以读取VOC2012语义分割数据集为例，具体见代码注释：

VocDataset.py

from PIL import Imageimport torchimport torch.utils.data as dataimport numpy as npimport osimport torchvisionimport torchvision.transforms as transformsimport time#VOC数据集分类对应颜色标签VOC_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]]#颜色标签空间转到序号标签空间，就他妈这里浪费巨量的时间,这里还他妈的有问题def voc_label_indices(colormap, colormap2label): """Assign label indices for Pascal VOC2012 Dataset.""" idx = ((colormap[:, :, 2] * 256 + colormap[ :, :,1]) * 256+ colormap[:, :,0]) #out = np.empty(idx.shape, dtype = np.int64) out = colormap2label[idx] out=out.astype(np.int64)#数据类型转换 end = time.time() return outclass MyDataset(data.Dataset):#创建自定义的数据读取类 def __init__(self, root, is_train, crop_size=(320,480)): self.rgb_mean =(0.485, 0.456, 0.406) self.rgb_std = (0.229, 0.224, 0.225) self.root=root self.crop_size=crop_size images = []#创建空列表存文件名称 txt_fname = '%s/ImageSets/Segmentation/%s' % (root, 'train.txt' if is_train else 'val.txt') with open(txt_fname, 'r') as f: self.images = f.read().split() #数据名称整理 self.files = [] for name in self.images: img_file = os.path.join(self.root, "JPEGImages/%s.jpg" % name) label_file = os.path.join(self.root, "SegmentationClass/%s.png" % name) self.files.append({ "img": img_file, "label": label_file, "name": name }) self.colormap2label = np.zeros(256**3) #整个循环的意思就是将颜色标签映射为单通道的数组索引 for i, cm in enumerate(VOC_COLORMAP): self.colormap2label[(cm[2] * 256 + cm[1]) * 256 + cm[0]] = i #按照索引读取每个元素的具体内容 def __getitem__(self, index): datafiles = self.files[index] name = datafiles["name"] image = Image.open(datafiles["img"]) label = Image.open(datafiles["label"]).convert('RGB')#打开的是PNG格式的图片要转到rgb的格式下，不然结果会比较要命 #以图像中心为中心截取固定大小图像，小于固定大小的图像则自动填0 imgCenterCrop = transforms.Compose([ transforms.CenterCrop(self.crop_size), transforms.ToTensor(), transforms.Normalize(self.rgb_mean, self.rgb_std),#图像数据正则化 ]) labelCenterCrop = transforms.CenterCrop(self.crop_size) cropImage=imgCenterCrop(image) croplabel=labelCenterCrop(label) croplabel=torch.from_numpy(np.array(croplabel)).long()#把标签数据类型转为torch #将颜色标签图转为序号标签图 mylabel=voc_label_indices(croplabel, self.colormap2label) return cropImage,mylabel #返回图像数据长度 def __len__(self): return len(self.files)

Train.py

import matplotlib.pyplot as pltimport torch.utils.data as dataimport torchvision.transforms as transformsimport numpy as npfrom PIL import Imagefrom VocDataset import MyDataset#VOC数据集分类对应颜色标签VOC_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]]root='../data/VOCdevkit/VOC2012'train_data=MyDataset(root,True)trainloader = data.DataLoader(train_data, 4)#从数据集中拿出一个批次的数据for i, data in enumerate(trainloader): getimgs, labels= data img = transforms.ToPILImage()(getimgs[0]) labels = labels.numpy()#tensor转numpy labels=labels[0]#获得批次标签集中的一张标签图像 labels = labels.transpose((1,0))#数组维度切换，将第1维换到第0维，第0维换到第1维 ##将单通道索引标签图片映射回颜色标签图片 newIm= Image.new('RGB', (480, 320))#创建一张与标签大小相同的图片，用以显示标签所对应的颜色 for i in range(0, 480): for j in range(0, 320): sele=labels[i][j]#取得坐标点对应像素的值 newIm.putpixel((i, j), (int(VOC_COLORMAP[sele][0]), int(VOC_COLORMAP[sele][1]), int(VOC_COLORMAP[sele][2]))) #显示图像和标签 plt.figure("image") ax1 = plt.subplot(1,2,1) ax2 = plt.subplot(1,2,2) plt.sca(ax1) plt.imshow(img) plt.sca(ax2) plt.imshow(newIm) plt.show()

以上这篇Pytorch 实现数据集自定义读取就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。