DeepLabV3+语义分割实战
语义分割是计算机视觉的一项重要任务，本文使用Jittor框架实现了DeepLabV3+语义分割模型。
DeepLabV3+论文：https://arxiv.org/pdf/1802.02611.pdf
完整代码：https://github.com/Jittor/deeplab-jittor

1. 数据集
   1.1 数据准备
   VOC2012数据集是目标检测、语义分割等任务常用的数据集之一， 本文使用VOC数据集的2012 trainaug (train + sbd set)作为训练集，2012 val set作为测试集。
   VOC数据集中的物体共包括20个前景类别：‘aeroplane’, ‘bicycle’, ‘bird’, ‘boat’, ‘bottle’, ‘bus’, ‘car’, ‘cat’, ‘chair’, ‘cow’, ‘diningtable’, ‘dog’, ‘horse’, ‘motorbike’, ‘person’, ‘pottedplant’, ‘sheep’, ‘sofa’, ‘train’, ‘tvmonitor’ 和背景类别
   

最终数据集的文件组织如下。

文件组织

根目录
|----voc_aug
| |----datalist
| | |----train.txt
| | |----val.txt
| |----images
| |----annotations
1.2 数据加载
使用jittor.dataset.dataset的基类Dataset可以构造自己的数据集，需要实现__init__、getitem、函数。

1. init: 定义数据路径，这里的data_root需设置为之前设定的 voc_aug, split 为 train val test 之一，表示选择训练集、验证集还是测试集。同时需要调用self.set_attr来指定数据集加载所需的参数batch_size，total_len、shuffle。
2. getitem: 返回单个item的数据。
   import numpy as np
   import os
   from PIL import Image
   import matplotlib.pyplot as plt
   from jittor.dataset.dataset import Dataset, dataset_root
   import jittor as jt
   import os
   import os.path as osp
   from PIL import Image, ImageOps, ImageFilter
   import numpy as np
   import scipy.io as sio
   import random

def fetch(image_path, label_path):
with open(image_path, ‘rb’) as fp:
image = Image.open(fp).convert(‘RGB’)

with open(label_path, 'rb') as fp:label = Image.open(fp).convert('P')return image, label

def scale(image, label):
SCALES = (0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0)
ratio = np.random.choice(SCALES)
w,h = image.size
nw = (int)(wratio)
nh = (int)(hratio)

image = image.resize((nw, nh), Image.BILINEAR)
label = label.resize((nw, nh), Image.NEAREST)return image, label

def pad(image, label):
w,h = image.size
crop_size = 513
pad_h = max(crop_size - h, 0)
pad_w = max(crop_size - w, 0)
image = ImageOps.expand(image, border=(0, 0, pad_w, pad_h), fill=0)
label = ImageOps.expand(label, border=(0, 0, pad_w, pad_h), fill=255)

return image, label

def crop(image, label):
w, h = image.size
crop_size = 513
x1 = random.randint(0, w - crop_size)
y1 = random.randint(0, h - crop_size)
image = image.crop((x1, y1, x1 + crop_size, y1 + crop_size))
label = label.crop((x1, y1, x1 + crop_size, y1 + crop_size))

return image, label

def normalize(image, label):
mean = (0.485, 0.456, 0.40)
std = (0.229, 0.224, 0.225)
image = np.array(image).astype(np.float32)
label = np.array(label).astype(np.float32)

image /= 255.0
image -= mean
image /= std
return image, label

def flip(image, label):
if random.random() < 0.5:
image = image.transpose(Image.FLIP_LEFT_RIGHT)
label = label.transpose(Image.FLIP_LEFT_RIGHT)
return image, label

class BaseDataset(Dataset):
def init(self, data_root=’/voc/’, split=‘train’, batch_size=1, shuffle=False):
super().init()
‘’’ total_len , batch_size, shuffle must be set ‘’’
self.data_root = data_root
self.split = split
self.batch_size = batch_size
self.shuffle = shuffle

    self.image_root = os.path.join(data_root, 'images')self.label_root = os.path.join(data_root, 'annotations')self.data_list_path = os.path.join(self.data_root,'/datalist/' + self.split + '.txt')self.image_path = []self.label_path = []with open(self.data_list_path, "r") as f:lines = f.read().splitlines()for idx, line in enumerate(lines):_img_path = os.path.join(self.image_root, line + '.jpg')_label_path = os.path.join(self.label_root, line + '.png')assert os.path.isfile(_img_path)assert os.path.isfile(_label_path)self.image_path.append(_img_path)self.label_path.append(_label_path)self.total_len = len(self.image_path)# set_attr must be called to set batch size total len and shuffle like __len__ function in pytorchself.set_attr(batch_size = self.batch_size, total_len = self.total_len, shuffle = self.shuffle) # bs , total_len, shuffledef __getitem__(self, image_id):return NotImplementedError

class TrainDataset(BaseDataset):
def init(self, data_root=’/voc/’, split=‘train’, batch_size=1, shuffle=False):
super(TrainDataset, self).init(data_root, split, batch_size, shuffle)

def __getitem__(self, image_id):image_path = self.image_path[image_id]label_path = self.label_path[image_id]image, label = fetch(image_path, label_path)image, label = scale(image, label)image, label = pad(image, label)image, label = crop(image, label)image, label = flip(image, label)image, label = normalize(image, label)image = np.array(image).astype(np.float).transpose(2, 0, 1)image = jt.array(image)label = jt.array(np.array(label).astype(np.int))return image, label

class ValDataset(BaseDataset):
def init(self, data_root=’/voc/’, split=‘train’, batch_size=1, shuffle=False):
super(ValDataset, self).init(data_root, split, batch_size, shuffle)

def __getitem__(self, image_id):image_path = self.image_path[image_id]label_path = self.label_path[image_id]image, label = fetch(image_path, label_path)image, label = normalize(image, label)image = np.array(image).astype(np.float).transpose(2, 0, 1)image = jt.array(image)label = jt.array(np.array(label).astype(np.int))return image, label

2. 模型定义
   

上图为DeepLabV3+论文给出的网络架构图。本文采用ResNe为backbone。输入图像尺寸为513*513。
整个网络可以分成 backbone aspp decoder 三个部分。
2.1 backbonb 这里使用最常见的ResNet，作为backbone并且在ResNet的最后两次使用空洞卷积来扩大感受野，其完整定义如下：
import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat, argmax_pool
import time

class Bottleneck(Module):
expansion = 4
def init(self, inplanes, planes, stride=1, dilation=1, downsample=None):
super(Bottleneck, self).init()
self.conv1 = nn.Conv(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm(planes)
self.conv2 = nn.Conv(planes, planes, kernel_size=3, stride=stride,
dilation=dilation, padding=dilation, bias=False)
self.bn2 = nn.BatchNorm(planes)
self.conv3 = nn.Conv(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm(planes * 4)
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride
self.dilation = dilation

def execute(self, x):residual = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)if self.downsample is not None:residual = self.downsample(x)out += residualout = self.relu(out)return out

class ResNet(Module):
def init(self, block, layers, output_stride):
super(ResNet, self).init()
self.inplanes = 64
blocks = [1, 2, 4]
if output_stride == 16:
strides = [1, 2, 2, 1]
dilations = [1, 1, 1, 2]
elif output_stride == 8:
strides = [1, 2, 1, 1]
dilations = [1, 1, 2, 4]
else:
raise NotImplementedError

    # Modulesself.conv1 = nn.Conv(3, 64, kernel_size=7, stride=2, padding=3, bias=False)self.bn1 = nn.BatchNorm(64)self.relu = nn.ReLU()# self.maxpool = nn.Pool(kernel_size=3, stride=2, padding=1)self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0])self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3])def _make_layer(self, block, planes, blocks, stride=1, dilation=1):downsample = Noneif stride != 1 or self.inplanes != planes * block.expansion:downsample = nn.Sequential(nn.Conv(self.inplanes, planes * block.expansion,kernel_size=1, stride=stride, bias=False),nn.BatchNorm(planes * block.expansion),)layers = []layers.append(block(self.inplanes, planes, stride, dilation, downsample))self.inplanes = planes * block.expansionfor i in range(1, blocks):layers.append(block(self.inplanes, planes, dilation=dilation))return nn.Sequential(*layers)def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1):downsample = Noneif stride != 1 or self.inplanes != planes * block.expansion:downsample = nn.Sequential(nn.Conv(self.inplanes, planes * block.expansion,kernel_size=1, stride=stride, bias=False),nn.BatchNorm(planes * block.expansion),)layers = []layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,downsample=downsample))self.inplanes = planes * block.expansionfor i in range(1, len(blocks)):layers.append(block(self.inplanes, planes, stride=1,dilation=blocks[i]*dilation))return nn.Sequential(*layers)def execute(self, input):x = self.conv1(input)x = self.bn1(x)x = self.relu(x)x = argmax_pool(x, 2, 2)x = self.layer1(x)low_level_feat = xx = self.layer2(x)x = self.layer3(x)x = self.layer4(x)return x, low_level_feat

def resnet50(output_stride):
model = ResNet(Bottleneck, [3,4,6,3], output_stride)
return model

def resnet101(output_stride):
model = ResNet(Bottleneck, [3,4,23,3], output_stride)
return model

2.2 ASPP
即使用不同尺寸的 dilation conv 对 backbone 得到的 feature map 进行卷积，最后 concat 并整合得到新的特征。
import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat

class Single_ASPPModule(Module):
def init(self, inplanes, planes, kernel_size, padding, dilation):
super(Single_ASPPModule, self).init()
self.atrous_conv = nn.Conv(inplanes, planes, kernel_size=kernel_size,
stride=1, padding=padding, dilation=dilation, bias=False)
self.bn = nn.BatchNorm(planes)
self.relu = nn.ReLU()

def execute(self, x):x = self.atrous_conv(x)x = self.bn(x)x = self.relu(x)return x

class ASPP(Module):
def init(self, output_stride):
super(ASPP, self).init()
inplanes = 2048
if output_stride == 16:
dilations = [1, 6, 12, 18]
elif output_stride == 8:
dilations = [1, 12, 24, 36]
else:
raise NotImplementedError

    self.aspp1 = Single_ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0])self.aspp2 = Single_ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1])self.aspp3 = Single_ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2])self.aspp4 = Single_ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3])self.global_avg_pool = nn.Sequential(GlobalPooling(),nn.Conv(inplanes, 256, 1, stride=1, bias=False),nn.BatchNorm(256),nn.ReLU())self.conv1 = nn.Conv(1280, 256, 1, bias=False)self.bn1 = nn.BatchNorm(256)self.relu = nn.ReLU()self.dropout = nn.Dropout(0.5)def execute(self, x):x1 = self.aspp1(x)x2 = self.aspp2(x)x3 = self.aspp3(x)x4 = self.aspp4(x)x5 = self.global_avg_pool(x)x5 = x5.broadcast((1,1,x4.shape[2],x4.shape[3]))x = concat((x1, x2, x3, x4, x5), dim=1)x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.dropout(x)return x

class GlobalPooling (Module):
def init(self):
super(GlobalPooling, self).init()
def execute (self, x):
return jt.mean(x, dims=[2,3], keepdims=1)

2.3 Decoder:
Decoder 将 ASPP 的特征放大后与 ResNet 的中间特征一起 concat， 得到最后分割所用的特征。
import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat
import time

class Decoder(nn.Module):
def init(self, num_classes):
super(Decoder, self).init()
low_level_inplanes = 256

    self.conv1 = nn.Conv(low_level_inplanes, 48, 1, bias=False)self.bn1 = nn.BatchNorm(48)self.relu = nn.ReLU()self.last_conv = nn.Sequential(nn.Conv(304, 256, kernel_size=3, stride=1, padding=1, bias=False),nn.BatchNorm(256),nn.ReLU(),nn.Dropout(0.5),nn.Conv(256, 256, kernel_size=3, stride=1, padding=1, bias=False),nn.BatchNorm(256),nn.ReLU(),nn.Dropout(0.1),nn.Conv(256, num_classes, kernel_size=1, stride=1, bias=True))def execute(self, x, low_level_feat):low_level_feat = self.conv1(low_level_feat)low_level_feat = self.bn1(low_level_feat)low_level_feat = self.relu(low_level_feat)x_inter = nn.resize(x, size=(low_level_feat.shape[2], low_level_feat.shape[3]) , mode='bilinear')x_concat = concat((x_inter, low_level_feat), dim=1)x = self.last_conv(x_concat)return x

2.4 完整的模型整合如下： 即将以上部分通过一个类连接起来。
import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat
from decoder import Decoder
from aspp import ASPP
from backbone import resnet50, resnet101

class DeepLab(Module):
def init(self, output_stride=16, num_classes=21):
super(DeepLab, self).init()
self.backbone = resnet101(output_stride=output_stride)
self.aspp = ASPP(output_stride)
self.decoder = Decoder(num_classes)

def execute(self, input):x, low_level_feat = self.backbone(input)x = self.aspp(x)x = self.decoder(x, low_level_feat)x = nn.resize(x, size=(input.shape[2], input.shape[3]), mode='bilinear')return x

3. 模型训练
   3.1 模型训练参数设定如下：

Learning parameters

batch_size = 8
learning_rate = 0.005
momentum = 0.9
weight_decay = 1e-4
epochs = 50

3.2 定义模型、优化器、数据加载器。
model = DeepLab(output_stride=16, num_classes=21)
optimizer = nn.SGD(model.parameters(),
lr,
momentum=momentum,
weight_decay=weight_decay)
train_loader = TrainDataset(data_root=’/vocdata/’,
split=‘train’,
batch_size=batch_size,
shuffle=True)
val_loader = ValDataset(data_root=’/vocdata/’,
split=‘val’,
batch_size=1,
shuffle=False)

3.3 模型训练与验证

lr scheduler

def poly_lr_scheduler(opt, init_lr, iter, epoch, max_iter, max_epoch):
new_lr = init_lr * (1 - float(epoch * max_iter + iter) / (max_epoch * max_iter)) ** 0.9
opt.lr = new_lr

train function

def train(model, train_loader, optimizer, epoch, init_lr):
model.train()
max_iter = len(train_loader)

for idx, (image, target) in enumerate(train_loader):poly_lr_scheduler(optimizer, init_lr, idx, epoch, max_iter, 50) # using poly_lr_scheduler image = image.float32()pred = model(image)loss = nn.cross_entropy_loss(pred, target, ignore_index=255)optimizer.step (loss)print ('Training in epoch {} iteration {} loss = {}'.format(epoch, idx, loss.data[0]))

val function

we omit evaluator code and you can

def val (model, val_loader, epoch, evaluator):
model.eval()
evaluator.reset()
for idx, (image, target) in enumerate(val_loader):
image = image.float32()
output = model(image)
pred = output.data
target = target.data
pred = np.argmax(pred, axis=1)
evaluator.add_batch(target, pred)
print (‘Test in epoch {} iteration {}’.format(epoch, idx))
Acc = evaluator.Pixel_Accuracy()
Acc_class = evaluator.Pixel_Accuracy_Class()
mIoU = evaluator.Mean_Intersection_over_Union()
FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union()
best_miou = 0.0

if (mIoU > best_miou):best_miou = mIoU
print ('Testing result of epoch {} miou = {} Acc = {} Acc_class = {} \FWIoU = {} Best Miou = {}'.format(epoch, mIoU, Acc, Acc_class, FWIoU, best_miou)) 

3.4 evaluator 写法：使用混淆矩阵计算 Pixel accuracy 和 mIoU。
class Evaluator(object):
def init(self, num_class):
self.num_class = num_class
self.confusion_matrix = np.zeros((self.num_class,)*2)

def Pixel_Accuracy(self):Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum()return Accdef Pixel_Accuracy_Class(self):Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1)Acc = np.nanmean(Acc)return Accdef Mean_Intersection_over_Union(self):MIoU = np.diag(self.confusion_matrix) / (np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0)-np.diag(self.confusion_matrix))MIoU = np.nanmean(MIoU)return MIoUdef Frequency_Weighted_Intersection_over_Union(self):freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix)iu = np.diag(self.confusion_matrix) / (np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) -np.diag(self.confusion_matrix))FWIoU = (freq[freq > 0] * iu[freq > 0]).sum()return FWIoUdef _generate_matrix(self, gt_image, pre_image):mask = (gt_image >= 0) & (gt_image < self.num_class)label = self.num_class * gt_image[mask].astype('int') + pre_image[mask]count = np.bincount(label, minlength=self.num_class**2)confusion_matrix = count.reshape(self.num_class, self.num_class)return confusion_matrixdef add_batch(self, gt_image, pre_image):assert gt_image.shape == pre_image.shapeself.confusion_matrix += self._generate_matrix(gt_image, pre_image)def reset(self):self.confusion_matrix = np.zeros((self.num_class,) * 2)

3.5 训练入口函数
epochs = 50
evaluator = Evaluator(21)
train_loader = TrainDataset(data_root=’/voc/data/path/’, split=‘train’, batch_size=8, shuffle=True)
val_loader = ValDataset(data_root=’/voc/data/path/’, split=‘val’, batch_size=1, shuffle=False)
learning_rate = 0.005
momentum = 0.9
weight_decay = 1e-4
optimizer = nn.SGD(model.parameters(), learning_rate, momentum, weight_decay)

for epoch in range (epochs):
train(model, train_loader, optimizer, epoch, learning_rate)
val(model, val_loader, epoch, evaluator)
4. 参考

1. pytorch-deeplab-xception
2. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

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