shuffle = False时，不打乱数据顺序
shuffle = True，随机打乱
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import numpy as np
import h5py
import torch
from 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)
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pytorch DataLoader使用细节
背景：
我一开始是对数据扩增这一块有疑问, 只看到了数据变换(torchvisiom.transforms),但是没看到数据扩增, 后来搞明白了, 数据扩增在pytorch指的是torchvisiom.transforms + torch.utils.data.DataLoader+多个epoch共同作用下完成的,
数据变换共有以下内容
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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])])
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简单的数据读取类, 进返回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
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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)
"""原始数据集共3张图片, 以batch_size=1, epoch为2 展示所有图片(共6张) """
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)
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从上述图片总可以看到, 在每个eopch阶段实际上是对原始图片重新使用了transform, , 这就造就了数据的扩增
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_35752161/article/details/110875040