transformer.py

import torch
from torchvision import datasets, transforms, models

data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'

def my_transforms(train_dir, valid_dir, test_dir):
    # TODO: Define your transforms for the training, validation, and testing sets
    train_transforms = transforms.Compose([
        transforms.RandomResizedCrop(224),  
        transforms.RandomHorizontalFlip(p=0.5),  
        transforms.ToTensor(),  
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])


    test_transforms = transforms.Compose([
        transforms.Resize(256),  
        transforms.CenterCrop(224),  
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    # TODO: Load the datasets with ImageFolder
    train_data = datasets.ImageFolder(train_dir, transform=train_transforms)
    valid_data = datasets.ImageFolder(valid_dir, transform=test_transforms)
    test_data = datasets.ImageFolder(test_dir, transform=test_transforms)

    # TODO: Using the image datasets and the trainforms, define the dataloaders
    train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
    valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=64)
    test_loader = torch.utils.data.DataLoader(test_data, batch_size=64)

    return train_loader, valid_loader, test_loader, train_data