MNN/source/backend/cpu/CPUGatherND.cpp

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- build: - unify schema building in core and converter; - add more build script for android; - add linux build script for python; - ops impl: - add floor mod support in binary; - use eltwise impl in add/max/sub/mul binary for optimization; - remove fake double support in cast; - fix 5d support for concat; - add adjX and adjY support for batch matmul; - optimize conv2d back prop filter; - add pad mode support for conv3d; - fix bug in conv2d & conv depthwise with very small feature map; - optimize binary without broacast; - add data types support for gather; - add gather ND support; - use uint8 data type in gather v2; - add transpose support for matmul; - add matrix band part; - add dim != 4 support for padding, reshape & tensor convert; - add pad type support for pool3d; - make ops based on TensorFlow Lite quantization optional; - add all & any support for reduction; - use type in parameter as output type in reduction; - add int support for unary; - add variable weight support for conv2d; - fix conv2d depthwise weights initialization; - fix type support for transpose; - fix grad outputs count for reduce grad and reshape grad; - fix priorbox & detection output; - fix metal softmax error; - python: - add runSessionWithCallBackInfo interface; - add max nodes limit (1400) for visualization tool; - fix save error in python3; - align default dim; - convert: - add extra design for optimization; - add more post converting optimizers; - add caffe v1 weights blob support; - add cast, unary, conv transpose support for onnx model; - optimize batchnorm, conv with variable weights, prelu, reshape, slice, upsample for onnx model; - add cos/sin/atan/tan support for unary for tensorflow model; - add any/all support for reduction for tensorflow model; - add elu, conv3d, pool3d support for tensorflow model; - optimize argmax, batchnorm, concat, batch to space, conv with variable weights, prelu, slice for tensorflow model; - others: - fix size computer lock; - fix thread pool deadlock; - add express & parameters in express; - rewrite blitter chooser without static map; - add tests for expr;
2019-10-29 13:37:26 +08:00
//
// CPUGatherND.cpp
// MNN
//
// Created by MNN on 2019/09/11.
// Copyright © 2018, Alibaba Group Holding Limited
//
/*Ref:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/kernels/internal/reference/reference_ops.h
*/
2019-12-27 22:16:57 +08:00
#include "backend/cpu/CPUGatherND.hpp"
- build: - unify schema building in core and converter; - add more build script for android; - add linux build script for python; - ops impl: - add floor mod support in binary; - use eltwise impl in add/max/sub/mul binary for optimization; - remove fake double support in cast; - fix 5d support for concat; - add adjX and adjY support for batch matmul; - optimize conv2d back prop filter; - add pad mode support for conv3d; - fix bug in conv2d & conv depthwise with very small feature map; - optimize binary without broacast; - add data types support for gather; - add gather ND support; - use uint8 data type in gather v2; - add transpose support for matmul; - add matrix band part; - add dim != 4 support for padding, reshape & tensor convert; - add pad type support for pool3d; - make ops based on TensorFlow Lite quantization optional; - add all & any support for reduction; - use type in parameter as output type in reduction; - add int support for unary; - add variable weight support for conv2d; - fix conv2d depthwise weights initialization; - fix type support for transpose; - fix grad outputs count for reduce grad and reshape grad; - fix priorbox & detection output; - fix metal softmax error; - python: - add runSessionWithCallBackInfo interface; - add max nodes limit (1400) for visualization tool; - fix save error in python3; - align default dim; - convert: - add extra design for optimization; - add more post converting optimizers; - add caffe v1 weights blob support; - add cast, unary, conv transpose support for onnx model; - optimize batchnorm, conv with variable weights, prelu, reshape, slice, upsample for onnx model; - add cos/sin/atan/tan support for unary for tensorflow model; - add any/all support for reduction for tensorflow model; - add elu, conv3d, pool3d support for tensorflow model; - optimize argmax, batchnorm, concat, batch to space, conv with variable weights, prelu, slice for tensorflow model; - others: - fix size computer lock; - fix thread pool deadlock; - add express & parameters in express; - rewrite blitter chooser without static map; - add tests for expr;
2019-10-29 13:37:26 +08:00
#include <string.h>
namespace MNN {
ErrorCode CPUGatherND::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto params = inputs[0];
auto indice = inputs[1];
mSliceN = 1;
mSliceSize = 1;
for (int i=0; i<indice->dimensions()-1; ++i) {
mSliceN *= indice->length(i);
}
auto indiceNd = indice->length(indice->dimensions()-1);
mDimsToCount.resize(indiceNd);
for (int i=indiceNd; i<params->dimensions(); ++i) {
mSliceSize *= params->length(i);
}
auto paramSize = params->elementSize();
for (int i=0; i<indiceNd; ++i) {
mDimsToCount[i] = paramSize / params->length(i);
paramSize = mDimsToCount[i];
}
mDimsToCount.resize(indiceNd);
return NO_ERROR;
}
ErrorCode CPUGatherND::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto params = inputs[0];
auto indice = inputs[1];
auto indiceNd = indice->length(indice->dimensions()-1);
auto indiceData = indice->host<int32_t>();
auto output = outputs[0];
auto bytes = output->getType().bytes();
for (int i=0; i<mSliceN; ++i) {
int fromPos = 0;
for (int j=0; j<indiceNd; ++j) {
fromPos += mDimsToCount[j] * indiceData[i*indiceNd + j];
}
::memcpy(output->host<uint8_t>() + bytes * i * mSliceSize, params->host<uint8_t>() + bytes * fromPos, bytes * mSliceSize);
}
return NO_ERROR;
}
class CPUGatherNDCreator : public CPUBackend::Creator {
public:
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
const MNN::Op *op, Backend *backend) const override {
return new CPUGatherND(backend);
}
};
REGISTER_CPU_OP_CREATOR(CPUGatherNDCreator, OpType_GatherND);
} // namespace MNN