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v811_spc009/packages/modules/NeuralNetworks/runtime/test/TestUnspecifiedDimensions.cpp

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/*
* Copyright (C) 2019 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 <android-base/macros.h>
#include <android/sharedmem.h>
#include <gtest/gtest.h>
#include <sys/mman.h>
#include <memory>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
#include "TestNeuralNetworksWrapper.h"
using namespace android::nn::test_wrapper;
namespace {
// We try the following model:
//
// op2 = ADD(op0, op1)
// op4 = TRANSPOSE(op2, op3)
//
// where op0 is a required model input, should be of dimension (A, B).
// op1 is a required constant, should be of dimension (A, 1).
// op2 is an internal operand, should be of dimension (A, B).
// op3 is an omitted optional constant / model input, should be of dimension (2).
// op4 is a model output, should be of dimension (B, A).
//
// For each operand, we test combinations of dimensions specification level during model
// construction time and execution time (if any). All other relevant combinations of the
// basic scenarios are then iterated over in TestAll. Note that we don't want to just use
// googletest's parametrized tests (TEST_P) as the 16k combinations generated too many
// lines of output for the test infrastructure to handle correctly.
// Which operand to test
enum class UnspecifiedOperand {
INPUT_MANDATORY,
CONST_MANDATORY,
TEMPORARY_VARIABLE,
INPUT_OPTIONAL,
CONST_OPTIONAL,
OUTPUT
};
// How well the dimensional information is specified
enum class SpecificationLevel {
FULLY_SPECIFIED, // all dimensions are clearly specified without any ambiguity
UNSPECIFIED_DIM, // certain dimension is set to 0 as unknown, but rank is well-specified
UNSPECIFIED_RANK, // rank is set to 0 as unknown, passing an empty vector for dims
UNSPECIFIED_TYPE // only during execution time, passing nullptr for operand type
};
using UnspecifiedDimensionsTestParam = std::tuple<UnspecifiedOperand,
SpecificationLevel, // model construction time
SpecificationLevel>; // execution time
// Indexing
constexpr uint32_t kIndex0_Model = 0; // op0, model
constexpr uint32_t kIndex1_Model = 1; // op1, model
constexpr uint32_t kIndex2_Model = 2; // op2, model
constexpr uint32_t kIndex3_Model = 3; // op3, model
constexpr uint32_t kIndex4_Model = 4; // op4, model
constexpr uint32_t kIndex0_Execution = 5; // op0, execution
constexpr uint32_t kIndex3_Execution = 6; // op3, execution
constexpr uint32_t kIndex4_Execution = 7; // op4, execution
constexpr uint32_t kIndexCount = 8; // count
constexpr int32_t kValueA = 0;
constexpr int32_t kValueB = 2;
constexpr uint32_t kDimAGood = 2;
constexpr uint32_t kDimABad = 3;
class UnspecifiedDimensionsTest : public ::testing::TestWithParam<UnspecifiedDimensionsTestParam> {
enum class OptionalType { CONST, INPUT }; // omitted operand op3 is an input or const
enum class BufferSize { LESS, EQUAL, MORE }; // only used for output buffer size
enum class OperandLocation { BUFFER, MEMORY }; // where the operand reside
enum class InOutType { INPUT, OUTPUT }; // parameter for setInOut()
// Whether input/output padding is implicitly disabled, enabled, or explicitly disabled
enum class PaddingEnabled { DEFAULT, ENABLED, DISABLED };
class SharedMemoryForTest {
public:
SharedMemoryForTest() : memory(nullptr), fd(-1), buffer(nullptr) {}
~SharedMemoryForTest() {
if (buffer != nullptr) {
munmap(buffer, kLength);
}
if (fd > -1) {
close(fd);
}
}
void initialize(size_t size, const void* data) {
fd = ASharedMemory_create(nullptr, kLength);
ASSERT_GT(fd, -1);
buffer = (uint8_t*)mmap(nullptr, kLength, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
ASSERT_NE(buffer, nullptr);
memcpy(buffer, data, size);
memory = std::make_shared<Memory>(kLength, PROT_READ | PROT_WRITE, fd, 0);
ASSERT_TRUE(memory->isValid());
}
const Memory* getMemory() const { return memory.get(); }
const uint8_t* getBuffer() const { return buffer; }
private:
DISALLOW_COPY_AND_ASSIGN(SharedMemoryForTest);
std::shared_ptr<Memory> memory;
int fd;
uint8_t* buffer;
// Always allocate an ashmem of 64 bytes. This is large enough for all use cases.
static constexpr size_t kLength = 64;
};
std::string toString(SpecificationLevel level) {
switch (level) {
case SpecificationLevel::FULLY_SPECIFIED:
return "FULLY_SPECIFIED";
case SpecificationLevel::UNSPECIFIED_DIM:
return "UNSPECIFIED_DIM";
case SpecificationLevel::UNSPECIFIED_RANK:
return "UNSPECIFIED_RANK";
case SpecificationLevel::UNSPECIFIED_TYPE:
return "UNSPECIFIED_TYPE";
default:
return "UNKNOWN";
}
}
std::string toString(BufferSize b) {
switch (b) {
case BufferSize::LESS:
return "LESS";
case BufferSize::EQUAL:
return "EQUAL";
case BufferSize::MORE:
return "MORE";
default:
return "UNKNOWN";
}
}
std::string toString(OperandLocation loc) {
switch (loc) {
case OperandLocation::BUFFER:
return "BUFFER";
case OperandLocation::MEMORY:
return "MEMORY";
default:
return "UNKNOWN";
}
}
std::string toString(PaddingEnabled enabled) {
switch (enabled) {
case PaddingEnabled::DEFAULT:
return "DEFAULT";
case PaddingEnabled::ENABLED:
return "ENABLED";
case PaddingEnabled::DISABLED:
return "DISABLED";
default:
return "UNKNOWN";
}
}
protected:
virtual void SetUp() {
uint32_t modelIndex, executionIndex;
switch (kUnspecifiedOperand) {
case UnspecifiedOperand::INPUT_MANDATORY:
modelIndex = kIndex0_Model;
executionIndex = kIndex0_Execution;
mBadIndexChoices = {kIndexCount, modelIndex, executionIndex};
mOperandLocationChoices = {OperandLocation::BUFFER, OperandLocation::MEMORY};
mBufferSizeChoices = {BufferSize::LESS, BufferSize::EQUAL, BufferSize::MORE};
mEnablePaddingChoices = {PaddingEnabled::DEFAULT, PaddingEnabled::ENABLED,
PaddingEnabled::DISABLED};
break;
case UnspecifiedOperand::CONST_MANDATORY:
modelIndex = kIndex1_Model;
executionIndex = kIndexCount;
mBadIndexChoices = {kIndexCount, modelIndex};
mOperandLocationChoices = {OperandLocation::BUFFER, OperandLocation::MEMORY};
break;
case UnspecifiedOperand::TEMPORARY_VARIABLE:
modelIndex = kIndex2_Model;
executionIndex = kIndexCount;
mBadIndexChoices = {kIndexCount, modelIndex};
mOperandLocationChoices = {OperandLocation::BUFFER};
break;
case UnspecifiedOperand::INPUT_OPTIONAL:
modelIndex = kIndex3_Model;
executionIndex = kIndex3_Execution;
mBadIndexChoices = {kIndexCount};
mOptionalType = OptionalType::INPUT;
mOperandLocationChoices = {OperandLocation::BUFFER};
break;
case UnspecifiedOperand::CONST_OPTIONAL:
modelIndex = kIndex3_Model;
executionIndex = kIndexCount;
mBadIndexChoices = {kIndexCount};
mOperandLocationChoices = {OperandLocation::BUFFER};
break;
case UnspecifiedOperand::OUTPUT:
modelIndex = kIndex4_Model;
executionIndex = kIndex4_Execution;
mBadIndexChoices = {kIndexCount, modelIndex, executionIndex};
mOperandLocationChoices = {OperandLocation::BUFFER, OperandLocation::MEMORY};
mBufferSizeChoices = {BufferSize::LESS, BufferSize::EQUAL, BufferSize::MORE};
mEnablePaddingChoices = {PaddingEnabled::DEFAULT, PaddingEnabled::ENABLED,
PaddingEnabled::DISABLED};
break;
default:
break;
}
std::vector<SpecificationLevel> levels{
SpecificationLevel::UNSPECIFIED_DIM, SpecificationLevel::FULLY_SPECIFIED,
SpecificationLevel::UNSPECIFIED_DIM, SpecificationLevel::FULLY_SPECIFIED,
SpecificationLevel::UNSPECIFIED_DIM, SpecificationLevel::FULLY_SPECIFIED,
SpecificationLevel::FULLY_SPECIFIED, SpecificationLevel::FULLY_SPECIFIED};
levels[modelIndex] = kSpecificationLevelModel;
if (executionIndex < kIndexCount) {
levels[executionIndex] = kSpecificationLevelExecution;
}
mSpecificationLevels = std::move(levels);
}
OperandType getType(uint32_t index, const std::vector<uint32_t>& dim) {
const SpecificationLevel l = mSpecificationLevels[index];
std::vector<uint32_t> setDim;
if (l != SpecificationLevel::UNSPECIFIED_RANK) {
for (auto d : dim) {
if (d == 0) {
setDim.push_back(mBadIndex != index ? kDimAGood : kDimABad);
} else {
setDim.push_back(l == SpecificationLevel::FULLY_SPECIFIED ? d : 0);
}
}
}
float scale = mOperandTypes[index] == Type::TENSOR_QUANT8_ASYMM ? 1.0 : 0.0;
return OperandType(mOperandTypes[index], setDim, scale, 0);
}
uint32_t getSize(uint32_t index, const std::vector<uint32_t>& dim,
BufferSize s = BufferSize::EQUAL) {
uint32_t n = 1;
for (auto d : dim) {
n *= (d == 0 ? (mBadIndex != index ? kDimAGood : kDimABad) : d);
}
if (s == BufferSize::LESS) {
n /= 2;
} else if (s == BufferSize::MORE) {
n *= 2;
}
return n;
};
template <typename T>
Result setInOut(Execution* execution, uint32_t index, uint32_t opIndex,
const std::vector<uint32_t>& dim, void* buffer,
const SharedMemoryForTest* memory, InOutType inOutType,
BufferSize bufferSize = BufferSize::EQUAL) {
const auto kLevel = mSpecificationLevels[index];
size_t size = (buffer == nullptr) ? 0 : getSize(index, dim, bufferSize) * sizeof(T);
auto type = getType(index, dim);
ANeuralNetworksOperandType* t =
(kLevel == SpecificationLevel::UNSPECIFIED_TYPE) ? nullptr : &type.operandType;
if (mOperandLocation == OperandLocation::MEMORY && memory != nullptr) {
if (inOutType == InOutType::INPUT) {
return execution->setInputFromMemory(opIndex, memory->getMemory(), 0, size, t);
} else {
return execution->setOutputFromMemory(opIndex, memory->getMemory(), 0, size, t);
}
} else {
if (inOutType == InOutType::INPUT) {
return execution->setInput(opIndex, buffer, size, t);
} else {
return execution->setOutput(opIndex, buffer, size, t);
}
}
return Result::NO_ERROR;
}
template <typename T, Type TensorType>
void TestOne() {
// Phase 1: Build Model
Model model;
auto type0 = getType(kIndex0_Model, {kValueA, kValueB});
auto type1 = getType(kIndex1_Model, {kValueA, 1});
auto type2 = getType(kIndex2_Model, {kValueA, kValueB});
auto type3 = getType(kIndex3_Model, {2});
auto type4 = getType(kIndex4_Model, {kValueB, kValueA});
OperandType typeActivation(Type::INT32, {}); // activation
auto op0 = model.addOperand(&type0);
auto op1 = model.addOperand(&type1);
auto op2 = model.addOperand(&type2);
auto op3 = model.addOperand(&type3);
auto op4 = model.addOperand(&type4);
auto act = model.addOperand(&typeActivation);
T bufferOp1[2] = {1, 2};
SharedMemoryForTest memoryOp1;
memoryOp1.initialize(sizeof(bufferOp1), bufferOp1);
if (mOperandLocation == OperandLocation::BUFFER) {
model.setOperandValue(op1, bufferOp1, sizeof(bufferOp1));
} else {
model.setOperandValueFromMemory(op1, memoryOp1.getMemory(), 0, sizeof(bufferOp1));
}
int32_t kActivation = 0;
model.setOperandValue(act, &kActivation, sizeof(int32_t));
if (mOptionalType == OptionalType::CONST) {
model.setOperandValue(op3, nullptr, 0);
}
model.addOperation(ANEURALNETWORKS_ADD, {op0, op1, act}, {op2});
model.addOperation(ANEURALNETWORKS_TRANSPOSE, {op2, op3}, {op4});
if (mOptionalType == OptionalType::CONST) {
model.identifyInputsAndOutputs({op0}, {op4});
} else {
model.identifyInputsAndOutputs({op0, op3}, {op4});
}
bool expected = expectModelIsValid();
ASSERT_EQ(model.isValid(), expected);
Result result = model.finish();
if (expected) {
ASSERT_EQ(result, Result::NO_ERROR);
} else {
// There is no contract (yet) for specific errors in NeuralNetworks.h,
// so we just assert on not being successful.
ASSERT_NE(result, Result::NO_ERROR);
return;
}
// Phase 2: Compile Model, should always pass
Compilation compilation(&model);
ASSERT_EQ(compilation.finish(), Result::NO_ERROR);
std::vector<uint32_t> valueBChoices = {1, 2};
for (const auto valueB : valueBChoices) {
SCOPED_TRACE("ValueB: " + std::to_string(valueB));
if (valueB != kValueB &&
(mSpecificationLevels[kIndex0_Model] == SpecificationLevel::FULLY_SPECIFIED ||
mSpecificationLevels[kIndex2_Model] == SpecificationLevel::FULLY_SPECIFIED ||
mSpecificationLevels[kIndex4_Model] == SpecificationLevel::FULLY_SPECIFIED)) {
continue;
}
// Phase 3: Set Execution Input/Output
Execution execution(&compilation);
// Enable padding
if (mEnablePadding == PaddingEnabled::ENABLED) {
ASSERT_EQ(execution.enableInputAndOutputPadding(true), Result::NO_ERROR);
} else if (mEnablePadding == PaddingEnabled::DISABLED) {
ASSERT_EQ(execution.enableInputAndOutputPadding(false), Result::NO_ERROR);
}
// Set input0
Result result;
T bufferOp0[6] = {1, 2, 3, 4, 5, 6};
SharedMemoryForTest memoryOp0;
memoryOp0.initialize(sizeof(bufferOp0), bufferOp0);
result = setInOut<T>(&execution, kIndex0_Execution, 0, {kValueA, valueB}, bufferOp0,
&memoryOp0, InOutType::INPUT, mBufferSize);
ASSERT_EQ(result, expectSetInput0());
if (result != Result::NO_ERROR) continue;
// Set input1, omitted
if (mOptionalType == OptionalType::INPUT) {
result = setInOut<T>(&execution, kIndex3_Execution, 1, {2}, nullptr, nullptr,
InOutType::INPUT);
ASSERT_EQ(result, expectSetInput1());
if (result != Result::NO_ERROR) continue;
}
// Set output0
T bufferOp4[16];
SharedMemoryForTest memoryOp4;
memoryOp4.initialize(sizeof(bufferOp4), bufferOp4);
result = setInOut<T>(&execution, kIndex4_Execution, 0, {valueB, kValueA}, bufferOp4,
&memoryOp4, InOutType::OUTPUT, mBufferSize);
ASSERT_EQ(result, expectSetOutput0());
if (result != Result::NO_ERROR) continue;
// Phase 4: Compute and Compare Results
result = execution.compute();
ASSERT_EQ(result, expectCompute());
if (result == Result::OP_FAILED) continue;
std::vector<uint32_t> outputShape;
ASSERT_EQ(execution.getOutputOperandDimensions(0, &outputShape), result);
std::vector<uint32_t> expectedOutputShape = {valueB, kDimAGood};
ASSERT_EQ(outputShape, expectedOutputShape);
if (result == Result::OUTPUT_INSUFFICIENT_SIZE) continue;
const T* outputBuffer = mOperandLocation == OperandLocation::MEMORY
? reinterpret_cast<const T*>(memoryOp4.getBuffer())
: bufferOp4;
T expected_1x2[2] = {2, 4};
T expected_2x2[4] = {2, 5, 3, 6};
for (uint32_t i = 0; i < kDimAGood * valueB; i++) {
ASSERT_EQ(outputBuffer[i], valueB == 1 ? expected_1x2[i] : expected_2x2[i]);
}
}
}
// Expect invalid model for the following cases
// - op1 is not fully specified (const operand must be fully specified)
// - op1 has bad dimension value (const operand size is checked with buffer size)
bool expectModelIsValid() {
const auto kLevel1_Model = mSpecificationLevels[kIndex1_Model];
if (kLevel1_Model != SpecificationLevel::FULLY_SPECIFIED || mBadIndex == kIndex1_Model) {
return false;
}
return true;
}
// Expect BAD_DATA on input0 for the following cases
// - the provided type is not fully specified
// - the provided type does not agree with the type set at model construction time
// - no type is provided and the type is not fully specified at model construction time
// - the buffer size (length) is less than needed
// - the buffer size (length) is more than needed and padding is not enabled
Result expectSetInput0() {
const auto kLevel0_Model = mSpecificationLevels[kIndex0_Model];
const auto kLevel0_Execution = mSpecificationLevels[kIndex0_Execution];
switch (kLevel0_Execution) {
case SpecificationLevel::UNSPECIFIED_DIM:
case SpecificationLevel::UNSPECIFIED_RANK:
return Result::BAD_DATA;
case SpecificationLevel::FULLY_SPECIFIED:
if ((mBadIndex == kIndex0_Execution || mBadIndex == kIndex0_Model) &&
kLevel0_Model != SpecificationLevel::UNSPECIFIED_RANK) {
return Result::BAD_DATA;
}
if (mBufferSize == BufferSize::LESS) {
return Result::BAD_DATA;
}
if (mEnablePadding != PaddingEnabled::ENABLED && mBufferSize == BufferSize::MORE) {
return Result::BAD_DATA;
}
break;
case SpecificationLevel::UNSPECIFIED_TYPE:
if (kLevel0_Model == SpecificationLevel::UNSPECIFIED_DIM ||
kLevel0_Model == SpecificationLevel::UNSPECIFIED_RANK) {
return Result::BAD_DATA;
}
if (mBufferSize == BufferSize::LESS) {
return Result::BAD_DATA;
}
if (mEnablePadding != PaddingEnabled::ENABLED && mBufferSize == BufferSize::MORE) {
return Result::BAD_DATA;
}
// This is the case when the dimension is incorrectly specified in the model.
// With incorrect dimension, the needed size is 2 * 3 = 6 data type size.
// BufferSize::EQUAL (2 * 2 = 4 data type size) cannot provide enough length.
if (mBadIndex == kIndex0_Model && mBufferSize == BufferSize::EQUAL) {
return Result::BAD_DATA;
}
break;
default:
break;
}
return Result::NO_ERROR;
}
// Expect BAD_DATA on input1 for the following cases
// - the provided type is less detailed as the type set at model construction time
Result expectSetInput1() {
const auto kLevel3_Model = mSpecificationLevels[kIndex3_Model];
const auto kLevel3_Execution = mSpecificationLevels[kIndex3_Execution];
switch (kLevel3_Execution) {
case SpecificationLevel::UNSPECIFIED_DIM:
if (kLevel3_Model == SpecificationLevel::FULLY_SPECIFIED) {
return Result::BAD_DATA;
}
break;
case SpecificationLevel::UNSPECIFIED_RANK:
if (kLevel3_Model != SpecificationLevel::UNSPECIFIED_RANK) {
return Result::BAD_DATA;
}
break;
default:
break;
}
return Result::NO_ERROR;
}
// Expect BAD_DATA on output0 for the following cases
// - the provided type is less detailed as the type set at model construction time
// - the provided type does not agree with the type set at model construction time
// - the buffer size (length) is less than needed
// - the buffer size (length) is more than needed and padding is not enabled
Result expectSetOutput0() {
const auto kLevel4_Model = mSpecificationLevels[kIndex4_Model];
const auto kLevel4_Execution = mSpecificationLevels[kIndex4_Execution];
switch (kLevel4_Execution) {
case SpecificationLevel::UNSPECIFIED_DIM:
if (kLevel4_Model == SpecificationLevel::FULLY_SPECIFIED ||
(kLevel4_Model == SpecificationLevel::UNSPECIFIED_DIM &&
(mBadIndex == kIndex4_Model || mBadIndex == kIndex4_Execution))) {
return Result::BAD_DATA;
}
break;
case SpecificationLevel::UNSPECIFIED_RANK:
if (kLevel4_Model != SpecificationLevel::UNSPECIFIED_RANK) {
return Result::BAD_DATA;
}
break;
case SpecificationLevel::FULLY_SPECIFIED:
if ((mBadIndex == kIndex4_Model || mBadIndex == kIndex4_Execution) &&
kLevel4_Model != SpecificationLevel::UNSPECIFIED_RANK) {
return Result::BAD_DATA;
}
if (mBufferSize == BufferSize::LESS) {
return Result::BAD_DATA;
}
if (mEnablePadding != PaddingEnabled::ENABLED && mBufferSize == BufferSize::MORE) {
return Result::BAD_DATA;
}
break;
case SpecificationLevel::UNSPECIFIED_TYPE:
if (kLevel4_Model == SpecificationLevel::FULLY_SPECIFIED) {
if (mBufferSize == BufferSize::LESS) {
return Result::BAD_DATA;
}
if (mEnablePadding != PaddingEnabled::ENABLED &&
mBufferSize == BufferSize::MORE) {
return Result::BAD_DATA;
}
// This is the case when the dimension is incorrectly specified in the model.
// With incorrect dimension, the needed size is 2 * 3 = 6 data type size.
// BufferSize::EQUAL (2 * 2 = 4 data type size) cannot provide enough length.
if (mBadIndex == kIndex4_Model && mBufferSize == BufferSize::EQUAL) {
return Result::BAD_DATA;
}
}
break;
default:
break;
}
return Result::NO_ERROR;
}
// Expect failure for the following cases
// - one of the operands has bad dimension -> OP_FAILED
// - insufficient output buffer -> OUTPUT_INSUFFICIENT_SIZE
Result expectCompute() {
if (mBadIndex < 8) {
return Result::OP_FAILED;
} else if (mBufferSize == BufferSize::LESS) {
return Result::OUTPUT_INSUFFICIENT_SIZE;
}
return Result::NO_ERROR;
}
// Iterate over combinations of
// - mBadIndexChoices: which operand has incorrect dimension
// - mOperandLocationChoices: where the operand reside, buffer or shared memory
// - mBufferSizeChoices: whether the provided buffer/memory size is sufficient
// - mEnablePaddingChoices: whether input/output memory padding is enabled
template <typename T, Type TensorType>
void TestAll() {
SCOPED_TRACE("Model: " + toString(kSpecificationLevelModel));
SCOPED_TRACE("Execution: " + toString(kSpecificationLevelExecution));
mOperandTypes = {TensorType, TensorType, TensorType, Type::TENSOR_INT32,
TensorType, TensorType, Type::TENSOR_INT32, TensorType};
for (const auto kBadIndex : mBadIndexChoices) {
mBadIndex = kBadIndex;
SCOPED_TRACE("Bad Index: " + std::to_string(mBadIndex));
if (mBadIndex < 8 &&
(mSpecificationLevels[mBadIndex] == SpecificationLevel::UNSPECIFIED_RANK ||
mSpecificationLevels[mBadIndex] == SpecificationLevel::UNSPECIFIED_TYPE)) {
continue;
}
for (const auto kOperandLocation : mOperandLocationChoices) {
mOperandLocation = kOperandLocation;
SCOPED_TRACE("Operand Location: " + toString(mOperandLocation));
for (const auto kBufferSize : mBufferSizeChoices) {
mBufferSize = kBufferSize;
SCOPED_TRACE("Buffer Size: " + toString(mBufferSize));
for (const auto kEnablePadding : mEnablePaddingChoices) {
mEnablePadding = kEnablePadding;
SCOPED_TRACE("Enable Padding: " + toString(mEnablePadding));
TestOne<T, TensorType>();
}
}
}
}
}
const UnspecifiedOperand kUnspecifiedOperand = std::get<0>(GetParam());
const SpecificationLevel kSpecificationLevelModel = std::get<1>(GetParam());
const SpecificationLevel kSpecificationLevelExecution = std::get<2>(GetParam());
std::vector<SpecificationLevel> mSpecificationLevels;
std::vector<Type> mOperandTypes;
OptionalType mOptionalType = OptionalType::CONST;
// Iterate all combinations in TestAll()
std::vector<uint32_t> mBadIndexChoices;
std::vector<OperandLocation> mOperandLocationChoices;
std::vector<BufferSize> mBufferSizeChoices = {BufferSize::EQUAL};
std::vector<PaddingEnabled> mEnablePaddingChoices = {PaddingEnabled::DEFAULT};
uint32_t mBadIndex;
OperandLocation mOperandLocation;
BufferSize mBufferSize;
PaddingEnabled mEnablePadding;
};
TEST_P(UnspecifiedDimensionsTest, Float32) {
TestAll<float, Type::TENSOR_FLOAT32>();
}
TEST_P(UnspecifiedDimensionsTest, Quant8) {
TestAll<uint8_t, Type::TENSOR_QUANT8_ASYMM>();
}
TEST_P(UnspecifiedDimensionsTest, Float16) {
TestAll<_Float16, Type::TENSOR_FLOAT16>();
}
static const auto kAllSpecificationLevelsModel =
testing::Values(SpecificationLevel::FULLY_SPECIFIED, SpecificationLevel::UNSPECIFIED_DIM,
SpecificationLevel::UNSPECIFIED_RANK);
static const auto kAllSpecificationLevelsExecution =
testing::Values(SpecificationLevel::FULLY_SPECIFIED, SpecificationLevel::UNSPECIFIED_DIM,
SpecificationLevel::UNSPECIFIED_RANK, SpecificationLevel::UNSPECIFIED_TYPE);
static const auto kFullySpecified = testing::Values(SpecificationLevel::FULLY_SPECIFIED);
INSTANTIATE_TEST_SUITE_P(ModelInputTest, UnspecifiedDimensionsTest,
testing::Combine(testing::Values(UnspecifiedOperand::INPUT_MANDATORY),
kAllSpecificationLevelsModel,
kAllSpecificationLevelsExecution));
INSTANTIATE_TEST_SUITE_P(ConstantParameterTest, UnspecifiedDimensionsTest,
testing::Combine(testing::Values(UnspecifiedOperand::CONST_MANDATORY),
kAllSpecificationLevelsModel, kFullySpecified));
INSTANTIATE_TEST_SUITE_P(TemporaryVariableTest, UnspecifiedDimensionsTest,
testing::Combine(testing::Values(UnspecifiedOperand::TEMPORARY_VARIABLE),
kAllSpecificationLevelsModel, kFullySpecified));
INSTANTIATE_TEST_SUITE_P(OptionalConstantTest, UnspecifiedDimensionsTest,
testing::Combine(testing::Values(UnspecifiedOperand::CONST_OPTIONAL),
kAllSpecificationLevelsModel, kFullySpecified));
INSTANTIATE_TEST_SUITE_P(OptionalInputTest, UnspecifiedDimensionsTest,
testing::Combine(testing::Values(UnspecifiedOperand::INPUT_OPTIONAL),
kAllSpecificationLevelsModel,
kAllSpecificationLevelsExecution));
INSTANTIATE_TEST_SUITE_P(ModelOutputTest, UnspecifiedDimensionsTest,
testing::Combine(testing::Values(UnspecifiedOperand::OUTPUT),
kAllSpecificationLevelsModel,
kAllSpecificationLevelsExecution));
} // end namespace
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