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MNN/source/backend/coreml/execution/CoreMLRaster.cpp

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//
// CoreMLRaster.cpp
// MNN
//
// Created by MNN on 2021/03/25.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "CoreMLRaster.hpp"
#include <cmath>
namespace MNN {
CoreMLRaster::CoreMLRaster(MNN::Backend *b, const MNN::Op *op, const std::vector<Tensor *> &inputs, const std::vector<MNN::Tensor *> &outputs) : CoreMLCommonExecution(b, op) {
initLayer();
}
static bool isTranspose(const Tensor::InsideDescribe::Region& region) {
int srcOne = -1, dstOne = -1;
for (int i = 0; i < 3; i++) {
if (region.src.stride[i] == 1 && region.size[i] != 1) {
if (srcOne >= 0 || region.size[i] < 4) {
return false;
}
srcOne = i;
}
if (region.dst.stride[i] == 1 && region.size[i] != 1) {
if (dstOne >= 0 || region.size[i] < 4) {
return false;
}
dstOne = i;
}
}
return srcOne >= 0 && dstOne >= 0 && srcOne != dstOne;
}
bool CoreMLRaster::buildReshape(CoreML__Specification__NeuralNetworkLayer* layer, const Tensor* input, const Tensor* output) {
mCoreMLBackend->setLayerName(layer, "Reshape");
layer->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_RESHAPE_STATIC;
layer->reshapestatic = mCoreMLBackend->create<CoreML__Specification__ReshapeStaticLayerParams>();
core_ml__specification__reshape_static_layer_params__init(layer->reshapestatic);
auto outputShape = output->shape();
layer->reshapestatic->n_targetshape = outputShape.size();
layer->reshapestatic->targetshape = mCoreMLBackend->create<int64_t>(layer->reshapestatic->n_targetshape);
for (int i = 0; i < outputShape.size(); i++) {
layer->reshapestatic->targetshape[i] = outputShape[i];
}
mCoreMLBackend->setLayerInputs(layer, {mCoreMLBackend->getTensorName(input)});
return true;
}
bool CoreMLRaster::buildPermute(CoreML__Specification__NeuralNetworkLayer* layer, const Tensor* input, const Tensor* output) {
bool needReshape = input->dimensions() != output->dimensions();
CoreML__Specification__NeuralNetworkLayer *permuteLayer = layer, *reshapeLayer = nullptr;
if (needReshape) {
permuteLayer = mCoreMLBackend->create<CoreML__Specification__NeuralNetworkLayer>();
core_ml__specification__neural_network_layer__init(permuteLayer);
reshapeLayer = layer;
}
mCoreMLBackend->setLayerName(permuteLayer, "Permute");
permuteLayer->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_PERMUTE;
permuteLayer->permute = mCoreMLBackend->create<CoreML__Specification__PermuteLayerParams>();
core_ml__specification__permute_layer_params__init(permuteLayer->permute);
permuteLayer->permute->n_axis = 4;
permuteLayer->permute->axis = mCoreMLBackend->create<uint64_t>(permuteLayer->permute->n_axis);
auto srcFormat = TensorUtils::getDescribe(input)->dimensionFormat;
auto dstFormat = TensorUtils::getDescribe(output)->dimensionFormat;
// NCHW -> NHWC
if ((srcFormat == MNN_DATA_FORMAT_NC4HW4 || srcFormat == MNN_DATA_FORMAT_NCHW)
&& dstFormat == MNN_DATA_FORMAT_NHWC) {
permuteLayer->permute->axis[0] = 0;
permuteLayer->permute->axis[1] = 2;
permuteLayer->permute->axis[2] = 3;
permuteLayer->permute->axis[3] = 1;
}
// NHWC -> NCHW
if ((dstFormat == MNN_DATA_FORMAT_NC4HW4 || srcFormat == MNN_DATA_FORMAT_NCHW)
&& srcFormat == MNN_DATA_FORMAT_NHWC) {
permuteLayer->permute->axis[0] = 0;
permuteLayer->permute->axis[1] = 3;
permuteLayer->permute->axis[2] = 1;
permuteLayer->permute->axis[3] = 2;
}
mCoreMLBackend->setLayerInputs(permuteLayer, {mCoreMLBackend->getTensorName(input)});
if (reshapeLayer) {
std::string middleName = mCoreMLBackend->getTensorName(input) + "_permute_" + mCoreMLBackend->getTensorName(output);
mCoreMLBackend->setLayerOutputs(permuteLayer, {middleName});
mCoreMLBackend->addLayer(permuteLayer);
mCoreMLBackend->setLayerName(reshapeLayer, "Permute_Reshape");
reshapeLayer->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_RESHAPE_STATIC;
reshapeLayer->reshapestatic = mCoreMLBackend->create<CoreML__Specification__ReshapeStaticLayerParams>();
core_ml__specification__reshape_static_layer_params__init(reshapeLayer->reshapestatic);
auto outputShape = output->shape();
reshapeLayer->reshapestatic->n_targetshape = outputShape.size();
reshapeLayer->reshapestatic->targetshape = mCoreMLBackend->create<int64_t>(reshapeLayer->reshapestatic->n_targetshape);
for (int i = 0; i < outputShape.size(); i++) {
reshapeLayer->reshapestatic->targetshape[i] = outputShape[i];
}
mCoreMLBackend->setLayerInputs(reshapeLayer, {middleName});
}
return true;
}
bool CoreMLRaster::buildPad(CoreML__Specification__NeuralNetworkLayer* layer, const Tensor* input, const Tensor* output) {
bool needPermute = TensorUtils::getDescribe(input)->dimensionFormat == MNN_DATA_FORMAT_NHWC;
CoreML__Specification__NeuralNetworkLayer *padLayer = layer, *postPermute = nullptr;
std::string inputName = mCoreMLBackend->getTensorName(input);
if (needPermute) {
padLayer = mCoreMLBackend->create<CoreML__Specification__NeuralNetworkLayer>();
core_ml__specification__neural_network_layer__init(padLayer);
postPermute = layer;
// NHWC -> NCHW
auto prePermute = mCoreMLBackend->create<CoreML__Specification__NeuralNetworkLayer>();
core_ml__specification__neural_network_layer__init(prePermute);
mCoreMLBackend->setLayerName(prePermute, "prePermute");
prePermute->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_PERMUTE;
prePermute->permute = mCoreMLBackend->create<CoreML__Specification__PermuteLayerParams>();
core_ml__specification__permute_layer_params__init(prePermute->permute);
prePermute->permute->n_axis = 4;
prePermute->permute->axis = mCoreMLBackend->create<uint64_t>(prePermute->permute->n_axis);
prePermute->permute->axis[0] = 0;
prePermute->permute->axis[1] = 3;
prePermute->permute->axis[2] = 1;
prePermute->permute->axis[3] = 2;
setLayerInputsAndOutputs(prePermute, {inputName}, {inputName + "-permute"});
inputName = inputName + "-permute";
mCoreMLBackend->addLayer(prePermute);
}
int padh = output->height() - input->height(), padw = output->width() - input->width();
int top = padh / 2, bottom = std::ceil(padh / 2.0), left = padw / 2, right = std::ceil(padw / 2.0);
mCoreMLBackend->setLayerName(padLayer, "Pad");
padLayer->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_PADDING;
padLayer->padding = mCoreMLBackend->create<CoreML__Specification__PaddingLayerParams>();
core_ml__specification__padding_layer_params__init(padLayer->padding);
padLayer->padding->padding_type_case = CORE_ML__SPECIFICATION__PADDING_LAYER_PARAMS__PADDING_TYPE_CONSTANT;
padLayer->padding->constant = mCoreMLBackend->create<CoreML__Specification__PaddingLayerParams__PaddingConstant>();
core_ml__specification__padding_layer_params__padding_constant__init(padLayer->padding->constant);
padLayer->padding->constant->value = 0;
padLayer->padding->paddingamounts = mCoreMLBackend->create<CoreML__Specification__BorderAmounts>();
core_ml__specification__border_amounts__init(padLayer->padding->paddingamounts);
padLayer->padding->paddingamounts->n_borderamounts = 2;
padLayer->padding->paddingamounts->borderamounts = mCoreMLBackend->create<CoreML__Specification__BorderAmounts__EdgeSizes*>(2);
padLayer->padding->paddingamounts->borderamounts[0] = mCoreMLBackend->create<CoreML__Specification__BorderAmounts__EdgeSizes>();
core_ml__specification__border_amounts__edge_sizes__init(padLayer->padding->paddingamounts->borderamounts[0]);
padLayer->padding->paddingamounts->borderamounts[0]->startedgesize = top;
padLayer->padding->paddingamounts->borderamounts[0]->endedgesize = bottom;
padLayer->padding->paddingamounts->borderamounts[1] = mCoreMLBackend->create<CoreML__Specification__BorderAmounts__EdgeSizes>();
core_ml__specification__border_amounts__edge_sizes__init(padLayer->padding->paddingamounts->borderamounts[1]);
padLayer->padding->paddingamounts->borderamounts[1]->startedgesize = left;
padLayer->padding->paddingamounts->borderamounts[1]->endedgesize = right;
mCoreMLBackend->setLayerInputs(padLayer, {inputName});
if (needPermute) {
inputName = inputName + "-pad";
mCoreMLBackend->setLayerOutputs(padLayer, {inputName});
mCoreMLBackend->addLayer(padLayer);
// NHWC -> NCHW
mCoreMLBackend->setLayerName(postPermute, "postPermute");
postPermute->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_PERMUTE;
postPermute->permute = mCoreMLBackend->create<CoreML__Specification__PermuteLayerParams>();
core_ml__specification__permute_layer_params__init(postPermute->permute);
postPermute->permute->n_axis = 4;
postPermute->permute->axis = mCoreMLBackend->create<uint64_t>(postPermute->permute->n_axis);
postPermute->permute->axis[0] = 0;
postPermute->permute->axis[1] = 2;
postPermute->permute->axis[2] = 3;
postPermute->permute->axis[3] = 1;
mCoreMLBackend->setLayerInputs(postPermute, {inputName});
}
return true;
}
bool CoreMLRaster::buildCrop(CoreML__Specification__NeuralNetworkLayer* layer, const Tensor* input, const Tensor* output) {
if (TensorUtils::getDescribe(input)->dimensionFormat == MNN_DATA_FORMAT_NHWC) {
return false;
}
int croph = input->height() - output->height(), cropw = input->width() - output->width();
int top = croph / 2, bottom = std::ceil(croph / 2.0), left = cropw / 2, right = std::ceil(cropw / 2.0);
mCoreMLBackend->setLayerName(layer, "Crop");
layer->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_CROP;
layer->crop = mCoreMLBackend->create<CoreML__Specification__CropLayerParams>();
core_ml__specification__crop_layer_params__init(layer->crop);
layer->crop->cropamounts = mCoreMLBackend->create<CoreML__Specification__BorderAmounts>();
core_ml__specification__border_amounts__init(layer->padding->paddingamounts);
layer->crop->cropamounts->n_borderamounts = 2;
layer->crop->cropamounts->borderamounts = mCoreMLBackend->create<CoreML__Specification__BorderAmounts__EdgeSizes*>(2);
layer->crop->cropamounts->borderamounts[0] = mCoreMLBackend->create<CoreML__Specification__BorderAmounts__EdgeSizes>();
core_ml__specification__border_amounts__edge_sizes__init(layer->crop->cropamounts->borderamounts[0]);
layer->crop->cropamounts->borderamounts[0]->startedgesize = top;
layer->crop->cropamounts->borderamounts[0]->endedgesize = bottom;
layer->crop->cropamounts->borderamounts[1] = mCoreMLBackend->create<CoreML__Specification__BorderAmounts__EdgeSizes>();
core_ml__specification__border_amounts__edge_sizes__init(layer->crop->cropamounts->borderamounts[1]);
layer->crop->cropamounts->borderamounts[1]->startedgesize = left;
layer->crop->cropamounts->borderamounts[1]->endedgesize = right;
mCoreMLBackend->setLayerInputs(layer, {mCoreMLBackend->getTensorName(input)});
return true;
}
bool CoreMLRaster::buildSlice(CoreML__Specification__NeuralNetworkLayer* layer, const Tensor* input, const Tensor* output) {
int endc = output->channel(), endh = output->height(), endw = output->width();
bool maskc = endc == input->channel(), maskh = endh == input->height(), maskw = endw == input->width();
mCoreMLBackend->setLayerName(layer, "Slice");
layer->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_SLICE_STATIC;
layer->slicestatic = mCoreMLBackend->create<CoreML__Specification__SliceStaticLayerParams>();
core_ml__specification__slice_static_layer_params__init(layer->slicestatic);
// [Seq, N, C, H, W] : [0:1:-1, 0:1:-1, 0:1:endc, 0:1:endh, 0:1:endw]
int dims = 5;
layer->slicestatic->n_beginids = dims;
layer->slicestatic->beginids = mCoreMLBackend->create<int64_t>(dims);
layer->slicestatic->n_beginmasks = dims;
layer->slicestatic->beginmasks = mCoreMLBackend->create<int>(dims);
layer->slicestatic->n_strides = dims;
layer->slicestatic->strides = mCoreMLBackend->create<int64_t>(dims);
for (int i = 0; i < dims; i++) {
layer->slicestatic->beginids[i] = 0;
layer->slicestatic->beginmasks[i] = true;
layer->slicestatic->strides[i] = 1;
}
layer->slicestatic->n_endids = dims;
layer->slicestatic->endids = mCoreMLBackend->create<int64_t>(dims);
layer->slicestatic->n_endmasks = dims;
layer->slicestatic->endmasks = mCoreMLBackend->create<int>(dims);
layer->slicestatic->endids[0] = -1;
layer->slicestatic->endids[1] = -1;
layer->slicestatic->endids[2] = endc;
layer->slicestatic->endids[3] = endh;
layer->slicestatic->endids[4] = endw;
layer->slicestatic->endmasks[0] = true;
layer->slicestatic->endmasks[1] = true;
layer->slicestatic->endmasks[2] = maskc;
layer->slicestatic->endmasks[3] = maskh;
layer->slicestatic->endmasks[4] = maskw;
mCoreMLBackend->setLayerInputs(layer, {mCoreMLBackend->getTensorName(input)});
return true;
}
bool CoreMLRaster::rasterOptimization(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
const auto& regions = TensorUtils::getDescribe(inputs[0])->regions;
const auto region = regions[0];
// region_size = 1: reshape, transpose
if (regions.size() == 1) {
int inputSize = 1, outputSize = 1;
for (int i = 0; i < region.origin->dimensions(); i++) {
inputSize *= region.origin->length(i);
}
for (int i = 0; i < outputs[0]->dimensions(); i++) {
outputSize *= outputs[0]->length(i);
}
// reshape, permute
if (inputSize == outputSize) {
// reshape
if (TensorUtils::isCopyRegion(region)) {
return buildReshape(mLayer_, region.origin, outputs[0]);
}
// transpose
if (isTranspose(region)) {
return buildPermute(mLayer_, region.origin, outputs[0]);
}
}
// pad
if (inputSize < outputSize) {
return buildPad(mLayer_, region.origin, outputs[0]);
}
// slice/crop
if (inputSize > outputSize) {
return false;
// TODO: Apple NPU will ANCE Error.
// return buildCrop(mLayer_, region.origin, outputs[0]);
// return buildSlice(mLayer_, region.origin, outputs[0]);
}
return false;
}
// region_size > 1: concat
{
int dim = outputs[0]->dimensions();
if (region.origin->dimensions() != dim) {
return false;
}
int axis = -1;
for (int i = 0; i < outputs[0]->dimensions(); i++) {
if (region.origin->length(i) != outputs[0]->length(i)) {
if (axis >= 0) {
return false;
}
axis = i;
}
}
int elemSize = region.size[0] * region.size[1] * region.size[2];
bool isSameShape = true;
for (int i = 1; i < regions.size(); i++) {
isSameShape &= (elemSize == regions[i].size[0] * regions[i].size[1] * regions[i].size[2]);
if (regions[i].origin->dimensions() != dim) {
return false;
}
for (int j = 0; j < dim; j++) {
if (j != axis && regions[i].origin->length(j) != outputs[0]->length(j)) {
return false;
}
}
}
if (isSameShape && (axis - dim == -3)) {
mCoreMLBackend->setLayerName(mLayer_, "Concat");
mLayer_->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_CONCAT;
mLayer_->concat = mCoreMLBackend->create<CoreML__Specification__ConcatLayerParams>();
core_ml__specification__concat_layer_params__init(mLayer_->concat);
mLayer_->concat->sequenceconcat = false;
} else {
mCoreMLBackend->setLayerName(mLayer_, "NDConcat");
mLayer_->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_CONCAT_ND;
mLayer_->concatnd = mCoreMLBackend->create<CoreML__Specification__ConcatNDLayerParams>();
core_ml__specification__concat_ndlayer_params__init(mLayer_->concatnd);
mLayer_->concatnd->axis = axis - dim;
}
std::vector<std::string> inputNames;
for (const auto& reg : regions) {
inputNames.push_back(mCoreMLBackend->getTensorName(reg.origin));
}
mCoreMLBackend->setLayerInputs(mLayer_, std::move(inputNames));
return true;
}
return false;
}
static void dumpRegion(const Tensor::InsideDescribe::Region& reg) {
printf("\n{\nsize: [%d, %d, %d], origin: %p\n", reg.size[0], reg.size[1], reg.size[2], reg.origin);
printf("src: { stride: [%d, %d, %d], offset: %d }\n", reg.src.stride[0],reg.src.stride[1],reg.src.stride[2],reg.src.offset);
printf("dst: { stride: [%d, %d, %d], offset: %d }\n}\n", reg.dst.stride[0],reg.dst.stride[1],reg.dst.stride[2],reg.dst.offset);
}
ErrorCode CoreMLRaster::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
MNN_ASSERT(inputs.size() == 1 && outputs.size() == 1);
if (!rasterOptimization(inputs, outputs)) {
/*
printf(">>> start\n");
for (const auto& reg : TensorUtils::getDescribe(inputs[0])->regions) {
printf("inputShape: ["); for (auto x : reg.origin->shape()) printf("%d, ", x); printf("]\n");
dumpRegion(reg);
}
printf("outputShape: ["); for (auto x : outputs[0]->shape()) printf("%d, ", x); printf("]\n");
printf(">>> end\n");*/
auto outputShape = outputs[0]->shape();
mLayer_->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_CUSTOM;
mLayer_->custom = mCoreMLBackend->create<CoreML__Specification__CustomLayerParams>();
core_ml__specification__custom_layer_params__init(mLayer_->custom);
mCoreMLBackend->copyName(&(mLayer_->custom->classname), "RasterLayer");
const auto& regions = TensorUtils::getDescribe(inputs[0])->regions;
mLayer_->custom->n_weights = regions.size() + 1;
mLayer_->custom->weights = mCoreMLBackend->create<CoreML__Specification__WeightParams*>(mLayer_->custom->n_weights);
std::vector<std::string> inputNames;
for (int i = 0; i <= regions.size(); i++) {
mLayer_->custom->weights[i] = mCoreMLBackend->create<CoreML__Specification__WeightParams>();
core_ml__specification__weight_params__init(mLayer_->custom->weights[i]);
if (i == 0) {
// first set outputShape
mLayer_->custom->weights[i]->n_floatvalue = outputShape.size();
mLayer_->custom->weights[i]->floatvalue = mCoreMLBackend->create<float>(mLayer_->custom->weights[i]->n_floatvalue);
memcpy(mLayer_->custom->weights[i]->floatvalue, outputShape.data(), outputShape.size() * sizeof(int));
} else {
// then set regions info
mLayer_->custom->weights[i]->n_floatvalue = 11;
mLayer_->custom->weights[i]->floatvalue = mCoreMLBackend->create<float>(mLayer_->custom->weights[i]->n_floatvalue);
memcpy(mLayer_->custom->weights[i]->floatvalue, &(regions[i-1]), 11 * sizeof(int));
inputNames.push_back(mCoreMLBackend->getTensorName(regions[i-1].origin));
}
}
mCoreMLBackend->setLayerInputs(mLayer_, std::move(inputNames));
}
mCoreMLBackend->setLayerOutputs(mLayer_, {mCoreMLBackend->getTensorName(outputs[0])});
mCoreMLBackend->addLayer(mLayer_);
return NO_ERROR;
}
REGISTER_COREML_OP_CREATOR(CoreMLRaster, OpType_Raster)
} // namespace MNN
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