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/*
* Copyright (C) 2018 The Android Open Source Project
*
* 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 "NeuralNetworks.h"
#include "NeuralNetworksOEM.h"
#include "NeuralNetworksWrapper.h"
#ifndef NNTEST_ONLY_PUBLIC_API
#include "Utils.h"
#endif
#include <gtest/gtest.h>
namespace {
using namespace android::nn::wrapper;
class OperandExtraParamsTest : public ::testing::Test {
protected:
virtual void SetUp() {
::testing::Test::SetUp();
ASSERT_EQ(ANeuralNetworksModel_create(&mModel), ANEURALNETWORKS_NO_ERROR);
nextOperandIndex = 0;
}
virtual void TearDown() {
ANeuralNetworksModel_free(mModel);
::testing::Test::TearDown();
}
static const uint32_t CHANNEL_DIM_SIZE = 4;
ANeuralNetworksOperandType createOperand(int32_t dataType) {
static uint32_t dims[4] = {1, 2, 3, CHANNEL_DIM_SIZE};
switch (dataType) {
case ANEURALNETWORKS_FLOAT32:
case ANEURALNETWORKS_FLOAT16:
case ANEURALNETWORKS_INT32:
case ANEURALNETWORKS_UINT32:
case ANEURALNETWORKS_BOOL:
case ANEURALNETWORKS_MODEL:
case ANEURALNETWORKS_OEM_SCALAR:
return {.type = dataType,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
case ANEURALNETWORKS_TENSOR_OEM_BYTE:
case ANEURALNETWORKS_TENSOR_FLOAT32:
case ANEURALNETWORKS_TENSOR_FLOAT16:
case ANEURALNETWORKS_TENSOR_INT32:
case ANEURALNETWORKS_TENSOR_BOOL8:
case ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL:
return {.type = dataType,
.dimensionCount = 4,
.dimensions = dims,
.scale = 0.0f,
.zeroPoint = 0};
case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM:
return {.type = dataType,
.dimensionCount = 4,
.dimensions = dims,
.scale = 1.0,
.zeroPoint = 128};
case ANEURALNETWORKS_TENSOR_QUANT8_SYMM:
return {.type = dataType,
.dimensionCount = 4,
.dimensions = dims,
.scale = 1.0,
.zeroPoint = 0};
case ANEURALNETWORKS_TENSOR_QUANT16_SYMM:
return {.type = dataType,
.dimensionCount = 4,
.dimensions = dims,
.scale = 1.0,
.zeroPoint = 0};
case ANEURALNETWORKS_TENSOR_QUANT16_ASYMM:
return {.type = dataType,
.dimensionCount = 4,
.dimensions = dims,
.scale = 1.0,
.zeroPoint = 32768};
case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED:
return {.type = dataType,
.dimensionCount = 4,
.dimensions = dims,
.scale = 1.0,
.zeroPoint = 1};
default:
ADD_FAILURE();
return {};
}
}
ANeuralNetworksSymmPerChannelQuantParams createSymmPerChannelQuantParams() {
static float scales[CHANNEL_DIM_SIZE] = {1.0, 2.0, 3.0, 4.0};
return {
.channelDim = 3,
.scaleCount = CHANNEL_DIM_SIZE,
.scales = scales,
};
}
void testAddingWithSymmPerChannelQuantParams(int32_t dataType,
ANeuralNetworksSymmPerChannelQuantParams params,
bool expectExtraParamsSuccess) {
ANeuralNetworksOperandType operandType = createOperand(dataType);
EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &operandType), ANEURALNETWORKS_NO_ERROR);
int operandIndex = nextOperandIndex++;
EXPECT_EQ(ANeuralNetworksModel_setOperandSymmPerChannelQuantParams(mModel, operandIndex,
¶ms),
expectExtraParamsSuccess ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_BAD_DATA);
}
ANeuralNetworksModel* mModel = nullptr;
int nextOperandIndex = 0;
};
const uint32_t kOperandCodeNoExtraParams[]{
ANEURALNETWORKS_FLOAT32,
ANEURALNETWORKS_FLOAT16,
ANEURALNETWORKS_INT32,
ANEURALNETWORKS_UINT32,
ANEURALNETWORKS_BOOL,
ANEURALNETWORKS_OEM_SCALAR,
ANEURALNETWORKS_TENSOR_OEM_BYTE,
ANEURALNETWORKS_TENSOR_FLOAT32,
ANEURALNETWORKS_TENSOR_INT32,
ANEURALNETWORKS_TENSOR_QUANT8_ASYMM,
ANEURALNETWORKS_TENSOR_QUANT16_ASYMM,
ANEURALNETWORKS_TENSOR_QUANT16_SYMM,
ANEURALNETWORKS_TENSOR_FLOAT16,
ANEURALNETWORKS_TENSOR_BOOL8,
ANEURALNETWORKS_TENSOR_QUANT8_SYMM,
ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED,
ANEURALNETWORKS_MODEL,
};
#ifndef NNTEST_ONLY_PUBLIC_API
// android::nn::k* consts are defined in private headers
static_assert(sizeof(kOperandCodeNoExtraParams) / sizeof(kOperandCodeNoExtraParams[0]) ==
android::nn::kNumberOfDataTypes + android::nn::kNumberOfDataTypesOEM - 1,
"New type added, OperandExtraParamsTest needs an update");
#endif
const uint32_t kOperandCodeChannelQuant[]{
ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL,
};
TEST_F(OperandExtraParamsTest, TestNoExtraParams) {
// Test for operands that are expected to not have additonal parameters
for (uint32_t dataType : kOperandCodeNoExtraParams) {
testAddingWithSymmPerChannelQuantParams(dataType, createSymmPerChannelQuantParams(),
/*expectExtraParamsSuccess=*/false);
}
}
TEST_F(OperandExtraParamsTest, TestChannelQuant) {
// Test for operands that are expected to have SymmPerChannelQuantParams value associated
for (uint32_t dataType : kOperandCodeChannelQuant) {
testAddingWithSymmPerChannelQuantParams(dataType, createSymmPerChannelQuantParams(),
/*expectExtraParamsSuccess=*/true);
}
}
TEST_F(OperandExtraParamsTest, TestChannelQuantValuesBadDim) {
// Bad .channelDim value
static float scales[4] = {1.0, 2.0, 3.0, 4.0};
ANeuralNetworksSymmPerChannelQuantParams ext = {
.channelDim = 7,
.scaleCount = 4,
.scales = scales,
};
for (uint32_t dataType : kOperandCodeChannelQuant) {
testAddingWithSymmPerChannelQuantParams(dataType, ext, /*expectExtraParamsSuccess=*/false);
}
}
TEST_F(OperandExtraParamsTest, TestChannelQuantValuesBadScalesCount) {
// Bad .scaleCount value
static float scales[4] = {1.0, 2.0, 3.0, 4.0};
ANeuralNetworksSymmPerChannelQuantParams lowScaleCountExt = {
.channelDim = 3,
.scaleCount = 3,
.scales = scales,
};
ANeuralNetworksSymmPerChannelQuantParams highScaleCountExt = {
.channelDim = 3,
.scaleCount = 10,
.scales = scales,
};
for (uint32_t dataType : kOperandCodeChannelQuant) {
testAddingWithSymmPerChannelQuantParams(dataType, lowScaleCountExt,
/*expectExtraParamsSuccess=*/false);
testAddingWithSymmPerChannelQuantParams(dataType, highScaleCountExt,
/*expectExtraParamsSuccess=*/false);
}
}
TEST_F(OperandExtraParamsTest, TestChannelQuantValuesBadScalesNegative) {
// Bad .scales value
static float scales[4] = {1.0, 2.0, -3.0, 4.0};
ANeuralNetworksSymmPerChannelQuantParams ext = {
.channelDim = 3,
.scaleCount = 4,
.scales = scales,
};
for (uint32_t dataType : kOperandCodeChannelQuant) {
testAddingWithSymmPerChannelQuantParams(dataType, ext, /*expectExtraParamsSuccess=*/false);
}
}
TEST_F(OperandExtraParamsTest, TestChannelQuantValuesNullScales) {
// .scales == nullptr value
ANeuralNetworksSymmPerChannelQuantParams ext = {
.channelDim = 3,
.scaleCount = 4,
.scales = nullptr,
};
for (uint32_t dataType : kOperandCodeChannelQuant) {
testAddingWithSymmPerChannelQuantParams(dataType, ext, /*expectExtraParamsSuccess=*/false);
}
}
TEST_F(OperandExtraParamsTest, TestChannelQuantValuesOperandScale) {
for (uint32_t dataType : kOperandCodeChannelQuant) {
ANeuralNetworksOperandType operandType = createOperand(dataType);
operandType.scale = 1.0f;
EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &operandType), ANEURALNETWORKS_BAD_DATA);
}
}
TEST_F(OperandExtraParamsTest, TestChannelQuantValuesOperandZeroPoint) {
for (uint32_t dataType : kOperandCodeChannelQuant) {
ANeuralNetworksOperandType operandType = createOperand(dataType);
operandType.zeroPoint = 1;
EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &operandType), ANEURALNETWORKS_BAD_DATA);
}
}
} // namespace
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