The ONNX model is modified to a custom node

Reference one

First, you need to modify the nodes in the ONNX model to custom nodes. To achieve this, you need to understand what custom nodes are and how to use them in ONNX. ONNX defines an interface for custom operators, which you can use to define your own operators and compile them into a format that ONNX models can recognize.

Specific steps are as follows:

1. Define an operator for a custom node: You need to define a custom node as a function with an input and an output. Functions should follow the interface defined in the ONNX specification.

2. Write code to generate custom nodes: You need to write code to generate custom nodes in ONNX models. Generated code is usually based on ONNX operator definitions, using custom nodes that you define.

3. Loading the model and weights: You need to load the ONNX model and weights to be able to modify and batch them.

4. Modify Node: Replaces an existing node with a custom node. During this process, you need to handle the weights and biases according to the ONNX specification in order to be the input of the custom node.

5. Export Modified Model: Export the modified model to ONNX format for later use.

In this process, you need to use the Python programming language, as well as the ONNX Python library and other related tools to process the ONNX model.

In the code example below, assume you have an ONNX model and you want to replace the nodes in it with custom nodes. The following is a simple code example to demonstrate how to use the ONNX Python library to read and modify ONNX models.

# 导入必要的库
import onnx
from onnx import helper, TensorProto
import numpy as np

# 加载模型
model = onnx.load("model.onnx")

# 定义自定义节点的运算符
def custom_node(inputs, outputs):
    output = ...
    return output

# 编写生成自定义节点的代码
node_def = helper.make_node(
    op_type='CustomNode',
    inputs=['input'],
    outputs=['output'],
)

# 加载权重
weight = onnx.load_tensor("weight.bin")

# 修改节点
for i, node in enumerate(model.graph.node):
    if node.op_type == 'OldNode':
        # 替换节点为自定义节点
        model.graph.node[i] = node_def
        # 将权重转移到自定义节点
        weight_value = np.array(weight[i]).astype(np.float32)
        tensor = onnx.helper.make_tensor(name='weight', data_type=onnx.TensorProto.FLOAT, dims=weight_value.shape, vals=weight_value)
        model.graph.initializer.append(tensor)

# 导出修改后的模型
onnx.save(model, "modified_model.onnx")

Note that this is just an example, you will need to modify it according to your custom nodes and models.

Reference two

To modify a node in an ONNX model to a custom node, you need to follow the steps below:

1. Load the ONNX model into memory.

2. Use the ONNX model API to parse the model and find the node you want to modify.

3. Replace this node with a custom node.

4. Make sure the new custom node has the same input and output properties as the original node.

5. Load weight parameters and combine them with new custom nodes.

Here is a code example:

import onnx
import numpy as np

# 加载ONNX模型
model = onnx.load('model.onnx')

# 找到要修改的节点
old_node = None
for n in model.graph.node:
    if n.op_type == 'OldNodeType':
        old_node = n
        break

# 如果找到了原来的节点，则替换为自定义节点
if old_node is not None:
    new_node = onnx.helper.make_node(
        'CustomNodeType',
        old_node.input,
        old_node.output,
        name='new_node',
        attr={
    
    'attribute_name': attribute_value}
    )
    model.graph.node.remove(old_node)
    model.graph.node.extend([new_node])

    # 加载权重参数并将其与新节点相结合
    weights = onnx.numpy_helper.load_tensor('weights.pb')
    new_node_weight = np.concatenate((weights, np.zeros((new_node.output[0],))))
    new_tensor = onnx.numpy_helper.from_array(new_node_weight, 'new_tensor')
    model.graph.initializer.extend([new_tensor])

# 保存新模型
onnx.save(model, 'new_model.onnx')

Please substitute the name and properties of the node you need to modify. The above code is for reference only, you need to modify and adjust according to the actual situation.

Reference three

Ok, I can give you some guidance.

ONNX GraphSurgeon is a Python library for modifying ONNX model graphs. You can add custom nodes to ONNX model graphs using GraphSurgeon. To add a custom node, you need to do the following steps:

1. Install ONNX GraphSurgeon:

pip install onnx-graphsurgeon

2. Load the ONNX model diagram:

import onnx
import onnx_graphsurgeon as gs

# 加载 ONNX 模型
onnx_model = onnx.load("model.onnx")

# 创建 GraphSurgeon 对象
graph = gs.import_onnx(onnx_model)

3. Define a custom node:

# 创建一个包含两个输入和一个输出的自定义节点
custom_node = gs.Node(
    op="CustomOp",
    inputs=["input1", "input2"],
    outputs=["output"]
)

# 将自定义节点添加到图中
graph.nodes.append(custom_node)

4. Import weight parameters:

# 将 ONNX 模型中的权重参数导入到 GraphSurgeon 图中
# 可以使用 onnxruntime 加载模型，然后使用 GraphSurgeon 将权重参数添加到图中。
import onnxruntime

# 加载 ONNX 模型
onnx_model = onnx.load("model.onnx")

# 创建 onnxruntime inference session
session = onnxruntime.InferenceSession("model.onnx")

# 遍历图中的所有张量
for tensor in graph.tensors:
    # 如果节点是权重张量，则从 ONNX runtime 会话中获取它们的值
    if tensor.name in session.get_inputs():
        tensor.value = session.run([], {
    
    session.get_inputs()[0].name: tensor.name})[0]

5. Export the modified ONNX model:

# 导出修改后的 ONNX 模型
# 您可以导出图，然后使用 onnxruntime 加载模型，并开始进行推断。
onnx_model = graph.make_model("custom_model")
onnx.save(onnx_model, "custom_model.onnx")

This is how you can modify the ONNX model graph and import weight parameters via ONNX GraphSurgeon.

Reference four

Hello! You need to replace one or more nodes in an ONNX computation graph with custom nodes and import weight parameters. Here are some basic steps to help you:

1. Load the ONNX model using the ONNX GraphSurgeon tool. You can load it with the following code:

import onnx
from onnx_graphsurgeon import GraphSurgeon

onnx_model = onnx.load("model.onnx")
graph = GraphSurgeon(onnx_model.graph)

2. Create custom nodes and add them to the graph. You can create a custom node with the following code:

from onnx_graphsurgeon import Node

node = Node(
    op="MyCustomOp",
    inputs=["input1", "input2"],
    outputs=["output"],
    attrs={
    
    "param": 123},
)

and add it to the computation graph:

# 获取需要替换的节点
old_node = graph.find_node_by_name("NodeToReplace")

# 将自定义节点添加到计算图中
new_node = graph.add_node(node)

# 更新新节点的输入和输出
new_node.inputs = old_node.inputs
new_node.outputs = old_node.outputs

# 删除旧节点
graph.remove_node(old_node)

Note that this code snippet only demonstrates how to add nodes to the computational graph. You need to modify it according to the actual needs of your custom node.

3. Import weight parameters. If your custom nodes contain parameters, you need to import these parameters into the computation graph. You can load weight parameters into an ONNX graph with the following code:

import numpy as np

# 使用 numpy 加载权重参数
weights = np.load("weights.npy")

# 创建一个常量节点，将权重参数添加到计算图中
const_node = onnx.helper.make_node("Constant", [], ["weights"], value=onnx.helper.make_tensor(name="const", data_type=onnx.TensorProto.FLOAT, dims=weights.shape, vals=weights.flatten()))

And add the original input and the new weight input to the new node:

# 将常量节点添加到计算图中
graph.append(const_node)

# 更新新节点输入
new_node.inputs.append("weights")

After all these steps, you can save the ONNX model back to disk:

onnx_model.graph = graph.export()
onnx.save(onnx_model, "new_model.onnx")

Hope this helps you in your task!