dataaug.py

from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from path import train_dir, val_dir, test_dir
from optimTarget import batch_size
import torch
import numpy as np
import random

seed = 3334
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) 
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(seed)
random.seed(seed)

train_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

val_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ])


test_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])


train_data = datasets.ImageFolder(train_dir)
val_data = datasets.ImageFolder(val_dir)
test_data = datasets.ImageFolder(test_dir)

train_data.transform = train_transform
val_data.transform = val_transform
test_data.transform = test_transform

train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=4)
val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=True, num_workers=4)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=4)