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+checkpoints/BFM/BFM_model_front.mat filter=lfs diff=lfs merge=lfs -text
+checkpoints/shape_predictor_68_face_landmarks.dat filter=lfs diff=lfs merge=lfs -text

checkpoints/30_net_gen.pth

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checkpoints/BFM/Exp_Pca.bin

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checkpoints/LNet.pth

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checkpoints/ParseNet-latest.pth

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checkpoints/face3d_pretrain_epoch_20.pth

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checkpoints/shape_predictor_68_face_landmarks.dat

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models/DNet.py

@@ -0,0 +1,118 @@
+# TODO
+import functools
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils import flow_util
+from models.base_blocks import LayerNorm2d, ADAINHourglass, FineEncoder, FineDecoder
+
+# DNet
+class DNet(nn.Module):
+    def __init__(self):  
+        super(DNet, self).__init__()
+        self.mapping_net = MappingNet()
+        self.warpping_net = WarpingNet()
+        self.editing_net = EditingNet()
+ 
+    def forward(self, input_image, driving_source, stage=None):
+        if stage == 'warp':
+            descriptor = self.mapping_net(driving_source)
+            output = self.warpping_net(input_image, descriptor)
+        else:
+            descriptor = self.mapping_net(driving_source)
+            output = self.warpping_net(input_image, descriptor)
+            output['fake_image'] = self.editing_net(input_image, output['warp_image'], descriptor)
+        return output
+
+class MappingNet(nn.Module):
+    def __init__(self, coeff_nc=73, descriptor_nc=256, layer=3):
+        super( MappingNet, self).__init__()
+
+        self.layer = layer
+        nonlinearity = nn.LeakyReLU(0.1)
+
+        self.first = nn.Sequential(
+            torch.nn.Conv1d(coeff_nc, descriptor_nc, kernel_size=7, padding=0, bias=True))
+
+        for i in range(layer):
+            net = nn.Sequential(nonlinearity,
+                torch.nn.Conv1d(descriptor_nc, descriptor_nc, kernel_size=3, padding=0, dilation=3))
+            setattr(self, 'encoder' + str(i), net)   
+
+        self.pooling = nn.AdaptiveAvgPool1d(1)
+        self.output_nc = descriptor_nc
+
+    def forward(self, input_3dmm):
+        out = self.first(input_3dmm)
+        for i in range(self.layer):
+            model = getattr(self, 'encoder' + str(i))
+            out = model(out) + out[:,:,3:-3]
+        out = self.pooling(out)
+        return out   
+
+class WarpingNet(nn.Module):
+    def __init__(
+        self, 
+        image_nc=3, 
+        descriptor_nc=256, 
+        base_nc=32, 
+        max_nc=256, 
+        encoder_layer=5, 
+        decoder_layer=3, 
+        use_spect=False
+        ):
+        super( WarpingNet, self).__init__()
+
+        nonlinearity = nn.LeakyReLU(0.1)
+        norm_layer = functools.partial(LayerNorm2d, affine=True) 
+        kwargs = {'nonlinearity':nonlinearity, 'use_spect':use_spect}
+
+        self.descriptor_nc = descriptor_nc 
+        self.hourglass = ADAINHourglass(image_nc, self.descriptor_nc, base_nc,
+                                       max_nc, encoder_layer, decoder_layer, **kwargs)
+
+        self.flow_out = nn.Sequential(norm_layer(self.hourglass.output_nc), 
+                                      nonlinearity,
+                                      nn.Conv2d(self.hourglass.output_nc, 2, kernel_size=7, stride=1, padding=3))
+
+        self.pool = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, input_image, descriptor):
+        final_output={}
+        output = self.hourglass(input_image, descriptor)
+        final_output['flow_field'] = self.flow_out(output)
+
+        deformation = flow_util.convert_flow_to_deformation(final_output['flow_field'])
+        final_output['warp_image'] = flow_util.warp_image(input_image, deformation)
+        return final_output
+
+
+class EditingNet(nn.Module):
+    def __init__(
+        self, 
+        image_nc=3, 
+        descriptor_nc=256, 
+        layer=3, 
+        base_nc=64, 
+        max_nc=256, 
+        num_res_blocks=2, 
+        use_spect=False):  
+        super(EditingNet, self).__init__()
+
+        nonlinearity = nn.LeakyReLU(0.1)
+        norm_layer = functools.partial(LayerNorm2d, affine=True) 
+        kwargs = {'norm_layer':norm_layer, 'nonlinearity':nonlinearity, 'use_spect':use_spect}
+        self.descriptor_nc = descriptor_nc
+
+        # encoder part
+        self.encoder = FineEncoder(image_nc*2, base_nc, max_nc, layer, **kwargs)
+        self.decoder = FineDecoder(image_nc, self.descriptor_nc, base_nc, max_nc, layer, num_res_blocks, **kwargs)
+
+    def forward(self, input_image, warp_image, descriptor):
+        x = torch.cat([input_image, warp_image], 1)
+        x = self.encoder(x)
+        gen_image = self.decoder(x, descriptor)
+        return gen_image

models/ENet.py

@@ -0,0 +1,139 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.base_blocks import ResBlock, StyleConv, ToRGB
+
+
+class ENet(nn.Module):
+    def __init__(
+        self, 
+        num_style_feat=512,
+        lnet=None,
+        concat=False
+        ):  
+        super(ENet, self).__init__()
+
+        self.low_res = lnet
+        for param in self.low_res.parameters():
+            param.requires_grad = False
+
+        channel_multiplier, narrow = 2, 1
+        channels = {
+            '4': int(512 * narrow),
+            '8': int(512 * narrow),
+            '16': int(512 * narrow),
+            '32': int(512 * narrow),
+            '64': int(256 * channel_multiplier * narrow),
+            '128': int(128 * channel_multiplier * narrow),
+            '256': int(64 * channel_multiplier * narrow),
+            '512': int(32 * channel_multiplier * narrow),
+            '1024': int(16 * channel_multiplier * narrow)
+        }
+
+        self.log_size = 8
+        first_out_size = 128
+        self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1) # 256 -> 128
+
+        # downsample
+        in_channels = channels[f'{first_out_size}']
+        self.conv_body_down = nn.ModuleList()
+        for i in range(8, 2, -1):
+            out_channels = channels[f'{2**(i - 1)}']
+            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down'))
+            in_channels = out_channels
+
+        self.num_style_feat = num_style_feat
+        linear_out_channel = num_style_feat
+        self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel)
+        self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1)
+
+        self.style_convs = nn.ModuleList()
+        self.to_rgbs = nn.ModuleList()
+        self.noises = nn.Module()
+        
+        self.concat = concat
+        if concat:
+            in_channels = 3 + 32 # channels['64']
+        else:
+            in_channels = 3
+
+        for i in range(7, 9):  # 128, 256
+            out_channels = channels[f'{2**i}'] # 
+            self.style_convs.append(
+                StyleConv(
+                    in_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode='upsample'))
+            self.style_convs.append(
+                StyleConv(
+                    out_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode=None))
+            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
+            in_channels = out_channels
+
+    def forward(self, audio_sequences, face_sequences, gt_sequences):
+        B = audio_sequences.size(0)
+        input_dim_size = len(face_sequences.size())
+        inp, ref = torch.split(face_sequences,3,dim=1)
+
+        if input_dim_size > 4:
+            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
+            inp = torch.cat([inp[:, :, i] for i in range(inp.size(2))], dim=0)
+            ref = torch.cat([ref[:, :, i] for i in range(ref.size(2))], dim=0)
+            gt_sequences = torch.cat([gt_sequences[:, :, i] for i in range(gt_sequences.size(2))], dim=0)
+        
+        # get the global style
+        feat = F.leaky_relu_(self.conv_body_first(F.interpolate(ref, size=(256,256), mode='bilinear')), negative_slope=0.2)
+        for i in range(self.log_size - 2):
+            feat = self.conv_body_down[i](feat)
+        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
+
+        # style code
+        style_code = self.final_linear(feat.reshape(feat.size(0), -1))
+        style_code = style_code.reshape(style_code.size(0), -1, self.num_style_feat)
+        
+        LNet_input = torch.cat([inp, gt_sequences], dim=1)
+        LNet_input = F.interpolate(LNet_input, size=(96,96), mode='bilinear')
+        
+        if self.concat:
+            low_res_img, low_res_feat = self.low_res(audio_sequences, LNet_input)
+            low_res_img.detach()
+            low_res_feat.detach()
+            out = torch.cat([low_res_img, low_res_feat], dim=1) 
+
+        else:
+            low_res_img = self.low_res(audio_sequences, LNet_input)
+            low_res_img.detach()
+            # 96 x 96
+            out = low_res_img 
+        
+        p2d = (2,2,2,2)
+        out = F.pad(out, p2d, "reflect", 0)
+        skip = out
+
+        for conv1, conv2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], self.to_rgbs):
+            out = conv1(out, style_code)  # 96, 192, 384
+            out = conv2(out, style_code)
+            skip = to_rgb(out, style_code, skip)
+        _outputs = skip
+
+        # remove padding
+        _outputs = _outputs[:,:,8:-8,8:-8]
+
+        if input_dim_size > 4:
+            _outputs = torch.split(_outputs, B, dim=0)
+            outputs = torch.stack(_outputs, dim=2)
+            low_res_img = F.interpolate(low_res_img, outputs.size()[3:])
+            low_res_img = torch.split(low_res_img, B, dim=0) 
+            low_res_img = torch.stack(low_res_img, dim=2)
+        else:
+            outputs = _outputs
+        return outputs, low_res_img

models/LNet.py

@@ -0,0 +1,139 @@
+import functools
+import torch
+import torch.nn as nn
+
+from models.transformer import RETURNX, Transformer
+from models.base_blocks import Conv2d, LayerNorm2d, FirstBlock2d, DownBlock2d, UpBlock2d, \
+                               FFCADAINResBlocks, Jump, FinalBlock2d
+
+
+class Visual_Encoder(nn.Module):
+    def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(Visual_Encoder, self).__init__()
+        self.layers = layers
+        self.first_inp = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
+        self.first_ref = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
+        for i in range(layers):
+            in_channels = min(ngf*(2**i), img_f)
+            out_channels = min(ngf*(2**(i+1)), img_f)
+            model_ref = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            model_inp = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            if i < 2:
+                ca_layer = RETURNX()
+            else:
+                ca_layer = Transformer(2**(i+1) * ngf,2,4,ngf,ngf*4)
+            setattr(self, 'ca' + str(i), ca_layer)
+            setattr(self, 'ref_down' + str(i), model_ref)
+            setattr(self, 'inp_down' + str(i), model_inp)
+        self.output_nc = out_channels * 2
+
+    def forward(self, maskGT, ref):
+        x_maskGT, x_ref = self.first_inp(maskGT), self.first_ref(ref)
+        out=[x_maskGT]
+        for i in range(self.layers):
+            model_ref = getattr(self, 'ref_down'+str(i))
+            model_inp = getattr(self, 'inp_down'+str(i))
+            ca_layer = getattr(self, 'ca'+str(i))
+            x_maskGT, x_ref = model_inp(x_maskGT), model_ref(x_ref)
+            x_maskGT = ca_layer(x_maskGT, x_ref)
+            if i < self.layers - 1:
+                out.append(x_maskGT)
+            else:           
+                out.append(torch.cat([x_maskGT, x_ref], dim=1)) # concat ref features !
+        return out
+
+
+class Decoder(nn.Module):
+    def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(Decoder, self).__init__()
+        self.layers = layers
+        for i in range(layers)[::-1]:
+            if  i == layers-1:
+                in_channels = ngf*(2**(i+1)) * 2
+            else:
+                in_channels = min(ngf*(2**(i+1)), img_f)
+            out_channels = min(ngf*(2**i), img_f)
+            up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            res = FFCADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
+            jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)
+
+            setattr(self, 'up' + str(i), up)
+            setattr(self, 'res' + str(i), res)            
+            setattr(self, 'jump' + str(i), jump)
+
+        self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'sigmoid')
+        self.output_nc = out_channels
+
+    def forward(self, x, z):
+        out = x.pop()
+        for i in range(self.layers)[::-1]:
+            res_model = getattr(self, 'res' + str(i))
+            up_model = getattr(self, 'up' + str(i))
+            jump_model = getattr(self, 'jump' + str(i))
+            out = res_model(out, z)
+            out = up_model(out)
+            out = jump_model(x.pop()) + out
+        out_image = self.final(out)
+        return out_image
+
+
+class LNet(nn.Module): 
+    def __init__(
+        self, 
+        image_nc=3, 
+        descriptor_nc=512, 
+        layer=3, 
+        base_nc=64, 
+        max_nc=512, 
+        num_res_blocks=9, 
+        use_spect=True,
+        encoder=Visual_Encoder,
+        decoder=Decoder
+        ):  
+        super(LNet, self).__init__()
+
+        nonlinearity = nn.LeakyReLU(0.1)
+        norm_layer = functools.partial(LayerNorm2d, affine=True) 
+        kwargs = {'norm_layer':norm_layer, 'nonlinearity':nonlinearity, 'use_spect':use_spect}
+        self.descriptor_nc = descriptor_nc
+
+        self.encoder = encoder(image_nc, base_nc, max_nc, layer, **kwargs)
+        self.decoder = decoder(image_nc, self.descriptor_nc, base_nc, max_nc, layer, num_res_blocks, **kwargs)
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, descriptor_nc, kernel_size=1, stride=1, padding=0),
+            )
+
+    def forward(self, audio_sequences, face_sequences):
+        B = audio_sequences.size(0)
+        input_dim_size = len(face_sequences.size())
+        if input_dim_size > 4:
+            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
+            face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
+        cropped, ref = torch.split(face_sequences, 3, dim=1)
+
+        vis_feat = self.encoder(cropped, ref)
+        audio_feat = self.audio_encoder(audio_sequences) 
+        _outputs = self.decoder(vis_feat, audio_feat)
+
+        if input_dim_size > 4:
+            _outputs = torch.split(_outputs, B, dim=0)
+            outputs = torch.stack(_outputs, dim=2) 
+        else:
+            outputs = _outputs
+        return outputs

models/__init__.py

@@ -0,0 +1,37 @@
+import torch
+from models.DNet import DNet
+from models.LNet import LNet
+from models.ENet import ENet
+
+
+def _load(checkpoint_path):
+    map_location=None if torch.cuda.is_available() else torch.device('cpu')
+    checkpoint = torch.load(checkpoint_path, map_location=map_location)
+    return checkpoint
+
+def load_checkpoint(path, model):
+    print("Load checkpoint from: {}".format(path))
+    checkpoint = _load(path)
+    s = checkpoint["state_dict"] if 'arcface' not in path else checkpoint
+    new_s = {}
+    for k, v in s.items():
+        if 'low_res' in k:
+            continue
+        else:
+            new_s[k.replace('module.', '')] = v
+    model.load_state_dict(new_s, strict=False)
+    return model
+
+def load_network(args):
+    L_net = LNet()
+    L_net = load_checkpoint(args.LNet_path, L_net)
+    E_net = ENet(lnet=L_net)
+    model = load_checkpoint(args.ENet_path, E_net)
+    return model.eval()
+
+def load_DNet(args):
+    D_Net = DNet()
+    print("Load checkpoint from: {}".format(args.DNet_path))
+    checkpoint =  torch.load(args.DNet_path, map_location=lambda storage, loc: storage)
+    D_Net.load_state_dict(checkpoint['net_G_ema'], strict=False)
+    return D_Net.eval()

models/base_blocks.py

@@ -0,0 +1,554 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torch.nn.utils.spectral_norm import spectral_norm as SpectralNorm
+
+from models.ffc import FFC
+from basicsr.archs.arch_util import default_init_weights
+
+
+class Conv2d(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
+                            nn.BatchNorm2d(cout)
+                            )
+        self.act = nn.ReLU()
+        self.residual = residual
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        if self.residual:
+            out += x
+        return self.act(out)
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, mode='down'):
+        super(ResBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
+        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
+        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
+        if mode == 'down':
+            self.scale_factor = 0.5
+        elif mode == 'up':
+            self.scale_factor = 2
+
+    def forward(self, x):
+        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
+        # upsample/downsample
+        out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
+        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
+        # skip
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
+        skip = self.skip(x)
+        out = out + skip
+        return out
+
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, n_out, affine=True):
+        super(LayerNorm2d, self).__init__()
+        self.n_out = n_out
+        self.affine = affine
+
+        if self.affine:
+          self.weight = nn.Parameter(torch.ones(n_out, 1, 1))
+          self.bias = nn.Parameter(torch.zeros(n_out, 1, 1))
+
+    def forward(self, x):
+        normalized_shape = x.size()[1:]
+        if self.affine:
+          return F.layer_norm(x, normalized_shape, \
+              self.weight.expand(normalized_shape), 
+              self.bias.expand(normalized_shape))    
+        else:
+          return F.layer_norm(x, normalized_shape)  
+
+
+def spectral_norm(module, use_spect=True):
+    if use_spect:
+        return SpectralNorm(module)
+    else:
+        return module
+
+
+class FirstBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FirstBlock2d, self).__init__()
+        kwargs = {'kernel_size': 7, 'stride': 1, 'padding': 3}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out 
+
+
+class DownBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(DownBlock2d, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+        pool = nn.AvgPool2d(kernel_size=(2, 2))
+
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity, pool)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity, pool)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out 
+
+
+class UpBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(UpBlock2d, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
+
+    def forward(self, x):
+        out = self.model(F.interpolate(x, scale_factor=2))
+        return out
+
+
+class ADAIN(nn.Module):
+    def __init__(self, norm_nc, feature_nc):
+        super().__init__()
+
+        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
+
+        nhidden = 128
+        use_bias=True
+
+        self.mlp_shared = nn.Sequential(
+            nn.Linear(feature_nc, nhidden, bias=use_bias),            
+            nn.ReLU()
+        )
+        self.mlp_gamma = nn.Linear(nhidden, norm_nc, bias=use_bias)    
+        self.mlp_beta = nn.Linear(nhidden, norm_nc, bias=use_bias)    
+
+    def forward(self, x, feature):
+
+        # Part 1. generate parameter-free normalized activations
+        normalized = self.param_free_norm(x)
+        # Part 2. produce scaling and bias conditioned on feature
+        feature = feature.view(feature.size(0), -1)
+        actv = self.mlp_shared(feature)
+        gamma = self.mlp_gamma(actv)
+        beta = self.mlp_beta(actv)
+
+        # apply scale and bias
+        gamma = gamma.view(*gamma.size()[:2], 1,1)
+        beta = beta.view(*beta.size()[:2], 1,1)
+        out = normalized * (1 + gamma) + beta
+        return out
+
+
+class FineADAINResBlock2d(nn.Module):
+    """
+    Define an Residual block for different types
+    """
+    def __init__(self, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineADAINResBlock2d, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        self.conv1 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
+        self.conv2 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
+        self.norm1 = ADAIN(input_nc, feature_nc)
+        self.norm2 = ADAIN(input_nc, feature_nc)
+        self.actvn = nonlinearity
+
+    def forward(self, x, z):
+        dx = self.actvn(self.norm1(self.conv1(x), z))
+        dx = self.norm2(self.conv2(x), z)
+        out = dx + x
+        return out  
+
+
+class FineADAINResBlocks(nn.Module):
+    def __init__(self, num_block, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineADAINResBlocks, self).__init__()                                
+        self.num_block = num_block
+        for i in range(num_block):
+            model = FineADAINResBlock2d(input_nc, feature_nc, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'res'+str(i), model)
+
+    def forward(self, x, z):
+        for i in range(self.num_block):
+            model = getattr(self, 'res'+str(i))
+            x = model(x, z)
+        return x   
+
+
+class ADAINEncoderBlock(nn.Module):       
+    def __init__(self, input_nc, output_nc, feature_nc, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(ADAINEncoderBlock, self).__init__()
+        kwargs_down = {'kernel_size': 4, 'stride': 2, 'padding': 1}
+        kwargs_fine = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+
+        self.conv_0 = spectral_norm(nn.Conv2d(input_nc,  output_nc, **kwargs_down), use_spect)
+        self.conv_1 = spectral_norm(nn.Conv2d(output_nc, output_nc, **kwargs_fine), use_spect)
+
+
+        self.norm_0 = ADAIN(input_nc, feature_nc)
+        self.norm_1 = ADAIN(output_nc, feature_nc)
+        self.actvn = nonlinearity
+
+    def forward(self, x, z):
+        x = self.conv_0(self.actvn(self.norm_0(x, z)))
+        x = self.conv_1(self.actvn(self.norm_1(x, z)))
+        return x
+
+
+class ADAINDecoderBlock(nn.Module):
+    def __init__(self, input_nc, output_nc, hidden_nc, feature_nc, use_transpose=True, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(ADAINDecoderBlock, self).__init__()        
+        # Attributes
+        self.actvn = nonlinearity
+        hidden_nc = min(input_nc, output_nc) if hidden_nc is None else hidden_nc
+
+        kwargs_fine = {'kernel_size':3, 'stride':1, 'padding':1}
+        if use_transpose:
+            kwargs_up = {'kernel_size':3, 'stride':2, 'padding':1, 'output_padding':1}
+        else:
+            kwargs_up = {'kernel_size':3, 'stride':1, 'padding':1}
+
+        # create conv layers
+        self.conv_0 = spectral_norm(nn.Conv2d(input_nc, hidden_nc, **kwargs_fine), use_spect)
+        if use_transpose:
+            self.conv_1 = spectral_norm(nn.ConvTranspose2d(hidden_nc, output_nc, **kwargs_up), use_spect)
+            self.conv_s = spectral_norm(nn.ConvTranspose2d(input_nc, output_nc, **kwargs_up), use_spect)
+        else:
+            self.conv_1 = nn.Sequential(spectral_norm(nn.Conv2d(hidden_nc, output_nc, **kwargs_up), use_spect),
+                                        nn.Upsample(scale_factor=2))
+            self.conv_s = nn.Sequential(spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs_up), use_spect),
+                                        nn.Upsample(scale_factor=2))
+        # define normalization layers
+        self.norm_0 = ADAIN(input_nc, feature_nc)
+        self.norm_1 = ADAIN(hidden_nc, feature_nc)
+        self.norm_s = ADAIN(input_nc, feature_nc)
+        
+    def forward(self, x, z):
+        x_s = self.shortcut(x, z)
+        dx = self.conv_0(self.actvn(self.norm_0(x, z)))
+        dx = self.conv_1(self.actvn(self.norm_1(dx, z)))
+        out = x_s + dx
+        return out
+
+    def shortcut(self, x, z):
+        x_s = self.conv_s(self.actvn(self.norm_s(x, z)))
+        return x_s   
+
+
+class FineEncoder(nn.Module):
+    """docstring for Encoder"""
+    def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineEncoder, self).__init__()
+        self.layers = layers
+        self.first = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
+        for i in range(layers):
+            in_channels = min(ngf*(2**i), img_f)
+            out_channels = min(ngf*(2**(i+1)), img_f)
+            model = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'down' + str(i), model)
+        self.output_nc = out_channels
+
+    def forward(self, x):
+        x = self.first(x)
+        out=[x]
+        for i in range(self.layers):
+            model = getattr(self, 'down'+str(i))
+            x = model(x)
+            out.append(x)
+        return out
+
+
+class FineDecoder(nn.Module):
+    """docstring for FineDecoder"""
+    def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineDecoder, self).__init__()
+        self.layers = layers
+        for i in range(layers)[::-1]:
+            in_channels = min(ngf*(2**(i+1)), img_f)
+            out_channels = min(ngf*(2**i), img_f)
+            up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            res = FineADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
+            jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'up' + str(i), up)
+            setattr(self, 'res' + str(i), res)            
+            setattr(self, 'jump' + str(i), jump)
+        self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'tanh')
+        self.output_nc = out_channels
+
+    def forward(self, x, z):
+        out = x.pop()
+        for i in range(self.layers)[::-1]:
+            res_model = getattr(self, 'res' + str(i))
+            up_model = getattr(self, 'up' + str(i))
+            jump_model = getattr(self, 'jump' + str(i))
+            out = res_model(out, z)
+            out = up_model(out)
+            out = jump_model(x.pop()) + out
+        out_image = self.final(out)
+        return out_image
+
+
+class ADAINEncoder(nn.Module):
+    def __init__(self, image_nc, pose_nc, ngf, img_f, layers, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(ADAINEncoder, self).__init__()
+        self.layers = layers
+        self.input_layer = nn.Conv2d(image_nc, ngf, kernel_size=7, stride=1, padding=3)
+        for i in range(layers):
+            in_channels = min(ngf * (2**i), img_f)
+            out_channels = min(ngf *(2**(i+1)), img_f)
+            model = ADAINEncoderBlock(in_channels, out_channels, pose_nc, nonlinearity, use_spect)
+            setattr(self, 'encoder' + str(i), model)
+        self.output_nc = out_channels
+        
+    def forward(self, x, z):
+        out = self.input_layer(x)
+        out_list = [out]
+        for i in range(self.layers):
+            model = getattr(self, 'encoder' + str(i))
+            out = model(out, z)
+            out_list.append(out)
+        return out_list
+        
+        
+class ADAINDecoder(nn.Module):
+    """docstring for ADAINDecoder"""
+    def __init__(self, pose_nc, ngf, img_f, encoder_layers, decoder_layers, skip_connect=True, 
+                 nonlinearity=nn.LeakyReLU(), use_spect=False):
+
+        super(ADAINDecoder, self).__init__()
+        self.encoder_layers = encoder_layers
+        self.decoder_layers = decoder_layers
+        self.skip_connect = skip_connect
+        use_transpose = True
+        for i in range(encoder_layers-decoder_layers, encoder_layers)[::-1]:
+            in_channels = min(ngf * (2**(i+1)), img_f)
+            in_channels = in_channels*2 if i != (encoder_layers-1) and self.skip_connect else in_channels
+            out_channels = min(ngf * (2**i), img_f)
+            model = ADAINDecoderBlock(in_channels, out_channels, out_channels, pose_nc, use_transpose, nonlinearity, use_spect)
+            setattr(self, 'decoder' + str(i), model)
+        self.output_nc = out_channels*2 if self.skip_connect else out_channels
+
+    def forward(self, x, z):
+        out = x.pop() if self.skip_connect else x
+        for i in range(self.encoder_layers-self.decoder_layers, self.encoder_layers)[::-1]:
+            model = getattr(self, 'decoder' + str(i))
+            out = model(out, z)
+            out = torch.cat([out, x.pop()], 1) if self.skip_connect else out
+        return out
+
+
+class ADAINHourglass(nn.Module):
+    def __init__(self, image_nc, pose_nc, ngf, img_f, encoder_layers, decoder_layers, nonlinearity, use_spect):
+        super(ADAINHourglass, self).__init__()
+        self.encoder = ADAINEncoder(image_nc, pose_nc, ngf, img_f, encoder_layers, nonlinearity, use_spect)
+        self.decoder = ADAINDecoder(pose_nc, ngf, img_f, encoder_layers, decoder_layers, True, nonlinearity, use_spect)
+        self.output_nc = self.decoder.output_nc
+
+    def forward(self, x, z):
+        return self.decoder(self.encoder(x, z), z)        
+
+
+class FineADAINLama(nn.Module):
+    def __init__(self, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineADAINLama, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        self.actvn = nonlinearity
+        ratio_gin = 0.75
+        ratio_gout = 0.75        
+        self.ffc = FFC(input_nc, input_nc, 3,
+                       ratio_gin, ratio_gout, 1, 1, 1,
+                       1, False, False, padding_type='reflect')
+        global_channels = int(input_nc * ratio_gout)
+        self.bn_l = ADAIN(input_nc - global_channels, feature_nc)
+        self.bn_g = ADAIN(global_channels, feature_nc)
+
+    def forward(self, x, z):
+        x_l, x_g = self.ffc(x)
+        x_l = self.actvn(self.bn_l(x_l,z))
+        x_g = self.actvn(self.bn_g(x_g,z))
+        return x_l, x_g
+
+
+class FFCResnetBlock(nn.Module):
+    def __init__(self, dim, feature_dim, padding_type='reflect', norm_layer=BatchNorm2d, activation_layer=nn.ReLU, dilation=1,
+                 spatial_transform_kwargs=None, inline=False, **conv_kwargs):
+        super().__init__()
+        self.conv1 = FineADAINLama(dim, feature_dim, **conv_kwargs)
+        self.conv2 = FineADAINLama(dim, feature_dim, **conv_kwargs)
+        self.inline = True
+
+    def forward(self, x, z):
+        if self.inline:
+            x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
+        else:
+            x_l, x_g = x if type(x) is tuple else (x, 0)
+
+        id_l, id_g = x_l, x_g
+        x_l, x_g = self.conv1((x_l, x_g), z)
+        x_l, x_g = self.conv2((x_l, x_g), z)
+
+        x_l, x_g = id_l + x_l, id_g + x_g
+        out = x_l, x_g
+        if self.inline:
+            out = torch.cat(out, dim=1)
+        return out
+
+
+class FFCADAINResBlocks(nn.Module):
+    def __init__(self, num_block, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FFCADAINResBlocks, self).__init__()                                
+        self.num_block = num_block
+        for i in range(num_block):
+            model = FFCResnetBlock(input_nc, feature_nc, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'res'+str(i), model)
+
+    def forward(self, x, z):
+        for i in range(self.num_block):
+            model = getattr(self, 'res'+str(i))
+            x = model(x, z)
+        return x 
+
+
+class Jump(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(Jump, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        conv = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(input_nc), nonlinearity)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out   
+
+
+class FinalBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, use_spect=False, tanh_or_sigmoid='tanh'):
+        super(FinalBlock2d, self).__init__()
+        kwargs = {'kernel_size': 7, 'stride': 1, 'padding':3}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+        if tanh_or_sigmoid == 'sigmoid':
+            out_nonlinearity = nn.Sigmoid()
+        else:
+            out_nonlinearity = nn.Tanh()            
+        self.model = nn.Sequential(conv, out_nonlinearity)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out    
+
+
+class ModulatedConv2d(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 num_style_feat,
+                 demodulate=True,
+                 sample_mode=None,
+                 eps=1e-8):
+        super(ModulatedConv2d, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.demodulate = demodulate
+        self.sample_mode = sample_mode
+        self.eps = eps
+
+        # modulation inside each modulated conv
+        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
+        # initialization
+        default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear')
+
+        self.weight = nn.Parameter(
+            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) /
+            math.sqrt(in_channels * kernel_size**2))
+        self.padding = kernel_size // 2
+
+    def forward(self, x, style):
+        b, c, h, w = x.shape   
+        style = self.modulation(style).view(b, 1, c, 1, 1)
+        weight = self.weight * style  
+
+        if self.demodulate:
+            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
+            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
+
+        weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
+
+        # upsample or downsample if necessary
+        if self.sample_mode == 'upsample':
+            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
+        elif self.sample_mode == 'downsample':
+            x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
+
+        b, c, h, w = x.shape
+        x = x.view(1, b * c, h, w)
+        out = F.conv2d(x, weight, padding=self.padding, groups=b)
+        out = out.view(b, self.out_channels, *out.shape[2:4])
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, '
+                f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})')
+
+
+class StyleConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None):
+        super(StyleConv, self).__init__()
+        self.modulated_conv = ModulatedConv2d(
+            in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode)
+        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
+        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
+        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x, style, noise=None):
+        # modulate
+        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
+        # noise injection
+        if noise is None:
+            b, _, h, w = out.shape
+            noise = out.new_empty(b, 1, h, w).normal_()
+        out = out + self.weight * noise
+        # add bias
+        out = out + self.bias
+        # activation
+        out = self.activate(out)
+        return out
+
+
+class ToRGB(nn.Module):
+    def __init__(self, in_channels, num_style_feat, upsample=True):
+        super(ToRGB, self).__init__()
+        self.upsample = upsample
+        self.modulated_conv = ModulatedConv2d(
+            in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
+        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+    def forward(self, x, style, skip=None):
+        out = self.modulated_conv(x, style)
+        out = out + self.bias
+        if skip is not None:
+            if self.upsample:
+                skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
+            out = out + skip
+        return out

models/ffc.py

@@ -0,0 +1,233 @@
+# Fast Fourier Convolution NeurIPS 2020
+# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
+# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+# from models.modules.squeeze_excitation import SELayer
+import torch.fft
+
+class SELayer(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SELayer, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Linear(channel // reduction, channel, bias=False),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        res = x * y.expand_as(x)
+        return res
+
+
+class FFCSE_block(nn.Module):
+    def __init__(self, channels, ratio_g):
+        super(FFCSE_block, self).__init__()
+        in_cg = int(channels * ratio_g)
+        in_cl = channels - in_cg
+        r = 16
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.conv1 = nn.Conv2d(channels, channels // r,
+                               kernel_size=1, bias=True)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.conv_a2l = None if in_cl == 0 else nn.Conv2d(
+            channels // r, in_cl, kernel_size=1, bias=True)
+        self.conv_a2g = None if in_cg == 0 else nn.Conv2d(
+            channels // r, in_cg, kernel_size=1, bias=True)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = x if type(x) is tuple else (x, 0)
+        id_l, id_g = x
+
+        x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)
+        x = self.avgpool(x)
+        x = self.relu1(self.conv1(x))
+
+        x_l = 0 if self.conv_a2l is None else id_l * \
+            self.sigmoid(self.conv_a2l(x))
+        x_g = 0 if self.conv_a2g is None else id_g * \
+            self.sigmoid(self.conv_a2g(x))
+        return x_l, x_g
+
+
+class FourierUnit(nn.Module):
+
+    def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
+                 spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
+        # bn_layer not used
+        super(FourierUnit, self).__init__()
+        self.groups = groups
+
+        self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
+                                          out_channels=out_channels * 2,
+                                          kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
+        self.bn = torch.nn.BatchNorm2d(out_channels * 2)
+        self.relu = torch.nn.ReLU(inplace=True)
+
+        # squeeze and excitation block
+        self.use_se = use_se
+        if use_se:
+            if se_kwargs is None:
+                se_kwargs = {}
+            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
+
+        self.spatial_scale_factor = spatial_scale_factor
+        self.spatial_scale_mode = spatial_scale_mode
+        self.spectral_pos_encoding = spectral_pos_encoding
+        self.ffc3d = ffc3d
+        self.fft_norm = fft_norm
+
+    def forward(self, x):
+        batch = x.shape[0]
+
+        if self.spatial_scale_factor is not None:
+            orig_size = x.shape[-2:]
+            x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)
+
+        r_size = x.size()
+        # (batch, c, h, w/2+1, 2)
+        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
+        ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
+        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
+        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
+        ffted = ffted.view((batch, -1,) + ffted.size()[3:])
+
+        if self.spectral_pos_encoding:
+            height, width = ffted.shape[-2:]
+            coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)
+            coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
+            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
+
+        if self.use_se:
+            ffted = self.se(ffted)
+
+        ffted = self.conv_layer(ffted)  # (batch, c*2, h, w/2+1)
+        ffted = self.relu(self.bn(ffted))
+
+        ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
+            0, 1, 3, 4, 2).contiguous()  # (batch,c, t, h, w/2+1, 2)
+        ffted = torch.complex(ffted[..., 0], ffted[..., 1])
+
+        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
+        output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
+
+        if self.spatial_scale_factor is not None:
+            output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
+
+        return output
+
+
+class SpectralTransform(nn.Module):
+    def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs):
+        # bn_layer not used
+        super(SpectralTransform, self).__init__()
+        self.enable_lfu = enable_lfu
+        if stride == 2:
+            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
+        else:
+            self.downsample = nn.Identity()
+
+        self.stride = stride
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels //
+                      2, kernel_size=1, groups=groups, bias=False),
+            nn.BatchNorm2d(out_channels // 2),
+            nn.ReLU(inplace=True)
+        )
+        self.fu = FourierUnit(
+            out_channels // 2, out_channels // 2, groups, **fu_kwargs)
+        if self.enable_lfu:
+            self.lfu = FourierUnit(
+                out_channels // 2, out_channels // 2, groups)
+        self.conv2 = torch.nn.Conv2d(
+            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)
+
+    def forward(self, x):
+        x = self.downsample(x)
+        x = self.conv1(x)
+        output = self.fu(x)
+
+        if self.enable_lfu:
+            n, c, h, w = x.shape
+            split_no = 2
+            split_s = h // split_no
+            xs = torch.cat(torch.split(
+                x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
+            xs = torch.cat(torch.split(xs, split_s, dim=-1),
+                           dim=1).contiguous()
+            xs = self.lfu(xs)
+            xs = xs.repeat(1, 1, split_no, split_no).contiguous()
+        else:
+            xs = 0
+
+        output = self.conv2(x + output + xs)
+        return output
+
+
+class FFC(nn.Module):
+
+    def __init__(self, in_channels, out_channels, kernel_size,
+                 ratio_gin, ratio_gout, stride=1, padding=0,
+                 dilation=1, groups=1, bias=False, enable_lfu=True,
+                 padding_type='reflect', gated=False, **spectral_kwargs):
+        super(FFC, self).__init__()
+
+        assert stride == 1 or stride == 2, "Stride should be 1 or 2."
+        self.stride = stride
+
+        in_cg = int(in_channels * ratio_gin)
+        in_cl = in_channels - in_cg
+        out_cg = int(out_channels * ratio_gout)
+        out_cl = out_channels - out_cg
+
+        self.ratio_gin = ratio_gin
+        self.ratio_gout = ratio_gout
+        self.global_in_num = in_cg
+
+        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
+        self.convl2l = module(in_cl, out_cl, kernel_size,
+                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
+        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
+        self.convl2g = module(in_cl, out_cg, kernel_size,
+                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
+        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
+        self.convg2l = module(in_cg, out_cl, kernel_size,
+                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
+        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
+        self.convg2g = module(
+            in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)
+
+        self.gated = gated
+        module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
+        self.gate = module(in_channels, 2, 1)
+
+    def forward(self, x):
+        x_l, x_g = x if type(x) is tuple else (x, 0)
+        out_xl, out_xg = 0, 0
+
+        if self.gated:
+            total_input_parts = [x_l]
+            if torch.is_tensor(x_g):
+                total_input_parts.append(x_g)
+            total_input = torch.cat(total_input_parts, dim=1)
+
+            gates = torch.sigmoid(self.gate(total_input))
+            g2l_gate, l2g_gate = gates.chunk(2, dim=1)
+        else:
+            g2l_gate, l2g_gate = 1, 1
+
+        if self.ratio_gout != 1:
+            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
+        if self.ratio_gout != 0:
+            out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
+
+        return out_xl, out_xg

models/transformer.py

@@ -0,0 +1,119 @@
+import torch
+from torch import nn
+
+from einops import rearrange
+
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+
+class GELU(nn.Module):
+    def __init__(self):
+        super(GELU, self).__init__()
+    def forward(self, x):
+        return 0.5*x*(1+F.tanh(np.sqrt(2/np.pi)*(x+0.044715*torch.pow(x,3))))
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+# classes
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+class DualPreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.normx = nn.LayerNorm(dim)
+        self.normy = nn.LayerNorm(dim)
+        self.fn = fn
+    def forward(self, x, y, **kwargs):
+        return self.fn(self.normx(x), self.normy(y), **kwargs)
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, hidden_dim),
+            GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, dim),
+            nn.Dropout(dropout)
+        )
+    def forward(self, x):
+        return self.net(x)
+
+class Attention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        project_out = not (heads == 1 and dim_head == dim)
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.attend = nn.Softmax(dim = -1)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias = False)
+        self.to_k = nn.Linear(dim, inner_dim, bias = False)
+        self.to_v = nn.Linear(dim, inner_dim, bias = False)
+
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        ) if project_out else nn.Identity()
+
+    def forward(self, x, y):
+        # qk = self.to_qk(x).chunk(2, dim = -1) #
+        q = rearrange(self.to_q(x), 'b n (h d) -> b h n d', h = self.heads) # q,k from the zero feature
+        k = rearrange(self.to_k(x), 'b n (h d) -> b h n d', h = self.heads) # v from the reference features
+        v = rearrange(self.to_v(y), 'b n (h d) -> b h n d', h = self.heads) 
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        attn = self.attend(dots)
+
+        out = torch.matmul(attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        return self.to_out(out)
+
+class Transformer(nn.Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                DualPreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
+            ]))
+
+
+    def forward(self, x, y): # x is the cropped, y is the foreign reference
+        bs,c,h,w = x.size()
+
+        # img to embedding
+        x = x.view(bs,c,-1).permute(0,2,1)
+        y = y.view(bs,c,-1).permute(0,2,1)
+
+        for attn, ff in self.layers:
+            x = attn(x, y) + x
+            x = ff(x) + x
+
+        x = x.view(bs,h,w,c).permute(0,3,1,2)
+        return x
+
+class RETURNX(nn.Module):
+    def __init__(self,):
+        super().__init__()
+
+    def forward(self, x, y): # x is the cropped, y is the foreign reference 
+        return x

temp/1.mp4_coeffs.npy

@@ -0,0 +1,3 @@
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temp/1.mp4_stablized.npy

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temp/dropbox5.mp4_coeffs.npy

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temp/dropbox5.mp4_stablized.npy

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third_part/GFPGAN/LICENSE

@@ -0,0 +1,351 @@
+Tencent is pleased to support the open source community by making GFPGAN available.
+
+Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
+
+GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
+
+
+Terms of the Apache License Version 2.0:
+---------------------------------------------
+Apache License
+
+Version 2.0, January 2004
+
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+1. Definitions.
+
+“License” shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
+“Licensor” shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
+
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+
+“You” (or “Your”) shall mean an individual or Legal Entity exercising permissions granted by this License.
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+
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+
+“Work” shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
+
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+Open Source Software licensed under the Apache 2.0 license and Other Licenses of the Third-Party Components therein:
+---------------------------------------------
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+StyleGAN2
+The codes are modified from the repository stylegan2-pytorch. Many thanks to the author - Kim Seonghyeon 😊 for translating from the official TensorFlow codes to PyTorch ones. Here is the license of stylegan2-pytorch.
+The official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.
+DFDNet
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+
+Open Source Software licensed under the BSD 3-Clause license:
+---------------------------------------------
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+Copyright (c) Soumith Chintala 2016,
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+Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
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+
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+Open Source Software licensed under the MIT license:
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+Please note that only files in cv2 package are used.
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+
+Terms of the MIT License:
+---------------------------------------------
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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+Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:
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+Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
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third_part/GFPGAN/gfpgan/__init__.py

@@ -0,0 +1,8 @@
+# flake8: noqa
+
+from .archs import *
+from .data import *
+from .models import *
+from .utils import *
+
+# from .version import *