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onnx.c
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/*
* onnx.c
*
* Copyright(c) 2007-2020 Jianjun Jiang <[email protected]>
* Mobile phone: +86-18665388956
* QQ: 8192542
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*/
#include <onnx.h>
#define ONNX_LOG(...) printf(__VA_ARGS__)
static void * resolver_default_create(void)
{
return NULL;
}
static void resolver_default_destroy(void * rctx)
{
}
static struct onnx_resolver_t resolver_default = {
.name = "default",
.create = resolver_default_create,
.destroy = resolver_default_destroy,
.op_Abs = resolver_default_op_Abs,
.op_Acos = resolver_default_op_Acos,
.op_Acosh = resolver_default_op_Acosh,
.op_Add = resolver_default_op_Add,
.op_And = resolver_default_op_And,
.op_ArgMax = resolver_default_op_ArgMax,
.op_ArgMin = resolver_default_op_ArgMin,
.op_Asin = resolver_default_op_Asin,
.op_Asinh = resolver_default_op_Asinh,
.op_Atan = resolver_default_op_Atan,
.op_Atanh = resolver_default_op_Atanh,
.op_AveragePool = resolver_default_op_AveragePool,
.op_BatchNormalization = resolver_default_op_BatchNormalization,
.op_BitShift = resolver_default_op_BitShift,
.op_Cast = resolver_default_op_Cast,
.op_Ceil = resolver_default_op_Ceil,
.op_Clip = resolver_default_op_Clip,
.op_Compress = resolver_default_op_Compress,
.op_Concat = resolver_default_op_Concat,
.op_ConcatFromSequence = resolver_default_op_ConcatFromSequence,
.op_Constant = resolver_default_op_Constant,
.op_ConstantOfShape = resolver_default_op_ConstantOfShape,
.op_Conv = resolver_default_op_Conv,
.op_ConvInteger = resolver_default_op_ConvInteger,
.op_ConvTranspose = resolver_default_op_ConvTranspose,
.op_Cos = resolver_default_op_Cos,
.op_Cosh = resolver_default_op_Cosh,
.op_CumSum = resolver_default_op_CumSum,
.op_DepthToSpace = resolver_default_op_DepthToSpace,
.op_DequantizeLinear = resolver_default_op_DequantizeLinear,
.op_Det = resolver_default_op_Det,
.op_Div = resolver_default_op_Div,
.op_Dropout = resolver_default_op_Dropout,
.op_Einsum = resolver_default_op_Einsum,
.op_Elu = resolver_default_op_Elu,
.op_Equal = resolver_default_op_Equal,
.op_Erf = resolver_default_op_Erf,
.op_Exp = resolver_default_op_Exp,
.op_Expand = resolver_default_op_Expand,
.op_EyeLike = resolver_default_op_EyeLike,
.op_Flatten = resolver_default_op_Flatten,
.op_Floor = resolver_default_op_Floor,
.op_GRU = resolver_default_op_GRU,
.op_Gather = resolver_default_op_Gather,
.op_GatherElements = resolver_default_op_GatherElements,
.op_GatherND = resolver_default_op_GatherND,
.op_Gemm = resolver_default_op_Gemm,
.op_GlobalAveragePool = resolver_default_op_GlobalAveragePool,
.op_GlobalLpPool = resolver_default_op_GlobalLpPool,
.op_GlobalMaxPool = resolver_default_op_GlobalMaxPool,
.op_Greater = resolver_default_op_Greater,
.op_HardSigmoid = resolver_default_op_HardSigmoid,
.op_Hardmax = resolver_default_op_Hardmax,
.op_Identity = resolver_default_op_Identity,
.op_If = resolver_default_op_If,
.op_InstanceNormalization = resolver_default_op_InstanceNormalization,
.op_IsInf = resolver_default_op_IsInf,
.op_IsNaN = resolver_default_op_IsNaN,
.op_LRN = resolver_default_op_LRN,
.op_LSTM = resolver_default_op_LSTM,
.op_LeakyRelu = resolver_default_op_LeakyRelu,
.op_Less = resolver_default_op_Less,
.op_Log = resolver_default_op_Log,
.op_Loop = resolver_default_op_Loop,
.op_LpNormalization = resolver_default_op_LpNormalization,
.op_LpPool = resolver_default_op_LpPool,
.op_MatMul = resolver_default_op_MatMul,
.op_MatMulInteger = resolver_default_op_MatMulInteger,
.op_Max = resolver_default_op_Max,
.op_MaxPool = resolver_default_op_MaxPool,
.op_MaxRoiPool = resolver_default_op_MaxRoiPool,
.op_MaxUnpool = resolver_default_op_MaxUnpool,
.op_Mean = resolver_default_op_Mean,
.op_Min = resolver_default_op_Min,
.op_Mod = resolver_default_op_Mod,
.op_Mul = resolver_default_op_Mul,
.op_Multinomial = resolver_default_op_Multinomial,
.op_Neg = resolver_default_op_Neg,
.op_NonMaxSuppression = resolver_default_op_NonMaxSuppression,
.op_NonZero = resolver_default_op_NonZero,
.op_Not = resolver_default_op_Not,
.op_OneHot = resolver_default_op_OneHot,
.op_Or = resolver_default_op_Or,
.op_PRelu = resolver_default_op_PRelu,
.op_Pad = resolver_default_op_Pad,
.op_Pow = resolver_default_op_Pow,
.op_QLinearConv = resolver_default_op_QLinearConv,
.op_QLinearMatMul = resolver_default_op_QLinearMatMul,
.op_QuantizeLinear = resolver_default_op_QuantizeLinear,
.op_RNN = resolver_default_op_RNN,
.op_RandomNormal = resolver_default_op_RandomNormal,
.op_RandomNormalLike = resolver_default_op_RandomNormalLike,
.op_RandomUniform = resolver_default_op_RandomUniform,
.op_RandomUniformLike = resolver_default_op_RandomUniformLike,
.op_Reciprocal = resolver_default_op_Reciprocal,
.op_ReduceL1 = resolver_default_op_ReduceL1,
.op_ReduceL2 = resolver_default_op_ReduceL2,
.op_ReduceLogSum = resolver_default_op_ReduceLogSum,
.op_ReduceLogSumExp = resolver_default_op_ReduceLogSumExp,
.op_ReduceMax = resolver_default_op_ReduceMax,
.op_ReduceMean = resolver_default_op_ReduceMean,
.op_ReduceMin = resolver_default_op_ReduceMin,
.op_ReduceProd = resolver_default_op_ReduceProd,
.op_ReduceSum = resolver_default_op_ReduceSum,
.op_ReduceSumSquare = resolver_default_op_ReduceSumSquare,
.op_Relu = resolver_default_op_Relu,
.op_Reshape = resolver_default_op_Reshape,
.op_Resize = resolver_default_op_Resize,
.op_ReverseSequence = resolver_default_op_ReverseSequence,
.op_RoiAlign = resolver_default_op_RoiAlign,
.op_Round = resolver_default_op_Round,
.op_Scan = resolver_default_op_Scan,
.op_Scatter = resolver_default_op_Scatter,
.op_ScatterElements = resolver_default_op_ScatterElements,
.op_ScatterND = resolver_default_op_ScatterND,
.op_Selu = resolver_default_op_Selu,
.op_SequenceAt = resolver_default_op_SequenceAt,
.op_SequenceConstruct = resolver_default_op_SequenceConstruct,
.op_SequenceEmpty = resolver_default_op_SequenceEmpty,
.op_SequenceErase = resolver_default_op_SequenceErase,
.op_SequenceInsert = resolver_default_op_SequenceInsert,
.op_SequenceLength = resolver_default_op_SequenceLength,
.op_Shape = resolver_default_op_Shape,
.op_Shrink = resolver_default_op_Shrink,
.op_Sigmoid = resolver_default_op_Sigmoid,
.op_Sign = resolver_default_op_Sign,
.op_Sin = resolver_default_op_Sin,
.op_Sinh = resolver_default_op_Sinh,
.op_Size = resolver_default_op_Size,
.op_Slice = resolver_default_op_Slice,
.op_Softplus = resolver_default_op_Softplus,
.op_Softsign = resolver_default_op_Softsign,
.op_SpaceToDepth = resolver_default_op_SpaceToDepth,
.op_Split = resolver_default_op_Split,
.op_SplitToSequence = resolver_default_op_SplitToSequence,
.op_Sqrt = resolver_default_op_Sqrt,
.op_Squeeze = resolver_default_op_Squeeze,
.op_StringNormalizer = resolver_default_op_StringNormalizer,
.op_Sub = resolver_default_op_Sub,
.op_Sum = resolver_default_op_Sum,
.op_Tan = resolver_default_op_Tan,
.op_Tanh = resolver_default_op_Tanh,
.op_TfIdfVectorizer = resolver_default_op_TfIdfVectorizer,
.op_ThresholdedRelu = resolver_default_op_ThresholdedRelu,
.op_Tile = resolver_default_op_Tile,
.op_TopK = resolver_default_op_TopK,
.op_Transpose = resolver_default_op_Transpose,
.op_Unique = resolver_default_op_Unique,
.op_Unsqueeze = resolver_default_op_Unsqueeze,
.op_Upsample = resolver_default_op_Upsample,
.op_Where = resolver_default_op_Where,
.op_Xor = resolver_default_op_Xor,
.op_Celu = resolver_default_op_Celu,
.op_DynamicQuantizeLinear = resolver_default_op_DynamicQuantizeLinear,
.op_GreaterOrEqual = resolver_default_op_GreaterOrEqual,
.op_LessOrEqual = resolver_default_op_LessOrEqual,
.op_LogSoftmax = resolver_default_op_LogSoftmax,
.op_MeanVarianceNormalization = resolver_default_op_MeanVarianceNormalization,
.op_NegativeLogLikelihoodLoss = resolver_default_op_NegativeLogLikelihoodLoss,
.op_Range = resolver_default_op_Range,
.op_Softmax = resolver_default_op_Softmax,
.op_SoftmaxCrossEntropyLoss = resolver_default_op_SoftmaxCrossEntropyLoss,
};
static void resolver_solve_operator(struct onnx_resolver_t * r, struct onnx_node_t * n)
{
void (*rop)(struct onnx_node_t *);
if(r && n)
{
switch(shash(n->proto->op_type))
{
case 0x0b87d47b: /* "Abs" */
rop = r->op_Abs;
break;
case 0x7c82680b: /* "Acos" */
rop = r->op_Acos;
break;
case 0x0ccf69d3: /* "Acosh" */
rop = r->op_Acosh;
break;
case 0x0b87d4ae: /* "Add" */
rop = r->op_Add;
break;
case 0x0b87d5f8: /* "And" */
rop = r->op_And;
break;
case 0xa7c70ea5: /* "ArgMax" */
rop = r->op_ArgMax;
break;
case 0xa7c70fa3: /* "ArgMin" */
rop = r->op_ArgMin;
break;
case 0x7c82ab50: /* "Asin" */
rop = r->op_Asin;
break;
case 0x0cd815b8: /* "Asinh" */
rop = r->op_Asinh;
break;
case 0x7c82ae89: /* "Atan" */
rop = r->op_Atan;
break;
case 0x0cd88011: /* "Atanh" */
rop = r->op_Atanh;
break;
case 0xf1a1e23a: /* "AveragePool" */
rop = r->op_AveragePool;
break;
case 0x2d3b46ee: /* "BatchNormalization" */
rop = r->op_BatchNormalization;
break;
case 0x0bfe45a2: /* "BitShift" */
rop = r->op_BitShift;
break;
case 0x7c8378d0: /* "Cast" */
rop = r->op_Cast;
break;
case 0x7c838882: /* "Ceil" */
rop = r->op_Ceil;
break;
case 0x7c83a64d: /* "Clip" */
rop = r->op_Clip;
break;
case 0xb7db9db1: /* "Compress" */
rop = r->op_Compress;
break;
case 0xac3f4a9d: /* "Concat" */
rop = r->op_Concat;
break;
case 0x5053caca: /* "ConcatFromSequence" */
rop = r->op_ConcatFromSequence;
break;
case 0xba6816ef: /* "Constant" */
rop = r->op_Constant;
break;
case 0xe468a875: /* "ConstantOfShape" */
rop = r->op_ConstantOfShape;
break;
case 0x7c83b3bb: /* "Conv" */
rop = r->op_Conv;
break;
case 0x8371dbe9: /* "ConvInteger" */
rop = r->op_ConvInteger;
break;
case 0x3903c4ba: /* "ConvTranspose" */
rop = r->op_ConvTranspose;
break;
case 0x0b87deaa: /* "Cos" */
rop = r->op_Cos;
break;
case 0x7c83b452: /* "Cosh" */
rop = r->op_Cosh;
break;
case 0xacab0fbf: /* "CumSum" */
rop = r->op_CumSum;
break;
case 0xc9c1d669: /* "DepthToSpace" */
rop = r->op_DepthToSpace;
break;
case 0xf9cc985a: /* "DequantizeLinear" */
rop = r->op_DequantizeLinear;
break;
case 0x0b87e1a2: /* "Det" */
rop = r->op_Det;
break;
case 0x0b87e228: /* "Div" */
rop = r->op_Div;
break;
case 0x883bca72: /* "Dropout" */
rop = r->op_Dropout;
break;
case 0xb07d4f76: /* "Einsum" */
rop = r->op_Einsum;
break;
case 0x0b87e6cb: /* "Elu" */
rop = r->op_Elu;
break;
case 0x0d1f905d: /* "Equal" */
rop = r->op_Equal;
break;
case 0x0b87e782: /* "Erf" */
rop = r->op_Erf;
break;
case 0x0b87e852: /* "Exp" */
rop = r->op_Exp;
break;
case 0xb18d8a45: /* "Expand" */
rop = r->op_Expand;
break;
case 0xe4c1560d: /* "EyeLike" */
rop = r->op_EyeLike;
break;
case 0x13363dd3: /* "Flatten" */
rop = r->op_Flatten;
break;
case 0x0d2ed347: /* "Floor" */
rop = r->op_Floor;
break;
case 0x0b87ebd3: /* "GRU" */
rop = r->op_GRU;
break;
case 0xb499f620: /* "Gather" */
rop = r->op_Gather;
break;
case 0x7c94d43d: /* "GatherElements" */
rop = r->op_GatherElements;
break;
case 0x42f00872: /* "GatherND" */
rop = r->op_GatherND;
break;
case 0x7c85ba8b: /* "Gemm" */
rop = r->op_Gemm;
break;
case 0x9289c84b: /* "GlobalAveragePool" */
rop = r->op_GlobalAveragePool;
break;
case 0x3f5a29ac: /* "GlobalLpPool" */
rop = r->op_GlobalLpPool;
break;
case 0x575f0fb6: /* "GlobalMaxPool" */
rop = r->op_GlobalMaxPool;
break;
case 0x6e6d652f: /* "Greater" */
rop = r->op_Greater;
break;
case 0x10341df0: /* "HardSigmoid" */
rop = r->op_HardSigmoid;
break;
case 0x94acb4aa: /* "Hardmax" */
rop = r->op_Hardmax;
break;
case 0xdfd9b28f: /* "Identity" */
rop = r->op_Identity;
break;
case 0x00597414: /* "If" */
rop = r->op_If;
break;
case 0xfb0902c1: /* "InstanceNormalization" */
rop = r->op_InstanceNormalization;
break;
case 0x0d68519e: /* "IsInf" */
rop = r->op_IsInf;
break;
case 0x0d68651e: /* "IsNaN" */
rop = r->op_IsNaN;
break;
case 0x0b880111: /* "LRN" */
rop = r->op_LRN;
break;
case 0x7c882885: /* "LSTM" */
rop = r->op_LSTM;
break;
case 0xea2c5c33: /* "LeakyRelu" */
rop = r->op_LeakyRelu;
break;
case 0x7c88793c: /* "Less" */
rop = r->op_Less;
break;
case 0x0b8804e7: /* "Log" */
rop = r->op_Log;
break;
case 0x7c88a33f: /* "Loop" */
rop = r->op_Loop;
break;
case 0x07f77ce8: /* "LpNormalization" */
rop = r->op_LpNormalization;
break;
case 0xc13f923b: /* "LpPool" */
rop = r->op_LpPool;
break;
case 0xc2987915: /* "MatMul" */
rop = r->op_MatMul;
break;
case 0x62fbd803: /* "MatMulInteger" */
rop = r->op_MatMulInteger;
break;
case 0x0b88076b: /* "Max" */
rop = r->op_Max;
break;
case 0x15f18a25: /* "MaxPool" */
rop = r->op_MaxPool;
break;
case 0x018c06cf: /* "MaxRoiPool" */
rop = r->op_MaxRoiPool;
break;
case 0x641501e8: /* "MaxUnpool" */
rop = r->op_MaxUnpool;
break;
case 0x7c890346: /* "Mean" */
rop = r->op_Mean;
break;
case 0x0b880869: /* "Min" */
rop = r->op_Min;
break;
case 0x0b880925: /* "Mod" */
rop = r->op_Mod;
break;
case 0x0b8809f3: /* "Mul" */
rop = r->op_Mul;
break;
case 0xaec55410: /* "Multinomial" */
rop = r->op_Multinomial;
break;
case 0x0b880c1f: /* "Neg" */
rop = r->op_Neg;
break;
case 0x254e25a1: /* "NonMaxSuppression" */
rop = r->op_NonMaxSuppression;
break;
case 0x82e45c50: /* "NonZero" */
rop = r->op_NonZero;
break;
case 0x0b880d76: /* "Not" */
rop = r->op_Not;
break;
case 0xc825b932: /* "OneHot" */
rop = r->op_OneHot;
break;
case 0x005974e6: /* "Or" */
rop = r->op_Or;
break;
case 0x0dd55b8d: /* "PRelu" */
rop = r->op_PRelu;
break;
case 0x0b88141a: /* "Pad" */
rop = r->op_Pad;
break;
case 0x0b8815fb: /* "Pow" */
rop = r->op_Pow;
break;
case 0xe569f427: /* "QLinearConv" */
rop = r->op_QLinearConv;
break;
case 0xfe108481: /* "QLinearMatMul" */
rop = r->op_QLinearMatMul;
break;
case 0x37138211: /* "QuantizeLinear" */
rop = r->op_QuantizeLinear;
break;
case 0x0b881a13: /* "RNN" */
rop = r->op_RNN;
break;
case 0xc100684f: /* "RandomNormal" */
rop = r->op_RandomNormal;
break;
case 0xa0b57174: /* "RandomNormalLike" */
rop = r->op_RandomNormalLike;
break;
case 0xf8e97c66: /* "RandomUniform" */
rop = r->op_RandomUniform;
break;
case 0x10a8b90b: /* "RandomUniformLike" */
rop = r->op_RandomUniformLike;
break;
case 0x73d06f69: /* "Reciprocal" */
rop = r->op_Reciprocal;
break;
case 0x7944853a: /* "ReduceL1" */
rop = r->op_ReduceL1;
break;
case 0x7944853b: /* "ReduceL2" */
rop = r->op_ReduceL2;
break;
case 0xeab46d14: /* "ReduceLogSum" */
rop = r->op_ReduceLogSum;
break;
case 0x9a057a01: /* "ReduceLogSumExp" */
rop = r->op_ReduceLogSumExp;
break;
case 0xa1d53763: /* "ReduceMax" */
rop = r->op_ReduceMax;
break;
case 0xdc7c323e: /* "ReduceMean" */
rop = r->op_ReduceMean;
break;
case 0xa1d53861: /* "ReduceMin" */
rop = r->op_ReduceMin;
break;
case 0xdc7e1072: /* "ReduceProd" */
rop = r->op_ReduceProd;
break;
case 0xa1d55372: /* "ReduceSum" */
rop = r->op_ReduceSum;
break;
case 0x20917223: /* "ReduceSumSquare" */
rop = r->op_ReduceSumSquare;
break;
case 0x7c8bc29d: /* "Relu" */
rop = r->op_Relu;
break;
case 0x9fdbcf8d: /* "Reshape" */
rop = r->op_Reshape;
break;
case 0xce8a9197: /* "Resize" */
rop = r->op_Resize;
break;
case 0x5d77301a: /* "ReverseSequence" */
rop = r->op_ReverseSequence;
break;
case 0x830cb9da: /* "RoiAlign" */
rop = r->op_RoiAlign;
break;
case 0x0e09b7cd: /* "Round" */
rop = r->op_Round;
break;
case 0x7c8c450a: /* "Scan" */
rop = r->op_Scan;
break;
case 0xe6ece5fb: /* "Scatter" */
rop = r->op_Scatter;
break;
case 0xb4db6f18: /* "ScatterElements" */
rop = r->op_ScatterElements;
break;
case 0x55be5b0d: /* "ScatterND" */
rop = r->op_ScatterND;
break;
case 0x7c8c4efe: /* "Selu" */
rop = r->op_Selu;
break;
case 0xe537ccd3: /* "SequenceAt" */
rop = r->op_SequenceAt;
break;
case 0xa52772e3: /* "SequenceConstruct" */
rop = r->op_SequenceConstruct;
break;
case 0x5e6e772d: /* "SequenceEmpty" */
rop = r->op_SequenceEmpty;
break;
case 0x5e70f50e: /* "SequenceErase" */
rop = r->op_SequenceErase;
break;
case 0x35a57cb3: /* "SequenceInsert" */
rop = r->op_SequenceInsert;
break;
case 0x3bff64e0: /* "SequenceLength" */
rop = r->op_SequenceLength;
break;
case 0x0e17a4d6: /* "Shape" */
rop = r->op_Shape;
break;
case 0xd11575d4: /* "Shrink" */
rop = r->op_Shrink;
break;
case 0xf5548151: /* "Sigmoid" */
rop = r->op_Sigmoid;
break;
case 0x7c8c5f56: /* "Sign" */
rop = r->op_Sign;
break;
case 0x0b8821ef: /* "Sin" */
rop = r->op_Sin;
break;
case 0x7c8c6037: /* "Sinh" */
rop = r->op_Sinh;
break;
case 0x7c8c61c0: /* "Size" */
rop = r->op_Size;
break;
case 0x0e19f6b5: /* "Slice" */
rop = r->op_Slice;
break;
case 0x6bec36a5: /* "Softplus" */
rop = r->op_Softplus;
break;
case 0x6bedcd32: /* "Softsign" */
rop = r->op_Softsign;
break;
case 0xa4436289: /* "SpaceToDepth" */
rop = r->op_SpaceToDepth;
break;
case 0x0e1c35d1: /* "Split" */
rop = r->op_Split;
break;
case 0x50e66fcd: /* "SplitToSequence" */
rop = r->op_SplitToSequence;
break;
case 0x7c8c82cf: /* "Sqrt" */
rop = r->op_Sqrt;
break;
case 0x08f69207: /* "Squeeze" */
rop = r->op_Squeeze;
break;
case 0xf404645f: /* "StringNormalizer" */
rop = r->op_StringNormalizer;
break;
case 0x0b88236f: /* "Sub" */
rop = r->op_Sub;
break;
case 0x0b88237a: /* "Sum" */
rop = r->op_Sum;
break;
case 0x0b882528: /* "Tan" */
rop = r->op_Tan;
break;
case 0x7c8cca90: /* "Tanh" */
rop = r->op_Tanh;
break;
case 0x46fbf3df: /* "TfIdfVectorizer" */
rop = r->op_TfIdfVectorizer;
break;
case 0xa646ea33: /* "ThresholdedRelu" */
rop = r->op_ThresholdedRelu;
break;
case 0x7c8cec53: /* "Tile" */
rop = r->op_Tile;
break;
case 0x7c8d0643: /* "TopK" */
rop = r->op_TopK;
break;
case 0x940b3944: /* "Transpose" */
rop = r->op_Transpose;
break;
case 0xd6278d9c: /* "Unique" */
rop = r->op_Unique;
break;
case 0xc836156a: /* "Unsqueeze" */
rop = r->op_Unsqueeze;
break;
case 0xae63c66c: /* "Upsample" */
rop = r->op_Upsample;
break;
case 0x0e601820: /* "Where" */
rop = r->op_Where;
break;
case 0x0b8837fe: /* "Xor" */
rop = r->op_Xor;
break;
case 0x7c8388ee: /* "Celu" */
rop = r->op_Celu;
break;
case 0x718dbc56: /* "DynamicQuantizeLinear" */
rop = r->op_DynamicQuantizeLinear;
break;
case 0x7b2541c8: /* "GreaterOrEqual" */
rop = r->op_GreaterOrEqual;
break;
case 0x60d9a535: /* "LessOrEqual" */
rop = r->op_LessOrEqual;
break;
case 0xf8c82769: /* "LogSoftmax" */
rop = r->op_LogSoftmax;
break;
case 0xbb8f2396: /* "MeanVarianceNormalization" */
rop = r->op_MeanVarianceNormalization;
break;
case 0x6ed111df: /* "NegativeLogLikelihoodLoss" */
rop = r->op_NegativeLogLikelihoodLoss;
break;
case 0x0e01ebd2: /* "Range" */
rop = r->op_Range;
break;
case 0x034529c7: /* "Softmax" */
rop = r->op_Softmax;
break;
case 0x522154a3: /* "SoftmaxCrossEntropyLoss" */
rop = r->op_SoftmaxCrossEntropyLoss;
break;
default:
rop = NULL;
break;
}
if(rop)
rop(n);
}
}
static struct onnx_tensor_t * onnx_tensor_alloc_from_value_info(Onnx__ValueInfoProto * v)
{
struct onnx_tensor_t * t;
enum onnx_tensor_type_t type;
int * dims = NULL;
int ndim;
int i;
if(!v || !v->name)
return NULL;
switch(v->type->value_case)
{
case ONNX__TYPE_PROTO__VALUE_TENSOR_TYPE:
type = (enum onnx_tensor_type_t)v->type->tensor_type->elem_type;
ndim = v->type->tensor_type->shape->n_dim;
if(ndim > 0)
{
dims = malloc(sizeof(int) * ndim);
if(dims)
{
for(i = 0; i < ndim; i++)
{
switch(v->type->tensor_type->shape->dim[i]->value_case)
{
case ONNX__TENSOR_SHAPE_PROTO__DIMENSION__VALUE_DIM_VALUE:
dims[i] = v->type->tensor_type->shape->dim[i]->dim_value;
break;
case ONNX__TENSOR_SHAPE_PROTO__DIMENSION__VALUE_DIM_PARAM:
if(strcmp(v->type->tensor_type->shape->dim[i]->dim_param, "batch_size") == 0)
dims[i] = 1;
else
dims[i] = 1;
break;
default:
dims[i] = 1;
break;
}
}
}
}
t = onnx_tensor_alloc(v->name, type, dims, ndim);
if(dims)
free(dims);
break;
case ONNX__TYPE_PROTO__VALUE_SEQUENCE_TYPE:
t = NULL;
break;
case ONNX__TYPE_PROTO__VALUE_MAP_TYPE:
t = NULL;
break;
default:
t = NULL;
break;
}
return t;
}
static void onnx_tensor_copy_from_tensor_proto(struct onnx_tensor_t * t, Onnx__TensorProto * o)
{
size_t n, i;
int sz;
if(t && o)
{
if(t->type == o->data_type)
{
sz = onnx_tensor_type_sizeof(t->type);
if(sz > 0)
{
if((o->raw_data.len > 0) && o->raw_data.data)
{
switch(o->data_type)
{
case ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT:
{
float * p = (float *)t->datas;
uint32_t * q = (uint32_t *)o->raw_data.data;
union { uint32_t u; float f; } v;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
{
v.u = le32_to_cpu(q[i]);
p[i] = v.f;
}
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__UINT8:
{
uint8_t * p = (uint8_t *)t->datas;
uint8_t * q = (uint8_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len);
memcpy(p, q, n);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__INT8:
{
int8_t * p = (int8_t *)t->datas;
int8_t * q = (int8_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len);
memcpy(p, q, n);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__UINT16:
{
uint16_t * p = (uint16_t *)t->datas;
uint16_t * q = (uint16_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le16_to_cpu(q[i]);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__INT16:
{
int16_t * p = (int16_t *)t->datas;
int16_t * q = (int16_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le16_to_cpu(q[i]);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__INT32:
{
int32_t * p = (int32_t *)t->datas;
int32_t * q = (int32_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le32_to_cpu(q[i]);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__INT64:
{
int64_t * p = (int64_t *)t->datas;
int64_t * q = (int64_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le64_to_cpu(q[i]);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__STRING:
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__BOOL:
{
uint8_t * p = (uint8_t *)t->datas;
uint8_t * q = (uint8_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len);
memcpy(p, q, n);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT16:
{
uint16_t * p = (uint16_t *)t->datas;
uint16_t * q = (uint16_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le16_to_cpu(q[i]);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__DOUBLE:
{
double * p = (double *)t->datas;
uint64_t * q = (uint64_t *)o->raw_data.data;
union { uint64_t u; double f; } v;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
{
v.u = le64_to_cpu(q[i]);
p[i] = v.f;
}
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__UINT32:
{
uint32_t * p = (uint32_t *)t->datas;
uint32_t * q = (uint32_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le32_to_cpu(q[i]);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__UINT64:
{
uint64_t * p = (uint64_t *)t->datas;
uint64_t * q = (uint64_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le64_to_cpu(q[i]);
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__COMPLEX64:
{
float * p = (float *)t->datas;
uint32_t * q = (uint32_t *)o->raw_data.data;
union { uint32_t u; float f; } v;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz) * 2;
for(i = 0; i < n; i++)
{
v.u = le32_to_cpu(q[i]);
p[i] = v.f;
}
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__COMPLEX128:
{
double * p = (double *)t->datas;
uint64_t * q = (uint64_t *)o->raw_data.data;
union { uint64_t u; double f; } v;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz) * 2;
for(i = 0; i < n; i++)
{
v.u = le64_to_cpu(q[i]);
p[i] = v.f;
}
}
}
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__BFLOAT16:
{
uint16_t * p = (uint16_t *)t->datas;
uint16_t * q = (uint16_t *)o->raw_data.data;
if(t->ndata > 0)
{
n = min(t->ndata, (size_t)o->raw_data.len / sz);
for(i = 0; i < n; i++)
p[i] = le16_to_cpu(q[i]);
}
}
break;
default:
break;
}
}
else
{
switch(o->data_type)
{
case ONNX__TENSOR_PROTO__DATA_TYPE__FLOAT:
n = min(t->ndata, (size_t)o->n_float_data);
if((n > 0) && t->datas && o->float_data)
memcpy(t->datas, o->float_data, sizeof(float) * n);
break;
case ONNX__TENSOR_PROTO__DATA_TYPE__UINT8:
case ONNX__TENSOR_PROTO__DATA_TYPE__INT8: