mirror of https://github.com/alibaba/MNN.git
183 lines
12 KiB
C++
183 lines
12 KiB
C++
//
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// NeuralNetWorkOp.hpp
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// MNN
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//
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// Created by MNN on 2019/06/27.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#ifndef MNN_NeuralNetWorkOp_HPP
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#define MNN_NeuralNetWorkOp_HPP
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#include <MNN/ImageProcess.hpp>
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namespace MNN {
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namespace Express {
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enum PaddingMode {CAFFE, VALID, SAME};
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enum PoolingMode {MAXPOOL, AVEPOOL};
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enum PadValueMode {CONSTANT, REFLECT, SYMMETRIC, EDGE};
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MNN_PUBLIC VARP _Input(INTS shape = {}, Dimensionformat data_format = NC4HW4, halide_type_t dtype = halide_type_of<float>()) ;
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MNN_PUBLIC VARP _Clone(VARP source, bool deepCopy = false);
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MNN_PUBLIC VARP _Scalar(const void* ptr, halide_type_t type);
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template <typename T>
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VARP _Scalar(T value) {
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return _Scalar(&value, halide_type_of<T>());
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}
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MNN_PUBLIC VARP _Const(float value, INTS shape = {}, Dimensionformat format = NHWC);
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MNN_PUBLIC VARP _Const(const void* ptr, INTS shape = {}, Dimensionformat format = NHWC,
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halide_type_t type = halide_type_of<float>());
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MNN_PUBLIC VARP _TrainableParam(float value, INTS dims, Dimensionformat format);
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MNN_PUBLIC VARP _TrainableParam(const void* ptr, INTS dims, Dimensionformat format,
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halide_type_t type = halide_type_of<float>());
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MNN_PUBLIC VARP _InnerProduct(std::vector<float>&& weight, std::vector<float>&& bias, VARP x, INTS outputShape);
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MNN_PUBLIC VARP _Conv(VARP weight, VARP bias, VARP x, PaddingMode pad = VALID, INTS stride = {1, 1},
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INTS dilate = {1, 1}, int group = 1, INTS pads = {0, 0});
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MNN_PUBLIC VARP _Conv(float weight, float bias, VARP x, INTS channel, INTS kernelSize, PaddingMode pad = VALID,
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INTS stride = {1, 1}, INTS dilate = {1, 1}, int group = 1);
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MNN_PUBLIC VARP _Conv(std::vector<int8_t>&& weight, std::vector<float>&& bias, VARP x, INTS channel, INTS kernelSize,
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PaddingMode pad = VALID, INTS stride = {1, 1}, INTS dilate = {1, 1}, int group = 1, INTS pads = {0, 0}, bool relu = false, bool relu6 = false, int nbits = 8);
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MNN_PUBLIC VARP _Conv(std::vector<float>&& weight, std::vector<float>&& bias, VARP x, INTS channel, INTS kernelSize,
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PaddingMode pad = VALID, INTS stride = {1, 1}, INTS dilate = {1, 1}, int group = 1, INTS pads = {0, 0}, bool relu = false, bool relu6 = false);
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MNN_PUBLIC VARP _Deconv(VARP weight, VARP bias, VARP x, PaddingMode pad = VALID, INTS stride = {1, 1},
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INTS dilate = {1, 1}, int group = 1, INTS pads = {0, 0});
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MNN_PUBLIC VARP _Deconv(std::vector<float>&& weight, std::vector<float>&& bias, VARP x, INTS channel, INTS kernelSize,
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PaddingMode pad, INTS stride = {1, 1}, INTS dilate = {1, 1}, int group = 1, INTS pads = {0, 0}, bool relu = false, bool relu6 = false);
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MNN_PUBLIC VARP _MaxPool(VARP x, INTS kernel, INTS stride = {1, 1}, PaddingMode pad = VALID, INTS pads= {0, 0});
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MNN_PUBLIC VARP _AvePool(VARP x, INTS kernel, INTS stride = {1, 1}, PaddingMode pad = VALID, INTS pads= {0, 0});
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MNN_PUBLIC VARP _Reshape(VARP x, INTS shape, Dimensionformat original_format = NCHW);
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MNN_PUBLIC VARP _Reshape(VARP x, VARP shape);
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MNN_PUBLIC VARP _Scale(VARP x, int channels, std::vector<float>&& scales, std::vector<float>&& bias);
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MNN_PUBLIC VARP _Relu(VARP x, float slope = 0.0f);
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MNN_PUBLIC VARP _Relu6(VARP x, float minValue = 0.0f, float maxValue = 6.0f);
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MNN_PUBLIC VARP _PRelu(VARP x, std::vector<float> &&slopes);
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MNN_PUBLIC VARP _Softmax(VARP logits, int axis = -1);
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MNN_PUBLIC VARP _Softplus(VARP features);
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MNN_PUBLIC VARP _Softsign(VARP features);
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MNN_PUBLIC std::vector<VARP> _Split(VARP value, INTS size_splits, int axis = 0);
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MNN_PUBLIC VARP _Slice(VARP x, VARP starts, VARP sizes);
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MNN_PUBLIC VARP _StridedSlice(VARP input, VARP begin, VARP end, VARP strided,
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int32_t beginMask, int32_t endMask, int32_t ellipsisMask,
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int32_t newAxisMask, int32_t shrinkAxisMask);
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MNN_PUBLIC VARP _StridedSliceWrite(VARP input, VARP begin, VARP end, VARP strided, VARP write,
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int32_t beginMask, int32_t endMask, int32_t ellipsisMask,
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int32_t newAxisMask, int32_t shrinkAxisMask);
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MNN_PUBLIC VARP _Concat(VARPS values, int axis);
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MNN_PUBLIC VARP _Convert(VARP input, Dimensionformat format);
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MNN_PUBLIC VARP _Transpose(VARP x, INTS perm);
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MNN_PUBLIC VARP _Transpose(VARP x, VARP perm);
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MNN_PUBLIC VARP _ChannelShuffle(VARP x, int group);
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MNN_PUBLIC VARP _ChangeInputFormat(VARP input, Dimensionformat format);
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MNN_PUBLIC VARP _Conv2DBackPropFilter(VARP input, VARP inputGrad, INTS kernelSize, PaddingMode pad = VALID, INTS stride = {1, 1}, INTS dilate = {1, 1}, int group = 1, INTS pads = {0, 0});
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MNN_PUBLIC VARP _PoolGrad(VARP originInput, VARP originOutput, VARP inputGrad, INTS kernel, INTS stride, PoolingMode type, PaddingMode pad = VALID, INTS pads= {0, 0});
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// FIXME: move the api to Array Ops
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MNN_PUBLIC VARP _Reverse(VARP x, VARP axis);
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MNN_PUBLIC VARP _ReverseSequence(VARP x, VARP y, int batchDim, int seqDim);
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// FIXME: move the api to Image Ops
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MNN_PUBLIC VARP _Crop(VARP images, VARP size, int axis, INTS offset);
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MNN_PUBLIC VARP _Resize(VARP images, float xScale, float yScale);
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MNN_PUBLIC VARP _Pad(VARP x, VARP paddings, PadValueMode mode = CONSTANT);
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MNN_PUBLIC VARP _ExpandDims(VARP input, int axis);
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MNN_PUBLIC VARP _ExpandDims(VARP input, VARP axis);
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MNN_PUBLIC VARP _Shape(VARP input, bool nchw = false);
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MNN_PUBLIC VARP _Stack(VARPS values, int axis=0);
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enum InterpolationMethod {BILINEAR, NEAREST};
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MNN_PUBLIC VARP _CropAndResize(VARP image, VARP boxes, VARP box_ind, VARP crop_size,
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InterpolationMethod method, float extrapolation_value = 0.0);
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MNN_PUBLIC VARP _Fill(VARP dims, VARP value);
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MNN_PUBLIC VARP _Tile(VARP input, VARP multiples);
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MNN_PUBLIC VARP _Gather(VARP params, VARP indices);
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MNN_PUBLIC VARP _GatherV2(VARP params, VARP indices, VARP axis = nullptr);
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MNN_PUBLIC VARP _Squeeze(VARP input, INTS axis = {});
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MNN_PUBLIC VARP _Unsqueeze(VARP input, INTS axis = {});
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MNN_PUBLIC VARP _BatchToSpaceND(VARP input, VARP block_shape, VARP crops);
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MNN_PUBLIC VARP _GatherND(VARP params, VARP indices);
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MNN_PUBLIC VARP _GatherElements(VARP params, VARP indices);
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MNN_PUBLIC VARP _GatherElements(VARP params, VARP indices, VARP axis);
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MNN_PUBLIC VARP _Selu(VARP features, float scale, float alpha);
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MNN_PUBLIC VARP _Size(VARP input);
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MNN_PUBLIC VARP _Elu(VARP features, float alpha=1.0);
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MNN_PUBLIC VARP _Threshold(VARP features, float alpha=1.0);
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MNN_PUBLIC VARP _MatrixBandPart(VARP input, VARP num_lower, VARP num_upper);
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MNN_PUBLIC std::vector<VARP> _Moments(VARP x, INTS axis, VARP shift, bool keepDims);
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MNN_PUBLIC VARP _SetDiff1D(VARP x, VARP y);
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MNN_PUBLIC VARP _SpaceToDepth(VARP input, int block_size);
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MNN_PUBLIC VARP _SpaceToBatchND(VARP input, VARP block_shape, VARP paddings);
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MNN_PUBLIC VARP _ZerosLike(VARP input);
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MNN_PUBLIC std::vector<VARP> _Unstack(VARP value, int axis=0);
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MNN_PUBLIC VARP _Rank(VARP input);
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MNN_PUBLIC VARP _Range(VARP start, VARP limit, VARP delta);
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MNN_PUBLIC VARP _DepthToSpace(VARP input, int block_size);
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MNN_PUBLIC VARP _PriorBox(VARP feature, VARP image,
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std::vector<float> min_size, std::vector<float> max_size, std::vector<float>aspect_ratio,
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bool flip, bool clip, std::vector<float>variance,
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unsigned int img_h, unsigned int img_w, float step_h, float step_w, float offset = 0.5);
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MNN_PUBLIC VARP _Permute(VARP input, INTS dims);
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MNN_PUBLIC VARP _DetectionOutput(VARP location, VARP confidence, VARP priorbox,
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unsigned int num_classes, bool share_location, int background_label_id,
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float nms_threshhold, int nms_topk, int code_type,
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bool variance_encoded_in_target,
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int keep_top_k, float confidence_threshold, float visualize_threshold);
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MNN_PUBLIC std::vector<VARP> _DetectionPostProcess(VARP encode_boxes, VARP class_predictions, VARP anchors,
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int num_classes, int max_detections,
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int max_class_per_detection, int detections_per_class,
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float nms_threshold, float iou_threshold,
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bool use_regular_nms, std::vector<float> centersize_encoding);
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MNN_PUBLIC VARP _Interp(VARPS xs, float widthScale, float heightScale, int outputWidth, int outputHeight, int resizeType, bool alignCorners);
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MNN_PUBLIC VARP _ZeroGrad(VARP x);
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// Int8 Inference
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MNN_PUBLIC VARP _Conv(std::vector<int8_t>&& weight, std::vector<int>&& bias, std::vector<float>&& scale, VARP x, INTS channel, INTS kernelSize,
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PaddingMode pad, INTS stride, INTS dilate, int group, INTS pads, bool relu, int nbits = 8);
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MNN_PUBLIC VARP _Conv(std::vector<int8_t>&& weight, std::vector<int>&& bias, std::vector<float>&& scale,
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VARP x, INTS channel, INTS kernelSize,
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PaddingMode pad, INTS stride, INTS dilate, int group, INTS pads, bool relu,
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int8_t inputZeroPoint, int8_t outputZeroPoint,
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int8_t minValue, int8_t maxValue, bool accumulateToInt16);
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MNN_PUBLIC VARP _Conv(std::vector<int8_t>&& weight, std::vector<float>&& bias, std::vector<float>&& weightScale,
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VARP x, INTS channel, INTS kernelSize,
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PaddingMode pad, INTS stride, INTS dilate, int group, INTS pads, bool relu,
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float scaleIn, float scaleOut,
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int8_t inputZeroPoint, int8_t outputZeroPoint,
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int8_t minValue, int8_t maxValue, float weightClampValue, bool accumulateToInt16);
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MNN_PUBLIC VARP _CosineSimilarity(VARP input0, VARP input1, VARP inputDim);
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enum GridSamplePaddingMode {GRID_SAMPLE_PADDING_ZEROS, GRID_SAMPLE_PADDING_BORDER, GRID_SAMPLE_PADDING_REFLECTION};
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MNN_PUBLIC VARP _GridSample(VARP input, VARP grid, InterpolationMethod mode=BILINEAR, GridSamplePaddingMode paddingMode=GRID_SAMPLE_PADDING_ZEROS, bool alignCorners=false);
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MNN_PUBLIC VARP _FloatToInt8(VARP x, VARP scale, char minValue, char maxValue);
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MNN_PUBLIC VARP _FloatToInt8(VARP x, VARP scale, int8_t minValue, int8_t maxValue, int8_t zeroPoint);
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MNN_PUBLIC VARP _Int8ToFloat(VARP x, VARP scale);
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MNN_PUBLIC VARP _Int8ToFloat(VARP x, VARP scale, int8_t zeroPoint);
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MNN_PUBLIC VARP _Select(VARP select, VARP input0, VARP input1);
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MNN_PUBLIC std::vector<VARP> _TopKV2(VARP input0, VARP input1);
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MNN_PUBLIC VARP _ImageProcess(VARP input, CV::ImageProcess::Config config, CV::Matrix matrix, int oh, int ow, int oc, int dtype, uint8_t padVal = 0);
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MNN_PUBLIC VARP _Where(VARP x);
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MNN_PUBLIC VARP _Sort(VARP x, int axis = -1, bool arg = false, bool descend = false);
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MNN_PUBLIC VARP _Raster(const std::vector<VARP>& vars, const std::vector<int>& regions, const std::vector<int>& shape);
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MNN_PUBLIC VARP _RasterRaw(const std::vector<VARP>& vars, const std::vector<int>& region, const std::vector<int>& shape, halide_type_t dataType, Dimensionformat format);
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MNN_PUBLIC VARP _Nms(VARP boxes, VARP scores, int maxDetections, float iouThreshold = -1, float scoreThreshold = -1);
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MNN_PUBLIC VARP _Im2Col(VARP x, INTS kernelSize, INTS dilate, INTS pads, INTS stride);
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MNN_PUBLIC VARP _Col2Im(VARP x, VARP outputShape, INTS kernelSize, INTS dilate, INTS pads, INTS stride);
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/**
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Onnx's Loop
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*/
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MNN_PUBLIC VARPS _Loop(VARPS x, const std::string& submoduleName);
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MNN_PUBLIC VARP _ROIPooling(VARP input, VARP roi, int pooledHeight, int pooledWidth, float spatialScale, bool outputGrad = false, VARP backwardDiff = nullptr);
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MNN_PUBLIC VARP _ROIAlign(VARP input, VARP roi, int pooledHeight, int pooledWidth, float spatialScale, int samplingRatio, bool aligned, PoolingMode poolType, bool outputGrad = false, VARP backwardDiff = nullptr);
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} // namespace Express
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} // namespace MNN
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#endif /* NeuralNetWorkOp_HPP */
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