语义分割 U-Net 应用入门_专栏

文章目录

1. 简介
2. 源码
3. 数据集
4. 网络结构
5. 训练
6. 预测

1. 简介

这里采用了一个在图像分割领域比较熟知的U-Net网络结构
是一个基于FCN做改进后的一个深度学习网络
包含下采样（编码器，特征提取）和上采样（解码器，分辨率还原）两个阶段，因模型结构比较像U型而命名为U-Net

2. 源码

根据PaddlePaddle飞桨开源框架上的文档代码进行一些更改：

整合和梳理工程文件
调整数据集地址
调整网络结构

可以通过以下渠道下载：

3. 数据集

3.1. 开源数据集

本案例使用原文里的一个例子的 Oxford-IIIT Pet数据集
里面包含了宠物照片和对应的标签数据
宠物图片在 /images
标签数据在 /annotations/trimaps
具体详情参考飞桨官方文档说明

3.2. 建立数据集文件夹

在工程中新建文件夹 /resources/Oxford-IIIT Pet/images，将所有数据原始图片均放置于此
在工程中新建文件夹 /resources/Oxford-IIIT Pet/masks，将所有数据标签图片均放置于此

3.3. 数据格式统一

宠物图片数据集里为jpg格式，这边利用tool_jpg2png.py将其统一为png吧

import os
from PIL import Image

# 原图和标签图片地址
resources_path = "./resources/Oxford-IIIT Pet"
origin_images_path = resources_path + "/images"
img_name_list = os.listdir(origin_images_path)

for img_name in img_name_list:
    if img_name[-3:] == "jpg":
        tp = Image.open(origin_images_path + '/' + img_name)
        tp.save(origin_images_path + '/' + img_name[:-3] + 'png')
        os.remove(origin_images_path + '/' + img_name)

4. 网络结构

网络结构在model.py中定义
根据U-Net的图片中设置相似的结构，具体如下：

-----------------------------------------------------------------------------
  Layer (type)        Input Shape          Output Shape         Param #
=============================================================================
    Conv2D-1       [[1, 3, 160, 160]]   [1, 16, 160, 160]         448
  BatchNorm2D-1   [[1, 16, 160, 160]]   [1, 16, 160, 160]         64
     ReLU-1       [[1, 16, 160, 160]]   [1, 16, 160, 160]          0
    Conv2D-2      [[1, 16, 160, 160]]   [1, 16, 160, 160]        2,320
  BatchNorm2D-2   [[1, 16, 160, 160]]   [1, 16, 160, 160]         64
     ReLU-2       [[1, 16, 160, 160]]   [1, 16, 160, 160]          0
   MaxPool2D-1    [[1, 16, 160, 160]]    [1, 16, 80, 80]           0
    Conv2D-3       [[1, 16, 80, 80]]     [1, 32, 80, 80]         4,640
  BatchNorm2D-3    [[1, 32, 80, 80]]     [1, 32, 80, 80]          128
     ReLU-3        [[1, 32, 80, 80]]     [1, 32, 80, 80]           0
    Conv2D-4       [[1, 32, 80, 80]]     [1, 32, 80, 80]         9,248
  BatchNorm2D-4    [[1, 32, 80, 80]]     [1, 32, 80, 80]          128
     ReLU-4        [[1, 32, 80, 80]]     [1, 32, 80, 80]           0
   MaxPool2D-2     [[1, 32, 80, 80]]     [1, 32, 40, 40]           0
    Conv2D-5       [[1, 32, 40, 40]]     [1, 64, 40, 40]        18,496
  BatchNorm2D-5    [[1, 64, 40, 40]]     [1, 64, 40, 40]          256
     ReLU-5        [[1, 64, 40, 40]]     [1, 64, 40, 40]           0
    Conv2D-6       [[1, 64, 40, 40]]     [1, 64, 40, 40]        36,928
  BatchNorm2D-6    [[1, 64, 40, 40]]     [1, 64, 40, 40]          256
     ReLU-6        [[1, 64, 40, 40]]     [1, 64, 40, 40]           0
   MaxPool2D-3     [[1, 64, 40, 40]]     [1, 64, 20, 20]           0
    Conv2D-7       [[1, 64, 20, 20]]     [1, 128, 20, 20]       73,856
  BatchNorm2D-7    [[1, 128, 20, 20]]    [1, 128, 20, 20]         512
     ReLU-7        [[1, 128, 20, 20]]    [1, 128, 20, 20]          0
    Conv2D-8       [[1, 128, 20, 20]]    [1, 128, 20, 20]       147,584
  BatchNorm2D-8    [[1, 128, 20, 20]]    [1, 128, 20, 20]         512
     ReLU-8        [[1, 128, 20, 20]]    [1, 128, 20, 20]          0
   MaxPool2D-4     [[1, 128, 20, 20]]    [1, 128, 10, 10]          0
    Conv2D-9       [[1, 128, 10, 10]]    [1, 256, 10, 10]       295,168
  BatchNorm2D-9    [[1, 256, 10, 10]]    [1, 256, 10, 10]        1,024
     ReLU-9        [[1, 256, 10, 10]]    [1, 256, 10, 10]          0
    Conv2D-10      [[1, 256, 10, 10]]    [1, 256, 10, 10]       590,080
 BatchNorm2D-10    [[1, 256, 10, 10]]    [1, 256, 10, 10]        1,024
     ReLU-10       [[1, 256, 10, 10]]    [1, 256, 10, 10]          0
   Upsample-1      [[1, 256, 10, 10]]    [1, 256, 20, 20]          0
    Conv2D-11      [[1, 256, 20, 20]]    [1, 128, 20, 20]       32,896
Conv2DTranspose-1  [[1, 128, 20, 20]]    [1, 128, 20, 20]       147,584
 BatchNorm2D-11    [[1, 128, 20, 20]]    [1, 128, 20, 20]         512
     ReLU-11       [[1, 128, 20, 20]]    [1, 128, 20, 20]          0
Conv2DTranspose-2  [[1, 128, 20, 20]]    [1, 128, 20, 20]       147,584
 BatchNorm2D-12    [[1, 128, 20, 20]]    [1, 128, 20, 20]         512
     ReLU-12       [[1, 128, 20, 20]]    [1, 128, 20, 20]          0
   Upsample-2      [[1, 128, 20, 20]]    [1, 128, 40, 40]          0
    Conv2D-12      [[1, 128, 40, 40]]    [1, 64, 40, 40]         8,256
Conv2DTranspose-3  [[1, 64, 40, 40]]     [1, 64, 40, 40]        36,928
 BatchNorm2D-13    [[1, 64, 40, 40]]     [1, 64, 40, 40]          256
     ReLU-13       [[1, 64, 40, 40]]     [1, 64, 40, 40]           0
Conv2DTranspose-4  [[1, 64, 40, 40]]     [1, 64, 40, 40]        36,928
 BatchNorm2D-14    [[1, 64, 40, 40]]     [1, 64, 40, 40]          256
     ReLU-14       [[1, 64, 40, 40]]     [1, 64, 40, 40]           0
   Upsample-3      [[1, 64, 40, 40]]     [1, 64, 80, 80]           0
    Conv2D-13      [[1, 64, 80, 80]]     [1, 32, 80, 80]         2,080
Conv2DTranspose-5  [[1, 32, 80, 80]]     [1, 32, 80, 80]         9,248
 BatchNorm2D-15    [[1, 32, 80, 80]]     [1, 32, 80, 80]          128
     ReLU-15       [[1, 32, 80, 80]]     [1, 32, 80, 80]           0
Conv2DTranspose-6  [[1, 32, 80, 80]]     [1, 32, 80, 80]         9,248
 BatchNorm2D-16    [[1, 32, 80, 80]]     [1, 32, 80, 80]          128
     ReLU-16       [[1, 32, 80, 80]]     [1, 32, 80, 80]           0
   Upsample-4      [[1, 32, 80, 80]]    [1, 32, 160, 160]          0
    Conv2D-14     [[1, 32, 160, 160]]   [1, 16, 160, 160]         528
Conv2DTranspose-7 [[1, 16, 160, 160]]   [1, 16, 160, 160]        2,320
 BatchNorm2D-17   [[1, 16, 160, 160]]   [1, 16, 160, 160]         64
     ReLU-17      [[1, 16, 160, 160]]   [1, 16, 160, 160]          0
Conv2DTranspose-8 [[1, 16, 160, 160]]   [1, 16, 160, 160]        2,320
 BatchNorm2D-18   [[1, 16, 160, 160]]   [1, 16, 160, 160]         64
     ReLU-18      [[1, 16, 160, 160]]   [1, 16, 160, 160]          0
    Conv2D-15     [[1, 16, 160, 160]]    [1, 4, 160, 160]         68
=============================================================================
Total params: 1,620,644
Trainable params: 1,614,756
Non-trainable params: 5,888
-----------------------------------------------------------------------------
Input size (MB): 0.29
Forward/backward pass size (MB): 91.31
Params size (MB): 6.18
Estimated Total Size (MB): 97.78
-----------------------------------------------------------------------------

5. 训练

根据自己电脑硬件合理调整参数进行训练，执行train.py文件
保存模型于 output 文件夹

$ python train.py

# Epoch 1/15
# step  30/416 [=>............................] - loss: 0.9846 - ETA: 5:49 - 907ms/step

6. 预测

执行predict.py文件，预测测试集中前两个数据

效果还可以吧

谢谢

语义分割 U-Net 应用入门