使用【PaddleSeg3D】搭建项目对肝脏进行3D分割
PaddleSeg3D 是一个易于使用的 3D 医学图像分割工具包。是基于PaddlePaddle进行开发的3D分割套件。由飞桨的CV兴趣小组进行开发和维护。
·
!git clone https://github.com/PaddleCV-SIG/PaddleSeg3D.git
1. PaddleSeg3D
从这个工具的github地址https://github.com/PaddleCV-SIG/PaddleSeg3D.git ，可以看出是飞桨的兴趣小组(PPSIG,飞桨特殊兴趣小组)的CV小组开发和维护的。如果对飞桨的分割套件PaddleSeg熟悉的话，知道PaddleSeg目前支持都是2D分割，没有3D分割这方面的工具，不过有很多用户或者行业有3D分割的需求，特别医疗行业的用户。现在飞桨兴趣小组开发了PaddleSeg3D的3D分割工具，后面估计会整合到PaddleSeg分割套件中。
现在尝试用PaddleSeg3D这个工具搭建3D分割项目，分割自己的医疗数据。
PaddleSeg3D跟PaddleSeg很像的，用起来也很方便，但是就是对自己的医疗数据预处理没有提供太多的脚本，例如PaddleSeg3D中快速运行的教程中，是对新冠胸部CT的数据集进行肺部的分割，里面的预处理都是根据肺部“打量的”，还不支持nii后缀名的医疗格式，所以如果你要分割自己的数据，器官不是肺部的话，那需要自己写脚本。不过除自己需要对数据进行预处理外，后面的训练，就很方便，PaddleSeg3D提供一行代码就可以训练、验证、评估。
希望PaddleSeg3D可以尽快加入PaddleSeg开发套件中。
# 安装依赖
!pip install -r ./PaddleSeg3D/requirements.txt
#解压数据
!unzip -o /home/aistudio/data/data129670/livers.zip -d /home/aistudio/PaddleSeg3D
2. 数据集介绍
这个项目主要分割肝脏和肿瘤，数据的模式是CT，属于薄层数据（体素约为0.7x0.7x0.7），在Z轴上可以达到500多层。通过ITK-SNAP中看到显示红色的是肝脏，显示绿色的是肿瘤。这个数据集一共有28例
#记得运行这行
%cd /home/aistudio/PaddleSeg3D/
/home/aistudio/PaddleSeg3D
#导入常用的库
import nibabel as nib
import SimpleITK as sitk
import matplotlib.pyplot as plt
import numpy as np
import os
from tqdm import tqdm
3. 对数据进行预处理
PaddleSeg3D的快速运行教程是分割胸部CT数据的肺部。对数据处理如下：
用nibabel读取医疗数据后，
if "nii.gz" in filename:
f_np = nib.load(f).get_fdata(dtype=np.float32)
对CT值裁剪，然后对数据的Size 重采样到[128，128，128]，再转换成numpy数据。
preprocess=[
HUNorm,
functools.partial(
resample, new_shape=[128, 128, 128], order=1)
],
HUNorm默认是 HU_min=-1000, HU_max=600
#这个值范围类似肺窗
如果是分割自己的数据（不是分割肺部）是不能用它提供的脚本，假如你的医疗数据是nii的话，直接不支持读取(脚本没有把nii的格式写进去),而且使用nibabel来读取nii的话，图像会旋转90度，对CT值裁剪也是针对肺部的。因此最好自己写脚本预处理自己的数据。
因此对这个项目的肝脏数据，自己写脚本进行如下的预处理：
- 用SimpleITK读取.nii医疗数据，而且用SimpleITK不会出现用nibabel读取后会旋转90度
- 对数据统一方向，避免SimpleITK读取后，图像上下翻转(统一方向后，用nibabel还是会旋转90度)
- 对数据的CT值根据窗宽窗位进行裁剪
- 对数据的Size 进行重采样到[128,128,128]
- 转换成numpy(.npy)文件
"""
发现nibabel读取会旋转90度，SImpleITK会上下翻转，为了解决这个问题，需要对数据进行方向统一.
不过统不统一方向，nibabel都会旋转90度，而PaddleSeg3D也是刚好用这库处理医学数据转换成numpy，
所以转换numpy时，我不用这分割套件自带的脚本，自己用SImpleITK写一个。
"""
plt.figure(figsize=(10,5))
f_nib = nib.load("./livers/data/volume-2.nii").get_fdata(dtype=np.float32)
print(f"nibabel读取文件转换numpy后形状为x,y,z{f_nib.shape}")
f_sitk= sitk.GetArrayFromImage(sitk.ReadImage('./livers/data/volume-2.nii'))
print(f"SimpleITK读取文件转换numpy后形状为z,y,x{f_sitk.shape}")
plt.subplot(1,2,1),plt.imshow(f_nib[:,:,60],'gray'),plt.title("nibabel")
plt.subplot(1,2,2),plt.imshow(f_sitk[60,:,:],'gray'),plt.title("SimpleITK")
plt.show()
nibabel读取文件转换numpy后形状为x,y,z(512, 512, 158)
SimpleITK读取文件转换numpy后形状为z,y,x(158, 512, 512)
![[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-JDK9eHZW-1646469900138)(output_8_1.png)]](https://i-blog.csdnimg.cn/blog_migrate/f67a4519eb467acb388c7a9c09cc217e.png)
"""
读取nii文件后，设置统一的Direction,
"""
def reorient_image(image):
"""Reorients an image to standard radiology view."""
dir = np.array(image.GetDirection()).reshape(len(image.GetSize()), -1)
ind = np.argmax(np.abs(dir), axis=0)
new_size = np.array(image.GetSize())[ind]
new_spacing = np.array(image.GetSpacing())[ind]
new_extent = new_size * new_spacing
new_dir = dir[:, ind]
flip = np.diag(new_dir) < 0
flip_diag = flip * -1
flip_diag[flip_diag == 0] = 1
flip_mat = np.diag(flip_diag)
new_origin = np.array(image.GetOrigin()) + np.matmul(new_dir, (new_extent * flip))
new_dir = np.matmul(new_dir, flip_mat)
resample = sitk.ResampleImageFilter()
resample.SetOutputSpacing(new_spacing.tolist())
resample.SetSize(new_size.tolist())
resample.SetOutputDirection(new_dir.flatten().tolist())
resample.SetOutputOrigin(new_origin.tolist())
resample.SetTransform(sitk.Transform())
resample.SetDefaultPixelValue(image.GetPixelIDValue())
resample.SetInterpolator(sitk.sitkNearestNeighbor)
return resample.Execute(image)
for f in tqdm(os.listdir("./livers/data")) :
d_path = os.path.join('./livers/data',f)
seg_path = os.path.join('./livers/mask',f.replace("volume","segmentation"))
d_sitkimg = sitk.ReadImage(d_path)
d_sitkimg = reorient_image(d_sitkimg)
seg_sitkimg = sitk.ReadImage(seg_path)
seg_sitkimg = reorient_image(seg_sitkimg)
sitk.WriteImage(d_sitkimg,d_path)
sitk.WriteImage(seg_sitkimg,seg_path)
#统一方向后，再用SimpleITK读取，不再是上下翻转了，显示正常了
plt.figure(figsize=(10,5))
img= sitk.GetArrayFromImage(sitk.ReadImage('./livers/data/volume-2.nii'))
seg= sitk.GetArrayFromImage(sitk.ReadImage('./livers/mask/segmentation-2.nii'))
plt.subplot(1,2,1),plt.imshow(img[60,:,:],'gray'),plt.title("origin")
plt.subplot(1,2,2),plt.imshow(seg[60,:,:],'gray'),plt.title("mask")
plt.show()
![[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-tHBMBSi0-1646469900138)(output_10_0.png)]](https://i-blog.csdnimg.cn/blog_migrate/706c311d1a84134b504e34e70f40e172.png)
"""
对已经CT值裁剪和size重采样的数据转换成numpy并保存.npy文件
"""
from paddleseg3d.datasets.preprocess_utils import HUNorm, resample
dataset_root = "livers/"
image_dir = os.path.join(dataset_root, "images") #images 不要改，存放转换后原始数据的npy文件
label_dir = os.path.join(dataset_root, "labels") #labels 不要改，存放转换后的标签npy文件
os.makedirs(image_dir, exist_ok=True)
os.makedirs(label_dir, exist_ok=True)
ww = 350
wc = 80
for f in tqdm(os.listdir("./livers/data")) :
d_path = os.path.join('./livers/data',f)
seg_path = os.path.join('./livers/mask',f.replace("volume","segmentation"))
d_img = sitk.GetArrayFromImage(sitk.ReadImage(d_path))
d_img = HUNorm(d_img,HU_min=int(wc-int(ww/2)), HU_max=int(wc+int(ww/2)))
d_img = resample(d_img,new_shape=[128, 128, 128], order=1).astype("float32")#new_shape=[z,y,x]
seg_img = sitk.GetArrayFromImage(sitk.ReadImage(seg_path))
seg_img = resample(seg_img,new_shape=[128, 128, 128], order=0).astype("int64")
np.save(os.path.join(image_dir,f.split('.')[0]), d_img)
np.save(os.path.join(label_dir,f.split('.')[0].replace("volume","segmentation")), seg_img)
# 划分数据集，训练集和测试集为8：2
import random
random.seed(1000)
path_origin = './livers/images/'
files = list(filter(lambda x: x.endswith('.npy'), os.listdir(path_origin)))
random.shuffle(files)
rate = int(len(files) * 0.8)#训练集和测试集8：2
train_txt = open('./livers/train_list.txt','w')
val_txt = open('./livers/val_list.txt','w')
for i,f in enumerate(files):
image_path = os.path.join('images', f)
label_path = image_path.replace("images", "labels").replace('volume','segmentation')
if i < rate:
train_txt.write(image_path + ' ' + label_path+ '\n')
else:
val_txt.write(image_path + ' ' + label_path+ '\n')
train_txt.close()
val_txt.close()
print('完成')
完成
"""
最后读取转换后的npy文件并展示，可见图像显示肝脏的窗宽窗位，形状大小也为128*128，数据都归一化到0到1之间
"""
data = np.load('livers/images/volume-0.npy')
print(data.shape)
print(np.max(data),np.min(data))#把数据归一化到0-1
img = data[60,:,:]
plt.imshow(img,'gray')
plt.show()
(128, 256, 256)
1.0 0.0
![[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-OlK2aeyY-1646469900139)(output_13_1.png)]](https://i-blog.csdnimg.cn/blog_migrate/c30d39e4da44b8bd4d02ecc4e52d08bf.png)
4. 设置训练配置文件
PaddleSeg3D通过配置yaml文件来设置训练参数的。
这次训练用到的yaml文件内容如下：
data_root: livers/ #随便设置，只要是一个路径即可，因为dataset_root用来绝对路径
batch_size: 2
iters: 15000
train_dataset:
type: LungCoronavirus #不用管
dataset_root: /home/aistudio/PaddleSeg3D/livers/ #这里用绝对路径省事好似
result_dir: None #目前好似没啥用
transforms:
- type: RandomResizedCrop3D #随机缩放裁剪
size: 128
scale: [0.8, 1.4]
- type: RandomRotation3D #随机旋转
degrees: 60 #旋转角度[-60 ,+60]度
- type: RandomFlip3D # 随机翻转
mode: train #训练模式
num_classes: 3 #分割类别数（背景，肝脏，肿瘤）
val_dataset:
type: LungCoronavirus
dataset_root: /home/aistudio/PaddleSeg3D/livers/
result_dir: None
num_classes: 3
transforms: []
mode: val
optimizer: #优化器设置
type: sgd
momentum: 0.9
weight_decay: 1.0e-4
lr_scheduler: #学习率设置
type: PolynomialDecay
decay_steps: 15000
learning_rate: 0.0003
end_lr: 0
power: 0.9
loss: #损失函数，交叉熵和dice结合一起用
types:
- type: MixedLoss
losses:
- type: CrossEntropyLoss
weight: Null
- type: DiceLoss
coef: [1, 1] #设置交叉熵和dice的权重
coef: [1]
model:
type: VNet #目前只有Unet3d和Vnet两个3D分割网络选择
elu: False
in_channels: 1
num_classes: 3
pretrained: null
5. VNet
VNet是医疗3D分割网络，结构上也是采用U型结构。比起Unet3D，层内采用残差结构(第一个红框)。用卷积层替代池化层(第二个红框)。论文中也强调了Dice损失函数的作用
#打印vnet的网络结构
!python paddleseg3d/models/vnet.py
/home/aistudio/PaddleSeg3D/paddleseg3d/models/../../paddleseg3d/cvlibs/manager.py:118: UserWarning: VNet exists already! It is now updated to <class '__main__.VNet'> !!!
component_name, component))
W0228 19:18:39.534801 27494 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0228 19:18:39.538172 27494 device_context.cc:465] device: 0, cuDNN Version: 7.6.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:653: UserWarning: When training, we now always track global mean and variance.
"When training, we now always track global mean and variance.")
out Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
[-0.45796004]) Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
[0.49996352])
-----------------------------------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===========================================================================================================
Conv3D-1 [[1, 1, 128, 256, 256]] [1, 16, 128, 256, 256] 2,016
BatchNorm3D-1 [[1, 16, 128, 256, 256]] [1, 16, 128, 256, 256] 64
PReLU-1 [[1, 16, 128, 256, 256]] [1, 16, 128, 256, 256] 16
InputTransition-1 [[1, 1, 128, 256, 256]] [1, 16, 128, 256, 256] 0
Conv3D-2 [[1, 16, 128, 256, 256]] [1, 32, 64, 128, 128] 4,128
BatchNorm3D-2 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 128
PReLU-2 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 32
Conv3D-3 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 128,032
BatchNorm3D-3 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 128
PReLU-4 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 32
LUConv-1 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 0
PReLU-3 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 32
DownTransition-1 [[1, 16, 128, 256, 256]] [1, 32, 64, 128, 128] 0
Conv3D-4 [[1, 32, 64, 128, 128]] [1, 64, 32, 64, 64] 16,448
BatchNorm3D-4 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 256
PReLU-5 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 64
Conv3D-5 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 512,064
BatchNorm3D-5 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 256
PReLU-7 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 64
LUConv-2 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 0
Conv3D-6 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 512,064
BatchNorm3D-6 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 256
PReLU-8 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 64
LUConv-3 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 0
PReLU-6 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 64
DownTransition-2 [[1, 32, 64, 128, 128]] [1, 64, 32, 64, 64] 0
Conv3D-7 [[1, 64, 32, 64, 64]] [1, 128, 16, 32, 32] 65,664
BatchNorm3D-7 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 512
PReLU-9 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 128
Dropout3D-3 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 0
Conv3D-8 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 2,048,128
BatchNorm3D-8 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 512
PReLU-11 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 128
LUConv-4 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 0
Conv3D-9 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 2,048,128
BatchNorm3D-9 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 512
PReLU-12 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 128
LUConv-5 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 0
Conv3D-10 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 2,048,128
BatchNorm3D-10 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 512
PReLU-13 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 128
LUConv-6 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 0
PReLU-10 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 128
DownTransition-3 [[1, 64, 32, 64, 64]] [1, 128, 16, 32, 32] 0
Conv3D-11 [[1, 128, 16, 32, 32]] [1, 256, 8, 16, 16] 262,400
BatchNorm3D-11 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 1,024
PReLU-14 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 256
Dropout3D-4 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 0
Conv3D-12 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 8,192,256
BatchNorm3D-12 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 1,024
PReLU-16 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 256
LUConv-7 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 0
Conv3D-13 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 8,192,256
BatchNorm3D-13 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 1,024
PReLU-17 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 256
LUConv-8 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 0
PReLU-15 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 256
DownTransition-4 [[1, 128, 16, 32, 32]] [1, 256, 8, 16, 16] 0
Dropout3D-5 [[1, 256, 8, 16, 16]] [1, 256, 8, 16, 16] 0
Dropout3D-6 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 0
Conv3DTranspose-1 [[1, 256, 8, 16, 16]] [1, 128, 16, 32, 32] 262,272
BatchNorm3D-14 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 512
PReLU-18 [[1, 128, 16, 32, 32]] [1, 128, 16, 32, 32] 128
Conv3D-14 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 8,192,256
BatchNorm3D-15 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 1,024
PReLU-20 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 256
LUConv-9 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 0
Conv3D-15 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 8,192,256
BatchNorm3D-16 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 1,024
PReLU-21 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 256
LUConv-10 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 0
PReLU-19 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 256
UpTransition-1 [[1, 256, 8, 16, 16], [1, 128, 16, 32, 32]] [1, 256, 16, 32, 32] 0
Dropout3D-7 [[1, 256, 16, 32, 32]] [1, 256, 16, 32, 32] 0
Dropout3D-8 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 0
Conv3DTranspose-2 [[1, 256, 16, 32, 32]] [1, 64, 32, 64, 64] 131,136
BatchNorm3D-17 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 256
PReLU-22 [[1, 64, 32, 64, 64]] [1, 64, 32, 64, 64] 64
Conv3D-16 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 2,048,128
BatchNorm3D-18 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 512
PReLU-24 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 128
LUConv-11 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 0
Conv3D-17 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 2,048,128
BatchNorm3D-19 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 512
PReLU-25 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 128
LUConv-12 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 0
PReLU-23 [[1, 128, 32, 64, 64]] [1, 128, 32, 64, 64] 128
UpTransition-2 [[1, 256, 16, 32, 32], [1, 64, 32, 64, 64]] [1, 128, 32, 64, 64] 0
Conv3DTranspose-3 [[1, 128, 32, 64, 64]] [1, 32, 64, 128, 128] 32,800
BatchNorm3D-20 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 128
PReLU-26 [[1, 32, 64, 128, 128]] [1, 32, 64, 128, 128] 32
Conv3D-18 [[1, 64, 64, 128, 128]] [1, 64, 64, 128, 128] 512,064
BatchNorm3D-21 [[1, 64, 64, 128, 128]] [1, 64, 64, 128, 128] 256
PReLU-28 [[1, 64, 64, 128, 128]] [1, 64, 64, 128, 128] 64
LUConv-13 [[1, 64, 64, 128, 128]] [1, 64, 64, 128, 128] 0
PReLU-27 [[1, 64, 64, 128, 128]] [1, 64, 64, 128, 128] 64
UpTransition-3 [[1, 128, 32, 64, 64], [1, 32, 64, 128, 128]] [1, 64, 64, 128, 128] 0
Conv3DTranspose-4 [[1, 64, 64, 128, 128]] [1, 16, 128, 256, 256] 8,208
BatchNorm3D-22 [[1, 16, 128, 256, 256]] [1, 16, 128, 256, 256] 64
PReLU-29 [[1, 16, 128, 256, 256]] [1, 16, 128, 256, 256] 16
Conv3D-19 [[1, 32, 128, 256, 256]] [1, 32, 128, 256, 256] 128,032
BatchNorm3D-23 [[1, 32, 128, 256, 256]] [1, 32, 128, 256, 256] 128
PReLU-31 [[1, 32, 128, 256, 256]] [1, 32, 128, 256, 256] 32
LUConv-14 [[1, 32, 128, 256, 256]] [1, 32, 128, 256, 256] 0
PReLU-30 [[1, 32, 128, 256, 256]] [1, 32, 128, 256, 256] 32
UpTransition-4 [[1, 64, 64, 128, 128], [1, 16, 128, 256, 256]] [1, 32, 128, 256, 256] 0
Conv3D-20 [[1, 32, 128, 256, 256]] [1, 2, 128, 256, 256] 8,002
BatchNorm3D-24 [[1, 2, 128, 256, 256]] [1, 2, 128, 256, 256] 8
PReLU-32 [[1, 2, 128, 256, 256]] [1, 2, 128, 256, 256] 2
Conv3D-21 [[1, 2, 128, 256, 256]] [1, 2, 128, 256, 256] 6
OutputTransition-1 [[1, 32, 128, 256, 256]] [1, 2, 128, 256, 256] 0
===========================================================================================================
Total params: 45,609,250
Trainable params: 45,598,618
Non-trainable params: 10,632
-----------------------------------------------------------------------------------------------------------
Input size (MB): 32.00
Forward/backward pass size (MB): 29372.00
Params size (MB): 173.99
Estimated Total Size (MB): 29577.99
-----------------------------------------------------------------------------------------------------------
Vnet test is complete
6. 开始训练
一行代码开始训练
#开始训练
"""
!python3 train.py --config /home/aistudio/vnet_livers.yml #yaml文件路径
--save_dir "/home/aistudio/output/livers" #模型保存路径
--save_interval 500 #多少iters保存模型参数
--log_iters 60 #多少iters打印一次信息
--iters 15000 #训练多少iters
--num_workers 6
--do_eval #是否训练期间验证模型性能
--use_vdl #是否使用VisualDL可视化训练指标的走势
"""
!python3 train.py --config /home/aistudio/vnet_livers.yml \
--save_dir "/home/aistudio/output/livers_vent128x128x128" \
--save_interval 500 --log_iters 20 \
--num_workers 6 --do_eval --use_vdl
7. 模型的验证和导出
2022-02-28 01:24:12 [INFO] [EVAL] #Images: 6, Dice: 0.6289, Loss: 0.634533
2022-02-28 01:24:12 [INFO] [EVAL] Class dice:
[0.9893 0.8679 0.0295]
用VNet跑了15000，验证集的效果如下：肝脏的dice为0.8679，肿瘤的dice为0.0295。对肿瘤这些小目标的检测效果比较差
#验证
"""
!python3 val.py --config /home/aistudio/vnet_livers.yml \ #yaml文件路径
--model_path /home/aistudio/output/livers/best_model/model.pdparams \ #最优模型的路径
--save_dir /home/aistudio/output/livers/best_model #验证后结果的保存路径
"""
!python3 val.py --config /home/aistudio/vnet_livers.yml \
--model_path /home/aistudio/output/livers/best_model/model.pdparams \
--save_dir /home/aistudio/output/livers/best_model
2022-02-28 01:24:04 [INFO]
---------------Config Information---------------
batch_size: 2
data_root: livers/
iters: 15000
loss:
coef:
- 1
types:
- coef:
- 0.7
- 0.3
losses:
- type: CrossEntropyLoss
weight: null
- type: DiceLoss
type: MixedLoss
lr_scheduler:
decay_steps: 15000
end_lr: 0
learning_rate: 0.0003
power: 0.9
type: PolynomialDecay
model:
elu: false
in_channels: 1
num_classes: 3
pretrained: null
type: VNet
optimizer:
momentum: 0.9
type: sgd
weight_decay: 0.0001
train_dataset:
dataset_root: /home/aistudio/PaddleSeg3D/livers/
mode: train
num_classes: 3
result_dir: None
transforms:
- scale:
- 0.8
- 1.2
size: 128
type: RandomResizedCrop3D
- degrees: 60
type: RandomRotation3D
- type: RandomFlip3D
type: LungCoronavirus
val_dataset:
dataset_root: /home/aistudio/PaddleSeg3D/livers/
mode: val
num_classes: 3
result_dir: None
transforms: []
type: LungCoronavirus
------------------------------------------------
W0228 01:24:04.360675 32748 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0228 01:24:04.360720 32748 device_context.cc:465] device: 0, cuDNN Version: 7.6.
2022-02-28 01:24:08 [INFO] Loading pretrained model from /home/aistudio/output/livers/best_model/model.pdparams
2022-02-28 01:24:09 [INFO] There are 178/178 variables loaded into VNet.
2022-02-28 01:24:09 [INFO] Loaded trained params of model successfully
2022-02-28 01:24:09 [INFO] Start evaluating (total_samples: 6, total_iters: 6)...
2022-02-28 01:24:10 [INFO] [EVAL] Sucessfully save iter 0 pred and label.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.bool, the right dtype will convert to paddle.float32
format(lhs_dtype, rhs_dtype, lhs_dtype))
1/6 [====>.........................] - ETA: 3s - batch_cost: 0.6990 - reader cost: 0.20632022-02-28 01:24:10 [INFO] [EVAL] Sucessfully save iter 1 pred and label.
2/6 [=========>....................] - ETA: 2s - batch_cost: 0.5683 - reader cost: 0.10322022-02-28 01:24:11 [INFO] [EVAL] Sucessfully save iter 2 pred and label.
3/6 [==============>...............] - ETA: 1s - batch_cost: 0.5247 - reader cost: 0.06882022-02-28 01:24:11 [INFO] [EVAL] Sucessfully save iter 3 pred and label.
4/6 [===================>..........] - ETA: 1s - batch_cost: 0.5027 - reader cost: 0.05172022-02-28 01:24:12 [INFO] [EVAL] Sucessfully save iter 4 pred and label.
5/6 [========================>.....] - ETA: 0s - batch_cost: 0.4912 - reader cost: 0.04132022-02-28 01:24:12 [INFO] [EVAL] Sucessfully save iter 5 pred and label.
6/6 [==============================] - 3s 483ms/step - batch_cost: 0.4830 - reader cost: 0.0345
2022-02-28 01:24:12 [INFO] [EVAL] #Images: 6, Dice: 0.6289, Loss: 0.634533
2022-02-28 01:24:12 [INFO] [EVAL] Class dice:
[0.9893 0.8679 0.0295]
#导出模型，方便推理和部署
"""
!python export.py --config /home/aistudio/vnet_livers.yml \ #yaml文件路径
--save_dir '/home/aistudio/save_model' \#导出模型的保存路径
--model_path /home/aistudio/output/livers/best_model/model.pdparams #需要导出的模型的路径
"""
!python export.py --config /home/aistudio/vnet_livers.yml \
--save_dir '/home/aistudio/save_model' \
--model_path /home/aistudio/output/livers/best_model/model.pdparams
#加载导出来的模型，对数据进行推理
!python deploy/python/infer.py --config /home/aistudio/save_model/deploy.yaml \
--image_path /home/aistudio/PaddleSeg3D/livers/images/volume-5.npy \
--save_dir '/home/aistudio'
#可视化推理后的结果
pre = np.load('/home/aistudio/volume-5.npy')
pre = np.squeeze(pre)
print(pre.shape)
img = pre[60,:,:]
plt.imshow(img,'gray')
plt.show()
(256, 128, 128)
![[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-iYgKTcE5-1646469900140)(output_23_1.png)]](https://i-blog.csdnimg.cn/blog_migrate/24002b8b7dfe38ffa188150f472449ad.png)
学大模型，用大模型上飞桨星河社区！每天8点V100G算力免费领！免费领取ERNIE 4.0 100w Token >>>
更多推荐
1
0- 0
已为社区贡献1438条内容
所有评论(0)