Pytorch网络模型转Onnx格式，多种方法（opencv、onnxruntime、c++）调用Onnx

Pytorch网络模型转Onnx格式，多种方法（opencv、onnxruntime、c++）调用Onnx

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code git

链接: https://pan.baidu.com/s/1Tcq-XJrWvEKRHgBsrI6gVg?pwd=adfh

提取码: adfh

import torch
import torchvision
import cv2
import onnx
import numpy as np
import timm
import os
from PIL import Image
from torchvision import transforms
import onnxruntime
from onnxsim import simplify

print(torch.__version__)
print(cv2.__version__)
print(np.__version__)
print(onnx.__version__)
classes = None
#class_file = r'F:\Pycharm\PyCharm_Study\Others\ONNX\Opencv-Onnx\classification_classes_ILSVRC2012.txt'

class_file = './classification_classes_ILSVRC2012.txt'
with open(class_file, 'rt') as f:
    classes = f.read().rstrip('\n').split('\n')

def init_model(model_name):
    if model_name == 'alexnet':
        model = torchvision.models.alexnet(pretrained=True)
    if model_name == 'densnet':
        model = torchvision.models.densenet121(pretrained=True)
    if model_name == 'resnet':
        model = torchvision.models.resnet50(pretrained=True)
    if model_name == 'mobilenet':
        model = torchvision.models.mobilenet_v2(pretrained=True)
    if model_name == 'squeezenet':
        model = torchvision.models.squeezenet1_1(pretrained=True)
    if model_name == 'inception':
        model = torchvision.models.inception_v3(pretrained=False)
    if model_name == 'googlenet':
        model = torchvision.models.googlenet(pretrained=True)
    if model_name == 'vgg16':
        model = torchvision.models.vgg16(pretrained=True)
    if model_name == 'vgg19':
        model = torchvision.models.vgg19(pretrained=True)
    if model_name == 'shufflenet':
        model = torchvision.models.shufflenet_v2_x1_0(pretrained=True)
    if model_name == 'cspdarknet53':
        model = timm.create_model('cspdarknet53', pretrained=True)
    if model_name == 'seresnet18':
        model = timm.create_model('seresnet18', pretrained=True)
    if model_name == 'senet154':
        model = timm.create_model('senet154', pretrained=True)
    if model_name == 'seresnet50':
        model = timm.create_model('seresnet50', pretrained=True)
    if model_name == 'resnest50d':
        model = timm.create_model('resnest50d', pretrained=True)
    if model_name == 'skresnet50':
        model = timm.create_model('skresnet50', pretrained=True)
    model.eval()
    if model_name == 'inception':
        dummy = torch.randn(1, 3, 299, 299)
    else:
        dummy = torch.randn(1, 3, 224, 224)
    return model, dummy

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model, dummy = init_model('vgg16')
model = model.to(device)
dummy = dummy.to(device)

#img_file = r'F:\Pycharm\PyCharm_Study\Others\ONNX\Opencv-Onnx\dog.jpg'

img_file = './dog.jpg'

################################torchvison模型推理#####################################
transform = transforms.Compose([  # [1]
    transforms.Resize(256),  # [2]
    transforms.CenterCrop(224),  # [3]
    transforms.ToTensor(),  # [4]
    transforms.Normalize(  # [5]
        mean=[0.485, 0.456, 0.406],  # [6]
        std=[0.229, 0.224, 0.225]  # [7]
    )])

img = Image.open(img_file)
img_t = transform(img).to(device) # 输入给模型的图像数据要先进行转换
batch_t = torch.unsqueeze(img_t, 0)
tc_out = model(batch_t).detach().cpu().numpy()
# Get a class with a highest score.
tc_out = tc_out.flatten()
classId = np.argmax(tc_out)
confidence = tc_out[classId]

label = '%s: %.4f' % (classes[classId] if classes else 'Class #%d' % classId, confidence)



################################ pytorch转Onnx ######################################
onnx_name = 'vgg16.onnx'
torch.onnx.export(model, dummy, onnx_name)

# 模型测试(可跳过)
print("----- 模型测试 -----")
# 可以跳过该步骤，一般不会有问题
# 检查输出
def check_onnx_output(filename, input_data, torch_output):
    session = onnxruntime.InferenceSession(filename)
    input_name = session.get_inputs()[0].name
    result = session.run([], {input_name: input_data.detach().cpu().numpy()})
    for test_result, gold_result in zip(result, torch_output.values()):
        np.testing.assert_almost_equal(
            gold_result.cpu().numpy(), test_result, decimal=3,
        )
    return result
# 检查模型
def check_onnx_model(model, onnx_filename, input_image):
    with torch.no_grad():
        torch_out = {"output": model(input_image)}
    check_onnx_output(onnx_filename, input_image, torch_out)
    onnx_model = onnx.load(onnx_filename)
    onnx.checker.check_model(onnx_model)
    print("模型测试成功")
    return onnx_model
# 检测导出的onnx模型是否完整
# 一般出现问题程序直接报错，不过很少出现问题
onnx_model = check_onnx_model(model, onnx_name, dummy)

# -----  模型简化
print("-----  模型简化 -----")
# 基于onnx-simplifier简化模型，https://github.com/daquexian/onnx-simplifier
# 也可以命令行输入python3 -m onnxsim input_onnx_model output_onnx_model
# 或者使用在线网站直接转换https://convertmodel.com/

# 输出模型名
filename = onnx_name + "sim.onnx"
# 简化模型
# 设置skip_fuse_bn=True表示跳过融合bn层，pytorch高版本融合bn层会出错
simplified_model, check = simplify(onnx_model, skip_fuse_bn=True)
onnx.save_model(simplified_model, filename)
onnx.checker.check_model(simplified_model)
# 如果出错
assert check, "简化模型失败"
print("模型简化成功")
################################ pytorch转Onnx ######################################



print(label)
################################ torchvison模型推理 ######################################


################################ Opencv dnn 调用 Onnx #####################################
# 载入onnx模块
model_ = onnx.load(onnx_name)
# 检查IR是否良好
onnx.checker.check_model(model_)
# opencv dnn加载
net = cv2.dnn.readNetFromONNX(onnx_name)

frame = cv2.imread(img_file)
# Create a 4D blob from a frame.
inpWidth = dummy.shape[-2]
inpHeight = dummy.shape[-2]
# blob = cv2.dnn.blobFromImage(frame, size=(inpWidth, inpHeight), crop=False)
blob = cv2.dnn.blobFromImage(frame,
                             scalefactor=1.0 / 255,
                             size=(inpWidth, inpHeight),
                             mean=[0.485, 0.456, 0.406],
                             swapRB=True,
                             crop=False)
# Run a model
net.setInput(blob)
out = net.forward()
print(out.shape)        # 得到推理的结果

# Get a class with a highest score.
out = out.flatten()
classId = np.argmax(out)
confidence = out[classId]

# Put efficiency information.
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
print(label)
cv2.putText(frame, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))

# Print predicted class.
label = '%s: %.4f' % (classes[classId] if classes else 'Class #%d' % classId, confidence)
print(label)
cv2.putText(frame, label, (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
winName = 'onnx'

cv2.imshow(winName, frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
################################ Opencv dnn 调用 Onnx #####################################