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/*
* 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");
* 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 "Wav2LetterPostprocess.hpp"
#include "Wav2LetterModel.hpp"
#include "ClassificationResult.hpp"
#include "BufAttributes.hpp"
#include <algorithm>
#include <catch.hpp>
#include <limits>
namespace arm {
namespace app {
static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
namespace asr {
extern uint8_t* GetModelPointer();
extern size_t GetModelLen();
} /* namespace asr */
} /* namespace app */
} /* namespace arm */
template <typename T>
static TfLiteTensor GetTestTensor(
std::vector<int>& shape,
T initVal,
std::vector<T>& vectorBuf)
{
REQUIRE(0 != shape.size());
shape.insert(shape.begin(), shape.size());
uint32_t sizeInBytes = sizeof(T);
for (size_t i = 1; i < shape.size(); ++i) {
sizeInBytes *= shape[i];
}
/* Allocate mem. */
vectorBuf = std::vector<T>(sizeInBytes, initVal);
TfLiteIntArray* dims = tflite::testing::IntArrayFromInts(shape.data());
return tflite::testing::CreateQuantizedTensor(
vectorBuf.data(), dims,
1, 0, "test-tensor");
}
TEST_CASE("Checking return value")
{
SECTION("Mismatched post processing parameters and tensor size")
{
const uint32_t outputCtxLen = 5;
arm::app::AsrClassifier classifier;
arm::app::Wav2LetterModel model;
model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::asr::GetModelPointer(),
arm::app::asr::GetModelLen());
std::vector<std::string> dummyLabels = {"a", "b", "$"};
const uint32_t blankTokenIdx = 2;
std::vector<arm::app::ClassificationResult> dummyResult;
std::vector <int> tensorShape = {1, 1, 1, 13};
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
tensorShape, 100, tensorVec);
arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
REQUIRE(!post.DoPostProcess());
}
SECTION("Post processing succeeds")
{
const uint32_t outputCtxLen = 5;
arm::app::AsrClassifier classifier;
arm::app::Wav2LetterModel model;
model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::asr::GetModelPointer(),
arm::app::asr::GetModelLen());
std::vector<std::string> dummyLabels = {"a", "b", "$"};
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);
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 not erase anything. */
REQUIRE(post.DoPostProcess());
}
}
TEST_CASE("Postprocessing - erasing required elements")
{
constexpr uint32_t outputCtxLen = 5;
constexpr uint32_t innerLen = 3;
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};
arm::app::AsrClassifier classifier;
arm::app::Wav2LetterModel model;
model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::asr::GetModelPointer(),
arm::app::asr::GetModelLen());
std::vector<std::string> dummyLabels = {"a", "b", "$"};
std::vector<arm::app::ClassificationResult> dummyResult;
SECTION("First and last iteration")
{
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;
/* This step should not erase anything. */
post.m_lastIteration = true;
REQUIRE(post.DoPostProcess());
REQUIRE(originalVec == tensorVec);
}
SECTION("Right context erase")
{
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;
//auto tensorData = tflite::GetTensorData<int8_t>(tensor);
/* This step should erase the right context only. */
post.m_lastIteration = false;
REQUIRE(post.DoPostProcess());
REQUIRE(originalVec != tensorVec);
/* The last ctxLen * 10 elements should be gone. */
for (size_t i = 0; i < outputCtxLen; ++i) {
for (size_t j = 0; j < nCols; ++j) {
/* Check right context elements are zeroed. Blank token idx should be set to 1 when erasing. */
if (j == blankTokenIdx) {
CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
} else {
CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
}
/* Check left context is preserved. */
CHECK(tensorVec[i*nCols + j] == originalVec[i*nCols + j]);
}
}
/* Check inner elements are preserved. */
for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
CHECK(tensorVec[i] == originalVec[i]);
}
}
SECTION("Left and right context erase")
{
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;
/* This step should erase right context. */
post.m_lastIteration = false;
REQUIRE(post.DoPostProcess());
/* Calling it the second time should erase the left context. */
REQUIRE(post.DoPostProcess());
REQUIRE(originalVec != tensorVec);
/* The first and last ctxLen * 10 elements should be gone. */
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[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 1);
CHECK(tensorVec[i*nCols + j] == 1);
} else {
CHECK(tensorVec[(outputCtxLen + innerLen) * nCols + i*nCols + j] == 0);
CHECK(tensorVec[i*nCols + j] == 0);
}
}
}
/* Check inner elements are preserved. */
for (size_t i = outputCtxLen * nCols; i < (outputCtxLen + innerLen) * nCols; ++i) {
/* Check left context is preserved. */
CHECK(tensorVec[i] == originalVec[i]);
}
}
SECTION("Try left context erase")
{
std::vector <int8_t> tensorVec;
TfLiteTensor tensor = GetTestTensor<int8_t>(
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. */
post.m_lastIteration = true;
REQUIRE(post.DoPostProcess());
REQUIRE(originalVec == tensorVec);
}
}
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