1

使用 Torchvision 的数据集后，我尝试在 Pytorch 中加载 HDF5 文件，但没有成功。我读到我应该定义自己的 Dataset 和 Dataloader 类，其中包含 getitem 以启用索引和 len 以返回数据集的长度。另外，我应该定义转换，因为 pytorch 的默认选项需要 PIL 图像。我尝试这样做，但我收到错误“ValueError：没有足够的值来解包（预期 2，得到 1）”我做错了什么？

#PyTorch packages
import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import transforms, datasets
from torch import optim
from torch.autograd import Variable 
from torch.utils import data

import h5py 
import numpy as np
import matplotlib.pyplot as plt

torch.manual_seed(0)

#open training file
with h5py.File('train_catvnoncat.h5', 'r') as hdf:
    ls = list(hdf.keys())
    print('List of datasets in this file: \n', ls)
    data = hdf.get('dataset')
    dataset1 = np.array(data)
    print('Shape of dataset1: \n', dataset1.shape)  
    length = len(h5py.File('train_catvnoncat.h5', 'r'))
    print(length)

#image size (64,64,3) 64*64*3=12,288. 
#209 training examples 
#50 test examples

# Example of a picture

#image size (64,64,3) 64*64*3=12,288. 
#209 training examples 
#50 test examples

#Def the dataloader for h5 files:

class HDF5Dataset(Dataset):   
    def __init__(self, h5_path):
        self.h5_path = '/Users/teff/Downloads/'
        self.train = train_catvnoncat.h5(h5_path, 'r')
        self.train = test_catvnoncat.h5(h5_path, 'r')
        self.length = len(h5py.File(h5_path, 'r')) 
        # self.transform = transform_hdf5    #I need to define the "transformToTensor"

    def __getitem__(self, index): #to enable indexing
        record = self.train[str(index)]
        return (
                record['X'].value,
                record['y'].value,
        )

    def __len__(self): #returns the lenght of the dataset
        return self.length


train_loader = torch.utils.data.DataLoader('train_catvnoncat.h5', shuffle=True)
test_loader = torch.utils.data.DataLoader('test_catvnoncat.h5', shuffle=True)
4

1 回答 1

0

您的数据集应如下所示：

import torchvision.transforms as transforms

class HDF5Dataset(Dataset):

    transform = transforms.Compose([
        transforms.RandomHorizontalFlip(p=0.5),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])

    def __init__(self, h5_path):
        self.h5_path = '/Users/teff/Downloads/'
        self.train = test_catvnoncat.h5(h5_path, 'r')
        self.length = len(h5py.File(h5_path, 'r')) 

    def __getitem__(self, index): #to enable indexing
        record = self.train[str(index)]
        image = record['X'].value

        # transform to PIL image
        image = Image.fromarray(pixels.astype('uint8'), 'RGB') # assume your data is  uint8 rgb
        label = record['y'].value

        # transformation here
        # torchvision PIL transformations accepts one image as input
        image = self.transform(image)
        return (
                image,
                label,
        )

    def __len__(self):
        return self.length

PS 看看关于 pytorch 数据加载的精彩教程。

于 2019-12-05T08:13:49.970 回答