463 lines
18 KiB
C++
463 lines
18 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 <vector>
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#include <gtest/gtest.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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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 {
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using ::testing::ElementsAreArray;
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class QuantizeOpModel : public SingleOpModel {
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public:
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QuantizeOpModel(const TensorData& input, const TensorData& output) {
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input_ = AddInput(input);
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output_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_QUANTIZE, BuiltinOptions_QuantizeOptions,
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CreateQuantizeOptions(builder_).Union());
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BuildInterpreter({GetShape(input_)});
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}
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void SetInput(std::initializer_list<float> data) {
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PopulateTensor(input_, data);
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}
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template <typename T>
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void SetInputAndQuantize(std::initializer_list<float> data) {
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QuantizeAndPopulate<T>(input_, data);
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}
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template <typename T>
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std::vector<T> GetOutput() {
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return ExtractVector<T>(output_);
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}
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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(QuantizeOpTest, UINT8) {
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// [-63.5, 64] -> scale=0.5 zero_point=127 for UINT8
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QuantizeOpModel m({TensorType_FLOAT32, {2, 5}},
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{TensorType_UINT8, {2, 5}, 0, 0, 0.5, 127});
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m.SetInput({-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<uint8_t>(),
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ElementsAreArray({0, 1, 2, 3, 4, 251, 252, 253, 254, 255}));
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}
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TEST(QuantizeOpTest, INT8) {
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// [-63.5, 64] -> scale=0.5, zero_point=1 for INT8
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QuantizeOpModel m({TensorType_FLOAT32, {2, 5}},
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{TensorType_INT8, {2, 5}, 0, 0, 0.5, -1});
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m.SetInput({-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray(
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{-128, -127, -126, -125, -124, 123, 124, 125, 126, 127}));
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}
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TEST(QuantizeOpTest, INT16) {
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QuantizeOpModel m({TensorType_FLOAT32, {2, 5}},
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{TensorType_INT16, {2, 5}, 0, 0, 0.005, 0});
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m.SetInput({-63.5, -63, -3, -2, -1, 1, 2, 3, 63.5, 64});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({-12700, -12600, -600, -400, -200, 200, 400, 600,
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12700, 12800}));
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}
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// rescale factor is around 2
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TEST(QuantizeOpTest, Int16Int16) {
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QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, -16383, 16384},
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{TensorType_INT16, {1, 1, 2, 5}, 0, 16384});
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m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({-32764, -32760, -32756, -32752, -32748, -32744,
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-32740, -32736, -32732, -32728}));
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}
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// zero point is -1, scale is 0.5
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TEST(QuantizeOpTest, Int16Int16SameScale) {
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QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, -16384, 16384},
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{TensorType_INT16, {1, 1, 2, 5}, -16384, 16384});
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m.SetInputAndQuantize<int16_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 37767});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({-1, 1, 3, 5, 7, 9, 11, 13, 15, 32767}));
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}
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// Input scale 0.500000, output scale 0.500000, input zeropoint -1, output
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// zeropoint -1
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TEST(QuantizeOpTest, Int8Int8SameScale) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -63.5, 64},
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{TensorType_INT8, {1, 1, 2, 5}, -63.5, 64});
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// Input will quantized to {1,3,5,7,9,11,13,15,17,19}.
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m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19}));
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}
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// Input scale 0.500000, output scale 1.000000, input zeropoint -1, output
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// zeropoint -1
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TEST(QuantizeOpTest, Int8Int8LargerScale) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -63.5, 64},
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{TensorType_INT8, {1, 1, 2, 5}, -127, 128});
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// Input will quantized to {1,3,5,7,9,11,13,15,17,19}.
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m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7, 8, 9}));
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}
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// Input scale 1.000000, output scale 0.500000, input zeropoint -1, output
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// zeropoint -1
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TEST(QuantizeOpTest, Int8Int8SmallerScale) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128},
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{TensorType_INT8, {1, 1, 2, 5}, -63.5, 64});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9}.
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m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19}));
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}
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, Int8Int8SmallerScaleNeonPath) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128},
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{TensorType_INT8, {1, 1, 4, 5}, -63.5, 64});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}.
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m.SetInputAndQuantize<int8_t>(
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19,
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19, 17, 15, 13, 11, 9, 7, 5, 3, 1}));
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}
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// Input scale 0.500000, output scale 0.500000, input zeropoint 127, output
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// zeropoint 127
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TEST(QuantizeOpTest, UInt8UInt8SameScale) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64},
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{TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64});
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// Input will quantized to {129,131,133,135,137,139,141,143,145,147}.
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m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147}));
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}
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// Input scale 0.500000, output scale 1.000000, input zeropoint 127, output
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// zeropoint 127
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TEST(QuantizeOpTest, Uint8Uint8LargerScale) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64},
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{TensorType_UINT8, {1, 1, 2, 5}, -127, 128});
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// Input will quantized to {129,131,133,135,137,139,141,143,145,147}.
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m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({128, 129, 130, 131, 132, 133, 134, 135, 136, 137}));
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}
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// Input scale 1.000000, output scale 0.500000, input zeropoint 127, output
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// zeropoint 127
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TEST(QuantizeOpTest, Uint8Uint8SmallerScale) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64});
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// Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137}.
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m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147}));
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}
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, Uint8Uint8SmallerScaleNeonPath) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 4, 5}, -63.5, 64});
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// Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
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// 137, 136, 135, 134, 133, 132, 131, 130, 129, 128}.
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m.SetInputAndQuantize<uint8_t>(
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147,
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147, 145, 143, 141, 139, 137, 135, 133, 131, 129}));
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}
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// Input scale 1.000000, output scale 1.000000, input zeropoint -1, output
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// zeropoint 127
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TEST(QuantizeOpTest, Int8Uint8SameScale) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 2, 5}, -127, 128});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9}.
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m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({128, 129, 130, 131, 132, 133, 134, 135, 136, 137}));
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}
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, Int8UInt8SameScaleNeonPath) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 4, 5}, -127, 128});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}.
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m.SetInputAndQuantize<int8_t>(
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
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137, 136, 135, 134, 133, 132, 131, 130, 129, 128}));
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}
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// Input scale 1.000000, output scale 0.500000, input zeropoint -1, output
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// zeropoint 127
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TEST(QuantizeOpTest, Int8Uint8SmallerScale) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9}.
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m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147}));
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}
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, Int8Uint8SmallerScaleNeonPath) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 4, 5}, -63.5, 64});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}.
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m.SetInputAndQuantize<int8_t>(
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147,
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147, 145, 143, 141, 139, 137, 135, 133, 131, 129}));
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}
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// Input scale 1.000000, output scale 2.000000, input zeropoint -1, output
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// zeropoint 127
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TEST(QuantizeOpTest, Int8Uint8LargerScale) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 2, 5}, -254, 256});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9}.
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m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({128, 128, 129, 129, 130, 130, 131, 131, 132, 132}));
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}
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, Int8Uint8LargerScaleNeonPath) {
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QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128},
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{TensorType_UINT8, {1, 1, 4, 5}, -254, 256});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}.
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m.SetInputAndQuantize<int8_t>(
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput<uint8_t>(),
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ElementsAreArray({128, 128, 129, 129, 130, 130, 131, 131, 132, 132,
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132, 132, 131, 131, 130, 130, 129, 129, 128, 128}));
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}
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// input scale 0.500000, output scale 0.500000, input zeropoint 127, output
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// zeropoint -1
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TEST(QuantizeOpTest, UInt8Int8SameScale128Diff) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -127, 128},
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{TensorType_INT8, {1, 1, 2, 5}, -127, 128});
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// Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137}.
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m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7, 8, 9}));
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}
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, UInt8Int8SameScale128DiffNeonPath) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, -127, 128},
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{TensorType_INT8, {1, 1, 4, 5}, -127, 128});
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// Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
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// 137, 136, 135, 134, 133, 132, 131, 130, 129, 128}.
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m.SetInputAndQuantize<uint8_t>(
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
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9, 8, 7, 6, 5, 4, 3, 2, 1, 0}));
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}
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// input scale 0.500000, output scale 0.500000, input zeropoint 0, output
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// zeropoint -1
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TEST(QuantizeOpTest, Uint8Int8SameScaleArbitraryDiff) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, 0, 127.5},
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{TensorType_INT8, {1, 1, 2, 5}, -63.5, 64});
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// Input will quantized to {2,4,6,8,10,12,14,16,18,20}.
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m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19}));
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}
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, Uint8Int8SameScaleArbitraryDiffNeonPath) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, 0, 127.5},
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{TensorType_INT8, {1, 1, 4, 5}, -63.5, 64});
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// Input will quantized to
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// {2,4,6,8,10,12,14,16,18,20,20,18,16,14,12,10,8,6,4,2}.
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m.SetInputAndQuantize<uint8_t>(
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
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ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19,
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19, 17, 15, 13, 11, 9, 7, 5, 3, 1}));
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}
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// input scale 0.500000, output scale 1.000000, input zeropoint 0, output
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// zeropoint -1
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TEST(QuantizeOpTest, Uint8Int8LargerScale) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, 0, 127.5},
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{TensorType_INT8, {1, 1, 2, 5}, -127, 128});
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// Input will quantized to {2,4,6,8,10,12,14,16,18,20}.
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m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
|
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ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7, 8, 9}));
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}
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|
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// Same as previous test, except more data to hit the neon path.
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TEST(QuantizeOpTest, Uint8Int8LargerScaleNeonPath) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, 0, 127.5},
|
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{TensorType_INT8, {1, 1, 4, 5}, -127, 128});
|
|
|
|
// Input will quantized to
|
|
// {2,4,6,8,10,12,14,16,18,20,20,18,16,14,12,10,8,6,4,2}.
|
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m.SetInputAndQuantize<uint8_t>(
|
|
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
|
|
m.Invoke();
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EXPECT_THAT(m.GetOutput<int8_t>(),
|
|
ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
|
|
9, 8, 7, 6, 5, 4, 3, 2, 1, 0}));
|
|
}
|
|
|
|
// input scale 1.000000, output scale 0.500000, input zeropoint 0, output
|
|
// zeropoint -1
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TEST(QuantizeOpTest, Uint8Int8SmallerScale) {
|
|
QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, 0, 255},
|
|
{TensorType_INT8, {1, 1, 2, 5}, -63.5, 64});
|
|
|
|
// Input will quantized to {1,2,3,4,5,6,7,8,9,10}.
|
|
m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetOutput<int8_t>(),
|
|
ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19}));
|
|
}
|
|
|
|
// Input scale 0.500000, output scale 0.500000, input zeropoint -1, output
|
|
// zeropoint -1
|
|
TEST(QuantizeOpTest, Int16Int8SameScale) {
|
|
QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, -63.5, 64},
|
|
{TensorType_INT8, {1, 1, 2, 5}, -63.5, 64});
|
|
|
|
// Input will quantized to {1,3,5,7,9,11,13,15,17,19}.
|
|
m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetOutput<int8_t>(),
|
|
ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19}));
|
|
}
|
|
|
|
// Input scale 0.500000, output scale 1.000000, input zeropoint -1, output
|
|
// zeropoint -1
|
|
TEST(QuantizeOpTest, Int16Int8LargerScale) {
|
|
QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, -63.5, 64},
|
|
{TensorType_INT8, {1, 1, 2, 5}, -127, 128});
|
|
|
|
m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetOutput<int8_t>(),
|
|
ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7, 8, 9}));
|
|
}
|
|
|
|
// Input scale 1.000000, output scale 0.500000, input zeropoint -1, output
|
|
// zeropoint -1
|
|
TEST(QuantizeOpTest, Int16Int8SmallerScale) {
|
|
QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, -127, 128},
|
|
{TensorType_INT8, {1, 1, 2, 5}, -63.5, 64});
|
|
|
|
m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetOutput<int8_t>(),
|
|
ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19}));
|
|
}
|
|
|
|
// Same as previous test, except more data to hit the neon path.
|
|
TEST(QuantizeOpTest, Int16Int8SmallerScaleNeonPath) {
|
|
QuantizeOpModel m({TensorType_INT16, {1, 1, 4, 5}, -127, 128},
|
|
{TensorType_INT8, {1, 1, 4, 5}, -63.5, 64});
|
|
|
|
m.SetInputAndQuantize<int16_t>(
|
|
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetOutput<int8_t>(),
|
|
ElementsAreArray({1, 3, 5, 7, 9, 11, 13, 15, 17, 19,
|
|
19, 17, 15, 13, 11, 9, 7, 5, 3, 1}));
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tflite
|