asp手机网站模板,宁波论坛招聘最新消息,怎么做繁体字网站,竞价排名采用什么计费方式onnx作为一个通用格式#xff0c;很少有中文教程#xff0c;因此开一篇文章对onnx 1.16文档进行翻译与进一步解释#xff0c; onnx 1.16官方文档#xff1a;https://onnx.ai/onnx/intro/index.html](https://onnx.ai/onnx/intro/index.html)#xff0c; 如果觉得有收获很少有中文教程因此开一篇文章对onnx 1.16文档进行翻译与进一步解释 onnx 1.16官方文档https://onnx.ai/onnx/intro/index.html](https://onnx.ai/onnx/intro/index.html) 如果觉得有收获麻烦点赞收藏关注目前仅在CSDN发布本博客会分为多个章节目前尚在连载中详见专栏链接 https://blog.csdn.net/qq_33345365/category_12581965.html
开始编辑时间2024/2/21最后编辑时间2024/2/21
这是本教程的第四篇其余内容见上述专栏链接。 ONNX with Python
本教程的第一篇介绍了ONNX的基本概念。
在本教程的第二篇介绍了ONNX关于Python的API具体涉及一个简单的线性回归例子和序列化。
本教程的第三篇包括python API的三个部分初始化器Initializer属性Attributes算子集和元数据Opset和Metadata
在本篇将继续对更多的内容进行介绍。具体介绍子图相关内容。
目录
子图测试与循环 IfScan 子图测试与循环 Subgraph: test and loops
通常情况下这些操作会被归类为控制流。一般来说最好避免使用它们因为它们不像矩阵操作那样高效而且矩阵操作经过高度优化速度更快。
If
可以使用 if 语句来实现一个test。它根据一个布尔值执行子图 A 或子图 B。这种用法不是很常见因为一个函数通常需要批处理比较的结果。下面的示例代码根据符号计算矩阵中所有浮点数的和并返回 1 或 -1。
import numpy
import onnx
from onnx.helper import (make_node, make_graph, make_model, make_tensor_value_info)
from onnx.numpy_helper import from_array
from onnx.checker import check_model
from onnxruntime import InferenceSession# initializers
value numpy.array([0], dtypenumpy.float32)
zero from_array(value, namezero)# Same as before, X is the input, Y is the output.
X make_tensor_value_info(X, onnx.TensorProto.FLOAT, [None, None])
Y make_tensor_value_info(Y, onnx.TensorProto.FLOAT, [None])# 第一个节点对所有轴求和。
rsum make_node(ReduceSum, [X], [rsum])
# 第二个节点将rsum与0比较输出为cond
cond make_node(Greater, [rsum, zero], [cond])# 构建if正确时的图
# Input for then
then_out make_tensor_value_info(then_out, onnx.TensorProto.FLOAT, None)
# 正确返回常数1
then_cst from_array(numpy.array([1]).astype(numpy.float32))# If正确时的节点并得到图
then_const_node make_node(Constant, inputs[],outputs[then_out],valuethen_cst, namecst1)then_body make_graph([then_const_node], then_body, [], [then_out])# 构建If错误时的图
else_out make_tensor_value_info(else_out, onnx.TensorProto.FLOAT, [5])
else_cst from_array(numpy.array([-1]).astype(numpy.float32))else_const_node make_node(Constant, inputs[],outputs[else_out],valueelse_cst, namecst2)else_body make_graph([else_const_node], else_body,[], [else_out])# 最后If节点把两个图当成属性
if_node onnx.helper.make_node(If, [cond], [Y],then_branchthen_body, else_branchelse_body)# 最后的图使用If节点和前两个节点。
graph make_graph([rsum, cond, if_node], if, [X], [Y], [zero])
onnx_model make_model(graph)
check_model(onnx_model)# 指定算子集
del onnx_model.opset_import[:]
opset onnx_model.opset_import.add()
opset.domain
opset.version 15
onnx_model.ir_version 8# 保存模型
with open(onnx_if_sign.onnx, wb) as f:f.write(onnx_model.SerializeToString())# 输出
sess InferenceSession(onnx_model.SerializeToString(),providers[CPUExecutionProvider])x numpy.ones((3, 2), dtypenumpy.float32)
res sess.run(None, {X: x})print(result, res)
print()print(onnx_model)这个代码的核心是构建if使用的两个图然后用着两个图构建节点参照输出进行进一步的了解
result [array([1.], dtypefloat32)]ir_version: 8
graph {node {input: Xoutput: rsumop_type: ReduceSum}node {input: rsuminput: zerooutput: condop_type: Greater}node {input: condoutput: Yop_type: Ifattribute {name: else_branchg {node {output: else_outname: cst2op_type: Constantattribute {name: valuet {dims: 1data_type: 1raw_data: \000\000\200\277}type: TENSOR}}name: else_bodyoutput {name: else_outtype {tensor_type {elem_type: 1shape {dim {dim_value: 5}}}}}}type: GRAPH}attribute {name: then_branchg {node {output: then_outname: cst1op_type: Constantattribute {name: valuet {dims: 1data_type: 1raw_data: \000\000\200?}type: TENSOR}}name: then_bodyoutput {name: then_outtype {tensor_type {elem_type: 1}}}}type: GRAPH}}name: ifinitializer {dims: 1data_type: 1name: zeroraw_data: \000\000\000\000}input {name: Xtype {tensor_type {elem_type: 1shape {dim {}dim {}}}}}output {name: Ytype {tensor_type {elem_type: 1shape {dim {}}}}}
}
opset_import {domain: version: 15
}在这个代码里“else”和“then”分支都非常简单甚至可以用一个“Where”节点替换“If”节点这样会更快。有趣的是当两个分支都比较大并且跳过一个分支效率更高时情况就变得复杂了。
Scan
根据规格描述Scan操作似乎有些复杂将张量的一个维度循环遍历并将其结果存储在预分配张量中这是一种很有用的方法。以下示例实现了经典的回归问题最近邻算法。第一步是计算输入特征 X 和训练集 W 之间的成对距离KaTeX parse error: Undefined control sequence: \norm at position 21: …X,W)(M_{ij})(\̲n̲o̲r̲m̲ ̲X_i-W^2_j)_{ij} . 然后使用一个 TopK 算子提取 k 个最近邻点。 注在原教程里此处的公式就是这样的不保真 ONNX scan 算子可以用于迭代执行一系列操作类似于 Python 中的 for 循环。 这个算子有些复杂我会在更多学习后给出更为简单的例子。 import numpy
from onnx import numpy_helper, TensorProto
from onnx.helper import (make_model, make_node, set_model_props, make_tensor, make_graph,make_tensor_value_info)
from onnx.checker import check_model# subgraph
initializers []
nodes []
inputs []
outputs []value make_tensor_value_info(next_in, 1, [None, 4])
inputs.append(value)
value make_tensor_value_info(next, 1, [None])
inputs.append(value)value make_tensor_value_info(next_out, 1, [None, None])
outputs.append(value)
value make_tensor_value_info(scan_out, 1, [None])
outputs.append(value)node make_node(Identity, [next_in], [next_out],namecdistd_17_Identity, domain)
nodes.append(node)node make_node(Sub, [next_in, next], [cdistdf_17_C0],namecdistdf_17_Sub, domain)
nodes.append(node)node make_node(ReduceSumSquare, [cdistdf_17_C0], [cdistdf_17_reduced0],namecdistdf_17_ReduceSumSquare, axes[1], keepdims0, domain)
nodes.append(node)node make_node(Identity, [cdistdf_17_reduced0],[scan_out], namecdistdf_17_Identity, domain)
nodes.append(node)graph make_graph(nodes, OnnxIdentity,inputs, outputs, initializers)# main graphinitializers []
nodes []
inputs []
outputs []opsets {: 15, ai.onnx.ml: 15}
target_opset 15 # subgraphs# initializers
list_value [23.29599822460675, -120.86516699239603, -144.70495899914215, -260.08772982740413,154.65272105889147, -122.23295157108991, 247.45232560871727, -182.83789715805776,-132.92727431421793, 147.48710175784703, 88.27761768038069, -14.87785569894749,111.71487894705504, 301.0518319089629, -29.64235742280055, -113.78493504731911,-204.41218591022718, 112.26561056133608, 66.04032954135549,-229.5428380626701, -33.549262642481615, -140.95737409864623, -87.8145187836131,-90.61397011283958, 57.185488100413366, 56.864151796743855, 77.09054590340892,-187.72501631246712, -42.779503579806025, -21.642642730674076, -44.58517761667535,78.56025104939847, -23.92423223842056, 234.9166231927213, -73.73512816431007,-10.150864499514297, -70.37105466673813, 65.5755688281476, 108.68676290979731, -78.36748960443065]
value numpy.array(list_value, dtypenumpy.float64).reshape((2, 20))
tensor numpy_helper.from_array(value, nameknny_ArrayFeatureExtractorcst)
initializers.append(tensor)list_value [1.1394007205963135, -0.6848101019859314, -1.234825849533081, 0.4023416340351105,0.17742614448070526, 0.46278226375579834, -0.4017809331417084, -1.630198359489441,-0.5096521973609924, 0.7774903774261475, -0.4380742907524109, -1.2527953386306763,-1.0485529899597168, 1.950775384902954, -1.420017957687378, -1.7062702178955078,1.8675580024719238, -0.15135720372200012, -0.9772778749465942, 0.9500884413719177,-2.5529897212982178, -0.7421650290489197, 0.653618574142456, 0.8644362092018127,1.5327792167663574, 0.37816253304481506, 1.4693588018417358, 0.154947429895401,-0.6724604368209839, -1.7262825965881348, -0.35955315828323364, -0.8131462931632996,-0.8707971572875977, 0.056165341287851334, -0.5788496732711792, -0.3115525245666504,1.2302906513214111, -0.302302747964859, 1.202379822731018, -0.38732680678367615,2.269754648208618, -0.18718385696411133, -1.4543657302856445, 0.04575851559638977,-0.9072983860969543, 0.12898291647434235, 0.05194539576768875, 0.7290905714035034,1.4940791130065918, -0.8540957570075989, -0.2051582634449005, 0.3130677044391632,1.764052391052246, 2.2408931255340576, 0.40015721321105957, 0.978738009929657,0.06651721894741058, -0.3627411723136902, 0.30247190594673157, -0.6343221068382263,-0.5108051300048828, 0.4283318817615509, -1.18063223361969, -0.02818222902715206,-1.6138978004455566, 0.38690251111984253, -0.21274028718471527, -0.8954665660858154,0.7610377073287964, 0.3336743414402008, 0.12167501449584961, 0.44386324286460876,-0.10321885347366333, 1.4542734622955322, 0.4105985164642334, 0.14404356479644775,-0.8877857327461243, 0.15634897351264954, -1.980796456336975, -0.34791216254234314]
value numpy.array(list_value, dtypenumpy.float32).reshape((20, 4))
tensor numpy_helper.from_array(value, nameSc_Scancst)
initializers.append(tensor)value numpy.array([2], dtypenumpy.int64)
tensor numpy_helper.from_array(value, nameTo_TopKcst)
initializers.append(tensor)value numpy.array([2, -1, 2], dtypenumpy.int64)
tensor numpy_helper.from_array(value, nameknny_Reshapecst)
initializers.append(tensor)# inputs
value make_tensor_value_info(input, 1, [None, 4])
inputs.append(value)# outputs
value make_tensor_value_info(variable, 1, [None, 2])
outputs.append(value)# nodes
# 这里是scan算子
node make_node(Scan, [input, Sc_Scancst], [UU032UU, UU033UU],nameSc_Scan, bodygraph, num_scan_inputs1, domain)
nodes.append(node)node make_node(Transpose, [UU033UU], [Tr_transposed0],nameTr_Transpose, perm[1, 0], domain)
nodes.append(node)node make_node(Sqrt, [Tr_transposed0], [Sq_Y0],nameSq_Sqrt, domain)
nodes.append(node)node make_node(TopK, [Sq_Y0, To_TopKcst], [To_Values0, To_Indices1],nameTo_TopK, largest0, sorted1, domain)
nodes.append(node)node make_node(Flatten, [To_Indices1], [knny_output0],nameknny_Flatten, domain)
nodes.append(node)node make_node(ArrayFeatureExtractor,[knny_ArrayFeatureExtractorcst, knny_output0], [knny_Z0],nameknny_ArrayFeatureExtractor, domainai.onnx.ml)
nodes.append(node)node make_node(Reshape, [knny_Z0, knny_Reshapecst], [knny_reshaped0],nameknny_Reshape, allowzero0, domain)
nodes.append(node)node make_node(Transpose, [knny_reshaped0], [knny_transposed0],nameknny_Transpose, perm[1, 0, 2], domain)
nodes.append(node)node make_node(Cast, [knny_transposed0], [Ca_output0],nameCa_Cast, toTensorProto.FLOAT, domain)
nodes.append(node)node make_node(ReduceMean, [Ca_output0], [variable],nameRe_ReduceMean, axes[2], keepdims0, domain)
nodes.append(node)# graph
graph make_graph(nodes, KNN regressor, inputs, outputs, initializers)# model
onnx_model make_model(graph)
onnx_model.ir_version 8
onnx_model.producer_name skl2onnx
onnx_model.producer_version
onnx_model.domain ai.onnx
onnx_model.model_version 0
onnx_model.doc_string
set_model_props(onnx_model, {})# opsets
del onnx_model.opset_import[:]
for dom, value in opsets.items():op_set onnx_model.opset_import.add()op_set.domain domop_set.version valuecheck_model(onnx_model)
with open(knnr.onnx, wb) as f:f.write(onnx_model.SerializeToString())print(onnx_model)输出如下所示
ir_version: 8
producer_name: skl2onnx
producer_version:
domain: ai.onnx
model_version: 0
doc_string:
graph {node {input: inputinput: Sc_Scancstoutput: UU032UUoutput: UU033UUname: Sc_Scanop_type: Scanattribute {name: bodyg {node {input: next_inoutput: next_outname: cdistd_17_Identityop_type: Identitydomain: }node {input: next_ininput: nextoutput: cdistdf_17_C0name: cdistdf_17_Subop_type: Subdomain: }node {input: cdistdf_17_C0output: cdistdf_17_reduced0name: cdistdf_17_ReduceSumSquareop_type: ReduceSumSquareattribute {name: axesints: 1type: INTS}attribute {name: keepdimsi: 0type: INT}domain: }node {input: cdistdf_17_reduced0output: scan_outname: cdistdf_17_Identityop_type: Identitydomain: }name: OnnxIdentityinput {name: next_intype {tensor_type {elem_type: 1shape {dim {}dim {dim_value: 4}}}}}input {name: nexttype {tensor_type {elem_type: 1shape {dim {}}}}}output {name: next_outtype {tensor_type {elem_type: 1shape {dim {}dim {}}}}}output {name: scan_outtype {tensor_type {elem_type: 1shape {dim {}}}}}}type: GRAPH}attribute {name: num_scan_inputsi: 1type: INT}domain: }node {input: UU033UUoutput: Tr_transposed0name: Tr_Transposeop_type: Transposeattribute {name: permints: 1ints: 0type: INTS}domain: }node {input: Tr_transposed0output: Sq_Y0name: Sq_Sqrtop_type: Sqrtdomain: }node {input: Sq_Y0input: To_TopKcstoutput: To_Values0output: To_Indices1name: To_TopKop_type: TopKattribute {name: largesti: 0type: INT}attribute {name: sortedi: 1type: INT}domain: }node {input: To_Indices1output: knny_output0name: knny_Flattenop_type: Flattendomain: }node {input: knny_ArrayFeatureExtractorcstinput: knny_output0output: knny_Z0name: knny_ArrayFeatureExtractorop_type: ArrayFeatureExtractordomain: ai.onnx.ml}node {input: knny_Z0input: knny_Reshapecstoutput: knny_reshaped0name: knny_Reshapeop_type: Reshapeattribute {name: allowzeroi: 0type: INT}domain: }node {input: knny_reshaped0output: knny_transposed0name: knny_Transposeop_type: Transposeattribute {name: permints: 1ints: 0ints: 2type: INTS}domain: }node {input: knny_transposed0output: Ca_output0name: Ca_Castop_type: Castattribute {name: toi: 1type: INT}domain: }node {input: Ca_output0output: variablename: Re_ReduceMeanop_type: ReduceMeanattribute {name: axesints: 2type: INTS}attribute {name: keepdimsi: 0type: INT}domain: }name: KNN regressorinitializer {dims: 2dims: 20data_type: 11name: knny_ArrayFeatureExtractorcstraw_data: ,\\\212\306K7\333z\345^7^\300\304\312,\006\217\026b\300Z9dWgAp\300.F\027\343Tc\203\330\264\255\350\216^\300\260\022\216sy\356n\237h\263\r\320\332f\300\224\277.;\254\235\300\336\370lV\226ob\261\201\362|\304\021Vc,[Mv\301-\300\322\214\240\223\300\355[)\036\262M\324\320rnE;\211q\244\300\021n5r\\\300\207\211\201\2400\215i\300H\232p\303\377\020\\\317K[\302\224\202P\306\355\355^\261l\300\301/\377N\306\300#w\001\317\242\236a\300$fd\023!\364U\300\204\327LIK\247V\300J\211\366\022\276\227L\262\345\254\206\234nLf{\013\201\313ES\234\343hU3wg\300\3370\367\305\306cE\300\336A\347;\204\2445\300f\374\242\031\347JF\300\325\2557\\333\243S\331\354\345{\232\3547\300\307o)\372T]m#\005\000W\014oR\300\\025\227\034M$\300\310\252\022\\\277\227Q\300l_\243\036\326dP\333kk\354\363[\223)\036\363\204\227S\300}initializer {dims: 20dims: 4data_type: 1name: Sc_Scancstraw_data: \342\327\221?\267O/\277\306\016\236\277\271\377\3153\2575\314\361\354;\266\315\276W\252\320\277\221x\002\277\234\tG?FK\340\276\231[\240\277\3746\206\277\002\263\371?\303\265\277\020g\332\277$\014\357?b\375\032\276\342.z\277\3778s?/d#\300\207\376\277\214S\?\261K]?\0342\304?\205\236\301\363\023\274?\212\252\036^,\277\324\366\334\277Z\027\270\276[*P\277\220\354^\277\241\rf~/\024\277\320\203\237\276*z\235?m\307\232\276\225\347\231?\263O\306\276\251C\021 \255?\276\250(\272\277Hm;\265Dh\277\031\024\004\262\304T\256\245:?\374\277?\005\246Z\277\002\025R\276iJ\240x\314\341?\313j\017h\341\314\223\216z?.:\2106\271\271\276\231\335\232\357b\\277 \304\002\277QN\333\365\036\227\277k\336\346\2744\224\316\277\026\030\306\227\330Y\276Le\277^\323B?]\327\252\3000\371\013B\343hd\323\275\242%\272?\3709\322(\200\023\355Ec\277\362\031 \275\212\375\277\213!\262\276}initializer {dims: 1data_type: 7name: To_TopKcstraw_data: \002\000\000\000\000\000\000\000}initializer {dims: 3data_type: 7name: knny_Reshapecstraw_data: \002\000\000\000\000\000\000\000\377\377\377\377\377\377\377\377\002\000\000\000\000\000\000\000}input {name: inputtype {tensor_type {elem_type: 1shape {dim {}dim {dim_value: 4}}}}}output {name: variabletype {tensor_type {elem_type: 1shape {dim {}dim {dim_value: 2}}}}}
}
opset_import {domain: version: 15
}
opset_import {domain: ai.onnx.mlversion: 15
}示意图如下所示 子图由操作符Scan执行。在本例中有一个scan输入这意味着操作符只构建一个输出。
node make_node(Scan, [X1, X2], [Y1, Y2],nameSc_Scan, bodygraph, num_scan_inputs1, domain)在第一步迭代中子图接收 X1 和 X2 的第一行作为输入。它会产生两个输出
第一个输出用于在下一轮迭代中替换 X1。第二个输出存储在一个容器中最终形成 Y2。
在第二步迭代中子图的第二个输入是 X2 的第二行。
以下是用颜色简要说明这个过程
绿色表示第一轮迭代。蓝色表示第二轮迭代。
