# 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(1):

    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(1):
    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()