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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (C) 2017 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.
*/
#ifndef __NNFW_RT_OPERATIONS_UTILS_H__
#define __NNFW_RT_OPERATIONS_UTILS_H__
#include "Utils.h"
#include <cstdint>
#include <vector>
// Macro to check if the input parameters for operation are valid or not.
#define NN_CHECK(v) \
do { \
if (!(v)) { \
LOG(ERROR) << "NN_CHECK failed: " << #v << "'\n"; \
return false; \
} \
} while(0);
#define NN_CHECK_EQ(actual, expected) \
NN_CHECK((actual) == (expected))
#define NN_OPS_CHECK NN_CHECK
namespace nnfw {
namespace rt {
enum PaddingScheme {
kPaddingUnknown = 0,
kPaddingSame = 1,
kPaddingValid = 2,
};
// The type and dimensions of an operand.
struct Shape {
OperandType type;
std::vector<uint32_t> dimensions;
float scale;
int32_t offset;
};
// Verifies that the two shapes are the same.
bool SameShape(const Shape& in1, const Shape& in2);
// Sets out to the same shape as in.
bool SetShape(const Shape& in, Shape* out);
// Return the total number of elements, i.e. all the dimensions multiplied
// together. For a scalar, returns one.
uint32_t getNumberOfElements(const Shape& shape);
uint32_t getNumberOfDimensions(const Shape& shape);
uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx);
inline uint32_t computeOutSize(uint32_t imageSize, uint32_t filterSize, uint32_t stride,
uint32_t paddingHead, uint32_t paddingTail) {
return (imageSize - filterSize + stride + paddingHead + paddingTail) / stride;
}
__wur
bool QuantizeMultiplierSmallerThanOne(double double_multiplier,
int32_t* quantized_multiplier,
int32_t* right_shift);
__wur
bool QuantizeMultiplierGreaterThanOne(double double_multiplier,
int32_t* quantized_multiplier,
int* left_shift);
__wur
bool GetQuantizedConvolutionMultipler(const Shape& inputShape,
const Shape& filterShape,
const Shape& biasShape,
const Shape& outputShape,
float* multiplier);
void CalculateActivationRangeUint8(int32_t activation,
const Shape& outputShape,
int32_t* act_min,
int32_t* act_max);
int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift);
inline void calculateExplicitPadding(int32_t in_size, int32_t stride,
int32_t filter_size, int32_t padding_implicit,
int32_t* padding_head, int32_t* padding_tail) {
*padding_head = 0;
*padding_tail = 0;
if (padding_implicit == kPaddingSame) {
int32_t out_size = (in_size + stride - 1) / stride;
int32_t tmp = (out_size - 1) * stride + filter_size;
if (tmp > in_size) {
*padding_head = (tmp - in_size) / 2;
*padding_tail = (tmp - in_size) - *padding_head;
}
}
}
inline PaddingScheme getPaddingScheme(int32_t inWidth, int32_t inHeight,
int32_t strideWidth, int32_t strideHeight,
int32_t filterWidth, int32_t filterHeight,
int32_t paddingLeft, int32_t paddingRight,
int32_t paddingTop, int32_t paddingBottom) {
if (paddingLeft == 0 && paddingRight == 0 && paddingTop == 0 && paddingBottom == 0) {
return kPaddingValid;
}
int32_t expectedPaddingLeft, expectedPaddingRight;
int32_t expectedPaddingTop, expectedPaddingBottom;
calculateExplicitPadding(inWidth, strideWidth, filterWidth, kPaddingSame,
&expectedPaddingLeft, &expectedPaddingRight);
calculateExplicitPadding(inHeight, strideHeight, filterHeight, kPaddingSame,
&expectedPaddingTop, &expectedPaddingBottom);
if (expectedPaddingLeft == paddingLeft && expectedPaddingRight == paddingRight &&
expectedPaddingTop == paddingTop && expectedPaddingBottom == paddingBottom) {
return kPaddingSame;
} else {
return kPaddingUnknown;
}
}
// Preparation functions for the corresponding ops
bool addMulPrepare(const Shape& in1, const Shape& in2, Shape* out1);
bool floorPrepare(const Shape& input, Shape* output);
bool dequantizePrepare(const Shape& input, Shape* output);
bool depthwiseConvPrepare(const Shape& input,
const Shape& filter,
const Shape& bias,
int32_t padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom,
int32_t stride_width, int32_t stride_height,
Shape* output);
bool convPrepare(const Shape& input,
const Shape& filter,
const Shape& bias,
int32_t padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom,
int32_t stride_width, int32_t stride_height,
Shape* output);
bool genericPoolingPrepare(const Shape& input,
int32_t padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom,
int32_t stride_width, int32_t stride_height,
int32_t filter_width, int32_t filter_height,
Shape* output);
bool genericActivationPrepare(const Shape& input, Shape* output);
bool fullyConnectedPrepare(const Shape& input,
const Shape& weights,
const Shape& bias,
Shape* output);
bool concatenationPrepare(const std::vector<Shape>& inputShapes,
int32_t axis,
Shape* output);
bool genericNormalizationPrepare(const Shape& input, Shape* output);
bool reshapePrepare(const Shape& input,
const int32_t* targetDims,
const int32_t targetDimsSize,
Shape* output);
bool resizeBilinearPrepare(const Shape& input,
int32_t height,
int32_t width,
Shape* output);
bool depthToSpacePrepare(const Shape& input,
int32_t blockSize,
Shape* output);
bool spaceToDepthPrepare(const Shape& input,
int32_t blockSize,
Shape* output);
bool embeddingLookupPrepare(const Shape &valueShape,
const Shape &lookupShape,
Shape *outputShape);
bool hashtableLookupPrepare(const Shape &lookupShape,
const Shape &keyShape,
const Shape &valueShape,
Shape *outputShape,
Shape *hitShape);
#define ANDROID_NN_MACRO_DISPATCH_INTERNAL(macro) \
case (int32_t) FusedActivationFunc::NONE: \
macro(kNone); \
break; \
case (int32_t) FusedActivationFunc::RELU: \
macro(kRelu); \
break; \
case (int32_t) FusedActivationFunc::RELU1: \
macro(kRelu1); \
break; \
case (int32_t) FusedActivationFunc::RELU6: \
macro(kRelu6); \
break;
#define ANDROID_NN_MACRO_DISPATCH(macro) \
switch (activation) { \
ANDROID_NN_MACRO_DISPATCH_INTERNAL(macro) \
default: \
LOG(ERROR) << "Unsupported fused activation function type"; \
return false; \
}
#define ANDROID_NN_MACRO_DISPATCH_WITH_DELETE(macro) \
switch (activation) { \
ANDROID_NN_MACRO_DISPATCH_INTERNAL(macro) \
default: \
LOG(ERROR) << "Unsupported fused activation function type"; \
if (im2colByteSize > kStaticBufferSize) { \
delete[] im2colData; \
} \
return false; \
}
} // namespace rt
} // namespace nnfw
#endif // __NNFW_RT_OPERATIONS_UTILS_H__
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