ds_train = datasets.VOCSegmentation(root='train_data', year='2012', image_set='train', download=False,transform=transform) print("the dataset has samples_num:"",len(ds_train)) dl_train = DataLoader(ds_train, batch_size=batch_size_train, shuffle=True, num_workers=1) for i, data in ...
这里只需要将Dataset丢到服务器里就可以了,其他都是不需要的。如下所示: 这个固定格式是有VOC.yaml文件所决定的: 简要来说,对于yolov5所需的的格式为: DataSet|---images|---train|---val (可以没有)|---test(可以没有)|---labels|---train|---val (可以没有)|---test(可以没有) 也就是在一个...
dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True) else: dataset = VOCSegmentation(voc_root=r"E:\note\cv\data\VOC_Train", shape=shape, txt_name="val.txt", ) dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True) return dataloader cm = np...
最后,有了上面两个子类,就可以使用torch.utils.data.DataLoader加载数据了。总结就是重写了classtorch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, num_workers=0,...)的dataset和sampler。 剩下的就是送入网络训练。
voc_root = r"work/VOCdevkit/VOC2012" crop_size = (320, 480) train_data = VOCSegData(voc_root=voc_root, train=True, crop_size=crop_size)#训练数据 val_data = VOCSegData(voc_root=voc_root, train=False, crop_size=crop_size)#测试数据 train_loader = paddle.io.DataLoader(train_data,...
inference_on_datasetfromdetectron2.dataimportMetadataCatalog,build_detection_test_loader,build_detection_train_loaderfromdetectron2.utils.loggerimportsetup_loggerfromdetectron2.modelingimportGeneralizedRCNNWithTTAfrompathlibimportPath# 获取数据集路径VOC_ROOT="/path/to/VOC2007"# 设置随机数种子,确保训练和验证集...