【onnx】——python简单解析打印onnx模型信息_打印onnx中的模型结构

onnx

前言：
什么是onnx，以及onnx的介绍可以参考：ONNX学习笔记。

当我们加载了一个ONNX之后，我们获得的就是一个ModelProto，它包含了一些版本信息，生产者信息和一个GraphProto。在GraphProto里面又包含了四个repeated数组，它们分别是node(NodeProto类型)，input(ValueInfoProto类型)，output(ValueInfoProto类型)和initializer(TensorProto类型)，其中node中存放了模型中所有的计算节点，input存放了模型的输入节点，output存放了模型中所有的输出节点，initializer存放了模型的所有权重参数。

code
这里用python，将onnx中包含的有用的信息打印出来，进行一个直观可视化。
整体流程：

• 解析graph input
• 解析graph output
• 解析graph initializer，模型的所有权重参数
• 解析graph node，打印op信息，输入输出，得到整个计算的graph

import onnx
import numpy as np
import logging
logging.basicConfig(level=logging.INFO)

def onnx_datatype_to_npType(data_type):
    if data_type == 1:
        return np.float32
    else:
        raise TypeError("don't support data type")


def parser_initializer(initializer):
    name = initializer.name
    logging.info(f"initializer name: {name}")

    dims = initializer.dims
    shape = [x for x in dims]
    logging.info(f"initializer with shape:{shape}")

    dtype = initializer.data_type
    logging.info(f"initializer with type: {onnx_datatype_to_npType(dtype)} ")
    
    # print tenth buffer
    weights = np.frombuffer(initializer.raw_data, dtype=onnx_datatype_to_npType(dtype))
    logging.info(f"initializer first 10 wights:{weights[:10]}")



def parser_tensor(tensor, use='normal'):
    name = tensor.name
    logging.info(f"{use} tensor name: {name}")

    data_type = tensor.type.tensor_type.elem_type
    logging.info(f"{use} tensor data type: {data_type}")

    dims = tensor.type.tensor_type.shape.dim
    shape = []
    for i, dim in enumerate(dims):
        shape.append(dim.dim_value)
    logging.info(f"{use} tensor with shape:{shape} ")


def parser_node(node):
    def attri_value(attri):
        if attri.type == 1:
            return attri.i
        elif attri.type == 7:
            return list(attri.ints)
        
    name = node.name
    logging.info(f"node name:{name}")

    opType = node.op_type
    logging.info(f"node op type:{opType}")

    inputs = list(node.input)
    logging.info(f"node with {len(inputs)} inputs:{inputs}")

    outputs = list(node.output)
    logging.info(f"node with {len(outputs)} outputs:{outputs}")
    
    attributes = node.attribute
    for attri in attributes:
        name = attri.name
        value = attri_value(attri)
        logging.info(f"{name} with value:{value}")


def parser_info(onnx_model):
    ir_version = onnx_model.ir_version
    producer_name = onnx_model.producer_name
    producer_version = onnx_model.producer_version
    for info in [ir_version, producer_name, producer_version]:
        logging.info("onnx model with info:{}".format(info))

def parser_inputs(onnx_graph):
    inputs = onnx_graph.input
    for input in inputs:
        parser_tensor(input, 'input')

def parser_outputs(onnx_graph):
    outputs = onnx_graph.output
    for output in outputs:
        parser_tensor(output, 'output')

def parser_graph_initializers(onnx_graph):
    initializers = onnx_graph.initializer
    for initializer in initializers:
        parser_initializer(initializer)


def parser_graph_nodes(onnx_graph):
    nodes = onnx_graph.node
    for node in nodes:
        parser_node(node)
        t = 1

def onnx_parser():
    model_path = './resblock.onnx'
    model = onnx.load(model_path)

    # 0.
    parser_info(model)

    graph = model.graph

    # 1.
    parser_inputs(graph)

    # 2. 
    parser_outputs(graph)

    # 3.
    parser_graph_initializers(graph)

    # 4. 
    parser_graph_nodes(graph)


if __name__ == '__main__':
    onnx_parser()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122

输出

INFO:root:onnx model with info:6
INFO:root:onnx model with info:pytorch
INFO:root:onnx model with info:1.7
INFO:root:input tensor name: input.1
INFO:root:input tensor data type: 1
INFO:root:input tensor with shape:[1, 3, 480, 640]
INFO:root:output tensor name: 22
INFO:root:output tensor data type: 1
INFO:root:output tensor with shape:[1, 64, 480, 640]
INFO:root:initializer name: conv1.bias
INFO:root:initializer with shape:[64]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[ 0.07732448 0.17457943 0.17851995 -0.05609495 0.14483029 -0.04734851
0.10502735 -0.18362212 0.12153099 0.16765712]
INFO:root:initializer name: conv1.weight
INFO:root:initializer with shape:[64, 3, 3, 3]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[ 0.02473598 -0.18695308 -0.12158576 -0.06351418 -0.13614939 -0.17964806
-0.0885106 0.11713844 -0.14802186 0.04773027]
INFO:root:initializer name: conv2.bias
INFO:root:initializer with shape:[64]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[ 0.02602181 0.00118999 0.00190271 -0.00422467 -0.01745024 0.04086005
0.01714195 0.00828495 0.03176006 0.00428755]
INFO:root:initializer name: conv2.weight
INFO:root:initializer with shape:[64, 64, 3, 3]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[-0.00695022 0.03211054 0.03908887 0.0133714 -0.000227 -0.00840447
0.03178968 -0.02974798 0.01581161 -0.01668321]
INFO:root:initializer name: conv3.bias
INFO:root:initializer with shape:[64]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[-0.18881151 -0.32025716 -0.0840579 0.23001562 0.3900114 -0.4994867
0.45777866 0.42241076 0.40811017 0.32145265]
INFO:root:initializer name: conv3.weight
INFO:root:initializer with shape:[64, 3, 1, 1]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[ 0.51609325 0.05624352 0.5465418 0.3039956 -0.11096366 -0.3982828
-0.4997819 0.19012491 -0.05253417 -0.09907632]
INFO:root:initializer name: conv4.bias
INFO:root:initializer with shape:[64]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[ 0.02621709 0.00735469 0.01134209 -0.02597461 0.03276851 0.00755459
0.02107707 -0.00700061 0.01922615 0.03978326]
INFO:root:initializer name: conv4.weight
INFO:root:initializer with shape:[64, 64, 3, 3]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[ 0.01240561 0.00703304 0.00477142 0.00702672 0.00661598 0.01905426
-0.00150001 0.01970465 -0.03535433 0.0147273 ]
INFO:root:initializer name: conv5.bias
INFO:root:initializer with shape:[64]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[-0.03412882 -0.01711171 0.00250292 -0.02079247 -0.01510924 0.03716683
-0.02143056 -0.03259912 -0.00563147 0.01869656]
INFO:root:initializer name: conv5.weight
INFO:root:initializer with shape:[64, 64, 3, 3]
INFO:root:initializer with type: <class ‘numpy.float32’>
INFO:root:initializer first 10 wights:[ 0.0076859 0.0182974 0.02970468 -0.03959073 0.0202815 0.01744279
0.02319324 -0.01895014 0.01619028 0.01473094]
INFO:root:node name:Conv_0
INFO:root:node op type:Conv
INFO:root:node with 3 inputs:[‘input.1’, ‘conv1.weight’, ‘conv1.bias’]
INFO:root:node with 1 outputs:[‘11’]
INFO:root:dilations with value:[1, 1]
INFO:root:group with value:None
INFO:root:kernel_shape with value:[3, 3]
INFO:root:pads with value:[1, 1, 1, 1]
INFO:root:strides with value:[1, 1]
INFO:root:node name:Relu_1
INFO:root:node op type:Relu
INFO:root:node with 1 inputs:[‘11’]
INFO:root:node with 1 outputs:[‘12’]
INFO:root:node name:Conv_2
INFO:root:node op type:Conv
INFO:root:node with 3 inputs:[‘12’, ‘conv2.weight’, ‘conv2.bias’]
INFO:root:node with 1 outputs:[‘13’]
INFO:root:dilations with value:[1, 1]
INFO:root:group with value:None
INFO:root:kernel_shape with value:[3, 3]
INFO:root:pads with value:[1, 1, 1, 1]
INFO:root:strides with value:[1, 1]
INFO:root:node name:Relu_3
INFO:root:node op type:Relu
INFO:root:node with 1 inputs:[‘13’]
INFO:root:node with 1 outputs:[‘14’]
INFO:root:node name:Conv_4
INFO:root:node op type:Conv
INFO:root:node with 3 inputs:[‘input.1’, ‘conv3.weight’, ‘conv3.bias’]
INFO:root:node with 1 outputs:[‘15’]
INFO:root:dilations with value:[1, 1]
INFO:root:group with value:None
INFO:root:kernel_shape with value:[1, 1]
INFO:root:pads with value:[0, 0, 0, 0]
INFO:root:strides with value:[1, 1]
INFO:root:node name:Relu_5
INFO:root:node op type:Relu
INFO:root:node with 1 inputs:[‘15’]
INFO:root:node with 1 outputs:[‘16’]
INFO:root:node name:Add_6
INFO:root:node op type:Add
INFO:root:node with 2 inputs:[‘14’, ‘16’]
INFO:root:node with 1 outputs:[‘17’]
INFO:root:node name:Conv_7
INFO:root:node op type:Conv
INFO:root:node with 3 inputs:[‘17’, ‘conv4.weight’, ‘conv4.bias’]
INFO:root:node with 1 outputs:[‘18’]
INFO:root:dilations with value:[1, 1]
INFO:root:group with value:None
INFO:root:kernel_shape with value:[3, 3]
INFO:root:pads with value:[1, 1, 1, 1]
INFO:root:strides with value:[1, 1]
INFO:root:node name:Relu_8
INFO:root:node op type:Relu
INFO:root:node with 1 inputs:[‘18’]
INFO:root:node with 1 outputs:[‘19’]
INFO:root:node name:Conv_9
INFO:root:node op type:Conv
INFO:root:node with 3 inputs:[‘19’, ‘conv5.weight’, ‘conv5.bias’]
INFO:root:node with 1 outputs:[‘20’]
INFO:root:dilations with value:[1, 1]
INFO:root:group with value:None
INFO:root:kernel_shape with value:[3, 3]
INFO:root:pads with value:[1, 1, 1, 1]
INFO:root:strides with value:[1, 1]
INFO:root:node name:Relu_10
INFO:root:node op type:Relu
INFO:root:node with 1 inputs:[‘20’]
INFO:root:node with 1 outputs:[‘21’]
INFO:root:node name:Add_11
INFO:root:node op type:Add
INFO:root:node with 2 inputs:[‘21’, ‘17’]
INFO:root:node with 1 outputs:[‘22’]

other

1. onnx中定义的repeated变量，再python中可以直接用list，很方便解析