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author | 2017-11-10 10:35:35 -0800 | |
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committer | 2017-11-10 16:14:42 -0800 | |
commit | 0b15439f8f0f2d4755587f4096c3ea04cb199d23 (patch) | |
tree | 9aa4fc8162bf9b4ee50112a7b85703f70ca4df08 /tensorflow/contrib/lite/kernels/svdf_test.cc | |
parent | 7ac140a5845553275427162aabd9d54987144b4a (diff) |
Internal Change.
PiperOrigin-RevId: 175307445
Diffstat (limited to 'tensorflow/contrib/lite/kernels/svdf_test.cc')
-rw-r--r-- | tensorflow/contrib/lite/kernels/svdf_test.cc | 312 |
1 files changed, 312 insertions, 0 deletions
diff --git a/tensorflow/contrib/lite/kernels/svdf_test.cc b/tensorflow/contrib/lite/kernels/svdf_test.cc new file mode 100644 index 0000000000..d956025e9d --- /dev/null +++ b/tensorflow/contrib/lite/kernels/svdf_test.cc @@ -0,0 +1,312 @@ +/* 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. +==============================================================================*/ +// Unit test for TFLite SVDF op. + +#include <vector> +#include <iomanip> + +#include <gmock/gmock.h> +#include <gtest/gtest.h> +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/model.h" + +namespace tflite { +namespace { + +using ::testing::ElementsAreArray; + +static float svdf_input[] = { + 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_rank_1[] = { + 0.014899, -0.0517661, -0.143725, -0.00271883, + -0.03004015, 0.09565311, 0.1587342, 0.00784263, + + 0.068281, -0.162217, -0.152268, 0.00323521, + 0.01582633, 0.03858774, -0.03001583, -0.02671271, + + -0.0317821, -0.0333089, 0.0609602, 0.0333759, + -0.01432795, 0.05524484, 0.1101355, -0.02382665, + + -0.00623099, -0.077701, -0.391193, -0.0136691, + -0.02333033, 0.02293761, 0.12338032, 0.04326871, + + 0.201551, -0.164607, -0.179462, -0.0592739, + 0.01064911, -0.17503069, 0.07821996, -0.00224009, + + 0.0886511, -0.0875401, -0.269283, 0.0281379, + -0.02282338, 0.09741908, 0.32973239, 0.12281385, + + -0.201174, -0.586145, -0.628624, -0.0330412, + 0.24780814, -0.39304617, -0.22473189, 0.02589256, + + -0.0839096, -0.299329, 0.108746, 0.109808, + 0.10084175, -0.06416984, 0.28936723, 0.0026358, + + 0.419114, -0.237824, -0.422627, 0.175115, + -0.2314795, -0.18584411, -0.4228974, -0.12928449, + + 0.36726, -0.522303, -0.456502, -0.175475, + 0.17012937, -0.34447709, 0.38505614, -0.28158101, +}; + +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, +}; + +// Derived class of SingleOpModel, which is used to test SVDF TFLite op. +class SVDFOpModel : public SingleOpModel { + public: + SVDFOpModel(int batches, int units, int input_size, int memory_size, int rank) + : batches_(batches), + units_(units), + input_size_(input_size), + memory_size_(memory_size), + rank_(rank) { + input_ = AddInput(TensorType_FLOAT32); + weights_feature_ = AddInput(TensorType_FLOAT32); + weights_time_ = AddInput(TensorType_FLOAT32); + bias_ = AddNullInput(); + state_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp( + BuiltinOperator_SVDF, BuiltinOptions_SVDFOptions, + CreateSVDFOptions(builder_, rank, ActivationFunctionType_NONE).Union()); + BuildInterpreter({ + {batches_, input_size_}, // Input tensor + {units_ * rank, input_size_}, // weights_feature tensor + {units_ * rank, memory_size_}, // weights_time tensor + {units_} // bias tensor + }); + } + + // Populates the weights_feature tensor. + void SetWeightsFeature(std::initializer_list<float> f) { + PopulateTensor(weights_feature_, f); + } + + // Populates the weights_time tensor. + void SetWeightsTime(std::initializer_list<float> f) { + PopulateTensor(weights_time_, f); + } + + // Populates the input tensor. + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + // Resets the state of SVDF op by filling it with 0's. + void ResetState() { + const int zero_buffer_size = rank_ * units_ * batches_ * memory_size_; + std::unique_ptr<float[]> zero_buffer(new float[zero_buffer_size]); + memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float)); + PopulateTensor(state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + } + + // Extracts the output tensor from the SVDF op. + std::vector<float> GetOutput() { return ExtractVector<float>(output_); } + + int input_size() { return input_size_; } + int num_units() { return units_; } + int num_batches() { return batches_; } + + private: + int input_; + int weights_feature_; + int weights_time_; + int bias_; + int state_; + int output_; + + int batches_; + int units_; + int input_size_; + int memory_size_; + int rank_; +}; + +TEST(SVDFOpTest, BlackBoxTestRank1) { + 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.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_rank_1 + 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.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_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 +} // namespace tflite + +int main(int argc, char** argv) { + // On Linux, add: tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} |