 +01:00
committed by Commit Bot
parent 40c3ea5c71
commit 812dc072c6
9 changed files with 98 additions and 103 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc
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@ -39,30 +39,20 @@ constexpr std::array<float, kFeatureVectorSize> kFeatures = {
-0.690268f, -0.925327f, -0.541354f, 0.58455f, -0.606726f, -0.0372358f,
0.565991f, 0.435854f, 0.420812f, 0.162198f, -2.13f, 10.0089f};
void WarmUpRnnVad(RnnBasedVad& rnn_vad) {
void WarmUpRnnVad(RnnVad& rnn_vad) {
for (int i = 0; i < 10; ++i) {
rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
}
}
void TestFullyConnectedLayer(FullyConnectedLayer* fc,
rtc::ArrayView<const float> input_vector,
rtc::ArrayView<const float> expected_output) {
RTC_CHECK(fc);
fc->ComputeOutput(input_vector);
ExpectNearAbsolute(expected_output, fc->GetOutput(), 1e-5f);
}
void TestGatedRecurrentLayer(
GatedRecurrentLayer& gru,
rtc::ArrayView<const float> input_sequence,
rtc::ArrayView<const float> expected_output_sequence) {
auto gru_output_view = gru.GetOutput();
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.output_size());
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.
@ -71,9 +61,9 @@ void TestGatedRecurrentLayer(
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.output_size(), gru.output_size());
ExpectNearAbsolute(expected_output, gru_output_view, 3e-6f);
const auto expected_output =
expected_output_sequence.subview(i * gru.size(), gru.size());
ExpectNearAbsolute(expected_output, gru, 3e-6f);
}
}
@ -190,8 +180,8 @@ TEST_P(RnnParametrization, CheckFullyConnectedLayerOutput) {
rnnoise::kInputLayerInputSize, rnnoise::kInputLayerOutputSize,
rnnoise::kInputDenseBias, rnnoise::kInputDenseWeights,
rnnoise::TansigApproximated, /*cpu_features=*/GetParam());
TestFullyConnectedLayer(&fc, kFullyConnectedInputVector,
kFullyConnectedExpectedOutput);
fc.ComputeOutput(kFullyConnectedInputVector);
ExpectNearAbsolute(kFullyConnectedExpectedOutput, fc, 1e-5f);
}
TEST_P(RnnParametrization, DISABLED_BenchmarkFullyConnectedLayer) {
@ -237,7 +227,7 @@ INSTANTIATE_TEST_SUITE_P(
// Checks that the speech probability is zero with silence.
TEST(RnnVadTest, CheckZeroProbabilityWithSilence) {
RnnBasedVad rnn_vad(GetAvailableCpuFeatures());
RnnVad rnn_vad(GetAvailableCpuFeatures());
WarmUpRnnVad(rnn_vad);
EXPECT_EQ(rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/true), 0.f);
}
@ -245,7 +235,7 @@ TEST(RnnVadTest, CheckZeroProbabilityWithSilence) {
// Checks that the same output is produced after reset given the same input
// sequence.
TEST(RnnVadTest, CheckRnnVadReset) {
RnnBasedVad rnn_vad(GetAvailableCpuFeatures());
RnnVad rnn_vad(GetAvailableCpuFeatures());
WarmUpRnnVad(rnn_vad);
float pre = rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
rnn_vad.Reset();
@ -257,7 +247,7 @@ TEST(RnnVadTest, CheckRnnVadReset) {
// Checks that the same output is produced after silence is observed given the
// same input sequence.
TEST(RnnVadTest, CheckRnnVadSilence) {
RnnBasedVad rnn_vad(GetAvailableCpuFeatures());
RnnVad rnn_vad(GetAvailableCpuFeatures());
WarmUpRnnVad(rnn_vad);
float pre = rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/true);