data.py

import zipfile
import os

import torchvision.transforms as transforms

# once the images are loaded, how do we pre-process them before being passed into the network
# by default, we resize the images to 64 x 64 in size
# and normalize them to mean = 0 and standard-deviation = 1 based on statistics collected from
# the training set
train_data_transforms = transforms.Compose([
    transforms.Resize(224),
    transforms.RandomHorizontalFlip(0.5),
    transforms.RandomRotation((-30, 30)),
    transforms.RandomCrop((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

data_transforms = transforms.Compose([
    transforms.Resize(224),
    transforms.CenterCrop((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])