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data_loader_custom.py
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import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
import numpy as np
from torch.autograd import Variable as V
import os
import random
import copy
import csv
from PIL import Image
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from torch.utils.data.dataset import Dataset
import numpy as np
NUM_WORKERS = 4
class Cifar10(Dataset):
def __init__(self, csv_file_path = './data/cifar10/train.csv',
file_pathR='./data/cifar10/train_PIr',
file_pathC = './data/cifar10/train_PIc',
file_pathOrg = './data/cifar10/train',
augment6 = None,
augment3 = None,
class_to_idx = {'airplane': 0,
'automobile': 1,
'bird': 2,
'cat': 3,
'deer': 4,
'dog': 5,
'frog': 6,
'horse': 7,
'ship': 8,
'truck': 9},
shuffle=True):
self.img_listR = []
self.img_listC = []
self.img_listOrg = []
self.img_label = []
self.augment6 = augment6
self.augment3 = augment3
self.class_to_idx = class_to_idx
with open(csv_file_path, "r") as fileDescriptor:
csvReader = csv.reader(fileDescriptor)
next(csvReader, None)
for line in csvReader:
imagePathR = os.path.join(file_pathR, line[0])
imagePathC = os.path.join(file_pathC, line[0])
imagePathOrg = os.path.join(file_pathOrg, line[0])
label = class_to_idx[line[1]]
self.img_listR.append(imagePathR)
self.img_listC.append(imagePathC)
self.img_listOrg.append(imagePathOrg)
self.img_label.append(label)
indexes = np.arange(len(self.img_listR))
if shuffle:
np.random.shuffle(indexes)
_img_listR, _img_listC, _img_listOrg, _img_label = copy.deepcopy(self.img_listR), copy.deepcopy(self.img_listC), copy.deepcopy(self.img_listOrg), copy.deepcopy(self.img_label)
self.img_listR = []
self.img_listC = []
self.img_listOrg = []
self.img_label = []
for i in indexes:
self.img_listR.append(_img_listR[i])
self.img_listC.append(_img_listC[i])
self.img_listOrg.append(_img_listOrg[i])
self.img_label.append(_img_label[i])
def __getitem__(self, index):
imagePathR = self.img_listR[index]
imagePathC = self.img_listC[index]
imagePathOrg = self.img_listOrg[index]
imageDataR = Image.open(imagePathR).convert('RGB')
imageDataC = Image.open(imagePathC).convert('RGB')
stu_imageData = np.concatenate((imageDataR, imageDataC), axis=2)
tea_imageData = Image.open(imagePathOrg).convert('RGB')
imageLabel = self.img_label[index]
if self.augment6 != None:
stu_imageData, tea_imageData = self.augment6(stu_imageData), self.augment3(tea_imageData)
return stu_imageData, tea_imageData, imageLabel
def __len__(self):
return len(self.img_listR)
def get_cifar(dataset_dir='./Data/cifar10', batch_size=128, crop=False):
normalizeT = transforms.Normalize(mean=[0.05165074, 0.05487815, 0.06697452, 0.05165074, 0.05487815, 0.06697452], std=[0.12468914, 0.12733056, 0.13768335, 0.12468914, 0.12733056, 0.13768335])
simple_transform6 = transforms.Compose([transforms.ToTensor(), normalizeT])
normalizeS = transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276])
simple_transform3 = transforms.Compose([transforms.ToTensor(), normalizeS])
if crop is True:
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
else:
train_transform = simple_transform6
trainset = Cifar10(csv_file_path = os.path.join(dataset_dir, 'train.csv'),
file_pathR = os.path.join(dataset_dir, 'train_PIr'),
file_pathC = os.path.join(dataset_dir, 'train_PIc'),
file_pathOrg = os.path.join(dataset_dir, 'train'),
augment6 = simple_transform6,
augment3 = simple_transform3)
testset = Cifar10(csv_file_path = os.path.join(dataset_dir, 'test.csv'),
file_pathR = os.path.join(dataset_dir, 'test_PIr'),
file_pathC = os.path.join(dataset_dir, 'test_PIc'),
file_pathOrg = os.path.join(dataset_dir, 'test'),
augment6 = simple_transform6,
augment3 = simple_transform3)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, num_workers=NUM_WORKERS,
pin_memory=True, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, num_workers=NUM_WORKERS,
pin_memory=True, shuffle=False)
return trainloader, testloader