114 lines
3.7 KiB
C++
114 lines
3.7 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <cstdint>
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#include <initializer_list>
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#include <memory>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "absl/memory/memory.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace ops {
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namespace builtin {
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TfLiteRegistration* Register_DENSIFY();
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} // namespace builtin
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} // namespace ops
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namespace {
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using ::testing::ElementsAreArray;
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template <typename T>
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class DensifyOpModel : public SingleOpModel {
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public:
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DensifyOpModel(const TensorData& input, std::initializer_list<T> input_data,
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int version = 1) {
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input_ = AddConstSparseInput(input, input_data);
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output_ = AddOutput({input.type, input.shape});
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SetBuiltinOp(BuiltinOperator_DENSIFY, BuiltinOptions_DensifyOptions,
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CreateDensifyOptions(builder_).Union());
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resolver_ = absl::make_unique<SingleOpResolver>(
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BuiltinOperator_DENSIFY, ops::builtin::Register_DENSIFY(), version);
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BuildInterpreter({input.shape});
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}
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std::vector<T> GetInput() { return ExtractVector<T>(input_); }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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private:
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int input_;
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int output_;
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};
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TEST(DensifyOpTest, Float) {
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std::initializer_list<float> dense_values = {6, 0, 9, 8, 0, 0,
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0, 0, 5, 0, 0, 7};
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std::initializer_list<float> sparse_values = {6, 9, 8, 5, 7};
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TensorData input = {};
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input.type = TensorType_FLOAT32;
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input.shape = {3, 4};
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input.traversal_order = {0, 1};
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input.format = {kTfLiteDimDense, kTfLiteDimSparseCSR};
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DensifyOpModel<float> m(input, dense_values);
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m.Invoke();
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EXPECT_THAT(m.GetInput(), ElementsAreArray(sparse_values));
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EXPECT_THAT(m.GetOutput(), ElementsAreArray(dense_values));
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}
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TEST(DensifyOpTest, Float3D) {
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std::initializer_list<float> dense_values = {6, 0, 9, 8, 0, 0,
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0, 0, 5, 0, 0, 7};
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std::initializer_list<float> sparse_values = {6, 9, 8, 5, 7};
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TensorData input = {};
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input.type = TensorType_FLOAT32;
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input.shape = {3, 2, 2};
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input.traversal_order = {0, 1, 2};
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input.format = {kTfLiteDimDense, kTfLiteDimDense, kTfLiteDimSparseCSR};
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DensifyOpModel<float> m(input, dense_values);
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m.Invoke();
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EXPECT_THAT(m.GetInput(), ElementsAreArray(sparse_values));
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EXPECT_THAT(m.GetOutput(), ElementsAreArray(dense_values));
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}
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TEST(DensifyOpTest, Int8) {
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std::initializer_list<int8_t> dense_values = {6, 0, 9, 8, 0, 0,
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0, 0, 5, 0, 0, 7};
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std::initializer_list<int8_t> sparse_values = {6, 9, 8, 5, 7};
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TensorData input = {};
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input.type = TensorType_INT8;
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input.shape = {3, 4};
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input.traversal_order = {0, 1};
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input.format = {kTfLiteDimDense, kTfLiteDimSparseCSR};
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DensifyOpModel<int8_t> m(input, dense_values);
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m.Invoke();
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EXPECT_THAT(m.GetInput(), ElementsAreArray(sparse_values));
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EXPECT_THAT(m.GetOutput(), ElementsAreArray(dense_values));
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}
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} // namespace
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} // namespace tflite
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