/* * Copyright (c) 2021 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 #include #include template static TfLiteTensor GetTestTensor(std::vector & shape, T initVal, std::vector& 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(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 ctxLen = 5; const uint32_t innerLen = 3; arm::app::audio::asr::Postprocess post{ctxLen, innerLen, 0}; std::vector tensorShape = {1, 1, 1, 13}; std::vector tensorVec; TfLiteTensor tensor = GetTestTensor( tensorShape, 100, tensorVec); REQUIRE(false == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false)); } 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 tensorShape = {1, 1, 13, 1}; std::vector tensorVec; TfLiteTensor tensor = GetTestTensor( tensorShape, 100, tensorVec); /* Copy elements to compare later. */ std::vector originalVec = tensorVec; /* This step should not erase anything. */ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false)); } } TEST_CASE("Postprocessing - erasing required elements") { constexpr uint32_t ctxLen = 5; constexpr uint32_t innerLen = 3; constexpr uint32_t nRows = 2*ctxLen + innerLen; constexpr uint32_t nCols = 10; constexpr uint32_t blankTokenIdx = nCols - 1; std::vector tensorShape = {1, 1, nRows, nCols}; SECTION("First and last iteration") { arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx}; std::vector tensorVec; TfLiteTensor tensor = GetTestTensor( tensorShape, 100, tensorVec); /* Copy elements to compare later. */ std::vector originalVec = tensorVec; /* This step should not erase anything. */ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true)); REQUIRE(originalVec == tensorVec); } SECTION("Right context erase") { arm::app::audio::asr::Postprocess post{ctxLen, innerLen, blankTokenIdx}; std::vector tensorVec; TfLiteTensor tensor = GetTestTensor( tensorShape, 100, tensorVec); /* Copy elements to compare later. */ std::vector originalVec = tensorVec; /* This step should erase the right context only. */ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false)); REQUIRE(originalVec != tensorVec); /* The last ctxLen * 10 elements should be gone. */ for (size_t i = 0; i < ctxLen; ++i) { for (size_t j = 0; j < nCols; ++j) { /* Check right context elements are zeroed. */ if (j == blankTokenIdx) { CHECK(tensorVec[(ctxLen + innerLen) * nCols + i*nCols + j] == 1); } else { CHECK(tensorVec[(ctxLen + 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 = ctxLen * nCols; i < (ctxLen + 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 tensorVec; TfLiteTensor tensor = GetTestTensor(tensorShape, 100, tensorVec); /* Copy elements to compare later. */ std::vector originalVec = tensorVec; /* This step should erase right context. */ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false)); /* Calling it the second time should erase the left context. */ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, false)); REQUIRE(originalVec != tensorVec); /* The first and last ctxLen * 10 elements should be gone. */ for (size_t i = 0; i < ctxLen; ++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); } else { CHECK(tensorVec[(ctxLen + 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) { /* Check left context is preserved. */ CHECK(tensorVec[i] == originalVec[i]); } } 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 tensorVec; TfLiteTensor tensor = GetTestTensor( tensorShape, 100, tensorVec); /* Copy elements to compare later. */ std::vector originalVec = tensorVec; /* Calling it the second time should erase the left context. */ REQUIRE(true == post.Invoke(&tensor, arm::app::Wav2LetterModel::ms_outputRowsIdx, true)); REQUIRE(originalVec == tensorVec); } }