486 lines
20 KiB
C++
486 lines
20 KiB
C++
/* Copyright 2017 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 <stddef.h>
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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 BaseMulOpModel : public SingleOpModel {
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public:
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BaseMulOpModel(const TensorData& input1, const TensorData& input2,
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const TensorData& output,
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ActivationFunctionType activation_type) {
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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_MUL, BuiltinOptions_MulOptions,
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CreateMulOptions(builder_, activation_type).Union());
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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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class FloatMulOpModel : public BaseMulOpModel {
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public:
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using BaseMulOpModel::BaseMulOpModel;
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std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
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};
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class IntegerMulOpModel : public BaseMulOpModel {
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public:
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using BaseMulOpModel::BaseMulOpModel;
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std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
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};
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// For quantized Mul, the error shouldn't exceed (2*step + step^2).
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// The param min=-1.0 & max=1.0 is used in the following tests.
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// The tolerance value is ~0.0157.
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const float kQuantizedStep = 2.0 / 255.0;
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const float kQuantizedTolerance =
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2.0 * kQuantizedStep + kQuantizedStep * kQuantizedStep;
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const float kQuantizedStepInt16 = 2.0 / 32767.0;
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const float kQuantizedToleranceInt16 =
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2.0 * kQuantizedStepInt16 + kQuantizedStepInt16 * kQuantizedStepInt16;
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class QuantizedMulOpModel : public BaseMulOpModel {
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public:
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using BaseMulOpModel::BaseMulOpModel;
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template <typename integer_dtype>
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std::vector<float> GetDequantizedOutput() {
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return Dequantize<integer_dtype>(ExtractVector<integer_dtype>(output_),
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GetScale(output_), GetZeroPoint(output_));
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}
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std::vector<float> GetDequantizedOutputInt16() {
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return Dequantize<int16_t>(ExtractVector<int16_t>(output_),
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GetScale(output_), GetZeroPoint(output_));
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}
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};
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TEST(FloatMulOpTest, NoActivation) {
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FloatMulOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4})));
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}
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TEST(FloatMulOpTest, ActivationRELU_N1_TO_1) {
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FloatMulOpModel m(
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{TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_RELU_N1_TO_1);
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m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 1.0})));
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}
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TEST(FloatMulOpTest, 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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FloatMulOpModel m({TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5, 1.1, 0.1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4, 1.21, 0.2})))
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<< "With shape number " << i;
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}
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}
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TEST(FloatMulOpTest, WithScalarBroadcast) {
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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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FloatMulOpModel m({TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, {}}, // always a scalar
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
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m.PopulateTensor<float>(m.input2(), {0.1});
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m.Invoke();
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-0.2, 0.02, 0.07, 0.08, 0.11, 0.2})))
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<< "With shape number " << i;
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}
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}
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TEST(FloatMulOpTest, WithBroadcast) {
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std::vector<std::vector<int>> test_shapes = {
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{2, 4}, {2, 1, 4}, {1, 2, 4}, {1, 2, 1, 4}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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FloatMulOpModel m({TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, {4}}, // always a scalar
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(),
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{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0, 1.1, 0.8});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear(
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{-0.2, 0.04, 0.21, 0.32, 0.11, 0.4, 0.33, 0.32})))
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<< "With shape number " << i;
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}
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}
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TEST(FloatMulOpTest, MixedBroadcast) {
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const std::vector<int> base_shape = {2, 3, 1, 2};
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std::vector<std::vector<int>> test_shapes = {
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{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
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std::vector<std::vector<float>> test_outputs = {
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{-0.06f, 0.69f, 0.12f, 1.15f, -0.30f, 2.07f, 0.18f, 0.15f, -0.36f,
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0.25f, 0.90f, 0.45f, 0.16f, -0.33f, -0.32f, -0.55f, 0.80f, -0.99f,
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0.24f, 0.84f, -0.48f, 1.40f, 1.20f, 2.52f, -0.32f, 0.00f, 0.64f,
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0.00f, -1.60f, 0.00f, 0.14f, -0.66f, -0.28f, -1.10f, 0.70f, -1.98f},
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{-0.06f, 0.69f, -0.36f, 0.25f, 0.80f, -0.99f, 0.24f, 0.84f, 0.64f, 0.00f,
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0.70f, -1.98f},
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{-0.06f, 0.46f, -0.09f, 0.69f, 0.12f, -0.92f, 0.18f, 0.10f, 0.27f,
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0.15f, -0.36f, -0.20f, 0.16f, -0.22f, 0.24f, -0.33f, -0.32f, 0.44f,
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0.60f, 1.40f, 1.20f, 2.80f, 1.08f, 2.52f, -0.80f, 0.00f, -1.60f,
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0.00f, -1.44f, 0.00f, 0.35f, -1.10f, 0.70f, -2.20f, 0.63f, -1.98f},
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{-0.06f, 0.46f, 0.27f, 0.15f, -0.32f, 0.44f, 0.60f, 1.40f, -1.60f, 0.00f,
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0.63f, -1.98f}};
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for (size_t i = 0; i < test_shapes.size(); ++i) {
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FloatMulOpModel model_fixture(
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{TensorType_FLOAT32, base_shape}, {TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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model_fixture.PopulateTensor<float>(
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model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
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2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
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model_fixture.PopulateTensor<float>(model_fixture.input2(),
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{0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
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model_fixture.Invoke();
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EXPECT_THAT(model_fixture.GetOutput(),
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ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f)))
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<< "With shape number " << i;
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}
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// Re-run with exchanged inputs.
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for (size_t i = 0; i < test_shapes.size(); ++i) {
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FloatMulOpModel model_fixture(
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{TensorType_FLOAT32, test_shapes[i]}, {TensorType_FLOAT32, base_shape},
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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model_fixture.PopulateTensor<float>(model_fixture.input1(),
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{0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
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model_fixture.PopulateTensor<float>(
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model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
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2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
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model_fixture.Invoke();
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EXPECT_THAT(model_fixture.GetOutput(),
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ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f)))
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<< "With shape number " << i;
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}
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}
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TEST(FloatMulOpTest, WithBroadcast2Elements) {
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std::vector<std::vector<int>> test_shapes = {
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{2, 2}, {2, 1, 2}, {1, 2, 2}, {1, 2, 1, 2}};
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for (int i = 0; i < test_shapes.size(); ++i) {
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FloatMulOpModel m({TensorType_FLOAT32, test_shapes[i]},
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{TensorType_FLOAT32, {2}}, // always a scalar
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{TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
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m.PopulateTensor<float>(m.input2(), {0.1, 0.2});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.07, 0.16})))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerMulOpTest, NoActivation) {
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IntegerMulOpModel m({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
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ActivationFunctionType_NONE);
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m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 4, 21, 40}));
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}
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TEST(IntegerMulOpTest, ActivationRELU_N1_TO_1) {
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IntegerMulOpModel m({TensorType_INT32, {1, 2, 2, 1}},
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{TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
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ActivationFunctionType_RELU_N1_TO_1);
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m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 1, 1}));
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}
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TEST(IntegerMulOpTest, 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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IntegerMulOpModel m({TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
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m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5, 11, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({-20, 4, 21, 40, 121, 20}))
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<< "With shape number " << i;
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}
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}
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TEST(IntegerMulOpTest, 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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IntegerMulOpModel m({TensorType_INT32, test_shapes[i]},
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{TensorType_INT32, {}}, // always a scalar
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{TensorType_INT32, {}}, ActivationFunctionType_NONE);
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m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
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m.PopulateTensor<int32_t>(m.input2(), {1});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray(ArrayFloatNear({-20, 2, 7, 8, 11, 20})))
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<< "With shape number " << i;
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}
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}
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template <TensorType tensor_type, typename integer_dtype>
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void NoActivation() {
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QuantizedMulOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
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{tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
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{tensor_type, {}, -1.0, 1.0},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<integer_dtype>(m.input1(), {-0.8, 0.2, 0.9, 0.7});
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m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.6, 0.4, 0.9, 0.8});
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m.Invoke();
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EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
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ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56},
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kQuantizedTolerance)));
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}
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template <TensorType tensor_type, typename integer_dtype>
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void NoActivationLargeMultiplier() {
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// Intentionally pathological output range much narrower than needed
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// to represent input values to exercise the multiplier>1 case.
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QuantizedMulOpModel m({tensor_type, {1, 2, 2, 1}, -100, 100},
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{tensor_type, {1, 2, 2, 1}, -100, 100},
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{tensor_type, {}, -10, 10},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<integer_dtype>(m.input1(), {-4, 2, 3, 1});
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m.QuantizeAndPopulate<integer_dtype>(m.input2(), {-1, -3, 4, 2});
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m.Invoke();
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// Note the large tolerance. This computation is inherently inaccurate.
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const float kTolerance = 1.4f;
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EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
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ElementsAreArray(ArrayFloatNear({4, -6, 10, 2}, kTolerance)));
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}
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TEST(QuantizedMulOpTest, NoActivationUInt8) {
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NoActivation<TensorType_UINT8, uint8_t>();
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NoActivationLargeMultiplier<TensorType_UINT8, uint8_t>();
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}
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TEST(QuantizedMulOpTest, NoActivationInt8) {
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NoActivation<TensorType_INT8, int8_t>();
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NoActivationLargeMultiplier<TensorType_INT8, int8_t>();
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}
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TEST(QuantizedMulOpTest, NoActivationInt16) {
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const float kMin = -1.f;
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const float kMax = 32767.f / 32768.f;
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QuantizedMulOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_INT16, {1, 2, 2, 1}, kMin, kMax},
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{TensorType_INT16, {}, kMin, kMax},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<int16_t>(m.input1(), {-0.8, 0.2, 0.9, 0.7});
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m.QuantizeAndPopulate<int16_t>(m.input2(), {0.6, 0.4, 0.9, 0.8});
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m.Invoke();
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EXPECT_THAT(m.GetDequantizedOutputInt16(),
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ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56},
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kQuantizedToleranceInt16)));
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}
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TEST(QuantizedMulOpTest, NoActivationInt16Scaled) {
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const float kMin = -2.f;
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const float kMax = 2.f * 32767.f / 32768.f;
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QuantizedMulOpModel m({TensorType_INT16, {1, 2, 3, 1}, kMin, kMax},
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{TensorType_INT16, {1, 2, 3, 1}, 2 * kMin, 2 * kMax},
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{TensorType_INT16, {}, 8 * kMin, 8 * kMax},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<int16_t>(m.input1(), {-1.8, 0.2, 0.9, 1.7, 0.1, -1.95});
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m.QuantizeAndPopulate<int16_t>(m.input2(),
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{3.6, -3.4, 3.9, 0.8, -1.0, -3.95});
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m.Invoke();
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const float kQuantizedToleranceInt16Scaled =
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6.0 * kQuantizedStepInt16 + kQuantizedStepInt16 * kQuantizedStepInt16;
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EXPECT_THAT(
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m.GetDequantizedOutputInt16(),
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ElementsAreArray(ArrayFloatNear({-6.48, -0.68, 3.51, 1.36, -0.1, 7.7025},
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kQuantizedToleranceInt16Scaled)));
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}
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template <TensorType tensor_type, typename integer_dtype>
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void NoActivationInt16With8BitOutput() {
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const float kMinInt16 = -1.f;
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const float kMaxInt16 = 32767.f / 32768.f;
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const float kMinUint8 = -1.f;
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const float kMaxUint8 = 127.f / 128.f;
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QuantizedMulOpModel m({TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16},
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{TensorType_INT16, {1, 2, 2, 1}, kMinInt16, kMaxInt16},
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{tensor_type, {}, kMinUint8, kMaxUint8},
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ActivationFunctionType_NONE);
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m.QuantizeAndPopulate<int16_t>(m.input1(), {-0.8, 0.2, 0.9, 0.7});
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m.QuantizeAndPopulate<int16_t>(m.input2(), {0.6, 0.4, 0.9, 0.8});
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m.Invoke();
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EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
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ElementsAreArray(ArrayFloatNear({-0.48, 0.08, 0.81, 0.56},
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|
kQuantizedTolerance)));
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}
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|
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TEST(QuantizedMulOpTest, NoActivationInt16WithUint8Output) {
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NoActivationInt16With8BitOutput<TensorType_UINT8, uint8_t>();
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}
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|
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TEST(QuantizedMulOpTest, NoActivationInt16Withint8Output) {
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NoActivationInt16With8BitOutput<TensorType_INT8, int8_t>();
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|
}
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|
|
|
// for quantized Mul, the error shouldn't exceed 2*step
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float GetTolerance(int min, int max) {
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float kQuantizedStep = (max - min) / 255.0;
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float kQuantizedTolerance = 2.0 * kQuantizedStep;
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return kQuantizedTolerance;
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|
}
|
|
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|
template <TensorType tensor_type, typename integer_dtype>
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void WithBroadcast() {
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float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
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|
std::vector<std::vector<int>> test_shapes = {
|
|
{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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QuantizedMulOpModel m({tensor_type, test_shapes[i], -3.0, 3.0},
|
|
{tensor_type, {}, -3.0, 3.0}, // always a scalar
|
|
{tensor_type, {}, -3.0, 3.0},
|
|
ActivationFunctionType_NONE);
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input1(),
|
|
{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
|
|
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.1});
|
|
m.Invoke();
|
|
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear(
|
|
{-0.2, 0.02, 0.07, 0.08, 0.11, 0.2}, kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
template <enum TensorType tensor_type, typename integer_dtype>
|
|
void QuantizedWithMixedBroadcast() {
|
|
float kQuantizedTolerance = GetTolerance(-3.f, 3.f);
|
|
const std::vector<int> base_shape = {2, 3, 1, 2};
|
|
std::vector<std::vector<int>> test_shapes = {
|
|
{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
|
|
std::vector<std::vector<float>> test_outputs = {
|
|
{-0.06f, 0.69f, 0.12f, 1.15f, -0.30f, 2.07f, 0.18f, 0.15f, -0.36f,
|
|
0.25f, 0.90f, 0.45f, 0.16f, -0.33f, -0.32f, -0.55f, 0.80f, -0.99f,
|
|
0.24f, 0.84f, -0.48f, 1.40f, 1.20f, 2.52f, -0.32f, 0.00f, 0.64f,
|
|
0.00f, -1.60f, 0.00f, 0.14f, -0.66f, -0.28f, -1.10f, 0.70f, -1.98f},
|
|
{-0.06f, 0.69f, -0.36f, 0.25f, 0.80f, -0.99f, 0.24f, 0.84f, 0.64f, 0.00f,
|
|
0.70f, -1.98f},
|
|
{-0.06f, 0.46f, -0.09f, 0.69f, 0.12f, -0.92f, 0.18f, 0.10f, 0.27f,
|
|
0.15f, -0.36f, -0.20f, 0.16f, -0.22f, 0.24f, -0.33f, -0.32f, 0.44f,
|
|
0.60f, 1.40f, 1.20f, 2.80f, 1.08f, 2.52f, -0.80f, 0.00f, -1.60f,
|
|
0.00f, -1.44f, 0.00f, 0.35f, -1.10f, 0.70f, -2.20f, 0.63f, -1.98f},
|
|
{-0.06f, 0.46f, 0.27f, 0.15f, -0.32f, 0.44f, 0.60f, 1.40f, -1.60f, 0.00f,
|
|
0.63f, -1.98f}};
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedMulOpModel model_fixture({tensor_type, base_shape, -3.f, 3.f},
|
|
{tensor_type, test_shapes[i], -3.f, 3.f},
|
|
{tensor_type, {}, -3.f, 3.f},
|
|
ActivationFunctionType_NONE);
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
|
|
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input2(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
|
|
model_fixture.Invoke();
|
|
EXPECT_THAT(
|
|
model_fixture.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
// Re-run with exchanged inputs.
|
|
for (size_t i = 0; i < test_shapes.size(); ++i) {
|
|
QuantizedMulOpModel model_fixture({tensor_type, test_shapes[i], -3.f, 3.f},
|
|
{tensor_type, base_shape, -3.f, 3.f},
|
|
{tensor_type, {}, -3.f, 3.f},
|
|
ActivationFunctionType_NONE);
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input1(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
|
|
model_fixture.QuantizeAndPopulate<integer_dtype>(
|
|
model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
|
|
2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
|
|
model_fixture.Invoke();
|
|
EXPECT_THAT(
|
|
model_fixture.GetDequantizedOutput<integer_dtype>(),
|
|
ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
|
|
<< "With shape number " << i;
|
|
}
|
|
}
|
|
|
|
TEST(QuantizedMulOpTest, WithBroadcastUInt8) {
|
|
WithBroadcast<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedMulOpTest, WithBroadcastInt8) {
|
|
WithBroadcast<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
TEST(QuantizedMulOpTest, QuantizedWithMixedBroadcastUInt8) {
|
|
QuantizedWithMixedBroadcast<TensorType_UINT8, uint8_t>();
|
|
}
|
|
|
|
TEST(QuantizedMulOpTest, QuantizedWithMixedBroadcastInt8) {
|
|
QuantizedWithMixedBroadcast<TensorType_INT8, int8_t>();
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace tflite
|