一、导出为dynamic shape

1）函数讲解（函数导出、输出检查）

①torch.onnx.export

    torch.onnx.export(clip_model,(tokens),onnx_path,verbose=True,opset_version=18,do_constant_folding=True,input_names=input_names,output_names=output_names,dynamic_axes=dynamic_axes,)

(1)export_params:默认为true，表示导出的 ONNX 模型文件会包含模型的所有参数（如权重、偏置等）。而当设置为 False 时，导出的 ONNX 模型文件仅包含模型的计算图结构，不包含模型的参数。这意味着导出的 ONNX 文件会小很多，因为它没有存储大量的参数数据
(2)verbose：为true表示，将会输出大量打印日志信息
(3)do_constant_folding：一般为true，是一个布尔类型的参数，其作用是控制在导出 ONNX 模型时是否进行常量折叠优化从而提高推理性能。为TRUE开启常量折叠优化。在导出 ONNX 模型时，会对图中所有仅包含常量输入的操作进行预先计算，并用计算结果替换这些操作，以此简化计算图，减少模型的计算量和复杂度。
(4)input_names和output_names：输入、输出参数
(5)dynamic_axes：是一个字典，其键为输入或输出张量的名称，值也是一个字典，用于指定该张量中哪些维度是动态的。内层字典的键是维度索引（从 0 开始），值是一个字符串，用于标识这个动态维度，通常在 ONNX 运行时会使用这个标识来指定具体的维度大小
(6)opset_version：指定optset的版本输入参数举例：dynamic_axes = {"x": {0: "batch_size"},"hint": {0: "batch_size"},"timesteps": {0: "batch_size"},"context": {0: "batch_size", 1: "sequence_length"},"output": {0: "batch_size", 1: "hint_height", 2: "hint_width"}}dynamic_axes = {"input_ids": {1: "S"}, "last_hidden_state": {1: "S"}}dynamic_axes = {"x": {0: "latent"},}

②误差检查

#onnx_path onnx文件目录
#input_dicts  输入参数
#torch_outputs  模型输出结果
def onnxruntime_check(onnx_path, input_dicts, torch_outputs):onnx_model = onnx.load(onnx_path)# onnx.checker.check_model(onnx_model)sess = rt.InferenceSession(onnx_path)# outputs = self.get_output_names()# latent input# data = np.zeros((4, 77), dtype=np.int32)result = sess.run(None, input_dicts)cnt = 0for i in range(0, len(torch_outputs)):ret = np.allclose(result[i], torch_outputs[i].detach().numpy(), rtol=1e-03, atol=1e-05, equal_nan=False)cnt = cnt +1if ret is False:#print(f"onnxruntime_check {i} ret:{ret}  result[i]:{result[i]}  torch_outputs[i]:{torch_outputs[i].detach().numpy()} ")print("Error onnxruntime_check")# import pdb; pdb.set_trace()#print("cnt:", cnt)

2）代码展示

• 代码

import numpy as np
from pytorch_fid import fid_score
from pytorch_fid.inception import InceptionV3
import cv2
import datetime
from share import *
import configimport cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
import osfrom pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.canny import CannyDetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from onnx import shape_inference
import onnx_graphsurgeon as gs
import onnx
import onnxruntime as rtdef optimize(onnx_path, opt_onnx_path):from onnxsim import simplifymodel = onnx.load(onnx_path)graph = gs.import_onnx(model)print(f"{onnx_path} simplify start !")# self.info("init", graph)model_simp, check = simplify(model)# self.info("opt", gs.import_onnx(model_simp))onnx.save(model_simp, opt_onnx_path, save_as_external_data=True)assert check, "Simplified ONNX model could not be validated"print(f"{onnx_path} simplify done !")def onnxruntime_check(onnx_path, input_dicts, torch_outputs):onnx_model = onnx.load(onnx_path)# onnx.checker.check_model(onnx_model)sess = rt.InferenceSession(onnx_path)# outputs = self.get_output_names()# latent input# data = np.zeros((4, 77), dtype=np.int32)result = sess.run(None, input_dicts)cnt = 0for i in range(0, len(torch_outputs)):ret = np.allclose(result[i], torch_outputs[i].detach().numpy(), rtol=1e-03, atol=1e-05, equal_nan=False)cnt = cnt +1if ret is False:#print(f"onnxruntime_check {i} ret:{ret}  result[i]:{result[i]}  torch_outputs[i]:{torch_outputs[i].detach().numpy()} ")print("Error onnxruntime_check")# import pdb; pdb.set_trace()#print("cnt:", cnt)class hackathon():def initialize(self):self.apply_canny = CannyDetector()self.model = create_model('./models/cldm_v15.yaml').cpu()self.model.load_state_dict(load_state_dict('./models/control_sd15_canny.pth', location='cpu'))# self.model.load_state_dict(load_state_dict('/home/player/ControlNet/models/control_sd15_canny.pth', location='cuda'))self.model = self.model.cpu()self.model.eval()self.ddim_sampler = DDIMSampler(self.model)hk = hackathon()
hk.initialize()def export_clip_model():clip_model = hk.model.cond_stage_modelimport typesdef forward(self, tokens):outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")if self.layer == "last":z = outputs.last_hidden_stateelif self.layer == "pooled":z = outputs.pooler_output[:, None, :]else:z = outputs.hidden_states[self.layer_idx]return zclip_model.forward = types.MethodType(forward, clip_model)onnx_path = "./onnx/CLIP.onnx"tokens = torch.zeros(1, 77, dtype=torch.int32)input_names = ["input_ids"]output_names = ["last_hidden_state"]dynamic_axes = {"input_ids": {1: "S"}, "last_hidden_state": {1: "S"}}torch.onnx.export(clip_model,(tokens),onnx_path,verbose=True,opset_version=18,do_constant_folding=True,input_names=input_names,output_names=output_names,dynamic_axes=dynamic_axes,)print("======================= CLIP model export onnx done!")# verify onnx modeloutput = clip_model(tokens)input_dicts = {"input_ids": tokens.numpy()}onnxruntime_check(onnx_path, input_dicts, [output])print("======================= CLIP onnx model verify done!")# opt_onnx_path = "./onnx/CLIP.opt.onnx"# optimize(onnx_path, opt_onnx_path)def export_control_net_model():control_net_model = hk.model.control_modelonnx_path = "./onnx/control_net_model.onnx"def get_shape(B=1,S=64):return [(B, 4, 32, 48),(B, 3, 256, 384),tuple([B])