Transform Image Data For The Model

transform-data.py

import numpy as np
import time
import copy
import os
import torch
import torch.optim as optim
import torch.nn as nn
import torchvision
import matplotlib.pyplot as plt
from torch.optim import lr_scheduler
from torchvision import datasets, models, transforms

# Set the transformation for each dataset folder
transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'test': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
}

# Import the dataset
data_dir = 'data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), transform=transforms[x])
                  for x in ['train', 'val', 'test']}

# Shuffle the dataset and create batches from the dataset
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4)
               for x in ['train', 'val', 'test']}

# Get the number of images in each folder
data_size = {x: len(image_datasets[x]) for x in ['train', 'val', 'test']}

# Get the class name
class_names = image_datasets['train'].classes

# Enable the GPU if it exists
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")