Use of dataloader

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Dataloader The example code of using is as follows ：

from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torchvision

#  Prepared test data set 
test_data = torchvision.datasets.CIFAR10(root="./CIFAR10",train=False,transform=torchvision.transforms.ToTensor(),download=True)
#  Preparation for loading dataset 
test_loader = DataLoader(dataset=test_data,batch_size=4,shuffle=True,num_workers=0,drop_last=False)
'''
batch_size: How many pieces of data to process each time 
shuffle: When fetching data in the second cycle , Whether the order is out of order ,True To disrupt 
num_workers: Number of processes selected 
drop_last: Whether to remove the redundant data in the tail ,True To remove 
'''
#  The first picture and classification results in the test data set 
img,target = test_data[0]
print(img.shape)
print(target)

writer = SummaryWriter("logs")
step = 0

#  Premise batch_size=4
for data in test_loader:
    imgs,targets = data
    #  example ：torch.Size([4, 3, 32, 32]) 4 representative 4 A picture  3 Number of channels representing the picture   The size is 32*32 Pictures of the 
    print(imgs.shape)
    #  example ：tensor([3, 1, 9, 0])  Categories of four pictures 
    print(targets)

    #  Use tensorboard Visualizing 
    writer.add_images("test_data",imgs,step)   #  Pay attention to the use of add_images instead of add_image Oh , because imgs yes 4 individual tensor A collection of data type pictures 
    step+=1

writer.close()

The above code results in tensorboard visualization ：


Every step is 4 Zhang , It can also be seen here that dataloader in batch_size The function of parameters ！

dataloader in batch_size It's the equivalent of dataset pack , Take the whole packaged part every time for subsequent operations , The figure above is easy to understand , take batch_size Set the value of the number of pictures (img) And classification results (target) Pack them separately into imgs,targets.

The above code should Be careful The place of ：

• writer.add_images(“test_data”,imgs,step) # Pay attention to the use of add_images instead of add_image Oh , because imgs yes 4 individual tensor A collection of data type pictures
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