import numpy as np

import torch

import torch.backends.cudnn as cudnn

import torch.nn as nn

import torch.nn.parallel

import torch.optim as optim

import torch.utils.data as data

import torchvision.datasets as datasets

import torchvision.models as models

import torchvision.transforms as transforms

from PIL import Image

import glob

import os

def loader(path, batch_size=16, num_workers=1, pin_memory=True):

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

return data.DataLoader(

datasets.ImageFolder(path,

transforms.Compose([

transforms.Resize(256),

transforms.RandomResizedCrop(224),

transforms.RandomHorizontalFlip(),

transforms.ToTensor(),

normalize,

])),

batch_size=batch_size,

shuffle=True,

num_workers=num_workers,

pin_memory=pin_memory)

def test_loader(path, batch_size=16, num_workers=1, pin_memory=True):

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

return data.DataLoader(

datasets.ImageFolder(path,

transforms.Compose([

transforms.Resize(256),

transforms.CenterCrop(224),

transforms.ToTensor(),

normalize,

])),

batch_size=batch_size,

shuffle=False,

num_workers=num_workers,

pin_memory=pin_memory)