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pytorch使用voc分割数据集训练FCN流程讲解

发布时间:2022-12-08 14:10:08来源:周小白软件园编辑:本站整理

语义分割是对图像中的每一个像素进行分类,从而完成图像分割的过程。分割主要用于医学图像领域和无人驾驶领域。

和其他算法一样,图像分割发展过程也经历了传统算法到深度学习算法的转变,传统的分割算法包括阈值分割、分水岭、边缘检测等等,面临的问题也跟其他传统图像处理算法一样,就是鲁棒性不够,但在一些场景单一不变的场合,传统图像处理依旧用的较多。

FCN是2014年的一篇论文,深度学习语义分割的开山之作,从思想上奠定了语义分割的基础。

Fully Convolutional Networks for Semantic Segmentation

Submitted on 14 Nov 2014

https://arxiv.org/abs/1411.4038

一、FCN理论介绍

上图是原论文中的截图,从整体架构上描绘了FCN的网络架构。其实就是图像经过一系列卷积运算,然后再上采样成原图大小,输出每一个像素的类别概率。

上图更加细致的描述了FCN的网络。backbone采用VGG16,把VGG的fully-connect层用卷积来表示,即conv6-7(一个大小和feature_map同样size的卷积核,就相当于全连接)。总的来说,网络有下列几个关键点:

1. Fully Convolution: 用于解决像素的预测问题。通过将基础网络(如VGG16)最后全连接层替换为卷积层,可实现任意大小的图像输入,并且输出图像大小与输入相对应;

2.Transpose Convolution: 上采样过程,用于恢复图片尺寸,方便后续进行逐个像素的预测;

3. Skip Architecture : 用于融合高底层特征信息。因为卷积是个下采样操作,而转置卷积虽然恢复了图像尺寸,但毕竟不是卷积的逆操作,所以信息肯定有丢失,而skip architecture可以融合千层的细粒度信息和深层的粗粒度信息,提高分割的精细程度。

FCN-32s: 没有跳连接,按照每层转置卷积放大2倍的速度放大,经过五层后放大32倍复原原图大小。

FCN-16s: 一个skip-connect,(1/32)放大为(1/16)后,再与vgg的(1/16)相加,然后继续放大,直到原图大小。

FCN-8s: 两个skip-connect,一个是(1/32)放大为(1/16)后,再与vgg的(1/16)相加;另外一个是(1/16)放大为(1/8)之后,再与vgg的(1/8)相加,然后继续放大,直到原图大小。

二、训练过程

pytorch训练深度学习模型主要实现三个文件即可,分别为data.py, model.py, train.py。其中data.py里实现数据批量处理功能,model.py定义网络模型,train.py实现训练步骤。

2.1 voc数据集介绍

下载地址:Pascal VOC Dataset Mirror

图片的名称在/ImageSets/Segmentation/train.txt ans val.txt里

图片都在./data/VOC2012/JPEGImages文件夹下面,需要在train.txt读取的每一行后面加上.jpg

标签都在./data/VOC2012/SegmentationClass文件夹下面,需要在读取的每一行后面加上.png

voc_seg_data.py

import torch import torch.nn as nn import torchvision.transforms as T from torch.utils.data import DataLoader,Dataset import numpy as np import os from PIL import Image from datetime import datetime class VOC_SEG(Dataset): def __init__(self, root, width, height, train=True, transforms=None): # 图像统一剪切尺寸(width, height) self.width = width self.height = height # VOC数据集中对应的标签 self.classes = ['background','aeroplane','bicycle','bird','boat',    'bottle','bus','car','cat','chair','cow','diningtable',    'dog','horse','motorbike','person','potted plant',    'sheep','sofa','train','tv/monitor'] # 各种标签所对应的颜色 self.colormap = [[0,0,0],[128,0,0],[0,128,0], [128,128,0], [0,0,128], [128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0], [64,128,0],[192,128,0],[64,0,128],[192,0,128], [64,128,128],[192,128,128],[0,64,0],[128,64,0], [0,192,0],[128,192,0],[0,64,128]] # 辅助变量 self.fnum = 0 if transforms is None: normalize = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) self.transforms = T.Compose([ T.ToTensor(), normalize ]) # 像素值(RGB)与类别label(0,1,3...)一一对应 self.cm2lbl = np.zeros(256**3) for i, cm in enumerate(self.colormap): self.cm2lbl[(cm[0]*256+cm[1])*256+cm[2]] = i if train: txt_fname = root+"/ImageSets/Segmentation/train.txt" else: txt_fname = root+"/ImageSets/Segmentation/val.txt" with open(txt_fname, 'r') as f: images = f.read().split() imgs = [os.path.join(root, "JPEGImages", item+".jpg") for item in images] labels = [os.path.join(root, "SegmentationClass", item+".png") for item in images] self.imgs = self._filter(imgs) self.labels = self._filter(labels) if train: print("训练集:加载了 " + str(len(self.imgs)) + " 张图片和标签" + ",过滤了" + str(self.fnum) + "张图片") else: print("测试集:加载了 " + str(len(self.imgs)) + " 张图片和标签" + ",过滤了" + str(self.fnum) + "张图片") def _crop(self, data, label): """ 切割函数,默认都是从图片的左上角开始切割。切割后的图片宽是width,高是height data和label都是Image对象 """ box = (0,0,self.width,self.height) data = data.crop(box) label = label.crop(box) return data, label def _image2label(self, im): data = np.array(im, dtype="int32") idx = (data[:,:,0]*256+data[:,:,1])*256+data[:,:,2] return np.array(self.cm2lbl[idx], dtype="int64") def _image_transforms(self, data, label): data, label = self._crop(data,label) data = self.transforms(data) label = self._image2label(label) label = torch.from_numpy(label) return data, label def _filter(self, imgs):  img = [] for im in imgs: if (Image.open(im).size[1] >= self.height and     Image.open(im).size[0] >= self.width): img.append(im) else: self.fnum  = self.fnum+1 return img def __getitem__(self, index: int): img_path = self.imgs[index] label_path = self.labels[index] img = Image.open(img_path) label = Image.open(label_path).convert("RGB") img, label = self._image_transforms(img, label) return img, label def __len__(self) : return len(self.imgs) if __name__=="__main__": root = "./VOCdevkit/VOC2012" height = 224 width = 224 voc_train = VOC_SEG(root, width, height, train=True) voc_test = VOC_SEG(root, width, height, train=False) # train_data = DataLoader(voc_train, batch_size=8, shuffle=True) # valid_data = DataLoader(voc_test, batch_size=8) for data, label in voc_train: print(data.shape) print(label.shape) break
我这里为了省事把一些辅助函数,如_crop(), _filter(),还是有变量colormap等都写到类里面了。实际上脱离出来另外写一个数据预处理的文件比较好,这样在训练结束后,推理测试时可以直接调用相应的处理函数。数据处理的结果是得到data, label。data是tensor格式的图像,label也是tensor,且已经把像素(RGB)替换为了int类别号。这样在训练时候,交叉熵函数直接会实现one-hot处理,就跟训练分类网络一样。

2.2 网络定义

fcn8s_net.py

import torch import torch.nn as nn from torch.autograd import Variable import torch.nn.functional as F from torchsummary import summary from torchvision import models class FCN8s(nn.Module): def __init__(self, num_classes=21): super(FCN8s,self).__init__() net = models.vgg16(pretrained=True)   # 从预训练模型加载VGG16网络参数 self.premodel = net.features  # 只使用Vgg16的五层卷积层(特征提取层)(3,224,224)----->(512,7,7) # self.conv6 = nn.Conv2d(512,512,kernel_size=1,stride=1,padding=0,dilation=1)  # self.conv7 = nn.Conv2d(512,512,kernel_size=1,stride=1,padding=0,dilation=1) # (512,7,7) self.relu = nn.ReLU(inplace=True) self.deconv1 = nn.ConvTranspose2d(512,512,kernel_size=3,stride=2,padding=1,dilation=1,output_padding=1)  # x2 self.bn1 = nn.BatchNorm2d(512) # (512, 14, 14) self.deconv2 = nn.ConvTranspose2d(512,256,kernel_size=3,stride=2,padding=1,dilation=1,output_padding=1)  # x2 self.bn2 = nn.BatchNorm2d(256) # (256, 28, 28) self.deconv3 = nn.ConvTranspose2d(256,128,kernel_size=3,stride=2,padding=1,dilation=1,output_padding=1)  # x2 self.bn3 = nn.BatchNorm2d(128) # (128, 56, 56) self.deconv4 = nn.ConvTranspose2d(128,64,kernel_size=3,stride=2,padding=1,dilation=1,output_padding=1)   # x2 self.bn4 = nn.BatchNorm2d(64) # (64, 112, 112) self.deconv5 = nn.ConvTranspose2d(64,32,kernel_size=3,stride=2,padding=1,dilation=1,output_padding=1)# x2 self.bn5 = nn.BatchNorm2d(32) # (32, 224, 224) self.classifier = nn.Conv2d(32, num_classes, kernel_size=1) # (num_classes, 224, 224) def forward(self, input): x = input for i in range(len(self.premodel)): x = self.premodel[i](x) if i == 16: x3 = x  # maxpooling3的feature map (1/8) if i == 23: x4 = x  # maxpooling4的feature map (1/16) if i == 30: x5 = x  # maxpooling5的feature map (1/32) # 五层转置卷积,每层size放大2倍,与VGG16刚好相反。两个skip-connect score = self.relu(self.deconv1(x5))   # out_size = 2*in_size (1/16) score = self.bn1(score + x4) score = self.relu(self.deconv2(score)) # out_size = 2*in_size (1/8)   score = self.bn2(score + x3) score = self.bn3(self.relu(self.deconv3(score)))  # out_size = 2*in_size (1/4) score = self.bn4(self.relu(self.deconv4(score)))  # out_size = 2*in_size (1/2) score = self.bn5(self.relu(self.deconv5(score)))  # out_size = 2*in_size (1) score = self.classifier(score)# size不变,使输出的channel等于类别数 return score if __name__ == "__main__": model = FCN8s() device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = model.to(device) print(model)

FCN的网络代码实现上,在网上查的都有所差异,不过总体都是卷积+转置卷积+跳链接的结构。实际上只要实现特征提取(提取抽象特征)——转置卷积(恢复原图大小)——给每一个像素分类的过程就够了。

本次实验采用vgg16的五层卷积层作为特征提取网络,然后接五个转置卷积(2x)恢复到原图大小,然后再接一个卷积层把feature map的通道调整为类别个数(21)。最后再softmax分类就行了。

2.3 训练

train.py

import torch import torch.nn as nn from torch.utils.data import DataLoader,Dataset from voc_seg_data import VOC_SEG from fcn_net import FCN8s import os import numpy as np # 计算混淆矩阵 def _fast_hist(label_true, label_pred, n_class): mask = (label_true >= 0) & (label_true < n_class) hist = np.bincount( n_class * label_true[mask].astype(int) + label_pred[mask], minlength=n_class ** 2).reshape(n_class, n_class) return hist # 根据混淆矩阵计算Acc和mIou def label_accuracy_score(label_trues, label_preds, n_class): """Returns accuracy score evaluation result.   - overall accuracy   - mean accuracy   - mean IU """ hist = np.zeros((n_class, n_class)) for lt, lp in zip(label_trues, label_preds): hist += _fast_hist(lt.flatten(), lp.flatten(), n_class) acc = np.diag(hist).sum() / hist.sum() with np.errstate(divide='ignore', invalid='ignore'): acc_cls = np.diag(hist) / hist.sum(axis=1) acc_cls = np.nanmean(acc_cls) with np.errstate(divide='ignore', invalid='ignore'): iu = np.diag(hist) / ( hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist) ) mean_iu = np.nanmean(iu) freq = hist.sum(axis=1) / hist.sum() return acc, acc_cls, mean_iu def main(): # 1. load dataset root = "./VOCdevkit/VOC2012" batch_size = 32 height = 224 width = 224 voc_train = VOC_SEG(root, width, height, train=True) voc_test = VOC_SEG(root, width, height, train=False) train_dataloader = DataLoader(voc_train,batch_size=batch_size,shuffle=True) val_dataloader = DataLoader(voc_test,batch_size=batch_size,shuffle=True) # 2. load model num_class = 21 model = FCN8s(num_classes=num_class) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = model.to(device) # 3. prepare super parameters criterion = nn.CrossEntropyLoss()  optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.7) epoch = 50 # 4. train val_acc_list = [] out_dir = "./checkpoints/" if not os.path.exists(out_dir): os.makedirs(out_dir) for epoch in range(0, epoch): print('\nEpoch: %d' % (epoch + 1)) model.train() sum_loss = 0.0 for batch_idx, (images, labels) in enumerate(train_dataloader): length = len(train_dataloader) images, labels = images.to(device), labels.to(device) optimizer.zero_grad() outputs = model(images) # torch.size([batch_size, num_class, width, height]) loss = criterion(outputs, labels) loss.backward() optimizer.step() sum_loss += loss.item() predicted = torch.argmax(outputs.data, 1) label_pred = predicted.data.cpu().numpy() label_true = labels.data.cpu().numpy() acc, acc_cls, mean_iu = label_accuracy_score(label_true,label_pred,num_class) print('[epoch:%d, iter:%d] Loss: %.03f | Acc: %.3f%% | Acc_cls: %.03f%% |Mean_iu: %.3f'  % (epoch + 1, (batch_idx + 1 + epoch * length), sum_loss / (batch_idx + 1),  100. *acc, 100.*acc_cls, mean_iu)) #get the ac with testdataset in each epoch print('Waiting Val...') mean_iu_epoch = 0.0 mean_acc = 0.0 mean_acc_cls = 0.0 with torch.no_grad(): for batch_idx, (images, labels) in enumerate(val_dataloader): model.eval() images, labels = images.to(device), labels.to(device) outputs = model(images) predicted = torch.argmax(outputs.data, 1) label_pred = predicted.data.cpu().numpy() label_true = labels.data.cpu().numpy() acc, acc_cls, mean_iu = label_accuracy_score(label_true,label_pred,num_class) # total += labels.size(0) # iou = torch.sum((predicted == labels.data), (1,2)) / float(width*height) # iou = torch.sum(iou) # correct += iou mean_iu_epoch += mean_iu mean_acc += acc mean_acc_cls += acc_cls print('Acc_epoch: %.3f%% | Acc_cls_epoch: %.03f%% |Mean_iu_epoch: %.3f'  % ((100. *mean_acc / len(val_dataloader)), (100.*mean_acc_cls/len(val_dataloader)), mean_iu_epoch/len(val_dataloader)) ) val_acc_list.append(mean_iu_epoch/len(val_dataloader)) torch.save(model.state_dict(), out_dir+"last.pt") if mean_iu_epoch/len(val_dataloader) == max(val_acc_list): torch.save(model.state_dict(), out_dir+"best.pt") print("save epoch {} model".format(epoch)) if __name__ == "__main__": main()

整体训练流程没问题,读者可以根据需要更改其模型评价标准和相关代码。在本次训练中,主要使用Acc作为评价指标,其实就是分类正确的像素个数除以全部像素个数。最终训练结果如下:

0.8

训练集的Acc来到了0.8, 验证集的Acc来到了0.77。由于有一些函数是复制过来的,如_hist等,所以其他指标暂时不参考。

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