STT-tensorflow/tensorflow/lite/kernels/numeric_verify_test.cc
Karim Nosir 2a96849f47 Update source files with used includes.
PiperOrigin-RevId: 316589177
Change-Id: I0aba0ed1cf9ff478e7890fa53a7749bf844bd26d
2020-06-15 18:42:14 -07:00

122 lines
4.2 KiB
C++

/* Copyright 2018 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 <string.h>
#include <cstdint>
#include <initializer_list>
#include <vector>
#include <gtest/gtest.h>
#include "absl/memory/memory.h"
#include "third_party/eigen3/Eigen/Core"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/internal/types.h"
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/schema/schema_generated.h"
namespace tflite {
namespace ops {
namespace custom {
TfLiteRegistration* Register_NUMERIC_VERIFY();
} // namespace custom
} // namespace ops
namespace {
class NumericVerifyOpModel : public SingleOpModel {
public:
NumericVerifyOpModel(TensorType type, std::initializer_list<int> shape,
float scale, int32_t zero_point, int version,
float tolerance = 5.0) {
const TensorData input_tensor_data = {type, shape, 0, 0, scale, zero_point};
input_ = AddInput(input_tensor_data);
ref_ = AddInput({TensorType_FLOAT32, shape});
std::vector<uint8_t> custom_options(sizeof(float));
memcpy(custom_options.data(), &tolerance, sizeof(float));
SetCustomOp("NUMERIC_VERIFY", custom_options,
ops::custom::Register_NUMERIC_VERIFY);
BuildInterpreter({GetShape(input_), GetShape(ref_)});
}
template <typename T>
void SetInputs(std::initializer_list<T> data,
std::initializer_list<float> ref_data) {
PopulateTensor(input_, data);
PopulateTensor(ref_, ref_data);
}
private:
int input_;
int ref_;
};
TEST(NumericVerifyOpTest, Uint8) {
// [-63.5, 64] -> scale=0.5 zero_point=127 for UINT8
NumericVerifyOpModel m(TensorType_UINT8, {2, 5}, 0.5, 127, 1);
m.SetInputs<uint8_t>({0, 1, 2, 3, 4, 251, 252, 253, 254, 255},
{-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64});
EXPECT_EQ(m.InvokeUnchecked(), kTfLiteOk);
}
TEST(NumericVerifyOpTest, Int8) {
// [-63.5, 64] -> scale=0.5, zero_point=1 for INT8
NumericVerifyOpModel m(TensorType_INT8, {2, 5}, 0.5, -1, 2);
m.SetInputs<int8_t>({-128, -127, -126, -125, -124, 123, 124, 125, 126, 127},
{-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64});
EXPECT_EQ(m.InvokeUnchecked(), kTfLiteOk);
}
TEST(NumericVerifyOpTest, Float16) {
NumericVerifyOpModel m(TensorType_FLOAT16, {2, 3}, 1.0f, 0, 3,
/*tolerance=*/0.1f);
std::vector<Eigen::half> half{Eigen::half{-535.54f}, Eigen::half{-100.0f},
Eigen::half{-1.0f}, Eigen::half{0.f},
Eigen::half{1.0f}, Eigen::half{100.32f}};
m.PopulateTensor(0, 0, reinterpret_cast<TfLiteFloat16*>(half.data()),
reinterpret_cast<TfLiteFloat16*>(half.data()) + half.size());
m.PopulateTensor(1, {-535.54f, -100.0f, -1.0f, 0.f, 1.0f, 100.32f});
EXPECT_EQ(m.InvokeUnchecked(), kTfLiteOk);
}
TEST(NumericVerifyOpTest, Int16) {
NumericVerifyOpModel m(TensorType_INT16, {2, 5}, 0.5, -1, 4);
m.SetInputs<int16_t>(
{-130, -127, -126, -125, -124, 123, 124, 125, 126, 130},
{-64.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 65.5});
EXPECT_EQ(m.InvokeUnchecked(), kTfLiteOk);
}
TEST(NumericVerifyOpFailedTest, Int8) {
// [-63.5, 64] -> scale=0.5, zero_point=1 for INT8
NumericVerifyOpModel m(TensorType_INT8, {2, 5}, 0.5, -1, 2);
// The 5th element is set to 0.
m.SetInputs<int8_t>({-128, -127, -126, -125, -124, 0, 124, 125, 126, 127},
{-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64});
EXPECT_EQ(m.InvokeUnchecked(), kTfLiteError);
}
} // namespace
} // namespace tflite