If you want to build datasets with PyTorch

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#!python3 -m pip install torch==1.8.0 \

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#--user --quiet --no-warn-script-location

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#!python3 -m pip install torchvision==0.9.0 \

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#--user --quiet --no-warn-script-location

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#spath='/home/sc_work/.sage/local/lib/python3.9/site-packages'

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#import sys; sys.path.append(spath)

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from torch.utils.data import Dataset as tds

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from torch.utils.data import DataLoader as tdl

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import numpy as np,pylab as pl,torch,zipfile,h5py,urllib

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from IPython.core.magic import register_line_magic

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class TData(tds):

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    def __init__(self,x,y):   

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        self.x=torch.tensor(x,dtype=torch.float32)

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        self.y=torch.tensor(y,dtype=torch.int32)

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    def __getitem__(self,index):

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        img,lbl=self.x[index],self.y[index]

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        return img,lbl

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    def __len__(self):

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        return self.y.shape[0]

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@register_line_magic

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def display_examples(data):

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    for images,labels in dataloaders[data]:  

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        print('Image dimensions: %s'%str(images.shape))

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        print('Label dimensions: %s'%str(labels.shape))

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        n=np.random.randint(1,3)

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        fig=pl.figure(figsize=(6,2))

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        for i in range(n,n+5):

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            ax=fig.add_subplot(1,5,i-n+1,xticks=[],yticks=[])

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            ax.set_title(names[labels[i].item()],fontsize=10)

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            ax.imshow(np.transpose(images[i],(1,2,0)))

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        pl.tight_layout(); pl.show(); break

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path='https://olgabelitskaya.github.io/'

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zf='TomatoCultivarImages.h5.zip'

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input_file=urllib.request.urlopen(path+zf)

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output_file=open(zf,'wb'); 

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output_file.write(input_file.read())

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output_file.close(); input_file.close()

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zipf=zipfile.ZipFile(zf,'r')

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zipf.extractall(''); zipf.close()

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f=h5py.File(zf[:-4],'r'); keys=list(f.keys())

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x_test=np.array(f[keys[0]])

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y_test=np.array(f[keys[1]])

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names=['Kumato','Beefsteak','Tigerella',

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       'Roma','Japanese Black Trifele',

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       'Yellow Pear','Sun Gold','Green Zebra',

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       'Cherokee Purple','Oxheart','Blue Berries',

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       'San Marzano','Banana Legs',

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       'German Orange Strawberry','Supersweet 100']

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num_classes=len(names); batch_size=int(16)

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x_test=np.transpose(x_test,(0,3,1,2))

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print(x_test.mean(),x_test.std())

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test=TData(x_test,y_test)

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dataloaders={'test':tdl(

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    dataset=test,shuffle=True,batch_size=batch_size)}

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%display_examples test

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