wehub-resource-sync
240 lines
9.6 KiB
C++
240 lines
9.6 KiB
C++
/* Copyright 2018 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 <stdint.h>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "flatbuffers/flatbuffers.h" // from @flatbuffers
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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 BaseSquaredDifferenceOpModel : public SingleOpModel {
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public:
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BaseSquaredDifferenceOpModel(const TensorData& input1,
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const TensorData& input2,
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const TensorData& output, bool allocate = true) {
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input1_ = AddInput(input1);
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input2_ = AddInput(input2);
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output_ = AddOutput(output);
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SetBuiltinOp(BuiltinOperator_SQUARED_DIFFERENCE,
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BuiltinOptions_SquaredDifferenceOptions,
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CreateSquaredDifferenceOptions(builder_).Union());
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SetBypassDefaultDelegates();
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BuildInterpreter({GetShape(input1_), GetShape(input2_)});
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}
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int input1() { return input1_; }
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int input2() { return input2_; }
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protected:
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int input1_;
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int input2_;
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int output_;
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};
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template <typename T>
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class SquaredDifferenceOpModel : public BaseSquaredDifferenceOpModel {
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public:
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using BaseSquaredDifferenceOpModel::BaseSquaredDifferenceOpModel;
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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};
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template <typename T>
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class FloatSquaredDifferenceTest : public ::testing::Test {};
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using FloatSquaredDifferenceTestTypes =
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::testing::Types<float, half, Eigen::bfloat16>;
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TYPED_TEST_SUITE(FloatSquaredDifferenceTest, FloatSquaredDifferenceTestTypes);
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class IntegerSquaredDifferenceOpModel : public BaseSquaredDifferenceOpModel {
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public:
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using BaseSquaredDifferenceOpModel::BaseSquaredDifferenceOpModel;
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std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
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};
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float GetTolerance(int min, int max) {
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float kQuantizedStep = (max - min) / 255.0;
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// Allow at most off-by-2.
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return kQuantizedStep * 2;
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}
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class QuantizedSquaredDifferenceOpModel : public BaseSquaredDifferenceOpModel {
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public:
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using BaseSquaredDifferenceOpModel::BaseSquaredDifferenceOpModel;
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template <typename integer_dtype>
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std::vector<float> GetDequantizedOutput() {
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return Dequantize<int8_t>(ExtractVector<int8_t>(output_), GetScale(output_),
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GetZeroPoint(output_));
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}
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};
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TYPED_TEST(FloatSquaredDifferenceTest, FloatType_SameShape) {
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using T = TypeParam;
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SquaredDifferenceOpModel<T> m({GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {1, 2, 2, 1}},
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{GetTensorType<T>(), {}}, /*allocate=*/false);
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TFLITE_ALLOCATE_AND_CHECK(T, &m);
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m.template PopulateTensor<T>(m.input1(), {-0.2, 0.2, -1.2, 0.8});
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m.template PopulateTensor<T>(m.input2(), {0.5, 0.2, -1.5, 0.5});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{0.49, 0.0, 0.09, 0.09}, NumericLimits<T>::epsilon() * 10)));
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}
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TYPED_TEST(FloatSquaredDifferenceTest, FloatType_VariousInputShapes) {
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using T = TypeParam;
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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SquaredDifferenceOpModel<T> m({GetTensorType<T>(), test_shapes[i]},
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{GetTensorType<T>(), test_shapes[i]},
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{GetTensorType<T>(), {}}, /*allocate=*/false);
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TFLITE_ALLOCATE_AND_CHECK(T, &m);
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m.template PopulateTensor<T>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0});
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m.template PopulateTensor<T>(m.input2(), {1.0, 0.2, 0.6, 0.4, -1.0, -0.0});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(
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{9.0, 0.0, 0.09, 0.16, 4.41, 4.0},
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NumericLimits<T>::epsilon() * 10)))
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<< "With shape number " << i;
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}
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}
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TYPED_TEST(FloatSquaredDifferenceTest, FloatType_WithBroadcast) {
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using T = TypeParam;
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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SquaredDifferenceOpModel<T> m({GetTensorType<T>(), test_shapes[i]},
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{GetTensorType<T>(), {}}, // always a scalar
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{GetTensorType<T>(), {}}, /*allocate=*/false);
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TFLITE_ALLOCATE_AND_CHECK(T, &m);
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m.template PopulateTensor<T>(m.input1(), {-0.2, 0.2, 0.5, 0.8, 0.11, 1.1});
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m.template PopulateTensor<T>(m.input2(), {0.1});
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TFLITE_INVOKE_AND_CHECK(T, &m);
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EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(
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{0.09, 0.01, 0.16, 0.49, 0.0001, 1.0},
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NumericLimits<T>::epsilon() * 10)))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerSquaredDifferenceOpTest, IntegerType_SameShape) {
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IntegerSquaredDifferenceOpModel m({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {}});
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m.PopulateTensor<int32_t>(m.input1(), {-2, 2, -15, 8});
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m.PopulateTensor<int32_t>(m.input2(), {5, -2, -3, 5});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({49, 16, 144, 9}));
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}
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TEST(IntegerSquaredDifferenceOpTest, IntegerType_VariousInputShapes) {
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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IntegerSquaredDifferenceOpModel m({TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, {}});
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m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 3, 8, 11, -20});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 6, 5, -5, -20});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({441, 0, 9, 9, 256, 0}))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerSquaredDifferenceOpTest, IntegerType_WithBroadcast) {
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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IntegerSquaredDifferenceOpModel m(
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{TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, {}}, // always a scalar
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{TensorType_INT32, {}});
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m.PopulateTensor<int32_t>(m.input1(), {-20, 10, 7, 3, 1, 13});
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m.PopulateTensor<int32_t>(m.input2(), {3});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({529, 49, 16, 0, 4, 100}))
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<< "With shape number " << i;
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}
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}
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TEST(QuantizedSquaredDifferenceOpTest, Quantized_SameShape) {
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float kQuantizedTolerance = GetTolerance(0, 1);
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QuantizedSquaredDifferenceOpModel m(
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{TensorType_INT8, {1, 2, 2, 1}, -1.2, 0.8},
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{TensorType_INT8, {1, 2, 2, 1}, -1.5, 0.5},
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{TensorType_INT8, {}, 0.0, 0.5});
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m.QuantizeAndPopulate<int8_t>(m.input1(), {-0.2, 0.2, -1.2, 0.8});
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m.QuantizeAndPopulate<int8_t>(m.input2(), {0.5, 0.2, -1.5, 0.5});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
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ElementsAreArray(ArrayFloatNear({0.49, 0.0, 0.09, 0.09},
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kQuantizedTolerance)));
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}
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TEST(QuantizedSquaredDifferenceOpTest, Quantized_VariousInputShapes) {
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float kQuantizedTolerance = GetTolerance(0, 9);
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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QuantizedSquaredDifferenceOpModel m(
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{TensorType_INT8, test_shapes[i], -2.0, 1.7},
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{TensorType_INT8, test_shapes[i], -1.0, 1.0},
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{TensorType_INT8, {}, 0.0, 9.0});
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m.QuantizeAndPopulate<int8_t>(m.input1(), {-2.0, 0.2, 0.3, 0.8, 1.1, -2.0});
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m.QuantizeAndPopulate<int8_t>(m.input2(), {1.0, 0.2, 0.6, 0.4, -1.0, -0.0});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(m.GetDequantizedOutput<int8_t>(),
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ElementsAreArray(ArrayFloatNear(
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{9.0, 0.0, 0.09, 0.16, 4.41, 4.0}, kQuantizedTolerance)))
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<< "With shape number " << i;
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}
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}
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TEST(QuantizedSquaredDifferenceOpTest, Quantized_WithBroadcast) {
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std::vector<std::vector<int>> test_shapes = {
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{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
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float kQuantizedTolerance = GetTolerance(0, 1);
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for (int i = 0; i < test_shapes.size(); ++i) {
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QuantizedSquaredDifferenceOpModel m(
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{TensorType_INT8, test_shapes[i], -0.2, 1.1},
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{TensorType_INT8, {}, 0.0, 0.1}, {TensorType_INT8, {}, 0.0, 1.0});
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m.QuantizeAndPopulate<int8_t>(m.input1(), {-0.2, 0.2, 0.5, 0.8, 0.11, 1.1});
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m.QuantizeAndPopulate<int8_t>(m.input2(), {0.1});
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ASSERT_EQ(m.Invoke(), kTfLiteOk);
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EXPECT_THAT(
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m.GetDequantizedOutput<int8_t>(),
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ElementsAreArray(ArrayFloatNear({0.09, 0.01, 0.16, 0.49, 0.0001, 1.0},
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kQuantizedTolerance)))
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<< "With shape number " << i;
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}
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}
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} // namespace
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} // namespace tflite
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