ELR/data_loader/data_loaders.py

import sys

from torchvision import datasets, transforms
from base import BaseDataLoader
from data_loader.cifar10 import get_cifar10
from data_loader.cifar100 import get_cifar100
from parse_config import ConfigParser
from PIL import Image


class CIFAR10DataLoader(BaseDataLoader):
    def __init__(self, data_dir, batch_size, shuffle=True, validation_split=0.0, num_batches=0,  training=True, num_workers=4,  pin_memory=True):
        config = ConfigParser.get_instance()
        cfg_trainer = config['trainer']
        
        transform_train = transforms.Compose([
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ])
        transform_val = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ])
        self.data_dir = data_dir

        noise_file='%sCIFAR10_%.1f_Asym_%s.json'%(config['data_loader']['args']['data_dir'],cfg_trainer['percent'],cfg_trainer['asym'])
        
        self.train_dataset, self.val_dataset = get_cifar10(config['data_loader']['args']['data_dir'], cfg_trainer, train=training,
                                                           transform_train=transform_train, transform_val=transform_val, noise_file = noise_file)

        super().__init__(self.train_dataset, batch_size, shuffle, validation_split, num_workers, pin_memory,
                         val_dataset = self.val_dataset)
    def run_loader(self, batch_size, shuffle, validation_split, num_workers, pin_memory):
        super().__init__(self.train_dataset, batch_size, shuffle, validation_split, num_workers, pin_memory,
                         val_dataset = self.val_dataset)



class CIFAR100DataLoader(BaseDataLoader):
    def __init__(self, data_dir, batch_size, shuffle=True, validation_split=0.0, num_batches=0, training=True,num_workers=4, pin_memory=True):
        config = ConfigParser.get_instance()
        cfg_trainer = config['trainer']
        
        transform_train = transforms.Compose([
                #transforms.ColorJitter(brightness= 0.4, contrast= 0.4, saturation= 0.4, hue= 0.1),
                transforms.RandomCrop(32, padding=4),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)),
            ])
        transform_val = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761)),
        ])
        self.data_dir = data_dir
        config = ConfigParser.get_instance()
        cfg_trainer = config['trainer']

        noise_file='%sCIFAR100_%.1f_Asym_%s.json'%(config['data_loader']['args']['data_dir'],cfg_trainer['percent'],cfg_trainer['asym'])

        self.train_dataset, self.val_dataset = get_cifar100(config['data_loader']['args']['data_dir'], cfg_trainer, train=training,
                                                           transform_train=transform_train, transform_val=transform_val, noise_file = noise_file)

        super().__init__(self.train_dataset, batch_size, shuffle, validation_split, num_workers, pin_memory,
                         val_dataset = self.val_dataset)
    def run_loader(self, batch_size, shuffle, validation_split, num_workers, pin_memory):
        super().__init__(self.train_dataset, batch_size, shuffle, validation_split, num_workers, pin_memory,
                         val_dataset = self.val_dataset)