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307 lines
14 KiB
307 lines
14 KiB
/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include <vector>
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#include "NeuralNetworksWrapper.h"
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#include "RNN.h"
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namespace android {
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namespace nn {
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namespace wrapper {
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using ::testing::Each;
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using ::testing::FloatNear;
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using ::testing::Matcher;
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namespace {
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std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
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float max_abs_error = 1.e-5) {
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std::vector<Matcher<float>> matchers;
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matchers.reserve(values.size());
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for (const float& v : values) {
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matchers.emplace_back(FloatNear(v, max_abs_error));
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}
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return matchers;
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}
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static float rnn_input[] = {
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0.23689353, 0.285385, 0.037029743, -0.19858193, -0.27569133, 0.43773448,
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0.60379338, 0.35562468, -0.69424844, -0.93421471, -0.87287879, 0.37144363,
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-0.62476718, 0.23791671, 0.40060222, 0.1356622, -0.99774903, -0.98858172,
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-0.38952237, -0.47685933, 0.31073618, 0.71511042, -0.63767755, -0.31729108,
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0.33468103, 0.75801885, 0.30660987, -0.37354088, 0.77002847, -0.62747043,
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-0.68572164, 0.0069220066, 0.65791464, 0.35130811, 0.80834007, -0.61777675,
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-0.21095741, 0.41213346, 0.73784804, 0.094794154, 0.47791874, 0.86496925,
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-0.53376222, 0.85315156, 0.10288584, 0.86684, -0.011186242, 0.10513687,
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0.87825835, 0.59929144, 0.62827742, 0.18899453, 0.31440187, 0.99059987,
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0.87170351, -0.35091716, 0.74861872, 0.17831337, 0.2755419, 0.51864719,
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0.55084288, 0.58982027, -0.47443086, 0.20875752, -0.058871567, -0.66609079,
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0.59098077, 0.73017097, 0.74604273, 0.32882881, -0.17503482, 0.22396147,
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0.19379807, 0.29120302, 0.077113032, -0.70331609, 0.15804303, -0.93407321,
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0.40182066, 0.036301374, 0.66521823, 0.0300982, -0.7747041, -0.02038002,
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0.020698071, -0.90300065, 0.62870288, -0.23068321, 0.27531278, -0.095755219,
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-0.712036, -0.17384434, -0.50593495, -0.18646687, -0.96508682, 0.43519354,
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0.14744234, 0.62589407, 0.1653645, -0.10651493, -0.045277178, 0.99032974,
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-0.88255352, -0.85147917, 0.28153265, 0.19455957, -0.55479527, -0.56042433,
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0.26048636, 0.84702539, 0.47587705, -0.074295521, -0.12287641, 0.70117295,
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0.90532446, 0.89782166, 0.79817224, 0.53402734, -0.33286154, 0.073485017,
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-0.56172788, -0.044897556, 0.89964068, -0.067662835, 0.76863563, 0.93455386,
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-0.6324693, -0.083922029};
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static float rnn_golden_output[] = {
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0.496726, 0, 0.965996, 0, 0.0584254, 0, 0, 0.12315,
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0, 0, 0.612266, 0.456601, 0, 0.52286, 1.16099, 0.0291232,
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0, 0, 0.524901, 0, 0, 0, 0, 1.02116,
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0, 1.35762, 0, 0.356909, 0.436415, 0.0355727, 0, 0,
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0, 0, 0, 0.262335, 0, 0, 0, 1.33992,
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0, 2.9739, 0, 0, 1.31914, 2.66147, 0, 0,
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0.942568, 0, 0, 0, 0.025507, 0, 0, 0,
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0.321429, 0.569141, 1.25274, 1.57719, 0.8158, 1.21805, 0.586239, 0.25427,
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1.04436, 0, 0.630725, 0, 0.133801, 0.210693, 0.363026, 0,
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0.533426, 0, 1.25926, 0.722707, 0, 1.22031, 1.30117, 0.495867,
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0.222187, 0, 0.72725, 0, 0.767003, 0, 0, 0.147835,
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0, 0, 0, 0.608758, 0.469394, 0.00720298, 0.927537, 0,
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0.856974, 0.424257, 0, 0, 0.937329, 0, 0, 0,
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0.476425, 0, 0.566017, 0.418462, 0.141911, 0.996214, 1.13063, 0,
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0.967899, 0, 0, 0, 0.0831304, 0, 0, 1.00378,
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0, 0, 0, 1.44818, 1.01768, 0.943891, 0.502745, 0,
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0.940135, 0, 0, 0, 0, 0, 0, 2.13243,
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0, 0.71208, 0.123918, 1.53907, 1.30225, 1.59644, 0.70222, 0,
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0.804329, 0, 0.430576, 0, 0.505872, 0.509603, 0.343448, 0,
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0.107756, 0.614544, 1.44549, 1.52311, 0.0454298, 0.300267, 0.562784, 0.395095,
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0.228154, 0, 0.675323, 0, 1.70536, 0.766217, 0, 0,
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0, 0.735363, 0.0759267, 1.91017, 0.941888, 0, 0, 0,
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0, 0, 1.5909, 0, 0, 0, 0, 0.5755,
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0, 0.184687, 0, 1.56296, 0.625285, 0, 0, 0,
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0, 0, 0.0857888, 0, 0, 0, 0, 0.488383,
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0.252786, 0, 0, 0, 1.02817, 1.85665, 0, 0,
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0.00981836, 0, 1.06371, 0, 0, 0, 0, 0,
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0, 0.290445, 0.316406, 0, 0.304161, 1.25079, 0.0707152, 0,
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0.986264, 0.309201, 0, 0, 0, 0, 0, 1.64896,
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0.346248, 0, 0.918175, 0.78884, 0.524981, 1.92076, 2.07013, 0.333244,
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0.415153, 0.210318, 0, 0, 0, 0, 0, 2.02616,
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0, 0.728256, 0.84183, 0.0907453, 0.628881, 3.58099, 1.49974, 0};
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} // anonymous namespace
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#define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \
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ACTION(Input) \
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ACTION(Weights) \
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ACTION(RecurrentWeights) \
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ACTION(Bias) \
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ACTION(HiddenStateIn)
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// For all output and intermediate states
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#define FOR_ALL_OUTPUT_TENSORS(ACTION) \
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ACTION(HiddenStateOut) \
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ACTION(Output)
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class BasicRNNOpModel {
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public:
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BasicRNNOpModel(uint32_t batches, uint32_t units, uint32_t size)
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: batches_(batches), units_(units), input_size_(size), activation_(kActivationRelu) {
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std::vector<uint32_t> inputs;
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OperandType InputTy(Type::TENSOR_FLOAT32, {batches_, input_size_});
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inputs.push_back(model_.addOperand(&InputTy));
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OperandType WeightTy(Type::TENSOR_FLOAT32, {units_, input_size_});
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inputs.push_back(model_.addOperand(&WeightTy));
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OperandType RecurrentWeightTy(Type::TENSOR_FLOAT32, {units_, units_});
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inputs.push_back(model_.addOperand(&RecurrentWeightTy));
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OperandType BiasTy(Type::TENSOR_FLOAT32, {units_});
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inputs.push_back(model_.addOperand(&BiasTy));
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OperandType HiddenStateTy(Type::TENSOR_FLOAT32, {batches_, units_});
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inputs.push_back(model_.addOperand(&HiddenStateTy));
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OperandType ActionParamTy(Type::INT32, {});
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inputs.push_back(model_.addOperand(&ActionParamTy));
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std::vector<uint32_t> outputs;
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outputs.push_back(model_.addOperand(&HiddenStateTy));
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OperandType OutputTy(Type::TENSOR_FLOAT32, {batches_, units_});
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outputs.push_back(model_.addOperand(&OutputTy));
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Input_.insert(Input_.end(), batches_ * input_size_, 0.f);
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HiddenStateIn_.insert(HiddenStateIn_.end(), batches_ * units_, 0.f);
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HiddenStateOut_.insert(HiddenStateOut_.end(), batches_ * units_, 0.f);
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Output_.insert(Output_.end(), batches_ * units_, 0.f);
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model_.addOperation(ANEURALNETWORKS_RNN, inputs, outputs);
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model_.identifyInputsAndOutputs(inputs, outputs);
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model_.finish();
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}
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#define DefineSetter(X) \
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void Set##X(const std::vector<float>& f) { X##_.insert(X##_.end(), f.begin(), f.end()); }
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FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter);
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#undef DefineSetter
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void SetInput(int offset, float* begin, float* end) {
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for (; begin != end; begin++, offset++) {
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Input_[offset] = *begin;
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}
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}
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void ResetHiddenState() {
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std::fill(HiddenStateIn_.begin(), HiddenStateIn_.end(), 0.f);
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std::fill(HiddenStateOut_.begin(), HiddenStateOut_.end(), 0.f);
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}
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const std::vector<float>& GetOutput() const { return Output_; }
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uint32_t input_size() const { return input_size_; }
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uint32_t num_units() const { return units_; }
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uint32_t num_batches() const { return batches_; }
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void Invoke() {
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ASSERT_TRUE(model_.isValid());
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HiddenStateIn_.swap(HiddenStateOut_);
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Compilation compilation(&model_);
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compilation.finish();
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Execution execution(&compilation);
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#define SetInputOrWeight(X) \
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ASSERT_EQ(execution.setInput(RNN::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \
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Result::NO_ERROR);
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FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);
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#undef SetInputOrWeight
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#define SetOutput(X) \
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ASSERT_EQ(execution.setOutput(RNN::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \
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Result::NO_ERROR);
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FOR_ALL_OUTPUT_TENSORS(SetOutput);
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#undef SetOutput
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ASSERT_EQ(execution.setInput(RNN::kActivationParam, &activation_, sizeof(activation_)),
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Result::NO_ERROR);
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ASSERT_EQ(execution.compute(), Result::NO_ERROR);
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}
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private:
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Model model_;
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const uint32_t batches_;
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const uint32_t units_;
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const uint32_t input_size_;
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const int activation_;
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#define DefineTensor(X) std::vector<float> X##_;
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FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor);
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FOR_ALL_OUTPUT_TENSORS(DefineTensor);
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#undef DefineTensor
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};
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TEST(RNNOpTest, BlackBoxTest) {
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BasicRNNOpModel rnn(2, 16, 8);
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rnn.SetWeights(
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{0.461459, 0.153381, 0.529743, -0.00371218, 0.676267, -0.211346, 0.317493,
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0.969689, -0.343251, 0.186423, 0.398151, 0.152399, 0.448504, 0.317662,
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0.523556, -0.323514, 0.480877, 0.333113, -0.757714, -0.674487, -0.643585,
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0.217766, -0.0251462, 0.79512, -0.595574, -0.422444, 0.371572, -0.452178,
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-0.556069, -0.482188, -0.685456, -0.727851, 0.841829, 0.551535, -0.232336,
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0.729158, -0.00294906, -0.69754, 0.766073, -0.178424, 0.369513, -0.423241,
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0.548547, -0.0152023, -0.757482, -0.85491, 0.251331, -0.989183, 0.306261,
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-0.340716, 0.886103, -0.0726757, -0.723523, -0.784303, 0.0354295, 0.566564,
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-0.485469, -0.620498, 0.832546, 0.697884, -0.279115, 0.294415, -0.584313,
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0.548772, 0.0648819, 0.968726, 0.723834, -0.0080452, -0.350386, -0.272803,
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0.115121, -0.412644, -0.824713, -0.992843, -0.592904, -0.417893, 0.863791,
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-0.423461, -0.147601, -0.770664, -0.479006, 0.654782, 0.587314, -0.639158,
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0.816969, -0.337228, 0.659878, 0.73107, 0.754768, -0.337042, 0.0960841,
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0.368357, 0.244191, -0.817703, -0.211223, 0.442012, 0.37225, -0.623598,
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-0.405423, 0.455101, 0.673656, -0.145345, -0.511346, -0.901675, -0.81252,
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-0.127006, 0.809865, -0.721884, 0.636255, 0.868989, -0.347973, -0.10179,
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-0.777449, 0.917274, 0.819286, 0.206218, -0.00785118, 0.167141, 0.45872,
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0.972934, -0.276798, 0.837861, 0.747958, -0.0151566, -0.330057, -0.469077,
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0.277308, 0.415818});
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rnn.SetBias({0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068, -0.23566568,
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-0.389184, 0.47481549, -0.4791103, 0.29931796, 0.10463274, 0.83918178, 0.37197268,
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0.61957061, 0.3956964, -0.37609905});
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rnn.SetRecurrentWeights(
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{0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0.1});
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rnn.ResetHiddenState();
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const int input_sequence_size =
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sizeof(rnn_input) / sizeof(float) / (rnn.input_size() * rnn.num_batches());
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for (int i = 0; i < input_sequence_size; i++) {
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float* batch_start = rnn_input + i * rnn.input_size();
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float* batch_end = batch_start + rnn.input_size();
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rnn.SetInput(0, batch_start, batch_end);
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rnn.SetInput(rnn.input_size(), batch_start, batch_end);
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rnn.Invoke();
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float* golden_start = rnn_golden_output + i * rnn.num_units();
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float* golden_end = golden_start + rnn.num_units();
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std::vector<float> expected;
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expected.insert(expected.end(), golden_start, golden_end);
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expected.insert(expected.end(), golden_start, golden_end);
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EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
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}
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}
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} // namespace wrapper
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} // namespace nn
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} // namespace android
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