语义分割系列5-Pspnet(pytorch实现)

Pspnet全名Pyramid Scene Parsing Network,论文地址:Pyramid Scene Parsing Network

论文名就是《Pyramid Scene Parsing Network》。

该模型提出是为了解决场景分析问题。针对FCN网络在场景分析数据集上存在的问题,Pspnet提出一系列改进方案,以提升场景分析中对于相似颜色、形状的物体的检测精度。

语义分割系列5-Pspnet(pytorch实现)_第1张图片 图1 ADE20k场景分析

作者在ADE20K数据集上进行实验时,主要发现有如下3个问题:

  1. 错误匹配,FCN模型把水里的船预测成汽车,但是汽车是不会在水上的。因此,作者认为FCN缺乏收集上下文能力,导致了分类错误。
  2. 作者发现相似的标签会导致一些奇怪的错误,比如earth和field,mountain和hill,wall,house,building和skyscraper。FCN模型会出现混淆。
  3. 第三是小目标的丢失问题,像一些路灯、信号牌这种小物体,很难被FCN所发现。相反的,一些特别大的物体预测中,在感受野不够大的情况下,往往会丢失一部分信息,导致预测不连续。

为了解决这些问题,作者提出了Pyramid Pooling Module。

Pyramid Pooling Module

作者在文章中提出了Pyramid Pooling Module(池化金字塔结构)这一模块。

作者提到,在深层网络中,感受野的大小大致上体现了模型能获得的上下文新消息。尽管在理论上Resnet的感受野已经大于图像尺寸,但是实际上会小得多。这就导致了很多网络不能充分的将上下文信息结合起来,于是作者就提出了一种全局的先验方法-全局平均池化。

作者在PPM模块中并联了四个不同大小的全局池化层,将原始的feature map池化生成不同级别的特征图,经过卷积和上采样恢复到原始大小。这种操作聚合了多尺度的图像特征,生成了一个“hierarchical global prior”,融合了不同尺度和不同子区域之间的信息。最后,这个先验信息再和原始特征图进行相加,输入到最后的卷积模块完成预测。

语义分割系列5-Pspnet(pytorch实现)_第2张图片 图2 Pspnet

Pspnet的核心就是PPM模块。其网络架构十分简单,backbone为resnet网络,将原始图像下采样8倍成特征图,特征图输入到PPM模块,并与其输出相加,最后经过卷积和8倍双线性差值上采样得到结果(图2)。

辅助损失

语义分割系列5-Pspnet(pytorch实现)_第3张图片 图3 辅助损失

论文中还有一个细节是辅助损失(auxiliary loss),在resnet101的res4b22层引出一条FCN分支,用于计算辅助损失。论文里设置了赋值损失loss2的权重为0.4。则最终的损失则为:

\large Loss = Loss_1 + 0.4*Loss_2

论文复现

本文主要在CamVid数据集上进行复现,数据集可以在另一篇博客中找到CamVid数据集的创建和使用。

Resnet

这里调用了pytorch官方写的ResNet101,替换最后两个layer为dialation模式,只采用8倍下采样。引出layer3的计算结果用于计算辅助损失。

from torchvision.models import resnet50, resnet101
from torchvision.models._utils import IntermediateLayerGetter
import torch
import torch.nn as nn

backbone=IntermediateLayerGetter(
            resnet101(pretrained=False, replace_stride_with_dilation=[False, True, True]),
            return_layers={'layer3':'aux','layer4': 'stage4'}
        )


x = torch.randn(1, 3, 224, 224).cpu()
result = backbone(x)
for k, v in result.items():
    print(k, v.shape)

pspnet

from torchvision.models import resnet50, resnet101
from torchvision.models._utils import IntermediateLayerGetter
import torch
import torch.nn as nn

class PPM(nn.ModuleList):
    def __init__(self, pool_sizes, in_channels, out_channels):
        super(PPM, self).__init__()
        self.pool_sizes = pool_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        
        for pool_size in pool_sizes:
            self.append(
                nn.Sequential(
                    nn.AdaptiveMaxPool2d(pool_size),
                    nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1),
                )
            )
            
    def forward(self, x):
        out_puts = []
        for ppm in self:
            ppm_out = nn.functional.interpolate(ppm(x), size=x.size()[-2:], mode='bilinear', align_corners=True)
            out_puts.append(ppm_out)
        return out_puts
 
    
class PSPHEAD(nn.Module):
    def __init__(self, in_channels, out_channels,pool_sizes = [1, 2, 3, 6],num_classes=3):
        super(PSPHEAD, self).__init__()
        self.pool_sizes = pool_sizes
        self.num_classes = num_classes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.psp_modules = PPM(self.pool_sizes, self.in_channels, self.out_channels)
        self.final = nn.Sequential(
            nn.Conv2d(self.in_channels + len(self.pool_sizes)*self.out_channels, self.out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(self.out_channels),
            nn.ReLU(),
        )
        
    def forward(self, x):
        out = self.psp_modules(x)
        out.append(x)
        out = torch.cat(out, 1)
        out = self.final(out)
        return out

# 构建一个FCN分割头,用于计算辅助损失
class Aux_Head(nn.Module):
    def __init__(self, in_channels=1024, num_classes=3):
        super(Aux_Head, self).__init__()
        self.num_classes = num_classes
        self.in_channels = in_channels

        self.decode_head = nn.Sequential(
            nn.Conv2d(self.in_channels, self.in_channels//2, kernel_size=3, padding=1),
            nn.BatchNorm2d(self.in_channels//2),
            nn.ReLU(),            
            
            nn.Conv2d(self.in_channels//2, self.in_channels//4, kernel_size=3, padding=1),
            nn.BatchNorm2d(self.in_channels//4),
            nn.ReLU(),            
            
            nn.Conv2d(self.in_channels//4, self.num_classes, kernel_size=3, padding=1),

        )
        
    def forward(self, x):

        return self.decode_head(x)

class Pspnet(nn.Module):
    def __init__(self, num_classes, aux_loss = True):
        super(Pspnet, self).__init__()
        self.num_classes = num_classes
        self.backbone = IntermediateLayerGetter(
            resnet50(pretrained=False, replace_stride_with_dilation=[False, True, True]),
            return_layers={'layer3':"aux" ,'layer4': 'stage4'}
        )
        self.aux_loss = aux_loss
        self.decoder = PSPHEAD(in_channels=2048, out_channels=512, pool_sizes = [1, 2, 3, 6], num_classes=self.num_classes)
        self.cls_seg = nn.Sequential(
            nn.Conv2d(512, self.num_classes, kernel_size=3, padding=1),
        )
        if self.aux_loss:
            self.aux_head = Aux_Head(in_channels=1024, num_classes=self.num_classes)

        
    def forward(self, x):
        _, _, h, w = x.size()
        feats = self.backbone(x) 
        x = self.decoder(feats["stage4"])
        x = self.cls_seg(x)
        x = nn.functional.interpolate(x, size=(h, w),mode='bilinear', align_corners=True)

        # 如果需要添加辅助损失
        if self.aux_loss:
            aux_output = self.aux_head(feats['aux'])
            aux_output = nn.functional.interpolate(aux_output, size=(h, w),mode='bilinear', align_corners=True)

            return {"output":x, "aux_output":aux_output}
        return {"output":x}


if __name__ == "__main__":
    model = Pspnet(num_classes=3, aux_loss=True)
    model = model.cuda()
    a = torch.ones([2, 3, 224, 224])
    a = a.cuda()

    for name, out in model(a).items():
        print(name,": ", out.shape)

数据集构建

# 导入库
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch import optim
from torch.utils.data import Dataset, DataLoader, random_split
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
import os.path as osp
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2

torch.manual_seed(17)
# 自定义数据集CamVidDataset
class CamVidDataset(torch.utils.data.Dataset):
    """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):
        self.transform = A.Compose([
            A.Resize(448, 448),
            A.HorizontalFlip(),
            A.VerticalFlip(),
            A.Normalize(),
            ToTensorV2(),
        ]) 
        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]

    
    def __getitem__(self, i):
        # read data
        image = np.array(Image.open(self.images_fps[i]).convert('RGB'))
        mask = np.array( Image.open(self.masks_fps[i]).convert('RGB'))
        image = self.transform(image=image,mask=mask)
        
        return image['image'], image['mask'][:,:,0]
        
    def __len__(self):
        return len(self.ids)
    
    
# 设置数据集路径
DATA_DIR = r'dataset\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')
    
train_dataset = CamVidDataset(
    x_train_dir, 
    y_train_dir, 
)
val_dataset = CamVidDataset(
    x_valid_dir, 
    y_valid_dir, 
)

train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True,drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=8, shuffle=True,drop_last=True)

模型训练

from d2l import torch as d2l
from tqdm import tqdm
import pandas as pd

model = Pspnet(num_classes=32, aux_loss=True)
model = model.cuda()

# training loop 100 epochs
epochs_num = 100
# 选用SGD优化器来训练
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
schedule = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50)

# 损失函数选用多分类交叉熵损失函数
lossf = nn.CrossEntropyLoss(ignore_index=255)


def evaluate(net, data_iter, device=torch.device('cuda:0')):
    net.eval()
    metric = d2l.Accumulator(3)
    with torch.no_grad():
        for X, y in data_iter:
            if isinstance(X, list):
                X = [x.to(device) for x in X]
            else:
                X = X.to(device)
            y = y.to(device)
            pred = net(X)['output']
            metric.add(d2l.accuracy(pred, y), d2l.size(y))
            
    return metric[0] / metric[1]


# 训练函数
def train_ch13(net, train_iter, test_iter, loss_func, optimizer, num_epochs, schedule, devices=d2l.try_all_gpus()):
    timer, num_batches = d2l.Timer(), len(train_iter)
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1], legend=['train loss', 'train acc', 'test acc'])
    net = nn.DataParallel(net, device_ids=devices).to(devices[0])
    # 用来保存一些训练参数

    loss_list = []
    train_acc_list = []
    test_acc_list = []
    epochs_list = []
    time_list = []
    lr_list = []
    

    for epoch in range(num_epochs):

        # metric: loss, accuracy, labels.shape[0], labels.numel(), 0.4*aux_loss
        metric = d2l.Accumulator(5)
        for i, (X, labels) in enumerate(train_iter):
            timer.start()
            if isinstance(X, list):
                X = [x.to(devices[0]) for x in X]
            else:
                X = X.to(devices[0])
            gt = labels.long().to(devices[0])

            net.train()
            optimizer.zero_grad()
            result = net(X)
            seg_loss = loss_func(result['output'], gt)
            aux_loss = loss_func(result['aux_output'], gt)

            loss_sum = seg_loss + 0.4*aux_loss

            l = loss_sum
            loss_sum.sum().backward()
            optimizer.step()

            acc = d2l.accuracy(result['output'], gt)
            metric.add(l, acc, labels.shape[0], labels.numel(), 0.4*aux_loss)

            timer.stop()
            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
                animator.add(epoch + (i + 1) / num_batches, (metric[0] / metric[2], metric[1] / metric[3], None, None))

            
        test_acc = evaluate(net, test_iter)
        animator.add(epoch + 1, (None, None, test_acc)) 
        schedule.step()
        print(f"epoch {epoch+1}/{epochs_num} --- loss {metric[0]/metric[2]:.3f} --- aux_loss {metric[4]/metric[2]:.3f} --- train acc {metric[1]/metric[3]:.3f} --- test acc {test_acc:.3f} --- lr {optimizer.state_dict()['param_groups'][0]['lr']} --- cost time {timer.sum()}")
        
        #---------保存训练数据---------------
        df = pd.DataFrame()
        loss_list.append(metric[0] / metric[2])
        train_acc_list.append(metric[1] / metric[3])
        test_acc_list.append(test_acc)
        epochs_list.append(epoch+1)
        time_list.append(timer.sum())
        lr_list.append(optimizer.state_dict()['param_groups'][0]['lr'])
        
        df['epoch'] = epochs_list
        df['loss'] = loss_list
        df['train_acc'] = train_acc_list
        df['test_acc'] = test_acc_list
        df["lr"] = lr_list
        df['time'] = time_list
        
        df.to_excel("../blork_file/savefile/PSPNET.xlsx")
        #----------------保存模型------------------- 
        if np.mod(epoch+1, 5) == 0:
            torch.save(net, f'../blork_file/checkpoints/PSPNET{epoch+1}.pth')

    # 保存下最后的model
    torch.save(net, f'../blork_file/checkpoints/PSPNET.pth')

训练结果

语义分割系列5-Pspnet(pytorch实现)_第4张图片

 

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