Draw a pipeline

There is no other way to look into one model stored in ONNX format than looking into its node with onnx. This example demonstrates how to draw a model and to retrieve it in json format.

Retrieve a model in JSON format

That’s the most simple way.

import skl2onnx
import onnxruntime
import sklearn
import numpy
import matplotlib.pyplot as plt
import os
from onnx.tools.net_drawer import GetPydotGraph, GetOpNodeProducer
from onnx import ModelProto
import onnx
from skl2onnx.algebra.onnx_ops import OnnxAdd, OnnxMul

onnx_fct = OnnxAdd(
    OnnxMul("X", numpy.array([2], dtype=numpy.float32), op_version=12),
    numpy.array([[1, 0], [0, 1]], dtype=numpy.float32),
    output_names=["Y"],
    op_version=12,
)

X = numpy.array([[4, 5], [-2, 3]], dtype=numpy.float32)
model = onnx_fct.to_onnx({"X": X}, target_opset=12)
print(model)

filename = "example1.onnx"
with open(filename, "wb") as f:
    f.write(model.SerializeToString())

ir_version: 7
opset_import {
  domain: ""
  version: 12
}
producer_name: "skl2onnx"
producer_version: "1.15.0"
domain: "ai.onnx"
model_version: 0
graph {
  node {
    input: "X"
    input: "Mu_Mulcst"
    output: "Mu_C0"
    name: "Mu_Mul"
    op_type: "Mul"
    domain: ""
  }
  node {
    input: "Mu_C0"
    input: "Ad_Addcst"
    output: "Y"
    name: "Ad_Add"
    op_type: "Add"
    domain: ""
  }
  name: "OnnxAdd"
  initializer {
    dims: 1
    data_type: 1
    float_data: 2
    name: "Mu_Mulcst"
  }
  initializer {
    dims: 2
    dims: 2
    data_type: 1
    float_data: 1
    float_data: 0
    float_data: 0
    float_data: 1
    name: "Ad_Addcst"
  }
  input {
    name: "X"
    type {
      tensor_type {
        elem_type: 1
        shape {
          dim {
          }
          dim {
            dim_value: 2
          }
        }
      }
    }
  }
  output {
    name: "Y"
    type {
      tensor_type {
        elem_type: 1
        shape {
          dim {
            dim_value: 2
          }
          dim {
            dim_value: 2
          }
        }
      }
    }
  }
}

Draw a model with ONNX

We use net_drawer.py included in onnx package. We use onnx to load the model in a different way than before.

model = ModelProto()
with open(filename, "rb") as fid:
    content = fid.read()
    model.ParseFromString(content)

We convert it into a graph.

pydot_graph = GetPydotGraph(
    model.graph,
    name=model.graph.name,
    rankdir="TB",
    node_producer=GetOpNodeProducer("docstring"),
)
pydot_graph.write_dot("graph.dot")

Then into an image

os.system("dot -O -Tpng graph.dot")

0

Which we display…

image = plt.imread("graph.dot.png")
plt.imshow(image)
plt.axis("off")

(-0.5, 431.5, 602.5, -0.5)

Versions used for this example

print("numpy:", numpy.__version__)
print("scikit-learn:", sklearn.__version__)
print("onnx: ", onnx.__version__)
print("onnxruntime: ", onnxruntime.__version__)
print("skl2onnx: ", skl2onnx.__version__)

numpy: 1.23.5
scikit-learn: 1.4.dev0
onnx:  1.15.0
onnxruntime:  1.16.0+cu118
skl2onnx:  1.15.0