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MNN/source/shape/ShapeStridedSlice.cpp

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//
// ShapeStridedSlice.cpp
// MNN
//
// Created by MNN on 2019/01/10.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <algorithm>
#include <array>
#include "shape/SizeComputer.hpp"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
namespace MNN {
class StridedSliceComputer : public SizeComputer {
public:
virtual bool onComputeSize(const MNN::Op *op, const std::vector<Tensor *> &inputs,
const std::vector<Tensor *> &outputs) const override {
MNN_ASSERT(3 <= inputs.size());
MNN_ASSERT(5 >= inputs.size());
MNN_ASSERT(1 == outputs.size());
Tensor *input = inputs[0];
const int inputDim = input->buffer().dimensions;
if (inputDim <= 0 || inputDim > MNN_MAX_TENSOR_DIM) {
return false;
}
auto parameter = op->main_as_StridedSliceParam();
int32_t beginMask = parameter->beginMask();
int32_t endMask = parameter->endMask();
int32_t shrinkAxisMask = parameter->shrinkAxisMask();
int32_t ellipsisMask = parameter->ellipsisMask();
int32_t newAxisMask = parameter->newAxisMask();
int32_t fromType = parameter->fromType();
// write to input
if (fromType == 0 && inputs.size() == 5) {
TensorUtils::copyShape(inputs[0], outputs[0], true);
outputs[0]->buffer().type = inputs[0]->buffer().type;
TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
return true;
}
if (ellipsisMask && (ellipsisMask & (ellipsisMask - 1))) {
MNN_ERROR("only one non-zero bit is allowed in ellipsisMask\n");
return false;
}
Tensor *begin = inputs[1];
Tensor *end = inputs[2];
int32_t strideSize = begin->length(0);
auto output = outputs[0];
MNN_ASSERT(begin->buffer().dimensions == end->buffer().dimensions);
int32_t inputShape[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t begins[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t ends[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t strides[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t axes[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t beginMasks[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t endMasks[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t shrinkAxisMasks[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t newAxisMasks[MNN_MAX_TENSOR_DIM] = { 0 };
for (int i = 0; i < inputDim; i++) {
inputShape[i] = input->length(i);
}
for (int i = 0; i < strideSize; i++) {
beginMasks[i] = beginMask & (1 << i);
}
for (int i = 0; i < strideSize; i++) {
endMasks[i] = endMask & (1 << i);
}
for (int i = 0; i < strideSize; i++) {
shrinkAxisMasks[i] = shrinkAxisMask & (1 << i);
}
for (int i = 0; i < strideSize; i++) {
newAxisMasks[i] = newAxisMask & (1 << i);
}
for(int i = 0; i < inputDim; i++) {
begins[i] = 0;
ends[i] = inputShape[i];
strides[i] = 1;
}
// broadcast begin end stride axis param
if (fromType == 1) {
Tensor *axis = nullptr;
if(inputs.size() >= 4) {
axis = inputs[3];
}
Tensor *step = nullptr;
if(inputs.size() == 5) {
step = inputs[4];
}
for(int i = 0; i < inputDim; i++) {
begins[i] = 0;
ends[i] = inputShape[i];
strides[i] = 1;
}
for (int i = 0; i < strideSize; i++) {
auto temp_axis = i;
if(axis != nullptr) {
temp_axis = axis->host<int>()[i];
temp_axis = temp_axis < 0 ? (temp_axis + inputDim) : temp_axis;
MNN_ASSERT(temp_axis < MNN_MAX_TENSOR_DIM);
}
if(step != nullptr) {
strides[temp_axis] = step->host<int>()[i];
}
auto shape = inputShape[temp_axis];
auto temp_value = begin->host<int>()[i];
temp_value = temp_value < 0 ? (temp_value + shape) : temp_value;
begins[temp_axis] = temp_value;
temp_value = end->host<int>()[i];
temp_value = temp_value < 0 ? (temp_value + shape) : temp_value;
ends[temp_axis] = temp_value;
}
strideSize = inputDim;
} else if(fromType == 0) {
Tensor *strided = nullptr;
if(inputs.size() >= 4) {
strided = inputs[3];
MNN_ASSERT(begin->buffer().dimensions == strided->buffer().dimensions);
}
// deal ellipsis, expand strides info
if (ellipsisMask > 0) {
int32_t beginMasksTmp[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t endMasksTmp[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t shrinkAxisMasksTmp[MNN_MAX_TENSOR_DIM] = { 0 };
int32_t newAxisMasksTmp[MNN_MAX_TENSOR_DIM] = { 0 };
// expand stride info
int ellipsisPos = -1;
for (int i = 0; i < strideSize; i++) {
int temp = ellipsisMask & (1 << i);
if (temp != 0) {
ellipsisPos = i;
break;
}
}
MNN_ASSERT(ellipsisPos >= 0 && ellipsisPos < strideSize);
/*
Example: foo's dim is [2, 3, 4, 5, 6, 7], foo[0:2, :, 3:5, 3:6]:
1. strideSize = 4, inputDim = 6, ellipsis = 2(0010)
2. left part: 0:2, right part: 3:5, 3:6
3. expand: foo[0:2, 0:3, 0:4, 3:5, 3:6]
*/
int ellpsisSize = inputDim - strideSize, strideIdx = 0;
for (int i = 0; i < inputDim; i++) {
if (i == ellipsisPos) {
strideIdx++;
}
if (i >= ellipsisPos && i <= ellipsisPos + ellpsisSize) {
begins[i] = 0;
ends[i] = inputShape[i];
strides[i] = 1;
beginMasksTmp[i] = 0;
endMasksTmp[i] = 0;
shrinkAxisMasksTmp[i] = 0;
} else {
begins[i] = begin->host<int32_t>()[strideIdx];
ends[i] = end->host<int32_t>()[strideIdx];
if(strided != nullptr) {
strides[i] = strided->host<int32_t>()[strideIdx];
}
beginMasksTmp[i] = beginMasks[strideIdx];
endMasksTmp[i] = endMasks[strideIdx];
shrinkAxisMasksTmp[i] = shrinkAxisMasks[strideIdx];
newAxisMasksTmp[i] = newAxisMasks[strideIdx++];
}
}
for (int i = 0; i < inputDim; i++) {
beginMasks[i] = beginMasksTmp[i];
endMasks[i] = endMasksTmp[i];
shrinkAxisMasks[i] = shrinkAxisMasksTmp[i];
newAxisMasks[i] = newAxisMasksTmp[i];
}
strideSize = inputDim;
} else {
for (int i = 0; i < strideSize; i++) {
begins[i] = begin->host<int>()[i];
ends[i] = end->host<int>()[i];
strides[i] = strided->host<int>()[i];
}
}
}
int32_t beginShape[MNN_MAX_TENSOR_DIM];
int32_t endShape[MNN_MAX_TENSOR_DIM];
int32_t stridedShape[MNN_MAX_TENSOR_DIM];
int32_t outputShape[MNN_MAX_TENSOR_DIM];
int32_t outputShapeShrinked[MNN_MAX_TENSOR_DIM];
int outputShapeSize = 0;
int outputShapeShrinkSize = 0;
int strideDealDims = 0;
auto beginAndEndShapeLimit = [](int shape, int dimSize, bool exclusive) -> int {
int maxShape = dimSize - 1, minShape = -dimSize;
if (exclusive) {
++maxShape;
--minShape;
}
shape = (shape > maxShape ? maxShape : shape);
shape = (shape < minShape ? minShape : shape);
if (shape < 0) {
shape += dimSize;
}
return shape;
};
int inputDimOffset = 0;
for (int i = 0; i < strideSize; i++) {
if (newAxisMasks[i] > 0) {
outputShape[outputShapeSize] = 1;
outputShapeSize++;
outputShapeShrinked[outputShapeShrinkSize] = 1;
outputShapeShrinkSize++;
continue;
}
auto inputDim = inputShape[inputDimOffset++];
strideDealDims++;
stridedShape[i] = shrinkAxisMasks[i] > 0 ? 1 : strides[i];
if (beginMasks[i] > 0) {
beginShape[i] = stridedShape[i] < 0 ? inputDim - 1 : 0;
} else {
beginShape[i] = stridedShape[i] < 0 ? beginAndEndShapeLimit(begins[i], inputDim, false) :
std::min(inputDim, begins[i]);
}
if (beginShape[i] < 0) {
auto temp = -beginShape[i];
beginShape[i] = UP_DIV(temp, inputDim) * inputDim + beginShape[i];
}
if (endMasks[i] > 0) {
endShape[i] = stridedShape[i] < 0 ? -1 : inputDim;
} else {
endShape[i] = stridedShape[i] < 0 ? std::max(-1, std::min(inputDim, ends[i])) :
beginAndEndShapeLimit(ends[i], inputDim, true);
}
if (endShape[i] < beginShape[i]) {
int t = beginShape[i];
beginShape[i] = endShape[i];
endShape[i] = t;
MNN_ASSERT(stridedShape[i] != 0);
if (stridedShape[i] < 0) {
stridedShape[i] = -stridedShape[i];
} else {
// MNN_ASSERT(false); // TODO: should be the wrong case, but there is one in linfeng's faster
// rcnn face model
beginShape[i] = endShape[i]; // TODO: temp solution
}
}
if (shrinkAxisMasks[i] == 0) {
int size = (endShape[i] - beginShape[i] - 1) / stridedShape[i] + 1;
outputShape[outputShapeSize] = size;
outputShapeSize++;
outputShapeShrinked[outputShapeShrinkSize] = size;
outputShapeShrinkSize++;
} else {
outputShape[outputShapeSize] = std::min(1, inputDim);
outputShapeSize++;
}
}
int outputDimensionsWithoutRemain = strideDealDims;
int dimensionRemained = input->buffer().dimensions - strideDealDims;
for (int i = 0; i < dimensionRemained; i++) {
outputShapeShrinked[outputShapeShrinkSize] = input->buffer().dim[outputDimensionsWithoutRemain + i].extent;
outputShapeShrinkSize++;
}
output->buffer().dimensions = outputShapeShrinkSize;
output->buffer().type = input->buffer().type;
for (int i = 0; i < outputShapeShrinkSize; i++) {
output->buffer().dim[i].extent = outputShapeShrinked[i];
}
TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
return true;
}
};
REGISTER_SHAPE_INPUTS(StridedSliceComputer, OpType_StridedSlice, (std::vector<int>{1,2,3}));
} // namespace MNN
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