Custom Dataloaders
A custom dataloader can be defined by wrapping the dataset along with torch.utils.data.DataLoader class. It enable us to control various aspects of data loader like batch size, number of workers, and whether to shuffle the data or not. We can define a custom data loader in Pytorch as follows:
Python3
# Defining a custom data loader dataloader = torch.utils.data.DataLoader( # Replace with the dataset object dataset=dataset, # Defining the batch size batch_size=4, # If true, shuffles the dataset at every epoch shuffle=True, # Number of parallel processes for loading the data num_workers=2) # Get the length of the dataloader # (Number of batches) print('Number of batches:',len(dataloader)) |
Output:
Number of batches: 274
Image Datasets, Dataloaders, and Transforms in Pytorch
Deep learning in Pytorch is becoming increasingly popular due to its ease of use, support for multiple hardware platforms, and efficient processing. Image datasets, dataloaders, and transforms are essential components for achieving successful results with deep learning models using Pytorch.
In this article, we will discuss Image datasets, dataloaders, and transforms in Python using the Pytorch library. Image datasets store collections of images that can be used in deep-learning models for training, testing, or validation. These images are collected from a variety of sources such as online websites, physical controllers, user-generated content, etc. Dataloaders are responsible for loading the image datasets and providing them in batches to the models. Transforms are algorithms used to alter certain aspects of the images such as color, size, shape, brightness, etc. In Pytorch, these components can be used to create deep learning models for tasks such as object recognition, image classification, and image segmentation.
Popular datasets such as ImageNet, CIFAR-10, and MNIST can be used as the basis for creating image datasets and Dataloaders. Popular image transforms such as random rotation, random crop, random horizontal or vertical flipping, normalization, and color augmentation can be used to create model-ready data. Dataloaders can be used to efficiently load batches of data from the dataset for model training.
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