参数配置

关于数据集的配置

    parser = argparse.ArgumentParser()# Datset Optionsparser.add_argument("--data_root", type=str, default=r'D:/',help="path to Dataset")parser.add_argument("--dataset", type=str, default='voc',choices=['voc', 'cityscapes'], help='Name of dataset')parser.add_argument("--num_classes", type=int, default=None,help="num classes (default: None)")# Deeplab Options# 选择模型的架构，特征提取模块分为moiblienet或者resnet50parser.add_argument("--model", type=str, default='deeplabv3plus_resnet50',choices=['deeplabv3_resnet50',  'deeplabv3plus_resnet50','deeplabv3_resnet101', 'deeplabv3plus_resnet101','deeplabv3_mobilenet', 'deeplabv3plus_mobilenet'], help='model name')parser.add_argument("--separable_conv", action='store_true', default=False,help="apply separable conv to decoder and aspp")parser.add_argument("--output_stride", type=int, default=16, choices=[8, 16])

训练集参数

  # Train Options# 制作测试parser.add_argument("--test_only", action='store_true', default=False)parser.add_argument("--save_val_results", action='store_true', default=False,help="save segmentation results to \"./results\"")parser.add_argument("--total_itrs", type=int, default=60e3,help="epoch number (default: 30k)")# 学习率parser.add_argument("--lr", type=float, default=0.01,help="learning rate (default: 0.01)")parser.add_argument("--lr_policy", type=str, default='poly', choices=['poly', 'step'],help="learning rate scheduler policy")parser.add_argument("--step_size", type=int, default=10000)parser.add_argument("--crop_val", action='store_true', default=False,help='crop validation (default: False)')parser.add_argument("--batch_size", type=int, default=8,help='batch size (default: 16)')parser.add_argument("--val_batch_size", type=int, default=4,help='batch size for validation (default: 4)')parser.add_argument("--crop_size", type=int, default=513)# 预训练权重路径parser.add_argument("--ckpt", default="./checkpoint/best_deeplabv3_resnet50_voc_os16.pth", type=str,help="restore from checkpoint")parser.add_argument("--continue_training", action='store_true', default=True)parser.add_argument("--loss_type", type=str, default='cross_entropy',choices=['cross_entropy', 'focal_loss'], help="loss type (default: False)")parser.add_argument("--gpu_id", type=str, default='0',help="GPU ID")# 正则化参数parser.add_argument("--weight_decay", type=float, default=1e-4,help='weight decay (default: 1e-4)')parser.add_argument("--random_seed", type=int, default=1,help="random seed (default: 1)")parser.add_argument("--print_interval", type=int, default=10,help="print interval of loss (default: 10)")parser.add_argument("--val_interval", type=int, default=100,help="epoch interval for eval (default: 100)")parser.add_argument("--download", action='store_true', default=False,help="download datasets")

数据预处理模块

分别针对训练集、验证集、测试集做三种数据增强变换

def get_dataset(opts):""" Dataset And Augmentation"""if opts.dataset == 'voc':train_transform = et.ExtCompose([#et.ExtResize(size=opts.crop_size),et.ExtRandomScale((0.5, 2.0)),et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size), pad_if_needed=True),et.ExtRandomHorizontalFlip(),et.ExtToTensor(),et.ExtNormalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),])if opts.crop_val:val_transform = et.ExtCompose([et.ExtResize(opts.crop_size),et.ExtCenterCrop(opts.crop_size),et.ExtToTensor(),et.ExtNormalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),])else:val_transform = et.ExtCompose([et.ExtToTensor(),et.ExtNormalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),])train_dst = VOCSegmentation(root=opts.data_root, year=opts.year,image_set='train', download=opts.download, transform=train_transform)val_dst = VOCSegmentation(root=opts.data_root, year=opts.year,image_set='val', download=False, transform=val_transform)if opts.dataset == 'cityscapes':train_transform = et.ExtCompose([#et.ExtResize( 512 ),et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size)),et.ExtColorJitter( brightness=0.5, contrast=0.5, saturation=0.5 ),et.ExtRandomHorizontalFlip(),et.ExtToTensor(),et.ExtNormalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),])val_transform = et.ExtCompose([#et.ExtResize( 512 ),et.ExtToTensor(),et.ExtNormalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),])train_dst = Cityscapes(root=opts.data_root,split='train', transform=train_transform)val_dst = Cityscapes(root=opts.data_root,split='val', transform=val_transform)return train_dst, val_dst

DataSet构建模块

def voc_cmap(N=256, normalized=False):def bitget(byteval, idx):return ((byteval & (1 << idx)) != 0)dtype = 'float32' if normalized else 'uint8'cmap = np.zeros((N, 3), dtype=dtype)for i in range(N):r = g = b = 0c = ifor j in range(8):r = r | (bitget(c, 0) << 7-j)g = g | (bitget(c, 1) << 7-j)b = b | (bitget(c, 2) << 7-j)c = c >> 3cmap[i] = np.array([r, g, b])cmap = cmap/255 if normalized else cmapreturn cmap
class VOCSegmentation(data.Dataset):"""`Pascal VOC <http://host.robots.ox.ac.uk/pascal/VOC/>`_ Segmentation Dataset.Args:root (string): Root directory of the VOC Dataset.year (string, optional): The dataset year, supports years 2007 to 2012.image_set (string, optional): Select the image_set to use, ``train``, ``trainval`` or ``val``download (bool, optional): If true, downloads the dataset from the internet andputs it in root directory. If dataset is already downloaded, it is notdownloaded again.transform (callable, optional): A function/transform that  takes in an PIL imageand returns a transformed version. E.g, ``transforms.RandomCrop``"""cmap = voc_cmap()def __init__(self,root,year='2012',image_set='train',download=False,transform=None):is_aug=Falseif year=='2012_aug':is_aug = Trueyear = '2012'self.root = os.path.expanduser(root)self.year = yearself.url = DATASET_YEAR_DICT[year]['url']self.filename = DATASET_YEAR_DICT[year]['filename']self.md5 = DATASET_YEAR_DICT[year]['md5']self.transform = transformself.image_set = image_setbase_dir = DATASET_YEAR_DICT[year]['base_dir']voc_root = os.path.join(self.root, base_dir)image_dir = os.path.join(voc_root, 'JPEGImages')if download:download_extract(self.url, self.root, self.filename, self.md5)if not os.path.isdir(voc_root):raise RuntimeError('Dataset not found or corrupted.' +' You can use download=True to download it')if is_aug and image_set=='train':mask_dir = os.path.join(voc_root, 'SegmentationClassAug')assert os.path.exists(mask_dir), "SegmentationClassAug not found, please refer to README.md and prepare it manually"split_f = os.path.join( self.root, 'train_aug.txt')#'./datasets/data/train_aug.txt'else:mask_dir = os.path.join(voc_root, 'SegmentationClass')splits_dir = os.path.join(voc_root, 'ImageSets/Segmentation')split_f = os.path.join(splits_dir, image_set.rstrip('\n') + '.txt')if not os.path.exists(split_f):raise ValueError('Wrong image_set entered! Please use image_set="train" ''or image_set="trainval" or image_set="val"')with open(os.path.join(split_f), "r") as f:file_names = [x.strip() for x in f.readlines()]self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]self.masks = [os.path.join(mask_dir, x + ".png") for x in file_names]assert (len(self.images) == len(self.masks))def __getitem__(self, index):"""Args:index (int): IndexReturns:tuple: (image, target) where target is the image segmentation."""img = Image.open(self.images[index]).convert('RGB')target = Image.open(self.masks[index])if self.transform is not None:img, target = self.transform(img, target)return img, targetdef __len__(self):return len(self.images)@classmethoddef decode_target(cls, mask):"""decode semantic mask to RGB image"""return cls.cmap[mask]

测试一下数据集

import numpy as npfrom datasets import VOCSegmentation
from utils import ext_transforms as et
import cv2
train_transform = et.ExtCompose([# et.ExtResize(size=opts.crop_size),et.ExtRandomScale((0.5, 2.0)),et.ExtRandomCrop(size=(224, 224), pad_if_needed=True),et.ExtRandomHorizontalFlip(),et.ExtToTensor(),et.ExtNormalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),
])
data = VOCSegmentation(root=r"D:/", year="2012", image_set='train', download=False, transform=train_transform)if __name__ == '__main__':print(data[0][0].shape)print(data[0][1].shape)res = data.decode_target(data[0][1])cv2.imshow("Res",np.array(res))cv2.waitKey(0)

去正则化

class Denormalize(object):def __init__(self, mean, std):mean = np.array(mean)std = np.array(std)self._mean = -mean/stdself._std = 1/stddef __call__(self, tensor):if isinstance(tensor, np.ndarray):return (tensor - self._mean.reshape(-1,1,1)) / self._std.reshape(-1,1,1)return normalize(tensor, self._mean, self._std)

模型加载模块

    # Set up modelmodel_map = {'deeplabv3_resnet50': network.deeplabv3_resnet50,'deeplabv3plus_resnet50': network.deeplabv3plus_resnet50,'deeplabv3_resnet101': network.deeplabv3_resnet101,'deeplabv3plus_resnet101': network.deeplabv3plus_resnet101,'deeplabv3_mobilenet': network.deeplabv3_mobilenet,'deeplabv3plus_mobilenet': network.deeplabv3plus_mobilenet}model = model_map[opts.model](num_classes=opts.num_classes, output_stride=opts.output_stride)def deeplabv3_resnet50(num_classes=21, output_stride=8, pretrained_backbone=True):"""Constructs a DeepLabV3 model with a ResNet-50 backbone.Args:num_classes (int): number of classes.output_stride (int): output stride for deeplab.pretrained_backbone (bool): If True, use the pretrained backbone."""return _load_model('deeplabv3', 'resnet50', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)def deeplabv3_resnet101(num_classes=21, output_stride=8, pretrained_backbone=True):"""Constructs a DeepLabV3 model with a ResNet-101 backbone.Args:num_classes (int): number of classes.output_stride (int): output stride for deeplab.pretrained_backbone (bool): If True, use the pretrained backbone."""return _load_model('deeplabv3', 'resnet101', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)def deeplabv3_mobilenet(num_classes=21, output_stride=8, pretrained_backbone=True, **kwargs):"""Constructs a DeepLabV3 model with a MobileNetv2 backbone.Args:num_classes (int): number of classes.output_stride (int): output stride for deeplab.pretrained_backbone (bool): If True, use the pretrained backbone."""return _load_model('deeplabv3', 'mobilenetv2', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)# Deeplab v3+def deeplabv3plus_resnet50(num_classes=21, output_stride=8, pretrained_backbone=True):"""Constructs a DeepLabV3 model with a ResNet-50 backbone.Args:num_classes (int): number of classes.output_stride (int): output stride for deeplab.pretrained_backbone (bool): If True, use the pretrained backbone."""return _load_model('deeplabv3plus', 'resnet50', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)

加载模块

def _load_model(arch_type, backbone, num_classes, output_stride, pretrained_backbone):if backbone=='mobilenetv2':model = _segm_mobilenet(arch_type, backbone, num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)elif backbone.startswith('resnet'):model = _segm_resnet(arch_type, backbone, num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone)else:raise NotImplementedErrorreturn modeldef _segm_resnet(name, backbone_name, num_classes, output_stride, pretrained_backbone):if output_stride==8:replace_stride_with_dilation=[False, True, True]aspp_dilate = [12, 24, 36]else:replace_stride_with_dilation=[False, False, True]aspp_dilate = [6, 12, 18]backbone = resnet.__dict__[backbone_name](pretrained=pretrained_backbone,replace_stride_with_dilation=replace_stride_with_dilation)inplanes = 2048low_level_planes = 256if name=='deeplabv3plus':return_layers = {'layer4': 'out', 'layer1': 'low_level'}#classifier = DeepLabHeadV3Plus(inplanes, low_level_planes, num_classes, aspp_dilate)elif name=='deeplabv3':return_layers = {'layer4': 'out'}classifier = DeepLabHead(inplanes , num_classes, aspp_dilate)#提取网络的第几层输出结果并给一个别名backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)model = DeepLabV3(backbone, classifier)return model

DeepLABV3+

class DeepLabHeadV3Plus(nn.Module):def __init__(self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36]):super(DeepLabHeadV3Plus, self).__init__()self.project = nn.Sequential( nn.Conv2d(low_level_channels, 48, 1, bias=False),nn.BatchNorm2d(48),nn.ReLU(inplace=True),)self.aspp = ASPP(in_channels, aspp_dilate)self.classifier = nn.Sequential(nn.Conv2d(304, 256, 3, padding=(1,1), bias=False),nn.BatchNorm2d(256),nn.ReLU(inplace=True),nn.Conv2d(256, num_classes, 1))self._init_weight()def forward(self, feature):#print(feature.shape)low_level_feature = self.project( feature['low_level'] )#return_layers = {'layer4': 'out', 'layer1': 'low_level'}#print(low_level_feature.shape)output_feature = self.aspp(feature['out'])#print(output_feature.shape)output_feature = F.interpolate(output_feature, size=low_level_feature.shape[2:], mode='bilinear', align_corners=False)#print(output_feature.shape)return self.classifier( torch.cat( [ low_level_feature, output_feature ], dim=1 ) )def _init_weight(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)

其中，空洞融合ASPP模块

class ASPP(nn.Module):def __init__(self, in_channels, atrous_rates):super(ASPP, self).__init__()out_channels = 256modules = []modules.append(nn.Sequential(nn.Conv2d(in_channels, out_channels, 1, bias=False),nn.BatchNorm2d(out_channels),nn.ReLU(inplace=True)))rate1, rate2, rate3 = tuple(atrous_rates)modules.append(ASPPConv(in_channels, out_channels, rate1))modules.append(ASPPConv(in_channels, out_channels, rate2))modules.append(ASPPConv(in_channels, out_channels, rate3))modules.append(ASPPPooling(in_channels, out_channels))self.convs = nn.ModuleList(modules)self.project = nn.Sequential(nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),nn.BatchNorm2d(out_channels),nn.ReLU(inplace=True),nn.Dropout(0.1),)def forward(self, x):res = []for conv in self.convs:#print(conv(x).shape)res.append(conv(x))res = torch.cat(res, dim=1)return self.project(res)

卷积转深度可分离卷积

def convert_to_separable_conv(module):new_module = moduleif isinstance(module, nn.Conv2d) and module.kernel_size[0]>1:new_module = AtrousSeparableConvolution(module.in_channels,module.out_channels, module.kernel_size,module.stride,module.padding,module.dilation,module.bias)for name, child in module.named_children():new_module.add_module(name, convert_to_separable_conv(child))return new_module
class AtrousSeparableConvolution(nn.Module):""" Atrous Separable Convolution"""def __init__(self, in_channels, out_channels, kernel_size,stride=1, padding=0, dilation=1, bias=True):super(AtrousSeparableConvolution, self).__init__()self.body = nn.Sequential(# Separable Convnn.Conv2d( in_channels, in_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, groups=in_channels ),# PointWise Convnn.Conv2d( in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias),)self._init_weight()def forward(self, x):return self.body(x)def _init_weight(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight)elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)

参考代码 --------
链接: https://pan.baidu.com/s/1E6CD5NQjbKeITacH9VjxAw?pwd=jqgk 提取码: jqgk
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