PYTORCH DATA LOADERS — 4 Types

PYTORCH DATA LOADERS — 4 Types

In this article I will show you how to setup Data loaders and Transformers in Pytorch, You need to import below for the same exercise.

In this article I will show you how to setup Data loaders and Transformers in Pytorch, You need to import below for the same exercise

import torchvision

import torch

import os

import matplotlib.pyplot as plt

import numpy as np

1. Define the Transform

Image Resize (256,256) or Any other size

Convert to Pytorch Tensors

Normalize the Image by calling torchvision.transform.Normalize

transform_img = torchvision.transforms.Compose([torchvision.transforms.Resize((256, 256)),

torchvision.transforms.ToTensor(),

torchvision.transforms.Normalize(mean=[0.485],std=[0.229])])

2. Create the DataSet from torchvision.datasets

Set some Directory Path, download = True will download the data into the directory specified, transform should be set to transform defined above

dir_path= ‘C:\Users\Asus\pytorch-basics-part2’

dataset_mnist_train = torchvision.datasets.MNIST(dir_path, train=True, transform=transform_img,

target_transform=None, download=True)

You can index this Dataset, dataset_mnist_train[i] will contain the Tuple of (Image, Label).

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PyTorch for Deep Learning | Data Science | Machine Learning | Python

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