I am trying to retrain a Stack-GAN-v2 model by saving the neural network and optimiser parameters. But, when I reload the parameters  the generator is unable to generate good quality images.
# ################## Shared functions ###################
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.orthogonal(m.weight.data, 1.0)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)
    elif classname.find('Linear') != -1:
        nn.init.orthogonal(m.weight.data, 1.0)
        if m.bias is not None:
            m.bias.data.fill_(0.0)


def load_params(model, new_param):
    for p, new_p in zip(model.parameters(), new_param):
        p.data.copy_(new_p)

def copy_G_params(model):
    flatten = deepcopy(list(p.data for p in model.parameters()))
    return flatten


def compute_inception_score(predictions, num_splits=1):
    # print('predictions', predictions.shape)
    scores = []
    for i in range(num_splits):
        istart = i * predictions.shape[0] // num_splits
        iend = (i + 1) * predictions.shape[0] // num_splits
        part = predictions[istart:iend, :]
        kl = part * \
            (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
        kl = np.mean(np.sum(kl, 1))
        scores.append(np.exp(kl))
    return np.mean(scores), np.std(scores)


def negative_log_posterior_probability(predictions, num_splits=1):
    # print('predictions', predictions.shape)
    scores = []
    for i in range(num_splits):
        istart = i * predictions.shape[0] // num_splits
        iend = (i + 1) * predictions.shape[0] // num_splits
        part = predictions[istart:iend, :]
        result = -1. * np.log(np.max(part, 1))
        result = np.mean(result)
        scores.append(result)
    return np.mean(scores), np.std(scores)


def load_network(gpus):
    netG = G_NET()
    netG.apply(weights_init)
    netG = torch.nn.DataParallel(netG, device_ids=gpus)
    print(netG)

    netsD = []
    netsD.append(D_NET64())
    netsD.append(D_NET128())

    for i in range(len(netsD)):
        netsD[i].apply(weights_init)
        netsD[i] = torch.nn.DataParallel(netsD[i], device_ids=gpus)
        # print(netsD[i])
    print('# of netsD', len(netsD))

    count = 0

    inception_model = INCEPTION_V3()

    #if cfg.CUDA:
    netG.cuda()
    for i in range(len(netsD)):
        netsD[i].cuda()
    inception_model = inception_model.cuda()
    inception_model.eval()

    return netG, netsD, len(netsD), inception_model, count


def define_optimizers(netG, netsD):
    optimizersD = []
    num_Ds = len(netsD)
    for i in range(num_Ds):
        opt = optim.Adam(netsD[i].parameters(),
                         lr=LEARNING_RATE,
                         betas=(0.5, 0.999))
        optimizersD.append(opt)

    optimizerG = optim.Adam(netG.parameters(),
                            lr=LEARNING_RATE,
                            betas=(0.5, 0.999))
    return optimizerG, optimizersD


def save_model(netG, avg_param_G, netsD, epoch, model_dir):
    load_params(netG, avg_param_G)
    torch.save(
        netG.state_dict(),
        '%s/netG_%d.pth' % (model_dir, epoch))
    for i in range(len(netsD)):
        netD = netsD[i]
        torch.save(
            netD.state_dict(),
            '%s/netD%d.pth' % (model_dir, i))
    print('Save G/Ds models.')


def save_img_results(imgs_tcpu, fake_imgs, num_imgs,
                     count, image_dir):
    num = VIS_COUNT

    # The range of real_img (i.e., self.imgs_tcpu[i][0:num])
    # is changed to [0, 1] by function vutils.save_image
    real_img = imgs_tcpu[-1][0:num]
    vutils.save_image(
        real_img, '%s/real_samples.png' % (image_dir),
        normalize=True)
    real_img_set = vutils.make_grid(real_img).numpy()
    real_img_set = np.transpose(real_img_set, (1, 2, 0))
    real_img_set = real_img_set * 255
    real_img_set = real_img_set.astype(np.uint8)

    for i in range(num_imgs):
        fake_img = fake_imgs[i][0:num]
        # The range of fake_img.data (i.e., self.fake_imgs[i][0:num])
        # is still [-1. 1]...
        vutils.save_image(
            fake_img.data, '%s/count_%09d_fake_samples%d.png' %
            (image_dir, count, i), normalize=True)

        fake_img_set = vutils.make_grid(fake_img.data).cpu().numpy()

        fake_img_set = np.transpose(fake_img_set, (1, 2, 0))
        fake_img_set = (fake_img_set + 1) * 255 / 2
        fake_img_set = fake_img_set.astype(np.uint8)


# ################# Text to image task############################ #
class condGANTrainer(object):
    def __init__(self, output_dir, data_loader, imsize):
        #if cfg.TRAIN.FLAG:
        self.model_dir = os.path.join(output_dir, 'Model')
        self.image_dir = os.path.join(output_dir, 'Image')
        self.log_dir = os.path.join(output_dir, 'Log')
        mkdir_p(self.model_dir)
        mkdir_p(self.image_dir)
        mkdir_p(self.log_dir)

        #s_gpus = cfg.GPU_ID.split(',')
        self.gpus = [0]
        self.num_gpus = len(self.gpus)
        torch.cuda.set_device(self.gpus[0])
        cudnn.benchmark = True

        self.batch_size = BATCH_SIZE
        self.max_epoch = EPOCHS
        self.snapshot_interval = SNAPSHOT_INTERVAL

        self.data_loader = data_loader
        self.num_batches = len(self.data_loader)

    def prepare_data(self, data):
        imgs, w_imgs, t_embedding, _ = data

        real_vimgs, wrong_vimgs = [], []
        vembedding = Variable(t_embedding).cuda()
        for i in range(self.num_Ds):
            real_vimgs.append(Variable(imgs[i]).cuda())
            wrong_vimgs.append(Variable(w_imgs[i]).cuda())
        return imgs, real_vimgs, wrong_vimgs, vembedding

    def train_Dnet(self, idx, count):
        flag = count % 100
        batch_size = self.real_imgs[0].size(0)
        criterion, mu = self.criterion, self.mu

        netD, optD = self.netsD[idx], self.optimizersD[idx]
        real_imgs = self.real_imgs[idx]
        wrong_imgs = self.wrong_imgs[idx]
        fake_imgs = self.fake_imgs[idx]
        #
        netD.zero_grad()
        # Forward
        real_labels = self.real_labels[:batch_size]
        fake_labels = self.fake_labels[:batch_size]
        # for real
        real_logits = netD(real_imgs, mu.detach())
        wrong_logits = netD(wrong_imgs, mu.detach())
        fake_logits = netD(fake_imgs.detach(), mu.detach())
        #
        errD_real = criterion(real_logits[0], real_labels)
        errD_wrong = criterion(wrong_logits[0], fake_labels)
        errD_fake = criterion(fake_logits[0], fake_labels)
        if len(real_logits) > 1 and UNCOND_LOSS_COEFF > 0:
            errD_real_uncond = UNCOND_LOSS_COEFF * \
                criterion(real_logits[1], real_labels)
            errD_wrong_uncond = UNCOND_LOSS_COEFF * \
                criterion(wrong_logits[1], real_labels)
            errD_fake_uncond = UNCOND_LOSS_COEFF * \
                criterion(fake_logits[1], fake_labels)
            #
            errD_real = errD_real + errD_real_uncond
            errD_wrong = errD_wrong + errD_wrong_uncond
            errD_fake = errD_fake + errD_fake_uncond
            #
            errD = errD_real + errD_wrong + errD_fake
        else:
            errD = errD_real + 0.5 * (errD_wrong + errD_fake)
        # backward
        errD.backward()
        # update parameters
        optD.step()
        return errD

    def train_Gnet(self, count):
        self.netG.zero_grad()
        errG_total = 0
        flag = count % 100
        batch_size = self.real_imgs[0].size(0)
        criterion, mu, logvar = self.criterion, self.mu, self.logvar
        real_labels = self.real_labels[:batch_size]
        for i in range(self.num_Ds):
            outputs = self.netsD[i](self.fake_imgs[i], mu)
            errG = criterion(outputs[0], real_labels)
            if len(outputs) > 1 and UNCOND_LOSS_COEFF > 0:
                errG_patch = UNCOND_LOSS_COEFF *\
                    criterion(outputs[1], real_labels)
                errG = errG + errG_patch
            errG_total = errG_total + errG

        kl_loss = KL_loss(mu, logvar) * KL_LOSS_COEFF
        errG_total = errG_total + kl_loss
        errG_total.backward()
        self.optimizerG.step()
        return kl_loss, errG_total

    def train(self):
        self.netG, self.netsD, self.num_Ds,\
            self.inception_model, start_count = load_network(self.gpus)
        avg_param_G = copy_G_params(self.netG)
        ############## LOADING MODEL ################
        self.netG.load_state_dict(torch.load('netG.pt'))
        self.netsD[0].load_state_dict(torch.load('netsD[0].pt'))
        self.netsD[1].load_state_dict(torch.load('netsD[1].pt'))

        self.optimizerG, self.optimizersD = \
            define_optimizers(self.netG, self.netsD)      
        ############ LOADING OPTIMIZER ##############
        self.optimizerG.load_state_dict(torch.load('netG_optim.pt'))
        self.optimizersD[0].load_state_dict(torch.load('netsD[0]_optim.pt'))
        self.optimizersD[1].load_state_dict(torch.load('netsD[1]_optim.pt'))
        

        self.criterion = nn.BCELoss()

        self.real_labels = \
            Variable(torch.FloatTensor(self.batch_size).fill_(1))
        self.fake_labels = \
            Variable(torch.FloatTensor(self.batch_size).fill_(0))

        self.gradient_one = torch.FloatTensor([1.0])
        self.gradient_half = torch.FloatTensor([0.5])

        nz = Z_DIM
        noise = Variable(torch.FloatTensor(self.batch_size, nz))
        fixed_noise = \
            Variable(torch.FloatTensor(self.batch_size, nz).normal_(0, 1))

        #if cfg.CUDA:
        self.criterion.cuda()
        self.real_labels = self.real_labels.cuda()
        self.fake_labels = self.fake_labels.cuda()
        self.gradient_one = self.gradient_one.cuda()
        self.gradient_half = self.gradient_half.cuda()
        noise, fixed_noise = noise.cuda(), fixed_noise.cuda()

        predictions = []
        count = start_count
        start_epoch = start_count // (self.num_batches)
        for epoch in range(start_epoch, self.max_epoch):
            start_t = time.time()

            for step, data in enumerate(self.data_loader, 0):                
                self.imgs_tcpu, self.real_imgs, self.wrong_imgs, \
                    self.txt_embedding = self.prepare_data(data)
               
                noise.data.normal_(0, 1)
                self.fake_imgs, self.mu, self.logvar = \
                    self.netG(noise, self.txt_embedding)
                
                errD_total = 0
                for i in range(self.num_Ds):
                    errD = self.train_Dnet(i, count)
                    errD_total += errD
             
                kl_loss, errG_total = self.train_Gnet(count)
                for p, avg_p in zip(self.netG.parameters(), avg_param_G):
                    avg_p.mul_(0.999).add_(0.001, p.data)

                # for inception score
                pred = self.inception_model(self.fake_imgs[-1].detach())
                predictions.append(pred.data.cpu().numpy())

                count = count + 1

                if count % SNAPSHOT_INTERVAL == 0:
                    ################# SAVING ####################
                    torch.save(self.netG.state_dict(),'netG.pt')
                    torch.save(self.optimizerG.state_dict(),'netG_optim.pt')
                    torch.save(self.netsD[0].state_dict(),'netsD[0].pt')
                    torch.save(self.optimizersD[0].state_dict(),'netsD[0]_optim.pt')
                    torch.save(self.netsD[1].state_dict(),'netsD[1].pt')
                    torch.save(self.optimizersD[1].state_dict(),'netsD[1]_optim.pt')
                    
                    save_model(self.netG, avg_param_G, self.netsD, count, self.model_dir)
                    # Save images
                    backup_para = copy_G_params(self.netG)
                    load_params(self.netG, avg_param_G)
                    #
                    self.fake_imgs, _, _ = \
                        self.netG(fixed_noise, self.txt_embedding)
                    save_img_results(self.imgs_tcpu, self.fake_imgs, self.num_Ds,
                                     count, self.image_dir)
                    #
                    load_params(self.netG, backup_para)

                    # Compute inception score
                    if len(predictions) > 500:
                        predictions = np.concatenate(predictions, 0)
                        mean, std = compute_inception_score(predictions, 10)
                        mean_nlpp, std_nlpp = \
                            negative_log_posterior_probability(predictions, 10)
                        predictions = []