MLECO-3173: Add AD, KWS_ASR and Noise reduction use case API's
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I36f61ce74bf17f7b327cdae9704a22ca54144f37
diff --git a/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc b/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
index 6fd7df3..e343b66 100644
--- a/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
+++ b/tests/use_case/kws_asr/Wav2LetterPostprocessingTest.cc
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,15 +16,17 @@
*/
#include "Wav2LetterPostprocess.hpp"
#include "Wav2LetterModel.hpp"
+#include "ClassificationResult.hpp"
#include <algorithm>
#include <catch.hpp>
#include <limits>
template <typename T>
-static TfLiteTensor GetTestTensor(std::vector <int>& shape,
- T initVal,
- std::vector<T>& vectorBuf)
+static TfLiteTensor GetTestTensor(
+ std::vector<int>& shape,
+ T initVal,
+ std::vector<T>& vectorBuf)
{
REQUIRE(0 != shape.size());
@@ -38,91 +40,112 @@
vectorBuf = std::vector<T>(sizeInBytes, initVal);
TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(shape.data());
return tflite::testing::CreateQuantizedTensor(
- vectorBuf.data(), dims,
- 1, 0, "test-tensor");
+ vectorBuf.data(), dims,
+ 1, 0, "test-tensor");
}
TEST_CASE("Checking return value")
{
SECTION("Mismatched post processing parameters and tensor size")
{
- const uint32_t ctxLen = 5;
- const uint32_t innerLen = 3;
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0};
-
+ const uint32_t outputCtxLen = 5;
+ arm::app::AsrClassifier classifier;
+ arm::app::Wav2LetterModel model;
+ model.Init();
+ std::vector<std::string> dummyLabels = {"a", "b", "$"};
+ const uint32_t blankTokenIdx = 2;
+ std::vector<arm::app::ClassificationResult> dummyResult;
std::vector <int> tensorShape = {1, 1, 1, 13};
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
- REQUIRE(false == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ tensorShape, 100, tensorVec);
+
+ arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
+
+ REQUIRE(!post.DoPostProcess());
}
SECTION("Post processing succeeds")
{
- const uint32_t ctxLen = 5;
- const uint32_t innerLen = 3;
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0};
-
- std::vector <int> tensorShape = {1, 1, 13, 1};
- std::vector <int8_t> tensorVec;
+ const uint32_t outputCtxLen = 5;
+ arm::app::AsrClassifier classifier;
+ arm::app::Wav2LetterModel model;
+ model.Init();
+ std::vector<std::string> dummyLabels = {"a", "b", "$"};
+ const uint32_t blankTokenIdx = 2;
+ std::vector<arm::app::ClassificationResult> dummyResult;
+ std::vector<int> tensorShape = {1, 1, 13, 1};
+ std::vector<int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ tensorShape, 100, tensorVec);
+
+ arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
- std::vector <int8_t> originalVec = tensorVec;
+ std::vector<int8_t> originalVec = tensorVec;
/* This step should not erase anything. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ REQUIRE(post.DoPostProcess());
}
}
+
TEST_CASE("Postprocessing - erasing required elements")
{
- constexpr uint32_t ctxLen = 5;
+ constexpr uint32_t outputCtxLen = 5;
constexpr uint32_t innerLen = 3;
- constexpr uint32_t nRows = 2*ctxLen + innerLen;
+ constexpr uint32_t nRows = 2*outputCtxLen + innerLen;
constexpr uint32_t nCols = 10;
constexpr uint32_t blankTokenIdx = nCols - 1;
- std::vector <int> tensorShape = {1, 1, nRows, nCols};
+ std::vector<int> tensorShape = {1, 1, nRows, nCols};
+ arm::app::AsrClassifier classifier;
+ arm::app::Wav2LetterModel model;
+ model.Init();
+ std::vector<std::string> dummyLabels = {"a", "b", "$"};
+ std::vector<arm::app::ClassificationResult> dummyResult;
SECTION("First and last iteration")
{
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
- std::vector <int8_t> tensorVec;
- TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ std::vector<int8_t> tensorVec;
+ TfLiteTensor tensor = GetTestTensor<int8_t>(tensorShape, 100, tensorVec);
+ arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
- std::vector <int8_t> originalVec = tensorVec;
+ std::vector<int8_t>originalVec = tensorVec;
/* This step should not erase anything. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true));
+ post.m_lastIteration = true;
+ REQUIRE(post.DoPostProcess());
REQUIRE(originalVec == tensorVec);
}
SECTION("Right context erase")
{
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ tensorShape, 100, tensorVec);
+ arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
- std::vector <int8_t> originalVec = tensorVec;
+ std::vector<int8_t> originalVec = tensorVec;
/* This step should erase the right context only. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ post.m_lastIteration = false;
+ REQUIRE(post.DoPostProcess());
REQUIRE(originalVec != tensorVec);
/* The last ctxLen * 10 elements should be gone. */
- for (size_t i = 0; i < ctxLen; ++i) {
+ for (size_t i = 0; i < outputCtxLen; ++i) {
for (size_t j = 0; j < nCols; ++j) {
- /* Check right context elements are zeroed. */
+ /* Check right context elements are zeroed. Blank token idx should be set to 1 when erasing. */
if (j == blankTokenIdx) {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 1);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
} else {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 0);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
}
/* Check left context is preserved. */
@@ -131,45 +154,47 @@
}
/* Check inner elements are preserved. */
- for (size_t i = ctxLen * nCols; i < (ctxLen + innerLen) * nCols; ++i) {
+ for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
CHECK(tensorVec[i] == originalVec[i]);
}
}
SECTION("Left and right context erase")
{
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
std::vector <int8_t> tensorVec;
- TfLiteTensor tensor = GetTestTensor<int8_t>(tensorShape, 100, tensorVec);
+ TfLiteTensor tensor = GetTestTensor<int8_t>(
+ tensorShape, 100, tensorVec);
+ arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
std::vector <int8_t> originalVec = tensorVec;
/* This step should erase right context. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ post.m_lastIteration = false;
+ REQUIRE(post.DoPostProcess());
/* Calling it the second time should erase the left context. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false));
+ REQUIRE(post.DoPostProcess());
REQUIRE(originalVec != tensorVec);
/* The first and last ctxLen * 10 elements should be gone. */
- for (size_t i = 0; i < ctxLen; ++i) {
+ for (size_t i = 0; i < outputCtxLen; ++i) {
for (size_t j = 0; j < nCols; ++j) {
/* Check left and right context elements are zeroed. */
if (j == blankTokenIdx) {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i * nCols + j] == 1);
- CHECK(tensorVec[i * nCols + j] == 1);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
+ CHECK(tensorVec[i*nCols + j] == 1);
} else {
- CHECK(tensorVec[(ctxLen + innerLen) * nCols + i * nCols + j] == 0);
- CHECK(tensorVec[i * nCols + j] == 0);
+ CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
+ CHECK(tensorVec[i*nCols + j] == 0);
}
}
}
/* Check inner elements are preserved. */
- for (size_t i = ctxLen * nCols; i < (ctxLen + innerLen) * nCols; ++i) {
+ for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
/* Check left context is preserved. */
CHECK(tensorVec[i] == originalVec[i]);
}
@@ -177,18 +202,21 @@
SECTION("Try left context erase")
{
- /* Should not be able to erase the left context if it is the first iteration. */
- arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx};
-
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
- tensorShape, 100, tensorVec);
+ tensorShape, 100, tensorVec);
+
+ /* Should not be able to erase the left context if it is the first iteration. */
+ arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
+ blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
/* Copy elements to compare later. */
std::vector <int8_t> originalVec = tensorVec;
/* Calling it the second time should erase the left context. */
- REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true));
+ post.m_lastIteration = true;
+ REQUIRE(post.DoPostProcess());
+
REQUIRE(originalVec == tensorVec);
}
-}
\ No newline at end of file
+}