| /* |
| * Copyright (C) 2017 The Android Open Source Project |
| * |
| * 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 <gmock/gmock.h> |
| #include <gtest/gtest.h> |
| |
| #include <vector> |
| |
| #include "NeuralNetworksWrapper.h" |
| #include "SVDF.h" |
| |
| using ::testing::FloatNear; |
| using ::testing::Matcher; |
| |
| namespace android { |
| namespace nn { |
| namespace wrapper { |
| |
| namespace { |
| |
| std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values, |
| float max_abs_error = 1.e-6) { |
| std::vector<Matcher<float>> matchers; |
| matchers.reserve(values.size()); |
| for (const float& v : values) { |
| matchers.emplace_back(FloatNear(v, max_abs_error)); |
| } |
| return matchers; |
| } |
| |
| } // namespace |
| |
| using ::testing::ElementsAreArray; |
| |
| static float svdf_input[] = { |
| 0.12609188, -0.46347019, -0.89598465, 0.12609188, -0.46347019, -0.89598465, |
| |
| 0.14278367, -1.64410412, -0.75222826, 0.14278367, -1.64410412, -0.75222826, |
| |
| 0.49837467, 0.19278903, 0.26584083, 0.49837467, 0.19278903, 0.26584083, |
| |
| -0.11186574, 0.13164264, -0.05349274, -0.11186574, 0.13164264, -0.05349274, |
| |
| -0.68892461, 0.37783599, 0.18263303, -0.68892461, 0.37783599, 0.18263303, |
| |
| -0.81299269, -0.86831826, 1.43940818, -0.81299269, -0.86831826, 1.43940818, |
| |
| -1.45006323, -0.82251364, -1.69082689, -1.45006323, -0.82251364, -1.69082689, |
| |
| 0.03966608, -0.24936394, -0.77526885, 0.03966608, -0.24936394, -0.77526885, |
| |
| 0.11771342, -0.23761693, -0.65898693, 0.11771342, -0.23761693, -0.65898693, |
| |
| -0.89477462, 1.67204106, -0.53235275, -0.89477462, 1.67204106, -0.53235275}; |
| |
| static float svdf_input_rank2[] = { |
| 0.12609188, -0.46347019, -0.89598465, 0.35867718, 0.36897406, 0.73463392, |
| |
| 0.14278367, -1.64410412, -0.75222826, -0.57290924, 0.12729003, 0.7567004, |
| |
| 0.49837467, 0.19278903, 0.26584083, 0.17660543, 0.52949083, -0.77931279, |
| |
| -0.11186574, 0.13164264, -0.05349274, -0.72674477, -0.5683046, 0.55900657, |
| |
| -0.68892461, 0.37783599, 0.18263303, -0.63690937, 0.44483393, -0.71817774, |
| |
| -0.81299269, -0.86831826, 1.43940818, -0.95760226, 1.82078898, 0.71135032, |
| |
| -1.45006323, -0.82251364, -1.69082689, -1.65087092, -1.89238167, 1.54172635, |
| |
| 0.03966608, -0.24936394, -0.77526885, 2.06740379, -1.51439476, 1.43768692, |
| |
| 0.11771342, -0.23761693, -0.65898693, 0.31088525, -1.55601168, -0.87661445, |
| |
| -0.89477462, 1.67204106, -0.53235275, -0.6230064, 0.29819036, 1.06939757, |
| }; |
| |
| static float svdf_golden_output[] = {0.014899, -0.0517661, -0.143725, -0.00271883, |
| 0.014899, -0.0517661, -0.143725, -0.00271883, |
| |
| 0.068281, -0.162217, -0.152268, 0.00323521, |
| 0.068281, -0.162217, -0.152268, 0.00323521, |
| |
| -0.0317821, -0.0333089, 0.0609602, 0.0333759, |
| -0.0317821, -0.0333089, 0.0609602, 0.0333759, |
| |
| -0.00623099, -0.077701, -0.391193, -0.0136691, |
| -0.00623099, -0.077701, -0.391193, -0.0136691, |
| |
| 0.201551, -0.164607, -0.179462, -0.0592739, |
| 0.201551, -0.164607, -0.179462, -0.0592739, |
| |
| 0.0886511, -0.0875401, -0.269283, 0.0281379, |
| 0.0886511, -0.0875401, -0.269283, 0.0281379, |
| |
| -0.201174, -0.586145, -0.628624, -0.0330412, |
| -0.201174, -0.586145, -0.628624, -0.0330412, |
| |
| -0.0839096, -0.299329, 0.108746, 0.109808, |
| -0.0839096, -0.299329, 0.108746, 0.109808, |
| |
| 0.419114, -0.237824, -0.422627, 0.175115, |
| 0.419114, -0.237824, -0.422627, 0.175115, |
| |
| 0.36726, -0.522303, -0.456502, -0.175475, |
| 0.36726, -0.522303, -0.456502, -0.175475}; |
| |
| static float svdf_golden_output_rank_2[] = { |
| -0.09623547, -0.10193135, 0.11083051, -0.0347917, |
| 0.1141196, 0.12965347, -0.12652366, 0.01007236, |
| |
| -0.16396809, -0.21247184, 0.11259045, -0.04156673, |
| 0.10132131, -0.06143532, -0.00924693, 0.10084561, |
| |
| 0.01257364, 0.0506071, -0.19287863, -0.07162561, |
| -0.02033747, 0.22673416, 0.15487903, 0.02525555, |
| |
| -0.1411963, -0.37054959, 0.01774767, 0.05867489, |
| 0.09607603, -0.0141301, -0.08995658, 0.12867066, |
| |
| -0.27142537, -0.16955489, 0.18521598, -0.12528358, |
| 0.00331409, 0.11167502, 0.02218599, -0.07309391, |
| |
| 0.09593632, -0.28361851, -0.0773851, 0.17199151, |
| -0.00075242, 0.33691186, -0.1536046, 0.16572715, |
| |
| -0.27916506, -0.27626723, 0.42615682, 0.3225764, |
| -0.37472126, -0.55655634, -0.05013514, 0.289112, |
| |
| -0.24418658, 0.07540751, -0.1940318, -0.08911639, |
| 0.00732617, 0.46737891, 0.26449674, 0.24888524, |
| |
| -0.17225097, -0.54660404, -0.38795233, 0.08389944, |
| 0.07736043, -0.28260678, 0.15666828, 1.14949894, |
| |
| -0.57454878, -0.64704704, 0.73235172, -0.34616736, |
| 0.21120001, -0.22927976, 0.02455296, -0.35906726, |
| }; |
| |
| #define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \ |
| ACTION(Input) \ |
| ACTION(WeightsFeature) \ |
| ACTION(WeightsTime) \ |
| ACTION(Bias) \ |
| ACTION(StateIn) |
| |
| // For all output and intermediate states |
| #define FOR_ALL_OUTPUT_TENSORS(ACTION) \ |
| ACTION(StateOut) \ |
| ACTION(Output) |
| |
| // Derived class of SingleOpModel, which is used to test SVDF TFLite op. |
| class SVDFOpModel { |
| public: |
| SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, |
| uint32_t rank) |
| : batches_(batches), |
| units_(units), |
| input_size_(input_size), |
| memory_size_(memory_size), |
| rank_(rank) { |
| std::vector<std::vector<uint32_t>> input_shapes{ |
| {batches_, input_size_}, // Input tensor |
| {units_ * rank_, input_size_}, // weights_feature tensor |
| {units_ * rank_, memory_size_}, // weights_time tensor |
| {units_}, // bias tensor |
| {batches_, memory_size * units_ * rank_}, // state in tensor |
| }; |
| std::vector<uint32_t> inputs; |
| auto it = input_shapes.begin(); |
| |
| // Input and weights |
| #define AddInput(X) \ |
| OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it++); \ |
| inputs.push_back(model_.addOperand(&X##OpndTy)); |
| |
| FOR_ALL_INPUT_AND_WEIGHT_TENSORS(AddInput); |
| |
| #undef AddInput |
| |
| // Parameters |
| OperandType RankParamTy(Type::INT32, {}); |
| inputs.push_back(model_.addOperand(&RankParamTy)); |
| OperandType ActivationParamTy(Type::INT32, {}); |
| inputs.push_back(model_.addOperand(&ActivationParamTy)); |
| |
| // Output and other intermediate state |
| std::vector<std::vector<uint32_t>> output_shapes{{batches_, memory_size_ * units_ * rank_}, |
| {batches_, units_}}; |
| std::vector<uint32_t> outputs; |
| |
| auto it2 = output_shapes.begin(); |
| |
| #define AddOutput(X) \ |
| OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it2++); \ |
| outputs.push_back(model_.addOperand(&X##OpndTy)); |
| |
| FOR_ALL_OUTPUT_TENSORS(AddOutput); |
| |
| #undef AddOutput |
| |
| Input_.insert(Input_.end(), batches_ * input_size_, 0.f); |
| StateIn_.insert(StateIn_.end(), batches_ * units_ * rank_ * memory_size_, 0.f); |
| |
| auto multiAll = [](const std::vector<uint32_t>& dims) -> uint32_t { |
| uint32_t sz = 1; |
| for (uint32_t d : dims) { |
| sz *= d; |
| } |
| return sz; |
| }; |
| |
| it2 = output_shapes.begin(); |
| |
| #define ReserveOutput(X) X##_.insert(X##_.end(), multiAll(*it2++), 0.f); |
| |
| FOR_ALL_OUTPUT_TENSORS(ReserveOutput); |
| |
| model_.addOperation(ANEURALNETWORKS_SVDF, inputs, outputs); |
| model_.identifyInputsAndOutputs(inputs, outputs); |
| |
| model_.finish(); |
| } |
| |
| void Invoke() { |
| ASSERT_TRUE(model_.isValid()); |
| |
| Compilation compilation(&model_); |
| compilation.finish(); |
| Execution execution(&compilation); |
| |
| StateIn_.swap(StateOut_); |
| |
| #define SetInputOrWeight(X) \ |
| ASSERT_EQ(execution.setInput(SVDF::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \ |
| Result::NO_ERROR); |
| |
| FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight); |
| |
| #undef SetInputOrWeight |
| |
| #define SetOutput(X) \ |
| EXPECT_TRUE(X##_.data() != nullptr); \ |
| ASSERT_EQ(execution.setOutput(SVDF::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \ |
| Result::NO_ERROR); |
| |
| FOR_ALL_OUTPUT_TENSORS(SetOutput); |
| |
| #undef SetOutput |
| |
| ASSERT_EQ(execution.setInput(SVDF::kRankParam, &rank_, sizeof(rank_)), Result::NO_ERROR); |
| |
| int activation = ActivationFn::kActivationNone; |
| ASSERT_EQ(execution.setInput(SVDF::kActivationParam, &activation, sizeof(activation)), |
| Result::NO_ERROR); |
| |
| ASSERT_EQ(execution.compute(), Result::NO_ERROR); |
| } |
| |
| #define DefineSetter(X) \ |
| void Set##X(const std::vector<float>& f) { X##_.insert(X##_.end(), f.begin(), f.end()); } |
| |
| FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter); |
| |
| #undef DefineSetter |
| |
| void SetInput(int offset, float* begin, float* end) { |
| for (; begin != end; begin++, offset++) { |
| Input_[offset] = *begin; |
| } |
| } |
| |
| // Resets the state of SVDF op by filling it with 0's. |
| void ResetState() { |
| std::fill(StateIn_.begin(), StateIn_.end(), 0.f); |
| std::fill(StateOut_.begin(), StateOut_.end(), 0.f); |
| } |
| |
| // Extracts the output tensor from the SVDF op. |
| const std::vector<float>& GetOutput() const { return Output_; } |
| |
| int input_size() const { return input_size_; } |
| int num_units() const { return units_; } |
| int num_batches() const { return batches_; } |
| |
| private: |
| Model model_; |
| |
| const uint32_t batches_; |
| const uint32_t units_; |
| const uint32_t input_size_; |
| const uint32_t memory_size_; |
| const uint32_t rank_; |
| |
| #define DefineTensor(X) std::vector<float> X##_; |
| |
| FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor); |
| FOR_ALL_OUTPUT_TENSORS(DefineTensor); |
| |
| #undef DefineTensor |
| }; |
| |
| TEST(SVDFOpTest, BlackBoxTest) { |
| SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, |
| /*memory_size=*/10, /*rank=*/1); |
| svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347, 0.22197971, 0.12416199, |
| 0.27901134, 0.27557442, 0.3905206, -0.36137494, -0.06634006, |
| -0.10640851}); |
| |
| svdf.SetWeightsTime({-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, |
| 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, |
| |
| 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, |
| -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, |
| |
| -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, |
| 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, |
| |
| -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, |
| -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657}); |
| |
| svdf.SetBias({}); |
| |
| svdf.ResetState(); |
| const int svdf_num_batches = svdf.num_batches(); |
| const int svdf_input_size = svdf.input_size(); |
| const int svdf_num_units = svdf.num_units(); |
| const int input_sequence_size = |
| sizeof(svdf_input) / sizeof(float) / (svdf_input_size * svdf_num_batches); |
| // Going over each input batch, setting the input tensor, invoking the SVDF op |
| // and checking the output with the expected golden values. |
| for (int i = 0; i < input_sequence_size; i++) { |
| float* batch_start = svdf_input + i * svdf_input_size * svdf_num_batches; |
| float* batch_end = batch_start + svdf_input_size * svdf_num_batches; |
| svdf.SetInput(0, batch_start, batch_end); |
| |
| svdf.Invoke(); |
| |
| float* golden_start = svdf_golden_output + i * svdf_num_units * svdf_num_batches; |
| float* golden_end = golden_start + svdf_num_units * svdf_num_batches; |
| std::vector<float> expected; |
| expected.insert(expected.end(), golden_start, golden_end); |
| |
| EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); |
| } |
| } |
| |
| TEST(SVDFOpTest, BlackBoxTestRank2) { |
| SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, |
| /*memory_size=*/10, /*rank=*/2); |
| svdf.SetWeightsFeature({-0.31930989, 0.0079667, 0.39296314, 0.37613347, 0.12416199, |
| 0.15785322, 0.27901134, 0.3905206, 0.21931258, -0.36137494, |
| -0.10640851, 0.31053296, -0.36118156, -0.0976817, -0.36916667, |
| 0.22197971, 0.15294972, 0.38031587, 0.27557442, 0.39635518, |
| -0.21580373, -0.06634006, -0.02702999, 0.27072677}); |
| |
| svdf.SetWeightsTime({-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, |
| 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, |
| |
| 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, |
| -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, |
| |
| -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, |
| 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, |
| |
| -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, |
| -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657, |
| |
| -0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486, |
| 0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187, |
| |
| -0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589, |
| 0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836, |
| |
| -0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277, |
| -0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214, |
| |
| 0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326, |
| 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763}); |
| |
| svdf.SetBias({}); |
| |
| svdf.ResetState(); |
| const int svdf_num_batches = svdf.num_batches(); |
| const int svdf_input_size = svdf.input_size(); |
| const int svdf_num_units = svdf.num_units(); |
| const int input_sequence_size = |
| sizeof(svdf_input_rank2) / sizeof(float) / (svdf_input_size * svdf_num_batches); |
| // Going over each input batch, setting the input tensor, invoking the SVDF op |
| // and checking the output with the expected golden values. |
| for (int i = 0; i < input_sequence_size; i++) { |
| float* batch_start = svdf_input_rank2 + i * svdf_input_size * svdf_num_batches; |
| float* batch_end = batch_start + svdf_input_size * svdf_num_batches; |
| svdf.SetInput(0, batch_start, batch_end); |
| |
| svdf.Invoke(); |
| |
| float* golden_start = svdf_golden_output_rank_2 + i * svdf_num_units * svdf_num_batches; |
| float* golden_end = golden_start + svdf_num_units * svdf_num_batches; |
| std::vector<float> expected; |
| expected.insert(expected.end(), golden_start, golden_end); |
| |
| EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); |
| } |
| } |
| |
| } // namespace wrapper |
| } // namespace nn |
| } // namespace android |
