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