wehub-resource-sync
740 lines
30 KiB
C++
740 lines
30 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 <limits>
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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/portable_tensor_utils.h"
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#include "tensorflow/lite/kernels/internal/tensor_utils.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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#include "tensorflow/lite/kernels/kernel_util.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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explicit QuantizeOpModel() {}
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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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std::vector<int8_t> GetOutputUnpackedInt4() {
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TfLiteTensor* t = interpreter_->tensor(output_);
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int num_elements = NumElements(t);
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std::vector<int8_t> unpacked_output(num_elements);
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tensor_utils::UnpackPackedIntToInt8(t->data.int8, num_elements,
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/*bit_width=*/4,
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unpacked_output.data());
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return unpacked_output;
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}
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std::vector<int8_t> GetOutputUnpackedUInt4() {
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TfLiteTensor* t = interpreter_->tensor(output_);
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int num_elements = NumElements(t);
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std::vector<int8_t> unpacked_output(num_elements);
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const int8_t* src_buffer = t->data.int8;
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for (int i = 0; i < num_elements; i += 2) {
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int8_t src_val = src_buffer[i / 2];
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unpacked_output[i] = src_val & 0x0F;
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if (i + 1 < num_elements) {
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unpacked_output[i + 1] = (src_val >> 4) & 0x0F;
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}
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}
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return unpacked_output;
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}
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protected:
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int input_;
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int output_;
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};
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class QuantizePerChannelOpModel : public QuantizeOpModel {
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public:
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QuantizePerChannelOpModel(TensorType inputType, TensorType outputType,
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std::initializer_list<int> shape,
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std::initializer_list<float> scales,
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std::initializer_list<int64_t> zero_points,
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int channel_dim) {
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std::vector<float> per_channel_scales(scales);
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std::vector<int64_t> per_channel_quantization_offsets(zero_points);
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const TensorData output_tensor_data = {outputType,
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shape,
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0 /*=min*/,
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0 /*=max*/,
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0.0f /*=scale*/,
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0 /*=zero_point*/,
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true /*=per_channel_quantization*/,
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per_channel_scales,
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per_channel_quantization_offsets,
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channel_dim};
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input_ = AddInput({inputType, shape});
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output_ = AddOutput(output_tensor_data);
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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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};
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// Per-node quantization tests.
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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TEST(QuantizeOpTest, INT4) {
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// [-3.5, 4] -> scale=0.5, zero_point=0 for INT4
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QuantizeOpModel m({TensorType_FLOAT32, {2, 5}},
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{TensorType_INT4, {2, 5}, 0, 0, 0.5, 0});
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m.SetInput({-4.5, -3, -2.5, -2, -1.5, 2, 2.5, 3, 3.5, 4});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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// Range of int4 is [-8, 7]. Values over the range are clamped to the range.
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EXPECT_THAT(m.GetOutputUnpackedInt4(),
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ElementsAreArray({-8, -6, -5, -4, -3, 4, 5, 6, 7, 7}));
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}
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TEST(QuantizeOpTest, UINT4) {
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// [-4, 3.5] -> scale=0.5, zero_point=8 for UINT4
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QuantizeOpModel m({TensorType_FLOAT32, {1, 8}},
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{TensorType_UINT4, {1, 8}, 0, 0, 0.5, 8});
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m.SetInput({-4.5, -4, -3.5, 0, 0.5, 3, 3.5, 4});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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// Range of uint4 is [0, 15]. Values over the range are clamped to the range.
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EXPECT_THAT(m.GetOutputUnpackedUInt4(),
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ElementsAreArray({0, 0, 1, 8, 9, 14, 15, 15}));
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}
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// Per-channel quantization tests.
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TEST(QuantizePerChannelOpTest, UINT8) {
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// [-63.5, 64] -> scale=0.5 zero_point=127 for UINT8
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QuantizePerChannelOpModel m(TensorType_FLOAT32, TensorType_UINT8, {2, 5},
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{0.5, 0.5}, {127, 127}, 0);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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(QuantizePerChannelOpTest, INT8) {
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// [-63.5, 64] -> scale=0.5, zero_point=1 for INT8
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QuantizePerChannelOpModel m(TensorType_FLOAT32, TensorType_INT8, {2, 5},
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{0.5, 0.5}, {-1, -1}, 0);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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(QuantizePerChannelOpTest, INT16) {
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// [-63.5, 64] -> scale=0.005, zero_point=0 for INT16
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QuantizePerChannelOpModel m(TensorType_FLOAT32, TensorType_INT16, {2, 5},
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{0.005, 0.005}, {0, 0}, 0);
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m.SetInput({-63.5, -63, -3, -2, -1, 1, 2, 3, 63.5, 64});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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// Requantization tests.
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// Input scale 1.000000, output scale 0.500000, input zeropoint 0, output
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// zeropoint 0
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TEST(QuantizeOpTest, Int32Int16) {
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QuantizeOpModel m({TensorType_INT32, {1, 1, 2, 5}, 0, 0, 1.0, 0},
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{TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0});
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m.SetInputAndQuantize<int32_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({2, 4, 6, 8, 10, 12, 14, 16, 18, 20}));
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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 0
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TEST(QuantizeOpTest, Int32Int16SameScale) {
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QuantizeOpModel m({TensorType_INT32, {1, 1, 2, 5}, 0, 0, 0.5, 0},
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{TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0});
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m.SetInputAndQuantize<int32_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 37767});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({0, 2, 4, 6, 8, 10, 12, 14, 16, 32767}));
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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, Int32Int8SameScale) {
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QuantizeOpModel m({TensorType_INT32, {1, 1, 2, 5}, 0, 0, 0.5, 0},
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{TensorType_INT8, {1, 1, 2, 5}, 0, 0, 0.5, -1});
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// Input will quantized to {1,3,5,7,9,11,13,15,17,19}.
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m.SetInputAndQuantize<int32_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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 0, output
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// zeropoint -1
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TEST(QuantizeOpTest, Int32Int8LargerScale) {
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QuantizeOpModel m({TensorType_INT32, {1, 1, 2, 5}, 0, 0, 0.5, 0},
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{TensorType_INT8, {1, 1, 2, 5}, 0, 0, 1.0, -1});
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// Input will quantized to {1,3,5,7,9,11,13,15,17,19}.
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m.SetInputAndQuantize<int32_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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 0, output
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// zeropoint -1
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TEST(QuantizeOpTest, Int32Int8SmallerScale) {
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QuantizeOpModel m({TensorType_INT32, {1, 1, 2, 5}, 0, 0, 1.0, 0},
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{TensorType_INT8, {1, 1, 2, 5}, 0, 0, 0.5, -1});
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// Input will quantized to {0,1,2,3,4,5,6,7,8,9}.
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m.SetInputAndQuantize<int32_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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 1.000000, output scale 0.500000, input zeropoint 0, output
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// zeropoint 0
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TEST(QuantizeOpTest, Int16Int16) {
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QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 1.0, 0},
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{TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0});
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m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({2, 4, 6, 8, 10, 12, 14, 16, 18, 20}));
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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 0
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TEST(QuantizeOpTest, Int16Int16SameScale) {
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QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0},
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{TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0});
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m.SetInputAndQuantize<int16_t>({0, 1, 2, 3, 4, 5, 6, 7, 8, 37767});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({0, 2, 4, 6, 8, 10, 12, 14, 16, 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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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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
|
|
// 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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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 0.500000, output scale 1.000000, input zeropoint -1, output
|
|
// zeropoint 127.
|
|
TEST(QuantizeOpTest, Int8Uint8LargerScale) {
|
|
QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, -63.5, 63},
|
|
{TensorType_UINT8, {1, 1, 2, 5}, -127, 128});
|
|
|
|
// Input will quantized to {2,4,6,8,10,12,14,16,18,20}.
|
|
m.SetInputAndQuantize<int8_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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, Int8Uint8LargerScaleNeonPath) {
|
|
QuantizeOpModel m({TensorType_INT8, {1, 1, 4, 5}, -63.5, 63},
|
|
{TensorType_UINT8, {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<int8_t>(
|
|
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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 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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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, Int16ZeroPointInt8) {
|
|
QuantizeOpModel m({TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, -1},
|
|
{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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
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 1.0, output scale 1.0, input zeropoint 0, output zeropoint 0
|
|
TEST(QuantizeOpTest, Int16Int32SameScale) {
|
|
QuantizeOpModel m({TensorType_INT16,
|
|
{1, 1, 2, 5},
|
|
std::numeric_limits<int16_t>::min(),
|
|
std::numeric_limits<int16_t>::max()},
|
|
{TensorType_INT32,
|
|
{1, 1, 2, 5},
|
|
std::numeric_limits<int32_t>::min(),
|
|
static_cast<float>(std::numeric_limits<int32_t>::max())});
|
|
|
|
// 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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<int32_t>(),
|
|
ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}));
|
|
}
|
|
|
|
// Input scale 0.500000, output scale 1.000000, input zeropoint -1, output
|
|
// zeropoint 0
|
|
TEST(QuantizeOpTest, Int16Int32LargerScale) {
|
|
QuantizeOpModel m({TensorType_INT16,
|
|
{1, 1, 2, 5},
|
|
std::numeric_limits<int16_t>::min() / 2.0,
|
|
std::numeric_limits<int16_t>::max() / 2.0},
|
|
{TensorType_INT32,
|
|
{1, 1, 2, 5},
|
|
std::numeric_limits<int32_t>::min(),
|
|
static_cast<float>(std::numeric_limits<int32_t>::max())});
|
|
|
|
m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<int32_t>(),
|
|
ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}));
|
|
}
|
|
|
|
// Input scale 1.000000, output scale 0.500000, input zeropoint -1, output
|
|
// zeropoint 0
|
|
TEST(QuantizeOpTest, Int16Int32SmallerScale) {
|
|
QuantizeOpModel m({TensorType_INT16,
|
|
{1, 1, 2, 5},
|
|
std::numeric_limits<int16_t>::min(),
|
|
std::numeric_limits<int16_t>::max()},
|
|
{TensorType_INT32,
|
|
{1, 1, 2, 5},
|
|
std::numeric_limits<int32_t>::min() / 2.0,
|
|
std::numeric_limits<int32_t>::max() / 2.0});
|
|
|
|
m.SetInputAndQuantize<int16_t>({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<int32_t>(),
|
|
ElementsAreArray({2, 4, 6, 8, 10, 12, 14, 16, 18, 20}));
|
|
}
|
|
|
|
// Input scale 0.5, output scale 0.5, input zeropoint -1, output zeropoint 0
|
|
TEST(QuantizeOpTest, Int8Int16SameScale) {
|
|
QuantizeOpModel m({TensorType_INT8, {1, 1, 2, 5}, 0, 0, 0.5, -1},
|
|
{TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0});
|
|
|
|
// 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});
|
|
ASSERT_EQ(m.Invoke(), kTfLiteOk);
|
|
EXPECT_THAT(m.GetOutput<int16_t>(),
|
|
ElementsAreArray({2, 4, 6, 8, 10, 12, 14, 16, 18, 20}));
|
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}
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// Input scale 0.5, output scale 0.5, input zeropoint 127, output zeropoint 0
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TEST(QuantizeOpTest, UInt8Int16SameScale) {
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QuantizeOpModel m({TensorType_UINT8, {1, 1, 2, 5}, 0, 0, 0.5, 127},
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{TensorType_INT16, {1, 1, 2, 5}, 0, 0, 0.5, 0});
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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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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput<int16_t>(),
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ElementsAreArray({2, 4, 6, 8, 10, 12, 14, 16, 18, 20}));
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}
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} // namespace
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} // namespace tflite
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