Google Git
Sign in
android / platform / packages / modules / NeuralNetworks / 11a7a0e8c5852b4e9bee8ec2fdae3fe16433be1f / . / runtime / test / TestValidateOperations.cpp
blob: a2e25b991f322d2e9f34b5dc93effcb7fb684347 [file] [log] [blame]
/*
* 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 "NeuralNetworksWrapper.h"
#include "NeuralNetworksOEM.h"
#include <gtest/gtest.h>
using namespace android::nn::wrapper;
namespace {
static const int32_t kAvailableOperandCodes[] = {
ANEURALNETWORKS_FLOAT32, ANEURALNETWORKS_INT32,
ANEURALNETWORKS_UINT32, ANEURALNETWORKS_TENSOR_FLOAT32,
ANEURALNETWORKS_TENSOR_INT32, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM,
ANEURALNETWORKS_BOOL, ANEURALNETWORKS_TENSOR_QUANT16_SYMM,
ANEURALNETWORKS_TENSOR_FLOAT16, ANEURALNETWORKS_TENSOR_BOOL8,
ANEURALNETWORKS_FLOAT16, ANEURALNETWORKS_TENSOR_OEM_BYTE};
ANeuralNetworksOperandType getOpType(int32_t opcode, uint32_t dimCount = 0, uint32_t* dim = nullptr,
float scale = 0.0f, int32_t zeroPoint = 0) {
return {.type = opcode,
.dimensionCount = dimCount,
.dimensions = dim,
.scale = scale,
.zeroPoint = zeroPoint};
}
class OperationTestBase {
public:
OperationTestBase(ANeuralNetworksOperationType opCode,
std::vector<ANeuralNetworksOperandType> validInputs,
std::vector<ANeuralNetworksOperandType> validOutputs)
: mOpCode(opCode),
mValidInputs(std::move(validInputs)),
mValidOutputs(std::move(validOutputs)) {}
// Add each operand separately and add the operation using these operands.
// This function does not cover the cases that a operand used mutiple times.
int32_t addOperation(const std::vector<ANeuralNetworksOperandType>& inputs,
const std::vector<ANeuralNetworksOperandType>& outputs) {
ANeuralNetworksModel* model = nullptr;
ANeuralNetworksModel_create(&model);
uint32_t opIdx = 0;
std::vector<uint32_t> inputIds;
std::vector<uint32_t> outputIds;
for (uint32_t i = 0; i < inputs.size(); i++) {
ANeuralNetworksModel_addOperand(model, &inputs[i]);
inputIds.push_back(opIdx++);
}
for (uint32_t i = 0; i < outputs.size(); i++) {
ANeuralNetworksModel_addOperand(model, &outputs[i]);
outputIds.push_back(opIdx++);
}
int32_t result = ANeuralNetworksModel_addOperation(model, mOpCode,
static_cast<uint32_t>(inputIds.size()),
inputIds.data(),
static_cast<uint32_t>(outputIds.size()),
outputIds.data());
ANeuralNetworksModel_free(model);
return result;
}
bool testMutatingInputOperandCode() {
for (uint32_t i = 0; i < mValidInputs.size(); i++) {
// LSH_PROJECTION's second argument is allowed to have any type.
// This is the only operation that currently has a type that can be
// anything independent from any other type. Changing the operand
// type to any other type will result in a valid model for
// LSH_PROJECTION. If this is the case, skip the test.
if (mOpCode == ANEURALNETWORKS_LSH_PROJECTION && i == 1) {
continue;
}
ANeuralNetworksOperandType newType = mValidInputs[i];
int32_t originalOperandCode = mValidInputs[i].type;
for (int32_t newOperandCode : kAvailableOperandCodes) {
if (newOperandCode == originalOperandCode) {
continue;
}
newType.type = newOperandCode;
std::vector<ANeuralNetworksOperandType> inputs = mValidInputs;
inputs[i] = newType;
int32_t result = addOperation(inputs, mValidOutputs);
if (ANEURALNETWORKS_NO_ERROR == result) {
return false;
}
}
}
return true;
}
bool testMutatingOutputOperandCode() {
for (uint32_t i = 0; i < mValidOutputs.size(); i++) {
// LSH_PROJECTION's second argument is allowed to have any type.
// This is the only operation that currently has a type that can be
// anything independent from any other type. Changing the operand
// type to any other type will result in a valid model for
// LSH_PROJECTION. If this is the case, skip the test.
if (mOpCode == ANEURALNETWORKS_LSH_PROJECTION && i == 1) {
continue;
}
ANeuralNetworksOperandType newType = mValidOutputs[i];
int32_t originalOperandCode = mValidOutputs[i].type;
for (int32_t newOperandCode : kAvailableOperandCodes) {
if (newOperandCode == originalOperandCode) {
continue;
}
// DEQUANTIZE's output can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.
if (mOpCode == ANEURALNETWORKS_DEQUANTIZE &&
(newOperandCode == ANEURALNETWORKS_TENSOR_FLOAT16 ||
newOperandCode == ANEURALNETWORKS_TENSOR_FLOAT32)) {
continue;
}
newType.type = newOperandCode;
std::vector<ANeuralNetworksOperandType> outputs = mValidOutputs;
outputs[i] = newType;
int32_t result = addOperation(mValidInputs, outputs);
if (ANEURALNETWORKS_NO_ERROR == result) {
return false;
}
}
}
return true;
}
bool testMutatingInputOperandCounts(uint32_t numToAdd = 5) {
std::vector<ANeuralNetworksOperandType> inputs = mValidInputs;
for (uint32_t i = 0; i < numToAdd; i++) {
inputs.push_back(inputs[0]);
if (ANEURALNETWORKS_NO_ERROR == addOperation(inputs, mValidOutputs)) {
return false;
}
}
return true;
}
bool testMutatingOutputOperandCounts() {
std::vector<ANeuralNetworksOperandType> outputs = mValidOutputs;
for (int i = 0; i < 5; i++) {
outputs.push_back(outputs[0]);
if (ANEURALNETWORKS_NO_ERROR == addOperation(mValidInputs, outputs)) {
return false;
}
}
return true;
}
private:
ANeuralNetworksOperationType mOpCode;
// The dimensions in the ANeuralNetworksOperandType must outlive the test object.
std::vector<ANeuralNetworksOperandType> mValidInputs;
std::vector<ANeuralNetworksOperandType> mValidOutputs;
};
TEST(OperationValidationTest, DEQUANTIZE_float16) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 1.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType output = {.type = ANEURALNETWORKS_TENSOR_FLOAT16,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase dequantizeTest(ANEURALNETWORKS_DEQUANTIZE, {input}, {output});
EXPECT_TRUE(dequantizeTest.testMutatingInputOperandCode());
EXPECT_TRUE(dequantizeTest.testMutatingInputOperandCounts());
EXPECT_TRUE(dequantizeTest.testMutatingOutputOperandCode());
EXPECT_TRUE(dequantizeTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, DEQUANTIZE_float32) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 1.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType output = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase dequantizeTest(ANEURALNETWORKS_DEQUANTIZE, {input}, {output});
EXPECT_TRUE(dequantizeTest.testMutatingInputOperandCode());
EXPECT_TRUE(dequantizeTest.testMutatingInputOperandCounts());
EXPECT_TRUE(dequantizeTest.testMutatingOutputOperandCode());
EXPECT_TRUE(dequantizeTest.testMutatingOutputOperandCounts());
}
void simpleMathOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input1 = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input1.scale = 0.5f;
}
ANeuralNetworksOperandType input2 = input1;
ANeuralNetworksOperandType output = input1;
ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase simpleMathTest(operationCode, {input1, input2, activation}, {output});
EXPECT_TRUE(simpleMathTest.testMutatingInputOperandCode());
EXPECT_TRUE(simpleMathTest.testMutatingInputOperandCounts());
EXPECT_TRUE(simpleMathTest.testMutatingOutputOperandCode());
EXPECT_TRUE(simpleMathTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, ADD_float16) {
simpleMathOpTest(ANEURALNETWORKS_ADD, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, ADD_float32) {
simpleMathOpTest(ANEURALNETWORKS_ADD, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, MUL_float32) {
simpleMathOpTest(ANEURALNETWORKS_MUL, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, SUB_float32) {
simpleMathOpTest(ANEURALNETWORKS_SUB, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, DIV_float32) {
simpleMathOpTest(ANEURALNETWORKS_DIV, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, ADD_quant8) {
simpleMathOpTest(ANEURALNETWORKS_ADD, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, MUL_quant8) {
simpleMathOpTest(ANEURALNETWORKS_MUL, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void binaryOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDimensions[] = {2, 2, 2, 2, 2};
ANeuralNetworksOperandType input1 = {.type = operandCode,
.dimensionCount = 5,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input1.scale = 0.5f;
}
ANeuralNetworksOperandType input2 = input1;
ANeuralNetworksOperandType output = input1;
OperationTestBase test(operationCode, {input1, input2}, {output});
EXPECT_TRUE(test.testMutatingInputOperandCode());
EXPECT_TRUE(test.testMutatingInputOperandCounts());
EXPECT_TRUE(test.testMutatingOutputOperandCode());
EXPECT_TRUE(test.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, MAXIMUM_float16) {
binaryOpTest(ANEURALNETWORKS_MAXIMUM, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, MAXIMUM_float32) {
binaryOpTest(ANEURALNETWORKS_MAXIMUM, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, MAXIMUM_int32) {
binaryOpTest(ANEURALNETWORKS_MAXIMUM, ANEURALNETWORKS_TENSOR_INT32);
}
TEST(OperationValidationTest, MAXIMUM_quant8) {
binaryOpTest(ANEURALNETWORKS_MAXIMUM, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, MINIMUM_float16) {
binaryOpTest(ANEURALNETWORKS_MINIMUM, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, MINIMUM_float32) {
binaryOpTest(ANEURALNETWORKS_MINIMUM, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, MINIMUM_int32) {
binaryOpTest(ANEURALNETWORKS_MINIMUM, ANEURALNETWORKS_TENSOR_INT32);
}
TEST(OperationValidationTest, MINIMUM_quant8) {
binaryOpTest(ANEURALNETWORKS_MINIMUM, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void activationOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 1.f / 256;
}
ANeuralNetworksOperandType output = input;
OperationTestBase test(operationCode, {input}, {output});
EXPECT_TRUE(test.testMutatingInputOperandCode());
EXPECT_TRUE(test.testMutatingInputOperandCounts());
EXPECT_TRUE(test.testMutatingOutputOperandCode());
EXPECT_TRUE(test.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, ABS_float16) {
activationOpTest(ANEURALNETWORKS_ABS, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, ABS_float32) {
activationOpTest(ANEURALNETWORKS_ABS, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, FLOOR_float16) {
activationOpTest(ANEURALNETWORKS_FLOOR, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, FLOOR_float32) {
activationOpTest(ANEURALNETWORKS_FLOOR, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, LOGICAL_NOT_bool) {
activationOpTest(ANEURALNETWORKS_LOGICAL_NOT, ANEURALNETWORKS_TENSOR_BOOL8);
}
TEST(OperationValidationTest, MEAN_float16) {
activationOpTest(ANEURALNETWORKS_MEAN, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, MEAN_float32) {
activationOpTest(ANEURALNETWORKS_MEAN, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, TANH_float32) {
activationOpTest(ANEURALNETWORKS_TANH, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, RELU_float32) {
activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, RELU1_float32) {
activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, RELU6_float32) {
activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, LOG_SOFTMAX_float16) {
activationOpTest(ANEURALNETWORKS_LOG_SOFTMAX, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, LOG_SOFTMAX_float32) {
activationOpTest(ANEURALNETWORKS_LOG_SOFTMAX, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, LOGISTIC_float32) {
activationOpTest(ANEURALNETWORKS_LOGISTIC, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, RELU_quant8) {
activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, RELU1_quant8) {
activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, RELU6_quant8) {
activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, LOGISTIC_quant8) {
activationOpTest(ANEURALNETWORKS_LOGISTIC, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void softmaxOpTest(int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 1.f / 256;
}
ANeuralNetworksOperandType output = input;
ANeuralNetworksOperandType beta = {.type = ANEURALNETWORKS_FLOAT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase softmaxTest(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
EXPECT_TRUE(softmaxTest.testMutatingInputOperandCode());
EXPECT_TRUE(softmaxTest.testMutatingInputOperandCounts());
EXPECT_TRUE(softmaxTest.testMutatingOutputOperandCode());
EXPECT_TRUE(softmaxTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType axis = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase softmaxAxisTest(ANEURALNETWORKS_SOFTMAX, {input, beta, axis}, {output});
EXPECT_TRUE(softmaxAxisTest.testMutatingInputOperandCode());
EXPECT_TRUE(softmaxAxisTest.testMutatingInputOperandCounts());
EXPECT_TRUE(softmaxAxisTest.testMutatingOutputOperandCode());
EXPECT_TRUE(softmaxAxisTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, SOFTMAX_float32) {
softmaxOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, SOFTMAX_quant8) {
softmaxOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void poolingOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 4, 4, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 1.f / 256;
}
ANeuralNetworksOperandType output = input;
ANeuralNetworksOperandType scalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType padLeft = scalar;
ANeuralNetworksOperandType padRight = scalar;
ANeuralNetworksOperandType padTop = scalar;
ANeuralNetworksOperandType padBottom = scalar;
ANeuralNetworksOperandType strideWidth = scalar;
ANeuralNetworksOperandType strideHeight = scalar;
ANeuralNetworksOperandType filterWidth = scalar;
ANeuralNetworksOperandType filterHeight = scalar;
ANeuralNetworksOperandType activation = scalar;
OperationTestBase explicitPoolingTest(operationCode,
{input,
padLeft, padRight, padTop, padBottom,
strideWidth, strideHeight,
filterWidth, filterHeight,
activation},
{output});
EXPECT_TRUE(explicitPoolingTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitPoolingTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitPoolingTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitPoolingTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType padImplicit = scalar;
OperationTestBase implicitPoolingTest(operationCode,
{input,
padImplicit,
strideWidth, strideHeight,
filterWidth, filterHeight,
activation},
{output});
EXPECT_TRUE(implicitPoolingTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitPoolingTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitPoolingTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitPoolingTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType layout = {.type = ANEURALNETWORKS_BOOL,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase explicitNchwPoolingTest(
operationCode,
{input, padLeft, padRight, padTop, padBottom, strideWidth, strideHeight, filterWidth,
filterHeight, activation, layout},
{output});
EXPECT_TRUE(explicitNchwPoolingTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitNchwPoolingTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitNchwPoolingTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitNchwPoolingTest.testMutatingOutputOperandCounts());
OperationTestBase implicitNchwPoolingTest(operationCode,
{input, padImplicit, strideWidth, strideHeight,
filterWidth, filterHeight, activation, layout},
{output});
EXPECT_TRUE(implicitNchwPoolingTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitNchwPoolingTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitNchwPoolingTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitNchwPoolingTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, AVERAGE_POOL_2D_float16) {
poolingOpTest(ANEURALNETWORKS_AVERAGE_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, AVERAGE_POOL_2D_float32) {
poolingOpTest(ANEURALNETWORKS_AVERAGE_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, MAX_POOL_2D_float32) {
poolingOpTest(ANEURALNETWORKS_MAX_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, MAX_POOL_2D_float16) {
poolingOpTest(ANEURALNETWORKS_MAX_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, L2_POOL_2D_float16) {
poolingOpTest(ANEURALNETWORKS_L2_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, L2_POOL_2D_float32) {
poolingOpTest(ANEURALNETWORKS_L2_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, AVERAGE_POOL_2D_quant8) {
poolingOpTest(ANEURALNETWORKS_AVERAGE_POOL_2D, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, MAX_POOL_2D_quant8) {
poolingOpTest(ANEURALNETWORKS_MAX_POOL_2D, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void spaceDepthOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 1.f / 256;
}
ANeuralNetworksOperandType block_size = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType output = input;
OperationTestBase spaceDepthTest(operationCode, {input, block_size}, {output});
EXPECT_TRUE(spaceDepthTest.testMutatingInputOperandCode());
EXPECT_TRUE(spaceDepthTest.testMutatingInputOperandCounts());
EXPECT_TRUE(spaceDepthTest.testMutatingOutputOperandCode());
EXPECT_TRUE(spaceDepthTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType layout = {.type = ANEURALNETWORKS_BOOL,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase spaceDepthNchwTest(operationCode, {input, block_size, layout}, {output});
EXPECT_TRUE(spaceDepthNchwTest.testMutatingInputOperandCode());
EXPECT_TRUE(spaceDepthNchwTest.testMutatingInputOperandCounts());
EXPECT_TRUE(spaceDepthNchwTest.testMutatingOutputOperandCode());
EXPECT_TRUE(spaceDepthNchwTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, SPACE_TO_DEPTH_float32) {
spaceDepthOpTest(ANEURALNETWORKS_SPACE_TO_DEPTH, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, DEPTH_TO_SPACE_float32) {
spaceDepthOpTest(ANEURALNETWORKS_DEPTH_TO_SPACE, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, SPACE_TO_DEPTH_quant8) {
spaceDepthOpTest(ANEURALNETWORKS_SPACE_TO_DEPTH, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, DEPTH_TO_SPACE_quant8) {
spaceDepthOpTest(ANEURALNETWORKS_DEPTH_TO_SPACE, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void spaceBatchOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 1.f / 256;
}
uint32_t blockDimensions[1] = {2};
ANeuralNetworksOperandType blockShape = {.type = ANEURALNETWORKS_TENSOR_INT32,
.dimensionCount = 1,
.dimensions = blockDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType layout = {.type = ANEURALNETWORKS_BOOL,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType padding = blockShape;
ANeuralNetworksOperandType output = input;
if (operationCode == ANEURALNETWORKS_SPACE_TO_BATCH_ND) {
OperationTestBase spaceBatchTest(operationCode, {input, blockShape, padding}, {output});
EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCode());
EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCounts());
EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCode());
EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCounts());
OperationTestBase spaceBatchNchwTest(operationCode, {input, blockShape, padding, layout},
{output});
EXPECT_TRUE(spaceBatchNchwTest.testMutatingInputOperandCode());
EXPECT_TRUE(spaceBatchNchwTest.testMutatingInputOperandCounts());
EXPECT_TRUE(spaceBatchNchwTest.testMutatingOutputOperandCode());
EXPECT_TRUE(spaceBatchNchwTest.testMutatingOutputOperandCounts());
} else {
OperationTestBase spaceBatchTest(operationCode, {input, blockShape}, {output});
EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCode());
EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCounts());
EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCode());
EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCounts());
OperationTestBase spaceBatchNchwTest(operationCode, {input, blockShape, layout}, {output});
EXPECT_TRUE(spaceBatchNchwTest.testMutatingInputOperandCode());
EXPECT_TRUE(spaceBatchNchwTest.testMutatingInputOperandCounts());
EXPECT_TRUE(spaceBatchNchwTest.testMutatingOutputOperandCode());
EXPECT_TRUE(spaceBatchNchwTest.testMutatingOutputOperandCounts());
}
}
TEST(OperationValidationTest, SPACE_TO_BATCH_ND_float32) {
spaceBatchOpTest(ANEURALNETWORKS_SPACE_TO_BATCH_ND, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, BATCH_TO_SPACE_ND_float32) {
spaceBatchOpTest(ANEURALNETWORKS_BATCH_TO_SPACE_ND, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, SPACE_TO_BATCH_ND_quant8) {
spaceBatchOpTest(ANEURALNETWORKS_SPACE_TO_BATCH_ND, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, BATCH_TO_SPACE_ND_quant8) {
spaceBatchOpTest(ANEURALNETWORKS_BATCH_TO_SPACE_ND, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void transposeAndSqueezeOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 1.f / 256;
}
uint32_t blockDimensions[1] = {4};
ANeuralNetworksOperandType dims = {.type = ANEURALNETWORKS_TENSOR_INT32,
.dimensionCount = 1,
.dimensions = blockDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType output = input;
OperationTestBase transposeAndSqueezeTest(operationCode, {input, dims}, {output});
EXPECT_TRUE(transposeAndSqueezeTest.testMutatingInputOperandCode());
EXPECT_TRUE(transposeAndSqueezeTest.testMutatingInputOperandCounts());
EXPECT_TRUE(transposeAndSqueezeTest.testMutatingOutputOperandCode());
EXPECT_TRUE(transposeAndSqueezeTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, TRANSPOSE_float32) {
transposeAndSqueezeOpTest(ANEURALNETWORKS_TRANSPOSE, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, SQUEEZE_float32) {
transposeAndSqueezeOpTest(ANEURALNETWORKS_SQUEEZE, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, TRANSPOSE_quant8) {
transposeAndSqueezeOpTest(ANEURALNETWORKS_TRANSPOSE, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, SQUEEZE_quant8) {
transposeAndSqueezeOpTest(ANEURALNETWORKS_SQUEEZE, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void convOpTest(int32_t operandCode) {
uint32_t inputDimensions[4] = {2, 4, 4, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 0.5f;
}
ANeuralNetworksOperandType filter = input;
ANeuralNetworksOperandType output = input;
uint32_t biasDimensions[1] = {2};
ANeuralNetworksOperandType bias = {.type = operandCode,
.dimensionCount = 1,
.dimensions = biasDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
bias.type = ANEURALNETWORKS_TENSOR_INT32;
bias.scale = 0.25f;
}
ANeuralNetworksOperandType scalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType padLeft = scalar;
ANeuralNetworksOperandType padRight = scalar;
ANeuralNetworksOperandType padTop = scalar;
ANeuralNetworksOperandType padBottom = scalar;
ANeuralNetworksOperandType strideWidth = scalar;
ANeuralNetworksOperandType strideHeight = scalar;
ANeuralNetworksOperandType activation = scalar;
OperationTestBase explicitConvTest(ANEURALNETWORKS_CONV_2D,
{input, filter, bias,
padLeft, padRight, padTop, padBottom,
strideWidth, strideHeight,
activation},
{output});
EXPECT_TRUE(explicitConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitConvTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType padImplicit = scalar;
OperationTestBase implicitConvTest(ANEURALNETWORKS_CONV_2D,
{input, filter, bias,
padImplicit,
strideWidth, strideHeight,
activation},
{output});
EXPECT_TRUE(implicitConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitConvTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType layout = {.type = ANEURALNETWORKS_BOOL,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase explicitNchwConvTest(
ANEURALNETWORKS_CONV_2D,
{input, filter, bias, padLeft, padRight, padTop, padBottom, strideWidth, strideHeight,
activation, layout},
{output});
EXPECT_TRUE(explicitNchwConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitNchwConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitNchwConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitNchwConvTest.testMutatingOutputOperandCounts());
OperationTestBase implicitNchwConvTest(
ANEURALNETWORKS_CONV_2D,
{input, filter, bias, padImplicit, strideWidth, strideHeight, activation, layout},
{output});
EXPECT_TRUE(implicitNchwConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitNchwConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitNchwConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitNchwConvTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, CONV_2D_float32) {
convOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, CONV_2D_quant8) {
convOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void depthwiseConvOpTest(int32_t operandCode) {
uint32_t inputDimensions[4] = {1, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 0.5f;
}
ANeuralNetworksOperandType filter = input;
ANeuralNetworksOperandType output = input;
uint32_t biasDimensions[1] = {2};
ANeuralNetworksOperandType bias = {.type = operandCode,
.dimensionCount = 1,
.dimensions = biasDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
bias.type = ANEURALNETWORKS_TENSOR_INT32;
bias.scale = 0.25f;
}
ANeuralNetworksOperandType scalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType padLeft = scalar;
ANeuralNetworksOperandType padRight = scalar;
ANeuralNetworksOperandType padTop = scalar;
ANeuralNetworksOperandType padBottom = scalar;
ANeuralNetworksOperandType strideWidth = scalar;
ANeuralNetworksOperandType strideHeight = scalar;
ANeuralNetworksOperandType multiplier = scalar;
ANeuralNetworksOperandType activation = scalar;
OperationTestBase explicitDepthwiseConvTest(ANEURALNETWORKS_DEPTHWISE_CONV_2D,
{input, filter, bias,
padLeft, padRight, padTop, padBottom,
strideWidth, strideHeight,
multiplier, activation},
{output});
EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType padImplicit = scalar;
OperationTestBase implicitDepthwiseConvTest(ANEURALNETWORKS_DEPTHWISE_CONV_2D,
{input, filter, bias,
padImplicit,
strideWidth, strideHeight,
multiplier, activation},
{output});
EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType layout = {.type = ANEURALNETWORKS_BOOL,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase explicitNchwDepthwiseConvTest(
ANEURALNETWORKS_DEPTHWISE_CONV_2D,
{input, filter, bias, padLeft, padRight, padTop, padBottom, strideWidth, strideHeight,
multiplier, activation, layout},
{output});
EXPECT_TRUE(explicitNchwDepthwiseConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitNchwDepthwiseConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitNchwDepthwiseConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitNchwDepthwiseConvTest.testMutatingOutputOperandCounts());
OperationTestBase implicitNchwDepthwiseConvTest(ANEURALNETWORKS_DEPTHWISE_CONV_2D,
{input, filter, bias, padImplicit, strideWidth,
strideHeight, multiplier, activation, layout},
{output});
EXPECT_TRUE(implicitNchwDepthwiseConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitNchwDepthwiseConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitNchwDepthwiseConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitNchwDepthwiseConvTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, DEPTHWISE_CONV_2D_float32) {
depthwiseConvOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, DEPTHWISE_CONV_2D_float16) {
depthwiseConvOpTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, DEPTHWISE_CONV_2D_quant8) {
depthwiseConvOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void fullyConnectedOpTest(int32_t operandCode) {
uint32_t inputDimensions[2] = {5, 5};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 2,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 0.5f;
}
ANeuralNetworksOperandType weights = input;
ANeuralNetworksOperandType output = input;
uint32_t biasDimensions[1] = {5};
ANeuralNetworksOperandType bias = {.type = operandCode,
.dimensionCount = 1,
.dimensions = biasDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
bias.type = ANEURALNETWORKS_TENSOR_INT32;
bias.scale = 0.25f;
}
ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase fullyConnectedTest(ANEURALNETWORKS_FULLY_CONNECTED,
{input, weights, bias, activation},
{output});
EXPECT_TRUE(fullyConnectedTest.testMutatingInputOperandCode());
EXPECT_TRUE(fullyConnectedTest.testMutatingInputOperandCounts());
EXPECT_TRUE(fullyConnectedTest.testMutatingOutputOperandCode());
EXPECT_TRUE(fullyConnectedTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, FULLY_CONNECTED_float16) {
fullyConnectedOpTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, FULLY_CONNECTED_float32) {
fullyConnectedOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, FULLY_CONNECTED_quant8) {
fullyConnectedOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void concatenationTest(int32_t operandCode) {
uint32_t inputDimensions[2] = {5, 5};
ANeuralNetworksOperandType input1 = {.type = operandCode,
.dimensionCount = 2,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input1.scale = 0.5f;
}
ANeuralNetworksOperandType input2 = input1;
ANeuralNetworksOperandType output = input1;
ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase concat2Test(ANEURALNETWORKS_CONCATENATION,
{input1, input2, activation}, {output});
EXPECT_TRUE(concat2Test.testMutatingInputOperandCode());
EXPECT_TRUE(concat2Test.testMutatingOutputOperandCode());
EXPECT_TRUE(concat2Test.testMutatingOutputOperandCounts());
OperationTestBase concat1Test(ANEURALNETWORKS_CONCATENATION,
{input1, activation}, {output});
EXPECT_TRUE(concat1Test.testMutatingInputOperandCode());
EXPECT_TRUE(concat1Test.testMutatingOutputOperandCode());
EXPECT_TRUE(concat1Test.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, CONCATENATION_float32) {
concatenationTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, CONCATENATION_quant8) {
concatenationTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, RESIZE_BILINEAR_float32) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType height = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType width = height;
ANeuralNetworksOperandType output = input;
OperationTestBase resizeTest(ANEURALNETWORKS_RESIZE_BILINEAR,
{input, height, width}, {output});
EXPECT_TRUE(resizeTest.testMutatingInputOperandCode());
EXPECT_TRUE(resizeTest.testMutatingInputOperandCounts());
EXPECT_TRUE(resizeTest.testMutatingOutputOperandCode());
EXPECT_TRUE(resizeTest.testMutatingOutputOperandCounts());
ANeuralNetworksOperandType layout = {.type = ANEURALNETWORKS_BOOL,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase resizeNchwTest(ANEURALNETWORKS_RESIZE_BILINEAR,
{input, height, width, layout}, {output});
EXPECT_TRUE(resizeNchwTest.testMutatingInputOperandCode());
EXPECT_TRUE(resizeNchwTest.testMutatingInputOperandCounts());
EXPECT_TRUE(resizeNchwTest.testMutatingOutputOperandCode());
EXPECT_TRUE(resizeNchwTest.testMutatingOutputOperandCounts());
}
void embeddingLookupTest(int32_t operandCode) {
uint32_t lookupDimensions[1] = {5};
ANeuralNetworksOperandType lookup = {.type = ANEURALNETWORKS_TENSOR_INT32,
.dimensionCount = 1,
.dimensions = lookupDimensions,
.scale = 0.0f,
.zeroPoint = 0};
uint32_t inputDimensions[2] = {5, 5};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 2,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 0.5f;
}
ANeuralNetworksOperandType output = input;
OperationTestBase embedLookupTest(ANEURALNETWORKS_EMBEDDING_LOOKUP,
{lookup, input}, {output});
EXPECT_TRUE(embedLookupTest.testMutatingInputOperandCode());
EXPECT_TRUE(embedLookupTest.testMutatingInputOperandCounts());
EXPECT_TRUE(embedLookupTest.testMutatingOutputOperandCode());
EXPECT_TRUE(embedLookupTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, EMBEDDING_LOOKUP_float32) {
embeddingLookupTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, EMBEDDING_LOOKUP_quant8) {
embeddingLookupTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void hashtableLookupTest(int32_t operandCode) {
uint32_t lookupDimensions[1] = {5};
ANeuralNetworksOperandType lookup = {.type = ANEURALNETWORKS_TENSOR_INT32,
.dimensionCount = 1,
.dimensions = lookupDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType keys = lookup;
uint32_t valuesDimensions[2] = {5, 5};
ANeuralNetworksOperandType values = {.type = operandCode,
.dimensionCount = 2,
.dimensions = valuesDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
values.scale = 0.5f;
}
ANeuralNetworksOperandType output = values;
ANeuralNetworksOperandType hits = lookup;
hits.type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM;
hits.scale = 1.0f;
OperationTestBase hashLookupTest(ANEURALNETWORKS_HASHTABLE_LOOKUP,
{lookup, keys, values}, {output, hits});
EXPECT_TRUE(hashLookupTest.testMutatingInputOperandCode());
EXPECT_TRUE(hashLookupTest.testMutatingInputOperandCounts());
EXPECT_TRUE(hashLookupTest.testMutatingOutputOperandCode());
EXPECT_TRUE(hashLookupTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, HASHTABLE_LOOKUP_float32) {
hashtableLookupTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, HASHTABLE_LOOKUP_quant8) {
hashtableLookupTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void lshProjectionTest(int32_t operandCode) {
uint32_t inputDimensions[2] = {5, 5};
ANeuralNetworksOperandType hash = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 2,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input = hash;
input.type = operandCode;
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 0.5f;
}
uint32_t weightDimensions[1] = {5};
ANeuralNetworksOperandType weight = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 1,
.dimensions = weightDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType type = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType output = weight;
output.type = ANEURALNETWORKS_TENSOR_INT32;
OperationTestBase lshProjTest(ANEURALNETWORKS_LSH_PROJECTION,
{hash, input, weight, type}, {output});
EXPECT_TRUE(lshProjTest.testMutatingInputOperandCode());
EXPECT_TRUE(lshProjTest.testMutatingInputOperandCounts());
EXPECT_TRUE(lshProjTest.testMutatingOutputOperandCode());
EXPECT_TRUE(lshProjTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, LSH_PROJECTION_float32) {
lshProjectionTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, LSH_PROJECTION_quant8) {
lshProjectionTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, LSTM_float32) {
uint32_t oneDimensional[1] = {5};
uint32_t twoDimensional[2] = {5, 5};
ANeuralNetworksOperandType floatTensor1D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 1,
.dimensions = oneDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 2,
.dimensions = twoDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType floatScalar = {.type = ANEURALNETWORKS_FLOAT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input = floatTensor2D;
ANeuralNetworksOperandType inputToInput = floatTensor2D;
ANeuralNetworksOperandType inputToForget = floatTensor2D;
ANeuralNetworksOperandType inputToCell = floatTensor2D;
ANeuralNetworksOperandType inputToOutput = floatTensor2D;
ANeuralNetworksOperandType recurrentToInput = floatTensor2D;
ANeuralNetworksOperandType recurrentToForget = floatTensor2D;
ANeuralNetworksOperandType recurrentToCell = floatTensor2D;
ANeuralNetworksOperandType recurrentToOutput = floatTensor2D;
ANeuralNetworksOperandType cellToInput = floatTensor1D;
ANeuralNetworksOperandType cellToForget = floatTensor1D;
ANeuralNetworksOperandType cellToOutput = floatTensor1D;
ANeuralNetworksOperandType inputGateBias = floatTensor1D;
ANeuralNetworksOperandType forgetGateBias = floatTensor1D;
ANeuralNetworksOperandType cellBias = floatTensor1D;
ANeuralNetworksOperandType outputGateBias = floatTensor1D;
ANeuralNetworksOperandType projWeights = floatTensor2D;
ANeuralNetworksOperandType projBias = floatTensor1D;
ANeuralNetworksOperandType outputStateIn = floatTensor2D;
ANeuralNetworksOperandType cellStateIn = floatTensor2D;
ANeuralNetworksOperandType activation = intScalar;
ANeuralNetworksOperandType clipCellState = floatScalar;
ANeuralNetworksOperandType clipProjLayer = floatScalar;
ANeuralNetworksOperandType scratch = floatTensor2D;
ANeuralNetworksOperandType outputStateOut = floatTensor2D;
ANeuralNetworksOperandType cellStateOut = floatTensor2D;
ANeuralNetworksOperandType output = floatTensor2D;
OperationTestBase lstmTest(ANEURALNETWORKS_LSTM,
{input, inputToInput, inputToForget, inputToCell, inputToOutput, recurrentToInput,
recurrentToForget, recurrentToCell, recurrentToOutput, cellToInput, cellToForget,
cellToOutput, inputGateBias, forgetGateBias, cellBias, outputGateBias, projWeights,
projBias, outputStateIn, cellStateIn, activation, clipCellState, clipProjLayer},
{scratch, outputStateOut, cellStateOut, output});
EXPECT_TRUE(lstmTest.testMutatingInputOperandCode());
EXPECT_TRUE(lstmTest.testMutatingInputOperandCounts(3));
EXPECT_TRUE(lstmTest.testMutatingOutputOperandCode());
EXPECT_TRUE(lstmTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, LSTM_V1_2_float32) {
uint32_t oneDimensional[1] = {5};
uint32_t twoDimensional[2] = {5, 5};
ANeuralNetworksOperandType floatTensor1D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 1,
.dimensions = oneDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 2,
.dimensions = twoDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType floatScalar = {.type = ANEURALNETWORKS_FLOAT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input = floatTensor2D;
ANeuralNetworksOperandType inputToInput = floatTensor2D;
ANeuralNetworksOperandType inputToForget = floatTensor2D;
ANeuralNetworksOperandType inputToCell = floatTensor2D;
ANeuralNetworksOperandType inputToOutput = floatTensor2D;
ANeuralNetworksOperandType recurrentToInput = floatTensor2D;
ANeuralNetworksOperandType recurrentToForget = floatTensor2D;
ANeuralNetworksOperandType recurrentToCell = floatTensor2D;
ANeuralNetworksOperandType recurrentToOutput = floatTensor2D;
ANeuralNetworksOperandType cellToInput = floatTensor1D;
ANeuralNetworksOperandType cellToForget = floatTensor1D;
ANeuralNetworksOperandType cellToOutput = floatTensor1D;
ANeuralNetworksOperandType inputGateBias = floatTensor1D;
ANeuralNetworksOperandType forgetGateBias = floatTensor1D;
ANeuralNetworksOperandType cellBias = floatTensor1D;
ANeuralNetworksOperandType outputGateBias = floatTensor1D;
ANeuralNetworksOperandType projWeights = floatTensor2D;
ANeuralNetworksOperandType projBias = floatTensor1D;
ANeuralNetworksOperandType outputStateIn = floatTensor2D;
ANeuralNetworksOperandType cellStateIn = floatTensor2D;
ANeuralNetworksOperandType activation = intScalar;
ANeuralNetworksOperandType clipCellState = floatScalar;
ANeuralNetworksOperandType clipProjLayer = floatScalar;
ANeuralNetworksOperandType inputLayerNormWeights = floatTensor1D;
ANeuralNetworksOperandType forgetLayerNormWeights = floatTensor1D;
ANeuralNetworksOperandType cellLayerNormWeights = floatTensor1D;
ANeuralNetworksOperandType outputLayerNormWeights = floatTensor1D;
ANeuralNetworksOperandType scratch = floatTensor2D;
ANeuralNetworksOperandType outputStateOut = floatTensor2D;
ANeuralNetworksOperandType cellStateOut = floatTensor2D;
ANeuralNetworksOperandType output = floatTensor2D;
OperationTestBase lstmTest(ANEURALNETWORKS_LSTM,
{input,
inputToInput,
inputToForget,
inputToCell,
inputToOutput,
recurrentToInput,
recurrentToForget,
recurrentToCell,
recurrentToOutput,
cellToInput,
cellToForget,
cellToOutput,
inputGateBias,
forgetGateBias,
cellBias,
outputGateBias,
projWeights,
projBias,
outputStateIn,
cellStateIn,
activation,
clipCellState,
clipProjLayer,
inputLayerNormWeights,
forgetLayerNormWeights,
cellLayerNormWeights,
outputLayerNormWeights},
{scratch, outputStateOut, cellStateOut, output});
EXPECT_TRUE(lstmTest.testMutatingInputOperandCode());
EXPECT_TRUE(lstmTest.testMutatingInputOperandCounts());
EXPECT_TRUE(lstmTest.testMutatingOutputOperandCode());
EXPECT_TRUE(lstmTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, RANDOM_MULTINOMIAL_float16) {
uint32_t twoDimensional[2] = {5, 5};
ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT16,
.dimensionCount = 2,
.dimensions = twoDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input = floatTensor2D;
ANeuralNetworksOperandType sample_count = intScalar;
ANeuralNetworksOperandType output = floatTensor2D;
OperationTestBase multinomialTest(ANEURALNETWORKS_RANDOM_MULTINOMIAL, {input, sample_count},
{output});
EXPECT_TRUE(multinomialTest.testMutatingInputOperandCode());
EXPECT_TRUE(multinomialTest.testMutatingInputOperandCounts());
EXPECT_TRUE(multinomialTest.testMutatingOutputOperandCode());
EXPECT_TRUE(multinomialTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, RANDOM_MULTINOMIAL_float32) {
uint32_t twoDimensional[2] = {5, 5};
ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 2,
.dimensions = twoDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input = floatTensor2D;
ANeuralNetworksOperandType sample_count = intScalar;
ANeuralNetworksOperandType output = floatTensor2D;
OperationTestBase multinomialTest(ANEURALNETWORKS_RANDOM_MULTINOMIAL, {input, sample_count},
{output});
EXPECT_TRUE(multinomialTest.testMutatingInputOperandCode());
EXPECT_TRUE(multinomialTest.testMutatingInputOperandCounts());
EXPECT_TRUE(multinomialTest.testMutatingOutputOperandCode());
EXPECT_TRUE(multinomialTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, RNN_float32) {
uint32_t oneDimensional[1] = {5};
uint32_t twoDimensional[2] = {5, 5};
ANeuralNetworksOperandType floatTensor1D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 1,
.dimensions = oneDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 2,
.dimensions = twoDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input = floatTensor2D;
ANeuralNetworksOperandType weights = floatTensor2D;
ANeuralNetworksOperandType recurrentWeights = floatTensor2D;
ANeuralNetworksOperandType bias = floatTensor1D;
ANeuralNetworksOperandType hiddenStateIn = floatTensor2D;
ANeuralNetworksOperandType activation = intScalar;
ANeuralNetworksOperandType hiddenStateOut = floatTensor2D;
ANeuralNetworksOperandType output = floatTensor2D;
OperationTestBase rnnTest(ANEURALNETWORKS_RNN,
{input, weights, recurrentWeights, bias, hiddenStateIn, activation},
{hiddenStateOut, output});
EXPECT_TRUE(rnnTest.testMutatingInputOperandCode());
EXPECT_TRUE(rnnTest.testMutatingInputOperandCounts());
EXPECT_TRUE(rnnTest.testMutatingOutputOperandCode());
EXPECT_TRUE(rnnTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, SVDF_float32) {
uint32_t oneDimensional[1] = {5};
uint32_t twoDimensional[2] = {5, 5};
ANeuralNetworksOperandType floatTensor1D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 1,
.dimensions = oneDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32,
.dimensionCount = 2,
.dimensions = twoDimensional,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input = floatTensor2D;
ANeuralNetworksOperandType weightsFeature = floatTensor2D;
ANeuralNetworksOperandType weightsTime = floatTensor2D;
ANeuralNetworksOperandType bias = floatTensor1D;
ANeuralNetworksOperandType stateIn = floatTensor2D;
ANeuralNetworksOperandType rank = intScalar;
ANeuralNetworksOperandType activation = intScalar;
ANeuralNetworksOperandType stateOut = floatTensor2D;
ANeuralNetworksOperandType output = floatTensor2D;
OperationTestBase svdfTest(ANEURALNETWORKS_SVDF,
{input, weightsFeature, weightsTime, bias, stateIn, rank, activation},
{stateOut, output});
EXPECT_TRUE(svdfTest.testMutatingInputOperandCode());
EXPECT_TRUE(svdfTest.testMutatingInputOperandCounts());
EXPECT_TRUE(svdfTest.testMutatingOutputOperandCode());
EXPECT_TRUE(svdfTest.testMutatingOutputOperandCounts());
}
void stridedSliceOpTest(int32_t operandCode) {
uint32_t inputDimensions[2] = {5, 5};
ANeuralNetworksOperandType input = {.type = operandCode,
.dimensionCount = 2,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input.scale = 0.5f;
}
ANeuralNetworksOperandType output = input;
uint32_t beginsDimensions[1] = {2};
ANeuralNetworksOperandType begins = {.type = ANEURALNETWORKS_TENSOR_INT32,
.dimensionCount = 1,
.dimensions = beginsDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType ends = begins;
ANeuralNetworksOperandType strides = begins;
ANeuralNetworksOperandType beginMask = {.type = ANEURALNETWORKS_INT32,
.dimensionCount = 0,
.dimensions = nullptr,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType endMask = beginMask;
ANeuralNetworksOperandType shrinkAxisMask = beginMask;
OperationTestBase stridedSliceTest(ANEURALNETWORKS_STRIDED_SLICE,
{input, begins, ends, strides,
beginMask, endMask, shrinkAxisMask},
{output});
EXPECT_TRUE(stridedSliceTest.testMutatingInputOperandCode());
EXPECT_TRUE(stridedSliceTest.testMutatingInputOperandCounts());
EXPECT_TRUE(stridedSliceTest.testMutatingOutputOperandCode());
EXPECT_TRUE(stridedSliceTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, STRIDED_SLICE_float32) {
stridedSliceOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, STRIDED_SLICE_float16) {
stridedSliceOpTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, STRIDED_SLICE_quant8) {
stridedSliceOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void roiAlignOpTest(int32_t inputOperandCode, int32_t roiOperandCode, int32_t scalarOperandCode) {
uint32_t inDim[] = {1, 4, 4, 1}, roiDim[] = {4, 4}, outShapeDim[] = {2};
uint32_t outDim[] = {4, 2, 2, 1};
OperationTestBase roiAlignTest(
ANEURALNETWORKS_ROI_ALIGN,
{getOpType(inputOperandCode, 4, inDim), getOpType(roiOperandCode, 2, roiDim),
getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, outShapeDim), getOpType(scalarOperandCode),
getOpType(ANEURALNETWORKS_INT32)},
{getOpType(inputOperandCode, 4, outDim)});
EXPECT_TRUE(roiAlignTest.testMutatingInputOperandCode());
EXPECT_TRUE(roiAlignTest.testMutatingInputOperandCounts());
EXPECT_TRUE(roiAlignTest.testMutatingOutputOperandCode());
EXPECT_TRUE(roiAlignTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, ROI_ALIGN_float16) {
roiAlignOpTest(ANEURALNETWORKS_TENSOR_FLOAT16, ANEURALNETWORKS_TENSOR_FLOAT16,
ANEURALNETWORKS_FLOAT16);
}
TEST(OperationValidationTest, ROI_ALIGN_float32) {
roiAlignOpTest(ANEURALNETWORKS_TENSOR_FLOAT32, ANEURALNETWORKS_TENSOR_FLOAT32,
ANEURALNETWORKS_FLOAT32);
}
TEST(OperationValidationTest, ROI_ALIGN_quant8) {
roiAlignOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, ANEURALNETWORKS_TENSOR_FLOAT32,
ANEURALNETWORKS_FLOAT32);
}
void roiPoolingOpTest(int32_t inputOperandCode, int32_t roiOperandCode, int32_t scalarOperandCode) {
uint32_t inDim[] = {1, 4, 4, 1}, roiDim[] = {4, 4}, outShapeDim[] = {2};
uint32_t outDim[] = {4, 2, 2, 1};
OperationTestBase roiPoolingTest(
ANEURALNETWORKS_ROI_POOLING,
{getOpType(inputOperandCode, 4, inDim), getOpType(roiOperandCode, 2, roiDim),
getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, outShapeDim), getOpType(scalarOperandCode)},
{getOpType(inputOperandCode, 4, outDim)});
EXPECT_TRUE(roiPoolingTest.testMutatingInputOperandCode());
EXPECT_TRUE(roiPoolingTest.testMutatingInputOperandCounts());
EXPECT_TRUE(roiPoolingTest.testMutatingOutputOperandCode());
EXPECT_TRUE(roiPoolingTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, ROI_POOLING_float16) {
roiPoolingOpTest(ANEURALNETWORKS_TENSOR_FLOAT16, ANEURALNETWORKS_TENSOR_FLOAT16,
ANEURALNETWORKS_FLOAT16);
}
TEST(OperationValidationTest, ROI_POOLING_float32) {
roiPoolingOpTest(ANEURALNETWORKS_TENSOR_FLOAT32, ANEURALNETWORKS_TENSOR_FLOAT32,
ANEURALNETWORKS_FLOAT32);
}
TEST(OperationValidationTest, ROI_POOLING_quant8) {
roiPoolingOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, ANEURALNETWORKS_TENSOR_FLOAT32,
ANEURALNETWORKS_FLOAT32);
}
void heatmapMaxKeypointOpTest(int32_t operandCode) {
uint32_t heatmapDim[] = {6, 4, 4, 1}, boxDim[] = {6, 4}, outDim[] = {6, 3, 1};
OperationTestBase heatmapMaxKeypointTest(
ANEURALNETWORKS_HEATMAP_MAX_KEYPOINT,
{getOpType(operandCode, 4, heatmapDim), getOpType(operandCode, 2, boxDim),
getOpType(ANEURALNETWORKS_BOOL)},
{getOpType(operandCode, 3, outDim)});
EXPECT_TRUE(heatmapMaxKeypointTest.testMutatingInputOperandCode());
EXPECT_TRUE(heatmapMaxKeypointTest.testMutatingInputOperandCounts());
EXPECT_TRUE(heatmapMaxKeypointTest.testMutatingOutputOperandCode());
EXPECT_TRUE(heatmapMaxKeypointTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, HEATMAP_MAX_KEYPOINT_float16) {
heatmapMaxKeypointOpTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, HEATMAP_MAX_KEYPOINT_float32) {
heatmapMaxKeypointOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
void groupedConvOpTest(int32_t operandCode) {
uint32_t inDim[] = {1, 3, 3, 2}, weightDim[] = {2, 2, 2, 1}, biasDim[] = {2};
uint32_t outDim[] = {1, 2, 2, 2};
ANeuralNetworksOperandType bias = (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM)
? getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, biasDim)
: getOpType(operandCode, 1, biasDim);
ANeuralNetworksOperandType scalar = getOpType(ANEURALNETWORKS_INT32);
ANeuralNetworksOperandType layout = getOpType(ANEURALNETWORKS_BOOL);
OperationTestBase explicitGroupedConvTest(
ANEURALNETWORKS_GROUPED_CONV_2D,
{getOpType(operandCode, 4, inDim), getOpType(operandCode, 4, weightDim), bias, scalar,
scalar, scalar, scalar, scalar, scalar, scalar, scalar, layout},
{getOpType(operandCode, 4, outDim)});
EXPECT_TRUE(explicitGroupedConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitGroupedConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitGroupedConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitGroupedConvTest.testMutatingOutputOperandCounts());
OperationTestBase implicitGroupedConvTest(
ANEURALNETWORKS_GROUPED_CONV_2D,
{getOpType(operandCode, 4, inDim), getOpType(operandCode, 4, weightDim), bias, scalar,
scalar, scalar, scalar, scalar, layout},
{getOpType(operandCode, 4, outDim)});
EXPECT_TRUE(implicitGroupedConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitGroupedConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitGroupedConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitGroupedConvTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, GROUPED_CONV_2D_float32) {
groupedConvOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, GROUPED_CONV_2D_quant8) {
groupedConvOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void transposeConvOpTest(int32_t operandCode) {
uint32_t inDim[] = {1, 2, 2, 2}, weightDim[] = {2, 3, 3, 1}, biasDim[] = {2};
uint32_t outDim[] = {1, 5, 5, 2}, outShapeDim[] = {4};
ANeuralNetworksOperandType bias = (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM)
? getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, biasDim)
: getOpType(operandCode, 1, biasDim);
ANeuralNetworksOperandType scalar = getOpType(ANEURALNETWORKS_INT32);
ANeuralNetworksOperandType layout = getOpType(ANEURALNETWORKS_BOOL);
OperationTestBase explicitTransposeConvTest(
ANEURALNETWORKS_TRANSPOSE_CONV_2D,
{getOpType(operandCode, 4, inDim), getOpType(operandCode, 4, weightDim), bias, scalar,
scalar, scalar, scalar, scalar, scalar, scalar, layout},
{getOpType(operandCode, 4, outDim)});
EXPECT_TRUE(explicitTransposeConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(explicitTransposeConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(explicitTransposeConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(explicitTransposeConvTest.testMutatingOutputOperandCounts());
OperationTestBase implicitTransposeConvTest(
ANEURALNETWORKS_TRANSPOSE_CONV_2D,
{getOpType(operandCode, 4, inDim), getOpType(operandCode, 4, weightDim), bias,
getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, outShapeDim), scalar, scalar, scalar,
scalar, layout},
{getOpType(operandCode, 4, outDim)});
EXPECT_TRUE(implicitTransposeConvTest.testMutatingInputOperandCode());
EXPECT_TRUE(implicitTransposeConvTest.testMutatingInputOperandCounts());
EXPECT_TRUE(implicitTransposeConvTest.testMutatingOutputOperandCode());
EXPECT_TRUE(implicitTransposeConvTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, TRANSPOSE_CONV_2D_float32) {
transposeConvOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, TRANSPOSE_CONV_2D_quant8) {
transposeConvOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void channelShuffleOpTest(int32_t operandCode) {
uint32_t inoutDim[] = {2, 2, 3, 12};
OperationTestBase channelShuffleTest(
ANEURALNETWORKS_CHANNEL_SHUFFLE,
{getOpType(operandCode, 2, inoutDim), getOpType(ANEURALNETWORKS_INT32),
getOpType(ANEURALNETWORKS_INT32)},
{getOpType(operandCode, 2, inoutDim)});
EXPECT_TRUE(channelShuffleTest.testMutatingInputOperandCode());
EXPECT_TRUE(channelShuffleTest.testMutatingInputOperandCounts());
EXPECT_TRUE(channelShuffleTest.testMutatingOutputOperandCode());
EXPECT_TRUE(channelShuffleTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, CHANNEL_SHUFFLE_float32) {
channelShuffleOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, CHANNEL_SHUFFLE_quant8) {
channelShuffleOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void preluOpTest(int32_t operandCode) {
uint32_t inoutDim[] = {1, 2, 2, 3}, alphaDim[] = {1, 1, 3};
OperationTestBase preluTest(
ANEURALNETWORKS_PRELU,
{getOpType(operandCode, 4, inoutDim), getOpType(operandCode, 3, alphaDim)},
{getOpType(operandCode, 4, inoutDim)});
EXPECT_TRUE(preluTest.testMutatingInputOperandCode());
EXPECT_TRUE(preluTest.testMutatingInputOperandCounts());
EXPECT_TRUE(preluTest.testMutatingOutputOperandCode());
EXPECT_TRUE(preluTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, PRELU_float16) {
preluOpTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, PRELU_float32) {
preluOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, PRELU_quant8) {
preluOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void normalizationOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) {
uint32_t inputDim[] = {2, 2, 2, 2};
OperationTestBase normalizationTest(operationCode, {getOpType(operandCode, 4, inputDim)},
{getOpType(operandCode, 4, inputDim)});
EXPECT_TRUE(normalizationTest.testMutatingInputOperandCode());
EXPECT_TRUE(normalizationTest.testMutatingInputOperandCounts());
EXPECT_TRUE(normalizationTest.testMutatingOutputOperandCode());
EXPECT_TRUE(normalizationTest.testMutatingOutputOperandCounts());
OperationTestBase normalizationAxisTest(
operationCode, {getOpType(operandCode, 4, inputDim), getOpType(ANEURALNETWORKS_INT32)},
{getOpType(operandCode, 4, inputDim)});
EXPECT_TRUE(normalizationAxisTest.testMutatingInputOperandCode());
EXPECT_TRUE(normalizationAxisTest.testMutatingInputOperandCounts());
EXPECT_TRUE(normalizationAxisTest.testMutatingOutputOperandCode());
EXPECT_TRUE(normalizationAxisTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, L2_NORMALIZATION_float16) {
normalizationOpTest(ANEURALNETWORKS_L2_NORMALIZATION, ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, L2_NORMALIZATION_float32) {
normalizationOpTest(ANEURALNETWORKS_L2_NORMALIZATION, ANEURALNETWORKS_TENSOR_FLOAT32);
}
void localResponseNormOpTest(int32_t operandCode) {
uint32_t inputDim[] = {2, 2, 2, 6};
OperationTestBase lrnTest(
ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION,
{getOpType(operandCode, 4, inputDim), getOpType(ANEURALNETWORKS_INT32),
getOpType(ANEURALNETWORKS_FLOAT32), getOpType(ANEURALNETWORKS_FLOAT32),
getOpType(ANEURALNETWORKS_FLOAT32)},
{getOpType(operandCode, 4, inputDim)});
EXPECT_TRUE(lrnTest.testMutatingInputOperandCode());
EXPECT_TRUE(lrnTest.testMutatingInputOperandCounts());
EXPECT_TRUE(lrnTest.testMutatingOutputOperandCode());
EXPECT_TRUE(lrnTest.testMutatingOutputOperandCounts());
OperationTestBase lrnAxisTest(
ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION,
{getOpType(operandCode, 4, inputDim), getOpType(ANEURALNETWORKS_INT32),
getOpType(ANEURALNETWORKS_FLOAT32), getOpType(ANEURALNETWORKS_FLOAT32),
getOpType(ANEURALNETWORKS_FLOAT32), getOpType(ANEURALNETWORKS_INT32)},
{getOpType(operandCode, 4, inputDim)});
EXPECT_TRUE(lrnAxisTest.testMutatingInputOperandCode());
EXPECT_TRUE(lrnAxisTest.testMutatingInputOperandCounts());
EXPECT_TRUE(lrnAxisTest.testMutatingOutputOperandCode());
EXPECT_TRUE(lrnAxisTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, LOCAL_RESPONSE_NORMALIZATION_float16) {
localResponseNormOpTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, LOCAL_RESPONSE_NORMALIZATION_float32) {
localResponseNormOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
void axisAlignedBBoxTransformOpTest(int32_t operandCode) {
uint32_t roiDim[] = {5, 5}, deltaDim[] = {5, 8}, imageDim[] = {5, 3}, weightDim[] = {4};
uint32_t outDim[] = {5, 8}, bsDim[] = {5};
OperationTestBase axisAlignedBBoxTransformTest(
ANEURALNETWORKS_AXIS_ALIGNED_BBOX_TRANSFORM,
{getOpType(operandCode, 2, roiDim), getOpType(operandCode, 2, deltaDim),
getOpType(operandCode, 2, imageDim), getOpType(operandCode, 1, weightDim),
getOpType(ANEURALNETWORKS_BOOL)},
{getOpType(operandCode, 2, outDim), getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, bsDim)});
EXPECT_TRUE(axisAlignedBBoxTransformTest.testMutatingInputOperandCode());
EXPECT_TRUE(axisAlignedBBoxTransformTest.testMutatingInputOperandCounts());
EXPECT_TRUE(axisAlignedBBoxTransformTest.testMutatingOutputOperandCode());
EXPECT_TRUE(axisAlignedBBoxTransformTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, AXIS_ALIGNED_BBOX_TRANSFORM_float16) {
axisAlignedBBoxTransformOpTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
TEST(OperationValidationTest, AXIS_ALIGNED_BBOX_TRANSFORM_float32) {
axisAlignedBBoxTransformOpTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
void rotatedBBoxTransformOpTest(int32_t tensorOperandCode, int32_t scalarOperandCode) {
uint32_t roiDim[] = {5, 5}, deltaDim[] = {5, 8}, imageDim[] = {5, 3}, weightDim[] = {4};
uint32_t outDim[] = {5, 8}, bsDim[] = {5};
OperationTestBase rotatedBBoxTransformTest(
ANEURALNETWORKS_ROTATED_BBOX_TRANSFORM,
{getOpType(tensorOperandCode, 2, roiDim), getOpType(tensorOperandCode, 2, deltaDim),
getOpType(tensorOperandCode, 2, imageDim), getOpType(tensorOperandCode, 1, weightDim),
getOpType(ANEURALNETWORKS_BOOL), getOpType(ANEURALNETWORKS_BOOL),
getOpType(ANEURALNETWORKS_INT32), getOpType(ANEURALNETWORKS_INT32),
getOpType(scalarOperandCode)},
{getOpType(tensorOperandCode, 2, outDim),
getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, bsDim)});
EXPECT_TRUE(rotatedBBoxTransformTest.testMutatingInputOperandCode());
EXPECT_TRUE(rotatedBBoxTransformTest.testMutatingInputOperandCounts());
EXPECT_TRUE(rotatedBBoxTransformTest.testMutatingOutputOperandCode());
EXPECT_TRUE(rotatedBBoxTransformTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, ROTATED_BBOX_TRANSFORM_float16) {
rotatedBBoxTransformOpTest(ANEURALNETWORKS_TENSOR_FLOAT16, ANEURALNETWORKS_FLOAT16);
}
TEST(OperationValidationTest, ROTATED_BBOX_TRANSFORM_float32) {
rotatedBBoxTransformOpTest(ANEURALNETWORKS_TENSOR_FLOAT32, ANEURALNETWORKS_FLOAT32);
}
void sliceTest(int32_t operandCode) {
uint32_t inputDim[] = {3, 3, 3};
uint32_t startDim[] = {3};
uint32_t sizeDim[] = {3};
uint32_t outputDim[] = {1, 2, 3};
OperationTestBase sliceTest(ANEURALNETWORKS_SLICE,
{getOpType(operandCode, 3, inputDim),
getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, startDim),
getOpType(ANEURALNETWORKS_TENSOR_INT32, 1, sizeDim)},
{getOpType(operandCode, 3, outputDim)});
EXPECT_TRUE(sliceTest.testMutatingInputOperandCode());
EXPECT_TRUE(sliceTest.testMutatingInputOperandCounts());
EXPECT_TRUE(sliceTest.testMutatingOutputOperandCode());
EXPECT_TRUE(sliceTest.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, SLICE_float32) {
sliceTest(ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, SLICE_int32) {
sliceTest(ANEURALNETWORKS_TENSOR_INT32);
}
TEST(OperationValidationTest, SLICE_uint8) {
sliceTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, SLICE_float16) {
sliceTest(ANEURALNETWORKS_TENSOR_FLOAT16);
}
void logicalTest(ANeuralNetworksOperationType operationCode) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input1 = {.type = ANEURALNETWORKS_TENSOR_BOOL8,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input2 = input1;
ANeuralNetworksOperandType output = input1;
OperationTestBase test(operationCode, {input1, input2}, {output});
EXPECT_TRUE(test.testMutatingInputOperandCode());
EXPECT_TRUE(test.testMutatingInputOperandCounts());
EXPECT_TRUE(test.testMutatingOutputOperandCode());
EXPECT_TRUE(test.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, LOGICAL_AND) {
logicalTest(ANEURALNETWORKS_LOGICAL_AND);
}
TEST(OperationValidationTest, LOGICAL_OR) {
logicalTest(ANEURALNETWORKS_LOGICAL_OR);
}
void comparisonTest(ANeuralNetworksOperationType operationCode, int32_t inputOperandType) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input1 = {.type = inputOperandType,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (inputOperandType == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input1.scale = 1.f / 256;
}
ANeuralNetworksOperandType input2 = input1;
ANeuralNetworksOperandType output = {.type = ANEURALNETWORKS_TENSOR_BOOL8,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
OperationTestBase test(operationCode, {input1, input2}, {output});
EXPECT_TRUE(test.testMutatingInputOperandCode());
EXPECT_TRUE(test.testMutatingInputOperandCounts());
EXPECT_TRUE(test.testMutatingOutputOperandCode());
EXPECT_TRUE(test.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, LESS) {
comparisonTest(ANEURALNETWORKS_LESS, ANEURALNETWORKS_TENSOR_BOOL8);
comparisonTest(ANEURALNETWORKS_LESS, ANEURALNETWORKS_TENSOR_FLOAT16);
comparisonTest(ANEURALNETWORKS_LESS, ANEURALNETWORKS_TENSOR_FLOAT32);
comparisonTest(ANEURALNETWORKS_LESS, ANEURALNETWORKS_TENSOR_INT32);
comparisonTest(ANEURALNETWORKS_LESS, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, LESS_EQUAL) {
comparisonTest(ANEURALNETWORKS_LESS_EQUAL, ANEURALNETWORKS_TENSOR_BOOL8);
comparisonTest(ANEURALNETWORKS_LESS_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT16);
comparisonTest(ANEURALNETWORKS_LESS_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT32);
comparisonTest(ANEURALNETWORKS_LESS_EQUAL, ANEURALNETWORKS_TENSOR_INT32);
comparisonTest(ANEURALNETWORKS_LESS_EQUAL, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, EQUAL) {
comparisonTest(ANEURALNETWORKS_EQUAL, ANEURALNETWORKS_TENSOR_BOOL8);
comparisonTest(ANEURALNETWORKS_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT16);
comparisonTest(ANEURALNETWORKS_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT32);
comparisonTest(ANEURALNETWORKS_EQUAL, ANEURALNETWORKS_TENSOR_INT32);
comparisonTest(ANEURALNETWORKS_EQUAL, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, NOT_EQUAL) {
comparisonTest(ANEURALNETWORKS_NOT_EQUAL, ANEURALNETWORKS_TENSOR_BOOL8);
comparisonTest(ANEURALNETWORKS_NOT_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT16);
comparisonTest(ANEURALNETWORKS_NOT_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT32);
comparisonTest(ANEURALNETWORKS_NOT_EQUAL, ANEURALNETWORKS_TENSOR_INT32);
comparisonTest(ANEURALNETWORKS_NOT_EQUAL, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, GREATER) {
comparisonTest(ANEURALNETWORKS_GREATER, ANEURALNETWORKS_TENSOR_BOOL8);
comparisonTest(ANEURALNETWORKS_GREATER, ANEURALNETWORKS_TENSOR_FLOAT16);
comparisonTest(ANEURALNETWORKS_GREATER, ANEURALNETWORKS_TENSOR_FLOAT32);
comparisonTest(ANEURALNETWORKS_GREATER, ANEURALNETWORKS_TENSOR_INT32);
comparisonTest(ANEURALNETWORKS_GREATER, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, GREATER_EQUAL) {
comparisonTest(ANEURALNETWORKS_GREATER_EQUAL, ANEURALNETWORKS_TENSOR_BOOL8);
comparisonTest(ANEURALNETWORKS_GREATER_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT16);
comparisonTest(ANEURALNETWORKS_GREATER_EQUAL, ANEURALNETWORKS_TENSOR_FLOAT32);
comparisonTest(ANEURALNETWORKS_GREATER_EQUAL, ANEURALNETWORKS_TENSOR_INT32);
comparisonTest(ANEURALNETWORKS_GREATER_EQUAL, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
void reduceOpTest(ANeuralNetworksOperationType operationCode, int32_t inputOperandType) {
bool isQuant = inputOperandType == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM;
float scale = isQuant ? 1.f / 256 : 0.0f;
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input1 = {
.type = inputOperandType,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = scale,
.zeroPoint = 0,
};
uint32_t axesDimensions[1] = {2};
ANeuralNetworksOperandType input2 = {
.type = ANEURALNETWORKS_TENSOR_INT32,
.dimensionCount = 1,
.dimensions = axesDimensions,
};
ANeuralNetworksOperandType input3 = {
.type = ANEURALNETWORKS_BOOL,
.dimensions = {},
};
ANeuralNetworksOperandType output = {
.type = inputOperandType,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = scale,
};
OperationTestBase test(operationCode, {input1, input2, input3}, {output});
EXPECT_TRUE(test.testMutatingInputOperandCode());
EXPECT_TRUE(test.testMutatingInputOperandCounts());
EXPECT_TRUE(test.testMutatingOutputOperandCode());
EXPECT_TRUE(test.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, REDUCE_PROD) {
reduceOpTest(ANEURALNETWORKS_REDUCE_PROD, ANEURALNETWORKS_TENSOR_FLOAT16);
reduceOpTest(ANEURALNETWORKS_REDUCE_PROD, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, REDUCE_SUM) {
reduceOpTest(ANEURALNETWORKS_REDUCE_SUM, ANEURALNETWORKS_TENSOR_FLOAT16);
reduceOpTest(ANEURALNETWORKS_REDUCE_SUM, ANEURALNETWORKS_TENSOR_FLOAT32);
}
TEST(OperationValidationTest, REDUCE_MAX) {
reduceOpTest(ANEURALNETWORKS_REDUCE_MAX, ANEURALNETWORKS_TENSOR_FLOAT16);
reduceOpTest(ANEURALNETWORKS_REDUCE_MAX, ANEURALNETWORKS_TENSOR_FLOAT32);
reduceOpTest(ANEURALNETWORKS_REDUCE_MAX, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, REDUCE_MIN) {
reduceOpTest(ANEURALNETWORKS_REDUCE_MIN, ANEURALNETWORKS_TENSOR_FLOAT16);
reduceOpTest(ANEURALNETWORKS_REDUCE_MIN, ANEURALNETWORKS_TENSOR_FLOAT32);
reduceOpTest(ANEURALNETWORKS_REDUCE_MIN, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
TEST(OperationValidationTest, REDUCE_ANY) {
reduceOpTest(ANEURALNETWORKS_REDUCE_ANY, ANEURALNETWORKS_TENSOR_BOOL8);
}
TEST(OperationValidationTest, REDUCE_ALL) {
reduceOpTest(ANEURALNETWORKS_REDUCE_ALL, ANEURALNETWORKS_TENSOR_BOOL8);
}
void selectTest(ANeuralNetworksOperationType operationCode, int32_t inputOperandType) {
uint32_t inputDimensions[4] = {2, 2, 2, 2};
ANeuralNetworksOperandType input0 = {.type = ANEURALNETWORKS_TENSOR_BOOL8,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
ANeuralNetworksOperandType input1 = {.type = inputOperandType,
.dimensionCount = 4,
.dimensions = inputDimensions,
.scale = 0.0f,
.zeroPoint = 0};
if (inputOperandType == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) {
input1.scale = 1.f / 256;
}
ANeuralNetworksOperandType input2 = input1;
ANeuralNetworksOperandType output = input1;
OperationTestBase test(operationCode, {input0, input1, input2}, {output});
EXPECT_TRUE(test.testMutatingInputOperandCode());
EXPECT_TRUE(test.testMutatingInputOperandCounts());
EXPECT_TRUE(test.testMutatingOutputOperandCode());
EXPECT_TRUE(test.testMutatingOutputOperandCounts());
}
TEST(OperationValidationTest, SELECT) {
selectTest(ANEURALNETWORKS_SELECT, ANEURALNETWORKS_TENSOR_FLOAT16);
selectTest(ANEURALNETWORKS_SELECT, ANEURALNETWORKS_TENSOR_FLOAT32);
selectTest(ANEURALNETWORKS_SELECT, ANEURALNETWORKS_TENSOR_INT32);
selectTest(ANEURALNETWORKS_SELECT, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM);
}
} // end namespace