diff options
Diffstat (limited to 'libs/support')
| -rw-r--r-- | libs/support/CMakeLists.txt | 2 | ||||
| -rw-r--r-- | libs/support/nnapi/CMakeLists.txt | 6 | ||||
| -rw-r--r-- | libs/support/nnapi/src/Utils.cpp | 29 | ||||
| -rw-r--r-- | libs/support/nnapi/src/feature/Utils.cpp | 43 | ||||
| -rw-r--r-- | libs/support/tflite/CMakeLists.txt | 12 | ||||
| -rw-r--r-- | libs/support/tflite/src/Diff.cpp | 538 | ||||
| -rw-r--r-- | libs/support/tflite/src/FeatureView.cpp | 73 | ||||
| -rw-r--r-- | libs/support/tflite/src/Quantization.cpp | 22 | ||||
| -rw-r--r-- | libs/support/tflite/src/TensorShapeUtils.cpp | 51 | ||||
| -rw-r--r-- | libs/support/tflite/src/TensorView.test.cpp | 53 | ||||
| -rw-r--r-- | libs/support/tflite/src/interp/FlatBufferBuilder.cpp | 46 | ||||
| -rw-r--r-- | libs/support/tflite/src/interp/FunctionBuilder.cpp | 40 | ||||
| -rw-r--r-- | libs/support/tflite/src/kernels/RSQRT.cpp | 83 | ||||
| -rw-r--r-- | libs/support/tflite/src/kernels/SquaredDifference.cpp | 115 | ||||
| -rw-r--r-- | libs/support/tflite/src/kernels/TensorFlowMax.cpp | 390 | ||||
| -rw-r--r-- | libs/support/tflite/src/kernels/register.cpp | 169 | ||||
| -rw-r--r-- | libs/support/tflite/src/nnapi_delegate.cpp | 720 | ||||
| -rw-r--r-- | libs/support/tflite/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc | 41 |
18 files changed, 0 insertions, 2433 deletions
diff --git a/libs/support/CMakeLists.txt b/libs/support/CMakeLists.txt deleted file mode 100644 index c91677266..000000000 --- a/libs/support/CMakeLists.txt +++ /dev/null @@ -1,2 +0,0 @@ -add_subdirectory(tflite) -add_subdirectory(nnapi) diff --git a/libs/support/nnapi/CMakeLists.txt b/libs/support/nnapi/CMakeLists.txt deleted file mode 100644 index 193bcbd4e..000000000 --- a/libs/support/nnapi/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -file(GLOB_RECURSE SOURCES "src/*.cpp") - -add_library(nnfw_support_nnapi ${SOURCES}) -set_property(TARGET nnfw_support_nnapi PROPERTY POSITION_INDEPENDENT_CODE ON) -target_include_directories(nnfw_support_nnapi PUBLIC ${CMAKE_SOURCE_DIR}/include) -target_link_libraries(nnfw_support_nnapi static_nnfw_util) diff --git a/libs/support/nnapi/src/Utils.cpp b/libs/support/nnapi/src/Utils.cpp deleted file mode 100644 index ae1076fd1..000000000 --- a/libs/support/nnapi/src/Utils.cpp +++ /dev/null @@ -1,29 +0,0 @@ -#include "support/nnapi/Utils.h" - -#include <cassert> - -namespace nnfw -{ -namespace support -{ -namespace nnapi -{ - -const char *to_string(const PaddingCode &code) -{ - assert((ANEURALNETWORKS_PADDING_SAME == code) || (ANEURALNETWORKS_PADDING_VALID == code)); - - switch (code) - { - case ANEURALNETWORKS_PADDING_SAME: - return "ANEURALNETWORKS_PADDING_SAME"; - case ANEURALNETWORKS_PADDING_VALID: - return "ANEURALNETWORKS_PADDING_VALID"; - } - - return nullptr; -} - -} // namespace nnapi -} // namespace support -} // namespace nnfw diff --git a/libs/support/nnapi/src/feature/Utils.cpp b/libs/support/nnapi/src/feature/Utils.cpp deleted file mode 100644 index 62939ff4a..000000000 --- a/libs/support/nnapi/src/feature/Utils.cpp +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/nnapi/feature/Utils.h" - -namespace nnfw -{ -namespace support -{ -namespace nnapi -{ -namespace feature -{ - -uint32_t indexOf(const nnfw::util::feature::Shape &shape, uint32_t ch, uint32_t row, uint32_t col) -{ - uint32_t res = 0; - - // NNAPI assumes that NHWC ordering for feature map - res += row * shape.W * shape.C; - res += col * shape.C; - res += ch; - - return res; -} - -} // namespace feature -} // namespace nnapi -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/CMakeLists.txt b/libs/support/tflite/CMakeLists.txt deleted file mode 100644 index 667b3bc11..000000000 --- a/libs/support/tflite/CMakeLists.txt +++ /dev/null @@ -1,12 +0,0 @@ -file(GLOB_RECURSE SOURCES "src/*.cpp") -file(GLOB_RECURSE TESTS "src/*.test.cpp") -list(REMOVE_ITEM SOURCES ${TESTS}) - -add_library(nnfw_support_tflite STATIC ${SOURCES}) -set_target_properties(nnfw_support_tflite PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(nnfw_support_tflite PUBLIC ${CMAKE_SOURCE_DIR}/include) -target_link_libraries(nnfw_support_tflite tensorflow-lite ${LIB_PTHREAD} dl) -target_link_libraries(nnfw_support_tflite static_nnfw_util) - -add_executable(nnfw_support_tflite_test_TensorView src/TensorView.test.cpp) -target_link_libraries(nnfw_support_tflite_test_TensorView nnfw_support_tflite) diff --git a/libs/support/tflite/src/Diff.cpp b/libs/support/tflite/src/Diff.cpp deleted file mode 100644 index e875571cb..000000000 --- a/libs/support/tflite/src/Diff.cpp +++ /dev/null @@ -1,538 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/Diff.h" -#include "support/tflite/nnapi_delegate.h" - -#include "util/fp32.h" - -#include "util/tensor/IndexIterator.h" -#include "util/tensor/IndexFormatter.h" -#include "util/tensor/Zipper.h" -#include "util/tensor/Comparator.h" - -#include "util/environment.h" - -#include <iostream> -#include <cassert> - -class DiffSummary : public nnfw::util::tensor::Comparator::Observer -{ -public: - DiffSummary() - : max_abs_diff_index(0), max_abs_diff_expected{0.0f}, max_abs_diff_obtained{0.0f}, - max_abs_diff_value{0.0f}, max_rel_diff_index(0), max_rel_diff_expected{0.0f}, - max_rel_diff_obtained{0.0f}, max_rel_diff_value{0.0f} - { - // DO NOTHING - } - -public: - void notify(const nnfw::util::tensor::Index &index, float expected, float obtained) override; - -public: - nnfw::util::tensor::Index max_abs_diff_index; - float max_abs_diff_expected; - float max_abs_diff_obtained; - float max_abs_diff_value; - - nnfw::util::tensor::Index max_rel_diff_index; - float max_rel_diff_expected; - float max_rel_diff_obtained; - float max_rel_diff_value; -}; - -void DiffSummary::notify(const nnfw::util::tensor::Index &index, float expected, float obtained) -{ - const auto abs_diff_value = std::fabs(expected - obtained); - - if (max_abs_diff_value < abs_diff_value) - { - max_abs_diff_index = index; - max_abs_diff_value = abs_diff_value; - max_abs_diff_expected = expected; - max_abs_diff_obtained = obtained; - } - - const auto rel_diff_value = nnfw::util::fp32::relative_diff(expected, obtained); - - if (max_rel_diff_value < rel_diff_value) - { - max_rel_diff_index = index; - max_rel_diff_value = rel_diff_value; - max_rel_diff_expected = expected; - max_rel_diff_obtained = obtained; - } -} - -template <typename T> -bool TfLiteInterpMatchApp::compareSingleTensorView( - const nnfw::support::tflite::TensorView<T> &expected, - const nnfw::support::tflite::TensorView<T> &obtained, int id) const -{ - std::vector<nnfw::util::tensor::Diff<T>> diffs; - assert(expected.shape() == obtained.shape()); - - using nnfw::util::tensor::zip; - using nnfw::util::tensor::Index; - - zip(expected.shape(), expected, obtained) - << [&](const Index &index, T expected_value, T obtained_value) { - if (expected_value != obtained_value) - { - diffs.emplace_back(index, expected_value, obtained_value); - } - }; - - // TODO Unify summary generation code - if (diffs.size() == 0) - { - std::cout << " Tensor #" << id << ": MATCHED" << std::endl; - } - else - { - std::cout << " Tensor #" << id << ": UNMATCHED" << std::endl; - std::cout << " " << diffs.size() << " diffs are detected" << std::endl; - } - - if (diffs.size() > 0 && _verbose != 0) - { - std::cout << " ---- Details ---" << std::endl; - for (const auto &diff : diffs) - { - std::cout << " Diff at [" << nnfw::util::tensor::IndexFormatter(diff.index) << "]" - << std::endl; - std::cout << " expected: " << diff.expected << std::endl; - std::cout << " obtained: " << diff.obtained << std::endl; - } - } - - return diffs.size() == 0; -} - -template <> -bool TfLiteInterpMatchApp::compareSingleTensorView<float>( - const nnfw::support::tflite::TensorView<float> &expected, - const nnfw::support::tflite::TensorView<float> &obtained, int id) const -{ - DiffSummary summary; - - assert(expected.shape() == obtained.shape()); - auto diffs = _comparator.compare(expected.shape(), expected, obtained, &summary); - - // TODO Unify summary generation code - if (diffs.size() == 0) - { - std::cout << " Tensor #" << id << ": MATCHED" << std::endl; - } - else - { - std::cout << " Tensor #" << id << ": UNMATCHED" << std::endl; - std::cout << " " << diffs.size() << " diffs are detected" << std::endl; - } - - // Print out max_diff - if (summary.max_abs_diff_value > 0) - { - std::cout << " Max absolute diff at [" - << nnfw::util::tensor::IndexFormatter(summary.max_abs_diff_index) << "]" << std::endl; - std::cout << " expected: " << summary.max_abs_diff_expected << std::endl; - std::cout << " obtained: " << summary.max_abs_diff_obtained << std::endl; - std::cout << " absolute diff: " << summary.max_abs_diff_value << std::endl; - } - - if (summary.max_rel_diff_value > 0) - { - const auto tolerance_level = summary.max_rel_diff_value / FLT_EPSILON; - - std::cout << " Max relative diff at [" - << nnfw::util::tensor::IndexFormatter(summary.max_rel_diff_index) << "]" << std::endl; - std::cout << " expected: " << summary.max_rel_diff_expected << std::endl; - std::cout << " obtained: " << summary.max_rel_diff_obtained << std::endl; - std::cout << " relative diff: " << summary.max_rel_diff_value << std::endl; - std::cout << " (tolerance level = " << tolerance_level << ")" << std::endl; - } - - if (diffs.size() > 0) - { - if (_verbose != 0) - { - std::cout << " ---- Details ---" << std::endl; - for (const auto &diff : diffs) - { - const auto absolute_diff = std::fabs(diff.expected - diff.obtained); - const auto relative_diff = nnfw::util::fp32::relative_diff(diff.expected, diff.obtained); - const auto tolerance_level = relative_diff / FLT_EPSILON; - - std::cout << " Diff at [" << nnfw::util::tensor::IndexFormatter(diff.index) << "]" - << std::endl; - std::cout << " expected: " << diff.expected << std::endl; - std::cout << " obtained: " << diff.obtained << std::endl; - std::cout << " absolute diff: " << absolute_diff << std::endl; - std::cout << " relative diff: " << relative_diff << std::endl; - std::cout << " (tolerance level = " << tolerance_level << ")" << std::endl; - } - } - - return false; - } - return true; -} - -#include <map> - -bool TfLiteInterpMatchApp::run(::tflite::Interpreter &interp, ::tflite::Interpreter &nnapi) const -{ - assert(interp.outputs() == nnapi.outputs()); - - bool all_matched = true; - - using Comparator = std::function<bool(int id, ::tflite::Interpreter &, ::tflite::Interpreter &)>; - - std::map<TfLiteType, Comparator> comparators; - - comparators[kTfLiteUInt8] = [this](int id, ::tflite::Interpreter &interp, - ::tflite::Interpreter &nnapi) { - const auto expected = nnfw::support::tflite::TensorView<uint8_t>::make(interp, id); - const auto obtained = nnfw::support::tflite::TensorView<uint8_t>::make(nnapi, id); - - return compareSingleTensorView(expected, obtained, id); - }; - - comparators[kTfLiteInt32] = [this](int id, ::tflite::Interpreter &interp, - ::tflite::Interpreter &nnapi) { - const auto expected = nnfw::support::tflite::TensorView<int32_t>::make(interp, id); - const auto obtained = nnfw::support::tflite::TensorView<int32_t>::make(nnapi, id); - - return compareSingleTensorView(expected, obtained, id); - }; - - comparators[kTfLiteFloat32] = [this](int id, ::tflite::Interpreter &interp, - ::tflite::Interpreter &nnapi) { - const auto expected = nnfw::support::tflite::TensorView<float>::make(interp, id); - const auto obtained = nnfw::support::tflite::TensorView<float>::make(nnapi, id); - - return compareSingleTensorView(expected, obtained, id); - }; - - for (const auto &id : interp.outputs()) - { - assert(interp.tensor(id)->type == nnapi.tensor(id)->type); - - auto it = comparators.find(interp.tensor(id)->type); - - if (it == comparators.end()) - { - throw std::runtime_error{"Not supported output type"}; - } - - const auto &comparator = it->second; - - if (!comparator(id, interp, nnapi)) - { - all_matched = false; - } - } - - return all_matched; -} - -#include "util/tensor/Object.h" - -using namespace std::placeholders; - -template <> uint8_t RandomGenerator::generate<uint8_t>(void) -{ - // The value of type_range is 255. - float type_range = static_cast<float>(std::numeric_limits<uint8_t>::max()) - - static_cast<float>(std::numeric_limits<uint8_t>::min()); - // Most _dist values range from -5.0 to 5.0. - float min_range = -5.0f; - float max_range = 5.0f; - return static_cast<uint8_t>((_dist(_rand) - min_range) * type_range / (max_range - min_range)); -} - -#include "support/tflite/TensorLogger.h" -// -// Random Test Runner -// -int RandomTestRunner::run(const nnfw::support::tflite::interp::Builder &builder) -{ - auto tfl_interp = builder.build(); - auto nnapi = builder.build(); - - tfl_interp->UseNNAPI(false); - - // Allocate Tensors - tfl_interp->AllocateTensors(); - nnapi->AllocateTensors(); - - assert(tfl_interp->inputs() == nnapi->inputs()); - - using ::tflite::Interpreter; - using Initializer = std::function<void(int id, Interpreter *, Interpreter *)>; - - std::map<TfLiteType, Initializer> initializers; - std::map<TfLiteType, Initializer> reseters; - - // Generate singed 32-bit integer (s32) input - initializers[kTfLiteInt32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteInt32); - assert(nnapi->tensor(id)->type == kTfLiteInt32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<int32_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<int32_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - int32_t value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - ++value; - }; - }; - - // Generate singed 32-bit integer (s32) input - reseters[kTfLiteInt32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteInt32); - assert(nnapi->tensor(id)->type == kTfLiteInt32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<int32_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<int32_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - int32_t value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - // TODO Generate random values - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - initializers[kTfLiteUInt8] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteUInt8); - assert(nnapi->tensor(id)->type == kTfLiteUInt8); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<uint8_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<uint8_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<uint8_t (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<uint8_t>); - const nnfw::util::tensor::Object<uint8_t> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - assert(tfl_interp_view.shape() == data.shape()); - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - const auto value = data.at(ind); - - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - reseters[kTfLiteUInt8] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteUInt8); - assert(nnapi->tensor(id)->type == kTfLiteUInt8); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<uint8_t>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<uint8_t>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<uint8_t (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<uint8_t>); - const nnfw::util::tensor::Object<uint8_t> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - assert(tfl_interp_view.shape() == data.shape()); - - uint8_t value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - initializers[kTfLiteFloat32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteFloat32); - assert(nnapi->tensor(id)->type == kTfLiteFloat32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<float>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<float>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<float (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<float>); - const nnfw::util::tensor::Object<float> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - - assert(tfl_interp_view.shape() == data.shape()); - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - const auto value = data.at(ind); - - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - reseters[kTfLiteFloat32] = [&](int id, Interpreter *tfl_interp, Interpreter *nnapi) { - assert(tfl_interp->tensor(id)->type == kTfLiteFloat32); - assert(nnapi->tensor(id)->type == kTfLiteFloat32); - - auto tfl_interp_view = nnfw::support::tflite::TensorView<float>::make(*tfl_interp, id); - auto nnapi_view = nnfw::support::tflite::TensorView<float>::make(*nnapi, id); - - assert(tfl_interp_view.shape() == nnapi_view.shape()); - - auto fp = static_cast<float (RandomGenerator::*)(const ::nnfw::util::tensor::Shape &, - const ::nnfw::util::tensor::Index &)>( - &RandomGenerator::generate<float>); - const nnfw::util::tensor::Object<float> data(tfl_interp_view.shape(), - std::bind(fp, _randgen, _1, _2)); - - assert(tfl_interp_view.shape() == data.shape()); - - float value = 0; - - nnfw::util::tensor::iterate(tfl_interp_view.shape()) - << [&](const nnfw::util::tensor::Index &ind) { - tfl_interp_view.at(ind) = value; - nnapi_view.at(ind) = value; - }; - }; - - // Fill IFM with random numbers - for (const auto id : tfl_interp->inputs()) - { - assert(tfl_interp->tensor(id)->type == nnapi->tensor(id)->type); - - auto it = initializers.find(tfl_interp->tensor(id)->type); - - if (it == initializers.end()) - { - throw std::runtime_error{"Not supported input type"}; - } - - it->second(id, tfl_interp.get(), nnapi.get()); - } - - // Fill OFM with 0 - for (const auto id : tfl_interp->outputs()) - { - assert(tfl_interp->tensor(id)->type == nnapi->tensor(id)->type); - - auto it = reseters.find(tfl_interp->tensor(id)->type); - - if (it == reseters.end()) - { - throw std::runtime_error{"Not supported input type"}; - } - - it->second(id, tfl_interp.get(), nnapi.get()); - } - - std::cout << "[NNAPI TEST] Run T/F Lite Interpreter without NNAPI" << std::endl; - tfl_interp->Invoke(); - - std::cout << "[NNAPI TEST] Run T/F Lite Interpreter with NNAPI" << std::endl; - - char *env = getenv("UPSTREAM_DELEGATE"); - - if (env && !std::string(env).compare("1")) - { - nnapi->UseNNAPI(true); - nnapi->Invoke(); - } - else - { - nnfw::NNAPIDelegate d; - - if (d.BuildGraph(nnapi.get())) - { - throw std::runtime_error{"Failed to BuildGraph"}; - } - - if (d.Invoke(nnapi.get())) - { - throw std::runtime_error{"Failed to BuildGraph"}; - } - } - - // Compare OFM - std::cout << "[NNAPI TEST] Compare the result" << std::endl; - - const auto tolerance = _param.tolerance; - - auto equals = [tolerance](float lhs, float rhs) { - // NOTE Hybrid approach - // TODO Allow users to set tolerance for absolute_epsilon_equal - if (nnfw::util::fp32::absolute_epsilon_equal(lhs, rhs)) - { - return true; - } - - return nnfw::util::fp32::epsilon_equal(lhs, rhs, tolerance); - }; - - nnfw::util::tensor::Comparator comparator(equals); - TfLiteInterpMatchApp app(comparator); - - app.verbose() = _param.verbose; - - bool res = app.run(*tfl_interp, *nnapi); - - if (!res) - { - return 255; - } - - std::cout << "[NNAPI TEST] PASSED" << std::endl; - - if (_param.tensor_logging) - nnfw::support::tflite::TensorLogger::instance().save(_param.log_path, *tfl_interp); - - return 0; -} - -RandomTestRunner RandomTestRunner::make(int seed) -{ - RandomTestParam param; - - param.verbose = 0; - param.tolerance = 1; - - nnfw::util::env::IntAccessor("VERBOSE").access(param.verbose); - nnfw::util::env::IntAccessor("TOLERANCE").access(param.tolerance); - - return RandomTestRunner{seed, param}; -} diff --git a/libs/support/tflite/src/FeatureView.cpp b/libs/support/tflite/src/FeatureView.cpp deleted file mode 100644 index 4c7636780..000000000 --- a/libs/support/tflite/src/FeatureView.cpp +++ /dev/null @@ -1,73 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/FeatureView.h" -#include "support/tflite/TensorUtils.h" - -#include <cassert> - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -nnfw::util::feature::Shape getFeatureShape(const TfLiteTensor *tensor) -{ - nnfw::util::feature::Shape shape{tensor->dims->data[3], tensor->dims->data[1], - tensor->dims->data[2]}; - - return shape; -} - -FeatureView<float>::FeatureView(::tflite::Interpreter &interp, const InputIndex &index) -{ - const auto tensor_index = interp.inputs().at(index.asInt()); - auto tensor_ptr = interp.tensor(tensor_index); - - assert(isFloatTensor(tensor_ptr)); - assert(isFeatureTensor(tensor_ptr)); - - _shape = getFeatureShape(tensor_ptr); - _base = interp.typed_tensor<float>(tensor_index); -} - -FeatureView<float>::FeatureView(::tflite::Interpreter &interp, const OutputIndex &index) -{ - const auto tensor_index = interp.outputs().at(index.asInt()); - auto tensor_ptr = interp.tensor(tensor_index); - - assert(isFloatTensor(tensor_ptr)); - assert(isFeatureTensor(tensor_ptr)); - - _shape = getFeatureShape(tensor_ptr); - _base = interp.typed_tensor<float>(tensor_index); -} - -float FeatureView<float>::at(uint32_t ch, uint32_t row, uint32_t col) const -{ - return *(_base + getElementOffset(ch, row, col)); -} - -float &FeatureView<float>::at(uint32_t ch, uint32_t row, uint32_t col) -{ - return *(_base + getElementOffset(ch, row, col)); -} - -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/Quantization.cpp b/libs/support/tflite/src/Quantization.cpp deleted file mode 100644 index b23204d41..000000000 --- a/libs/support/tflite/src/Quantization.cpp +++ /dev/null @@ -1,22 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/Quantization.h" - -TfLiteQuantizationParams make_default_quantization(void) -{ - return TfLiteQuantizationParams{0.0f, 0}; -} diff --git a/libs/support/tflite/src/TensorShapeUtils.cpp b/libs/support/tflite/src/TensorShapeUtils.cpp deleted file mode 100644 index 611ba920e..000000000 --- a/libs/support/tflite/src/TensorShapeUtils.cpp +++ /dev/null @@ -1,51 +0,0 @@ -#include "support/tflite/TensorShapeUtils.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ - -nnfw::util::tensor::Shape broadcast(const nnfw::util::tensor::Shape &lhs_shape, - const nnfw::util::tensor::Shape &rhs_shape) -{ - const uint32_t lhs_rank = lhs_shape.rank(); - const uint32_t rhs_rank = rhs_shape.rank(); - const uint32_t out_rank = std::max(lhs_rank, rhs_rank); - - // TODO Simplify implementation - std::vector<int32_t> lhs_normalized_dims; - std::vector<int32_t> rhs_normalized_dims; - - for (uint32_t n = 0; n < out_rank - lhs_rank; ++n) - { - lhs_normalized_dims.emplace_back(1); - } - for (uint32_t axis = 0; axis < lhs_rank; ++axis) - { - lhs_normalized_dims.emplace_back(lhs_shape.dim(axis)); - } - - for (uint32_t n = 0; n < out_rank - rhs_rank; ++n) - { - rhs_normalized_dims.emplace_back(1); - } - for (uint32_t axis = 0; axis < rhs_rank; ++axis) - { - rhs_normalized_dims.emplace_back(rhs_shape.dim(axis)); - } - - nnfw::util::tensor::Shape out_shape(out_rank); - - for (uint32_t axis = 0; axis < out_rank; ++axis) - { - out_shape.dim(axis) = std::max(lhs_normalized_dims.at(axis), rhs_normalized_dims.at(axis)); - } - - return out_shape; -} - -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/TensorView.test.cpp b/libs/support/tflite/src/TensorView.test.cpp deleted file mode 100644 index 1d3a70500..000000000 --- a/libs/support/tflite/src/TensorView.test.cpp +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/TensorView.h" - -#include <cassert> - -void int_test(void) -{ - int value[6] = {1, 2, 3, 4, 5, 6}; - - const nnfw::util::tensor::Shape shape{2, 3}; - const nnfw::support::tflite::TensorView<int> view{shape, value}; - - assert(view.at(nnfw::util::tensor::Index{0, 0}) == 1); - assert(view.at(nnfw::util::tensor::Index{0, 1}) == 2); - assert(view.at(nnfw::util::tensor::Index{0, 2}) == 3); - assert(view.at(nnfw::util::tensor::Index{1, 0}) == 4); - assert(view.at(nnfw::util::tensor::Index{1, 1}) == 5); - assert(view.at(nnfw::util::tensor::Index{1, 2}) == 6); -} - -int main(int argc, char **argv) -{ - float value[6] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}; - - const nnfw::util::tensor::Shape shape{2, 3}; - const nnfw::support::tflite::TensorView<float> view{shape, value}; - - assert(view.at(nnfw::util::tensor::Index{0, 0}) == 1.0f); - assert(view.at(nnfw::util::tensor::Index{0, 1}) == 2.0f); - assert(view.at(nnfw::util::tensor::Index{0, 2}) == 3.0f); - assert(view.at(nnfw::util::tensor::Index{1, 0}) == 4.0f); - assert(view.at(nnfw::util::tensor::Index{1, 1}) == 5.0f); - assert(view.at(nnfw::util::tensor::Index{1, 2}) == 6.0f); - - int_test(); - - return 0; -} diff --git a/libs/support/tflite/src/interp/FlatBufferBuilder.cpp b/libs/support/tflite/src/interp/FlatBufferBuilder.cpp deleted file mode 100644 index 67df13f34..000000000 --- a/libs/support/tflite/src/interp/FlatBufferBuilder.cpp +++ /dev/null @@ -1,46 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/interp/FlatBufferBuilder.h" - -#include "support/tflite/kernels/register.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ -namespace interp -{ - -std::unique_ptr<::tflite::Interpreter> FlatBufferBuilder::build(void) const -{ - std::unique_ptr<::tflite::Interpreter> interpreter; - - ::tflite::ops::builtin::BuiltinOpResolver resolver; - - ::tflite::InterpreterBuilder builder(_model, resolver); - - builder(&interpreter); - - return std::move(interpreter); -} - -} // namespace interp -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/interp/FunctionBuilder.cpp b/libs/support/tflite/src/interp/FunctionBuilder.cpp deleted file mode 100644 index 65783bd37..000000000 --- a/libs/support/tflite/src/interp/FunctionBuilder.cpp +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/interp/FunctionBuilder.h" - -namespace nnfw -{ -namespace support -{ -namespace tflite -{ -namespace interp -{ - -std::unique_ptr<::tflite::Interpreter> FunctionBuilder::build(void) const -{ - auto res = std::unique_ptr<::tflite::Interpreter>{new ::tflite::Interpreter}; - - _fn(*res); - - return std::move(res); -} - -} // namespace interp -} // namespace tflite -} // namespace support -} // namespace nnfw diff --git a/libs/support/tflite/src/kernels/RSQRT.cpp b/libs/support/tflite/src/kernels/RSQRT.cpp deleted file mode 100644 index 13efe0ed9..000000000 --- a/libs/support/tflite/src/kernels/RSQRT.cpp +++ /dev/null @@ -1,83 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/kernels/RSQRT.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <cmath> -#include <iostream> - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace RSQRT -{ - -void *InitRSQRT(TfLiteContext *context, const char *buffer, size_t length) { return nullptr; } - -void FreeRSQRT(TfLiteContext *context, void *buffer) {} - -TfLiteStatus PrepareRSQRT(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - const TfLiteTensor *input = GetInput(context, node, 0); - TfLiteTensor *output = GetOutput(context, node, 0); - TF_LITE_ENSURE_EQ(context, input->type, output->type); - // Quantized float is not supported yet. - TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); - return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input->dims)); -} - -inline TfLiteStatus Eval(TfLiteContext *context, TfLiteNode *node, float float_func(float)) -{ - const TfLiteTensor *input = GetInput(context, node, 0); - TfLiteTensor *output = GetOutput(context, node, 0); - switch (input->type) - { - case kTfLiteFloat32: - { - size_t elements = NumElements(input); - const float *in = input->data.f; - const float *in_end = in + elements; - float *out = output->data.f; - for (; in < in_end; in++, out++) - *out = float_func(*in); - return kTfLiteOk; - } - default: - { - context->ReportError(context, "Input type is %d, requires float32", input->type); - return kTfLiteError; - } - } -} - -TfLiteStatus EvalRSQRT(TfLiteContext *context, TfLiteNode *node) -{ - return Eval(context, node, [](float f) { return 1.f / std::sqrt(f); }); -} - -} // namespace RSQRT -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/kernels/SquaredDifference.cpp b/libs/support/tflite/src/kernels/SquaredDifference.cpp deleted file mode 100644 index 25e10a8ed..000000000 --- a/libs/support/tflite/src/kernels/SquaredDifference.cpp +++ /dev/null @@ -1,115 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/kernels/SquaredDifference.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace SquaredDifference -{ - -void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length) -{ - return nullptr; -} - -void FreeSquaredDifference(TfLiteContext *context, void *buffer) {} - -TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - const TfLiteTensor *input1 = GetInput(context, node, 0); - const TfLiteTensor *input2 = GetInput(context, node, 1); - TfLiteTensor *output = GetOutput(context, node, 0); - - TF_LITE_ENSURE_EQ(context, input1->type, input2->type); - TF_LITE_ENSURE_EQ(context, input1->type, output->type); - - return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input1->dims)); -} - -TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node) -{ - - const TfLiteTensor *input1 = GetInput(context, node, 0); - const TfLiteTensor *input2 = GetInput(context, node, 1); - - TfLiteTensor *output = GetOutput(context, node, 0); - - size_t elements = NumElements(input1); - - switch (input1->type) - { - case kTfLiteFloat32: - { - const float *in1 = input1->data.f; - const float *in2 = input2->data.f; - const float *in_end1 = in1 + elements; - float *out = output->data.f; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - case kTfLiteInt32: - { - const int *in1 = input1->data.i32; - const int *in2 = input2->data.i32; - const int *in_end1 = in1 + elements; - int *out = output->data.i32; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - case kTfLiteInt64: - { - const int64_t *in1 = input1->data.i64; - const int64_t *in2 = input1->data.i64; - const int64_t *in_end1 = in1 + elements; - int64_t *out = output->data.i64; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - default: - { - context->ReportError(context, "InputType is %d Unsupported", input1->type); - return kTfLiteError; - } - } -} - -} // namespace SquaredDifference -} // nnfw -} // namespace custom -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/kernels/TensorFlowMax.cpp b/libs/support/tflite/src/kernels/TensorFlowMax.cpp deleted file mode 100644 index abc6fda4e..000000000 --- a/libs/support/tflite/src/kernels/TensorFlowMax.cpp +++ /dev/null @@ -1,390 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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 "support/tflite/kernels/TensorFlowMax.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace tflite -{ -namespace ops -{ -namespace custom -{ -namespace nnfw -{ -namespace TensorFlowMax -{ - -struct TensorFlowMaxOp -{ - TensorFlowMaxOp(TfLiteContext *context, TfLiteNode *node) - { - input = tflite::GetInput(context, node, 0); - axis = tflite::GetInput(context, node, 1); - output = tflite::GetOutput(context, node, 0); - } - const TfLiteTensor *input; - const TfLiteTensor *axis; - TfLiteTensor *output; -}; - -void *InitTensorFlowMax(TfLiteContext *context, const char *buffer, size_t length) -{ - // Creates two temp tensors to store index and axis for internal - // implementation only. - auto *scratch_tensor_index = new int; - context->AddTensors(context, 2, scratch_tensor_index); - return scratch_tensor_index; -} - -void FreeTensorFlowMax(TfLiteContext *context, void *buffer) -{ - delete static_cast<TensorFlowMaxOp *>(buffer); -} - -// Resizes the temp tensor that stores resolved axis. -TfLiteStatus ResizeTempAxis(TfLiteContext *context, TensorFlowMaxOp *op_context, - TfLiteTensor *resolved_axis) -{ - TfLiteIntArray *axis_size = TfLiteIntArrayCreate(1); - axis_size->data[0] = static_cast<int>(tflite::NumElements(op_context->axis)); - return context->ResizeTensor(context, resolved_axis, axis_size); -} - -// Resizes output array based on the input size and resolved axis. -TfLiteStatus ResizeOutputTensor(TfLiteContext *context, TensorFlowMaxOp *op_context) -{ - size_t num_axis = tflite::NumElements(op_context->axis); - const TfLiteIntArray *input_dims = op_context->input->dims; - int input_num_dims = tflite::NumDimensions(op_context->input); - const int *axis = op_context->axis->data.i32; - - { - // Calculates size of reducing axis. - int num_reduce_axis = num_axis; - for (int i = 0; i < num_axis; ++i) - { - int current = axis[i]; - if (current < 0) - { - current += input_num_dims; - } - TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims); - for (int j = 0; j < i; ++j) - { - int previous = axis[j]; - if (previous < 0) - { - previous += input_num_dims; - } - if (current == previous) - { - --num_reduce_axis; - break; - } - } - } - // Determines output dimensions. - TfLiteIntArray *output_dims = TfLiteIntArrayCreate(input_num_dims - num_reduce_axis); - int num_skip_axis = 0; - for (int idx = 0; idx < input_num_dims; ++idx) - { - bool is_axis = false; - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) - { - ++num_skip_axis; - is_axis = true; - break; - } - } - if (!is_axis) - { - output_dims->data[idx - num_skip_axis] = input_dims->data[idx]; - } - } - return context->ResizeTensor(context, op_context->output, output_dims); - } -} - -// Initializes temp tensors to store index and resolved axis. -TfLiteStatus InitializeTemporaries(TfLiteContext *context, TfLiteNode *node, - TensorFlowMaxOp *op_context) -{ - // Creates a temp index to iterate through input data. - int *scratch_tensor_index = reinterpret_cast<int *>(node->user_data); - TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(2); - node->temporaries->data[0] = *scratch_tensor_index; - TfLiteTensor *scratch_tensor = &context->tensors[node->temporaries->data[0]]; - scratch_tensor->type = kTfLiteInt32; - scratch_tensor->allocation_type = kTfLiteArenaRw; - TfLiteIntArray *index_size = TfLiteIntArrayCreate(1); - index_size->data[0] = tflite::NumDimensions(op_context->input); - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, index_size)); - - // Creates a temp tensor to store resolved axis given input data. - node->temporaries->data[1] = *scratch_tensor_index + 1; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - resolved_axis->type = kTfLiteInt32; - return kTfLiteOk; -} - -TfLiteStatus PrepareTensorFlowMax(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, tflite::NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, tflite::NumOutputs(node), 1); - - TensorFlowMaxOp op_context(context, node); - TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); - - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Leaves work to Eval if axis is not constant; else resizes output. - if (!tflite::IsConstantTensor(op_context.axis)) - { - tflite::SetTensorToDynamic(op_context.output); - tflite::SetTensorToDynamic(resolved_axis); - return kTfLiteOk; - } - resolved_axis->allocation_type = kTfLiteArenaRw; - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - return ResizeOutputTensor(context, &op_context); -} - -// Gets offset of index if expanded on axis. When expanded, the flattened offset -// will not change, if the output index changes on the given axis. For example, -// if you have a 2D tensor and you are expanding to 3D on axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map from the same flattened -// offset. -inline size_t ExpandedInputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - int out_idx = 0; - for (int in_idx = 0; in_idx < num_dims; ++in_idx) - { - // if we need to expand this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (in_idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[in_idx]) + static_cast<size_t>(index[out_idx]); - out_idx++; - } - else - { - offset = offset * static_cast<size_t>(dims[in_idx]); - } - } - return offset; -} - -// Gets offset of index if reducing on axis. When reducing, the flattened offset -// will not change, if the input index changes on the given axis. For example, -// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened -// offset. -// TODO(kanlig): uses Dims to represent dimensions. -inline size_t ReducedOutputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) - { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[idx]) + static_cast<size_t>(index[idx]); - } - } - return offset; -} - -// Gets next index to iterate through a multidimensional array. -inline bool NextIndex(TfLiteContext *context, const int num_dims, const int *dims, int *current) -{ - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) - { - int current_val = current[idx] + carry; - TF_LITE_ENSURE(context, (dims[idx] >= current_val)); - if (dims[idx] == current_val) - { - current[idx] = 0; - } - else - { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -template <typename T> -inline TfLiteStatus -CustomMax(TfLiteContext *context, T *input_data, const int *input_dims, const int input_num_dims, - T *output_data, const int *output_dims, const int output_num_dims, const int *axis, - const int num_axis_dimensions, bool keep_dims, int *temp_index, int *resolved_axis) -{ - // resolves axis. - int num_resolved_axis = 0; - for (int idx = 0; idx < num_axis_dimensions; ++idx) - { - int current = axis[idx]; - TF_LITE_ENSURE(context, (current < input_num_dims && current + input_num_dims >= 0)); - if (current < 0) - { - current += input_num_dims; - } - bool is_dup = false; - for (int j = 0; j < num_resolved_axis; ++j) - { - if (resolved_axis[j] == current) - { - is_dup = true; - break; - } - } - if (!is_dup) - { - resolved_axis[num_resolved_axis++] = current; - } - } - - TF_LITE_ENSURE(context, (input_num_dims > 0)); - TF_LITE_ENSURE(context, (input_dims != nullptr)); - TF_LITE_ENSURE(context, (temp_index != nullptr)); - - // resets output data. - for (int idx = 0; idx < output_num_dims; ++idx) - { - temp_index[idx] = 0; - } - for (bool has_next = true; has_next; - has_next = NextIndex(context, output_num_dims, output_dims, temp_index)) - { - size_t output_offset = - ReducedOutputOffset(output_num_dims, output_dims, temp_index, 0, nullptr); - size_t input_offset = ExpandedInputOffset(input_num_dims, input_dims, temp_index, - num_resolved_axis, resolved_axis); - output_data[output_offset] = input_data[input_offset]; - } - - // resets temp index. - for (int idx = 0; idx < input_num_dims; ++idx) - { - temp_index[idx] = 0; - } - - // iterates through input_data. - for (bool has_next = true; has_next; - has_next = NextIndex(context, input_num_dims, input_dims, temp_index)) - { - size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr); - size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, - num_resolved_axis, resolved_axis); - if (output_data[output_offset] < input_data[input_offset]) - { - output_data[output_offset] = input_data[input_offset]; - } - } - - return kTfLiteOk; -} - -TfLiteStatus EvalTensorFlowMax(TfLiteContext *context, TfLiteNode *node) -{ - - TensorFlowMaxOp op_context(context, node); - int num_axis = static_cast<int>(tflite::NumElements(op_context.axis)); - TfLiteTensor *temp_index = &context->tensors[node->temporaries->data[0]]; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Resize the output tensor if the output tensor is dynamic. - if (tflite::IsDynamicTensor(op_context.output)) - { - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - } - - TfLiteStatus returnStatus = kTfLiteOk; - switch (op_context.input->type) - { - case kTfLiteFloat32: - returnStatus = CustomMax<float>( - context, op_context.input->data.f, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.f, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt32: - returnStatus = CustomMax<int>(context, op_context.input->data.i32, - op_context.input->dims->data, op_context.input->dims->size, - op_context.output->data.i32, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteUInt8: - returnStatus = CustomMax<uint8_t>( - context, op_context.input->data.uint8, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.uint8, - op_context.output->dims->data, op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt64: - returnStatus = CustomMax<int64_t>( - context, op_context.input->data.i64, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.i64, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - default: - returnStatus = kTfLiteError; - } - - return returnStatus; -} -} // namespace TensorFlowMax -} // namespace nnfw -} // namespace custom -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/kernels/register.cpp b/libs/support/tflite/src/kernels/register.cpp deleted file mode 100644 index 6700b4de4..000000000 --- a/libs/support/tflite/src/kernels/register.cpp +++ /dev/null @@ -1,169 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -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. -==============================================================================*/ - -// NOTE This code is derived from the following file (in TensorFlow) -// 'externals/tensorflow/tensorflow/contrib/lite/kernels/register.cc' -#include "support/tflite/kernels/register.h" -#include "support/tflite/kernels/CustomOps.h" - -// TODO Use namespace nnfw -namespace tflite -{ -namespace ops -{ -namespace builtin -{ - -TfLiteRegistration *Register_RELU(); -TfLiteRegistration *Register_RELU_N1_TO_1(); -TfLiteRegistration *Register_RELU6(); -TfLiteRegistration *Register_TANH(); -TfLiteRegistration *Register_LOGISTIC(); -TfLiteRegistration *Register_AVERAGE_POOL_2D(); -TfLiteRegistration *Register_MAX_POOL_2D(); -TfLiteRegistration *Register_L2_POOL_2D(); -TfLiteRegistration *Register_CONV_2D(); -TfLiteRegistration *Register_DEPTHWISE_CONV_2D(); -TfLiteRegistration *Register_SVDF(); -TfLiteRegistration *Register_RNN(); -TfLiteRegistration *Register_BIDIRECTIONAL_SEQUENCE_RNN(); -TfLiteRegistration *Register_UNIDIRECTIONAL_SEQUENCE_RNN(); -TfLiteRegistration *Register_EMBEDDING_LOOKUP(); -TfLiteRegistration *Register_EMBEDDING_LOOKUP_SPARSE(); -TfLiteRegistration *Register_FULLY_CONNECTED(); -TfLiteRegistration *Register_LSH_PROJECTION(); -TfLiteRegistration *Register_HASHTABLE_LOOKUP(); -TfLiteRegistration *Register_SOFTMAX(); -TfLiteRegistration *Register_CONCATENATION(); -TfLiteRegistration *Register_ADD(); -TfLiteRegistration *Register_SPACE_TO_BATCH_ND(); -TfLiteRegistration *Register_DIV(); -TfLiteRegistration *Register_SUB(); -TfLiteRegistration *Register_BATCH_TO_SPACE_ND(); -TfLiteRegistration *Register_MUL(); -TfLiteRegistration *Register_L2_NORMALIZATION(); -TfLiteRegistration *Register_LOCAL_RESPONSE_NORMALIZATION(); -TfLiteRegistration *Register_LSTM(); -TfLiteRegistration *Register_BIDIRECTIONAL_SEQUENCE_LSTM(); -TfLiteRegistration *Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); -TfLiteRegistration *Register_PAD(); -TfLiteRegistration *Register_PADV2(); -TfLiteRegistration *Register_RESHAPE(); -TfLiteRegistration *Register_RESIZE_BILINEAR(); -TfLiteRegistration *Register_SKIP_GRAM(); -TfLiteRegistration *Register_SPACE_TO_DEPTH(); -TfLiteRegistration *Register_GATHER(); -TfLiteRegistration *Register_TRANSPOSE(); -TfLiteRegistration *Register_MEAN(); -TfLiteRegistration *Register_SPLIT(); -TfLiteRegistration *Register_SQUEEZE(); -TfLiteRegistration *Register_STRIDED_SLICE(); -TfLiteRegistration *Register_EXP(); -TfLiteRegistration *Register_TOPK_V2(); -TfLiteRegistration *Register_LOG_SOFTMAX(); -TfLiteRegistration *Register_CAST(); -TfLiteRegistration *Register_DEQUANTIZE(); -TfLiteRegistration *Register_PRELU(); -TfLiteRegistration *Register_MAXIMUM(); -TfLiteRegistration *Register_MINIMUM(); -TfLiteRegistration *Register_ARG_MAX(); -TfLiteRegistration *Register_GREATER(); -TfLiteRegistration *Register_GREATER_EQUAL(); -TfLiteRegistration *Register_LESS(); -TfLiteRegistration *Register_LESS_EQUAL(); -TfLiteRegistration *Register_FLOOR(); -TfLiteRegistration *Register_NEG(); -TfLiteRegistration *Register_SELECT(); -TfLiteRegistration *Register_SLICE(); -TfLiteRegistration *Register_SIN(); -TfLiteRegistration *Register_TRANSPOSE_CONV(); -TfLiteRegistration *Register_SPARSE_TO_DENSE(); - -BuiltinOpResolver::BuiltinOpResolver() -{ - AddBuiltin(BuiltinOperator_RELU, Register_RELU()); - AddBuiltin(BuiltinOperator_RELU_N1_TO_1, Register_RELU_N1_TO_1()); - AddBuiltin(BuiltinOperator_RELU6, Register_RELU6()); - AddBuiltin(BuiltinOperator_TANH, Register_TANH()); - AddBuiltin(BuiltinOperator_LOGISTIC, Register_LOGISTIC()); - AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, Register_AVERAGE_POOL_2D()); - AddBuiltin(BuiltinOperator_MAX_POOL_2D, Register_MAX_POOL_2D()); - AddBuiltin(BuiltinOperator_L2_POOL_2D, Register_L2_POOL_2D()); - AddBuiltin(BuiltinOperator_CONV_2D, Register_CONV_2D()); - AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, Register_DEPTHWISE_CONV_2D()); - AddBuiltin(BuiltinOperator_SVDF, Register_SVDF()); - AddBuiltin(BuiltinOperator_RNN, Register_RNN()); - AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, Register_BIDIRECTIONAL_SEQUENCE_RNN()); - AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, Register_UNIDIRECTIONAL_SEQUENCE_RNN()); - AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, Register_EMBEDDING_LOOKUP()); - AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, Register_EMBEDDING_LOOKUP_SPARSE()); - AddBuiltin(BuiltinOperator_FULLY_CONNECTED, Register_FULLY_CONNECTED()); - AddBuiltin(BuiltinOperator_LSH_PROJECTION, Register_LSH_PROJECTION()); - AddBuiltin(BuiltinOperator_HASHTABLE_LOOKUP, Register_HASHTABLE_LOOKUP()); - AddBuiltin(BuiltinOperator_SOFTMAX, Register_SOFTMAX()); - AddBuiltin(BuiltinOperator_CONCATENATION, Register_CONCATENATION()); - AddBuiltin(BuiltinOperator_ADD, Register_ADD()); - AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, Register_SPACE_TO_BATCH_ND()); - AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND, Register_BATCH_TO_SPACE_ND()); - AddBuiltin(BuiltinOperator_MUL, Register_MUL()); - AddBuiltin(BuiltinOperator_L2_NORMALIZATION, Register_L2_NORMALIZATION()); - AddBuiltin(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, Register_LOCAL_RESPONSE_NORMALIZATION()); - AddBuiltin(BuiltinOperator_LSTM, Register_LSTM()); - AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, Register_BIDIRECTIONAL_SEQUENCE_LSTM()); - AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, Register_UNIDIRECTIONAL_SEQUENCE_LSTM()); - AddBuiltin(BuiltinOperator_PAD, Register_PAD()); - AddBuiltin(BuiltinOperator_PADV2, Register_PADV2()); - AddBuiltin(BuiltinOperator_RESHAPE, Register_RESHAPE()); - AddBuiltin(BuiltinOperator_RESIZE_BILINEAR, Register_RESIZE_BILINEAR()); - AddBuiltin(BuiltinOperator_SKIP_GRAM, Register_SKIP_GRAM()); - AddBuiltin(BuiltinOperator_SPACE_TO_DEPTH, Register_SPACE_TO_DEPTH()); - AddBuiltin(BuiltinOperator_GATHER, Register_GATHER()); - AddBuiltin(BuiltinOperator_TRANSPOSE, Register_TRANSPOSE()); - AddBuiltin(BuiltinOperator_MEAN, Register_MEAN()); - AddBuiltin(BuiltinOperator_DIV, Register_DIV()); - AddBuiltin(BuiltinOperator_SUB, Register_SUB()); - AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT()); - AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE()); - AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE()); - AddBuiltin(BuiltinOperator_EXP, Register_EXP()); - AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2()); - AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); - AddBuiltin(BuiltinOperator_CAST, Register_CAST()); - AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); - AddBuiltin(BuiltinOperator_PRELU, Register_PRELU()); - AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM()); - AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); - AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX()); - AddBuiltin(BuiltinOperator_GREATER, Register_GREATER()); - AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL()); - AddBuiltin(BuiltinOperator_LESS, Register_LESS()); - AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL()); - AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR()); - AddBuiltin(BuiltinOperator_NEG, Register_NEG()); - AddBuiltin(BuiltinOperator_SELECT, Register_SELECT()); - AddBuiltin(BuiltinOperator_SLICE, Register_SLICE()); - AddBuiltin(BuiltinOperator_SIN, Register_SIN()); - AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); - AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); - - AddCustom("TensorFlowMax", tflite::ops::custom::nnfw::Register_TensorFlowMax()); - AddCustom("RSQRT", tflite::ops::custom::nnfw::Register_RSQRT()); - AddCustom("SquaredDifference", tflite::ops::custom::nnfw::Register_SquaredDifference()); -} - -} // namespace builtin -} // namespace ops -} // namespace tflite diff --git a/libs/support/tflite/src/nnapi_delegate.cpp b/libs/support/tflite/src/nnapi_delegate.cpp deleted file mode 100644 index 1eada4bca..000000000 --- a/libs/support/tflite/src/nnapi_delegate.cpp +++ /dev/null @@ -1,720 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -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. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This code is derived from the following file (in TensorFlow) -// 'externals/tensorflow/tensorflow/contrib/lite/nnapi_delegate.cc' -#include "support/tflite/nnapi_delegate.h" -#include <fcntl.h> -#include <sys/mman.h> -#include <sys/stat.h> -#include <sys/types.h> -#include "tensorflow/contrib/lite/builtin_op_data.h" -#include "tensorflow/contrib/lite/error_reporter.h" -#include "tensorflow/contrib/lite/model.h" -#include "NeuralNetworksShim.h" -#include "NeuralNetworksExShim.h" - -#ifdef __ANDROID__ -#include <sys/system_properties.h> -#endif - -namespace nnfw -{ - -// TODO(aselle): FATAL leaves resources hanging. -void FATAL(const char* format, ...) { - va_list args; - va_start(args, format); - vfprintf(stderr, format, args); - va_end(args); - fflush(stderr); - exit(1); -} - -// TODO(aselle): Change the error model to use status codes. -#define CHECK_TFLITE_SUCCESS(x) \ - if (x != kTfLiteOk) { \ - FATAL("Aborting since tflite returned failure."); \ - } - -#define CHECK_NN(x) \ - if (x != ANEURALNETWORKS_NO_ERROR) { \ - FATAL("Aborting since tflite returned failure."); \ - } - -namespace { - -int32_t GetAndroidSdkVersion() { -#ifdef __ANDROID__ - const char* sdkProp = "ro.build.version.sdk"; - char sdkVersion[PROP_VALUE_MAX]; - int length = __system_property_get(sdkProp, sdkVersion); - if (length != 0) { - for (int i = 0; i < length; ++i) { - int digit = sdkVersion[i] - '0'; - if (digit < 0 || digit > 9) { - // Non-numeric SDK version, assume it's higher then expected; - return 0xFFFF; - } - } - return atoi(sdkVersion); - } - FATAL("No %s prop", sdkProp); -#endif // __ANDROID__ - return 0; -} - -static const int32_t kAndroidSdkVersion = GetAndroidSdkVersion(); - -} // namespace - -NNAPIAllocation::NNAPIAllocation(const char* filename, - ::tflite::ErrorReporter* error_reporter) - : MMAPAllocation(filename, error_reporter) { - if (mmapped_buffer_ != MAP_FAILED) - CHECK_NN(ANeuralNetworksMemory_createFromFd(buffer_size_bytes_, PROT_READ, - mmap_fd_, 0, &handle_)); -} - -NNAPIAllocation::~NNAPIAllocation() { - if (handle_) { - ANeuralNetworksMemory_free(handle_); - } -} - -NNAPIDelegate::~NNAPIDelegate() { - if (nn_compiled_model_) { - ANeuralNetworksCompilation_free(nn_compiled_model_); - nn_compiled_model_ = nullptr; - } - if (nn_model_) { - ANeuralNetworksModel_free(nn_model_); - nn_model_ = nullptr; - // TODO(aselle): Is this thread-safe and callable multiple times? - } - // ANeuralNetworksShutdown(); -} - -// Adds the tensors of the interpreter to the NN API model. -// Returns the number of operands added. -uint32_t addTensorOperands(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, - const std::vector<uint32_t>& skip_list) { - uint32_t next_id = 0; - for (size_t i = 0; i < interpreter->tensors_size(); i++) { - // skip temporaries tensors. - bool shouldSkip = false; - for (auto skip_idx : skip_list) { - if (i == skip_idx) { - shouldSkip = true; - break; - } - } - if (shouldSkip) continue; - - int32_t nn_type = 0; - // NNAPI requires 32-bit float scale to be zero, tflite doesn't care - float scale = 0.0f; - int32_t zeroPoint = 0; - TfLiteTensor* tensor = interpreter->tensor(i); - switch (tensor->type) { - case kTfLiteNoType: - // Tensors added during initialization of Ops don't have a type yet and - // should not be registered with the NNAPI. - continue; - case kTfLiteFloat32: - nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; - break; - case kTfLiteUInt8: - nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; - scale = tensor->params.scale; - // FIXME The next line is a workaround because currently zero scale is - // passed down from TF - // Lite. Note that the latest NeuralNetworks.h (see - // https://android.googlesource.com/platform/frameworks/ml/+/master/nn/runtime/include/NeuralNetworks.h) - // requires scale to be greater than zero. Remove this workaround - // when the scale - // value is correctly passed. - scale = (scale == 0.0f) ? 1.0f : scale; - zeroPoint = tensor->params.zero_point; - break; - case kTfLiteInt32: - nn_type = ANEURALNETWORKS_TENSOR_INT32; - scale = tensor->params.scale; - zeroPoint = tensor->params.zero_point; - break; - default: - FATAL("Unsupported type."); - } - // TODO(aselle): Note, many of these are intermediate results. Do I need - // to ever specify these sizes. I am currently below doing setValue - // on all of them, but I shouldn't in the future. - // Answer(jeanluc): If all the operators can set the dimension correctly, - // you won't need to. - ANeuralNetworksOperandType operand_type{ - nn_type, static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), scale, zeroPoint}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - // TODO(aselle): Based on Michael's suggestion, limiting this to read - // only memory - if (tensor->allocation_type == kTfLiteMmapRo) { - if (const NNAPIAllocation* alloc = dynamic_cast<const NNAPIAllocation*>( - static_cast<const ::tflite::Allocation*>(tensor->allocation))) { - CHECK_NN(ANeuralNetworksModel_setOperandValueFromMemory( - nn_model, next_id, alloc->memory(), alloc->offset(tensor->data.raw), - tensor->bytes)); - } else { - CHECK_NN(ANeuralNetworksModel_setOperandValue( - nn_model, next_id, tensor->data.raw, tensor->bytes)); - } - } else if (tensor->bytes == 0) { - // These size 0 tensors are optional tensors reserved. - CHECK_NN( - ANeuralNetworksModel_setOperandValue(nn_model, next_id, nullptr, 0)); - } - - ++next_id; - } - return next_id; -} - -// Adds the operations and their parameters to the NN API model. -// 'next-id' is the operand ID of the next operand of the model. -void AddOpsAndParams(tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, uint32_t next_id, - std::vector<int>* model_state_inputs, - std::vector<int>* model_state_outputs) { - for (size_t i = 0; i < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - const TfLiteRegistration& registration = node_and_registration->second; - tflite::BuiltinOperator builtin = - static_cast<tflite::BuiltinOperator>(registration.builtin_code); - - // Add the parameters. - std::vector<uint32_t> augmented_inputs( - node.inputs->data, node.inputs->data + node.inputs->size); - std::vector<uint32_t> augmented_outputs( - node.outputs->data, node.outputs->data + node.outputs->size); - - auto add_scalar_int32 = [&nn_model, &augmented_inputs, - &next_id](int value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_INT32}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(int32_t))) - augmented_inputs.push_back(next_id++); - }; - - auto add_scalar_float32 = [&nn_model, &augmented_inputs, - &next_id](float value) { - ANeuralNetworksOperandType operand_type{.type = ANEURALNETWORKS_FLOAT32}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(float))) - augmented_inputs.push_back(next_id++); - }; - - // Handle state tensors of RNN, LSTM, SVDF. - // For each state_out tensor, a corresponding state_in operand needs to be - // created for NNAPI. - auto duplicate_state_tensor_float32 = - [interpreter, &nn_model, &next_id, &augmented_inputs, - &model_state_inputs, &model_state_outputs](int tensor_id) { - const TfLiteTensor* tensor = interpreter->tensor(tensor_id); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), - tensor->params.scale, tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_inputs.push_back(next_id); - model_state_inputs->push_back(next_id); - model_state_outputs->push_back(tensor_id); - next_id++; - }; - - auto add_add_params = [&add_scalar_int32]() { add_scalar_int32(0); }; - - auto add_pooling_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLitePoolParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->filter_width); - add_scalar_int32(builtin->filter_height); - add_scalar_int32(builtin->activation); - }; - - auto add_convolution_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->activation); - }; - - auto add_depthwise_conv_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteDepthwiseConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->depth_multiplier); - add_scalar_int32(builtin->activation); - }; - - auto add_fully_connected_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteFullyConnectedParams*>(data); - add_scalar_int32(builtin->activation); - }; - - auto add_concatenation_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteConcatenationParams*>(data); - add_scalar_int32(builtin->axis); - if (builtin->activation != kTfLiteActNone) { - FATAL("Concatenation does not support fused activation in NNAPI"); - } - }; - - auto add_softmax_params = [&add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteSoftmaxParams*>(data); - add_scalar_float32(builtin->beta); - }; - - auto add_space_to_depth_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSpaceToDepthParams*>(data); - add_scalar_int32(builtin->block_size); - }; - - auto add_lstm_params = [&add_scalar_int32, - &add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteLSTMParams*>(data); - add_scalar_int32(builtin->activation); - add_scalar_float32(builtin->cell_clip); - add_scalar_float32(builtin->proj_clip); - }; - - // LSTM in NNAPI requires scratch tensor as an output operand. - auto add_lstm_scratch_tensor_float32 = [interpreter, &node, &nn_model, - &next_id, &augmented_outputs]() { - int scratch_buffer_index = node.temporaries->data[0]; - const TfLiteTensor* tensor = interpreter->tensor(scratch_buffer_index); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), tensor->params.scale, - tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_outputs.insert(augmented_outputs.begin(), next_id++); - }; - - auto add_mean_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteMeanParams*>(data); - add_scalar_int32(builtin->keep_dims); - }; - - auto add_svdf_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSVDFParams*>(data); - add_scalar_int32(builtin->rank); - add_scalar_int32(builtin->activation); - }; - - auto add_rnn_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteRNNParams*>(data); - add_scalar_int32(builtin->activation); - }; - - // Handle optional input tensors. - auto add_optional_tensors = [&nn_model, &augmented_inputs, - &next_id](int nn_type) { - for (size_t idx = 0; idx < augmented_inputs.size(); idx++) { - if (augmented_inputs[idx] == kOptionalTensor) { - const std::vector<uint32_t> dim = {0, 0}; - ANeuralNetworksOperandType operand_type{nn_type, 2, dim.data(), 0, 0}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, - nullptr, 0)) - augmented_inputs[idx] = next_id++; - } - } - }; - - int nnapi_version = 10; -#include "nnapi_delegate_ex_AddOpsAndParams_lambda.inc" - - ANeuralNetworksOperationType nn_op_type; - - switch (builtin) { - case tflite::BuiltinOperator_ADD: - nn_op_type = ANEURALNETWORKS_ADD; - add_add_params(); - break; - case tflite::BuiltinOperator_MUL: - nn_op_type = ANEURALNETWORKS_MUL; - add_add_params(); - break; - case tflite::BuiltinOperator_AVERAGE_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_AVERAGE_POOL_2D; - break; - case tflite::BuiltinOperator_MAX_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_MAX_POOL_2D; - break; - case tflite::BuiltinOperator_L2_POOL_2D: - add_pooling_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_L2_POOL_2D; - break; - case tflite::BuiltinOperator_CONV_2D: - add_convolution_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_CONV_2D; - break; - case tflite::BuiltinOperator_RELU: - nn_op_type = ANEURALNETWORKS_RELU; - break; - case tflite::BuiltinOperator_RELU_N1_TO_1: - nn_op_type = ANEURALNETWORKS_RELU1; - break; - case tflite::BuiltinOperator_RELU6: - nn_op_type = ANEURALNETWORKS_RELU6; - break; - case tflite::BuiltinOperator_TANH: - nn_op_type = ANEURALNETWORKS_TANH; - break; - case tflite::BuiltinOperator_FLOOR: - nn_op_type = ANEURALNETWORKS_FLOOR; - break; - case tflite::BuiltinOperator_LOGISTIC: - nn_op_type = ANEURALNETWORKS_LOGISTIC; - break; - case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: - add_depthwise_conv_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_DEPTHWISE_CONV_2D; - break; - case tflite::BuiltinOperator_CONCATENATION: - add_concatenation_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_CONCATENATION; - break; - case tflite::BuiltinOperator_SOFTMAX: - add_softmax_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SOFTMAX; - break; - case tflite::BuiltinOperator_FULLY_CONNECTED: - add_fully_connected_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_FULLY_CONNECTED; - break; - case tflite::BuiltinOperator_RESHAPE: - nn_op_type = ANEURALNETWORKS_RESHAPE; - // add_reshape_params(node.builtin_data); - break; - case tflite::BuiltinOperator_RESIZE_BILINEAR: - add_resize_bilinear_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RESIZE_BILINEAR; - break; - case tflite::BuiltinOperator_SPACE_TO_DEPTH: - add_space_to_depth_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SPACE_TO_DEPTH; - break; - case tflite::BuiltinOperator_LSTM: { - duplicate_state_tensor_float32( - node.outputs->data[/*kOutputStateTensor*/ 0]); - duplicate_state_tensor_float32( - node.outputs->data[/*kCellStateTensor*/ 1]); - add_lstm_params(node.builtin_data); - add_lstm_scratch_tensor_float32(); - add_optional_tensors(ANEURALNETWORKS_TENSOR_FLOAT32); - nn_op_type = ANEURALNETWORKS_LSTM; - break; - } - case tflite::BuiltinOperator_DEQUANTIZE: - nn_op_type = ANEURALNETWORKS_DEQUANTIZE; - break; - case tflite::BuiltinOperator_SVDF: { - duplicate_state_tensor_float32(node.outputs->data[/*kStateTensor*/ 0]); - add_svdf_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SVDF; - break; - } - case tflite::BuiltinOperator_RNN: { - duplicate_state_tensor_float32( - node.outputs->data[/*kHiddenStateTensor*/ 0]); - add_rnn_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RNN; - break; - } - case tflite::BuiltinOperator_EMBEDDING_LOOKUP: - nn_op_type = ANEURALNETWORKS_EMBEDDING_LOOKUP; - break; - case tflite::BuiltinOperator_PAD: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_PAD; - break; - case tflite::BuiltinOperator_MEAN: - nnapi_version = 11; // require NNAPI 1.1 - add_mean_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_MEAN; - break; - case tflite::BuiltinOperator_DIV: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_DIV; - add_add_params(); - break; - case tflite::BuiltinOperator_SUB: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_SUB; - add_add_params(); - break; - case tflite::BuiltinOperator_STRIDED_SLICE: - add_strided_slice_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_STRIDED_SLICE; - break; - case tflite::BuiltinOperator_CAST: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_CAST_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_TOPK_V2: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TOPK_V2_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_GATHER: - add_gather_ex_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_GATHER_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_SPLIT: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SPLIT_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_TRANSPOSE: - nn_op_type = ANEURALNETWORKS_TRANSPOSE; - // param is almost same as reshape - break; - case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: - case tflite::BuiltinOperator_LSH_PROJECTION: - case tflite::BuiltinOperator_HASHTABLE_LOOKUP: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_L2_NORMALIZATION: - case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: - case tflite::BuiltinOperator_PADV2: - case tflite::BuiltinOperator_CALL: - case tflite::BuiltinOperator_SKIP_GRAM: - case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: - case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: - case tflite::BuiltinOperator_SQUEEZE: - case tflite::BuiltinOperator_EXP: - case tflite::BuiltinOperator_LOG_SOFTMAX: - case tflite::BuiltinOperator_DELEGATE: - case tflite::BuiltinOperator_PRELU: - case tflite::BuiltinOperator_MAXIMUM: - case tflite::BuiltinOperator_MINIMUM: - case tflite::BuiltinOperator_ARG_MAX: - case tflite::BuiltinOperator_GREATER: - case tflite::BuiltinOperator_GREATER_EQUAL: - case tflite::BuiltinOperator_LESS: - case tflite::BuiltinOperator_LESS_EQUAL: - case tflite::BuiltinOperator_NEG: - case tflite::BuiltinOperator_SELECT: - case tflite::BuiltinOperator_SLICE: - case tflite::BuiltinOperator_SIN: - case tflite::BuiltinOperator_TRANSPOSE_CONV: - case tflite::BuiltinOperator_SPARSE_TO_DENSE: - FATAL("Op code %d is currently not delegated to NNAPI", builtin); - nn_op_type = -1; // set to invalid - break; - case tflite::BuiltinOperator_CUSTOM: - std::string custom_name(registration.custom_name); - if (custom_name.compare("TensorFlowMax") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TENSORFLOW_MAX_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - else if (custom_name.compare("RSQRT") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_RSQRT_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - else if (custom_name.compare("SquaredDifference") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SQUARED_DIFFERENCE_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - - FATAL("Custom operations are not supported when using NNAPI."); - nn_op_type = -1; // set to invalid - break; - } - - //if (nnapi_version == 11 && kAndroidSdkVersion < 28) { - // FATAL("Op %d needs NNAPI1.1", builtin); - //} - - // Add the operation. - CHECK_NN(ANeuralNetworksModel_addOperation( - nn_model, nn_op_type, static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<uint32_t*>(augmented_outputs.data()))); - } -} - -TfLiteStatus NNAPIDelegate::BuildGraph(::tflite::Interpreter* interpreter) { - // TODO(aselle): This is not correct. need to handle resize invalidation. - if (nn_model_ && nn_compiled_model_) return kTfLiteOk; - - if (!nn_model_) { - CHECK_NN(ANeuralNetworksModel_create(&nn_model_)); - - // Find all the temporary tensors and put them in a skip_list. - std::vector<uint32_t> skip_list; - for (size_t i = 0; i < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - if (node.temporaries != nullptr) { - for (int j = 0; j < node.temporaries->size; j++) { - skip_list.push_back(static_cast<uint32_t>(node.temporaries->data[j])); - } - } - } - - uint32_t next_id = addTensorOperands(interpreter, nn_model_, skip_list); - AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, - &model_states_outputs_); - - std::vector<int> augmented_inputs = interpreter->inputs(); - std::vector<int> augmented_outputs = interpreter->outputs(); - - // All state tensors input/output need to be treated as model input/output. - augmented_inputs.insert(augmented_inputs.end(), - model_states_inputs_.begin(), - model_states_inputs_.end()); - augmented_outputs.insert(augmented_outputs.end(), - model_states_outputs_.begin(), - model_states_outputs_.end()); - - CHECK_NN(ANeuralNetworksModel_identifyInputsAndOutputs( - nn_model_, static_cast<uint32_t>(augmented_inputs.size()), - reinterpret_cast<const uint32_t*>(augmented_inputs.data()), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<const uint32_t*>(augmented_outputs.data()))); - CHECK_NN(ANeuralNetworksModel_finish(nn_model_)); - } - if (!nn_compiled_model_) { - CHECK_NN(ANeuralNetworksCompilation_create(nn_model_, &nn_compiled_model_)); - CHECK_NN(ANeuralNetworksCompilation_finish(nn_compiled_model_)); - } - return kTfLiteOk; -} - -TfLiteStatus NNAPIDelegate::Invoke(::tflite::Interpreter* interpreter) { - if (!nn_model_) { - TF_LITE_ENSURE_STATUS(BuildGraph(interpreter)); - } - - ANeuralNetworksExecution* execution = nullptr; - CHECK_NN(ANeuralNetworksExecution_create(nn_compiled_model_, &execution)); - - // Currently perform deep copy of input buffer - for (size_t i = 0; i < interpreter->inputs().size(); i++) { - int input = interpreter->inputs()[i]; - // TODO(aselle): Is this what we want or do we want input instead? - // TODO(aselle): This should be called setInputValue maybe to be cons. - TfLiteTensor* tensor = interpreter->tensor(input); - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - - // Tell nn api where to place final data. - for (size_t i = 0; i < interpreter->outputs().size(); i++) { - int output = interpreter->outputs()[i]; - TfLiteTensor* tensor = interpreter->tensor(output); - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - - // The state_out of previous invocation need to be mapped to state_in of - // current invocation. - for (size_t i = 0; i < model_states_outputs_.size(); i++) { - int state_tensor_idx = model_states_outputs_[i]; - TfLiteTensor* tensor = interpreter->tensor(state_tensor_idx); - // Here we are using a deep copy for state_in tensors so that we are not - // reading and writing into the same buffer during a invocation. - // TODO(miaowang): using double shared buffer to minimize the copies. - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i + interpreter->inputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - // Tell NNAPI where to output the state_out. - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i + interpreter->outputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - } - - // Currently use blocking compute. - ANeuralNetworksEvent* event = nullptr; - CHECK_NN(ANeuralNetworksExecution_startCompute(execution, &event)); - CHECK_NN(ANeuralNetworksEvent_wait(event)); - ANeuralNetworksEvent_free(event); - ANeuralNetworksExecution_free(execution); - -#if 0 - printf("From the NN API:\n"); - TfLiteTensor* tensor = interpreter->tensor(interpreter->outputs()[0]); - if (float* data = - interpreter->typed_tensor<float>(interpreter->outputs()[0])) { - size_t num = tensor->bytes / sizeof(float); - for (float* p = data; p < data + num; p++) { - printf(" %f", *p); - } - printf("\n"); - } -#endif - - return kTfLiteOk; -} - -} // namespace nnfw - -// clang-format on diff --git a/libs/support/tflite/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc b/libs/support/tflite/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc deleted file mode 100644 index ea485fe45..000000000 --- a/libs/support/tflite/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc +++ /dev/null @@ -1,41 +0,0 @@ -// This file is included from AddOpsAndParams defined in nnapi_delegate.cc -// and contains lambda for extened implementation to original Tensorflow Lite. - auto add_resize_bilinear_params = [&add_scalar_int32, &interpreter, &augmented_inputs](void* data) { - auto builtin = reinterpret_cast<TfLiteResizeBilinearParams*>(data); - if (builtin->align_corners) { - FATAL("Resize bilinear does not support align corners in NNAPI"); - } - - TfLiteTensor* tensor = interpreter->tensor(augmented_inputs.back()); - assert(tensor->type == kTfLiteInt32); - assert(tensor->bytes == sizeof(int)*2); - augmented_inputs.pop_back(); - - int height = ((int*)(tensor->data.raw))[1]; - int width = ((int*)(tensor->data.raw))[0]; - add_scalar_int32(height); - add_scalar_int32(width); - }; - - auto add_strided_slice_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteStridedSliceParams*>(data); - add_scalar_int32(builtin->begin_mask); - add_scalar_int32(builtin->end_mask); - // ellipsis_mask and new_axis_mask are not supported on nn runtime - // cf) tflite interpreter supports both operations - if (builtin->ellipsis_mask) { - FATAL("STRIDE_SLICE does not support ellipsis_mask in NNAPI"); - } - if (builtin->new_axis_mask) { - FATAL("STRIDE_SLICE does not support new_axis_mask in NNAPI"); - } - add_scalar_int32(builtin->shrink_axis_mask); - }; - - auto add_gather_ex_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteGatherParams*>(data); - add_scalar_int32(builtin->axis); - if (builtin->axis != 0) { - FATAL("GATHER does not support axis>0 in NNAPI"); - } - }; |