前言

最近找到一个比较好玩的Unet分割项目，Unet的出现就是为了在医学上进行分割(比如细胞或者血管)，这里进行眼底血管的分割，用的backbone是VGG16，结构如下如所示(项目里面的图片，借用的！借用标记出处，尊重别人的知识产权)，模型比较小，但是效果感觉还不错的。

相关的算法发介绍就不写了接下来从PYTORCH、ONNX、rknn三个方面看看效果

全部代码地址: https://pan.baidu.com/s/1QkOz5tvRSF-UkJhmpI__lA 提取码: 8twv

检测原图

1. Pytroch推理代码

gpu_test文件夹

├── predict.py:推理代码
├── test_result_cuda.png: 检测结果
├── save_weights:模型文件夹
├── images:图片文件夹
├── src:相关库文件夹
└── mask:mask图片文件夹

import os
import time

import torch
from torchvision import transforms
import numpy as np
from PIL import Image

from src import UNet


def time_synchronized():
    torch.cuda.synchronize() if torch.cuda.is_available() else None
    return time.time()


def main():
    classes = 1  # exclude background
    # 模型路径
    weights_path = "./save_weights/best_model.pth"
    # 检测图片路径
    img_path = "./images/01_test.tif"
    # mask图片路径
    roi_mask_path = "./mask/01_test_mask.gif"
    assert os.path.exists(weights_path), f"weights {weights_path} not found."
    assert os.path.exists(img_path), f"image {img_path} not found."
    assert os.path.exists(roi_mask_path), f"image {roi_mask_path} not found."

    mean = (0.709, 0.381, 0.224)
    std = (0.127, 0.079, 0.043)

    # get devices
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    # 用cpu推理
    # device = "cpu"
    print("using {} device.".format(device))

    # create model
    model = UNet(in_channels=3, num_classes=classes+1, base_c=32)

    # load weights
    model.load_state_dict(torch.load(weights_path, map_location='cpu')['model'])
    model.to(device)

    # dummy_input = torch.randn(1, 3, 584, 565)
    # torch.onnx.export(model, dummy_input, 'eyes_unet.onnx', verbose=True, opset_version=11)
    # load roi mask
    roi_img = Image.open(roi_mask_path).convert('L')
    roi_img = np.array(roi_img)

    # load image
    original_img = Image.open(img_path).convert('RGB')

    # from pil image to tensor and normalize
    data_transform = transforms.Compose([transforms.ToTensor(),
                                         transforms.Normalize(mean=mean, std=std)])
    img = data_transform(original_img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    model.eval()  # 进入验证模式
    with torch.no_grad():
        # init model
        img_height, img_width = img.shape[-2:]
        init_img = torch.zeros((1, 3, img_height, img_width), device=device)
        model(init_img)

        t_start = time_synchronized()
        output = model(img.to(device))
        print(output["out"].shape)
        t_end = time_synchronized()
        print("inference time: {}".format(t_end - t_start))

        prediction = output['out'].argmax(1).squeeze(0)
        prediction = prediction.to("cpu").numpy().astype(np.uint8)
        # np.save("cuda_unet.npy", prediction)
        print(prediction.shape)
        # 将前景对应的像素值改成255(白色)
        prediction[prediction == 1] = 255
        # 将不敢兴趣的区域像素设置成0(黑色)
        prediction[roi_img == 0] = 0
        mask = Image.fromarray(prediction)
        mask.save("test_result_cuda.png")


if __name__ == '__main__':
    main()

检测结果

2. ONNX代码推理

onnx_test文件夹

├── images : 检测图片文件夹
├── test_result_onnx.png: 检测结果
├── predict_onnx.py:推理代码
├── mask:mask图片文件夹
└── eyes_unet-sim.onnx :模型文件

import os
import time
from torchvision import transforms
import numpy as np
from PIL import Image
import onnxruntime as rt


def main():
    # classes = 1  # exclude background
    img_path = "./images/01_test.tif"
    roi_mask_path = "./mask/01_test_mask.gif"
    assert os.path.exists(img_path), f"image {img_path} not found."
    assert os.path.exists(roi_mask_path), f"image {roi_mask_path} not found."

    mean = (0.709, 0.381, 0.224)
    std = (0.127, 0.079, 0.043)

    # load roi mask
    roi_img = Image.open(roi_mask_path).convert('L')
    roi_img = np.array(roi_img)
    # load image
    original_img = Image.open(img_path).convert('RGB')
    # from pil image to tensor and normalize
    data_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
    img = data_transform(original_img)
    # expand batch dimension
    img = img.numpy()
    img = img[np.newaxis, :]
    t_start = time.time()
    sess = rt.InferenceSession('./eyes_unet-sim.onnx')
    # 模型的输入和输出节点名，可以通过netron查看
    input_name = 'input.1'
    outputs_name = ['437']
    # 模型推理:模型输出节点名，模型输入节点名，输入数据(注意节点名的格式！！！！！)
    output = sess.run(outputs_name, {input_name: img})
    output = np.array(output).reshape(1, 2, 584, 565)
    t_end = time.time()
    print("inference time: {}".format(t_end - t_start))
    prediction = np.squeeze(np.argmax(output, axis=1))
    print(prediction.shape)
    prediction = prediction.astype(np.uint8)
    # 将前景对应的像素值改成255(白色)
    prediction[prediction == 1] = 255
    # 将不敢兴趣的区域像素设置成0(黑色)
    prediction[roi_img == 0] = 0
    mask = Image.fromarray(prediction)
    mask.save("test_result_onnx.png")


if __name__ == '__main__':
    main()

检测结果

3. RKNN模型转化

rknn_trans_1808_3588文件夹

├── dataset.txt: 量化数据集路径
├── images :量化数据集
├── trans_1808.py :适用1808的rknn模型
├── trans_3588.py :适用3588的rknn模型
├── mask:没用到
└── eyes_unet-sim.onnx:原始onnx模型
这个没什么好说的，装好环境，直接在相应的环境里面转就好啦，大家应该都会的(不会就拉出去，或者收藏留言，嘿嘿，我看看，出不出教程呢)

4. RKNN模型推理

4.1 rk1808_test文件夹

├── 01_test_mask.gif:mask图片
├── eyes_unet-sim-1808.rknn:rk1808适用模型
├── predict_rknn_1808.py:推理代码
├── test_result_1808.png :检测结果
└── 01_test.tif:检测图片

import os
import time
import numpy as np
from PIL import Image
from rknn.api import RKNN


def main():
    # classes = 1  # exclude background
    RKNN_MODEL = "./eyes_unet-sim-1808.rknn"
    img_path = "./01_test.tif"
    roi_mask_path = "./01_test_mask.gif"
    assert os.path.exists(img_path), f"image {img_path} not found."
    assert os.path.exists(roi_mask_path), f"image {roi_mask_path} not found."


    # load roi mask
    roi_img = Image.open(roi_mask_path).convert('L')
    roi_img = np.array(roi_img)


    # load image
    original_img = Image.open(img_path).convert('RGB')
    # from pil image to tensor and normalize
    # data_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
    # img = data_transform(original_img)
    # expand batch dimension
    img = np.array(original_img)
    img = img[np.newaxis, :]


    # Create RKNN object
    rknn = RKNN(verbose=False)
    ret = rknn.load_rknn(RKNN_MODEL)


    # Init runtime environment
    print('--> Init runtime environment')

    ret = rknn.init_runtime(target='rk1808')
    if ret != 0:
        print('Init runtime environment failed!')
        exit(ret)
    print('done')
    
    t_start = time.time()
    output = rknn.inference(inputs=[img])
    t_end = time.time()
    print("inference time: {}".format(t_end - t_start))
    output = np.array(output).reshape(1, 2, 584, 565)
    prediction = np.squeeze(np.argmax(output, axis=1))
    print(prediction.shape)


    prediction = prediction.astype(np.uint8)
    # 将前景对应的像素值改成255(白色)
    prediction[prediction == 1] = 255
    # 将不敢兴趣的区域像素设置成0(黑色)
    prediction[roi_img == 0] = 0
    mask = Image.fromarray(prediction)
    mask.save("test_result_1808.png")

    rknn.release()


if __name__ == '__main__':
    main()

检测结果

4.2 rk3588_test文件夹

├── 01_test_mask.gif:mask图片
├── test_result_3588.png:检测结果
├── eyes_unet-sim-3588.rknn:rk1808适用模型
├── 01_test.tif:检测图片
└── predict_3588.py:推理代码

import os
import time
import numpy as np
from PIL import Image
from rknnlite.api import RKNNLite


def main():
    # classes = 1  # exclude background
    RKNN_MODEL = "./eyes_unet-sim.rknn"
    img_path = "./01_test.tif"
    roi_mask_path = "./01_test_mask.gif"
    assert os.path.exists(img_path), f"image {img_path} not found."
    assert os.path.exists(roi_mask_path), f"image {roi_mask_path} not found."


    # load roi mask
    roi_img = Image.open(roi_mask_path).convert('L')
    roi_img = np.array(roi_img)


    # load image
    original_img = Image.open(img_path).convert('RGB')
    # from pil image to tensor and normalize
    # data_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)])
    # img = data_transform(original_img)
    # expand batch dimension
    img = np.array(original_img)
    img = img[np.newaxis, :]


    # Create RKNN object
    rknn_lite = RKNNLite(verbose=False)
    ret = rknn_lite.load_rknn(RKNN_MODEL)


    # Init runtime environment
    print('--> Init runtime environment')

    ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_AUTO)
    if ret != 0:
        print('Init runtime environment failed!')
        exit(ret)
    print('done')
    
    t_start = time.time()
    output = rknn_lite.inference(inputs=[img])
    t_end = time.time()
    print("inference time: {}".format(t_end - t_start))
    output = np.array(output).reshape(1, 2, 584, 565)
    prediction = np.squeeze(np.argmax(output, axis=1))
    print(prediction.shape)


    prediction = prediction.astype(np.uint8)
    np.save("int8_unet.npy", prediction)
    # 将前景对应的像素值改成255(白色)
    prediction[prediction == 1] = 255
    # 将不敢兴趣的区域像素设置成0(黑色)
    prediction[roi_img == 0] = 0
    mask = Image.fromarray(prediction)
    mask.save("test_result_3588.png")

    rknn_lite.release()


if __name__ == '__main__':
    main()

检测结果