从上图可以看出原始模型的输入维度是（10，3，32，32）输出537的维度是（10，1，8，8）；538的维度是（10，2，8，8）；539的维度是（10，2，8，8）；540的维度是（10，2，8，8）

2.模型修改

由于我们的实际图片大小是1920*1080，所有要对网络的输入输出维度进行修改

import onnx
import math
input_size =(1080,1920)
model = onnx.load_model("centerface.onnx")
d = model.graph.input[0].type.tensor_type.shape.dim
print(d)
rate = (int(math.ceil(input_size[0]/d[2].dim_value)),int(math.ceil(input_size[1]/d[3].dim_value)))
print("rare",rate)
d[0].dim_value = 1
d[2].dim_value *= rate[0]
d[3].dim_value *= rate[1]
for output in model.graph.output:
    d = output.type.tensor_type.shape.dim
    print(d)
    d[0].dim_value = 1
    d[2].dim_value  *= rate[0]
    d[3].dim_value  *= rate[1]
onnx.save_model(model,"centerface_1088_1920.onnx" )    

再次查看 centerface_1088_1920.onn模型的输入输出维度如下图：

也可以使用代码方式查看网络结构，代码如下

import onnx
from onnx import helper
import sys,getopt
#加载模型
def loadOnnxModel(path):
    model = onnx.load(path)
    return model
#获取节点和节点的输入输出名列表，一般节点的输入将来自于上一层的输出放在列表前面，参数放在列表后面
def getNodeAndIOname(nodename,model):
    for i in range(len(model.graph.node)):
        if model.graph.node[i].name == nodename:
            Node = model.graph.node[i]
            input_name = model.graph.node[i].input
            output_name = model.graph.node[i].output
    return Node,input_name,output_name
#获取对应输入信息
def getInputTensorValueInfo(input_name,model):
    in_tvi = []
    for name in input_name:
        for params_input in model.graph.input:
            if params_input.name == name:
               in_tvi.append(params_input)
        for inner_output in model.graph.value_info:
            if inner_output.name == name:
                in_tvi.append(inner_output)
    return in_tvi
#获取对应输出信息
def getOutputTensorValueInfo(output_name,model):
    out_tvi = []
    for name in output_name:
        out_tvi = [inner_output for inner_output in model.graph.value_info if inner_output.name == name]
        if name == model.graph.output[0].name:
            out_tvi.append(model.graph.output[0])
    return out_tvi
#获取对应超参数值
def getInitTensorValue(input_name,model):
    init_t = []
    for name in input_name:
        init_t = [init for init in model.graph.initializer if init.name == name]
    return init_t
#构建单个节点onnx模型
def createSingelOnnxModel(ModelPath,nodename,SaveType="",SavePath=""):
    model = loadOnnxModel(str(ModelPath))
    Node,input_name,output_name = getNodeAndIOname(nodename,model)
    in_tvi = getInputTensorValueInfo(input_name,model)
    out_tvi = getOutputTensorValueInfo(output_name,model)
    init_t = getInitTensorValue(input_name,model)
    graph_def = helper.make_graph(
                [Node],
                nodename,
                inputs=in_tvi,  # 输入
                outputs=out_tvi,  # 输出
                initializer=init_t,  # initalizer
    model_def = helper.make_model(graph_def, producer_name='onnx-example')
    print(nodename+"onnx模型生成成功！")

#获取节点数量
def getNodeNum(model):
    return len(model.graph.node)
#获取节点类型
def getNodetype(model):
    op_name = []
    for i in range(len(model.graph.node)):
        if model.graph.node[i].op_type not in op_name:
            op_name.append(model.graph.node[i].op_type)
    return op_name
#获取节点名列表
def getNodeNameList(model):
    NodeNameList = []
    for i in range(len(model.graph.node)):
        NodeNameList.append(model.graph.node[i].name)
    return NodeNameList
#获取模型的输入信息
def getModelInputInfo(model):
    return model.graph.input[0]
#获取模型的输出信息
def getModelOutputInfo(model):
    return model.graph.output[0:4]