shuffle = False時，不打亂數(shù)據(jù)順序
shuffle = True，隨機(jī)打亂
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import numpy as npimport h5pyimport torchfrom torch.utils.data import DataLoader, Dataset h5f = h5py.File('train.h5', 'w');data1 = np.array([[1,2,3], [2,5,6], [3,5,6], [4,5,6]])data2 = np.array([[1,1,1], [1,2,6], [1,3,6], [1,4,6]])h5f.create_dataset(str('data'), data=data1)h5f.create_dataset(str('label'), data=data2)class Dataset(Dataset): def __init__(self): h5f = h5py.File('train.h5', 'r') self.data = h5f['data'] self.label = h5f['label'] def __getitem__(self, index): data = torch.from_numpy(self.data[index]) label = torch.from_numpy(self.label[index]) return data, label def __len__(self): assert self.data.shape[0] == self.label.shape[0], "wrong data length" return self.data.shape[0] dataset_train = Dataset()loader_train = DataLoader(dataset=dataset_train, batch_size=2, shuffle = True) for i, data in enumerate(loader_train): train_data, label = data print(train_data) |
pytorch DataLoader使用細(xì)節(jié)
背景：
我一開始是對數(shù)據(jù)擴(kuò)增這一塊有疑問, 只看到了數(shù)據(jù)變換(torchvisiom.transforms),但是沒看到數(shù)據(jù)擴(kuò)增, 后來搞明白了, 數(shù)據(jù)擴(kuò)增在pytorch指的是torchvisiom.transforms + torch.utils.data.DataLoader+多個epoch共同作用下完成的,
數(shù)據(jù)變換共有以下內(nèi)容
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composed = transforms.Compose([transforms.Resize((448, 448)), # resize transforms.RandomCrop(300), # random crop transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], # normalize std=[0.5, 0.5, 0.5])]) |
簡單的數(shù)據(jù)讀取類, 進(jìn)返回PIL格式的image:
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class MyDataset(data.Dataset): def __init__(self, labels_file, root_dir, transform=None): with open(labels_file) as csvfile: self.labels_file = list(csv.reader(csvfile)) self.root_dir = root_dir self.transform = transform def __len__(self): return len(self.labels_file) def __getitem__(self, idx): im_name = os.path.join(root_dir, self.labels_file[idx][0]) im = Image.open(im_name) if self.transform: im = self.transform(im) return im |
下面是主程序
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labels_file = "F:/test_temp/labels.csv"root_dir = "F:/test_temp"dataset_transform = MyDataset(labels_file, root_dir, transform=composed)dataloader = data.DataLoader(dataset_transform, batch_size=1, shuffle=False)"""原始數(shù)據(jù)集共3張圖片, 以batch_size=1, epoch為2 展示所有圖片(共6張) """for eopch in range(2): plt.figure(figsize=(6, 6)) for ind, i in enumerate(dataloader): a = i[0, :, :, :].numpy().transpose((1, 2, 0)) plt.subplot(1, 3, ind+1) plt.imshow(a) |
從上述圖片總可以看到, 在每個eopch階段實(shí)際上是對原始圖片重新使用了transform, , 這就造就了數(shù)據(jù)的擴(kuò)增
以上為個人經(jīng)驗(yàn)，希望能給大家一個參考，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://blog.csdn.net/qq_35752161/article/details/110875040
