from __future__ import print_function
import argparse
import os
from math import log10

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from models import G, D, weights_init
from data import get_training_set, get_test_set
import torch.backends.cudnn as cudnn
import torchvision.utils as vutils

# Training settings
parser = argparse.ArgumentParser(description='pix2pix-PyTorch-implementation')
parser.add_argument('--dataset', required=True, help='facades')
parser.add_argument('--batchSize', type=int, default=16, help='training batch size')
parser.add_argument('--testBatchSize', type=int, default=1, help='testing batch size')
parser.add_argument('--nEpochs', type=int, default=200, help='number of epochs to train for')
parser.add_argument('--input_nc', type=int, default=3, help='input image channels')
parser.add_argument('--output_nc', type=int, default=3, help='output image channels')
parser.add_argument('--ngf', type=int, default=64, help='generator filters in first conv layer')
parser.add_argument('--ndf', type=int, default=64, help='discriminator filters in first conv layer')
parser.add_argument('--lr', type=float, default=0.0002, help='Learning Rate. Default=0.002')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
parser.add_argument('--cuda', action='store_true', help='use cuda?')
parser.add_argument('--threads', type=int, default=4, help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')
parser.add_argument('--lamb', type=int, default=100, help='weight on L1 term in objective')
parser.add_argument('--netG', default='', help="path to netG (to continue training)")
parser.add_argument('--netD', default='', help="path to netD (to continue training)")
opt = parser.parse_args()

print(opt)

if opt.cuda and not torch.cuda.is_available():
    raise Exception("No GPU found, please run without --cuda")

cudnn.benchmark = True

torch.manual_seed(opt.seed)
if opt.cuda:
    torch.cuda.manual_seed(opt.seed)

print('===> Loading datasets')
root_path = "dataset/"
train_set = get_training_set(root_path + opt.dataset)
test_set = get_test_set(root_path + opt.dataset)
training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)
testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False)

print('===> Building model')
if opt.netG:
    netG = torch.load(opt.netG)
    print('==> Loaded model for G.')
else:
    netG = G(opt.input_nc, opt.output_nc, opt.ngf)
    netG.apply(weights_init)

if opt.netD:
    netG = torch.load(opt.netD)
    print('==> Loaded model for D.')
else:
    netD = D(opt.input_nc, opt.output_nc, opt.ndf)
    netD.apply(weights_init)

criterion = nn.BCELoss()
criterion_l1 = nn.L1Loss()
criterion_mse = nn.MSELoss()

real_A = torch.FloatTensor(opt.batchSize, opt.input_nc, 256, 256)
real_B = torch.FloatTensor(opt.batchSize, opt.output_nc, 256, 256)
label = torch.FloatTensor(opt.batchSize)
real_label = 1
fake_label = 0

if opt.cuda:
    netD = netD.cuda()
    netG = netG.cuda()
    criterion = criterion.cuda()
    criterion_l1 = criterion_l1.cuda()
    criterion_mse = criterion_mse.cuda()
    real_A = real_A.cuda()
    real_B = real_B.cuda()
    label = label.cuda()


real_A = Variable(real_A)
real_B = Variable(real_B)
label = Variable(label)

# setup optimizer
optimizerD = optim.Adam(netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))


def train(epoch):
    for iteration, batch in enumerate(training_data_loader, 1):
        ############################
        # (1) Update D network: maximize log(D(x,y)) + log(1 - D(x,G(x)))
        ###########################
        for p in netD.parameters(): # reset requires_grad
            p.requires_grad = True # they are set to False below in netG update

        # train with real
        netD.zero_grad()
        real_a_cpu, real_b_cpu = batch[0], batch[1]
        real_A.data.resize_(real_a_cpu.size()).copy_(real_a_cpu)
        real_B.data.resize_(real_b_cpu.size()).copy_(real_b_cpu)

        output = netD(torch.cat((real_A, real_B), 1))
        label.data.resize_(output.size()).fill_(real_label)
        err_d_real = criterion(output, label)
        err_d_real.backward()
        d_x_y = output.data.mean()

        # train with fake
        fake_b = netG(real_A)
        output = netD(torch.cat((real_A, fake_b.detach()), 1))
        label.data.resize_(output.size()).fill_(fake_label)
        err_d_fake = criterion(output, label)
        err_d_fake.backward()
        d_x_gx = output.data.mean()

        err_d = (err_d_real + err_d_fake) / 2.0
        optimizerD.step()

        ############################
        # (2) Update G network: maximize log(D(x,G(x))) + L1(y,G(x))
        ###########################
        for p in netD.parameters():
            p.requires_grad = False # to avoid computation
        netG.zero_grad()
        output = netD(torch.cat((real_A, fake_b), 1))
        label.data.resize_(output.size()).fill_(real_label)
        err_g = criterion(output, label) + opt.lamb * criterion_l1(fake_b, real_B)
        err_g.backward()
        d_x_gx_2 = output.data.mean()
        optimizerG.step()

        print("===> Epoch[{}]({}/{}): Loss_D: {:.4f} Loss_G: {:.4f} D(x): {:.4f} D(G(z)): {:.4f}/{:.4f}".format(
            epoch, iteration, len(training_data_loader), err_d.data[0], err_g.data[0], d_x_y, d_x_gx, d_x_gx_2))

        if iteration % 200 == 1:
            vutils.save_image(real_a_cpu,
                    '%s/input_samples_epoch_%03d_%03d.png' % ("out", epoch,iteration))
            # fake = netG(fixed_noise)
            vutils.save_image(fake_b.data,
                    '%s/output_samples_epoch_%03d_%03d.png' % ("out", epoch, iteration))


def test():
    avg_psnr = 0
    for batch in testing_data_loader:
        input, target = Variable(batch[0]), Variable(batch[1])
        if opt.cuda:
            input = input.cuda()
            target = target.cuda()

        prediction = netG(input)
        mse = criterion_mse(prediction, target)
        psnr = 10 * log10(1 / mse.data[0])
        avg_psnr += psnr
    print("===> Avg. PSNR: {:.4f} dB".format(avg_psnr / len(testing_data_loader)))


def checkpoint(epoch):
    if not os.path.exists("checkpoint"):
        os.mkdir("checkpoint")
    if not os.path.exists(os.path.join("checkpoint", opt.dataset)):
        os.mkdir(os.path.join("checkpoint", opt.dataset))
    net_g_model_out_path = "checkpoint/{}/netG_model_epoch_{}.pth".format(opt.dataset, epoch)
    net_d_model_out_path = "checkpoint/{}/netD_model_epoch_{}.pth".format(opt.dataset, epoch)
    torch.save(netG, net_g_model_out_path)
    torch.save(netD, net_d_model_out_path)
    print("Checkpoint saved to {}".format("checkpoint" + opt.dataset))

for epoch in range(1, opt.nEpochs + 1):
    train(epoch)
    #test()
    if epoch % 5 == 0:
        checkpoint(epoch)