DataLoader.py

import torch
import torch.nn as nn
import torchvision.datasets as dset
import torch.utils.data
import torchvision.transforms as transforms
import os
import json
import numpy as np
from torch.utils.data import Dataset, DataLoader

def imagenet(state):
    
    if 'defense' in state and state['defense']:
        mean = np.array([0.5, 0.5, 0.5])
        std = np.array([0.5, 0.5, 0.5])
    else:               
        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])

    transform = transforms.Compose(
        [transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.ToTensor()])

    train_loader = torch.utils.data.DataLoader(
        dset.ImageFolder(state['train_path'], transform=transform),
        batch_size=state['batch_size'], shuffle=False,
        num_workers=8, pin_memory=True)

    test_loader = torch.utils.data.DataLoader(
        dset.ImageFolder(state['data_path'], transform=transform),
        batch_size=state['batch_size'], shuffle=False,
        num_workers=8, pin_memory=True)

    nlabels = 1000

    return train_loader, test_loader, nlabels, mean, std


def gvision(state):

    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])

    transform = transforms.Compose(
        [transforms.Resize(256),
         transforms.CenterCrop(224),
         transforms.ToTensor()])


    test_loader = torch.utils.data.DataLoader(
        dset.ImageFolder(state['data_path'], transform=transform),
        batch_size=1, shuffle=False,
        num_workers=1, pin_memory=True)

    nlabels = 1
    labels = ["Cat"]


    return test_loader, nlabels, labels, mean, std