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diff --git a/data/__init__.py b/data/__init__.py
new file mode 100755
index 0000000..b44ca1f
--- /dev/null
+++ b/data/__init__.py
@@ -0,0 +1,74 @@
+import importlib
+import torch.utils.data
+from data.base_data_loader import BaseDataLoader
+from data.base_dataset import BaseDataset
+
+def find_dataset_using_name(dataset_name):
+    # Given the option --dataset_mode [datasetname],
+    # the file "data/datasetname_dataset.py"
+    # will be imported.
+    dataset_filename = "data." + dataset_name + "_dataset"
+    datasetlib = importlib.import_module(dataset_filename)
+
+    # In the file, the class called DatasetNameDataset() will
+    # be instantiated. It has to be a subclass of BaseDataset,
+    # and it is case-insensitive.
+    dataset = None
+    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
+    for name, cls in datasetlib.__dict__.items():
+        if name.lower() == target_dataset_name.lower() \
+           and issubclass(cls, BaseDataset):
+            dataset = cls
+            
+    if dataset is None:
+        print("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
+        exit(0)
+
+    return dataset
+
+
+def get_option_setter(dataset_name):    
+    dataset_class = find_dataset_using_name(dataset_name)
+    return dataset_class.modify_commandline_options
+
+
+def create_dataset(opt):
+    dataset = find_dataset_using_name(opt.dataset_mode)
+    instance = dataset()
+    instance.initialize(opt)
+    print("dataset [%s] was created" % (instance.name()))
+    return instance
+
+
+def CreateDataLoader(opt):
+    data_loader = CustomDatasetDataLoader()
+    data_loader.initialize(opt)
+    return data_loader
+
+
+## Wrapper class of Dataset class that performs
+## multi-threaded data loading
+class CustomDatasetDataLoader(BaseDataLoader):
+    def name(self):
+        return 'CustomDatasetDataLoader'
+
+    def initialize(self, opt):
+        BaseDataLoader.initialize(self, opt)
+        self.dataset = create_dataset(opt)
+        self.dataloader = torch.utils.data.DataLoader(
+            self.dataset,
+            batch_size=opt.batchSize,
+            shuffle=not opt.serial_batches,
+            num_workers=int(opt.nThreads))
+
+    def load_data(self):
+        return self
+
+    def __len__(self):
+        return min(len(self.dataset), self.opt.max_dataset_size)
+
+    def __iter__(self):
+        for i, data in enumerate(self.dataloader):
+            if i * self.opt.batchSize >= self.opt.max_dataset_size:
+                break
+            yield data
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diff --git a/data/aligned_dataset.py b/data/aligned_dataset.py
new file mode 100755
index 0000000..6e4c66e
--- /dev/null
+++ b/data/aligned_dataset.py
@@ -0,0 +1,81 @@
+import os.path
+import random
+import torchvision.transforms as transforms
+import torch
+from data.base_dataset import BaseDataset
+from data.image_folder import make_dataset
+from PIL import Image
+
+
+class AlignedDataset(BaseDataset):
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        return parser
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.root = opt.dataroot
+        self.dir_AB = os.path.join(opt.dataroot, opt.phase)
+        self.AB_paths = sorted(make_dataset(self.dir_AB))
+        assert(opt.resize_or_crop == 'resize_and_crop')
+
+    def __getitem__(self, index):
+        AB_path = self.AB_paths[index]
+        ABCD = Image.open(AB_path).convert('RGB')
+        w, h = ABCD.size
+        w2 = int(w / 4)
+        A = ABCD.crop((0, 0, w2, h)).resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
+        B = ABCD.crop((w2, 0, w2+w2, h)).resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
+        C = ABCD.crop((w2+w2, 0, w2+w2+w2, h)).resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
+        D = ABCD.crop((w2+w2+w2, 0, w, h)).resize((self.opt.loadSize, self.opt.loadSize), Image.BICUBIC)
+        # A.show()
+        # B.show()
+        # C.show()
+        # D.show()
+
+        A = transforms.ToTensor()(A)
+        B = transforms.ToTensor()(B)
+        C = transforms.ToTensor()(C)
+        D = transforms.ToTensor()(D)
+        w_offset = random.randint(0, max(0, self.opt.loadSize - self.opt.fineSize - 1))
+        h_offset = random.randint(0, max(0, self.opt.loadSize - self.opt.fineSize - 1))
+
+        A = A[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]
+        B = B[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]
+        C = C[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]
+        D = D[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]
+
+        A = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(A)
+        B = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(B)
+        C = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(C)
+        D = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))(D)
+
+        if self.opt.which_direction == 'BtoA':
+            input_nc = self.opt.output_nc
+            output_nc = self.opt.input_nc
+        else:
+            input_nc = self.opt.input_nc
+            output_nc = self.opt.output_nc
+
+        if (not self.opt.no_flip) and random.random() < 0.5:
+            idx = [i for i in range(A.size(2) - 1, -1, -1)]
+            idx = torch.LongTensor(idx)
+            A = A.index_select(2, idx)
+            B = B.index_select(2, idx)
+
+        if input_nc == 1:  # RGB to gray
+            tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114
+            A = tmp.unsqueeze(0)
+
+        if output_nc == 1:  # RGB to gray
+            tmp = B[0, ...] * 0.299 + B[1, ...] * 0.587 + B[2, ...] * 0.114
+            B = tmp.unsqueeze(0)
+
+        return {'A': A, 'B': B, 'C': C, 'D': D,
+                'A_paths': AB_path, 'B_paths': AB_path}
+
+    def __len__(self):
+        return len(self.AB_paths)
+
+    def name(self):
+        return 'AlignedDataset'
diff --git a/data/base_data_loader.py b/data/base_data_loader.py
new file mode 100755
index 0000000..ae5a168
--- /dev/null
+++ b/data/base_data_loader.py
@@ -0,0 +1,10 @@
+class BaseDataLoader():
+    def __init__(self):
+        pass
+
+    def initialize(self, opt):
+        self.opt = opt
+        pass
+
+    def load_data():
+        return None
diff --git a/data/base_dataset.py b/data/base_dataset.py
new file mode 100755
index 0000000..1a82083
--- /dev/null
+++ b/data/base_dataset.py
@@ -0,0 +1,103 @@
+import torch.utils.data as data
+from PIL import Image
+import torchvision.transforms as transforms
+
+
+class BaseDataset(data.Dataset):
+    def __init__(self):
+        super(BaseDataset, self).__init__()
+
+    def name(self):
+        return 'BaseDataset'
+
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        return parser
+
+    def initialize(self, opt):
+        pass
+
+    def __len__(self):
+        return 0
+
+
+def get_transform(opt):
+    transform_list = []
+    if opt.resize_or_crop == 'resize_and_crop':
+        osize = [opt.loadSize, opt.loadSize]
+        transform_list.append(transforms.Resize(osize, Image.BICUBIC))
+        transform_list.append(transforms.RandomCrop(opt.fineSize))
+    elif opt.resize_or_crop == 'crop':
+        transform_list.append(transforms.RandomCrop(opt.fineSize))
+    elif opt.resize_or_crop == 'scale_width':
+        transform_list.append(transforms.Lambda(
+            lambda img: __scale_width(img, opt.fineSize)))
+    elif opt.resize_or_crop == 'scale_width_and_crop':
+        transform_list.append(transforms.Lambda(
+            lambda img: __scale_width(img, opt.loadSize)))
+        transform_list.append(transforms.RandomCrop(opt.fineSize))
+    elif opt.resize_or_crop == 'none':
+        transform_list.append(transforms.Lambda(
+            lambda img: __adjust(img)))
+    else:
+        raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop)
+
+    if opt.isTrain and not opt.no_flip:
+        transform_list.append(transforms.RandomHorizontalFlip())
+
+    transform_list += [transforms.ToTensor(),
+                       transforms.Normalize((0.5, 0.5, 0.5),
+                                            (0.5, 0.5, 0.5))]
+    return transforms.Compose(transform_list)
+
+# just modify the width and height to be multiple of 4
+def __adjust(img):
+    ow, oh = img.size
+
+    # the size needs to be a multiple of this number, 
+    # because going through generator network may change img size
+    # and eventually cause size mismatch error
+    mult = 4 
+    if ow % mult == 0 and oh % mult == 0:
+        return img
+    w = (ow - 1) // mult
+    w = (w + 1) * mult
+    h = (oh - 1) // mult
+    h = (h + 1) * mult
+
+    if ow != w or oh != h:
+        __print_size_warning(ow, oh, w, h)
+        
+    return img.resize((w, h), Image.BICUBIC)
+
+
+def __scale_width(img, target_width):
+    ow, oh = img.size
+    
+    # the size needs to be a multiple of this number, 
+    # because going through generator network may change img size
+    # and eventually cause size mismatch error    
+    mult = 4
+    assert target_width % mult == 0, "the target width needs to be multiple of %d." % mult
+    if (ow == target_width and oh % mult == 0):
+        return img
+    w = target_width
+    target_height = int(target_width * oh / ow)
+    m = (target_height - 1) // mult
+    h = (m + 1) * mult
+
+    if target_height != h:
+        __print_size_warning(target_width, target_height, w, h)
+    
+    return img.resize((w, h), Image.BICUBIC)
+
+
+def __print_size_warning(ow, oh, w, h):
+    if not hasattr(__print_size_warning, 'has_printed'):
+        print("The image size needs to be a multiple of 4. "
+              "The loaded image size was (%d, %d), so it was adjusted to "
+              "(%d, %d). This adjustment will be done to all images "
+              "whose sizes are not multiples of 4" % (ow, oh, w, h))
+        __print_size_warning.has_printed = True
+
+
diff --git a/data/image_folder.py b/data/image_folder.py
new file mode 100755
index 0000000..898200b
--- /dev/null
+++ b/data/image_folder.py
@@ -0,0 +1,68 @@
+###############################################################################
+# Code from
+# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
+# Modified the original code so that it also loads images from the current
+# directory as well as the subdirectories
+###############################################################################
+
+import torch.utils.data as data
+
+from PIL import Image
+import os
+import os.path
+
+IMG_EXTENSIONS = [
+    '.jpg', '.JPG', '.jpeg', '.JPEG',
+    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
+]
+
+
+def is_image_file(filename):
+    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+def make_dataset(dir):
+    images = []
+    assert os.path.isdir(dir), '%s is not a valid directory' % dir
+
+    for root, _, fnames in sorted(os.walk(dir)):
+        for fname in fnames:
+            if is_image_file(fname):
+                path = os.path.join(root, fname)
+                images.append(path)
+
+    return images
+
+
+def default_loader(path):
+    return Image.open(path).convert('RGB')
+
+
+class ImageFolder(data.Dataset):
+
+    def __init__(self, root, transform=None, return_paths=False,
+                 loader=default_loader):
+        imgs = make_dataset(root)
+        if len(imgs) == 0:
+            raise(RuntimeError("Found 0 images in: " + root + "\n"
+                               "Supported image extensions are: " +
+                               ",".join(IMG_EXTENSIONS)))
+
+        self.root = root
+        self.imgs = imgs
+        self.transform = transform
+        self.return_paths = return_paths
+        self.loader = loader
+
+    def __getitem__(self, index):
+        path = self.imgs[index]
+        img = self.loader(path)
+        if self.transform is not None:
+            img = self.transform(img)
+        if self.return_paths:
+            return img, path
+        else:
+            return img
+
+    def __len__(self):
+        return len(self.imgs)
diff --git a/data/single_dataset.py b/data/single_dataset.py
new file mode 100755
index 0000000..c9f515b
--- /dev/null
+++ b/data/single_dataset.py
@@ -0,0 +1,42 @@
+import os.path
+from data.base_dataset import BaseDataset, get_transform
+from data.image_folder import make_dataset
+from PIL import Image
+
+
+class SingleDataset(BaseDataset):
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        return parser
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.root = opt.dataroot
+        self.dir_A = os.path.join(opt.dataroot)
+
+        self.A_paths = make_dataset(self.dir_A)
+
+        self.A_paths = sorted(self.A_paths)
+
+        self.transform = get_transform(opt)
+
+    def __getitem__(self, index):
+        A_path = self.A_paths[index]
+        A_img = Image.open(A_path).convert('RGB')
+        A = self.transform(A_img)
+        if self.opt.which_direction == 'BtoA':
+            input_nc = self.opt.output_nc
+        else:
+            input_nc = self.opt.input_nc
+
+        if input_nc == 1:  # RGB to gray
+            tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114
+            A = tmp.unsqueeze(0)
+
+        return {'A': A, 'A_paths': A_path}
+
+    def __len__(self):
+        return len(self.A_paths)
+
+    def name(self):
+        return 'SingleImageDataset'
diff --git a/data/unaligned_dataset.py b/data/unaligned_dataset.py
new file mode 100755
index 0000000..06938b7
--- /dev/null
+++ b/data/unaligned_dataset.py
@@ -0,0 +1,62 @@
+import os.path
+from data.base_dataset import BaseDataset, get_transform
+from data.image_folder import make_dataset
+from PIL import Image
+import random
+
+
+class UnalignedDataset(BaseDataset):
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        return parser
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.root = opt.dataroot
+        self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
+        self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
+
+        self.A_paths = make_dataset(self.dir_A)
+        self.B_paths = make_dataset(self.dir_B)
+
+        self.A_paths = sorted(self.A_paths)
+        self.B_paths = sorted(self.B_paths)
+        self.A_size = len(self.A_paths)
+        self.B_size = len(self.B_paths)
+        self.transform = get_transform(opt)
+
+    def __getitem__(self, index):
+        A_path = self.A_paths[index % self.A_size]
+        if self.opt.serial_batches:
+            index_B = index % self.B_size
+        else:
+            index_B = random.randint(0, self.B_size - 1)
+        B_path = self.B_paths[index_B]
+        # print('(A, B) = (%d, %d)' % (index_A, index_B))
+        A_img = Image.open(A_path).convert('RGB')
+        B_img = Image.open(B_path).convert('RGB')
+
+        A = self.transform(A_img)
+        B = self.transform(B_img)
+        if self.opt.which_direction == 'BtoA':
+            input_nc = self.opt.output_nc
+            output_nc = self.opt.input_nc
+        else:
+            input_nc = self.opt.input_nc
+            output_nc = self.opt.output_nc
+
+        if input_nc == 1:  # RGB to gray
+            tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114
+            A = tmp.unsqueeze(0)
+
+        if output_nc == 1:  # RGB to gray
+            tmp = B[0, ...] * 0.299 + B[1, ...] * 0.587 + B[2, ...] * 0.114
+            B = tmp.unsqueeze(0)
+        return {'A': A, 'B': B,
+                'A_paths': A_path, 'B_paths': B_path}
+
+    def __len__(self):
+        return max(self.A_size, self.B_size)
+
+    def name(self):
+        return 'UnalignedDataset'
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diff --git a/models/__init__.py b/models/__init__.py
new file mode 100755
index 0000000..4d92091
--- /dev/null
+++ b/models/__init__.py
@@ -0,0 +1,39 @@
+import importlib
+from models.base_model import BaseModel
+
+
+def find_model_using_name(model_name):
+    # Given the option --model [modelname],
+    # the file "models/modelname_model.py"
+    # will be imported.
+    model_filename = "models." + model_name + "_model"
+    modellib = importlib.import_module(model_filename)
+
+    # In the file, the class called ModelNameModel() will
+    # be instantiated. It has to be a subclass of BaseModel,
+    # and it is case-insensitive.
+    model = None
+    target_model_name = model_name.replace('_', '') + 'model'
+    for name, cls in modellib.__dict__.items():
+        if name.lower() == target_model_name.lower() \
+           and issubclass(cls, BaseModel):
+            model = cls
+
+    if model is None:
+        print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
+        exit(0)
+
+    return model
+
+
+def get_option_setter(model_name):
+    model_class = find_model_using_name(model_name)
+    return model_class.modify_commandline_options
+
+
+def create_model(opt):
+    model = find_model_using_name(opt.model)
+    instance = model()
+    instance.initialize(opt)
+    print("model [%s] was created" % (instance.name()))
+    return instance
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diff --git a/models/base_model.py b/models/base_model.py
new file mode 100755
index 0000000..461727c
--- /dev/null
+++ b/models/base_model.py
@@ -0,0 +1,159 @@
+import os
+import torch
+from collections import OrderedDict
+from . import networks
+
+
+class BaseModel():
+
+    # modify parser to add command line options,
+    # and also change the default values if needed
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        return parser
+
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+        if opt.resize_or_crop != 'scale_width':
+            torch.backends.cudnn.benchmark = True
+        self.loss_names = []
+        self.model_names = []
+        self.visual_names = []
+        self.image_paths = []
+
+    def set_input(self, input):
+        self.input = input
+
+    def forward(self):
+        pass
+
+    # load and print networks; create schedulers
+    def setup(self, opt, parser=None):
+        if self.isTrain:
+            self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
+
+        if not self.isTrain or opt.continue_train:
+            self.load_networks(opt.which_epoch)
+        self.print_networks(opt.verbose)
+
+    # make models eval mode during test time
+    def eval(self):
+        for name in self.model_names:
+            if isinstance(name, str):
+                net = getattr(self, 'net' + name)
+                net.eval()
+
+    # used in test time, wrapping `forward` in no_grad() so we don't save
+    # intermediate steps for backprop
+    def test(self):
+        with torch.no_grad():
+            self.forward()
+
+    # get image paths
+    def get_image_paths(self):
+        return self.image_paths
+
+    def optimize_parameters(self):
+        pass
+
+    # update learning rate (called once every epoch)
+    def update_learning_rate(self):
+        for scheduler in self.schedulers:
+            scheduler.step()
+        lr = self.optimizers[0].param_groups[0]['lr']
+        print('learning rate = %.7f' % lr)
+
+    # return visualization images. train.py will display these images, and save the images to a html
+    def get_current_visuals(self):
+        visual_ret = OrderedDict()
+        for name in self.visual_names:
+            if isinstance(name, str):
+                visual_ret[name] = getattr(self, name)
+        return visual_ret
+
+    # return traning losses/errors. train.py will print out these errors as debugging information
+    def get_current_losses(self):
+        errors_ret = OrderedDict()
+        for name in self.loss_names:
+            if isinstance(name, str):
+                # float(...) works for both scalar tensor and float number
+                errors_ret[name] = float(getattr(self, 'loss_' + name))
+        return errors_ret
+
+    # save models to the disk
+    def save_networks(self, which_epoch):
+        for name in self.model_names:
+            if isinstance(name, str):
+                save_filename = '%s_net_%s.pth' % (which_epoch, name)
+                save_path = os.path.join(self.save_dir, save_filename)
+                net = getattr(self, 'net' + name)
+
+                if len(self.gpu_ids) > 0 and torch.cuda.is_available():
+                    torch.save(net.module.cpu().state_dict(), save_path)
+                    net.cuda(self.gpu_ids[0])
+                else:
+                    torch.save(net.cpu().state_dict(), save_path)
+
+    def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
+        key = keys[i]
+        if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
+            if module.__class__.__name__.startswith('InstanceNorm') and \
+                    (key == 'running_mean' or key == 'running_var'):
+                if getattr(module, key) is None:
+                    state_dict.pop('.'.join(keys))
+            if module.__class__.__name__.startswith('InstanceNorm') and \
+                    (key == 'num_batches_tracked'):
+                state_dict.pop('.'.join(keys))
+        else:
+            self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
+
+    # load models from the disk
+    def load_networks(self, which_epoch):
+        for name in self.model_names:
+            if isinstance(name, str):
+                load_filename = '%s_net_%s.pth' % (which_epoch, name)
+                load_path = os.path.join(self.save_dir, load_filename)
+                net = getattr(self, 'net' + name)
+                if isinstance(net, torch.nn.DataParallel):
+                    net = net.module
+                print('loading the model from %s' % load_path)
+                # if you are using PyTorch newer than 0.4 (e.g., built from
+                # GitHub source), you can remove str() on self.device
+                state_dict = torch.load(load_path, map_location=str(self.device))
+                if hasattr(state_dict, '_metadata'):
+                    del state_dict._metadata
+
+                # patch InstanceNorm checkpoints prior to 0.4
+                for key in list(state_dict.keys()):  # need to copy keys here because we mutate in loop
+                    self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
+                net.load_state_dict(state_dict)
+
+    # print network information
+    def print_networks(self, verbose):
+        print('---------- Networks initialized -------------')
+        for name in self.model_names:
+            if isinstance(name, str):
+                net = getattr(self, 'net' + name)
+                num_params = 0
+                for param in net.parameters():
+                    num_params += param.numel()
+                if verbose:
+                    print(net)
+                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
+        print('-----------------------------------------------')
+
+    # set requies_grad=Fasle to avoid computation
+    def set_requires_grad(self, nets, requires_grad=False):
+        if not isinstance(nets, list):
+            nets = [nets]
+        for net in nets:
+            if net is not None:
+                for param in net.parameters():
+                    param.requires_grad = requires_grad
\ No newline at end of file
diff --git a/models/gesturegan_twocycle_model.py b/models/gesturegan_twocycle_model.py
new file mode 100755
index 0000000..c8cd3ae
--- /dev/null
+++ b/models/gesturegan_twocycle_model.py
@@ -0,0 +1,219 @@
+import torch
+from util.image_pool import ImagePool
+from .base_model import BaseModel
+from . import networks
+import itertools
+
+class GestureGANTwoCycleModel(BaseModel):
+    def name(self):
+        return 'GestureGANTwoCycleModel'
+
+    @staticmethod
+    def modify_commandline_options(parser, is_train=True):
+
+        # changing the default values to match the pix2pix paper
+        # (https://phillipi.github.io/pix2pix/)
+        # parser.set_defaults(pool_size=0, no_lsgan=True, norm='batch')
+        parser.set_defaults(pool_size=0, no_lsgan=True, norm='instance')
+        parser.set_defaults(dataset_mode='aligned')
+        parser.set_defaults(which_model_netG='resnet_9blocks')
+        parser.add_argument('--REGULARIZATION', type=float, default=1e-6)
+        if is_train:
+            parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss')
+            parser.add_argument('--cyc_L1', type=float, default=100.0, help='weight for L1 loss')
+            parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')
+            parser.add_argument('--lambda_identity', type=float, default=5.0, help='weight for identity loss')
+
+        return parser
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        self.isTrain = opt.isTrain
+        # specify the training losses you want to print out. The program will call base_model.get_current_losses
+        # self.loss_names = ['G_GAN_D1', 'Gi_L1', 'G' , 'D1_real', 'D1_fake','D1']
+        self.loss_names = ['G_GAN_D1',  'G_GAN_D2', 'G_L1', 'G_VGG', 'reg', 'G','D1','D2']
+        # specify the images you want to save/display. The program will call base_model.get_current_visuals
+        self.visual_names = ['real_A', 'real_D', 'fake_B', 'real_B', 'real_C', 'recovery_A']
+        # self.visual_names = ['fake_B', 'fake_D']
+        # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks
+        if self.isTrain:
+            self.model_names = ['Gi','D1','D2']
+        else:  # during test time, only load Gs
+            self.model_names = ['Gi']
+        # load/define networks
+        self.netGi = networks.define_G(6, 3, opt.ngf,
+                                      opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
+
+        if self.isTrain:
+            use_sigmoid = opt.no_lsgan
+            self.netD1 = networks.define_D(6, opt.ndf,
+                                          opt.which_model_netD,
+                                          opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
+            self.netD2 = networks.define_D(9, opt.ndf,
+                                          opt.which_model_netD,
+                                          opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, opt.init_gain, self.gpu_ids)
+
+
+        if self.isTrain:
+            self.fake_AB_pool = ImagePool(opt.pool_size)
+
+            # define loss functions
+            self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan).to(self.device)
+            self.criterionL1 = torch.nn.L1Loss()
+            self.criterionVGG = networks.VGGLoss(self.gpu_ids)
+
+            # initialize optimizers
+            self.optimizers = []
+            # self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
+            #                                     lr=opt.lr, betas=(opt.beta1, 0.999))
+            # self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
+            #                                     lr=opt.lr, betas=(opt.beta1, 0.999))
+
+            self.optimizer_G = torch.optim.Adam(self.netGi.parameters(),
+                                                lr=opt.lr, betas=(opt.beta1, 0.999))
+            self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD1.parameters(),self.netD2.parameters()),
+                                                lr=opt.lr, betas=(opt.beta1, 0.999))
+            self.optimizers.append(self.optimizer_G)
+            self.optimizers.append(self.optimizer_D)
+
+
+    def set_input(self, input):
+        AtoB = self.opt.which_direction == 'AtoB'
+        self.real_A = input['A' if AtoB else 'B'].to(self.device)
+        self.real_B = input['B' if AtoB else 'A'].to(self.device)
+        self.real_C = input['C'].to(self.device)
+        self.real_D = input['D'].to(self.device)
+        self.image_paths = input['A_paths' if AtoB else 'B_paths']
+
+    def forward(self):
+        combine_realA_realD=torch.cat((self.real_A, self.real_D), 1)
+        # combine_ACD=torch.cat((self.real_A, self.real_D), 1)
+        self.fake_B = self.netGi(combine_realA_realD)
+        combine_fakeB_realC=torch.cat((self.fake_B, self.real_C), 1)
+        self.recovery_A = self.netGi(combine_fakeB_realC)
+
+        combine_realB_real_C=torch.cat((self.real_B, self.real_C), 1)
+        self.fake_A = self.netGi(combine_realB_real_C)
+        combine_fakeA_realD=torch.cat((self.fake_A, self.real_D), 1)
+        self.recovery_B = self.netGi(combine_fakeA_realD)
+
+
+        combine_realA_realC=torch.cat((self.real_A, self.real_C), 1)
+        self.identity_A = self.netGi(combine_realA_realC)
+        combine_realB_realD=torch.cat((self.real_B, self.real_D), 1)
+        self.identity_B = self.netGi(combine_realB_realD)
+
+    def backward_D1(self):
+        # Fake
+        # stop backprop to the generator by detaching fake_B
+        realA_fakeB = self.fake_AB_pool.query(torch.cat((self.real_A, self.fake_B), 1))
+        pred_D1_realA_fakeB = self.netD1(realA_fakeB.detach())
+        self.loss_D1_realA_fakeB = self.criterionGAN(pred_D1_realA_fakeB, False)
+
+        # Real
+        realA_realB = torch.cat((self.real_A, self.real_B), 1)
+        pred_D1_realA_realB = self.netD1(realA_realB)
+        self.loss_D1_realA_realB = self.criterionGAN(pred_D1_realA_realB, True)
+
+        # Combined loss
+        self.loss_D1 = (self.loss_D1_realA_fakeB + self.loss_D1_realA_realB) * 0.5
+
+
+        realB_fakeA = self.fake_AB_pool.query(torch.cat((self.real_B, self.fake_A), 1))
+        pred_D1_realB_fakeA = self.netD1(realB_fakeA.detach())
+        self.loss_D1_realB_fakeA = self.criterionGAN(pred_D1_realB_fakeA, False)
+
+        # Combined loss
+        self.loss_D1 = (self.loss_D1_realB_fakeA + self.loss_D1_realA_realB) * 0.5 + self.loss_D1
+
+        self.loss_D1.backward()
+
+    def backward_D2(self):
+        # Fake
+        # stop backprop to the generator by detaching fake_B
+        realA_realD_fakeB = self.fake_AB_pool.query(torch.cat((self.real_A, self.real_D, self.fake_B), 1))
+        pred_D2_realA_realD_fakeB = self.netD2(realA_realD_fakeB.detach())
+        self.loss_D2_realA_realD_fakeB = self.criterionGAN(pred_D2_realA_realD_fakeB, False)
+
+        # Real
+        realA_realD_realB = torch.cat((self.real_A, self.real_D, self.real_B), 1)
+        pred_D2_realA_realD_realB = self.netD2(realA_realD_realB)
+        self.loss_D2_realA_realD_realB = self.criterionGAN(pred_D2_realA_realD_realB, True)
+
+        # Combined loss
+        self.loss_D2 = (self.loss_D2_realA_realD_fakeB + self.loss_D2_realA_realD_realB) * 0.5
+
+        realB_realC_fakeA = self.fake_AB_pool.query(torch.cat((self.real_B, self.real_C, self.fake_A), 1))
+        pred_D2_realB_realC_fakeA = self.netD2(realB_realC_fakeA.detach())
+        self.loss_D2_realB_realC_fakeA = self.criterionGAN(pred_D2_realB_realC_fakeA, False)
+
+        # Real
+        realB_realC_realA = torch.cat((self.real_B, self.real_C, self.real_A), 1)
+        pred_D2_realB_realC_realA = self.netD2(realB_realC_realA)
+        self.loss_D2_realB_realC_realA = self.criterionGAN(pred_D2_realB_realC_realA, True)
+
+        # Combined loss
+        self.loss_D2 = (self.loss_D2_realB_realC_fakeA + self.loss_D2_realB_realC_realA) * 0.5 + self.loss_D2
+
+        self.loss_D2.backward()
+
+
+    def backward_G(self):
+        # First, G(A) should fake the discriminator
+        realA_fakeB = torch.cat((self.real_A, self.fake_B), 1)
+        pred_D1_realA_fakeB = self.netD1(realA_fakeB)
+        self.loss_G_GAN_D1 = self.criterionGAN(pred_D1_realA_fakeB, True)
+
+        realB_fakeA = torch.cat((self.real_B, self.fake_A), 1)
+        pred_D1_realB_fakeA = self.netD1(realB_fakeA)
+        self.loss_G_GAN_D1 = self.criterionGAN(pred_D1_realB_fakeA, True) + self.loss_G_GAN_D1      
+
+        realA_realD_fakeB = torch.cat((self.real_A, self.real_D, self.fake_B), 1)
+        pred_D2_realA_realD_fakeB = self.netD2(realA_realD_fakeB)
+        self.loss_G_GAN_D2 = self.criterionGAN(pred_D2_realA_realD_fakeB, True)
+
+        realB_realC_fakeA = torch.cat((self.real_B, self.real_C, self.fake_A), 1)
+        pred_D2_realB_realC_fakeA = self.netD2(realB_realC_fakeA)
+        self.loss_G_GAN_D2 = self.criterionGAN(pred_D2_realB_realC_fakeA, True) + self.loss_G_GAN_D2
+
+        self.fake_B_red = self.fake_B[:,0:1,:,:]
+        self.fake_B_green = self.fake_B[:,1:2,:,:]
+        self.fake_B_blue = self.fake_B[:,2:3,:,:]
+        # print(self.fake_A_red.size())
+        self.real_B_red = self.real_B[:,0:1,:,:]
+        self.real_B_green = self.real_B[:,1:2,:,:]
+        self.real_B_blue = self.real_B[:,2:3,:,:]
+
+        self.fake_A_red = self.fake_A[:,0:1,:,:]
+        self.fake_A_green = self.fake_A[:,1:2,:,:]
+        self.fake_A_blue = self.fake_A[:,2:3,:,:]
+        # print(self.fake_A_red.size())
+        self.real_A_red = self.real_A[:,0:1,:,:]
+        self.real_A_green = self.real_A[:,1:2,:,:]
+        self.real_A_blue = self.real_A[:,2:3,:,:]
+
+        # second, G(A)=B
+        self.loss_G_L1 = (self.criterionL1(self.fake_B_red, self.real_B_red) + self.criterionL1(self.fake_B_green, self.real_B_green) + self.criterionL1(self.fake_B_blue, self.real_B_blue)) * self.opt.lambda_L1 + self.criterionL1(self.fake_B, self.real_B) * self.opt.lambda_L1 + self.criterionL1(self.recovery_A, self.real_A) * self.opt.cyc_L1 + self.criterionL1(self.identity_A, self.real_A) * self.opt.lambda_identity + (self.criterionL1(self.fake_A_red, self.real_A_red) + self.criterionL1(self.fake_A_green, self.real_A_green) + self.criterionL1(self.fake_A_blue, self.real_A_blue)) * self.opt.lambda_L1 + self.criterionL1(self.fake_A, self.real_A) * self.opt.lambda_L1 + self.criterionL1(self.recovery_B, self.real_B) * self.opt.cyc_L1 + self.criterionL1(self.identity_B, self.real_B) * self.opt.lambda_identity
+
+        self.loss_G_VGG = self.criterionVGG(self.fake_B, self.real_B) * self.opt.lambda_feat + self.criterionVGG(self.fake_A, self.real_A) * self.opt.lambda_feat
+
+        self.loss_reg = self.opt.REGULARIZATION * (torch.sum(torch.abs(self.fake_B[:, :, :, :-1] - self.fake_B[:, :, :, 1:])) + torch.sum(torch.abs(self.fake_B[:, :, :-1, :] - self.fake_B[:, :, 1:, :]))) + self.opt.REGULARIZATION * (torch.sum(torch.abs(self.fake_A[:, :, :, :-1] - self.fake_A[:, :, :, 1:])) + torch.sum(torch.abs(self.fake_A[:, :, :-1, :] - self.fake_A[:, :, 1:, :])))
+
+        self.loss_G = self.loss_G_GAN_D1 + self.loss_G_GAN_D2 + self.loss_G_L1 + self.loss_G_VGG + self.loss_reg 
+
+        self.loss_G.backward()
+
+    def optimize_parameters(self):
+        self.forward()
+        # update D
+        self.set_requires_grad([self.netD1, self.netD2], True)
+        self.optimizer_D.zero_grad()
+        self.backward_D1()
+        self.backward_D2()
+        self.optimizer_D.step()
+
+        # update G
+        self.set_requires_grad([self.netD1, self.netD2], False)
+        self.optimizer_G.zero_grad()
+        self.backward_G()
+        self.optimizer_G.step()
diff --git a/models/networks.py b/models/networks.py
new file mode 100755
index 0000000..ad8bc06
--- /dev/null
+++ b/models/networks.py
@@ -0,0 +1,429 @@
+import torch
+import torch.nn as nn
+from torch.nn import init
+import functools
+from torch.optim import lr_scheduler
+
+###############################################################################
+# Helper Functions
+###############################################################################
+
+
+def get_norm_layer(norm_type='instance'):
+    if norm_type == 'batch':
+        norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
+    elif norm_type == 'instance':
+        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=True)
+    elif norm_type == 'none':
+        norm_layer = None
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return norm_layer
+
+
+def get_scheduler(optimizer, opt):
+    if opt.lr_policy == 'lambda':
+        def lambda_rule(epoch):
+            lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1)
+            return lr_l
+        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
+    elif opt.lr_policy == 'step':
+        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
+    elif opt.lr_policy == 'plateau':
+        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
+    else:
+        return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
+    return scheduler
+
+
+def init_weights(net, init_type='normal', gain=0.02):
+    def init_func(m):
+        classname = m.__class__.__name__
+        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
+            if init_type == 'normal':
+                init.normal_(m.weight.data, 0.0, gain)
+            elif init_type == 'xavier':
+                init.xavier_normal_(m.weight.data, gain=gain)
+            elif init_type == 'kaiming':
+                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
+            elif init_type == 'orthogonal':
+                init.orthogonal_(m.weight.data, gain=gain)
+            else:
+                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
+            if hasattr(m, 'bias') and m.bias is not None:
+                init.constant_(m.bias.data, 0.0)
+        elif classname.find('BatchNorm2d') != -1:
+            init.normal_(m.weight.data, 1.0, gain)
+            init.constant_(m.bias.data, 0.0)
+
+    print('initialize network with %s' % init_type)
+    net.apply(init_func)
+
+
+def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        net.to(gpu_ids[0])
+        net = torch.nn.DataParallel(net, gpu_ids)
+    init_weights(net, init_type, gain=init_gain)
+    return net
+
+class VGGLoss(nn.Module):
+    def __init__(self, gpu_ids):
+        super(VGGLoss, self).__init__()        
+        self.vgg = Vgg19().cuda()
+        self.criterion = nn.L1Loss()
+        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]        
+
+    def forward(self, x, y):              
+        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
+        loss = 0
+        for i in range(len(x_vgg)):
+            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())        
+        return loss
+
+
+def define_G(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
+    netG = None
+    norm_layer = get_norm_layer(norm_type=norm)
+
+    if which_model_netG == 'resnet_9blocks':
+        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
+    elif which_model_netG == 'resnet_6blocks':
+        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
+    elif which_model_netG == 'unet_128':
+        netG = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    elif which_model_netG == 'unet_256':
+        netG = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
+    else:
+        raise NotImplementedError('Generator model name [%s] is not recognized' % which_model_netG)
+    return init_net(netG, init_type, init_gain, gpu_ids)
+
+
+def define_D(input_nc, ndf, which_model_netD,
+             n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
+    netD = None
+    norm_layer = get_norm_layer(norm_type=norm)
+
+    if which_model_netD == 'basic':
+        netD = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid)
+    elif which_model_netD == 'n_layers':
+        netD = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid)
+    elif which_model_netD == 'pixel':
+        netD = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid)
+    else:
+        raise NotImplementedError('Discriminator model name [%s] is not recognized' %
+                                  which_model_netD)
+    return init_net(netD, init_type, init_gain, gpu_ids)
+
+
+##############################################################################
+# Classes
+##############################################################################
+
+
+# Defines the GAN loss which uses either LSGAN or the regular GAN.
+# When LSGAN is used, it is basically same as MSELoss,
+# but it abstracts away the need to create the target label tensor
+# that has the same size as the input
+class GANLoss(nn.Module):
+    def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0):
+        super(GANLoss, self).__init__()
+        self.register_buffer('real_label', torch.tensor(target_real_label))
+        self.register_buffer('fake_label', torch.tensor(target_fake_label))
+        if use_lsgan:
+            self.loss = nn.MSELoss()
+        else:
+            self.loss = nn.BCELoss()
+
+    def get_target_tensor(self, input, target_is_real):
+        if target_is_real:
+            target_tensor = self.real_label
+        else:
+            target_tensor = self.fake_label
+        return target_tensor.expand_as(input)
+
+    def __call__(self, input, target_is_real):
+        target_tensor = self.get_target_tensor(input, target_is_real)
+        return self.loss(input, target_tensor)
+
+
+# Defines the generator that consists of Resnet blocks between a few
+# downsampling/upsampling operations.
+# Code and idea originally from Justin Johnson's architecture.
+# https://github.com/jcjohnson/fast-neural-style/
+class ResnetGenerator(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
+        assert(n_blocks >= 0)
+        super(ResnetGenerator, self).__init__()
+        self.input_nc = input_nc
+        self.output_nc = output_nc
+        self.ngf = ngf
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+
+        model = [nn.ReflectionPad2d(3),
+                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0,
+                           bias=use_bias),
+                 norm_layer(ngf),
+                 nn.ReLU(True)]
+
+        n_downsampling = 2
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,
+                                stride=2, padding=1, bias=use_bias),
+                      norm_layer(ngf * mult * 2),
+                      nn.ReLU(True)]
+
+        mult = 2**n_downsampling
+        for i in range(n_blocks):
+            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
+
+        for i in range(n_downsampling):
+            mult = 2**(n_downsampling - i)
+            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
+                                         kernel_size=3, stride=2,
+                                         padding=1, output_padding=1,
+                                         bias=use_bias),
+                      norm_layer(int(ngf * mult / 2)),
+                      nn.ReLU(True)]
+        model += [nn.ReflectionPad2d(3)]
+        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
+        model += [nn.Tanh()]
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, input):
+        return self.model(input)
+
+
+# Define a resnet block
+class ResnetBlock(nn.Module):
+    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
+        super(ResnetBlock, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
+
+    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
+        conv_block = []
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
+                       norm_layer(dim),
+                       nn.ReLU(True)]
+        if use_dropout:
+            conv_block += [nn.Dropout(0.5)]
+
+        p = 0
+        if padding_type == 'reflect':
+            conv_block += [nn.ReflectionPad2d(1)]
+        elif padding_type == 'replicate':
+            conv_block += [nn.ReplicationPad2d(1)]
+        elif padding_type == 'zero':
+            p = 1
+        else:
+            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
+        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
+                       norm_layer(dim)]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        out = x + self.conv_block(x)
+        return out
+
+
+# Defines the Unet generator.
+# |num_downs|: number of downsamplings in UNet. For example,
+# if |num_downs| == 7, image of size 128x128 will become of size 1x1
+# at the bottleneck
+class UnetGenerator(nn.Module):
+    def __init__(self, input_nc, output_nc, num_downs, ngf=64,
+                 norm_layer=nn.BatchNorm2d, use_dropout=False):
+        super(UnetGenerator, self).__init__()
+
+        # construct unet structure
+        unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)
+        for i in range(num_downs - 5):
+            unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
+        unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)
+
+        self.model = unet_block
+
+    def forward(self, input):
+        return self.model(input)
+
+
+# Defines the submodule with skip connection.
+# X -------------------identity---------------------- X
+#   |-- downsampling -- |submodule| -- upsampling --|
+class UnetSkipConnectionBlock(nn.Module):
+    def __init__(self, outer_nc, inner_nc, input_nc=None,
+                 submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
+        super(UnetSkipConnectionBlock, self).__init__()
+        self.outermost = outermost
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+        if input_nc is None:
+            input_nc = outer_nc
+        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
+                             stride=2, padding=1, bias=use_bias)
+        downrelu = nn.LeakyReLU(0.2, True)
+        downnorm = norm_layer(inner_nc)
+        uprelu = nn.ReLU(True)
+        upnorm = norm_layer(outer_nc)
+
+        if outermost:
+            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1)
+            down = [downconv]
+            up = [uprelu, upconv, nn.Tanh()]
+            model = down + [submodule] + up
+        elif innermost:
+            upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias)
+            down = [downrelu, downconv]
+            up = [uprelu, upconv, upnorm]
+            model = down + up
+        else:
+            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=use_bias)
+            down = [downrelu, downconv, downnorm]
+            up = [uprelu, upconv, upnorm]
+
+            if use_dropout:
+                model = down + [submodule] + up + [nn.Dropout(0.5)]
+            else:
+                model = down + [submodule] + up
+
+        self.model = nn.Sequential(*model)
+
+    def forward(self, x):
+        if self.outermost:
+            return self.model(x)
+        else:
+            return torch.cat([x, self.model(x)], 1)
+
+
+# Defines the PatchGAN discriminator with the specified arguments.
+class NLayerDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(NLayerDiscriminator, self).__init__()
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+
+        kw = 4
+        padw = 1
+        sequence = [
+            nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
+            nn.LeakyReLU(0.2, True)
+        ]
+
+        nf_mult = 1
+        nf_mult_prev = 1
+        for n in range(1, n_layers):
+            nf_mult_prev = nf_mult
+            nf_mult = min(2**n, 8)
+            sequence += [
+                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,
+                          kernel_size=kw, stride=2, padding=padw, bias=use_bias),
+                norm_layer(ndf * nf_mult),
+                nn.LeakyReLU(0.2, True)
+            ]
+
+        nf_mult_prev = nf_mult
+        nf_mult = min(2**n_layers, 8)
+        sequence += [
+            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,
+                      kernel_size=kw, stride=1, padding=padw, bias=use_bias),
+            norm_layer(ndf * nf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+
+        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]
+
+        if use_sigmoid:
+            sequence += [nn.Sigmoid()]
+
+        self.model = nn.Sequential(*sequence)
+
+    def forward(self, input):
+        return self.model(input)
+
+
+class PixelDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
+        super(PixelDiscriminator, self).__init__()
+        if type(norm_layer) == functools.partial:
+            use_bias = norm_layer.func == nn.InstanceNorm2d
+        else:
+            use_bias = norm_layer == nn.InstanceNorm2d
+
+        self.net = [
+            nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
+            norm_layer(ndf * 2),
+            nn.LeakyReLU(0.2, True),
+            nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]
+
+        if use_sigmoid:
+            self.net.append(nn.Sigmoid())
+
+        self.net = nn.Sequential(*self.net)
+
+    def forward(self, input):
+        return self.net(input)
+
+from torchvision import models
+class Vgg19(torch.nn.Module):
+    def __init__(self, requires_grad=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(21, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)        
+        h_relu3 = self.slice3(h_relu2)        
+        h_relu4 = self.slice4(h_relu3)        
+        h_relu5 = self.slice5(h_relu4)                
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
\ No newline at end of file
diff --git a/models/test_model.py b/models/test_model.py
new file mode 100755
index 0000000..4d73f70
--- /dev/null
+++ b/models/test_model.py
@@ -0,0 +1,46 @@
+from .base_model import BaseModel
+from . import networks
+from .cycle_gan_model import CycleGANModel
+
+
+class TestModel(BaseModel):
+    def name(self):
+        return 'TestModel'
+
+    @staticmethod
+    def modify_commandline_options(parser, is_train=True):
+        assert not is_train, 'TestModel cannot be used in train mode'
+        parser = CycleGANModel.modify_commandline_options(parser, is_train=False)
+        parser.set_defaults(dataset_mode='single')
+
+        parser.add_argument('--model_suffix', type=str, default='',
+                            help='In checkpoints_dir, [which_epoch]_net_G[model_suffix].pth will'
+                            ' be loaded as the generator of TestModel')
+
+        return parser
+
+    def initialize(self, opt):
+        assert(not opt.isTrain)
+        BaseModel.initialize(self, opt)
+
+        # specify the training losses you want to print out. The program will call base_model.get_current_losses
+        self.loss_names = []
+        # specify the images you want to save/display. The program will call base_model.get_current_visuals
+        self.visual_names = ['real_A', 'fake_B']
+        # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks
+        self.model_names = ['G' + opt.model_suffix]
+
+        self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.which_model_netG,
+                                      opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
+
+        # assigns the model to self.netG_[suffix] so that it can be loaded
+        # please see BaseModel.load_networks
+        setattr(self, 'netG' + opt.model_suffix, self.netG)
+
+    def set_input(self, input):
+        # we need to use single_dataset mode
+        self.real_A = input['A'].to(self.device)
+        self.image_paths = input['A_paths']
+
+    def forward(self):
+        self.fake_B = self.netG(self.real_A)
\ No newline at end of file
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diff --git a/options/base_options.py b/options/base_options.py
new file mode 100755
index 0000000..3e8fc64
--- /dev/null
+++ b/options/base_options.py
@@ -0,0 +1,119 @@
+import argparse
+import os
+from util import util
+import torch
+import models
+import data
+
+
+class BaseOptions():
+    def __init__(self):
+        self.initialized = False
+
+    def initialize(self, parser):
+        parser.add_argument('--dataroot', type=str, default='/home/csdept/projects/pytorch-CycleGAN-and-pix2pix_sg2/datasets/dayton', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
+        parser.add_argument('--batchSize', type=int, default=4, help='input batch size')
+        parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
+        parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
+        parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
+        parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
+        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
+        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
+        parser.add_argument('--which_model_netD', type=str, default='basic', help='selects model to use for netD')
+        parser.add_argument('--which_model_netG', type=str, default='resnet_9blocks', help='selects model to use for netG')
+        parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
+        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        parser.add_argument('--name', type=str, default='ego2top_x_sg2_1', help='name of the experiment. It decides where to store samples and models')
+        parser.add_argument('--dataset_mode', type=str, default='aligned', help='chooses how datasets are loaded. [unaligned | aligned | single]')
+        parser.add_argument('--model', type=str, default='pix2pix',
+                            help='chooses which model to use. cycle_gan, pix2pix, test')
+        parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA')
+        parser.add_argument('--nThreads', default=4, type=int, help='# threads for loading data')
+        parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
+        parser.add_argument('--norm', type=str, default='batch', help='instance normalization or batch normalization')
+        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
+        parser.add_argument('--display_winsize', type=int, default=256, help='display window size')
+        parser.add_argument('--display_id', type=int, default=1, help='window id of the web display')
+        parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display')
+        parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")')
+        parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')
+        parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
+        parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
+        parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
+        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
+        parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]')
+        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
+        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
+        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{which_model_netG}_size{loadSize}')
+        self.initialized = True
+        return parser
+
+    def gather_options(self):
+        # initialize parser with basic options
+        if not self.initialized:
+            parser = argparse.ArgumentParser(
+                formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+            parser = self.initialize(parser)
+
+        # get the basic options
+        opt, _ = parser.parse_known_args()
+
+        # modify model-related parser options
+        model_name = opt.model
+        model_option_setter = models.get_option_setter(model_name)
+        parser = model_option_setter(parser, self.isTrain)
+        opt, _ = parser.parse_known_args()  # parse again with the new defaults
+
+        # modify dataset-related parser options
+        dataset_name = opt.dataset_mode
+        dataset_option_setter = data.get_option_setter(dataset_name)
+        parser = dataset_option_setter(parser, self.isTrain)
+
+        self.parser = parser
+
+        return parser.parse_args()
+
+    def print_options(self, opt):
+        message = ''
+        message += '----------------- Options ---------------\n'
+        for k, v in sorted(vars(opt).items()):
+            comment = ''
+            default = self.parser.get_default(k)
+            if v != default:
+                comment = '\t[default: %s]' % str(default)
+            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
+        message += '----------------- End -------------------'
+        print(message)
+
+        # save to the disk
+        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
+        util.mkdirs(expr_dir)
+        file_name = os.path.join(expr_dir, 'opt.txt')
+        with open(file_name, 'wt') as opt_file:
+            opt_file.write(message)
+            opt_file.write('\n')
+
+    def parse(self):
+
+        opt = self.gather_options()
+        opt.isTrain = self.isTrain   # train or test
+
+        # process opt.suffix
+        if opt.suffix:
+            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
+            opt.name = opt.name + suffix
+
+        self.print_options(opt)
+
+        # set gpu ids
+        str_ids = opt.gpu_ids.split(',')
+        opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                opt.gpu_ids.append(id)
+        if len(opt.gpu_ids) > 0:
+            torch.cuda.set_device(opt.gpu_ids[0])
+
+        self.opt = opt
+        return self.opt
diff --git a/options/test_options.py b/options/test_options.py
new file mode 100755
index 0000000..e114ec7
--- /dev/null
+++ b/options/test_options.py
@@ -0,0 +1,21 @@
+from .base_options import BaseOptions
+
+
+class TestOptions(BaseOptions):
+    def initialize(self, parser):
+        parser = BaseOptions.initialize(self, parser)
+        parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
+        parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
+        parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        #  Dropout and Batchnorm has different behavioir during training and test.
+        parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
+        parser.add_argument('--how_many', type=int, default=10000000, help='how many test images to run')
+
+        parser.set_defaults(model='pix2pix')
+        # To avoid cropping, the loadSize should be the same as fineSize
+        parser.set_defaults(loadSize=parser.get_default('fineSize'))
+        
+        self.isTrain = False
+        return parser
diff --git a/options/train_options.py b/options/train_options.py
new file mode 100755
index 0000000..fdfb4d6
--- /dev/null
+++ b/options/train_options.py
@@ -0,0 +1,28 @@
+from .base_options import BaseOptions
+
+
+class TrainOptions(BaseOptions):
+    def initialize(self, parser):
+        parser = BaseOptions.initialize(self, parser)
+        parser.add_argument('--display_freq', type=int, default=10, help='frequency of showing training results on screen')
+        parser.add_argument('--display_ncols', type=int, default=5, help='if positive, display all images in a single visdom web panel with certain number of images per row.')
+        parser.add_argument('--update_html_freq', type=int, default=100, help='frequency of saving training results to html')
+        parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
+        parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
+        parser.add_argument('--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs')
+        parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
+        parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')
+        parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
+        parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        parser.add_argument('--niter', type=int, default=20, help='# of iter at starting learning rate')
+        parser.add_argument('--niter_decay', type=int, default=15, help='# of iter to linearly decay learning rate to zero')
+        parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
+        parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
+        parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')
+        parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')
+        parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
+        parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau')
+        parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')
+
+        self.isTrain = True
+        return parser
diff --git a/test.py b/test.py
new file mode 100755
index 0000000..3df0965
--- /dev/null
+++ b/test.py
@@ -0,0 +1,41 @@
+import os
+from options.test_options import TestOptions
+from data import CreateDataLoader
+from models import create_model
+from util.visualizer import save_images
+from util import html
+
+
+if __name__ == '__main__':
+    opt = TestOptions().parse()
+    opt.nThreads = 1   # test code only supports nThreads = 1
+    opt.batchSize = 1  # test code only supports batchSize = 1
+    opt.serial_batches = True  # no shuffle
+    opt.no_flip = True  # no flip
+    opt.display_id = -1  # no visdom display
+    data_loader = CreateDataLoader(opt)
+    dataset = data_loader.load_data()
+    model = create_model(opt)
+    model.setup(opt)
+    # create website
+    web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
+    webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
+    # test
+
+    # Set eval mode.
+    # This only affects layers like batch norm and drop out. But we do use batch norm in pix2pix.
+    if opt.eval:
+        model.eval()
+
+    for i, data in enumerate(dataset):
+        if i >= opt.how_many:
+            break
+        model.set_input(data)
+        model.test()
+        visuals = model.get_current_visuals()
+        img_path = model.get_image_paths()
+        if i % 5 == 0:
+            print('processing (%04d)-th image... %s' % (i, img_path))
+        save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
+
+    webpage.save()
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100755
index 0000000..0877a35
--- /dev/null
+++ b/train.py
@@ -0,0 +1,59 @@
+import time
+from options.train_options import TrainOptions
+from data import CreateDataLoader
+from models import create_model
+from util.visualizer import Visualizer
+
+if __name__ == '__main__':
+    opt = TrainOptions().parse()
+    data_loader = CreateDataLoader(opt)
+    dataset = data_loader.load_data()
+    dataset_size = len(data_loader)
+    print('#training images = %d' % dataset_size)
+
+    model = create_model(opt)
+    model.setup(opt)
+    visualizer = Visualizer(opt)
+    total_steps = 0
+
+    for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
+        epoch_start_time = time.time()
+        iter_data_time = time.time()
+        epoch_iter = 0
+
+        for i, data in enumerate(dataset):
+            iter_start_time = time.time()
+            if total_steps % opt.print_freq == 0:
+                t_data = iter_start_time - iter_data_time
+            visualizer.reset()
+            total_steps += opt.batchSize
+            epoch_iter += opt.batchSize
+            model.set_input(data)
+            model.optimize_parameters()
+
+            if total_steps % opt.display_freq == 0:
+                save_result = total_steps % opt.update_html_freq == 0
+                visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
+
+            if total_steps % opt.print_freq == 0:
+                losses = model.get_current_losses()
+                t = (time.time() - iter_start_time) / opt.batchSize
+                visualizer.print_current_losses(epoch, epoch_iter, losses, t, t_data)
+                if opt.display_id > 0:
+                    visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, opt, losses)
+
+            if total_steps % opt.save_latest_freq == 0:
+                print('saving the latest model (epoch %d, total_steps %d)' %
+                      (epoch, total_steps))
+                model.save_networks('latest')
+
+            iter_data_time = time.time()
+        if epoch % opt.save_epoch_freq == 0:
+            print('saving the model at the end of epoch %d, iters %d' %
+                  (epoch, total_steps))
+            model.save_networks('latest')
+            model.save_networks(epoch)
+
+        print('End of epoch %d / %d \t Time Taken: %d sec' %
+              (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
+        model.update_learning_rate()
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diff --git a/util/get_data.py b/util/get_data.py
new file mode 100755
index 0000000..6325605
--- /dev/null
+++ b/util/get_data.py
@@ -0,0 +1,115 @@
+from __future__ import print_function
+import os
+import tarfile
+import requests
+from warnings import warn
+from zipfile import ZipFile
+from bs4 import BeautifulSoup
+from os.path import abspath, isdir, join, basename
+
+
+class GetData(object):
+    """
+
+    Download CycleGAN or Pix2Pix Data.
+
+    Args:
+        technique : str
+            One of: 'cyclegan' or 'pix2pix'.
+        verbose : bool
+            If True, print additional information.
+
+    Examples:
+        >>> from util.get_data import GetData
+        >>> gd = GetData(technique='cyclegan')
+        >>> new_data_path = gd.get(save_path='./datasets')  # options will be displayed.
+
+    """
+
+    def __init__(self, technique='cyclegan', verbose=True):
+        url_dict = {
+            'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets',
+            'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets'
+        }
+        self.url = url_dict.get(technique.lower())
+        self._verbose = verbose
+
+    def _print(self, text):
+        if self._verbose:
+            print(text)
+
+    @staticmethod
+    def _get_options(r):
+        soup = BeautifulSoup(r.text, 'lxml')
+        options = [h.text for h in soup.find_all('a', href=True)
+                   if h.text.endswith(('.zip', 'tar.gz'))]
+        return options
+
+    def _present_options(self):
+        r = requests.get(self.url)
+        options = self._get_options(r)
+        print('Options:\n')
+        for i, o in enumerate(options):
+            print("{0}: {1}".format(i, o))
+        choice = input("\nPlease enter the number of the "
+                       "dataset above you wish to download:")
+        return options[int(choice)]
+
+    def _download_data(self, dataset_url, save_path):
+        if not isdir(save_path):
+            os.makedirs(save_path)
+
+        base = basename(dataset_url)
+        temp_save_path = join(save_path, base)
+
+        with open(temp_save_path, "wb") as f:
+            r = requests.get(dataset_url)
+            f.write(r.content)
+
+        if base.endswith('.tar.gz'):
+            obj = tarfile.open(temp_save_path)
+        elif base.endswith('.zip'):
+            obj = ZipFile(temp_save_path, 'r')
+        else:
+            raise ValueError("Unknown File Type: {0}.".format(base))
+
+        self._print("Unpacking Data...")
+        obj.extractall(save_path)
+        obj.close()
+        os.remove(temp_save_path)
+
+    def get(self, save_path, dataset=None):
+        """
+
+        Download a dataset.
+
+        Args:
+            save_path : str
+                A directory to save the data to.
+            dataset : str, optional
+                A specific dataset to download.
+                Note: this must include the file extension.
+                If None, options will be presented for you
+                to choose from.
+
+        Returns:
+            save_path_full : str
+                The absolute path to the downloaded data.
+
+        """
+        if dataset is None:
+            selected_dataset = self._present_options()
+        else:
+            selected_dataset = dataset
+
+        save_path_full = join(save_path, selected_dataset.split('.')[0])
+
+        if isdir(save_path_full):
+            warn("\n'{0}' already exists. Voiding Download.".format(
+                save_path_full))
+        else:
+            self._print('Downloading Data...')
+            url = "{0}/{1}".format(self.url, selected_dataset)
+            self._download_data(url, save_path=save_path)
+
+        return abspath(save_path_full)
diff --git a/util/html.py b/util/html.py
new file mode 100755
index 0000000..c7956f1
--- /dev/null
+++ b/util/html.py
@@ -0,0 +1,64 @@
+import dominate
+from dominate.tags import *
+import os
+
+
+class HTML:
+    def __init__(self, web_dir, title, reflesh=0):
+        self.title = title
+        self.web_dir = web_dir
+        self.img_dir = os.path.join(self.web_dir, 'images')
+        if not os.path.exists(self.web_dir):
+            os.makedirs(self.web_dir)
+        if not os.path.exists(self.img_dir):
+            os.makedirs(self.img_dir)
+        # print(self.img_dir)
+
+        self.doc = dominate.document(title=title)
+        if reflesh > 0:
+            with self.doc.head:
+                meta(http_equiv="reflesh", content=str(reflesh))
+
+    def get_image_dir(self):
+        return self.img_dir
+
+    def add_header(self, str):
+        with self.doc:
+            h3(str)
+
+    def add_table(self, border=1):
+        self.t = table(border=border, style="table-layout: fixed;")
+        self.doc.add(self.t)
+
+    def add_images(self, ims, txts, links, width=400):
+        self.add_table()
+        with self.t:
+            with tr():
+                for im, txt, link in zip(ims, txts, links):
+                    with td(style="word-wrap: break-word;", halign="center", valign="top"):
+                        with p():
+                            with a(href=os.path.join('images', link)):
+                                img(style="width:%dpx" % width, src=os.path.join('images', im))
+                            br()
+                            p(txt)
+
+    def save(self):
+        html_file = '%s/index.html' % self.web_dir
+        f = open(html_file, 'wt')
+        f.write(self.doc.render())
+        f.close()
+
+
+if __name__ == '__main__':
+    html = HTML('web/', 'test_html')
+    html.add_header('hello world')
+
+    ims = []
+    txts = []
+    links = []
+    for n in range(4):
+        ims.append('image_%d.png' % n)
+        txts.append('text_%d' % n)
+        links.append('image_%d.png' % n)
+    html.add_images(ims, txts, links)
+    html.save()
diff --git a/util/image_pool.py b/util/image_pool.py
new file mode 100755
index 0000000..52413e0
--- /dev/null
+++ b/util/image_pool.py
@@ -0,0 +1,32 @@
+import random
+import torch
+
+
+class ImagePool():
+    def __init__(self, pool_size):
+        self.pool_size = pool_size
+        if self.pool_size > 0:
+            self.num_imgs = 0
+            self.images = []
+
+    def query(self, images):
+        if self.pool_size == 0:
+            return images
+        return_images = []
+        for image in images:
+            image = torch.unsqueeze(image.data, 0)
+            if self.num_imgs < self.pool_size:
+                self.num_imgs = self.num_imgs + 1
+                self.images.append(image)
+                return_images.append(image)
+            else:
+                p = random.uniform(0, 1)
+                if p > 0.5:
+                    random_id = random.randint(0, self.pool_size - 1)  # randint is inclusive
+                    tmp = self.images[random_id].clone()
+                    self.images[random_id] = image
+                    return_images.append(tmp)
+                else:
+                    return_images.append(image)
+        return_images = torch.cat(return_images, 0)
+        return return_images
diff --git a/util/util.py b/util/util.py
new file mode 100755
index 0000000..ba7b083
--- /dev/null
+++ b/util/util.py
@@ -0,0 +1,60 @@
+from __future__ import print_function
+import torch
+import numpy as np
+from PIL import Image
+import os
+
+
+# Converts a Tensor into an image array (numpy)
+# |imtype|: the desired type of the converted numpy array
+def tensor2im(input_image, imtype=np.uint8):
+    if isinstance(input_image, torch.Tensor):
+        image_tensor = input_image.data
+    else:
+        return input_image
+    image_numpy = image_tensor[0].cpu().float().numpy()
+    if image_numpy.shape[0] == 1:
+        image_numpy = np.tile(image_numpy, (3, 1, 1))
+    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
+    return image_numpy.astype(imtype)
+
+
+def diagnose_network(net, name='network'):
+    mean = 0.0
+    count = 0
+    for param in net.parameters():
+        if param.grad is not None:
+            mean += torch.mean(torch.abs(param.grad.data))
+            count += 1
+    if count > 0:
+        mean = mean / count
+    print(name)
+    print(mean)
+
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+
+def print_numpy(x, val=True, shp=False):
+    x = x.astype(np.float64)
+    if shp:
+        print('shape,', x.shape)
+    if val:
+        x = x.flatten()
+        print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
+            np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
+
+
+def mkdirs(paths):
+    if isinstance(paths, list) and not isinstance(paths, str):
+        for path in paths:
+            mkdir(path)
+    else:
+        mkdir(paths)
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
diff --git a/util/visualizer.py b/util/visualizer.py
new file mode 100755
index 0000000..69cf926
--- /dev/null
+++ b/util/visualizer.py
@@ -0,0 +1,163 @@
+import numpy as np
+import os
+import ntpath
+import time
+from . import util
+from . import html
+from scipy.misc import imresize
+
+
+# save image to the disk
+def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
+    image_dir = webpage.get_image_dir()
+    short_path = ntpath.basename(image_path[0])
+    name = os.path.splitext(short_path)[0]
+
+    webpage.add_header(name)
+    ims, txts, links = [], [], []
+
+    for label, im_data in visuals.items():
+        im = util.tensor2im(im_data)
+        image_name = '%s_%s.png' % (name, label)
+        save_path = os.path.join(image_dir, image_name)
+        h, w, _ = im.shape
+        if aspect_ratio > 1.0:
+            im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
+        if aspect_ratio < 1.0:
+            im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
+        util.save_image(im, save_path)
+
+        ims.append(image_name)
+        txts.append(label)
+        links.append(image_name)
+    webpage.add_images(ims, txts, links, width=width)
+
+
+class Visualizer():
+    def __init__(self, opt):
+        self.display_id = opt.display_id
+        self.use_html = opt.isTrain and not opt.no_html
+        self.win_size = opt.display_winsize
+        self.name = opt.name
+        self.opt = opt
+        self.saved = False
+        if self.display_id > 0:
+            import visdom
+            self.ncols = opt.display_ncols
+            self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env, raise_exceptions=True, use_incoming_socket=False)
+
+        if self.use_html:
+            self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
+            self.img_dir = os.path.join(self.web_dir, 'images')
+            print('create web directory %s...' % self.web_dir)
+            util.mkdirs([self.web_dir, self.img_dir])
+        self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
+        with open(self.log_name, "a") as log_file:
+            now = time.strftime("%c")
+            log_file.write('================ Training Loss (%s) ================\n' % now)
+
+    def reset(self):
+        self.saved = False
+
+    def throw_visdom_connection_error(self):
+        print('\n\nCould not connect to Visdom server (https://github.com/facebookresearch/visdom) for displaying training progress.\nYou can suppress connection to Visdom using the option --display_id -1. To install visdom, run \n$ pip install visdom\n, and start the server by \n$ python -m visdom.server.\n\n')
+        exit(1)
+
+    # |visuals|: dictionary of images to display or save
+    def display_current_results(self, visuals, epoch, save_result):
+        if self.display_id > 0:  # show images in the browser
+            ncols = self.ncols
+            if ncols > 0:
+                ncols = min(ncols, len(visuals))
+                h, w = next(iter(visuals.values())).shape[:2]
+                table_css = """<style>
+                        table {border-collapse: separate; border-spacing:4px; white-space:nowrap; text-align:center}
+                        table td {width: %dpx; height: %dpx; padding: 4px; outline: 4px solid black}
+                        </style>""" % (w, h)
+                title = self.name
+                label_html = ''
+                label_html_row = ''
+                images = []
+                idx = 0
+                for label, image in visuals.items():
+                    image_numpy = util.tensor2im(image)
+                    label_html_row += '<td>%s</td>' % label
+                    images.append(image_numpy.transpose([2, 0, 1]))
+                    idx += 1
+                    if idx % ncols == 0:
+                        label_html += '<tr>%s</tr>' % label_html_row
+                        label_html_row = ''
+                white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
+                while idx % ncols != 0:
+                    images.append(white_image)
+                    label_html_row += '<td></td>'
+                    idx += 1
+                if label_html_row != '':
+                    label_html += '<tr>%s</tr>' % label_html_row
+                # pane col = image row
+                try:
+                    self.vis.images(images, nrow=ncols, win=self.display_id + 1,
+                                    padding=2, opts=dict(title=title + ' images'))
+                    label_html = '<table>%s</table>' % label_html
+                    self.vis.text(table_css + label_html, win=self.display_id + 2,
+                                  opts=dict(title=title + ' labels'))
+                except ConnectionError:
+                    self.throw_visdom_connection_error()
+
+            else:
+                idx = 1
+                for label, image in visuals.items():
+                    image_numpy = util.tensor2im(image)
+                    self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label),
+                                   win=self.display_id + idx)
+                    idx += 1
+
+        if self.use_html and (save_result or not self.saved):  # save images to a html file
+            self.saved = True
+            for label, image in visuals.items():
+                image_numpy = util.tensor2im(image)
+                img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
+                util.save_image(image_numpy, img_path)
+            # update website
+            webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1)
+            for n in range(epoch, 0, -1):
+                webpage.add_header('epoch [%d]' % n)
+                ims, txts, links = [], [], []
+
+                for label, image_numpy in visuals.items():
+                    image_numpy = util.tensor2im(image)
+                    img_path = 'epoch%.3d_%s.png' % (n, label)
+                    ims.append(img_path)
+                    txts.append(label)
+                    links.append(img_path)
+                webpage.add_images(ims, txts, links, width=self.win_size)
+            webpage.save()
+
+    # losses: dictionary of error labels and values
+    def plot_current_losses(self, epoch, counter_ratio, opt, losses):
+        if not hasattr(self, 'plot_data'):
+            self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())}
+        self.plot_data['X'].append(epoch + counter_ratio)
+        self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
+        try:
+            self.vis.line(
+                X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
+                Y=np.array(self.plot_data['Y']),
+                opts={
+                    'title': self.name + ' loss over time',
+                    'legend': self.plot_data['legend'],
+                    'xlabel': 'epoch',
+                    'ylabel': 'loss'},
+                win=self.display_id)
+        except ConnectionError:
+            self.throw_visdom_connection_error()
+
+    # losses: same format as |losses| of plot_current_losses
+    def print_current_losses(self, epoch, i, losses, t, t_data):
+        message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, i, t, t_data)
+        for k, v in losses.items():
+            message += '%s: %.3f ' % (k, v)
+
+        print(message)
+        with open(self.log_name, "a") as log_file:
+            log_file.write('%s\n' % message)