/*
* 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 "SVDF.h"
#include "NeuralNetworksWrapper.h"
#include "gmock/gmock-matchers.h"
#include "gtest/gtest.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 = TfLiteFusedActivation::kTfLiteActNone;
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