Initial commit

models/base_model.py

+import os
+import torch
+import sys
+
+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)
+                    if self.opt.verbose:
+                        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)
+                    for k, v in pretrained_dict.items():                      
+                        if v.size() == model_dict[k].size():
+                            model_dict[k] = v
+
+                    if sys.version_info >= (3,0):
+                        not_initialized = set()
+                    else:
+                        from sets import Set
+                        not_initialized = Set()                    
+
+                    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

models/models.py

+import torch
+
+def create_model(opt):
+    if opt.model == 'pix2pixHD':
+        from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
+        if opt.isTrain:
+            model = Pix2PixHDModel()
+        else:
+            model = InferenceModel()
+    else:
+    	from .ui_model import UIModel
+    	model = UIModel()
+    model.initialize(opt)
+    if opt.verbose:
+        print("model [%s] was created" % (model.name()))
+
+    if opt.isTrain and len(opt.gpu_ids) and not opt.fp16:
+        model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids)
+
+    return model

models/networks.py

+import torch
+import torch.nn as nn
+import functools
+from torch.autograd import Variable
+import numpy as np
+
+###############################################################################
+# 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(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(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:
+            for b in range(input.size()[0]):
+                indices = (inst[b:b+1] == int(i)).nonzero() # n x 4            
+                for j in range(self.output_nc):
+                    output_ins = outputs[indices[:,0] + b, indices[:,1] + j, indices[:,2], indices[:,3]]                    
+                    mean_feat = torch.mean(output_ins).expand_as(output_ins)                                        
+                    outputs_mean[indices[:,0] + b, 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

models/pix2pixHD_model.py

+import numpy as np
+import torch
+import os
+from torch.autograd import Variable
+from util.image_pool import ImagePool
+from .base_model import BaseModel
+from . import networks
+
+class Pix2PixHDModel(BaseModel):
+    def name(self):
+        return 'Pix2PixHDModel'
+    
+    def init_loss_filter(self, use_gan_feat_loss, use_vgg_loss):
+        flags = (True, use_gan_feat_loss, use_vgg_loss, True, True)
+        def loss_filter(g_gan, g_gan_feat, g_vgg, d_real, d_fake):
+            return [l for (l,f) in zip((g_gan,g_gan_feat,g_vgg,d_real,d_fake),flags) if f]
+        return loss_filter
+    
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+        if opt.resize_or_crop != 'none' or not opt.isTrain: # 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
+        input_nc = opt.label_nc if opt.label_nc != 0 else opt.input_nc
+
+        ##### define networks        
+        # Generator network
+        netG_input_nc = input_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 = input_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)  
+        if self.opt.verbose:
+                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.loss_filter = self.init_loss_filter(not opt.no_ganFeat_loss, not opt.no_vgg_loss)
+            
+            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 = self.loss_filter('G_GAN','G_GAN_Feat','G_VGG','D_real', 'D_fake')
+
+            # initialize optimizers
+            # optimizer G
+            if opt.niter_fix_global > 0:                
+                import sys
+                if sys.version_info >= (3,0):
+                    finetune_list = set()
+                else:
+                    from sets import Set
+                    finetune_list = Set()
+
+                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 += [value]
+                        finetune_list.add(key.split('.')[0])  
+                print('------------- Only training the local enhancer network (for %d epochs) ------------' % opt.niter_fix_global)
+                print('The layers that are finetuned are ', sorted(finetune_list))                         
+            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):             
+        if self.opt.label_nc == 0:
+            input_label = label_map.data.cuda()
+        else:
+            # 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)
+            if self.opt.data_type == 16:
+                input_label = input_label.half()
+
+        # 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())
+            if self.opt.label_feat:
+                inst_map = label_map.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 [ self.loss_filter( 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, image=None):
+        # Encode Inputs        
+        image = Variable(image) if image is not None else None
+        input_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)
+
+        # Fake Generation
+        if self.use_features:
+            if self.opt.use_encoded_image:
+                # encode the real image to get feature map
+                feat_map = self.netE.forward(real_image, inst_map)
+            else:
+                # 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        
+           
+        if torch.__version__.startswith('0.4'):
+            with torch.no_grad():
+                fake_image = self.netG.forward(input_concat)
+        else:
+            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, encoding='latin1').item()
+
+        # randomly sample from the feature clusters
+        inst_np = inst.cpu().numpy().astype(int)                                      
+        feat_map = self.Tensor(inst.size()[0], 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 == int(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]
+        if self.opt.data_type==16:
+            feat_map = feat_map.half()
+        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 == int(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,:])
+        if self.opt.data_type==16:
+            return edge.half()
+        else:
+            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))
+        if self.opt.verbose:
+            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
+        if self.opt.verbose:
+            print('update learning rate: %f -> %f' % (self.old_lr, lr))
+        self.old_lr = lr
+
+class InferenceModel(Pix2PixHDModel):
+    def forward(self, inp):
+        label, inst = inp
+        return self.inference(label, inst)
+
+        

models/ui_model.py

+import torch
+from torch.autograd import Variable
+from collections import OrderedDict
+import numpy as np
+import os
+from PIL import Image
+import util.util as util
+from .base_model import BaseModel
+from . import networks
+
+class UIModel(BaseModel):
+    def name(self):
+        return 'UIModel'
+
+    def initialize(self, opt):
+        assert(not opt.isTrain)
+        BaseModel.initialize(self, opt)
+        self.use_features = opt.instance_feat or opt.label_feat
+
+        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)            
+        self.load_network(self.netG, 'G', opt.which_epoch)
+
+        print('---------- Networks initialized -------------')
+
+    def toTensor(self, img, normalize=False):
+        tensor = torch.from_numpy(np.array(img, np.int32, copy=False))
+        tensor = tensor.view(1, img.size[1], img.size[0], len(img.mode))    
+        tensor = tensor.transpose(1, 2).transpose(1, 3).contiguous()
+        if normalize:
+            return (tensor.float()/255.0 - 0.5) / 0.5        
+        return tensor.float()
+
+    def load_image(self, label_path, inst_path, feat_path):
+        opt = self.opt
+        # read label map
+        label_img = Image.open(label_path)    
+        if label_path.find('face') != -1:
+            label_img = label_img.convert('L')
+        ow, oh = label_img.size    
+        w = opt.loadSize
+        h = int(w * oh / ow)    
+        label_img = label_img.resize((w, h), Image.NEAREST)
+        label_map = self.toTensor(label_img)           
+        
+        # onehot vector input for label map
+        self.label_map = label_map.cuda()
+        oneHot_size = (1, opt.label_nc, h, w)
+        input_label = self.Tensor(torch.Size(oneHot_size)).zero_()
+        self.input_label = input_label.scatter_(1, label_map.long().cuda(), 1.0)
+
+        # read instance map
+        if not opt.no_instance:
+            inst_img = Image.open(inst_path)        
+            inst_img = inst_img.resize((w, h), Image.NEAREST)            
+            self.inst_map = self.toTensor(inst_img).cuda()
+            self.edge_map = self.get_edges(self.inst_map)          
+            self.net_input = Variable(torch.cat((self.input_label, self.edge_map), dim=1), volatile=True)
+        else:
+            self.net_input = Variable(self.input_label, volatile=True)  
+        
+        self.features_clustered = np.load(feat_path).item()
+        self.object_map = self.inst_map if opt.instance_feat else self.label_map 
+                       
+        object_np = self.object_map.cpu().numpy().astype(int) 
+        self.feat_map = self.Tensor(1, opt.feat_num, h, w).zero_()                 
+        self.cluster_indices = np.zeros(self.opt.label_nc, np.uint8)
+        for i in np.unique(object_np):    
+            label = i if i < 1000 else i//1000
+            if label in self.features_clustered:
+                feat = self.features_clustered[label]
+                np.random.seed(i+1)
+                cluster_idx = np.random.randint(0, feat.shape[0])
+                self.cluster_indices[label] = cluster_idx
+                idx = (self.object_map == i).nonzero()                    
+                self.set_features(idx, feat, cluster_idx)
+
+        self.net_input_original = self.net_input.clone()        
+        self.label_map_original = self.label_map.clone()
+        self.feat_map_original = self.feat_map.clone()
+        if not opt.no_instance:
+            self.inst_map_original = self.inst_map.clone()        
+
+    def reset(self):
+        self.net_input = self.net_input_prev = self.net_input_original.clone()        
+        self.label_map = self.label_map_prev = self.label_map_original.clone()
+        self.feat_map = self.feat_map_prev = self.feat_map_original.clone()
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev = self.inst_map_original.clone()
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+
+    def undo(self):        
+        self.net_input = self.net_input_prev
+        self.label_map = self.label_map_prev
+        self.feat_map = self.feat_map_prev
+        if not self.opt.no_instance:
+            self.inst_map = self.inst_map_prev
+        self.object_map = self.inst_map if self.opt.instance_feat else self.label_map 
+            
+    # get boundary map from instance map
+    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()
+
+    # change the label at the source position to the label at the target position
+    def change_labels(self, click_src, click_tgt): 
+        y_src, x_src = click_src[0], click_src[1]
+        y_tgt, x_tgt = click_tgt[0], click_tgt[1]
+        label_src = int(self.label_map[0, 0, y_src, x_src])
+        inst_src = self.inst_map[0, 0, y_src, x_src]
+        label_tgt = int(self.label_map[0, 0, y_tgt, x_tgt])
+        inst_tgt = self.inst_map[0, 0, y_tgt, x_tgt]
+
+        idx_src = (self.inst_map == inst_src).nonzero()         
+        # need to change 3 things: label map, instance map, and feature map
+        if idx_src.shape:
+            # backup current maps
+            self.backup_current_state() 
+
+            # change both the label map and the network input
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_src, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                                    
+            
+            # update the instance map (and the network input)
+            if inst_tgt > 1000:
+                # if different instances have different ids, give the new object a new id
+                tgt_indices = (self.inst_map > label_tgt * 1000) & (self.inst_map < (label_tgt+1) * 1000)
+                inst_tgt = self.inst_map[tgt_indices].max() + 1
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = inst_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+
+            # also copy the source features to the target position      
+            idx_tgt = (self.inst_map == inst_tgt).nonzero()    
+            if idx_tgt.shape:
+                self.copy_features(idx_src, idx_tgt[0,:])
+
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add strokes of target label in the image
+    def add_strokes(self, click_src, label_tgt, bw, save):
+        # get the region of the new strokes (bw is the brush width)        
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        idx_src = torch.LongTensor(bw**2, 4).fill_(0)
+        for i in range(bw):
+            idx_src[i*bw:(i+1)*bw, 2] = min(h-1, max(0, click_src[0]-bw//2 + i))
+            for j in range(bw):
+                idx_src[i*bw+j, 3] = min(w-1, max(0, click_src[1]-bw//2 + j))
+        idx_src = idx_src.cuda()
+        
+        # again, need to update 3 things
+        if idx_src.shape:
+            # backup current maps
+            if save:
+                self.backup_current_state()
+
+            # update the label map (and the network input) in the stroke region            
+            self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            for k in range(self.opt.label_nc):
+                self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+            self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1                 
+
+            # update the instance map (and the network input)
+            self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+            self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+            
+            # also update the features if available
+            if self.opt.instance_feat:                                            
+                feat = self.features_clustered[label_tgt]
+                #np.random.seed(label_tgt+1)   
+                #cluster_idx = np.random.randint(0, feat.shape[0])
+                cluster_idx = self.cluster_indices[label_tgt]
+                self.set_features(idx_src, feat, cluster_idx)                                                  
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    # add an object to the clicked position with selected style
+    def add_objects(self, click_src, label_tgt, mask, style_id=0):
+        y, x = click_src[0], click_src[1]
+        mask = np.transpose(mask, (2, 0, 1))[np.newaxis,...]        
+        idx_src = torch.from_numpy(mask).cuda().nonzero()        
+        idx_src[:,2] += y
+        idx_src[:,3] += x
+
+        # backup current maps
+        self.backup_current_state()
+
+        # update label map
+        self.label_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt        
+        for k in range(self.opt.label_nc):
+            self.net_input[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = 0
+        self.net_input[idx_src[:,0], idx_src[:,1] + label_tgt, idx_src[:,2], idx_src[:,3]] = 1            
+
+        # update instance map
+        self.inst_map[idx_src[:,0], idx_src[:,1], idx_src[:,2], idx_src[:,3]] = label_tgt
+        self.net_input[:,-1,:,:] = self.get_edges(self.inst_map)
+                
+        # update feature map
+        self.set_features(idx_src, self.feat, style_id)                
+        
+        self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+
+    def single_forward(self, net_input, feat_map):
+        net_input = torch.cat((net_input, feat_map), dim=1)
+        fake_image = self.netG.forward(net_input)
+
+        if fake_image.size()[0] == 1:
+            return fake_image.data[0]        
+        return fake_image.data
+
+
+    # generate all outputs for different styles
+    def style_forward(self, click_pt, style_id=-1):           
+        if click_pt is None:            
+            self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))
+            self.crop = None
+            self.mask = None        
+        else:                       
+            instToChange = int(self.object_map[0, 0, click_pt[0], click_pt[1]])
+            self.instToChange = instToChange
+            label = instToChange if instToChange < 1000 else instToChange//1000        
+            self.feat = self.features_clustered[label]
+            self.fake_image = []
+            self.mask = self.object_map == instToChange
+            idx = self.mask.nonzero()
+            self.get_crop_region(idx)            
+            if idx.size():                
+                if style_id == -1:
+                    (min_y, min_x, max_y, max_x) = self.crop
+                    ### original
+                    for cluster_idx in range(self.opt.multiple_output):
+                        self.set_features(idx, self.feat, cluster_idx)
+                        fake_image = self.single_forward(self.net_input, self.feat_map)
+                        fake_image = util.tensor2im(fake_image[:,min_y:max_y,min_x:max_x])
+                        self.fake_image.append(fake_image)    
+                    """### To speed up previewing different style results, either crop or downsample the label maps
+                    if instToChange > 1000:
+                        (min_y, min_x, max_y, max_x) = self.crop                                                
+                        ### crop                                                
+                        _, _, h, w = self.net_input.size()
+                        offset = 512
+                        y_start, x_start = max(0, min_y-offset), max(0, min_x-offset)
+                        y_end, x_end = min(h, (max_y + offset)), min(w, (max_x + offset))
+                        y_region = slice(y_start, y_start+(y_end-y_start)//16*16)
+                        x_region = slice(x_start, x_start+(x_end-x_start)//16*16)
+                        net_input = self.net_input[:,:,y_region,x_region]                    
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            fake_image = self.single_forward(net_input, self.feat_map[:,:,y_region,x_region])                            
+                            fake_image = util.tensor2im(fake_image[:,min_y-y_start:max_y-y_start,min_x-x_start:max_x-x_start])
+                            self.fake_image.append(fake_image)
+                    else:
+                        ### downsample
+                        (min_y, min_x, max_y, max_x) = [crop//2 for crop in self.crop]                    
+                        net_input = self.net_input[:,:,::2,::2]                    
+                        size = net_input.size()
+                        net_input_batch = net_input.expand(self.opt.multiple_output, size[1], size[2], size[3])             
+                        for cluster_idx in range(self.opt.multiple_output):  
+                            self.set_features(idx, self.feat, cluster_idx)
+                            feat_map = self.feat_map[:,:,::2,::2]
+                            if cluster_idx == 0:
+                                feat_map_batch = feat_map
+                            else:
+                                feat_map_batch = torch.cat((feat_map_batch, feat_map), dim=0)
+                        fake_image_batch = self.single_forward(net_input_batch, feat_map_batch)
+                        for i in range(self.opt.multiple_output):
+                            self.fake_image.append(util.tensor2im(fake_image_batch[i,:,min_y:max_y,min_x:max_x]))"""
+                                        
+                else:
+                    self.set_features(idx, self.feat, style_id)
+                    self.cluster_indices[label] = style_id
+                    self.fake_image = util.tensor2im(self.single_forward(self.net_input, self.feat_map))        
+
+    def backup_current_state(self):
+        self.net_input_prev = self.net_input.clone()
+        self.label_map_prev = self.label_map.clone() 
+        self.inst_map_prev = self.inst_map.clone() 
+        self.feat_map_prev = self.feat_map.clone() 
+
+    # crop the ROI and get the mask of the object
+    def get_crop_region(self, idx):
+        size = self.net_input.size()
+        h, w = size[2], size[3]
+        min_y, min_x = idx[:,2].min(), idx[:,3].min()
+        max_y, max_x = idx[:,2].max(), idx[:,3].max()             
+        crop_min = 128
+        if max_y - min_y < crop_min:
+            min_y = max(0, (max_y + min_y) // 2 - crop_min // 2)
+            max_y = min(h-1, min_y + crop_min)
+        if max_x - min_x < crop_min:
+            min_x = max(0, (max_x + min_x) // 2 - crop_min // 2)
+            max_x = min(w-1, min_x + crop_min)
+        self.crop = (min_y, min_x, max_y, max_x)           
+        self.mask = self.mask[:,:, min_y:max_y, min_x:max_x]
+
+    # update the feature map once a new object is added or the label is changed
+    def update_features(self, cluster_idx, mask=None, click_pt=None):        
+        self.feat_map_prev = self.feat_map.clone()
+        # adding a new object
+        if mask is not None:
+            y, x = click_pt[0], click_pt[1]
+            mask = np.transpose(mask, (2,0,1))[np.newaxis,...]        
+            idx = torch.from_numpy(mask).cuda().nonzero()        
+            idx[:,2] += y
+            idx[:,3] += x    
+        # changing the label of an existing object 
+        else:            
+            idx = (self.object_map == self.instToChange).nonzero()              
+
+        # update feature map
+        self.set_features(idx, self.feat, cluster_idx)        
+
+    # set the class features to the target feature
+    def set_features(self, idx, feat, cluster_idx):        
+        for k in range(self.opt.feat_num):
+            self.feat_map[idx[:,0], idx[:,1] + k, idx[:,2], idx[:,3]] = feat[cluster_idx, k] 
+
+    # copy the features at the target position to the source position
+    def copy_features(self, idx_src, idx_tgt):        
+        for k in range(self.opt.feat_num):
+            val = self.feat_map[idx_tgt[0], idx_tgt[1] + k, idx_tgt[2], idx_tgt[3]]
+            self.feat_map[idx_src[:,0], idx_src[:,1] + k, idx_src[:,2], idx_src[:,3]] = val 
+
+    def get_current_visuals(self, getLabel=False):                              
+        mask = self.mask     
+        if self.mask is not None:
+            mask = np.transpose(self.mask[0].cpu().float().numpy(), (1,2,0)).astype(np.uint8)        
+
+        dict_list = [('fake_image', self.fake_image), ('mask', mask)]
+
+        if getLabel: # only output label map if needed to save bandwidth
+            label = util.tensor2label(self.net_input.data[0], self.opt.label_nc)                    
+            dict_list += [('label', label)]
+
+        return OrderedDict(dict_list)
\ No newline at end of file

options/base_options.py

+import argparse
+import os
+from util import util
+import torch
+
+class BaseOptions():
+    def __init__(self):
+        self.parser = argparse.ArgumentParser()
+        self.initialized = False
+
+    def initialize(self):    
+        # experiment specifics
+        self.parser.add_argument('--name', type=str, default='label2city', help='name of the experiment. It decides where to store samples and models')        
+        self.parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        self.parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
+        self.parser.add_argument('--model', type=str, default='pix2pixHD', help='which model to use')
+        self.parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')        
+        self.parser.add_argument('--use_dropout', action='store_true', help='use dropout for the generator')
+        self.parser.add_argument('--data_type', default=32, type=int, choices=[8, 16, 32], help="Supported data type i.e. 8, 16, 32 bit")
+        self.parser.add_argument('--verbose', action='store_true', default=False, help='toggles verbose')
+        self.parser.add_argument('--fp16', action='store_true', default=False, help='train with AMP')
+        self.parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
+
+        # input/output sizes       
+        self.parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
+        self.parser.add_argument('--loadSize', type=int, default=1024, help='scale images to this size')
+        self.parser.add_argument('--fineSize', type=int, default=512, help='then crop to this size')
+        self.parser.add_argument('--label_nc', type=int, default=35, help='# of input label channels')
+        self.parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
+        self.parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
+
+        # for setting inputs
+        self.parser.add_argument('--dataroot', type=str, default='./datasets/cityscapes/') 
+        self.parser.add_argument('--resize_or_crop', type=str, default='scale_width', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop]')
+        self.parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')        
+        self.parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data argumentation') 
+        self.parser.add_argument('--nThreads', default=2, type=int, help='# threads for loading data')                
+        self.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.')
+
+        # for displays
+        self.parser.add_argument('--display_winsize', type=int, default=512,  help='display window size')
+        self.parser.add_argument('--tf_log', action='store_true', help='if specified, use tensorboard logging. Requires tensorflow installed')
+
+        # for generator
+        self.parser.add_argument('--netG', type=str, default='global', help='selects model to use for netG')
+        self.parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
+        self.parser.add_argument('--n_downsample_global', type=int, default=4, help='number of downsampling layers in netG') 
+        self.parser.add_argument('--n_blocks_global', type=int, default=9, help='number of residual blocks in the global generator network')
+        self.parser.add_argument('--n_blocks_local', type=int, default=3, help='number of residual blocks in the local enhancer network')
+        self.parser.add_argument('--n_local_enhancers', type=int, default=1, help='number of local enhancers to use')        
+        self.parser.add_argument('--niter_fix_global', type=int, default=0, help='number of epochs that we only train the outmost local enhancer')        
+
+        # for instance-wise features
+        self.parser.add_argument('--no_instance', action='store_true', help='if specified, do *not* add instance map as input')        
+        self.parser.add_argument('--instance_feat', action='store_true', help='if specified, add encoded instance features as input')
+        self.parser.add_argument('--label_feat', action='store_true', help='if specified, add encoded label features as input')        
+        self.parser.add_argument('--feat_num', type=int, default=3, help='vector length for encoded features')        
+        self.parser.add_argument('--load_features', action='store_true', help='if specified, load precomputed feature maps')
+        self.parser.add_argument('--n_downsample_E', type=int, default=4, help='# of downsampling layers in encoder') 
+        self.parser.add_argument('--nef', type=int, default=16, help='# of encoder filters in the first conv layer')        
+        self.parser.add_argument('--n_clusters', type=int, default=10, help='number of clusters for features')        
+
+        self.initialized = True
+
+    def parse(self, save=True):
+        if not self.initialized:
+            self.initialize()
+        self.opt = self.parser.parse_args()
+        self.opt.isTrain = self.isTrain   # train or test
+
+        str_ids = self.opt.gpu_ids.split(',')
+        self.opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                self.opt.gpu_ids.append(id)
+        
+        # set gpu ids
+        if len(self.opt.gpu_ids) > 0:
+            torch.cuda.set_device(self.opt.gpu_ids[0])
+
+        args = vars(self.opt)
+
+        print('------------ Options -------------')
+        for k, v in sorted(args.items()):
+            print('%s: %s' % (str(k), str(v)))
+        print('-------------- End ----------------')
+
+        # save to the disk        
+        expr_dir = os.path.join(self.opt.checkpoints_dir, self.opt.name)
+        util.mkdirs(expr_dir)
+        if save and not self.opt.continue_train:
+            file_name = os.path.join(expr_dir, 'opt.txt')
+            with open(file_name, 'wt') as opt_file:
+                opt_file.write('------------ Options -------------\n')
+                for k, v in sorted(args.items()):
+                    opt_file.write('%s: %s\n' % (str(k), str(v)))
+                opt_file.write('-------------- End ----------------\n')
+        return self.opt

options/test_options.py

+from .base_options import BaseOptions
+
+class TestOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        self.parser.add_argument('--ntest', type=int, default=float("inf"), help='# of test examples.')
+        self.parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
+        self.parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        self.parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--how_many', type=int, default=50, help='how many test images to run')       
+        self.parser.add_argument('--cluster_path', type=str, default='features_clustered_010.npy', help='the path for clustered results of encoded features')
+        self.parser.add_argument('--use_encoded_image', action='store_true', help='if specified, encode the real image to get the feature map')
+        self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
+        self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
+        self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
+        self.isTrain = False

options/train_options.py

+from .base_options import BaseOptions
+
+class TrainOptions(BaseOptions):
+    def initialize(self):
+        BaseOptions.initialize(self)
+        # for displays
+        self.parser.add_argument('--display_freq', type=int, default=100, help='frequency of showing training results on screen')
+        self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
+        self.parser.add_argument('--save_latest_freq', type=int, default=1000, help='frequency of saving the latest results')
+        self.parser.add_argument('--save_epoch_freq', type=int, default=10, help='frequency of saving checkpoints at the end of epochs')        
+        self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
+        self.parser.add_argument('--debug', action='store_true', help='only do one epoch and displays at each iteration')
+
+        # for training
+        self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
+        self.parser.add_argument('--load_pretrain', type=str, default='', help='load the pretrained model from the specified location')
+        self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
+        self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')
+        self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')
+        self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
+        self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
+
+        # for discriminators        
+        self.parser.add_argument('--num_D', type=int, default=2, help='number of discriminators to use')
+        self.parser.add_argument('--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers')
+        self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')    
+        self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss')                
+        self.parser.add_argument('--no_ganFeat_loss', action='store_true', help='if specified, do *not* use discriminator feature matching loss')
+        self.parser.add_argument('--no_vgg_loss', action='store_true', help='if specified, do *not* use VGG feature matching loss')        
+        self.parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')
+        self.parser.add_argument('--pool_size', type=int, default=0, help='the size of image buffer that stores previously generated images')
+
+        self.isTrain = True

pix2pixHD_README.md

+<img src='imgs/teaser_720.gif' align="right" width=360>
+
+<br><br><br><br>
+
+# pix2pixHD
+### [Project](https://tcwang0509.github.io/pix2pixHD/) | [Youtube](https://youtu.be/3AIpPlzM_qs) | [Paper](https://arxiv.org/pdf/1711.11585.pdf) <br>
+Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. <br><br>
+[High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/)  
+ [Ting-Chun Wang](https://tcwang0509.github.io/)<sup>1</sup>, [Ming-Yu Liu](http://mingyuliu.net/)<sup>1</sup>, [Jun-Yan Zhu](http://people.eecs.berkeley.edu/~junyanz/)<sup>2</sup>, Andrew Tao<sup>1</sup>, [Jan Kautz](http://jankautz.com/)<sup>1</sup>, [Bryan Catanzaro](http://catanzaro.name/)<sup>1</sup>  
+ <sup>1</sup>NVIDIA Corporation, <sup>2</sup>UC Berkeley  
+ In CVPR 2018.  
+
+## Image-to-image translation at 2k/1k resolution
+- Our label-to-streetview results
+<p align='center'>  
+  <img src='imgs/teaser_label.png' width='400'/>
+  <img src='imgs/teaser_ours.jpg' width='400'/>
+</p>
+- Interactive editing results
+<p align='center'>  
+  <img src='imgs/teaser_style.gif' width='400'/>
+  <img src='imgs/teaser_label.gif' width='400'/>
+</p>
+- Additional streetview results
+<p align='center'>
+  <img src='imgs/cityscapes_1.jpg' width='400'/>
+  <img src='imgs/cityscapes_2.jpg' width='400'/>
+</p>
+<p align='center'>
+  <img src='imgs/cityscapes_3.jpg' width='400'/>
+  <img src='imgs/cityscapes_4.jpg' width='400'/>
+</p>
+
+- Label-to-face and interactive editing results
+<p align='center'>
+  <img src='imgs/face1_1.jpg' width='250'/>
+  <img src='imgs/face1_2.jpg' width='250'/>
+  <img src='imgs/face1_3.jpg' width='250'/>
+</p>
+<p align='center'>
+  <img src='imgs/face2_1.jpg' width='250'/>
+  <img src='imgs/face2_2.jpg' width='250'/>
+  <img src='imgs/face2_3.jpg' width='250'/>
+</p>
+
+- Our editing interface
+<p align='center'>
+  <img src='imgs/city_short.gif' width='330'/>
+  <img src='imgs/face_short.gif' width='450'/>
+</p>
+
+## Prerequisites
+- Linux or macOS
+- Python 2 or 3
+- NVIDIA GPU (11G memory or larger) + CUDA cuDNN
+
+## Getting Started
+### Installation
+- Install PyTorch and dependencies from http://pytorch.org
+- Install python libraries [dominate](https://github.com/Knio/dominate).
+```bash
+pip install dominate
+```
+- Clone this repo:
+```bash
+git clone https://github.com/NVIDIA/pix2pixHD
+cd pix2pixHD
+```
+
+
+### Testing
+- A few example Cityscapes test images are included in the `datasets` folder.
+- Please download the pre-trained Cityscapes model from [here](https://drive.google.com/file/d/1h9SykUnuZul7J3Nbms2QGH1wa85nbN2-/view?usp=sharing) (google drive link), and put it under `./checkpoints/label2city_1024p/`
+- Test the model (`bash ./scripts/test_1024p.sh`):
+```bash
+#!./scripts/test_1024p.sh
+python test.py --name label2city_1024p --netG local --ngf 32 --resize_or_crop none
+```
+The test results will be saved to a html file here: `./results/label2city_1024p/test_latest/index.html`.
+
+More example scripts can be found in the `scripts` directory.
+
+
+### Dataset
+- We use the Cityscapes dataset. To train a model on the full dataset, please download it from the [official website](https://www.cityscapes-dataset.com/) (registration required).
+After downloading, please put it under the `datasets` folder in the same way the example images are provided.
+
+
+### Training
+- Train a model at 1024 x 512 resolution (`bash ./scripts/train_512p.sh`):
+```bash
+#!./scripts/train_512p.sh
+python train.py --name label2city_512p
+```
+- To view training results, please checkout intermediate results in `./checkpoints/label2city_512p/web/index.html`.
+If you have tensorflow installed, you can see tensorboard logs in `./checkpoints/label2city_512p/logs` by adding `--tf_log` to the training scripts.
+
+### Multi-GPU training
+- Train a model using multiple GPUs (`bash ./scripts/train_512p_multigpu.sh`):
+```bash
+#!./scripts/train_512p_multigpu.sh
+python train.py --name label2city_512p --batchSize 8 --gpu_ids 0,1,2,3,4,5,6,7
+```
+Note: this is not tested and we trained our model using single GPU only. Please use at your own discretion.
+
+### Training with Automatic Mixed Precision (AMP) for faster speed
+- To train with mixed precision support, please first install apex from: https://github.com/NVIDIA/apex
+- You can then train the model by adding `--fp16`. For example,
+```bash
+#!./scripts/train_512p_fp16.sh
+python -m torch.distributed.launch train.py --name label2city_512p --fp16
+```
+In our test case, it trains about 80% faster with AMP on a Volta machine.
+
+### Training at full resolution
+- To train the images at full resolution (2048 x 1024) requires a GPU with 24G memory (`bash ./scripts/train_1024p_24G.sh`), or 16G memory if using mixed precision (AMP).
+- If only GPUs with 12G memory are available, please use the 12G script (`bash ./scripts/train_1024p_12G.sh`), which will crop the images during training. Performance is not guaranteed using this script.
+
+### Training with your own dataset
+- If you want to train with your own dataset, please generate label maps which are one-channel whose pixel values correspond to the object labels (i.e. 0,1,...,N-1, where N is the number of labels). This is because we need to generate one-hot vectors from the label maps. Please also specity `--label_nc N` during both training and testing.
+- If your input is not a label map, please just specify `--label_nc 0` which will directly use the RGB colors as input. The folders should then be named `train_A`, `train_B` instead of `train_label`, `train_img`, where the goal is to translate images from A to B.
+- If you don't have instance maps or don't want to use them, please specify `--no_instance`.
+- The default setting for preprocessing is `scale_width`, which will scale the width of all training images to `opt.loadSize` (1024) while keeping the aspect ratio. If you want a different setting, please change it by using the `--resize_or_crop` option. For example, `scale_width_and_crop` first resizes the image to have width `opt.loadSize` and then does random cropping of size `(opt.fineSize, opt.fineSize)`. `crop` skips the resizing step and only performs random cropping. If you don't want any preprocessing, please specify `none`, which will do nothing other than making sure the image is divisible by 32.
+
+## More Training/Test Details
+- Flags: see `options/train_options.py` and `options/base_options.py` for all the training flags; see `options/test_options.py` and `options/base_options.py` for all the test flags.
+- Instance map: we take in both label maps and instance maps as input. If you don't want to use instance maps, please specify the flag `--no_instance`.
+
+
+## Citation
+
+If you find this useful for your research, please use the following.
+
+```
+@inproceedings{wang2018pix2pixHD,
+  title={High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs},
+  author={Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Andrew Tao and Jan Kautz and Bryan Catanzaro},  
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2018}
+}
+```
+
+## Acknowledgments
+This code borrows heavily from [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).

precompute_feature_maps.py

+from options.train_options import TrainOptions
+from data.data_loader import CreateDataLoader
+from models.models import create_model
+import os
+import util.util as util
+from torch.autograd import Variable
+import torch.nn as nn
+
+opt = TrainOptions().parse()
+opt.nThreads = 1
+opt.batchSize = 1 
+opt.serial_batches = True 
+opt.no_flip = True
+opt.instance_feat = True
+
+name = 'features'
+save_path = os.path.join(opt.checkpoints_dir, opt.name)
+
+############ Initialize #########
+data_loader = CreateDataLoader(opt)
+dataset = data_loader.load_data()
+dataset_size = len(data_loader)
+model = create_model(opt)
+util.mkdirs(os.path.join(opt.dataroot, opt.phase + '_feat'))
+
+######## Save precomputed feature maps for 1024p training #######
+for i, data in enumerate(dataset):
+	print('%d / %d images' % (i+1, dataset_size)) 
+	feat_map = model.module.netE.forward(Variable(data['image'].cuda(), volatile=True), data['inst'].cuda())
+	feat_map = nn.Upsample(scale_factor=2, mode='nearest')(feat_map)
+	image_numpy = util.tensor2im(feat_map.data[0])
+	save_path = data['path'][0].replace('/train_label/', '/train_feat/')
+	util.save_image(image_numpy, save_path)
\ No newline at end of file

run_engine.py

+import os
+import sys
+from random import randint
+import numpy as np
+import tensorrt
+
+try:
+    from PIL import Image
+    import pycuda.driver as cuda
+    import pycuda.gpuarray as gpuarray
+    import pycuda.autoinit
+    import argparse
+except ImportError as err:
+    sys.stderr.write("""ERROR: failed to import module ({})
+Please make sure you have pycuda and the example dependencies installed.
+https://wiki.tiker.net/PyCuda/Installation/Linux
+pip(3) install tensorrt[examples]
+""".format(err))
+    exit(1)
+
+try:
+    import tensorrt as trt
+    from tensorrt.parsers import caffeparser
+    from tensorrt.parsers import onnxparser    
+except ImportError as err:
+    sys.stderr.write("""ERROR: failed to import module ({})
+Please make sure you have the TensorRT Library installed
+and accessible in your LD_LIBRARY_PATH
+""".format(err))
+    exit(1)
+
+
+G_LOGGER = trt.infer.ConsoleLogger(trt.infer.LogSeverity.INFO)
+
+class Profiler(trt.infer.Profiler):
+    """
+    Example Implimentation of a Profiler
+    Is identical to the Profiler class in trt.infer so it is possible
+    to just use that instead of implementing this if further
+    functionality is not needed
+    """
+    def __init__(self, timing_iter):
+        trt.infer.Profiler.__init__(self)
+        self.timing_iterations = timing_iter
+        self.profile = []
+
+    def report_layer_time(self, layerName, ms):
+        record = next((r for r in self.profile if r[0] == layerName), (None, None))
+        if record == (None, None):
+            self.profile.append((layerName, ms))
+        else:
+            self.profile[self.profile.index(record)] = (record[0], record[1] + ms)
+
+    def print_layer_times(self):
+        totalTime = 0
+        for i in range(len(self.profile)):
+            print("{:40.40} {:4.3f}ms".format(self.profile[i][0], self.profile[i][1] / self.timing_iterations))
+            totalTime += self.profile[i][1]
+        print("Time over all layers: {:4.2f} ms per iteration".format(totalTime / self.timing_iterations))
+
+
+def get_input_output_names(trt_engine):
+    nbindings = trt_engine.get_nb_bindings();
+    maps = []
+
+    for b in range(0, nbindings):
+        dims = trt_engine.get_binding_dimensions(b).to_DimsCHW()
+        name = trt_engine.get_binding_name(b)
+        type = trt_engine.get_binding_data_type(b)
+        
+        if (trt_engine.binding_is_input(b)):
+            maps.append(name)
+            print("Found input: ", name)
+        else:
+            maps.append(name)
+            print("Found output: ", name)
+
+        print("shape=" + str(dims.C()) + " , " + str(dims.H()) + " , " + str(dims.W()))
+        print("dtype=" + str(type))
+    return maps
+
+def create_memory(engine, name,  buf, mem, batchsize, inp, inp_idx):
+    binding_idx = engine.get_binding_index(name)
+    if binding_idx == -1:
+        raise AttributeError("Not a valid binding")
+    print("Binding: name={}, bindingIndex={}".format(name, str(binding_idx)))
+    dims = engine.get_binding_dimensions(binding_idx).to_DimsCHW()
+    eltCount = dims.C() * dims.H() * dims.W() * batchsize
+
+    if engine.binding_is_input(binding_idx):
+        h_mem = inp[inp_idx]
+        inp_idx = inp_idx + 1
+    else:
+        h_mem = np.random.uniform(0.0, 255.0, eltCount).astype(np.dtype('f4'))
+
+    d_mem = cuda.mem_alloc(eltCount * 4)
+    cuda.memcpy_htod(d_mem, h_mem)
+    buf.insert(binding_idx, int(d_mem))
+    mem.append(d_mem)
+    return inp_idx
+
+
+#Run inference on device
+def time_inference(engine, batch_size, inp):
+    bindings = []
+    mem = []
+    inp_idx = 0
+    for io in get_input_output_names(engine):
+        inp_idx = create_memory(engine, io,  bindings, mem,
+                                batch_size, inp, inp_idx)
+
+    context = engine.create_execution_context()
+    g_prof = Profiler(500)
+    context.set_profiler(g_prof)
+    for i in range(iter):
+        context.execute(batch_size, bindings)
+    g_prof.print_layer_times()
+    
+    context.destroy() 
+    return
+
+
+def convert_to_datatype(v):
+    if v==8:
+        return trt.infer.DataType.INT8
+    elif v==16:
+        return trt.infer.DataType.HALF
+    elif v==32:
+        return trt.infer.DataType.FLOAT
+    else:
+        print("ERROR: Invalid model data type bit depth: " + str(v))
+        return trt.infer.DataType.INT8
+
+def run_trt_engine(engine_file, bs, it):
+    engine = trt.utils.load_engine(G_LOGGER, engine_file)
+    time_inference(engine, bs, it)
+
+def run_onnx(onnx_file, data_type, bs, inp):
+    # Create onnx_config
+    apex = onnxparser.create_onnxconfig()
+    apex.set_model_file_name(onnx_file)
+    apex.set_model_dtype(convert_to_datatype(data_type))
+
+     # create parser
+    trt_parser = onnxparser.create_onnxparser(apex)
+    assert(trt_parser)
+    data_type = apex.get_model_dtype()
+    onnx_filename = apex.get_model_file_name()
+    trt_parser.parse(onnx_filename, data_type)
+    trt_parser.report_parsing_info()
+    trt_parser.convert_to_trtnetwork()
+    trt_network = trt_parser.get_trtnetwork()
+    assert(trt_network)
+
+    # create infer builder
+    trt_builder = trt.infer.create_infer_builder(G_LOGGER)
+    trt_builder.set_max_batch_size(max_batch_size)
+    trt_builder.set_max_workspace_size(max_workspace_size)
+    
+    if (apex.get_model_dtype() == trt.infer.DataType_kHALF):
+        print("-------------------  Running FP16 -----------------------------")
+        trt_builder.set_half2_mode(True)
+    elif (apex.get_model_dtype() == trt.infer.DataType_kINT8): 
+        print("-------------------  Running INT8 -----------------------------")
+        trt_builder.set_int8_mode(True)
+    else:
+        print("-------------------  Running FP32 -----------------------------")
+        
+    print("----- Builder is Done -----")
+    print("----- Creating Engine -----")
+    trt_engine = trt_builder.build_cuda_engine(trt_network)
+    print("----- Engine is built -----")
+    time_inference(engine, bs, inp)

scripts/test_1024p.sh

+#!/bin/bash
+################################ Testing ################################
+# labels only
+python test.py --name label2city_1024p --netG local --ngf 32 --resize_or_crop none $@

scripts/test_1024p_feat.sh

+################################ Testing ################################
+# first precompute and cluster all features
+python encode_features.py --name label2city_1024p_feat --netG local --ngf 32 --resize_or_crop none;
+# use instance-wise features
+python test.py --name label2city_1024p_feat ---netG local --ngf 32 --resize_or_crop none --instance_feat
\ No newline at end of file

scripts/test_512p.sh

+################################ Testing ################################
+# labels only
+python test.py --name label2city_512p
\ No newline at end of file

scripts/test_512p_feat.sh

+################################ Testing ################################
+# first precompute and cluster all features
+python encode_features.py --name label2city_512p_feat;
+# use instance-wise features
+python test.py --name label2city_512p_feat --instance_feat
\ No newline at end of file