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本文尝试将pytorch搭建的ViT模型转为onnx模型。

首先将博主上一篇文章中搭建的模型ViT Vision Transformer超详细解析，网络构建，可视化，数据预处理，全流程实例教程-CSDN博客转存为.pth

torch.save(model, 'my_vit_model.pth')

然后新建一个py文件，要新建py文件的原因是，博主上一篇文章的main.py文件引用了很多torch相关的库，如果还是在main.py文件中运行转onnx的代码，回报错circle import 重复循环引用的错误，所以姑且将.pth作为一个中转。

新建一个py文件，写入

import importlib
torch = importlib.import_module('torch')model = torch.load("my_vit_model.pth")model.cpu()
# 创建一个随机的输入张量
dummy_input = torch.randn(1, 3, 16, 16)
torch.onnx.export(model, dummy_input, 'model.onnx', opset_version=18)

引入importlib，通过它来引用torch也是为了解决循环引用的问题。

这时运行这段代码，会报错onnx 不支持aten::unflatten运算。这里有两种解决方法，一种是将自己pytorch模型中的unflatten运算全部换成onnx支持的reshape函数（参见文章：https://www.cnblogs.com/antelx/p/17564039.html）

还有一种方法是，修改onnx库中的 symbolic_opset18.py 文件（/home/.local/lib/python3.8/site-packages/torch/onnx），改为如下形式

"""This file exports ONNX ops for opset 18.Note [ONNX Operators that are added/updated in opset 18]~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-18-of-the-default-onnx-operator-set
New operators:CenterCropPadCol2ImMishOptionalGetElementOptionalHasElementPadResizeScatterElementsScatterND
"""import functools
from typing import Sequenceimport torch
import torch._C._onnx as _C_onnx
from torch.onnx import (_constants,_type_utils,errors,symbolic_helper,symbolic_opset11 as opset11,symbolic_opset9 as opset9,utils,
)
from torch.onnx._internal import _beartype, jit_utils, registrationfrom torch import _C
from torch.onnx import symbolic_helper
from torch.onnx._internal import _beartype, registration# EDITING THIS FILE? READ THIS FIRST!
# see Note [Edit Symbolic Files] in symbolic_helper.py__all__ = ["col2im"]_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=18)@_onnx_symbolic("aten::col2im")
@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is")
@_beartype.beartype
def col2im(g,input: _C.Value,output_size: _C.Value,kernel_size: _C.Value,dilation: Sequence[int],padding: Sequence[int],stride: Sequence[int],
):# convert [i0, i1, ..., in] into [i0, i0, i1, i1, ..., in, in]adjusted_padding = []for pad in padding:for _ in range(2):adjusted_padding.append(pad)num_dimensional_axis = symbolic_helper._get_tensor_sizes(output_size)[0]if not adjusted_padding:adjusted_padding = [0, 0] * num_dimensional_axisif not dilation:dilation = [1] * num_dimensional_axisif not stride:stride = [1] * num_dimensional_axisreturn g.op("Col2Im",input,output_size,kernel_size,dilations_i=dilation,pads_i=adjusted_padding,strides_i=stride,)@_onnx_symbolic("aten::unflatten")
def unflatten(g:jit_utils.GraphContext, input, dim, unflattened_size):input_dim = symbolic_helper._get_tensor_rank(input)if input_dim is None:return symbolic_helper._unimplemented("dim","ONNX and PyTorch use different strategies to split the input. ""Input rank must be known at export time.",)# dim could be negativeinput_dim = g.op("Constant", value_t=torch.tensor([input_dim], dtype=torch.int64))dim = g.op("Add", input_dim, dim)dim = g.op("Mod", dim, input_dim)input_size = g.op("Shape", input)head_start_idx = g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64))head_end_idx = g.op("Reshape", dim, g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64)))head_part_rank = g.op("Slice", input_size, head_start_idx, head_end_idx)dim_plus_one = g.op("Add", dim, g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64)))tail_start_idx = g.op("Reshape",dim_plus_one,g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64)),)tail_end_idx = g.op("Constant", value_t=torch.tensor([_constants.INT64_MAX], dtype=torch.int64))tail_part_rank = g.op("Slice", input_size, tail_start_idx, tail_end_idx)final_shape = g.op("Concat", head_part_rank, unflattened_size, tail_part_rank, axis_i=0)return symbolic_helper._reshape_helper(g, input, final_shape)

这里这样做是相当于自己在onnx库中注册aten::unflatten运算。

再新建一个py文件，写入

import onnxruntime as rt
import numpy as np# 加载模型
sess = rt.InferenceSession("model.onnx")# 获取输入和输出名称
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name# 创建输入数据
input_data = np.random.rand(1, 3, 16, 16).astype(np.float32)# 运行模型
pred_onnx = sess.run([output_name], {input_name: input_data})# 打印预测结果
print(pred_onnx)

就可以运行onnx模型了。