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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 "fuzzing/operation_signatures/OperationSignatureUtils.h"
namespace android {
namespace nn {
namespace fuzzing_test {
static void roiTensorConstructor(Type dataType, uint32_t, RandomOperand* op) {
op->dataType = dataType;
if (dataType == Type::TENSOR_QUANT8_ASYMM) {
op->dataType = Type::TENSOR_QUANT16_ASYMM;
op->scale = 0.125f;
op->zeroPoint = 0;
}
}
// TODO: Have a version that makes roi tensor an input.
static const OperandSignature kInputRoiTensor = {.type = RandomOperandType::CONST,
.constructor = roiTensorConstructor};
static const OperandSignature kOutputRoiTensor = {.type = RandomOperandType::OUTPUT,
.constructor = roiTensorConstructor};
static void roiConstructor(Type, uint32_t rank, RandomOperation* op) {
NN_FUZZER_CHECK(rank == 4);
bool useNchw;
if (op->opType == ANEURALNETWORKS_ROI_ALIGN) {
useNchw = op->inputs[9]->value<bool8>();
} else {
useNchw = op->inputs[7]->value<bool8>();
}
op->inputs[0]->dimensions = {RandomVariableType::FREE, RandomVariableType::FREE,
RandomVariableType::FREE, RandomVariableType::FREE};
op->inputs[1]->dimensions = {RandomVariableType::FREE, 4};
op->inputs[2]->dimensions = {op->inputs[1]->dimensions[0]};
auto outBatch = op->inputs[1]->dimensions[0];
auto outDepth = op->inputs[0]->dimensions[useNchw ? 1 : 3];
auto outHeight = op->inputs[3]->value<RandomVariable>();
auto outWidth = op->inputs[4]->value<RandomVariable>();
if (useNchw) {
op->outputs[0]->dimensions = {outBatch, outDepth, outHeight, outWidth};
} else {
op->outputs[0]->dimensions = {outBatch, outHeight, outWidth, outDepth};
}
if (op->opType == ANEURALNETWORKS_ROI_POOLING) {
setSameQuantization(op->outputs[0], op->inputs[0]);
}
}
template <typename T>
inline void fillRoiTensor(uint32_t numRois, T maxH, T maxW, RandomOperand* op) {
for (uint32_t i = 0; i < numRois; i++) {
T low = getUniform<T>(0, maxW);
op->value<T>(i * 4) = low;
op->value<T>(i * 4 + 2) = getUniform<T>(low, maxW);
low = getUniform<T>(0, maxH);
op->value<T>(i * 4 + 1) = low;
op->value<T>(i * 4 + 3) = getUniform<T>(low, maxH);
}
}
static void roiFinalizer(RandomOperation* op) {
bool useNchw;
if (op->opType == ANEURALNETWORKS_ROI_ALIGN) {
useNchw = op->inputs[9]->value<bool8>();
} else {
useNchw = op->inputs[7]->value<bool8>();
}
uint32_t batch = op->inputs[0]->dimensions[0].getValue();
uint32_t height = op->inputs[0]->dimensions[useNchw ? 2 : 1].getValue();
uint32_t width = op->inputs[0]->dimensions[useNchw ? 3 : 2].getValue();
uint32_t numRois = op->inputs[1]->dimensions[0].getValue();
// Fill values to the roi tensor with format [x1, y1, x2, y2].
switch (op->inputs[1]->dataType) {
case Type::TENSOR_FLOAT32: {
float maxH = static_cast<float>(height) * op->inputs[5]->value<float>();
float maxW = static_cast<float>(width) * op->inputs[6]->value<float>();
fillRoiTensor<float>(numRois, maxH, maxW, op->inputs[1].get());
} break;
case Type::TENSOR_QUANT16_ASYMM: {
uint16_t maxH = static_cast<float>(height) * op->inputs[5]->value<float>();
uint16_t maxW = static_cast<float>(width) * op->inputs[6]->value<float>();
fillRoiTensor<uint16_t>(numRois, maxH, maxW, op->inputs[1].get());
} break;
default:
NN_FUZZER_CHECK(false) << "Unsupported data type.";
}
// Fill values to the batch index tensor.
std::vector<int32_t> batchIndex(numRois);
for (uint32_t i = 0; i < numRois; i++) batchIndex[i] = getUniform<int32_t>(0, batch - 1);
// Same batches are grouped together.
std::sort(batchIndex.begin(), batchIndex.end());
for (uint32_t i = 0; i < numRois; i++) op->inputs[2]->value<int32_t>(i) = batchIndex[i];
}
// Type::TENSOR_FLOAT16 is intentionally excluded for all bounding box ops because
// 1. It has limited precision for compuation on bounding box indices, which will lead to poor
// accuracy evaluation.
// 2. There is no actual graph that uses this data type on bounding boxes.
DEFINE_OPERATION_SIGNATURE(ROI_ALIGN_V1_2){
.opType = ANEURALNETWORKS_ROI_ALIGN,
.supportedDataTypes = {Type::TENSOR_FLOAT32, Type::TENSOR_QUANT8_ASYMM},
.supportedRanks = {4},
.version = HalVersion::V1_2,
.inputs =
{
INPUT_DEFAULT,
kInputRoiTensor,
PARAMETER_NONE(Type::TENSOR_INT32),
RANDOM_INT_FREE,
RANDOM_INT_FREE,
PARAMETER_FLOAT_RANGE(0.1f, 10.0f),
PARAMETER_FLOAT_RANGE(0.1f, 10.0f),
PARAMETER_RANGE(Type::INT32, 0, 10),
PARAMETER_RANGE(Type::INT32, 0, 10),
PARAMETER_CHOICE(Type::BOOL, true, false),
},
.outputs = {OUTPUT_DEFAULT},
.constructor = roiConstructor,
.finalizer = roiFinalizer};
DEFINE_OPERATION_SIGNATURE(ROI_POOLING_V1_2){
.opType = ANEURALNETWORKS_ROI_POOLING,
.supportedDataTypes = {Type::TENSOR_FLOAT32, Type::TENSOR_QUANT8_ASYMM},
.supportedRanks = {4},
.version = HalVersion::V1_2,
.inputs =
{
INPUT_DEFAULT,
kInputRoiTensor,
PARAMETER_NONE(Type::TENSOR_INT32),
RANDOM_INT_FREE,
RANDOM_INT_FREE,
PARAMETER_FLOAT_RANGE(0.1f, 10.0f),
PARAMETER_FLOAT_RANGE(0.1f, 10.0f),
PARAMETER_CHOICE(Type::BOOL, true, false),
},
.outputs = {OUTPUT_DEFAULT},
.constructor = roiConstructor,
.finalizer = roiFinalizer};
static void heatmapMaxKeypointConstructor(Type, uint32_t rank, RandomOperation* op) {
NN_FUZZER_CHECK(rank == 4);
bool useNchw = op->inputs[2]->value<bool8>();
RandomVariable heatmapSize = RandomVariableType::FREE;
RandomVariable numRois = RandomVariableType::FREE;
RandomVariable numKeypoints = RandomVariableType::FREE;
heatmapSize.setRange(2, kInvalidValue);
if (useNchw) {
op->inputs[0]->dimensions = {numRois, numKeypoints, heatmapSize, heatmapSize};
} else {
op->inputs[0]->dimensions = {numRois, heatmapSize, heatmapSize, numKeypoints};
}
op->inputs[1]->dimensions = {numRois, 4};
op->outputs[0]->dimensions = {numRois, numKeypoints};
op->outputs[1]->dimensions = {numRois, numKeypoints, 2};
// TODO: This is an ugly fix due to the limitation of the current generator that can not handle
// the dimension dependency within an input. Without the following line, most of the generated
// HEATMAP_MAX_KEYPOINT graphs will be invalid and triggers retry.
RandomVariableNetwork::get()->addDimensionProd(
{numRois, numKeypoints, heatmapSize * heatmapSize});
}
static void heatmapMaxKeypointFinalizer(RandomOperation* op) {
uint32_t numRois = op->inputs[0]->dimensions[0].getValue();
uint32_t heatmapSize = op->inputs[0]->dimensions[2].getValue();
// Fill values to the roi tensor with format [x1, y1, x2, y2].
switch (op->inputs[1]->dataType) {
case Type::TENSOR_FLOAT32: {
float maxSize = heatmapSize;
fillRoiTensor<float>(numRois, maxSize, maxSize, op->inputs[1].get());
} break;
case Type::TENSOR_QUANT16_ASYMM: {
uint16_t maxSize = static_cast<uint16_t>(heatmapSize * 8);
fillRoiTensor<uint16_t>(numRois, maxSize, maxSize, op->inputs[1].get());
} break;
default:
NN_FUZZER_CHECK(false) << "Unsupported data type.";
}
}
DEFINE_OPERATION_SIGNATURE(HEATMAP_MAX_KEYPOINT_V1_2){
.opType = ANEURALNETWORKS_HEATMAP_MAX_KEYPOINT,
.supportedDataTypes = {Type::TENSOR_FLOAT32, Type::TENSOR_QUANT8_ASYMM},
.supportedRanks = {4},
.version = HalVersion::V1_2,
.inputs = {INPUT_DEFAULT, kInputRoiTensor, PARAMETER_CHOICE(Type::BOOL, true, false)},
.outputs = {OUTPUT_DEFAULT, kOutputRoiTensor},
.constructor = heatmapMaxKeypointConstructor,
.finalizer = heatmapMaxKeypointFinalizer};
} // namespace fuzzing_test
} // namespace nn
} // namespace android
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