嵌入式AI Arm_linux_第一个Demo_让IPU跑起来

conda create -n classification python==3.10
conda activate classification
##此处建议在你的工作目录下创建一个文件夹
mkdir pyclass
cd pyclass
git clone https://github.com/WZMIAOMIAO/deep-learning-for-image-processing.git
cd deep-learning-for-image-processing/pytorch_classification/Test6_mobilenet

pytorch 安装配置（建议去官网选择合适的版本安装）
pip3 install torch torchvision

onnx安装

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pip install onnx -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install onnx-simplifier -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install onnxscript

5.以mobilenetv2为例实际操作

5.1 创建 mobilenetv2 文件夹

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mkdir mobilenetv2
cd mobilenetv2
git clone https://github.com/WZMIAOMIAO/deep-learning-for-image-processing.git
cd deep-learning-for-image-processing/pytorch_classification/Test6_mobilenet

5.2 验证模型

在这个

deep-learning-for-image-processing/pytorch_classification/Test6_mobilenet

路径下创建infer.py文件，验证模型正确与否

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vi infer.py

输入以下内容

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import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

from model_v2 import MobileNetV2

def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    data_transform = transforms.Compose(
        [transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # load image
    img_path = "./apple.jpg"
    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
    img = Image.open(img_path)
    plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    # create model
    model = MobileNetV2(num_classes=1000).to(device)
    # load model weights
    model_weight_path = "./opendla/mobilenet_v2-b0353104.pth"
    model.load_state_dict(torch.load(model_weight_path, map_location=device))
    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squeeze(model(img.to(device))).cpu()
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()
        print("class id", predict_cla)

if __name__ == '__main__':
    main()

然后在 conda 的 classification 环境下运行

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python infer.py

5.3 转换模型为onnx模型

在这个

deep-learning-for-image-processing/pytorch_classification/Test6_mobilenet

路径下创建export.py文件，验证模型正确与否

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vi export.py

输入以下内容

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import os
import json

import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt

from model_v2 import MobileNetV2

def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    data_transform = transforms.Compose(
        [transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

    # load image
    img_path = "./apple.jpg"
    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
    img = Image.open(img_path)
    plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    # create model
    model = MobileNetV2(num_classes=1000).to(device)
    # load model weights
    model_weight_path = "./opendla/mobilenet_v2-b0353104.pth"
    model.load_state_dict(torch.load(model_weight_path, map_location=device))
    model.eval()
    torch.onnx.export(
        model,
        img.to(device),
        "./opendla/mobilenetv2.onnx",
        input_names=['images'],
        output_names=['output'],
        do_constant_folding=False
    )

if __name__ == '__main__':
    main()

优化图结构

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python -m onnxsim opendla/mobilenetv2.onnx opendla/mobilenetv2_sim.onnx

6.使用 comake 社区的端侧云ai仿真转换离线模型

详情可参考Comake 社区AI在线开发平台使用

7.拷贝到板端，离线运行模型

将6中转换好的模型img文件，拷贝至板端

这里先用sdk/verify/opendla/classification的代码验证一下

在板端执行：

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/customer/opendla/prog_classification -m mobilenet_v2_224.img -i apple.jpg