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关于python:ONNX-开始

jiezi

环境

根底

  • Anaconda
conda create -n onnx python=3.8 -y
conda activate onnx

# ONNX
#  https://github.com/onnx/onnx
conda install -c conda-forge onnx -y
python -c "import onnx; print(onnx.__version__)"
import onnx
model = onnx.load("model.onnx")

简化 

# ONNX Simplifier
#  https://github.com/daquexian/onnx-simplifier
pip install onnx-simplifier
python -m onnxsim -h
import onnxsim
model_simp, check = onnxsim.simplify(model, perform_optimization=False)
assert check, "Simplified ONNX model could not be validated"

<!–

转换 

# ONNX to Caffe
#  https://github.com/MTlab/onnx2caffe
git clone https://github.com/MTLab/onnx2caffe.git

# ONNX to PyTorch
#  https://github.com/ToriML/onnx2pytorch
pip install onnx2pytorch

–>

应用

给出些 ONNX 模型应用的示例办法。

提取子模型 

import onnx

input_path = "path/to/the/original/model.onnx"
output_path = "path/to/save/the/extracted/model.onnx"
input_names = ["input_0", "input_1", "input_2"]
output_names = ["output_0", "output_1"]

onnx.utils.extract_model(input_path, output_path, input_names, output_names)

批改输入输出名称 

def _onnx_rename(model, names, names_new):
  for node in model.graph.node:
    for i, n in enumerate(node.input):
      if n in names:
        node.input[i] = names_new[names.index(n)]
    for i, n in enumerate(node.output):
      if n in names:
        node.output[i] = names_new[names.index(n)]
  for node in model.graph.input:
    if node.name in names:
      node.name = names_new[names.index(node.name)]
  # print(model.graph.input)
  for node in model.graph.output:
    if node.name in names:
      node.name = names_new[names.index(node.name)]
  # print(model.graph.output)

_onnx_rename(model, ["input", "output"], ["input_new", "output_new"])

批改输入输出维度

此为批改模型的。如果要批改某节点的，见参考 onnx_cut.py 的 _onnx_specify_shapes()。

from onnx.tools import update_model_dims

update_model_dims.update_inputs_outputs_dims(model,
  {"input": [1, 3, 512, 512]},
  {"scores": [100, 1], "boxes": [100, 4]}
)

推理模型节点维度

指明模型输出维度后，可主动推理后续节点的维度。

model_infer = onnx.shape_inference.infer_shapes(model)

获取图属性名称索引

辅助找出指定名称的图属性。

def _onnx_graph_name_map(graph_prop_list):
  m = {}
  for n in graph_prop_list:
    m[n.name] = n
  return m

node_map = _onnx_graph_name_map(graph.node)
initializer_map = _onnx_graph_name_map(graph.initializer)
input_map = _onnx_graph_name_map(graph.input)
output_map = _onnx_graph_name_map(graph.output)
value_info_map = _onnx_graph_name_map(graph.value_info)

获取节点输出名称索引

辅助找出指定输出名称的节点列表。输入同样。

def _onnx_node_input_map(node_list):
  m = {}
  for n in node_list:
    for n_input in n.input:
      if n_input in m:
        m[n_input].append(n)
      else:
        m[n_input] = [n]
  return m

node_input_map = _onnx_node_input_map(graph.node)

获取图属性地位

辅助找出图某属性所在列表地位。

def _onnx_graph_index(graph_prop_list, prop, by_name=False):
  for i, n in enumerate(graph_prop_list):
    if by_name:
      if n.name == prop.name:
        return i
    else:
      if n == prop:
        return i
  return -1

node_i = _onnx_graph_index(graph.node, node)

获取某区间的节点

辅助找出某区间的节点字典。

def _onnx_node_between(node_beg, node_end, node_input_map):
  nodes = {}
  def _between(beg, end):
    if beg.name == end.name:
      return
    for n_output in beg.output:
      for n in node_input_map[n_output]:
        if n.name == end.name or n.name in nodes:
          continue
        nodes[n.name] = n
        _between(n, end)
  _between(node_beg, node_end)
  return nodes

替换某个节点

替换或批改某个节点的过程。

from onnx import helper

node = graph.node[100]
node_i = _onnx_graph_index(graph.node, node)

graph.node.remove(node)

node_new = helper.make_node(
  'Pad',                  # name
  ['X', 'pads', 'value'], # inputs
  ['Y'],                  # outputs
  mode='constant',        # attributes
)
graph.node.insert(node_i, node_new)

模型运行推理

模型运行推理，失去输入的过程。

import cv2 as cv
import numpy as np
import onnxruntime as nxrun

onnx_session = nxrun.InferenceSession("path/to/model.onnx")

img = cv.imread("path/to/image.png", cv.IMREAD_COLOR)
# _, _, h, w = input_node.shape  # BCHW
# img = cv.resize(src=img, dsize=(w, h), interpolation=cv.INTER_LINEAR_EXACT)
input_data = np.swapaxes(img, 0, -1)
input_data = input_data[np.newaxis, :].astype(np.float32)

def _get_output_names(onnx_session):
  names = []
  for node in onnx_session.get_outputs():
    names.append(node.name)
  return names

output_names = _get_output_names(onnx_session)

outputs = onnx_session.run(output_names, input_feed={"input": input_data}
)

参考

  • onnx_cut.py
  • ONNX Python API

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