<gh_stars>10-100
import torch
import torch.nn as nn
import torch.optim as optim
from torch import cuda
from data_loader import *
from model import *
import time
import datetime
import os
from utils.util import *
from torch.backends import cudnn
from scipy.linalg import block_diag
import lpips
loss_fn = lpips.LPIPS(net='alex')


class Run(object):
    def __init__(self, config):
        self.data_loader = get_loader(config['DATA_PATH'],
                                    crop_size=config['CROP_SIZE'], resize=config['RESIZE'], 
                                    batch_size=config['BATCH_SIZE'], dataset=config['DATASET'], 
                                    mode=config['MODE'], num_workers=config['NUM_WORKERS'])

        self.config = config
        self.device = torch.device("cuda:%d" % (int(config['GPU1'])) if torch.cuda.is_available() else "cpu")
        print(self.device)

        C = self.config['G']['CONTENT_DIM'] # The number of channels of the content feature.
        n_mem = C // self.config['N_GROUP'] # The number of blocks in the coloring matrix: G, The number of elements for each block: n_members^2
        self.mask = self.get_block_diagonal_mask(n_mem) # This is used in generators to make the coloring matrix the block diagonal form.

        self.make_dir()
        self.init_network()
        self.loss = {}

        print(config)

        if config['LOAD_MODEL']:
            self.load_pretrained_model(self.config['START'])

    def get_block_diagonal_mask(self, n_member):
        G = self.config['N_GROUP']
        ones = np.ones((n_member,n_member)).tolist()
        mask = block_diag(ones,ones)
        for i in range(G-2):
            mask = block_diag(mask,ones)
        return torch.from_numpy(mask).to(self.device).float()

    def make_dir(self):
        if not os.path.exists(self.config['MODEL_SAVE_PATH']):
            os.makedirs(self.config['MODEL_SAVE_PATH'])

    def init_network(self):
        """Create a generator and a discriminator."""
        G_opts = self.config['G']
        D_opts = self.config['D']
        
        self.G_A = Generator(G_opts['FIRST_DIM'], G_opts['N_RES_BLOCKS'], self.mask, self.config['N_GROUP'],
                           G_opts['MLP_DIM'], G_opts['BIAS_DIM'], G_opts['CONTENT_DIM'], self.device)
        self.G_B = Generator(G_opts['FIRST_DIM'], G_opts['N_RES_BLOCKS'], self.mask, self.config['N_GROUP'],
                           G_opts['MLP_DIM'], G_opts['BIAS_DIM'], G_opts['CONTENT_DIM'], self.device)

        G_params = list(self.G_A.parameters()) + list(self.G_B.parameters()) # + list(blah)
        self.G_optimizer = torch.optim.Adam([p for p in G_params if p.requires_grad], self.config['G_LR'], [self.config['BETA1'], self.config['BETA2']], weight_decay=self.config['WEIGHT_DECAY'])
        self.G_scheduler = get_scheduler(self.G_optimizer, config)
        self.G_A.apply(weights_init(self.config['INIT']))
        self.G_B.apply(weights_init(self.config['INIT']))

        if self.config['MODE'] == 'train':
            self.D_A = Discriminator(3, D_opts)
            self.D_B = Discriminator(3, D_opts)
            D_params = list(self.D_A.parameters()) + list(self.D_B.parameters())
            self.D_optimizer = torch.optim.Adam([p for p in D_params if p.requires_grad], self.config['D_LR'], [self.config['BETA1'], self.config['BETA2']], weight_decay=self.config['WEIGHT_DECAY'])
            self.D_scheduler = get_scheduler(self.D_optimizer, config)
            self.D_A.apply(weights_init('gaussian'))
            self.D_B.apply(weights_init('gaussian'))
        
        # print_network(self.G, 'G')
        # print_network(self.D, 'D')

        self.set_gpu()

    def set_gpu(self):
        def multi_gpu(gpu1, gpu2, model):
            model = nn.DataParallel(model, device_ids=[gpu1, gpu2])
            return model

        gpu1 = int(self.config['GPU1'])
        gpu2 = int(self.config['GPU2'])
        if self.config['DATA_PARALLEL']:
            self.G_A = multi_gpu(gpu1, gpu2, self.G_A)
            self.G_B = multi_gpu(gpu1, gpu2, self.G_B)
            if self.config['MODE'] == 'train':
                self.D_A = multi_gpu(gpu1, gpu2, self.D_A)
                self.D_B = multi_gpu(gpu1, gpu2, self.D_B)

        self.G_A.to(self.device)
        self.G_B.to(self.device)
        if self.config['MODE'] == 'train':
            self.D_A.to(self.device)
            self.D_B.to(self.device)

    def l1_criterion(self, input, target):
        return torch.mean(torch.abs(input - target))

    def reg(self, x_arr):
        # whitening_reg: G,C//G,C//G
        I = torch.eye(x_arr[0][0].size(1)).unsqueeze(0).to(self.device) # 1,C//G,C//G
        loss = torch.FloatTensor([0]).to(self.device)
        for x in x_arr:
            x = torch.cat(x,dim=0) # G*(# of style),C//G,C//G
            loss = loss + torch.mean(torch.abs(x-I))
        return loss / len(x_arr)

    def model_save(self, iteration):
        self.G_A = self.G_A.cpu()
        self.G_B = self.G_B.cpu()
        self.D_A = self.D_A.cpu()
        self.D_B = self.D_B.cpu()

        torch.save(self.G_A.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'G_A_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        torch.save(self.G_B.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'G_B_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        torch.save(self.D_A.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'D_A_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        torch.save(self.D_B.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'D_B_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        
        self.set_gpu()

    def load_pretrained_model(self, iteration):
        self.G_A.load_state_dict(torch.load(os.path.join(
            self.config['MODEL_SAVE_PATH'], 'G_A_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))
        self.G_B.load_state_dict(torch.load(os.path.join(
            self.config['MODEL_SAVE_PATH'], 'G_B_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))
        if self.config['MODE'] == 'train':
            self.D_A.load_state_dict(torch.load(os.path.join(
                self.config['MODEL_SAVE_PATH'], 'D_A_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))
            self.D_B.load_state_dict(torch.load(os.path.join(
                self.config['MODEL_SAVE_PATH'], 'D_B_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))

    def update_learning_rate(self):
        if self.G_scheduler is not None:
            self.G_scheduler.step()
        if self.D_scheduler is not None:
            self.D_scheduler.step()

    def train_ready(self):
        self.G_A.train()
        self.G_B.train()
        self.D_A.train()
        self.D_B.train()

    def test_ready(self):
        self.G_A.eval()
        self.G_B.eval()

    def clamping_alpha(self,G):
        for gdwct in G.decoder.gdwct_modules:
            gdwct.alpha.data.clamp_(0,1)

    def update_G(self, x_A, x_B, isTrain=True):
        G_A = self.G_A.module if self.config['DATA_PARALLEL'] else self.G_A
        G_B = self.G_B.module if self.config['DATA_PARALLEL'] else self.G_B
        #D_A = self.D_A.module if self.config['DATA_PARALLEL'] else self.D_A
        #D_B = self.D_B.module if self.config['DATA_PARALLEL'] else self.D_B

        self.clamping_alpha(G_A)
        self.clamping_alpha(G_B)

        '''
        ### 1st stage
        # cov_reg: G,C//G,C//G
        # W_reg: B*G,C//G,C//G
        '''
        # get content
        c_A = G_A.c_encoder(x_A)
        c_B = G_B.c_encoder(x_B)

        # get style
        s_A = G_A.s_encoder(x_A)
        s_B = G_B.s_encoder(x_B)

        # from A to B
        x_AB = G_B(c_A, s_B)

        # from B to A
        x_BA = G_A(c_B, s_A)

        if isTrain:

            '''
            ### 2nd stage
            '''
            c_BA = G_A.c_encoder(x_BA)
            c_AB = G_B.c_encoder(x_AB)

            s_AB = G_B.s_encoder(x_AB)
            s_BA = G_A.s_encoder(x_BA)

            # from AB to A
            x_ABA = G_A(c_AB, s_BA)

            # from BA to B
            x_BAB = G_B(c_BA, s_AB)

            # from A to A
            x_AA= G_A(c_A, s_A)
            
            # from B to B
            x_BB= G_B(c_B, s_B)

            # Compute the losses
            g_loss_fake = self.D_A.calc_gen_loss(x_BA) + self.D_B.calc_gen_loss(x_AB)

            loss_cross_rec = self.l1_criterion(x_ABA, x_A) + self.l1_criterion(x_BAB, x_B)
            loss_ae_rec = self.l1_criterion(x_AA, x_A) + self.l1_criterion(x_BB, x_B)

            loss_cross_s = self.l1_criterion(s_AB, s_B) + self.l1_criterion(s_BA, s_A)
            
            loss_cross_c = self.l1_criterion(c_AB, c_A) + self.l1_criterion(c_BA, c_B)

            #loss_whitening_reg = self.reg([whitening_reg_AB, whitening_reg_BA, whitening_reg_ABA, whitening_reg_BAB])
            #loss_coloring_reg = self.reg([coloring_reg_AB, coloring_reg_BA, coloring_reg_ABA, coloring_reg_BAB])

            # Backward and optimize.
            g_loss = g_loss_fake + \
                     self.config['LAMBDA_X_REC'] * (loss_ae_rec) + \
                     self.config['LAMBDA_X_CYC'] * loss_cross_rec + \
                     self.config['LAMBDA_S'] * loss_cross_s + \
                     self.config['LAMBDA_C'] * loss_cross_c
                     #self.config['LAMBDA_W_REG'] * loss_whitening_reg + \
                     #self.config['LAMBDA_C_REG'] * loss_coloring_reg
            
            self.G_optimizer.zero_grad()
            g_loss.backward()

            torch.nn.utils.clip_grad_norm_(G_A.parameters(), 5)
            torch.nn.utils.clip_grad_norm_(G_B.parameters(), 5)
            self.G_optimizer.step()

            # Logging.
            self.loss['G/loss_fake'] = g_loss_fake.item()
            self.loss['G/loss_cross_rec'] = self.config['LAMBDA_X_REC']* loss_cross_rec.item()
            self.loss['G/loss_ae_rec'] = self.config['LAMBDA_X_REC'] * loss_ae_rec.item()
            self.loss['G/loss_latent_c'] = self.config['LAMBDA_C'] * loss_cross_c.item()
            self.loss['G/loss_latent_s'] = self.config['LAMBDA_S'] * loss_cross_s.item()
            #self.loss['G/loss_whitening_reg'] = self.config['LAMBDA_W_REG'] * loss_whitening_reg.item()
            #self.loss['G/loss_coloring_reg'] = self.config['LAMBDA_C_REG'] * loss_coloring_reg.item()

        return (x_AB, x_BA)

    def update_D(self, x_A, x_B):
        G_A = self.G_A.module if self.config['DATA_PARALLEL'] else self.G_A
        G_B = self.G_B.module if self.config['DATA_PARALLEL'] else self.G_B
        D_A = self.D_A.module if self.config['DATA_PARALLEL'] else self.D_A
        D_B = self.D_B.module if self.config['DATA_PARALLEL'] else self.D_B

        c_A = G_A.c_encoder(x_A)
        c_B = G_B.c_encoder(x_B)

        s_A = G_A.s_encoder(x_A)
        s_B = G_B.s_encoder(x_B)

        x_AB = G_B(c_A, s_B)
        x_BA = G_A(c_B, s_A)

        # D loss
        d_loss_a = D_A.calc_dis_loss(x_BA.detach(), x_A)
        d_loss_b = D_B.calc_dis_loss(x_AB.detach(), x_B)
        
        d_loss = d_loss_a + d_loss_b

        self.D_optimizer.zero_grad()
        d_loss.backward()
        torch.nn.utils.clip_grad_norm_(D_A.parameters(), 5)
        torch.nn.utils.clip_grad_norm_(D_B.parameters(), 5)
        self.D_optimizer.step()

        self.loss['D/loss'] = d_loss.item()

    def train(self):

        data_loader = self.data_loader

        print('# iters: %d' % (len(data_loader)))
        print('# data: %d' % (len(data_loader)*self.config['BATCH_SIZE']))
        # Fetch fixed inputs for debugging.
        data_iter = iter(data_loader)

        self.train_ready()
        print("Start training ~ Ayo:)!")
        start_time = time.time()

        
        for i in range(self.config['START'], self.config['NUM_ITERS']):

        ### Preprocess input data ###
            # Fetch real images and labels.
            try:
                x_A, x_B = next(data_iter)
                if x_A.size(0) != self.config['BATCH_SIZE'] or x_B.size(0) != self.config['BATCH_SIZE']:
                    x_A, x_B = next(data_iter)
            except:
                data_iter = iter(data_loader)
                x_A, x_B = next(data_iter)
                if x_A.size(0) != self.config['BATCH_SIZE'] or x_B.size(0) != self.config['BATCH_SIZE']:
                    x_A, x_B = next(data_iter)
            
            x_A = x_A.to(self.device)   # Input images.
            x_B = x_B.to(self.device)   # Exemplar images corresponding with target labels.

        ### Training ###    
            self.update_D(x_A, x_B)
            (x_AB, x_BA)= \
            self.update_G(x_A, x_B)
            #save_img([x_A, x_AB, x_B, x_BA], self.config['SAVE_NAME'], 1, 'train_results')
        ### ETC ###
            if i % self.config['SAVE_EVERY'] == 0:

                elapsed = time.time() - start_time
                elapsed = str(datetime.timedelta(seconds=elapsed))

                print('=====================================================')
                print("Elapsed [{}], Iter [{}/{}]".format(
                    elapsed, i, self.config['NUM_ITERS']))
                print('=====================================================')
                print('D/loss: %.5f' % (self.loss['D/loss']))
                print('G/loss_fake: %.5f' % (self.loss['G/loss_fake']))
                print('G/loss_cross_rec: %.5f' % (self.loss['G/loss_cross_rec']))
                print('G/loss_ae_rec: %.5f' % (self.loss['G/loss_ae_rec']))
                print('G/loss_latent_s: %.5f' % (self.loss['G/loss_latent_s']))
                print('G/loss_latent_c: %.5f' % (self.loss['G/loss_latent_c']))
                #print('G/loss_whitening_reg: %.5f' % (self.loss['G/loss_whitening_reg']))
                #print('G/loss_coloring_reg: %.5f' % (self.loss['G/loss_coloring_reg']))
                
                save_img([x_A, x_AB, x_B, x_BA], self.config['SAVE_NAME'], i, 'train_results')
                self.model_save(i)

            if i > self.config['NUM_ITERS_DECAY']:
                self.update_learning_rate()


    def test(self):
        print("test start")
        self.test_ready()
        loss_fn.to(self.device)
        data_loader = self.data_loader
        lpips_score = []
        with torch.no_grad():
            for i, (x_A, x_B) in enumerate(data_loader):
                print(i)
                x_A = x_A.to(self.device)
                x_B = x_B.to(self.device)
                N = x_A.size(0)
                x_AB, x_BA = \
                self.update_G(x_A, x_B, isTrain=False)
                d = loss_fn.forward(x_B,x_BA)
                lpips_score.append(d.mean())
                for j in range(N):
                    save_img([x_A[j:j+1]], self.config['SAVE_NAME'], i*N+j, 'test_results/style')
                    save_img([x_B[j:j+1]], self.config['SAVE_NAME'], i*N+j, 'test_results/content')
                    save_img([x_BA[j:j+1]], self.config['SAVE_NAME'], i*N+j, 'test_results/output')
                #save_img([x_A, x_B, x_AB, x_BA], self.config['SAVE_NAME'], i, 'test_results')
        print("LPIPS Score:", sum(lpips_score)/len(lpips_score))


def main():
    
    # For fast training
    cudnn.benchmark = True

    run = Run(config)
    if config['MODE'] == 'train':
        run.train()
    else:
        run.test()

config = ges_Aonfig('configs/config.yaml')

main()
import torch
import torch.nn as nn
import torch.optim as optim
from torch import cuda
from data_loader import *
from model import *
import time
import datetime
import os
from utils.util import *
from torch.backends import cudnn
from scipy.linalg import block_diag
import lpips
loss_fn = lpips.LPIPS(net='alex')


class Run(object):
    def __init__(self, config):
        self.data_loader = get_loader(config['DATA_PATH'],
                                    crop_size=config['CROP_SIZE'], resize=config['RESIZE'], 
                                    batch_size=config['BATCH_SIZE'], dataset=config['DATASET'], 
                                    mode=config['MODE'], num_workers=config['NUM_WORKERS'])

        self.config = config
        self.device = torch.device("cuda:%d" % (int(config['GPU1'])) if torch.cuda.is_available() else "cpu")
        print(self.device)

        C = self.config['G']['CONTENT_DIM'] # The number of channels of the content feature.
        n_mem = C // self.config['N_GROUP'] # The number of blocks in the coloring matrix: G, The number of elements for each block: n_members^2
        self.mask = self.get_block_diagonal_mask(n_mem) # This is used in generators to make the coloring matrix the block diagonal form.

        self.make_dir()
        self.init_network()
        self.loss = {}

        print(config)

        if config['LOAD_MODEL']:
            self.load_pretrained_model(self.config['START'])

    def get_block_diagonal_mask(self, n_member):
        G = self.config['N_GROUP']
        ones = np.ones((n_member,n_member)).tolist()
        mask = block_diag(ones,ones)
        for i in range(G-2):
            mask = block_diag(mask,ones)
        return torch.from_numpy(mask).to(self.device).float()

    def make_dir(self):
        if not os.path.exists(self.config['MODEL_SAVE_PATH']):
            os.makedirs(self.config['MODEL_SAVE_PATH'])

    def init_network(self):
        """Create a generator and a discriminator."""
        G_opts = self.config['G']
        D_opts = self.config['D']
        
        self.G_A = Generator(G_opts['FIRST_DIM'], G_opts['N_RES_BLOCKS'], self.mask, self.config['N_GROUP'],
                           G_opts['MLP_DIM'], G_opts['BIAS_DIM'], G_opts['CONTENT_DIM'], self.device)
        self.G_B = Generator(G_opts['FIRST_DIM'], G_opts['N_RES_BLOCKS'], self.mask, self.config['N_GROUP'],
                           G_opts['MLP_DIM'], G_opts['BIAS_DIM'], G_opts['CONTENT_DIM'], self.device)

        G_params = list(self.G_A.parameters()) + list(self.G_B.parameters()) # + list(blah)
        self.G_optimizer = torch.optim.Adam([p for p in G_params if p.requires_grad], self.config['G_LR'], [self.config['BETA1'], self.config['BETA2']], weight_decay=self.config['WEIGHT_DECAY'])
        self.G_scheduler = get_scheduler(self.G_optimizer, config)
        self.G_A.apply(weights_init(self.config['INIT']))
        self.G_B.apply(weights_init(self.config['INIT']))

        if self.config['MODE'] == 'train':
            self.D_A = Discriminator(3, D_opts)
            self.D_B = Discriminator(3, D_opts)
            D_params = list(self.D_A.parameters()) + list(self.D_B.parameters())
            self.D_optimizer = torch.optim.Adam([p for p in D_params if p.requires_grad], self.config['D_LR'], [self.config['BETA1'], self.config['BETA2']], weight_decay=self.config['WEIGHT_DECAY'])
            self.D_scheduler = get_scheduler(self.D_optimizer, config)
            self.D_A.apply(weights_init('gaussian'))
            self.D_B.apply(weights_init('gaussian'))
        
        # print_network(self.G, 'G')
        # print_network(self.D, 'D')

        self.set_gpu()

    def set_gpu(self):
        def multi_gpu(gpu1, gpu2, model):
            model = nn.DataParallel(model, device_ids=[gpu1, gpu2])
            return model

        gpu1 = int(self.config['GPU1'])
        gpu2 = int(self.config['GPU2'])
        if self.config['DATA_PARALLEL']:
            self.G_A = multi_gpu(gpu1, gpu2, self.G_A)
            self.G_B = multi_gpu(gpu1, gpu2, self.G_B)
            if self.config['MODE'] == 'train':
                self.D_A = multi_gpu(gpu1, gpu2, self.D_A)
                self.D_B = multi_gpu(gpu1, gpu2, self.D_B)

        self.G_A.to(self.device)
        self.G_B.to(self.device)
        if self.config['MODE'] == 'train':
            self.D_A.to(self.device)
            self.D_B.to(self.device)

    def l1_criterion(self, input, target):
        return torch.mean(torch.abs(input - target))

    def reg(self, x_arr):
        # whitening_reg: G,C//G,C//G
        I = torch.eye(x_arr[0][0].size(1)).unsqueeze(0).to(self.device) # 1,C//G,C//G
        loss = torch.FloatTensor([0]).to(self.device)
        for x in x_arr:
            x = torch.cat(x,dim=0) # G*(# of style),C//G,C//G
            loss = loss + torch.mean(torch.abs(x-I))
        return loss / len(x_arr)

    def model_save(self, iteration):
        self.G_A = self.G_A.cpu()
        self.G_B = self.G_B.cpu()
        self.D_A = self.D_A.cpu()
        self.D_B = self.D_B.cpu()

        torch.save(self.G_A.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'G_A_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        torch.save(self.G_B.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'G_B_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        torch.save(self.D_A.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'D_A_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        torch.save(self.D_B.state_dict(),
            os.path.join(self.config['MODEL_SAVE_PATH'], 'D_B_%s_%d.pth' % (self.config['SAVE_NAME'],iteration)))
        
        self.set_gpu()

    def load_pretrained_model(self, iteration):
        self.G_A.load_state_dict(torch.load(os.path.join(
            self.config['MODEL_SAVE_PATH'], 'G_A_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))
        self.G_B.load_state_dict(torch.load(os.path.join(
            self.config['MODEL_SAVE_PATH'], 'G_B_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))
        if self.config['MODE'] == 'train':
            self.D_A.load_state_dict(torch.load(os.path.join(
                self.config['MODEL_SAVE_PATH'], 'D_A_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))
            self.D_B.load_state_dict(torch.load(os.path.join(
                self.config['MODEL_SAVE_PATH'], 'D_B_%s_%d.pth' % (self.config['SAVE_NAME'], iteration))))

    def update_learning_rate(self):
        if self.G_scheduler is not None:
            self.G_scheduler.step()
        if self.D_scheduler is not None:
            self.D_scheduler.step()

    def train_ready(self):
        self.G_A.train()
        self.G_B.train()
        self.D_A.train()
        self.D_B.train()

    def test_ready(self):
        self.G_A.eval()
        self.G_B.eval()

    def clamping_alpha(self,G):
        for gdwct in G.decoder.gdwct_modules:
            gdwct.alpha.data.clamp_(0,1)

    def update_G(self, x_A, x_B, isTrain=True):
        G_A = self.G_A.module if self.config['DATA_PARALLEL'] else self.G_A
        G_B = self.G_B.module if self.config['DATA_PARALLEL'] else self.G_B
        #D_A = self.D_A.module if self.config['DATA_PARALLEL'] else self.D_A
        #D_B = self.D_B.module if self.config['DATA_PARALLEL'] else self.D_B

        self.clamping_alpha(G_A)
        self.clamping_alpha(G_B)

        '''
        ### 1st stage
        # cov_reg: G,C//G,C//G
        # W_reg: B*G,C//G,C//G
        '''
        # get content
        c_A = G_A.c_encoder(x_A)
        c_B = G_B.c_encoder(x_B)

        # get style
        s_A = G_A.s_encoder(x_A)
        s_B = G_B.s_encoder(x_B)

        # from A to B
        x_AB = G_B(c_A, s_B)

        # from B to A
        x_BA = G_A(c_B, s_A)

        if isTrain:

            '''
            ### 2nd stage
            '''
            c_BA = G_A.c_encoder(x_BA)
            c_AB = G_B.c_encoder(x_AB)

            s_AB = G_B.s_encoder(x_AB)
            s_BA = G_A.s_encoder(x_BA)

            # from AB to A
            x_ABA = G_A(c_AB, s_BA)

            # from BA to B
            x_BAB = G_B(c_BA, s_AB)

            # from A to A
            x_AA= G_A(c_A, s_A)
            
            # from B to B
            x_BB= G_B(c_B, s_B)

            # Compute the losses
            g_loss_fake = self.D_A.calc_gen_loss(x_BA) + self.D_B.calc_gen_loss(x_AB)

            loss_cross_rec = self.l1_criterion(x_ABA, x_A) + self.l1_criterion(x_BAB, x_B)
            loss_ae_rec = self.l1_criterion(x_AA, x_A) + self.l1_criterion(x_BB, x_B)

            loss_cross_s = self.l1_criterion(s_AB, s_B) + self.l1_criterion(s_BA, s_A)
            
            loss_cross_c = self.l1_criterion(c_AB, c_A) + self.l1_criterion(c_BA, c_B)

            #loss_whitening_reg = self.reg([whitening_reg_AB, whitening_reg_BA, whitening_reg_ABA, whitening_reg_BAB])
            #loss_coloring_reg = self.reg([coloring_reg_AB, coloring_reg_BA, coloring_reg_ABA, coloring_reg_BAB])

            # Backward and optimize.
            g_loss = g_loss_fake + \
                     self.config['LAMBDA_X_REC'] * (loss_ae_rec) + \
                     self.config['LAMBDA_X_CYC'] * loss_cross_rec + \
                     self.config['LAMBDA_S'] * loss_cross_s + \
                     self.config['LAMBDA_C'] * loss_cross_c
                     #self.config['LAMBDA_W_REG'] * loss_whitening_reg + \
                     #self.config['LAMBDA_C_REG'] * loss_coloring_reg
            
            self.G_optimizer.zero_grad()
            g_loss.backward()

            torch.nn.utils.clip_grad_norm_(G_A.parameters(), 5)
            torch.nn.utils.clip_grad_norm_(G_B.parameters(), 5)
            self.G_optimizer.step()

            # Logging.
            self.loss['G/loss_fake'] = g_loss_fake.item()
            self.loss['G/loss_cross_rec'] = self.config['LAMBDA_X_REC']* loss_cross_rec.item()
            self.loss['G/loss_ae_rec'] = self.config['LAMBDA_X_REC'] * loss_ae_rec.item()
            self.loss['G/loss_latent_c'] = self.config['LAMBDA_C'] * loss_cross_c.item()
            self.loss['G/loss_latent_s'] = self.config['LAMBDA_S'] * loss_cross_s.item()
            #self.loss['G/loss_whitening_reg'] = self.config['LAMBDA_W_REG'] * loss_whitening_reg.item()
            #self.loss['G/loss_coloring_reg'] = self.config['LAMBDA_C_REG'] * loss_coloring_reg.item()

        return (x_AB, x_BA)

    def update_D(self, x_A, x_B):
        G_A = self.G_A.module if self.config['DATA_PARALLEL'] else self.G_A
        G_B = self.G_B.module if self.config['DATA_PARALLEL'] else self.G_B
        D_A = self.D_A.module if self.config['DATA_PARALLEL'] else self.D_A
        D_B = self.D_B.module if self.config['DATA_PARALLEL'] else self.D_B

        c_A = G_A.c_encoder(x_A)
        c_B = G_B.c_encoder(x_B)

        s_A = G_A.s_encoder(x_A)
        s_B = G_B.s_encoder(x_B)

        x_AB = G_B(c_A, s_B)
        x_BA = G_A(c_B, s_A)

        # D loss
        d_loss_a = D_A.calc_dis_loss(x_BA.detach(), x_A)
        d_loss_b = D_B.calc_dis_loss(x_AB.detach(), x_B)
        
        d_loss = d_loss_a + d_loss_b

        self.D_optimizer.zero_grad()
        d_loss.backward()
        torch.nn.utils.clip_grad_norm_(D_A.parameters(), 5)
        torch.nn.utils.clip_grad_norm_(D_B.parameters(), 5)
        self.D_optimizer.step()

        self.loss['D/loss'] = d_loss.item()

    def train(self):

        data_loader = self.data_loader

        print('# iters: %d' % (len(data_loader)))
        print('# data: %d' % (len(data_loader)*self.config['BATCH_SIZE']))
        # Fetch fixed inputs for debugging.
        data_iter = iter(data_loader)

        self.train_ready()
        print("Start training ~ Ayo:)!")
        start_time = time.time()

        
        for i in range(self.config['START'], self.config['NUM_ITERS']):

        ### Preprocess input data ###
            # Fetch real images and labels.
            try:
                x_A, x_B = next(data_iter)
                if x_A.size(0) != self.config['BATCH_SIZE'] or x_B.size(0) != self.config['BATCH_SIZE']:
                    x_A, x_B = next(data_iter)
            except:
                data_iter = iter(data_loader)
                x_A, x_B = next(data_iter)
                if x_A.size(0) != self.config['BATCH_SIZE'] or x_B.size(0) != self.config['BATCH_SIZE']:
                    x_A, x_B = next(data_iter)
            
            x_A = x_A.to(self.device)   # Input images.
            x_B = x_B.to(self.device)   # Exemplar images corresponding with target labels.

        ### Training ###    
            self.update_D(x_A, x_B)
            (x_AB, x_BA)= \
            self.update_G(x_A, x_B)
            #save_img([x_A, x_AB, x_B, x_BA], self.config['SAVE_NAME'], 1, 'train_results')
        ### ETC ###
            if i % self.config['SAVE_EVERY'] == 0:

                elapsed = time.time() - start_time
                elapsed = str(datetime.timedelta(seconds=elapsed))

                print('=====================================================')
                print("Elapsed [{}], Iter [{}/{}]".format(
                    elapsed, i, self.config['NUM_ITERS']))
                print('=====================================================')
                print('D/loss: %.5f' % (self.loss['D/loss']))
                print('G/loss_fake: %.5f' % (self.loss['G/loss_fake']))
                print('G/loss_cross_rec: %.5f' % (self.loss['G/loss_cross_rec']))
                print('G/loss_ae_rec: %.5f' % (self.loss['G/loss_ae_rec']))
                print('G/loss_latent_s: %.5f' % (self.loss['G/loss_latent_s']))
                print('G/loss_latent_c: %.5f' % (self.loss['G/loss_latent_c']))
                #print('G/loss_whitening_reg: %.5f' % (self.loss['G/loss_whitening_reg']))
                #print('G/loss_coloring_reg: %.5f' % (self.loss['G/loss_coloring_reg']))
                
                save_img([x_A, x_AB, x_B, x_BA], self.config['SAVE_NAME'], i, 'train_results')
                self.model_save(i)

            if i > self.config['NUM_ITERS_DECAY']:
                self.update_learning_rate()


    def test(self):
        print("test start")
        self.test_ready()
        loss_fn.to(self.device)
        data_loader = self.data_loader
        lpips_score = []
        with torch.no_grad():
            for i, (x_A, x_B) in enumerate(data_loader):
                print(i)
                x_A = x_A.to(self.device)
                x_B = x_B.to(self.device)
                N = x_A.size(0)
                x_AB, x_BA = \
                self.update_G(x_A, x_B, isTrain=False)
                d = loss_fn.forward(x_B,x_BA)
                lpips_score.append(d.mean())
                for j in range(N):
                    save_img([x_A[j:j+1]], self.config['SAVE_NAME'], i*N+j, 'test_results/style')
                    save_img([x_B[j:j+1]], self.config['SAVE_NAME'], i*N+j, 'test_results/content')
                    save_img([x_BA[j:j+1]], self.config['SAVE_NAME'], i*N+j, 'test_results/output')
                #save_img([x_A, x_B, x_AB, x_BA], self.config['SAVE_NAME'], i, 'test_results')
        print("LPIPS Score:", sum(lpips_score)/len(lpips_score))


def main():
    
    # For fast training
    cudnn.benchmark = True

    run = Run(config)
    if config['MODE'] == 'train':
        run.train()
    else:
        run.test()

config = ges_Aonfig('configs/config.yaml')

main()
# The number of channels of the content feature. # The number of blocks in the coloring matrix: G, The number of elements for each block: n_members^2 # This is used in generators to make the coloring matrix the block diagonal form. Create a generator and a discriminator. # + list(blah) # print_network(self.G, 'G') # print_network(self.D, 'D') # whitening_reg: G,C//G,C//G # 1,C//G,C//G # G*(# of style),C//G,C//G #D_A = self.D_A.module if self.config['DATA_PARALLEL'] else self.D_A #D_B = self.D_B.module if self.config['DATA_PARALLEL'] else self.D_B ### 1st stage
         # cov_reg: G,C//G,C//G
         # W_reg: B*G,C//G,C//G # get content # get style # from A to B # from B to A ### 2nd stage # from AB to A # from BA to B # from A to A # from B to B # Compute the losses #loss_whitening_reg = self.reg([whitening_reg_AB, whitening_reg_BA, whitening_reg_ABA, whitening_reg_BAB]) #loss_coloring_reg = self.reg([coloring_reg_AB, coloring_reg_BA, coloring_reg_ABA, coloring_reg_BAB]) # Backward and optimize. #self.config['LAMBDA_W_REG'] * loss_whitening_reg + \ #self.config['LAMBDA_C_REG'] * loss_coloring_reg # Logging. #self.loss['G/loss_whitening_reg'] = self.config['LAMBDA_W_REG'] * loss_whitening_reg.item() #self.loss['G/loss_coloring_reg'] = self.config['LAMBDA_C_REG'] * loss_coloring_reg.item() # D loss # Fetch fixed inputs for debugging. ### Preprocess input data ### # Fetch real images and labels. # Input images. # Exemplar images corresponding with target labels. ### Training ### #save_img([x_A, x_AB, x_B, x_BA], self.config['SAVE_NAME'], 1, 'train_results') ### ETC ### #print('G/loss_whitening_reg: %.5f' % (self.loss['G/loss_whitening_reg'])) #print('G/loss_coloring_reg: %.5f' % (self.loss['G/loss_coloring_reg'])) #save_img([x_A, x_B, x_AB, x_BA], self.config['SAVE_NAME'], i, 'test_results') # For fast training