甘肃兰州天气预报,seo专业优化方法,安徽建设工程信息中标,南京市网站基于UNet和camvid数据集的道路分割h(1.3.0)#xff1a;
背景 语义分割是深度学习中的一个非常重要的研究方向#xff0c;并且UNet是语义分割中一个非常经典的模型。在本次博客中#xff0c;我尝试用UNet对camvid dataset数据集进行道路分割#xff0c;大致期望的效果如下)
背景 语义分割是深度学习中的一个非常重要的研究方向并且UNet是语义分割中一个非常经典的模型。在本次博客中我尝试用UNet对camvid dataset数据集进行道路分割大致期望的效果如下
原图 道路分割效果 本博客的代码参考了以下链接
https://github.com/milesial/Pytorch-UNet https://github.com/qubvel/segmentation_models.pytorch 1 2 数据集介绍及处理 之前的博客里我几乎不怎么介绍数据集因为用到的数据集比较简单但是在使用camvid dataset的时候我脑袋都大了用了两三个小时才搞清楚这个数据集到底是啥情况。
数据集下载链接 虽然数据集的主页还可以访问但是下载链接好像都失效了所以最后还是用了aws上存储链接。
https://s3.amazonaws.com/fast-ai-imagelocal/camvid.tgz 1 数据说明 camvid数据集里包括三种重要信息分别是RGB影像、语义分割图和标签说明。 RGB影像就不用多少了为三通道RGB。 语义分割图为单通道其中像素值代表了当前像素的类别其对应关系存储在标签说明里。 标签说明对应了语义分割图像素值和类别的关系如下
0 Animal 1 Archway 2 Bicyclist 3 Bridge 4 Building 5 Car 6 CartLuggagePram 7 Child 8 Column_Pole 9 Fence 10 LaneMkgsDriv 11 LaneMkgsNonDriv 12 Misc_Text 13 MotorcycleScooter 14 OtherMoving 15 ParkingBlock 16 Pedestrian 17 Road 18 RoadShoulder 19 Sidewalk 20 SignSymbol 21 Sky 22 SUVPickupTruck 23 TrafficCone 24 TrafficLight 25 Train 26 Tree 27 Truck_Bus 28 Tunnel 29 VegetationMisc 30 Void 31 Wall 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 数据处理 下载后数据后会有一个压缩包包括images和labels分别对应的是RGB的影像和像素的标签。 首先要做以下的一些处理包括
重命名labels的名称去掉名称里的_P保证labels和images的名称一致 将原始数据集按照7:2:1的规则分割成train:valid:test rename.py import os,sys
cur_path D:/camvid/camvid/labels # 你的数据集路径
labels os.listdir(cur_path)
for label in labels: old_label str(label) new_label label.replace(_P.png,.png) print(old_label, new_label) os.rename(os.path.join(cur_path,old_label),os.path.join(cur_path,new_label)) 1 2 3 4 5 6 7 8 9 10 11 12 split_dataset.py import os import random import shutil
# 数据集路径 dataset_path D:/camvid/camvid images_path D:/camvid/camvid/images labels_path D:/camvid/camvid/labels
images_name os.listdir(images_path) images_num len(images_name) alpha int( images_num * 0.7 ) beta int( images_num * 0.9 )
print(images_num)
random.shuffle(images_name)
train_list images_name[0:alpha] valid_list images_name[alpha:beta] test_list images_name[beta:images_num]
# 确认分割正确 print(train list: ,len(train_list)) print(valid list: ,len(valid_list)) print(test list: ,len(test_list)) print(total num: ,len(test_list)len(valid_list)len(train_list))
# 创建train,valid和test的文件夹 train_images_path os.path.join(dataset_path,train_images) train_labels_path os.path.join(dataset_path,train_labels) if os.path.exists(train_images_path)False: os.mkdir(train_images_path ) if os.path.exists(train_labels_path)False: os.mkdir(train_labels_path)
valid_images_path os.path.join(dataset_path,valid_images) valid_labels_path os.path.join(dataset_path,valid_labels) if os.path.exists(valid_images_path)False: os.mkdir(valid_images_path ) if os.path.exists(valid_labels_path)False: os.mkdir(valid_labels_path)
test_images_path os.path.join(dataset_path,test_images) test_labels_path os.path.join(dataset_path,test_labels) if os.path.exists(test_images_path)False: os.mkdir(test_images_path ) if os.path.exists(test_labels_path)False: os.mkdir(test_labels_path)
# 拷贝影像到指定目录 for image in train_list: shutil.copy(os.path.join(images_path,image), os.path.join(train_images_path,image)) shutil.copy(os.path.join(labels_path,image), os.path.join(train_labels_path,image))
for image in valid_list: shutil.copy(os.path.join(images_path,image), os.path.join(valid_images_path,image)) shutil.copy(os.path.join(labels_path,image), os.path.join(valid_labels_path,image))
for image in test_list: shutil.copy(os.path.join(images_path,image), os.path.join(test_images_path,image)) shutil.copy(os.path.join(labels_path,image), os.path.join(test_labels_path,image)) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 代码 代码链接https://github.com/Yannnnnnnnnnnn/learnPyTorch/blob/master/road%20segmentation%20(camvid).ipynb
# 导入库 import os os.environ[CUDA_VISIBLE_DEVICES] 0 import numpy as np import cv2 import matplotlib.pyplot as plt # 设置数据集路径 DATA_DIR D:/camvid/camvid # 根据自己的路径来设置
x_train_dir os.path.join(DATA_DIR, train_images) y_train_dir os.path.join(DATA_DIR, train_labels)
x_valid_dir os.path.join(DATA_DIR, valid_images) y_valid_dir os.path.join(DATA_DIR, valid_labels)
x_test_dir os.path.join(DATA_DIR, test_images) y_test_dir os.path.join(DATA_DIR, test_labels)
# 导入pytorch import torch from torch.utils.data import DataLoader from torch.utils.data import Dataset as BaseDataset import torch.nn as nn import torch.nn.functional as F from torch import optim
# 自定义Dataloader class Dataset(BaseDataset): CamVid Dataset. Read images, apply augmentation and preprocessing transformations. Args: images_dir (str): path to images folder masks_dir (str): path to segmentation masks folder class_values (list): values of classes to extract from segmentation mask augmentation (albumentations.Compose): data transfromation pipeline (e.g. flip, scale, etc.) preprocessing (albumentations.Compose): data preprocessing (e.g. noralization, shape manipulation, etc.) def __init__( self, images_dir, masks_dir, augmentationNone, ): self.ids os.listdir(images_dir) self.images_fps [os.path.join(images_dir, image_id) for image_id in self.ids] self.masks_fps [os.path.join(masks_dir, image_id) for image_id in self.ids] self.augmentation augmentation def __getitem__(self, i): # read data image cv2.imread(self.images_fps[i]) image cv2.cvtColor(image, cv2.COLOR_BGR2RGB) mask cv2.imread(self.masks_fps[i], 0) # 抱歉代码写的这么粗暴意思就是讲mask里的道路设置为前景而其他设置为背景 # road mask (mask17) mask mask.astype(float) # apply augmentations if self.augmentation: sample self.augmentation(imageimage, maskmask) image, mask sample[image], sample[mask] # 这里必须设置一个mask的shape因为前边的形状是320,320 return image, mask.reshape(1,320,320) def __len__(self): return len(self.ids)
# 数据增强 # 关于albumentations 怎么用我就不废话了 # 需要说明的是我本身是打算用pytorch自带的transform # 然而我实在没有搞明白怎么同时对image和mask进行增强 # 如果连续调用两次transform那么image和mask的增强方式都不一致肯定不行 # 如果将[image;mask]堆砌在一起放到transform里image和mask的增强方式倒是一样了但是transform最后一步的toTensor会把mask归一化这肯定也是不行的 import albumentations as albu def get_training_augmentation(): train_transform [ albu.HorizontalFlip(p0.5), albu.Resize(height320, width320, always_applyTrue), albu.ShiftScaleRotate(scale_limit0.1, rotate_limit20, shift_limit0.1, p1, border_mode0), ] return albu.Compose(train_transform)
def get_test_augmentation(): train_transform [ albu.Resize(height320, width320, always_applyTrue), ] return albu.Compose(train_transform)
augmented_dataset Dataset( x_train_dir, y_train_dir, augmentationget_training_augmentation(), ) # 定义UNet的基本模块 # 代码来自https://github.com/milesial/Pytorch-UNet class DoubleConv(nn.Module): (convolution [BN] ReLU) * 2 def __init__(self, in_channels, out_channels): super().__init__() self.double_conv nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size3, padding1), nn.BatchNorm2d(out_channels), nn.ReLU(inplaceTrue), nn.Conv2d(out_channels, out_channels, kernel_size3, padding1), nn.BatchNorm2d(out_channels), nn.ReLU(inplaceTrue) ) def forward(self, x): return self.double_conv(x)
class Down(nn.Module): Downscaling with maxpool then double conv def __init__(self, in_channels, out_channels): super().__init__() self.maxpool_conv nn.Sequential( nn.MaxPool2d(2), DoubleConv(in_channels, out_channels) ) def forward(self, x): return self.maxpool_conv(x)
class Up(nn.Module): Upscaling then double conv def __init__(self, in_channels, out_channels, bilinearTrue): super().__init__() # if bilinear, use the normal convolutions to reduce the number of channels if bilinear: self.up nn.Upsample(scale_factor2, modebilinear, align_cornersTrue) else: self.up nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size2, stride2) self.conv DoubleConv(in_channels, out_channels) def forward(self, x1, x2): x1 self.up(x1) # input is CHW diffY torch.tensor([x2.size()[2] - x1.size()[2]]) diffX torch.tensor([x2.size()[3] - x1.size()[3]]) x1 F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) # if you have padding issues, see # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd x torch.cat([x2, x1], dim1) return self.conv(x) class OutConv(nn.Module): def __init__(self, in_channels, out_channels): super(OutConv, self).__init__() self.conv nn.Conv2d(in_channels, out_channels, kernel_size1) def forward(self, x): return self.conv(x)
# UNet class UNet(nn.Module): def __init__(self, n_channels, n_classes, bilinearTrue): super(UNet, self).__init__() self.n_channels n_channels self.n_classes n_classes self.bilinear bilinear # 考虑到我电脑的显卡大小我降低了参数~~无奈之举 self.inc DoubleConv(n_channels, 32) self.down1 Down(32, 64) self.down2 Down(64, 128) self.down3 Down(128, 256) self.down4 Down(256, 256) self.up1 Up(512, 128, bilinear) self.up2 Up(256, 64, bilinear) self.up3 Up(128, 32, bilinear) self.up4 Up(64, 32, bilinear) self.outc OutConv(32, n_classes) self.out torch.sigmoid #此处记得有sigmoid def forward(self, x): x1 self.inc(x) x2 self.down1(x1) x3 self.down2(x2) x4 self.down3(x3) x5 self.down4(x4) x self.up1(x5, x4) x self.up2(x, x3) x self.up3(x, x2) x self.up4(x, x1) logits self.outc(x) logits self.out(logits) return logits
# 设置train数据集 # 原谅我偷懒并没有valid因为我并没有train多少epoch train_dataset Dataset( x_train_dir, y_train_dir, augmentationget_training_augmentation(), ) train_loader DataLoader(train_dataset, batch_size8, shuffleTrue)
# 准备训练定义模型我只做了两分类偷懒 # 另外由于我修改了UNet模型所以encoder部分肯定不能用预训练模型 # 并且我真的很反感每次都用预训练模型没啥成就感。。。 net UNet(n_channels3, n_classes1)
# 训练 from torch.autograd import Variable net.cuda()
# 这里我说一下我是怎么train的 # 先lr0.01,train大概40个epoch # 然后lr0.005,train大概40个epoch # 最后在lr0.0001,train大概20个epoch optimizer optim.RMSprop(net.parameters(), lr0.4, weight_decay1e-8)
# 这个loss是专门用于二分类的吴恩达的课程我记得前几节课就讲了 criterion nn.BCELoss()
device cuda for epoch in range(10): net.train() epoch_loss 0 for data in train_loader: # 修改一下数据格式 images,labels data images images.permute(0,3,1,2) # 交换通道顺序 images images/255. # 把image的值归一化到[0,1] images Variable(images.to(devicedevice, dtypetorch.float32)) labels Variable(labels.to(devicedevice, dtypetorch.float32)) pred net(images) # 这里我不知道是看了哪里的代码 # 最开始犯傻写成了 loss criterion(pred.view(-1), labels.view(-1)) # 结果loss很久都不下降 # 还不知道为啥 loss criterion(pred, labels) epoch_loss loss.item() optimizer.zero_grad() loss.backward() optimizer.step() print(loss: , loss.item()) # 测试 test_dataset_noaug Dataset( x_train_dir, y_train_dir, augmentationget_test_augmentation(), )
image, mask test_dataset_noaug[77] show_image image with torch.no_grad(): image image/255. image image.astype(float32) image torch.from_numpy(image) image image.permute(2,0,1) image image.to() print(image.shape) pred net(image.unsqueeze(0).cuda()) pred pred.cpu()
# 大于0.5我才认为是对的 pred pred0.5 # 展示图如下 visualize(imageshow_image,GTmask[0,:,:],Predpred[0,0,:,:]) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 结果及分析 看一下最终结果做一下分析讨论总结经验。
结果 关于结果这里随便展示几个吧感觉还行。
分析 这是我第一次train分割的网络有一些经验写一写。
最开始train的时候我比较心贪用的原始分辨率的影像720*960结果网络参数太多根本train不了而且训练效果也不好最后降采样才正常了且效果变好了。 在训练之前务必搞清楚数据集的格式不然都不知道在train啥。 我在选择分割对象的时候其实最开始也是用car,但是明显这个类别在影像里特别少效果一直不好最后选取了sky,road和wall这种样本较多的效果才比较好这说明样本数量还是很重要的。 ———————————————— 版权声明本文为CSDN博主「Stone_Yannn」的原创文章遵循CC 4.0 BY-SA版权协议转载请附上原文出处链接及本声明。 原文链接https://blog.csdn.net/u012348774/article/details/104300366
