| /* Copyright 2019 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. |
| ==============================================================================*/ |
| #include <cstdint> |
| #include <initializer_list> |
| #include <vector> |
| |
| #include <gtest/gtest.h> |
| #include "flatbuffers/flatbuffers.h" // from @flatbuffers |
| #include "tensorflow/lite/kernels/internal/types.h" |
| #include "tensorflow/lite/kernels/test_util.h" |
| #include "tensorflow/lite/schema/schema_generated.h" |
| |
| namespace tflite { |
| namespace { |
| |
| using ::testing::ElementsAreArray; |
| |
| class QuantizeOpModel : public SingleOpModel { |
| public: |
| QuantizeOpModel(const TensorData& input, const TensorData& output) { |
| input_ = AddInput(input); |
| output_ = AddOutput(output); |
| SetBuiltinOp(BuiltinOperator_QUANTIZE, BuiltinOptions_QuantizeOptions, |
| CreateQuantizeOptions(builder_).Union()); |
| |
| BuildInterpreter({GetShape(input_)}); |
| } |
| |
| void SetInput(std::initializer_list<float> data) { |
| PopulateTensor(input_, data); |
| } |
| |
| template <typename T> |
| void SetInputAndQuantize(std::initializer_list<float> data) { |
| QuantizeAndPopulate<T>(input_, data); |
| } |
| |
| template <typename T> |
| std::vector<T> GetOutput() { |
| return ExtractVector<T>(output_); |
| } |
| |
| private: |
| int input_; |
| int output_; |
| }; |
| |
| TEST(QuantizeOpTest, UINT8) { |
| // [-63.5, 64] -> scale=0.5 zero_point=127 for UINT8 |
| QuantizeOpModel m({TensorType_FLOAT32, {2, 5}}, |
| {TensorType_UINT8, {2, 5}, 0, 0, 0.5, 127}); |
| |
| m.SetInput({-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64}); |
| m.Invoke(); |
| EXPECT_THAT(m.GetOutput<uint8_t>(), |
| ElementsAreArray({0, 1, 2, 3, 4, 251, 252, 253, 254, 255})); |
| } |
| |
| TEST(QuantizeOpTest, INT8) { |
| // [-63.5, 64] -> scale=0.5, zero_point=1 for INT8 |
| QuantizeOpModel m({TensorType_FLOAT32, {2, 5}}, |
| {TensorType_INT8, {2, 5}, 0, 0, 0.5, -1}); |
| |
| m.SetInput({-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64}); |
| m.Invoke(); |
| EXPECT_THAT(m.GetOutput<int8_t>(), |
| ElementsAreArray( |
| {-128, -127, -126, -125, -124, 123, 124, 125, 126, 127})); |
| } |
| |
| TEST(QuantizeOpTest, INT16) { |
| QuantizeOpModel m({TensorType_FLOAT32, {2, 5}}, |
| {TensorType_INT16, {2, 5}, 0, 0, 0.005, 0}); |
| |
| m.SetInput({-63.5, -63, -3, -2, -1, 1, 2, 3, 63.5, 64}); |
| m.Invoke(); |
| EXPECT_THAT(m.GetOutput<int16_t>(), |
| ElementsAreArray({-12700, -12600, -600, -400, -200, 200, 400, 600, |
| 12700, 12800})); |
| } |
| |
| // Input scale 1.000000, output scale 0.500000, input zeropoint 0, output |
| // zeropoint 0 |
| TEST(QuantizeOpTest, Int16Int16) { |
| QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 1.0, 0}, |
| {TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0}); |
| |
| m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); |
| m.Invoke(); |
| EXPECT_THAT(m.GetOutput<int16_t>(), |
| ElementsAreArray({2, 4, 6, 8, 10, 12, 14, 16, 18, 20})); |
| } |
| |
| // Input scale 0.500000, output scale 0.500000, input zeropoint 0, output |
| // zeropoint 0 |
| TEST(QuantizeOpTest, Int16Int16SameScale) { |
| QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0}, |
| {TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0}); |
| m.SetInputAndQuantize<int16_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 37767}); |
| m.Invoke(); |
| EXPECT_THAT(m.GetOutput<int16_t>(), |
| ElementsAreArray({0, 2, 4, 6, 8, 10, 12, 14, 16, 32767})); |
| } |
| |
| // Input scale 0.500000, output scale 0.500000, input zeropoint -1, output |
| // zeropoint -1 |
| TEST(QuantizeOpTest, Int8Int8SameScale) { |
| QuantizeOpModel m({TensorType_INT8, {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<int8_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, Int8Int8LargerScale) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -63.5, 64}, |
| {TensorType_INT8, {1, 1, 2, 5}, -127, 128}); |
| |
| // Input will quantized to {1,3,5,7,9,11,13,15,17,19}. |
| m.SetInputAndQuantize<int8_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, Int8Int8SmallerScale) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128}, |
| {TensorType_INT8, {1, 1, 2, 5}, -63.5, 64}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9}. |
| m.SetInputAndQuantize<int8_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, Int8Int8SmallerScaleNeonPath) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128}, |
| {TensorType_INT8, {1, 1, 4, 5}, -63.5, 64}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}. |
| m.SetInputAndQuantize<int8_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})); |
| } |
| |
| // Input scale 0.500000, output scale 0.500000, input zeropoint 127, output |
| // zeropoint 127 |
| TEST(QuantizeOpTest, UInt8UInt8SameScale) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64}, |
| {TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64}); |
| |
| // Input will quantized to {129,131,133,135,137,139,141,143,145,147}. |
| m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); |
| m.Invoke(); |
| EXPECT_THAT( |
| m.GetOutput<uint8_t>(), |
| ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147})); |
| } |
| |
| // Input scale 0.500000, output scale 1.000000, input zeropoint 127, output |
| // zeropoint 127 |
| TEST(QuantizeOpTest, Uint8Uint8LargerScale) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64}, |
| {TensorType_UINT8, {1, 1, 2, 5}, -127, 128}); |
| |
| // Input will quantized to {129,131,133,135,137,139,141,143,145,147}. |
| m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); |
| m.Invoke(); |
| EXPECT_THAT( |
| m.GetOutput<uint8_t>(), |
| ElementsAreArray({128, 129, 130, 131, 132, 133, 134, 135, 136, 137})); |
| } |
| |
| // Input scale 1.000000, output scale 0.500000, input zeropoint 127, output |
| // zeropoint 127 |
| TEST(QuantizeOpTest, Uint8Uint8SmallerScale) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64}); |
| |
| // Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137}. |
| m.SetInputAndQuantize<uint8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); |
| m.Invoke(); |
| EXPECT_THAT( |
| m.GetOutput<uint8_t>(), |
| ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147})); |
| } |
| |
| // Same as previous test, except more data to hit the neon path. |
| TEST(QuantizeOpTest, Uint8Uint8SmallerScaleNeonPath) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 4, 5}, -63.5, 64}); |
| |
| // Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137, |
| // 137, 136, 135, 134, 133, 132, 131, 130, 129, 128}. |
| 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(); |
| EXPECT_THAT( |
| m.GetOutput<uint8_t>(), |
| ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147, |
| 147, 145, 143, 141, 139, 137, 135, 133, 131, 129})); |
| } |
| |
| // Input scale 1.000000, output scale 1.000000, input zeropoint -1, output |
| // zeropoint 127 |
| TEST(QuantizeOpTest, Int8Uint8SameScale) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 2, 5}, -127, 128}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9}. |
| m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); |
| m.Invoke(); |
| EXPECT_THAT( |
| m.GetOutput<uint8_t>(), |
| ElementsAreArray({128, 129, 130, 131, 132, 133, 134, 135, 136, 137})); |
| } |
| |
| // Same as previous test, except more data to hit the neon path. |
| TEST(QuantizeOpTest, Int8UInt8SameScaleNeonPath) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 4, 5}, -127, 128}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}. |
| m.SetInputAndQuantize<int8_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<uint8_t>(), |
| ElementsAreArray({128, 129, 130, 131, 132, 133, 134, 135, 136, 137, |
| 137, 136, 135, 134, 133, 132, 131, 130, 129, 128})); |
| } |
| |
| // Input scale 1.000000, output scale 0.500000, input zeropoint -1, output |
| // zeropoint 127 |
| TEST(QuantizeOpTest, Int8Uint8SmallerScale) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 2, 5}, -63.5, 64}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9}. |
| m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); |
| m.Invoke(); |
| EXPECT_THAT( |
| m.GetOutput<uint8_t>(), |
| ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147})); |
| } |
| |
| // Same as previous test, except more data to hit the neon path. |
| TEST(QuantizeOpTest, Int8Uint8SmallerScaleNeonPath) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 4, 5}, -63.5, 64}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}. |
| m.SetInputAndQuantize<int8_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<uint8_t>(), |
| ElementsAreArray({129, 131, 133, 135, 137, 139, 141, 143, 145, 147, |
| 147, 145, 143, 141, 139, 137, 135, 133, 131, 129})); |
| } |
| |
| // Input scale 1.000000, output scale 2.000000, input zeropoint -1, output |
| // zeropoint 127 |
| TEST(QuantizeOpTest, Int8Uint8LargerScale) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 2, 5}, -254, 256}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9}. |
| m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); |
| m.Invoke(); |
| EXPECT_THAT( |
| m.GetOutput<uint8_t>(), |
| ElementsAreArray({128, 128, 129, 129, 130, 130, 131, 131, 132, 132})); |
| } |
| |
| // Same as previous test, except more data to hit the neon path. |
| TEST(QuantizeOpTest, Int8Uint8LargerScaleNeonPath) { |
| QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -127, 128}, |
| {TensorType_UINT8, {1, 1, 4, 5}, -254, 256}); |
| |
| // Input will quantized to {0,1,2,3,4,5,6,7,8,9,9,8,7,6,5,4,3,2,1,0}. |
| m.SetInputAndQuantize<int8_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<uint8_t>(), |
| ElementsAreArray({128, 128, 129, 129, 130, 130, 131, 131, 132, 132, |
| 132, 132, 131, 131, 130, 130, 129, 129, 128, 128})); |
| } |
| |
| // input scale 0.500000, output scale 0.500000, input zeropoint 127, output |
| // zeropoint -1 |
| TEST(QuantizeOpTest, UInt8Int8SameScale128Diff) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, -127, 128}, |
| {TensorType_INT8, {1, 1, 2, 5}, -127, 128}); |
| |
| // Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137}. |
| m.SetInputAndQuantize<uint8_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})); |
| } |
| |
| // Same as previous test, except more data to hit the neon path. |
| TEST(QuantizeOpTest, UInt8Int8SameScale128DiffNeonPath) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, -127, 128}, |
| {TensorType_INT8, {1, 1, 4, 5}, -127, 128}); |
| |
| // Input will quantized to {128, 129, 130, 131, 132, 133, 134, 135, 136, 137, |
| // 137, 136, 135, 134, 133, 132, 131, 130, 129, 128}. |
| 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(); |
| 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 0.500000, output scale 0.500000, input zeropoint 0, output |
| // zeropoint -1 |
| TEST(QuantizeOpTest, Uint8Int8SameScaleArbitraryDiff) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, 0, 127.5}, |
| {TensorType_INT8, {1, 1, 2, 5}, -63.5, 64}); |
| |
| // Input will quantized to {2,4,6,8,10,12,14,16,18,20}. |
| 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})); |
| } |
| |
| // Same as previous test, except more data to hit the neon path. |
| TEST(QuantizeOpTest, Uint8Int8SameScaleArbitraryDiffNeonPath) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, 0, 127.5}, |
| {TensorType_INT8, {1, 1, 4, 5}, -63.5, 64}); |
| |
| // Input will quantized to |
| // {2,4,6,8,10,12,14,16,18,20,20,18,16,14,12,10,8,6,4,2}. |
| 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(); |
| 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})); |
| } |
| |
| // input scale 0.500000, output scale 1.000000, input zeropoint 0, output |
| // zeropoint -1 |
| TEST(QuantizeOpTest, Uint8Int8LargerScale) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, 0, 127.5}, |
| {TensorType_INT8, {1, 1, 2, 5}, -127, 128}); |
| |
| // Input will quantized to {2,4,6,8,10,12,14,16,18,20}. |
| m.SetInputAndQuantize<uint8_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})); |
| } |
| |
| // Same as previous test, except more data to hit the neon path. |
| TEST(QuantizeOpTest, Uint8Int8LargerScaleNeonPath) { |
| QuantizeOpModel m({TensorType_UINT8, {1, 1, 4, 5}, 0, 127.5}, |
| {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}. |
| 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(); |
| 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 |
| 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 0, output |
| // zeropoint -1 |
| TEST(QuantizeOpTest, Int16Int8SameScale) { |
| QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0}, |
| {TensorType_INT8, {1, 1, 2, 5}, 0, 0, 0.5, -1}); |
| |
| // Input will quantized to {2,4,6,8,10,12,14,16,18,20}. |
| 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 0, output |
| // zeropoint -1 |
| TEST(QuantizeOpTest, Int16Int8LargerScale) { |
| QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0}, |
| {TensorType_INT8, {1, 1, 2, 5}, 0, 0, 1.0, -1}); |
| |
| 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 0, output |
| // zeropoint -1 |
| TEST(QuantizeOpTest, Int16Int8SmallerScale) { |
| QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 1.0, 0}, |
| {TensorType_INT8, {1, 1, 2, 5}, 0, 0, 0.5, -1}); |
| |
| 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}, 0, 0, 1.0, 0}, |
| {TensorType_INT8, {1, 1, 4, 5}, 0, 0, 0.5, -1}); |
| |
| 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 |