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Loading demo ImageNet vision dataset in torchvision using Pytorch. Click here to download the dataset by signing up.

Python3




# import the torch and
# torchvision dataset packages.
import torch
import torchvision
 
# access the dataset in torchvision package using
# .datasets followed by dataset name.
imagenet_data = torchvision.datasets.ImageNet('path/to/imagenet_root/')
 






                        









													
													
												

												
												

													
													
Code Explanation:

													
													
												

												
												

													
													
    

  • The procedure is almost the same as loading the audio data.
    • 

  • Here, instead of torchaudio, torchvision has to be imported.
    • 

  • Use the torchvision function with the datasets accessor, followed by the dataset name.
    • 

  • Now, pass the path in which the dataset is present. Since the ImageNet dataset is no longer publicly accessible, download the root data in your local system and pass the path to this function. This will comfortably load the vision data.
    • 


													
													
												

												
												

													
													
To load your custom image data, use torch.utils.data.DataLoader(data, batch_size, shuffle) as mentioned above.

													
													
												

												
												

													
													




Python3















                                    




                                    

                                    




                                    




                                    

                                    

                                    
                                




















# import necessary function


# from torchvision package


from torchvision import transforms, datasets


import matplotlib.pyplot as plt


 


# specify the image dataset folder


data_dir = r'path to dataset\train'


 


# perform some transformations like resizing,


# centering and tensorconversion


# using transforms function


transform = transforms.Compose(


    [transforms.Resize(255),


     transforms.CenterCrop(224),


     transforms.ToTensor()])


 


# pass the image data folder and


# transform function to the datasets


# .imagefolder function


dataset = datasets.ImageFolder(data_dir, 


                               transform=transform)


 


# now use dataloder function load the


# dataset in the specified transformation.


dataloader = torch.utils.data.DataLoader(dataset,


                                         batch_size=32,


                                         shuffle=True)


 


# iter function iterates through all the


# images and labels and stores in two variables


images, labels = next(iter(dataloader))


 


# print the total no of samples


print('Number of samples: ', len(images))


image = images[2][0]  # load 3rd sample


 


# visualize the image


plt.imshow(image, cmap='gray')


 


# print the size of image


print("Image Size: ", image.size())


 


# print the label


print(label)



















                        







                        









													
													
												

												
												

													
													
Output:

													
													
												

												
												

													
													
Image size: torch.Size([224,224])
tensor([0, 0, 0, 1, 1, 1])

													
													
												

												
												

													
													

													
													
												

												
											

											
											

												
Loading Data in Pytorch

												
												

													
													
In this article, we will discuss how to load different kinds of data in PyTorch.

													
													
												

												
												

													
													
For demonstration purposes, Pytorch comes with 3 divisions of datasets namely torchaudio, torchvision, and torchtext. We can leverage these demo datasets to understand how to load Sound, Image, and text data using Pytorch.

													
													
												

												
											

										


										
										

											
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														Loading demo ImageNet vision dataset in torchvision using Pytorch. Click here to download the dataset by signing up....
													

												
											

											
										


									

									

										
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												Torchaudio Dataset
											
											
										

										

											
											
												Torchtext Dataset