diff --git a/HW89/homework/ckpt/discriminator_epoch_19.pt b/HW89/homework/ckpt/discriminator_epoch_19.pt
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diff --git a/HW89/homework/ckpt/discriminator_epoch_5.pt b/HW89/homework/ckpt/discriminator_epoch_5.pt
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diff --git a/HW89/homework/ckpt/generator_epoch_19.pt b/HW89/homework/ckpt/generator_epoch_19.pt
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diff --git a/HW89/homework/ckpt/generator_epoch_5.pt b/HW89/homework/ckpt/generator_epoch_5.pt
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diff --git a/HW89/homework/dcgan/__init__.py b/HW89/homework/dcgan/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/HW89/homework/dcgan/dcgan.py b/HW89/homework/dcgan/dcgan.py
new file mode 100644
index 0000000..65c208d
--- /dev/null
+++ b/HW89/homework/dcgan/dcgan.py
@@ -0,0 +1,69 @@
+
+#куски кода взяты с https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/dcgan/dcgan.py
+
+import torch.nn as nn
+
+
+class DCGenerator(nn.Module):
+
+    def __init__(self, image_size):
+        super(DCGenerator, self).__init__()
+        self.channels = 3
+        
+        self.init_size = image_size // 4
+        self.l1 = nn.Sequential(nn.Linear(100, 128*self.init_size**2))
+
+        self.conv_blocks = nn.Sequential(
+            nn.BatchNorm2d(128),
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(128, 128, 3, stride=1, padding=1),
+            nn.BatchNorm2d(128, 0.8),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(128, 64, 3, stride=1, padding=1),
+            nn.BatchNorm2d(64, 0.8),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv2d(64, self.channels, 3, stride=1, padding=1),
+            nn.Tanh()
+        )
+
+    def forward(self, data):
+        out = self.l1(data.squeeze())
+        out = out.view(out.shape[0], 128, self.init_size, self.init_size)
+        img = self.conv_blocks(out)
+        return img
+
+
+class DCDiscriminator(nn.Module):
+
+    def __init__(self, image_size):
+        super(DCDiscriminator, self).__init__()
+        
+        self.channels = 3
+        
+        def discriminator_block(in_filters, out_filters, bn=True):
+            block = [   nn.Conv2d(in_filters, out_filters, 3, 2, 1),
+                        nn.LeakyReLU(0.2, inplace=True),
+                        nn.Dropout2d(0.25)]
+            if bn:
+                block.append(nn.BatchNorm2d(out_filters, 0.8))
+            return block
+
+        self.model = nn.Sequential(
+            *discriminator_block(self.channels, 16, bn=False),
+            *discriminator_block(16, 32),
+            *discriminator_block(32, 64),
+            *discriminator_block(64, 128),
+        )
+
+        # The height and width of downsampled image
+        ds_size = image_size // 2**4
+        self.adv_layer = nn.Sequential( nn.Linear(128*ds_size**2, 1),
+                                        nn.Sigmoid())
+
+    def forward(self, data):
+        out = self.model(data)
+        out = out.view(out.shape[0], -1)
+        validity = self.adv_layer(out)
+
+        return validity
diff --git a/HW89/homework/dcgan/trainer.py b/HW89/homework/dcgan/trainer.py
new file mode 100644
index 0000000..202dc12
--- /dev/null
+++ b/HW89/homework/dcgan/trainer.py
@@ -0,0 +1,116 @@
+import logging
+import os
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.utils.data
+import torchvision.utils as vutils
+from tensorboardX import SummaryWriter
+
+import metric
+
+
+class DCGANTrainer:
+
+    def __init__(self, discriminator, generator, optimizer_d, optimizer_g, latent_size=100,
+                 device='cpu', metrics_dir='metrics', save_root='ckpt', log_dir=None, start_epoch=0):
+        self.net_g = generator
+        self.net_d = discriminator
+        self.optimizer_d = optimizer_d
+        self.optimizer_g = optimizer_g
+        self.latent_size = latent_size
+        self.device = device
+
+        self.metric_dir = metrics_dir
+        self.save_root = save_root
+
+        self.net_g.to(device)
+        self.net_d.to(device)
+        
+        self.start_epoch = start_epoch
+        if self.start_epoch == 0:
+            self.net_g.apply(self._weights_init)
+            self.net_d.apply(self._weights_init)
+
+        self.writer = SummaryWriter(log_dir=log_dir)
+
+    @staticmethod
+    def _weights_init(m):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            m.weight.data.normal_(0.0, 0.02)
+        elif classname.find('BatchNorm') != -1:
+            m.weight.data.normal_(1.0, 0.02)
+            m.bias.data.fill_(0)
+
+    def save(self, epoch):
+        os.makedirs(self.save_root, exist_ok=True)
+        torch.save(self.net_g.state_dict(), os.path.join(self.save_root, 'generator_epoch_{}.pt'.format(epoch)))
+        torch.save(self.net_d.state_dict(), os.path.join(self.save_root, 'discriminator_epoch_{}.pt'.format(epoch)))
+
+    def train(self, dataloader, n_epoch=25, n_show_samples=8, show_img_every=10, log_metrics_every=100,
+              metrics_dataset='cifar10', metrics_to_log=('inception-score', 'mode-score', 'fid')):
+        criterion = nn.BCELoss()
+
+        global_step = 0
+        for epoch in range(self.start_epoch, n_epoch):
+            for i, data in enumerate(dataloader):
+
+                self.net_d.zero_grad()
+                real, _ = data
+                real = real.to(self.device)
+
+                target = torch.ones(real.size()[0], device=self.device)
+
+                output = self.net_d(real)
+                err_d_real = criterion(output, target)
+
+                noise = torch.randn(real.size()[0], self.latent_size, 1, 1, device=self.device)
+                fake = self.net_g(noise)
+
+                if global_step % show_img_every == 0:
+                    x = vutils.make_grid(fake[:n_show_samples, :, :, :], normalize=True, scale_each=True)
+                    self.writer.add_image('img/fake', x, global_step)
+
+                    y = vutils.make_grid(real[:n_show_samples, :, :, :], normalize=True, scale_each=True)
+                    self.writer.add_image('img/real', y, global_step)
+
+                target = torch.zeros(real.size()[0], device=self.device)
+                output = self.net_d(fake.detach())
+                err_d_fake = criterion(output, target)
+
+                err_d = err_d_real + err_d_fake
+                err_d.backward()
+                self.optimizer_d.step()
+
+                self.net_g.zero_grad()
+                target = torch.ones(real.size()[0], device=self.device)
+                output = self.net_d(fake)
+                err_g = criterion(output, target)
+                err_g.backward()
+                self.optimizer_g.step()
+
+                #logging.info(f'epoch: [{epoch}/{n_epoch}] iter: [{i}/{len(dataloader)}] loss_D: {err_d:.4f} '
+                logging.info('epoch: [{}/{}] iter: [{}/{}] loss_D: {:.4f} '\
+                .format(epoch, n_epoch, i, len(dataloader), err_d)
+                             #f'loss_G: {err_g:.4f}')
+                             + 'loss_G: {:.4f}'.format(err_g))
+                self.writer.add_scalar('data/loss_discriminator', err_d, global_step)
+                self.writer.add_scalar('data/loss_generator', err_g, global_step)
+
+                self.net_g.eval()
+                if global_step % log_metrics_every == 0:
+                    image_size = real.shape[-1]
+                    report_dict = metric.compute_metrics(metrics_dataset,
+                                                         image_size=image_size,
+                                                         metrics_root=Path(self.metric_dir),
+                                                         batch_size=dataloader.batch_size, netG=self.net_g)
+
+                    for mtrc in metrics_to_log:
+                        self.writer.add_scalar('data/{}'.format(mtrc), report_dict[mtrc], global_step)
+                self.net_g.train()
+                global_step += 1
+
+            self.save(epoch)
diff --git a/HW89/homework/metric.py b/HW89/homework/metric.py
new file mode 100644
index 0000000..6db8bbc
--- /dev/null
+++ b/HW89/homework/metric.py
@@ -0,0 +1,408 @@
+# copyed from https://github.com/xuqiantong/GAN-Metrics/blob/master/metric.py
+
+import math
+import os
+from pathlib import Path
+
+import numpy as np
+import ot
+import torch
+import torch.nn.functional as F
+import torchvision.datasets as dset
+import torchvision.models as models
+import torchvision.transforms as transforms
+import torchvision.utils as vutils
+from scipy import linalg
+from torch import nn
+
+
+def giveName(iter):  # 7 digit name.
+    ans = str(iter)
+    return ans.zfill(7)
+
+
+def sampleFake(netG, nz, sampleSize, batchSize, saveFolder):
+    saveFolder = os.path.join(saveFolder, '0')
+
+    try:
+        os.makedirs(saveFolder)
+    except OSError:
+        pass
+
+    #noise = torch.FloatTensor(batchSize, nz, 1, 1).cpu()
+    noise = torch.FloatTensor(batchSize, nz, 1, 1)
+    iter = 0
+    for i in range(0, 1 + sampleSize // batchSize):
+        noise.data.normal_(0, 1)
+        fake = netG(noise)
+        for j in range(0, len(fake.data)):
+            if iter < sampleSize:
+                vutils.save_image(fake.data[j].mul(0.5).add(
+                    0.5), os.path.join(saveFolder, giveName(iter) + ".png"))
+            iter += 1
+            if iter >= sampleSize:
+                break
+
+
+def sampleTrue(dataset, imageSize, dataroot, sampleSize, batchSize, saveFolder):
+    saveFolder = os.path.join(saveFolder, '0')
+
+    workers = 4
+    if dataset in ['imagenet', 'folder', 'lfw']:
+        # folder dataset
+        dataset = dset.ImageFolder(root=dataroot,
+                                   transform=transforms.Compose([
+                                       transforms.Resize(imageSize),
+                                       transforms.CenterCrop(imageSize),
+                                       transforms.ToTensor(),
+                                       transforms.Normalize(
+                                           (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+                                   ]))
+    elif dataset == 'lsun':
+        dataset = dset.LSUN(db_path=dataroot, classes=['bedroom_train'],
+                            transform=transforms.Compose([
+                                transforms.Resize(imageSize),
+                                transforms.CenterCrop(imageSize),
+                                transforms.ToTensor(),
+                                transforms.Normalize(
+                                    (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+                            ]))
+    elif dataset == 'cifar10':
+        dataset = dset.CIFAR10(root=str(dataroot), download=True,
+                               transform=transforms.Compose([
+                                   transforms.Resize(imageSize),
+                                   transforms.ToTensor(),
+                                   transforms.Normalize(
+                                       (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+                               ]))
+    elif dataset == 'celeba':
+        dataset = dset.ImageFolder(root=dataroot,
+                                   transform=transforms.Compose([
+                                       transforms.CenterCrop(138),
+                                       transforms.Resize(imageSize),
+                                       transforms.ToTensor(),
+                                       transforms.Normalize(
+                                           (0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+                                   ]))
+
+    assert dataset
+    dataloader = torch.utils.data.DataLoader(
+        dataset, shuffle=True, batch_size=batchSize, num_workers=int(workers))
+
+    if not os.path.exists(saveFolder):
+        try:
+            os.makedirs(saveFolder)
+        except OSError:
+            pass
+
+        iter = 0
+        for i, data in enumerate(dataloader, 0):
+            img, _ = data
+            for j in range(0, len(img)):
+
+                vutils.save_image(img[j].mul(0.5).add(
+                    0.5), os.path.join(saveFolder, giveName(iter) + ".png"))
+                iter += 1
+                if iter >= sampleSize:
+                    break
+            if iter >= sampleSize:
+                break
+
+
+class ConvNetFeatureSaver(object):
+    def __init__(self, model='resnet34', workers=4, batchSize=64):
+        '''
+        model: inception_v3, vgg13, vgg16, vgg19, resnet18, resnet34,
+               resnet50, resnet101, or resnet152
+        '''
+        self.model = model
+        self.batch_size = batchSize
+        self.workers = workers
+        if self.model.find('vgg') >= 0:
+            self.vgg = getattr(models, model)(pretrained=True).cpu().eval()
+            self.trans = transforms.Compose([
+                transforms.Resize(224),
+                transforms.ToTensor(),
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225)),
+            ])
+        elif self.model.find('resnet') >= 0:
+            resnet = getattr(models, model)(pretrained=True)
+            resnet.cpu().eval()
+            resnet_feature = nn.Sequential(resnet.conv1, resnet.bn1,
+                                           resnet.relu,
+                                           resnet.maxpool, resnet.layer1,
+                                           resnet.layer2, resnet.layer3,
+                                           resnet.layer4).cpu().eval()
+            self.resnet = resnet
+            self.resnet_feature = resnet_feature
+            self.trans = transforms.Compose([
+                transforms.Resize(224),
+                transforms.ToTensor(),
+                transforms.Normalize((0.485, 0.456, 0.406),
+                                     (0.229, 0.224, 0.225)),
+            ])
+        elif self.model == 'inception' or self.model == 'inception_v3':
+            inception = models.inception_v3(
+                pretrained=True, transform_input=False).cpu().eval()
+            inception_feature = nn.Sequential(inception.Conv2d_1a_3x3,
+                                              inception.Conv2d_2a_3x3,
+                                              inception.Conv2d_2b_3x3,
+                                              nn.MaxPool2d(3, 2),
+                                              inception.Conv2d_3b_1x1,
+                                              inception.Conv2d_4a_3x3,
+                                              nn.MaxPool2d(3, 2),
+                                              inception.Mixed_5b,
+                                              inception.Mixed_5c,
+                                              inception.Mixed_5d,
+                                              inception.Mixed_6a,
+                                              inception.Mixed_6b,
+                                              inception.Mixed_6c,
+                                              inception.Mixed_6d,
+                                              inception.Mixed_7a,
+                                              inception.Mixed_7b,
+                                              inception.Mixed_7c,
+                                              ).cpu().eval()
+            self.inception = inception
+            self.inception_feature = inception_feature
+            self.trans = transforms.Compose([
+                transforms.Resize(299),
+                transforms.ToTensor(),
+                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ])
+        else:
+            raise NotImplementedError
+
+    def save(self, imgFolder, save2disk=False):
+        dataset = dset.ImageFolder(root=imgFolder, transform=self.trans)
+        dataloader = torch.utils.data.DataLoader(
+            dataset, batch_size=self.batch_size, num_workers=self.workers)
+
+        feature_pixl, feature_conv, feature_smax, feature_logit = [], [], [], []
+        for img, _ in dataloader:
+            with torch.no_grad():
+                input = img.cpu()
+                if self.model == 'vgg' or self.model == 'vgg16':
+                    fconv = self.vgg.features(input).view(input.size(0), -1)
+                    flogit = self.vgg.classifier(fconv)
+                elif self.model.find('resnet') >= 0:
+                    fconv = self.resnet_feature(
+                        input).mean(3).mean(2).squeeze()
+                    flogit = self.resnet.fc(fconv)
+                elif self.model == 'inception' or self.model == 'inception_v3':
+                    fconv = self.inception_feature(
+                        input).mean(3).mean(2).squeeze()
+                    flogit = self.inception.fc(fconv)
+                else:
+                    raise NotImplementedError
+                fsmax = F.softmax(flogit, dim=0)
+                feature_pixl.append(img)
+                feature_conv.append(fconv.data.cpu())
+                feature_logit.append(flogit.data.cpu())
+                feature_smax.append(fsmax.data.cpu())
+
+        feature_pixl = torch.cat(feature_pixl, 0)
+        feature_conv = torch.cat(feature_conv, 0)
+        feature_logit = torch.cat(feature_logit, 0)
+        feature_smax = torch.cat(feature_smax, 0)
+
+        if save2disk:
+            torch.save(feature_conv, os.path.join(
+                imgFolder, 'feature_pixl.pth'))
+            torch.save(feature_conv, os.path.join(
+                imgFolder, 'feature_conv.pth'))
+            torch.save(feature_logit, os.path.join(
+                imgFolder, 'feature_logit.pth'))
+            torch.save(feature_smax, os.path.join(
+                imgFolder, 'feature_smax.pth'))
+
+        return feature_pixl, feature_conv, feature_logit, feature_smax
+
+
+def distance(X, Y, sqrt):
+    nX = X.size(0)
+    nY = Y.size(0)
+    X = X.view(nX, -1).cpu()
+    X2 = (X * X).sum(1).resize_(nX, 1)
+    Y = Y.view(nY, -1).cpu()
+    Y2 = (Y * Y).sum(1).resize_(nY, 1)
+
+    M = torch.zeros(nX, nY)
+    M.copy_(X2.expand(nX, nY) + Y2.expand(nY, nX).transpose(0, 1) -
+            2 * torch.mm(X, Y.transpose(0, 1)))
+
+    del X, X2, Y, Y2
+
+    if sqrt:
+        M = ((M + M.abs()) / 2).sqrt()
+
+    return M
+
+
+def wasserstein(M, sqrt):
+    if sqrt:
+        M = M.abs().sqrt()
+    emd = ot.emd2([], [], M.numpy())
+
+    return emd
+
+
+class Score_knn:
+    acc = 0
+    acc_real = 0
+    acc_fake = 0
+    precision = 0
+    recall = 0
+    tp = 0
+    fp = 0
+    fn = 0
+    ft = 0
+
+
+def knn(Mxx, Mxy, Myy, k, sqrt):
+    n0 = Mxx.size(0)
+    n1 = Myy.size(0)
+    label = torch.cat((torch.ones(n0), torch.zeros(n1)))
+    M = torch.cat((torch.cat((Mxx, Mxy), 1), torch.cat(
+        (Mxy.transpose(0, 1), Myy), 1)), 0)
+    if sqrt:
+        M = M.abs().sqrt()
+    INFINITY = float('inf')
+    val, idx = (M + torch.diag(INFINITY * torch.ones(n0 + n1))
+                ).topk(k, 0, False)
+
+    count = torch.zeros(n0 + n1)
+    for i in range(0, k):
+        count = count + label.index_select(0, idx[i])
+    pred = torch.ge(count, (float(k) / 2) * torch.ones(n0 + n1)).float()
+
+    s = Score_knn()
+    s.tp = (pred * label).sum()
+    s.fp = (pred * (1 - label)).sum()
+    s.fn = ((1 - pred) * label).sum()
+    s.tn = ((1 - pred) * (1 - label)).sum()
+    s.precision = s.tp / (s.tp + s.fp + 1e-10)
+    s.recall = s.tp / (s.tp + s.fn + 1e-10)
+    s.acc_t = s.tp / (s.tp + s.fn)
+    s.acc_f = s.tn / (s.tn + s.fp)
+    s.acc = torch.eq(label, pred).float().mean()
+    s.k = k
+
+    return s
+
+
+def mmd(Mxx, Mxy, Myy, sigma):
+    scale = Mxx.mean()
+    Mxx = torch.exp(-Mxx / (scale * 2 * sigma * sigma))
+    Mxy = torch.exp(-Mxy / (scale * 2 * sigma * sigma))
+    Myy = torch.exp(-Myy / (scale * 2 * sigma * sigma))
+    mmd = math.sqrt(Mxx.mean() + Myy.mean() - 2 * Mxy.mean())
+
+    return mmd
+
+
+eps = 1e-20
+
+
+def inception_score(X):
+    kl = X * ((X + eps).log() - (X.mean(0) + eps).log().expand_as(X))
+    score = np.exp(kl.sum(1).mean())
+
+    return score
+
+
+def mode_score(X, Y):
+    kl1 = X * ((X + eps).log() - (X.mean(0) + eps).log().expand_as(X))
+    kl2 = X.mean(0) * ((X.mean(0) + eps).log() - (Y.mean(0) + eps).log())
+    score = np.exp(kl1.sum(1).mean() - kl2.sum())
+
+    return score
+
+
+def fid(X, Y):
+    m = X.mean(0)
+    m_w = Y.mean(0)
+    X_np = X.numpy()
+    Y_np = Y.numpy()
+
+    C = np.cov(X_np.transpose())
+    C_w = np.cov(Y_np.transpose())
+    C_C_w_sqrt = linalg.sqrtm(C.dot(C_w), True).real
+
+    score = m.dot(m) + m_w.dot(m_w) - 2 * m_w.dot(m) + np.trace(C + C_w - 2 * C_C_w_sqrt)
+    return np.sqrt(score)
+
+
+class Score:
+    emd = 0
+    mmd = 0
+    knn = None
+
+
+def compute_score(real, fake, k=1, sigma=1, sqrt=True):
+    Mxx = distance(real, real, False)
+    Mxy = distance(real, fake, False)
+    Myy = distance(fake, fake, False)
+
+    s = Score()
+    s.emd = wasserstein(Mxy, sqrt)
+    s.mmd = mmd(Mxx, Mxy, Myy, sigma)
+    s.knn = knn(Mxx, Mxy, Myy, k, sqrt)
+
+    return s
+
+
+def compute_score_raw(dataset, imageSize, dataroot, sampleSize, batchSize,
+                      saveFolder_r, saveFolder_f, netG, nz,
+                      conv_model='resnet34'):
+    sampleTrue(dataset, imageSize, dataroot, sampleSize, batchSize,
+               saveFolder_r)
+    sampleFake(netG, nz, sampleSize, batchSize, saveFolder_f)
+
+    convnet_feature_saver = ConvNetFeatureSaver(model=conv_model,
+                                                batchSize=batchSize)
+    feature_r = convnet_feature_saver.save(saveFolder_r)
+    feature_f = convnet_feature_saver.save(saveFolder_f)
+
+    # 4 feature spaces and 7 scores + incep + modescore + fid
+    score = np.zeros(4 * 7 + 3)
+    for i in range(0, 4):
+        Mxx = distance(feature_r[i], feature_r[i], False)
+        Mxy = distance(feature_r[i], feature_f[i], False)
+        Myy = distance(feature_f[i], feature_f[i], False)
+
+        score[i * 7] = wasserstein(Mxy, True)
+        score[i * 7 + 1] = mmd(Mxx, Mxy, Myy, 1)
+        tmp = knn(Mxx, Mxy, Myy, 1, False)
+        score[(i * 7 + 2):(i * 7 + 7)] = \
+            tmp.acc, tmp.acc_t, tmp.acc_f, tmp.precision, tmp.recall
+
+    score[28] = inception_score(feature_f[3])
+    score[29] = mode_score(feature_r[3], feature_f[3])
+    score[30] = fid(feature_r[3], feature_f[3])
+    return score
+
+
+def compute_metrics(dataset, image_size, metrics_root: Path, batch_size, netG, nz=100,
+                    conv_model='resnet34'):
+    data_root = metrics_root / 'data'
+    real_path = metrics_root / 'real'
+    fake_path = metrics_root / 'fake'
+
+    real_path.mkdir(parents=True, exist_ok=True)
+    fake_path.mkdir(parents=True, exist_ok=True)
+
+    scores = compute_score_raw(dataset, imageSize=image_size, dataroot=data_root, sampleSize=batch_size,
+                               batchSize=batch_size, saveFolder_r=str(real_path),
+                               saveFolder_f=str(fake_path), netG=netG, nz=nz,
+                               conv_model=conv_model)
+
+    report_dict = {}
+    metrics = ['wasserstein', 'mmd', 'knn-acc', 'knn-acc-t', 'knn-acc-f', 'knn-prec', 'knn-recall']
+    for i, metric in enumerate(metrics):
+        report_dict[metric] = np.mean(scores[i::len(metrics)])
+
+    report_dict['inception-score'] = scores[28]
+    report_dict['mode-score'] = scores[29]
+    report_dict['fid'] = scores[30]
+    return report_dict
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diff --git a/HW89/homework/task.md b/HW89/homework/task.md
new file mode 100644
index 0000000..5fdf9fd
--- /dev/null
+++ b/HW89/homework/task.md
@@ -0,0 +1,5 @@
+## HW8 Generative models
+
+* Implement deep convolutional GAN in dcgan module
+* Fill the gaps in vae trainer
+* Provide the script for VAE training experiment 
\ No newline at end of file
diff --git a/HW89/homework/train_dcgan.py b/HW89/homework/train_dcgan.py
new file mode 100644
index 0000000..f556b0a
--- /dev/null
+++ b/HW89/homework/train_dcgan.py
@@ -0,0 +1,74 @@
+import argparse
+import logging
+import os
+
+import torch
+import torchvision.datasets as datasets
+from torch.optim import Adam
+from torchvision import transforms
+
+from dcgan.dcgan import DCGenerator, DCDiscriminator
+from dcgan.trainer import DCGANTrainer
+
+
+def get_config():
+    parser = argparse.ArgumentParser(description='Training DCGAN on CIFAR10')
+
+    parser.add_argument('--log-root', type=str, default='../logs')
+    parser.add_argument('--data-root', type=str, default='data')
+    parser.add_argument('--log-name', type=str, default='train_dcgan.log')
+    parser.add_argument('--no-cuda', action='store_true', default=True,
+                        help='enables CUDA training')
+    parser.add_argument('--batch-size', type=int, default=128,
+                        help='input batch size for training')
+    parser.add_argument('--epochs', type=int, default=30,
+                        help='number of epochs to train ')
+    parser.add_argument('--image-size', type=int, default=32,
+                        help='size of images to generate')
+    parser.add_argument('--n_show_samples', type=int, default=8)
+    parser.add_argument('--show_img_every', type=int, default=10)
+    parser.add_argument('--log_metrics_every', type=int, default=100)
+    config = parser.parse_args()
+    config.cuda = not config.no_cuda and torch.cuda.is_available()
+
+    return config
+
+
+def main():
+    config = get_config()
+    logging.basicConfig(
+        format='%(asctime)s | %(message)s',
+        handlers=[
+            logging.FileHandler(os.path.join(config.log_root,
+                                             config.log_name)),
+            logging.StreamHandler()],
+        level=logging.INFO)
+
+    transform = transforms.Compose([transforms.Scale(config.image_size), transforms.ToTensor(),
+                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+    dataset = datasets.CIFAR10(root=config.data_root, download=True,
+                               transform=transform)
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.batch_size, shuffle=True,
+                                             num_workers=4, pin_memory=True)
+
+    discriminator, generator = DCDiscriminator(config.image_size), DCGenerator(config.image_size)
+    
+    start_epoch = 5
+    if start_epoch >= 0:
+        disc_path = './ckpt/discriminator_epoch_{}.pt'.format(start_epoch)
+        gen_path = './ckpt/generator_epoch_{}.pt'.format(start_epoch)
+        discriminator.load_state_dict(torch.load(disc_path))
+        generator.load_state_dict(torch.load(gen_path))
+        print('state loaded from', disc_path, gen_path)
+
+    trainer = DCGANTrainer(generator=generator, discriminator=discriminator,
+                           optimizer_d=Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999)),
+                           optimizer_g=Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999)),
+                           metrics_dir='metrics',
+                           start_epoch=start_epoch+1)
+
+    trainer.train(dataloader, config.epochs, config.n_show_samples, config.show_img_every, config.log_metrics_every)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/HW89/homework/train_vae.py b/HW89/homework/train_vae.py
new file mode 100644
index 0000000..67e01c2
--- /dev/null
+++ b/HW89/homework/train_vae.py
@@ -0,0 +1,52 @@
+
+# куски кода взяты из https://github.com/pytorch/examples/blob/master/vae/main.py
+
+import vae.trainer
+import vae.vae
+import argparse
+import torch
+import torch.utils.data
+from torchvision import datasets, transforms
+
+parser = argparse.ArgumentParser(description='VAE MNIST Example')
+parser.add_argument('--batch-size', type=int, default=128, metavar='N',
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                    help='number of epochs to train (default: 10)')
+parser.add_argument('--no-cuda', action='store_true', default=True,
+                    help='enables CUDA training')
+parser.add_argument('--seed', type=int, default=1, metavar='S',
+                    help='random seed (default: 1)')
+parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+                    help='how many batches to wait before logging training status')
+args = parser.parse_args()
+args.cuda = not args.no_cuda and torch.cuda.is_available()
+
+torch.manual_seed(args.seed)
+
+device = torch.device("cuda" if args.cuda else "cpu")
+kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
+
+train_loader = torch.utils.data.DataLoader(
+    datasets.FashionMNIST('data', train=True, download=True,
+                   transform=transforms.ToTensor()),
+                    batch_size=args.batch_size, shuffle=True, **kwargs)
+test_loader = torch.utils.data.DataLoader(
+    datasets.FashionMNIST('data', train=False, transform=transforms.ToTensor()),
+        batch_size=args.batch_size, shuffle=True, **kwargs)
+
+model = vae.vae.VAE().to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+    
+def main():
+    
+    trainer = vae.trainer.Trainer(model, train_loader, test_loader, optimizer, vae.vae.loss_function, device)    
+    
+    for epoch in range(args.epochs + 1):
+        trainer.train(epoch, args.log_interval)
+        trainer.test(epoch, args.batch_size, args.log_interval)
+        
+
+
+if __name__ == '__main__':
+    main()
diff --git a/HW89/homework/vae/trainer.py b/HW89/homework/vae/trainer.py
new file mode 100644
index 0000000..ed5711f
--- /dev/null
+++ b/HW89/homework/vae/trainer.py
@@ -0,0 +1,108 @@
+import logging
+import os
+
+import torch
+import torchvision.utils as vutils
+from tensorboardX import SummaryWriter
+
+
+class Trainer:
+
+    def __init__(self, model, train_loader, test_loader, optimizer,
+                 loss_function, device='cpu'):
+        self.model = model
+        self.train_loader = train_loader
+        self.test_loader = test_loader
+        self.optimizer = optimizer
+        self.loss_function = loss_function
+        self.device = device
+        self.writer = SummaryWriter()
+
+    def train(self, epoch, log_interval):
+        self.model.train()
+        epoch_loss = 0
+
+        for batch_idx, (data, _) in enumerate(self.train_loader):
+            # ############################
+            
+            data = data.to(self.device)
+            self.optimizer.zero_grad()
+            recon_batch, mu, logvar = self.model(data)
+            loss = self.loss_function(recon_batch, data, mu, logvar)
+            loss.backward()
+            
+            train_loss = loss.item()
+            #######################
+            epoch_loss += train_loss
+            norm_train_loss = train_loss / len(data)
+
+            self.optimizer.step()
+            if batch_idx % log_interval == 0:
+                msg = 'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                    epoch, batch_idx * len(data),
+                    len(self.train_loader.dataset),
+                    100. * batch_idx / len(self.train_loader),
+                    norm_train_loss)
+                logging.info(msg)
+
+                batch_size = self.train_loader.batch_size
+                train_size = len(self.train_loader.dataset)
+                batches_per_epoch_train = train_size // batch_size
+                self.writer.add_scalar(tag='data/train_loss',
+                                       scalar_value=norm_train_loss,
+                                       global_step=batches_per_epoch_train * epoch + batch_idx)
+
+        epoch_loss /= len(self.train_loader.dataset)
+        #logging.info(f'====> Epoch: {epoch} Average loss: {epoch_loss:.4f}')
+        logging.info('====> Epoch: {} Average loss: {:.4f}'.format(epoch, epoch_loss))
+        self.writer.add_scalar(tag='data/train_epoch_loss',
+                               scalar_value=epoch_loss,
+                               global_step=epoch)
+
+    def test(self, epoch, batch_size, log_interval):
+        self.model.eval()
+        test_epoch_loss = 0
+
+        for batch_idx, (data, _) in enumerate(self.test_loader):
+            # #############################################
+            
+            data = data.to(self.device)
+            recon_batch, mu, logvar = self.model(data)
+            
+            test_loss = self.loss_function(recon_batch, data, mu, logvar).item()
+            
+            ###############################################
+            test_epoch_loss += test_loss
+
+            if batch_idx % log_interval == 0:
+                msg = 'Test Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                    epoch, batch_idx * len(data),
+                    len(self.test_loader.dataset),
+                    100. * batch_idx / len(self.test_loader),
+                    test_loss / len(data))
+                logging.info(msg)
+
+                batches_per_epoch_test = len(self.test_loader.dataset) // batch_size
+                self.writer.add_scalar(tag='data/test_loss',
+                                       scalar_value=test_loss / len(data),
+                                       global_step=batches_per_epoch_test * (epoch - 1) + batch_idx)
+
+        test_epoch_loss /= len(self.test_loader.dataset)
+        logging.info('====> Test set loss: {:.4f}'.format(test_epoch_loss))
+        self.writer.add_scalar(tag='data/test_epoch_loss',
+                               scalar_value=test_epoch_loss,
+                               global_step=epoch)
+        self.plot_generated(epoch, batch_size)
+
+    def plot_generated(self, epoch, batch_size):
+        with torch.no_grad():
+            sample = torch.randn(64, 20).to(self.device)
+            sample = self.model.decode(sample).cpu()
+            vutils.save_image(sample.view(64, 1, 28, 28),
+                'vae_results/sample_' + str(epoch) + '.png')
+        
+
+    def save(self, checkpoint_path):
+        dir_name = os.path.dirname(checkpoint_path)
+        os.makedirs(dir_name, exist_ok=True)
+        torch.save(self.model.state_dict(), checkpoint_path)
diff --git a/HW89/homework/vae/vae.py b/HW89/homework/vae/vae.py
new file mode 100644
index 0000000..065c39c
--- /dev/null
+++ b/HW89/homework/vae/vae.py
@@ -0,0 +1,57 @@
+import torch
+import torch.utils.data
+from torch import nn
+from torch.nn import functional as F  # noqa: F401
+
+
+class VAE(nn.Module):
+    def __init__(self, image_size=28, enc_hidden=400,
+                 latent_size=20, dec_hidden=400):
+        super().__init__()
+        self.latent_size = latent_size
+
+        self.fc1 = nn.Linear(image_size * image_size, enc_hidden)
+        self.fc21 = nn.Linear(enc_hidden, latent_size)
+        self.fc22 = nn.Linear(enc_hidden, latent_size)
+        self.fc3 = nn.Linear(latent_size, dec_hidden)
+        self.fc4 = nn.Linear(dec_hidden, image_size * image_size)
+
+    def encode(self, x):
+        h1 = F.relu(self.fc1(x))
+        return self.fc21(h1), self.fc22(h1)
+
+    def reparameterize(self, mu, logvar):
+        if self.training:
+            std = torch.exp(0.5*logvar)
+            eps = torch.randn_like(std)
+            return eps.mul(std).add_(mu)
+        else:
+            return mu
+
+    def decode(self, z):
+        h3 = F.relu(self.fc3(z))
+        return F.sigmoid(self.fc4(h3))
+
+    def forward(self, x):
+        mu, logvar = self.encode(x.view(-1, 784))
+        latent = self.reparameterize(mu, logvar)
+        return self.decode(latent), mu, logvar
+
+    def embed(self, x):
+        with torch.no_grad():
+            mu, logvar = self.encode(x.view(-1, 784))
+            z = self.reparameterize(mu, logvar)
+        return z
+
+
+def loss_function(recon_x, x, mu, logvar):
+    """
+    see Appendix B from VAE paper:
+    Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
+    https://arxiv.org/abs/1312.6114
+    0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+    """
+    bce = F.binary_cross_entropy(recon_x, x.view(-1, 784), size_average=False)
+    kld = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+
+    return bce + kld
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