Training and testing dataset
Now, we will combine all these knowledge and use to define train and test dataset. We will perform preprocessing on both dataset while we will only perform augmentation on train dataset. The Pytorch implementation is as follows:
Python3
# File path to the dataset, replace this with your path train_path = f'./maps/train'test_path = f'./maps/val' # Defining the train and test transforms train_transform = torchvision.transforms.Compose([ CustomTransform(), CustomAugmentation(), ]) test_transform = torchvision.transforms.Compose([ CustomTransform(), ]) # Creating the train and test datasets train_dataset = ImageDataset(train_path, transform=train_transform) test_dataset = ImageDataset(test_path, transform=test_transform) # Creating the train and test dataloaders train_dataloader = torch.utils.data.DataLoader( dataset=train_dataset, batch_size=4, shuffle=True, num_workers=2) test_dataloader = torch.utils.data.DataLoader( dataset=test_dataset, batch_size=1, shuffle=False, num_workers=2) # Printing the length of the train and test dataloaders print('Number of training batches:',len(train_dataloader)) print('Number of testing batches:',len(test_dataloader)) |
Output:
Number of training batches: 274
Number of testing batches: 1098
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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