calculate each data's variance, kl divergence

+# run train.py --dataset cifar10 --model resnet18 --data_augmentation --cutout --length 16
+# run train.py --dataset cifar100 --model resnet18 --data_augmentation --cutout --length 8
+# run train.py --dataset svhn --model wideresnet --learning_rate 0.01 --epochs 160 --cutout --length 20
+
+import pdb
+import argparse
+import numpy as np
+from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.backends.cudnn as cudnn
+from torch.optim.lr_scheduler import MultiStepLR
+
+from torchvision.utils import make_grid
+from torchvision import datasets, transforms
+
+from torch.utils.data.dataloader import RandomSampler
+from util.misc import CSVLogger
+from util.cutout import Cutout
+
+from model.resnet import ResNet18
+from model.wide_resnet import WideResNet
+
+model_options = ['resnet18', 'wideresnet']
+dataset_options = ['cifar10', 'cifar100', 'svhn']
+
+parser = argparse.ArgumentParser(description='CNN')
+parser.add_argument('--dataset', '-d', default='cifar10',
+                    choices=dataset_options)
+parser.add_argument('--model', '-a', default='resnet18',
+                    choices=model_options)
+parser.add_argument('--batch_size', type=int, default=128,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=200,
+                    help='number of epochs to train (default: 20)')
+parser.add_argument('--learning_rate', type=float, default=0.1,
+                    help='learning rate')
+parser.add_argument('--data_augmentation', action='store_true', default=False,
+                    help='augment data by flipping and cropping')
+parser.add_argument('--cutout', action='store_true', default=False,
+                    help='apply cutout')
+parser.add_argument('--n_holes', type=int, default=1,
+                    help='number of holes to cut out from image')
+parser.add_argument('--length', type=int, default=16,
+                    help='length of the holes')
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='enables CUDA training')
+parser.add_argument('--seed', type=int, default=0,
+                    help='random seed (default: 1)')
+
+args = parser.parse_args()
+args.cuda = not args.no_cuda and torch.cuda.is_available()
+cudnn.benchmark = True  # Should make training should go faster for large models
+
+torch.manual_seed(args.seed)
+if args.cuda:
+    torch.cuda.manual_seed(args.seed)
+
+test_id = args.dataset + '_' + args.model
+
+print(args)
+
+# Image Preprocessing
+if args.dataset == 'svhn':
+    normalize = transforms.Normalize(mean=[x / 255.0 for x in[109.9, 109.7, 113.8]],
+                                     std=[x / 255.0 for x in [50.1, 50.6, 50.8]])
+else:
+    normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
+                                     std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
+
+train_transform = transforms.Compose([])
+if args.data_augmentation:
+    train_transform.transforms.append(transforms.RandomCrop(32, padding=4))
+    train_transform.transforms.append(transforms.RandomHorizontalFlip())
+train_transform.transforms.append(transforms.ToTensor())
+train_transform.transforms.append(normalize)
+if args.cutout:
+    train_transform.transforms.append(Cutout(n_holes=args.n_holes, length=args.length))
+
+
+test_transform = transforms.Compose([
+    transforms.ToTensor(),
+    normalize])
+
+if args.dataset == 'cifar10':
+    num_classes = 10
+    train_dataset = datasets.CIFAR10(root='data/',
+                                     train=True,
+                                     transform=train_transform,
+                                     download=True)
+
+    test_dataset = datasets.CIFAR10(root='data/',
+                                    train=False,
+                                    transform=test_transform,
+                                    download=True)
+elif args.dataset == 'cifar100':
+    num_classes = 100
+    train_dataset = datasets.CIFAR100(root='data/',
+                                      train=True,
+                                      transform=train_transform,
+                                      download=True)
+
+    test_dataset = datasets.CIFAR100(root='data/',
+                                     train=False,
+                                     transform=test_transform,
+                                     download=True)
+elif args.dataset == 'svhn':
+    num_classes = 10
+    train_dataset = datasets.SVHN(root='data/',
+                                  split='train',
+                                  transform=train_transform,
+                                  download=True)
+
+    extra_dataset = datasets.SVHN(root='data/',
+                                  split='extra',
+                                  transform=train_transform,
+                                  download=True)
+
+    # Combine both training splits (https://arxiv.org/pdf/1605.07146.pdf)
+    data = np.concatenate([train_dataset.data, extra_dataset.data], axis=0)
+    labels = np.concatenate([train_dataset.labels, extra_dataset.labels], axis=0)
+    train_dataset.data = data
+    train_dataset.labels = labels
+
+    test_dataset = datasets.SVHN(root='data/',
+                                 split='test',
+                                 transform=test_transform,
+                                 download=True)
+
+# Data Loader (Input Pipeline)
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=args.batch_size,
+                                           shuffle=False,
+                                        #    sampler=RandomSampler(train_dataset, True, 40000),
+                                           pin_memory=True,
+                                           num_workers=0)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                          batch_size=args.batch_size,
+                                          shuffle=False,
+                                          pin_memory=True,
+                                          num_workers=0)
+
+if args.model == 'resnet18':
+    cnn = ResNet18(num_classes=num_classes)
+elif args.model == 'wideresnet':
+    if args.dataset == 'svhn':
+        cnn = WideResNet(depth=16, num_classes=num_classes, widen_factor=8,
+                         dropRate=0.4)
+    else:
+        cnn = WideResNet(depth=28, num_classes=num_classes, widen_factor=10,
+                         dropRate=0.3)
+checkpoint = torch.load('/content/drive/MyDrive/capstone/Cutout/checkpoints/baseline_cifar10_resnet18.pt', map_location = torch.device('cuda:0'))
+cnn = cnn.cuda()
+cnn.load_state_dict(checkpoint)
+criterion = nn.CrossEntropyLoss().cuda()
+cnn_optimizer = torch.optim.SGD(cnn.parameters(), lr=args.learning_rate,
+                                momentum=0.9, nesterov=True, weight_decay=5e-4)
+
+if args.dataset == 'svhn':
+    scheduler = MultiStepLR(cnn_optimizer, milestones=[80, 120], gamma=0.1)
+else:
+    scheduler = MultiStepLR(cnn_optimizer, milestones=[60, 120, 160], gamma=0.2)
+
+filename = 'logs/' + test_id + '.csv'
+csv_logger = CSVLogger(args=args, fieldnames=['epoch', 'train_acc', 'test_acc'], filename=filename)
+
+
+def test(loader):
+    cnn.eval()    # Change model to 'eval' mode (BN uses moving mean/var).
+    correct = 0.
+    total = 0.
+    for images, labels in loader:
+        images = images.cuda()
+        labels = labels.cuda()
+
+        with torch.no_grad():
+            pred = cnn(images)
+
+        pred = torch.max(pred.data, 1)[1]
+        
+        total += labels.size(0)
+        correct += (pred == labels).sum().item()
+
+    val_acc = correct / total
+    cnn.train()
+    return val_acc
+
+kl_sum = 0
+y_bar = torch.Tensor([0] * 10).detach().cuda()
+
+# y_bar 구하는 epoch
+for epoch in range(args.epochs):
+
+    cnn.eval()
+    xentropy_loss_avg = 0.
+    correct = 0.
+    total = 0.
+    norm_const = 0
+
+    kldiv = 0
+    # pred_sum = torch.Tensor([0] * 10).detach().cuda()
+    
+    progress_bar = tqdm(train_loader)
+    for i, (images, labels) in enumerate(progress_bar):
+        progress_bar.set_description('Epoch ' + str(epoch))
+
+        images = images.cuda()
+        labels = labels.cuda()
+
+        cnn.zero_grad()
+        pred = cnn(images)
+        xentropy_loss = criterion(pred, labels)
+        # xentropy_loss.backward()
+        # cnn_optimizer.step()
+
+        xentropy_loss_avg += xentropy_loss.item()
+
+        pred_softmax = nn.functional.softmax(pred).cuda()
+        # Calculate running average of accuracy
+        pred = torch.max(pred.data, 1)[1]
+        total += labels.size(0)
+        correct += (pred == labels.data).sum().item()
+        accuracy = correct / total
+        for a in range(pred_softmax.data.size()[0]):
+            for b in range(y_bar.size()[0]):
+                y_bar[b] += torch.log(pred_softmax.data[a][b])
+        
+
+        # expectation(log y_hat)        
+        # y_bar = [x / pred.data.size()[0] for x in y_bar]
+
+        # print(pred.data.size()[0], y_bar.size()[0]) # 128, 10
+        
+
+        # print(pred)
+        # y_hat : 모델별 예측값  --> pred_softmax
+        # y_bar : 예측값들 평균값  -- > pred / total : pred_sum
+        # labes.data : ground_truth
+
+        # y_bar = pred_sum / (i+1)
+        # kl = torch.nn.functional.kl_div(pred, y_bar)
+        # kl_sum += kl
+        
+
+        
+        # for문 추가안하면 epoch별 iter마다 xentropy_loss_avg값의 1/iter이 xentropy값으로 출력
+        # for문 추가하면 epoch 별 iter 마다 xentropy_loss_avg 값은 동일하나 xentropy값 출력이 x_l_avg 값의 1/10으로 출력
+        # for문 상관 없이 pred, labels 값은 동일하게 확인됨.
+
+        # for a in range(list(pred_sum.size())[0]):
+        #     for b in range(list(pred.size())[0]):
+        #         if pred[b] == a:
+        #             pred_sum[a] += 1
+        
+        # variance calculate : E[KL_div(y_bar, y_hat)] -> expectation of KLDivLoss(pred_sum, pred)
+        # 한 epoch마다 계산해서 출력해야 할듯
+        # nn.functional.kl_div(pred_sum, pred)
+        
+        
+        # print('\n',i, ' ', xentropy_loss_avg)
+        progress_bar.set_postfix(
+            # y_hat = '%.5f' % pred,
+            # y_bar = '%.5f' % y_bar,
+            # groun_truth = '%.5f' % labels.data,
+            # kl = '%.3f' % kl.item(),
+            # kl_sum = '%.3f' % (kl_sum.item()),
+            # kl_div = '%.3f' % (kl_sum.item() / (i + 1)), # kl_div 호출
+            xentropy='%.3f' % (xentropy_loss_avg / (i + 1)),
+            acc='%.3f' % accuracy)
+    # pred_sum = [x / 40000 for x in pred_sum]
+    y_bar = torch.Tensor([x / 50000 for x in y_bar]).cuda()
+    y_bar = torch.exp(y_bar)
+    # print(y_bar)
+    for index in range(y_bar.size()[0]):
+        norm_const += y_bar[index]
+    print(y_bar)
+    print(norm_const)
+    # print(norm_const)
+    for index in range(y_bar.size()[0]):
+        y_bar[index] = y_bar[index] / norm_const
+    print(y_bar)
+    # print(y_bar)
+    # print(pred_softmax)
+    # print(y_bar)
+    # kldiv = torch.nn.functional.kl_div(y_bar, pred_softmax, reduction='batchmean')
+    # kl_sum += kldiv
+    # print(kldiv, kl_sum)
+    y_bar_copy = y_bar.clone().detach()
+    test_acc = test(test_loader)
+    # print(pred, labels.data)
+    tqdm.write('test_acc: %.3f' % (test_acc))
+
+    scheduler.step(epoch)  # Use this line for PyTorch <1.4
+    # scheduler.step()     # Use this line for PyTorch >=1.4
+
+    row = {'epoch': str(epoch), 'train_acc': str(accuracy), 'test_acc': str(test_acc)
+    }
+    csv_logger.writerow(row)
+    del pred
+    torch.cuda.empty_cache()
+
+# kl_div 구하는 epoch
+for epoch in range(args.epochs):
+    cnn.eval()
+    kldiv = 0
+    for i, (images, labels) in enumerate(progress_bar):
+        progress_bar.set_description('Epoch ' + str(epoch) + ': Calculate kl_div')
+
+        images = images.cuda()
+        labels = labels.cuda()
+
+        cnn.zero_grad()
+        pred = cnn(images)
+
+        pred_softmax = nn.functional.softmax(pred).cuda()
+
+        # 입력 두 개의 shape이 다르면 batchsize로 평균을 내서 반환.
+        kldiv = torch.nn.functional.kl_div(y_bar_copy, pred_softmax, reduction='sum')
+        kl_sum += kldiv.detach()
+        # print(y_bar_copy.size(), pred_softmax.size())
+        # print(kl_sum)
+    print("Average KL_div : ", abs(kl_sum / 50000))
+        # y_bar = torch.Tensor([x / 40000 for x in y_bar]).cuda()
+        # y_bar = torch.exp(y_bar)
+        # # print(y_bar)
+        # for index in range(y_bar.size()[0]):
+        #     norm_const += y_bar[index]
+        # # print(norm_const)
+        # for index in range(y_bar.size()[0]):
+        #     y_bar[index] = y_bar[index] / norm_const
+        # # print(y_bar)
+        # # print(pred_softmax)
+        # # print(y_bar)
+        # kldiv = torch.nn.functional.kl_div(y_bar, pred_softmax, reduction='batchmean')
+        # kl_sum += kldiv
+        # print(kldiv, kl_sum)
+
+torch.save(cnn.state_dict(), 'checkpoints/' + test_id + '.pt')
+csv_logger.close()