'''Train CIFAR10 with PyTorch.'''
from __future__ import print_function

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn

import torchvision
import torchvision.transforms as transforms

import os
import argparse

from models import *
from utils import progress_bar


import matplotlib.pyplot as plt
#graphing
import pickle
#save the list

parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
# ArgumentParser(prog=None, usage=None, description=None, epilog=None)
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
args = parser.parse_args()
# Convert argument strings to objects and assign them as attributes of the namespace. Return the populated namespace.

device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0  # best test accuracy
start_epoch = 0  # start from epoch 0 or last checkpoint epoch

# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(0.5),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4824, 0.4467), (0.2471, 0.2435, 0.2616)),
])
#  transforms.Normalize is about the RBG color

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4824, 0.4467), (0.2471, 0.2435, 0.2616)),
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

# Model
print('==> Building model..')
# net = VGG('VGG19')
net = ResNet110()
# net = PreActResNet18()
# net = GoogLeNet()
# net = DenseNet121()
# net = ResNeXt29_2x64d()
# net = MobileNet()
# net = MobileNetV2()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
net = net.to(device)
if device == 'cuda':
    net = torch.nn.DataParallel(net)
    cudnn.benchmark = True

if args.resume:
    # Load checkpoint.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
    checkpoint = torch.load('./checkpoint/ckpt_resnet110.t7')
    net.load_state_dict(checkpoint['net'])
    best_acc = checkpoint['acc']
    start_epoch = checkpoint['epoch']

criterion = nn.CrossEntropyLoss()
# the method we choose
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=0.0001)
# Implements stochastic gradient descent (optionally with momentum)
# torch.optim.SGD(params, lr=<object object>, momentum=0, dampening=0, weight_decay=0, nesterov=False)[s

#learning rate schedualing
def adjust_learning_rate(optimizer, epoch):
    if epoch<10:
        lr=0.01
    elif epoch<15:
        lr=0.001
    else:
        lr=0.0001
    for param_group in optimizer.param_groups:
        param_group['li']=lr
        
# Training
def train(epoch):
    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
            % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))

    return epoch, train_loss/(batch_idx+1)
    #return things in function(progress_bar)

def test(epoch):
    global best_acc
    net.eval()
    test_loss = 0
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (inputs, targets) in enumerate(testloader):
            inputs, targets = inputs.to(device), targets.to(device)
            outputs = net(inputs)
            loss = criterion(outputs, targets)

            test_loss += loss.item()
            _, predicted = outputs.max(1)
            total += targets.size(0)
            correct += predicted.eq(targets).sum().item()

            progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
                % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
    return epoch, 100.*correct/total
    #return things in function(progress_bar)
    
    # Save checkpoint.
    acc = 100.*correct/total
    if acc > best_acc:
        print('Saving..')
        state = {
            'net': net.state_dict(),
            'acc': acc,
            'epoch': epoch,
        }
        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(state, './checkpoint/ckpt_resnet110.t7')
        best_acc = acc
#use pickle function to save epoch, loss,and accurate rate.
x = []
y = []
u = []
v = []
for epoch in range(start_epoch, start_epoch+5):
    #how many epoch you want to train, the default I set is 4.
    
    epoch, loss = train(epoch)
    x.append(epoch)
    y.append(loss)
    with open("./resnet/resnet110_epoch.txt", "wb") as fp:   #Pickling x
     pickle.dump(x, fp)
    with open("./resnet/resnet110_loss.txt", "wb") as fp:   #Pickling y
     pickle.dump(y, fp)
    epoch, accuracy=test(epoch)
    u.append(epoch)
    v.append(accuracy)
    with open("./resnet/resnet110_epoch1.txt", "wb") as fp:   #Pickling u
     pickle.dump(u, fp)
    with open("./resnet/resnet110_acc.txt", "wb") as fp:   #Pickling v
     pickle.dump(v, fp)
print(x,y)
print(u,v)
