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Portrait_net_G.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:247e6582d90d36917a18df0e0909d5ec0b7d79ebe762e649842e36f7574d6c27
+size 729793853

model.py

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+from networks import ResnetBlock
+import functools
+import torch
+import torch.nn as nn
+
+# class GenerativeModel():
+#     def __init__(self):
+#         self.model = networks.define_G(3, 3,64, "global", 4, 9, 1,3, "instance", gpu_ids=[0])
+
+class GlobalGenerator(nn.Module):
+    def __init__(self, input_nc=3, output_nc=3, ngf=64, n_downsampling=4, n_blocks=9, norm_layer=functools.partial(nn.InstanceNorm2d, affine=False), 
+                 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)           

networks.py

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+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