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diff --git a/models/__init__.py b/models/__init__.py
new file mode 100755
index 0000000..e69de29
--- /dev/null
+++ b/models/__init__.py
diff --git a/models/base_model.py b/models/base_model.py
new file mode 100755
index 0000000..d3879d0
--- /dev/null
+++ b/models/base_model.py
@@ -0,0 +1,86 @@
+### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
+### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+import os
+import torch
+
+class BaseModel(torch.nn.Module):
+    def name(self):
+        return 'BaseModel'
+
+    def initialize(self, opt):
+        self.opt = opt
+        self.gpu_ids = opt.gpu_ids
+        self.isTrain = opt.isTrain
+        self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor
+        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
+
+    def set_input(self, input):
+        self.input = input
+
+    def forward(self):
+        pass
+
+    # used in test time, no backprop
+    def test(self):
+        pass
+
+    def get_image_paths(self):
+        pass
+
+    def optimize_parameters(self):
+        pass
+
+    def get_current_visuals(self):
+        return self.input
+
+    def get_current_errors(self):
+        return {}
+
+    def save(self, label):
+        pass
+
+    # helper saving function that can be used by subclasses
+    def save_network(self, network, network_label, epoch_label, gpu_ids):
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        save_path = os.path.join(self.save_dir, save_filename)
+        torch.save(network.cpu().state_dict(), save_path)
+        if len(gpu_ids) and torch.cuda.is_available():
+            network.cuda()
+
+    # helper loading function that can be used by subclasses
+    def load_network(self, network, network_label, epoch_label, save_dir=''):        
+        save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
+        if not save_dir:
+            save_dir = self.save_dir
+        save_path = os.path.join(save_dir, save_filename)        
+        if not os.path.isfile(save_path):
+            print('%s not exists yet!' % save_path)
+            if network_label == 'G':
+                raise('Generator must exist!')
+        else:
+            #network.load_state_dict(torch.load(save_path))
+            try:
+                network.load_state_dict(torch.load(save_path))
+            except:   
+                pretrained_dict = torch.load(save_path)                
+                model_dict = network.state_dict()
+                try:
+                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}                    
+                    network.load_state_dict(pretrained_dict)
+                    print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
+                except:
+                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
+                    from sets import Set
+                    not_initialized = Set()
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    for k, v in model_dict.items():
+                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
+                            not_initialized.add(k.split('.')[0])                            
+                    print(sorted(not_initialized))
+                    network.load_state_dict(model_dict)                  
+
+    def update_learning_rate():
+        pass
diff --git a/models/models.py b/models/models.py
new file mode 100755
index 0000000..351483c
--- /dev/null
+++ b/models/models.py
@@ -0,0 +1,14 @@
+### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
+### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+import torch
+
+def create_model(opt):
+    from .pix2pixHD_model import Pix2PixHDModel
+    model = Pix2PixHDModel()    
+    model.initialize(opt)
+    print("model [%s] was created" % (model.name()))
+
+    if opt.isTrain and len(opt.gpu_ids):
+        model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
+
+    return model
diff --git a/models/networks.py b/models/networks.py
new file mode 100755
index 0000000..a673a56
--- /dev/null
+++ b/models/networks.py
@@ -0,0 +1,421 @@
+### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
+### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+import torch
+import torch.nn as nn
+from torch.nn import init
+import functools
+from torch.autograd import Variable
+import numpy as np
+import math
+import torch.nn.functional as F
+import copy
+
+###############################################################################
+# Functions
+###############################################################################
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv') != -1:
+        m.weight.data.normal_(0.0, 0.02)
+    elif classname.find('BatchNorm2d') != -1:
+        m.weight.data.normal_(1.0, 0.02)
+        m.bias.data.fill_(0)
+
+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)
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return norm_layer
+
+def define_G(input_nc, output_nc, ngf, netG, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, 
+             n_blocks_local=3, norm='instance', gpu_ids=[]):    
+    norm_layer = get_norm_layer(norm_type=norm)     
+    if netG == 'global':    
+        netG = GlobalGenerator(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, norm_layer)       
+    elif netG == 'local':        
+        netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global, n_blocks_global, 
+                                  n_local_enhancers, n_blocks_local, norm_layer)
+    elif netG == 'encoder':
+        netG = Encoder(input_nc, output_nc, ngf, n_downsample_global, norm_layer)
+    else:
+        raise('generator not implemented!')
+    print(netG)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())   
+        netG.cuda(device_id=gpu_ids[0])
+    netG.apply(weights_init)
+    return netG
+
+def define_D(input_nc, ndf, n_layers_D, norm='instance', use_sigmoid=False, num_D=1, getIntermFeat=False, gpu_ids=[]):        
+    norm_layer = get_norm_layer(norm_type=norm)   
+    netD = MultiscaleDiscriminator(input_nc, ndf, n_layers_D, norm_layer, use_sigmoid, num_D, getIntermFeat)   
+    print(netD)
+    if len(gpu_ids) > 0:
+        assert(torch.cuda.is_available())
+        netD.cuda(device_id=gpu_ids[0])
+    netD.apply(weights_init)
+    return netD
+
+def print_network(net):
+    if isinstance(net, list):
+        net = net[0]
+    num_params = 0
+    for param in net.parameters():
+        num_params += param.numel()
+    print(net)
+    print('Total number of parameters: %d' % num_params)
+
+##############################################################################
+# Losses
+##############################################################################
+class GANLoss(nn.Module):
+    def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0,
+                 tensor=torch.FloatTensor):
+        super(GANLoss, self).__init__()
+        self.real_label = target_real_label
+        self.fake_label = target_fake_label
+        self.real_label_var = None
+        self.fake_label_var = None
+        self.Tensor = tensor
+        if use_lsgan:
+            self.loss = nn.MSELoss()
+        else:
+            self.loss = nn.BCELoss()
+
+    def get_target_tensor(self, input, target_is_real):
+        target_tensor = None
+        if target_is_real:
+            create_label = ((self.real_label_var is None) or
+                            (self.real_label_var.numel() != input.numel()))
+            if create_label:
+                real_tensor = self.Tensor(input.size()).fill_(self.real_label)
+                self.real_label_var = Variable(real_tensor, requires_grad=False)
+            target_tensor = self.real_label_var
+        else:
+            create_label = ((self.fake_label_var is None) or
+                            (self.fake_label_var.numel() != input.numel()))
+            if create_label:
+                fake_tensor = self.Tensor(input.size()).fill_(self.fake_label)
+                self.fake_label_var = Variable(fake_tensor, requires_grad=False)
+            target_tensor = self.fake_label_var
+        return target_tensor
+
+    def __call__(self, input, target_is_real):
+        if isinstance(input[0], list):
+            loss = 0
+            for input_i in input:
+                pred = input_i[-1]
+                target_tensor = self.get_target_tensor(pred, target_is_real)
+                loss += self.loss(pred, target_tensor)
+            return loss
+        else:            
+            target_tensor = self.get_target_tensor(input[-1], target_is_real)
+            return self.loss(input[-1], target_tensor)
+
+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
+
+##############################################################################
+# Generator
+##############################################################################
+class LocalEnhancer(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsample_global=3, n_blocks_global=9, 
+                 n_local_enhancers=1, n_blocks_local=3, norm_layer=nn.BatchNorm2d, padding_type='reflect'):        
+        super(LocalEnhancer, self).__init__()
+        self.n_local_enhancers = n_local_enhancers
+        
+        ###### global generator model #####           
+        ngf_global = ngf * (2**n_local_enhancers)
+        model_global = GlobalGenerator(input_nc, output_nc, ngf_global, n_downsample_global, n_blocks_global, norm_layer).model        
+        model_global = [model_global[i] for i in range(len(model_global)-3)] # get rid of final convolution layers        
+        self.model = nn.Sequential(*model_global)                
+
+        ###### local enhancer layers #####
+        for n in range(1, n_local_enhancers+1):
+            ### downsample            
+            ngf_global = ngf * (2**(n_local_enhancers-n))
+            model_downsample = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0), 
+                                norm_layer(ngf_global), nn.ReLU(True),
+                                nn.Conv2d(ngf_global, ngf_global * 2, kernel_size=3, stride=2, padding=1), 
+                                norm_layer(ngf_global * 2), nn.ReLU(True)]
+            ### residual blocks
+            model_upsample = []
+            for i in range(n_blocks_local):
+                model_upsample += [ResnetBlock(ngf_global * 2, padding_type=padding_type, norm_layer=norm_layer)]
+
+            ### upsample
+            model_upsample += [nn.ConvTranspose2d(ngf_global * 2, ngf_global, kernel_size=3, stride=2, padding=1, output_padding=1), 
+                               norm_layer(ngf_global), nn.ReLU(True)]      
+
+            ### final convolution
+            if n == n_local_enhancers:                
+                model_upsample += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]                       
+            
+            setattr(self, 'model'+str(n)+'_1', nn.Sequential(*model_downsample))
+            setattr(self, 'model'+str(n)+'_2', nn.Sequential(*model_upsample))                  
+        
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def forward(self, input): 
+        ### create input pyramid
+        input_downsampled = [input]
+        for i in range(self.n_local_enhancers):
+            input_downsampled.append(self.downsample(input_downsampled[-1]))
+
+        ### output at coarest level
+        output_prev = self.model(input_downsampled[-1])        
+        ### build up one layer at a time
+        for n_local_enhancers in range(1, self.n_local_enhancers+1):
+            model_downsample = getattr(self, 'model'+str(n_local_enhancers)+'_1')
+            model_upsample = getattr(self, 'model'+str(n_local_enhancers)+'_2')            
+            input_i = input_downsampled[self.n_local_enhancers-n_local_enhancers]            
+            output_prev = model_upsample(model_downsample(input_i) + output_prev)
+        return output_prev
+
+class GlobalGenerator(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, 
+                 padding_type='reflect'):
+        assert(n_blocks >= 0)
+        super(GlobalGenerator, self).__init__()        
+        activation = nn.ReLU(True)        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), norm_layer(ngf), activation]
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), activation]
+
+        ### resnet blocks
+        mult = 2**n_downsampling
+        for i in range(n_blocks):
+            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer)]
+        
+        ### upsample         
+        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),
+                       norm_layer(int(ngf * mult / 2)), activation]
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), 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, activation=nn.ReLU(True), use_dropout=False):
+        super(ResnetBlock, self).__init__()
+        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout)
+
+    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout):
+        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),
+                       norm_layer(dim),
+                       activation]
+        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),
+                       norm_layer(dim)]
+
+        return nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        out = x + self.conv_block(x)
+        return out
+
+class Encoder(nn.Module):
+    def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d):
+        super(Encoder, self).__init__()        
+        self.output_nc = output_nc        
+
+        model = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), 
+                 norm_layer(ngf), nn.ReLU(True)]             
+        ### downsample
+        for i in range(n_downsampling):
+            mult = 2**i
+            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
+                      norm_layer(ngf * mult * 2), nn.ReLU(True)]
+
+        ### upsample         
+        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),
+                       norm_layer(int(ngf * mult / 2)), nn.ReLU(True)]        
+
+        model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]
+        self.model = nn.Sequential(*model) 
+
+    def forward(self, input, inst):
+        outputs = self.model(input)
+
+        # instance-wise average pooling
+        outputs_mean = outputs.clone()        
+        inst_list = np.unique(inst.cpu().numpy().astype(int))        
+        for i in inst_list:
+            indices = (inst == i).nonzero() # n x 4            
+            for j in range(self.output_nc):
+                output_ins = outputs[indices[:,0], indices[:,1] + j, indices[:,2], indices[:,3]]                    
+                mean_feat = torch.mean(output_ins).expand_as(output_ins)                                        
+                outputs_mean[indices[:,0], indices[:,1] + j, indices[:,2], indices[:,3]] = mean_feat                        
+        return outputs_mean
+
+class MultiscaleDiscriminator(nn.Module):
+    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, 
+                 use_sigmoid=False, num_D=3, getIntermFeat=False):
+        super(MultiscaleDiscriminator, self).__init__()
+        self.num_D = num_D
+        self.n_layers = n_layers
+        self.getIntermFeat = getIntermFeat
+     
+        for i in range(num_D):
+            netD = NLayerDiscriminator(input_nc, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat)
+            if getIntermFeat:                                
+                for j in range(n_layers+2):
+                    setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j)))                                   
+            else:
+                setattr(self, 'layer'+str(i), netD.model)
+
+        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
+
+    def singleD_forward(self, model, input):
+        if self.getIntermFeat:
+            result = [input]
+            for i in range(len(model)):
+                result.append(model[i](result[-1]))
+            return result[1:]
+        else:
+            return [model(input)]
+
+    def forward(self, input):        
+        num_D = self.num_D
+        result = []
+        input_downsampled = input
+        for i in range(num_D):
+            if self.getIntermFeat:
+                model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)]
+            else:
+                model = getattr(self, 'layer'+str(num_D-1-i))
+            result.append(self.singleD_forward(model, input_downsampled))
+            if i != (num_D-1):
+                input_downsampled = self.downsample(input_downsampled)
+        return result
+        
+# 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, getIntermFeat=False):
+        super(NLayerDiscriminator, self).__init__()
+        self.getIntermFeat = getIntermFeat
+        self.n_layers = n_layers
+
+        kw = 4
+        padw = int(np.ceil((kw-1.0)/2))
+        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]]
+
+        nf = ndf
+        for n in range(1, n_layers):
+            nf_prev = nf
+            nf = min(nf * 2, 512)
+            sequence += [[
+                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
+                norm_layer(nf), nn.LeakyReLU(0.2, True)
+            ]]
+
+        nf_prev = nf
+        nf = min(nf * 2, 512)
+        sequence += [[
+            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
+            norm_layer(nf),
+            nn.LeakyReLU(0.2, True)
+        ]]
+
+        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
+
+        if use_sigmoid:
+            sequence += [nn.Sigmoid()]
+
+        if getIntermFeat:
+            for n in range(len(sequence)):
+                setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
+        else:
+            sequence_stream = []
+            for n in range(len(sequence)):
+                sequence_stream += sequence[n]
+            self.model = nn.Sequential(*sequence_stream)
+
+    def forward(self, input):
+        if self.getIntermFeat:
+            res = [input]
+            for n in range(self.n_layers+2):
+                model = getattr(self, 'model'+str(n))
+                res.append(model(res[-1]))
+            return res[1:]
+        else:
+            return self.model(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
diff --git a/models/pix2pixHD_model.py b/models/pix2pixHD_model.py
new file mode 100755
index 0000000..ba44e53
--- /dev/null
+++ b/models/pix2pixHD_model.py
@@ -0,0 +1,260 @@
+### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
+### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+import numpy as np
+import torch
+import os
+from collections import OrderedDict
+from torch.autograd import Variable
+import util.util as util
+from util.image_pool import ImagePool
+from .base_model import BaseModel
+from . import networks
+
+class Pix2PixHDModel(BaseModel):
+    def name(self):
+        return 'Pix2PixHDModel'
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none': # when training at full res this causes OOM
+            torch.backends.cudnn.benchmark = True
+        self.isTrain = opt.isTrain
+        self.use_features = opt.instance_feat or opt.label_feat
+        self.gen_features = self.use_features and not self.opt.load_features
+
+        ##### define networks        
+        # Generator network
+        netG_input_nc = opt.label_nc
+        if not opt.no_instance:
+            netG_input_nc += 1
+        if self.use_features:
+            netG_input_nc += opt.feat_num                  
+        self.netG = networks.define_G(netG_input_nc, opt.output_nc, opt.ngf, opt.netG, 
+                                      opt.n_downsample_global, opt.n_blocks_global, opt.n_local_enhancers, 
+                                      opt.n_blocks_local, opt.norm, gpu_ids=self.gpu_ids)        
+
+        # Discriminator network
+        if self.isTrain:
+            use_sigmoid = opt.no_lsgan
+            netD_input_nc = opt.label_nc + opt.output_nc
+            if not opt.no_instance:
+                netD_input_nc += 1
+            self.netD = networks.define_D(netD_input_nc, opt.ndf, opt.n_layers_D, opt.norm, use_sigmoid, 
+                                          opt.num_D, not opt.no_ganFeat_loss, gpu_ids=self.gpu_ids)
+
+        ### Encoder network
+        if self.gen_features:          
+            self.netE = networks.define_G(opt.output_nc, opt.feat_num, opt.nef, 'encoder', 
+                                          opt.n_downsample_E, norm=opt.norm, gpu_ids=self.gpu_ids)  
+            
+        print('---------- Networks initialized -------------')
+
+        # load networks
+        if not self.isTrain or opt.continue_train or opt.load_pretrain:
+            pretrained_path = '' if not self.isTrain else opt.load_pretrain
+            self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)            
+            if self.isTrain:
+                self.load_network(self.netD, 'D', opt.which_epoch, pretrained_path)  
+            if self.gen_features:
+                self.load_network(self.netE, 'E', opt.which_epoch, pretrained_path)              
+
+        # set loss functions and optimizers
+        if self.isTrain:
+            if opt.pool_size > 0 and (len(self.gpu_ids)) > 1:
+                raise NotImplementedError("Fake Pool Not Implemented for MultiGPU")
+            self.fake_pool = ImagePool(opt.pool_size)
+            self.old_lr = opt.lr
+
+            # define loss functions
+            self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)   
+            self.criterionFeat = torch.nn.L1Loss()
+            if not opt.no_vgg_loss:             
+                self.criterionVGG = networks.VGGLoss(self.gpu_ids)
+        
+            # Names so we can breakout loss
+            self.loss_names = ['G_GAN', 'G_GAN_Feat', 'G_VGG', 'D_real', 'D_fake']
+
+            # initialize optimizers
+            # optimizer G
+            if opt.niter_fix_global > 0:
+                print('------------- Only training the local enhancer network (for %d epochs) ------------' % opt.niter_fix_global)
+                params_dict = dict(self.netG.named_parameters())
+                params = []
+                for key, value in params_dict.items():       
+                    if key.startswith('model' + str(opt.n_local_enhancers)):
+                        params += [{'params':[value],'lr':opt.lr}]
+                    else:
+                        params += [{'params':[value],'lr':0.0}]                            
+            else:
+                params = list(self.netG.parameters())
+            if self.gen_features:              
+                params += list(self.netE.parameters())         
+            self.optimizer_G = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))                            
+
+            # optimizer D                        
+            params = list(self.netD.parameters())    
+            self.optimizer_D = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
+
+    def encode_input(self, label_map, inst_map=None, real_image=None, feat_map=None, infer=False):     
+        # create one-hot vector for label map 
+        size = label_map.size()
+        oneHot_size = (size[0], self.opt.label_nc, size[2], size[3])
+        input_label = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
+        input_label = input_label.scatter_(1, label_map.data.long().cuda(), 1.0)
+
+        # get edges from instance map
+        if not self.opt.no_instance:
+            inst_map = inst_map.data.cuda()
+            edge_map = self.get_edges(inst_map)
+            input_label = torch.cat((input_label, edge_map), dim=1) 
+        input_label = Variable(input_label, volatile=infer)
+
+        # real images for training
+        if real_image is not None:
+            real_image = Variable(real_image.data.cuda())
+
+        # instance map for feature encoding
+        if self.use_features:
+            # get precomputed feature maps
+            if self.opt.load_features:
+                feat_map = Variable(feat_map.data.cuda())
+
+        return input_label, inst_map, real_image, feat_map
+
+    def discriminate(self, input_label, test_image, use_pool=False):
+        input_concat = torch.cat((input_label, test_image.detach()), dim=1)
+        if use_pool:            
+            fake_query = self.fake_pool.query(input_concat)
+            return self.netD.forward(fake_query)
+        else:
+            return self.netD.forward(input_concat)
+
+    def forward(self, label, inst, image, feat, infer=False):
+        # Encode Inputs
+        input_label, inst_map, real_image, feat_map = self.encode_input(label, inst, image, feat)  
+
+        # Fake Generation
+        if self.use_features:
+            if not self.opt.load_features:
+                feat_map = self.netE.forward(real_image, inst_map)                     
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label
+        fake_image = self.netG.forward(input_concat)
+
+        # Fake Detection and Loss
+        pred_fake_pool = self.discriminate(input_label, fake_image, use_pool=True)
+        loss_D_fake = self.criterionGAN(pred_fake_pool, False)        
+
+        # Real Detection and Loss        
+        pred_real = self.discriminate(input_label, real_image)
+        loss_D_real = self.criterionGAN(pred_real, True)
+
+        # GAN loss (Fake Passability Loss)        
+        pred_fake = self.netD.forward(torch.cat((input_label, fake_image), dim=1))        
+        loss_G_GAN = self.criterionGAN(pred_fake, True)               
+        
+        # GAN feature matching loss
+        loss_G_GAN_Feat = 0
+        if not self.opt.no_ganFeat_loss:
+            feat_weights = 4.0 / (self.opt.n_layers_D + 1)
+            D_weights = 1.0 / self.opt.num_D
+            for i in range(self.opt.num_D):
+                for j in range(len(pred_fake[i])-1):
+                    loss_G_GAN_Feat += D_weights * feat_weights * \
+                        self.criterionFeat(pred_fake[i][j], pred_real[i][j].detach()) * self.opt.lambda_feat
+                   
+        # VGG feature matching loss
+        loss_G_VGG = 0
+        if not self.opt.no_vgg_loss:
+            loss_G_VGG = self.criterionVGG(fake_image, real_image) * self.opt.lambda_feat
+        
+        # Only return the fake_B image if necessary to save BW
+        return [ [ loss_G_GAN, loss_G_GAN_Feat, loss_G_VGG, loss_D_real, loss_D_fake ], None if not infer else fake_image ]
+
+    def inference(self, label, inst):
+        # Encode Inputs        
+        input_label, inst_map, _, _ = self.encode_input(Variable(label), Variable(inst), infer=True)
+
+        # Fake Generation
+        if self.use_features:       
+            # sample clusters from precomputed features             
+            feat_map = self.sample_features(inst_map)
+            input_concat = torch.cat((input_label, feat_map), dim=1)                        
+        else:
+            input_concat = input_label                
+        fake_image = self.netG.forward(input_concat)
+        return fake_image
+
+    def sample_features(self, inst): 
+        # read precomputed feature clusters 
+        cluster_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, self.opt.cluster_path)        
+        features_clustered = np.load(cluster_path).item()
+
+        # randomly sample from the feature clusters
+        inst_np = inst.cpu().numpy().astype(int)                                      
+        feat_map = torch.cuda.FloatTensor(1, self.opt.feat_num, inst.size()[2], inst.size()[3])                   
+        for i in np.unique(inst_np):    
+            label = i if i < 1000 else i//1000
+            if label in features_clustered:
+                feat = features_clustered[label]
+                cluster_idx = np.random.randint(0, feat.shape[0]) 
+                                            
+                idx = (inst == i).nonzero()
+                for k in range(self.opt.feat_num):                                    
+                    feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k] 
+        return feat_map
+
+    def encode_features(self, image, inst):
+        image = Variable(image.cuda(), volatile=True)
+        feat_num = self.opt.feat_num
+        h, w = inst.size()[2], inst.size()[3]
+        block_num = 32
+        feat_map = self.netE.forward(image, inst.cuda())
+        inst_np = inst.cpu().numpy().astype(int)
+        feature = {}
+        for i in range(self.opt.label_nc):
+            feature[i] = np.zeros((0, feat_num+1))
+        for i in np.unique(inst_np):
+            label = i if i < 1000 else i//1000
+            idx = (inst == i).nonzero()
+            num = idx.size()[0]
+            idx = idx[num//2,:]
+            val = np.zeros((1, feat_num+1))                        
+            for k in range(feat_num):
+                val[0, k] = feat_map[idx[0], idx[1] + k, idx[2], idx[3]].data[0]            
+            val[0, feat_num] = float(num) / (h * w // block_num)
+            feature[label] = np.append(feature[label], val, axis=0)
+        return feature
+
+    def get_edges(self, t):
+        edge = torch.cuda.ByteTensor(t.size()).zero_()
+        edge[:,:,:,1:] = edge[:,:,:,1:] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,:,:-1] = edge[:,:,:,:-1] | (t[:,:,:,1:] != t[:,:,:,:-1])
+        edge[:,:,1:,:] = edge[:,:,1:,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        edge[:,:,:-1,:] = edge[:,:,:-1,:] | (t[:,:,1:,:] != t[:,:,:-1,:])
+        return edge.float()
+
+    def save(self, which_epoch):
+        self.save_network(self.netG, 'G', which_epoch, self.gpu_ids)
+        self.save_network(self.netD, 'D', which_epoch, self.gpu_ids)
+        if self.gen_features:
+            self.save_network(self.netE, 'E', which_epoch, self.gpu_ids)
+
+    def update_fixed_params(self):
+        # after fixing the global generator for a number of iterations, also start finetuning it
+        params = list(self.netG.parameters())
+        if self.gen_features:
+            params += list(self.netE.parameters())           
+        self.optimizer_G = torch.optim.Adam(params, lr=self.opt.lr, betas=(self.opt.beta1, 0.999)) 
+        print('------------ Now also finetuning global generator -----------')
+
+    def update_learning_rate(self):
+        lrd = self.opt.lr / self.opt.niter_decay
+        lr = self.old_lr - lrd        
+        for param_group in self.optimizer_D.param_groups:
+            param_group['lr'] = lr
+        for param_group in self.optimizer_G.param_groups:
+            param_group['lr'] = lr
+        print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr