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animation/SadTalker/src/facerender/animate.py

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+import os
+import cv2
+import yaml
+import numpy as np
+import warnings
+from skimage import img_as_ubyte
+import safetensors
+import safetensors.torch 
+warnings.filterwarnings('ignore')
+
+
+import imageio
+import torch
+import torchvision
+
+
+from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector
+from src.facerender.modules.mapping import MappingNet
+from src.facerender.modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator
+from src.facerender.modules.make_animation import make_animation 
+
+from pydub import AudioSegment 
+from src.utils.face_enhancer import enhancer_generator_with_len, enhancer_list
+from src.utils.paste_pic import paste_pic
+from src.utils.videoio import save_video_with_watermark
+
+try:
+    import webui  # in webui
+    in_webui = True
+except:
+    in_webui = False
+
+class AnimateFromCoeff():
+
+    def __init__(self, sadtalker_path, device):
+
+        with open(sadtalker_path['facerender_yaml']) as f:
+            config = yaml.safe_load(f)
+
+        generator = OcclusionAwareSPADEGenerator(**config['model_params']['generator_params'],
+                                                    **config['model_params']['common_params'])
+        kp_extractor = KPDetector(**config['model_params']['kp_detector_params'],
+                                    **config['model_params']['common_params'])
+        he_estimator = HEEstimator(**config['model_params']['he_estimator_params'],
+                               **config['model_params']['common_params'])
+        mapping = MappingNet(**config['model_params']['mapping_params'])
+
+        generator.to(device)
+        kp_extractor.to(device)
+        he_estimator.to(device)
+        mapping.to(device)
+        for param in generator.parameters():
+            param.requires_grad = False
+        for param in kp_extractor.parameters():
+            param.requires_grad = False 
+        for param in he_estimator.parameters():
+            param.requires_grad = False
+        for param in mapping.parameters():
+            param.requires_grad = False
+
+        if sadtalker_path is not None:
+            if 'checkpoint' in sadtalker_path: # use safe tensor
+                self.load_cpk_facevid2vid_safetensor(sadtalker_path['checkpoint'], kp_detector=kp_extractor, generator=generator, he_estimator=None)
+            else:
+                self.load_cpk_facevid2vid(sadtalker_path['free_view_checkpoint'], kp_detector=kp_extractor, generator=generator, he_estimator=he_estimator)
+        else:
+            raise AttributeError("Checkpoint should be specified for video head pose estimator.")
+
+        if  sadtalker_path['mappingnet_checkpoint'] is not None:
+            self.load_cpk_mapping(sadtalker_path['mappingnet_checkpoint'], mapping=mapping)
+        else:
+            raise AttributeError("Checkpoint should be specified for video head pose estimator.") 
+
+        self.kp_extractor = kp_extractor
+        self.generator = generator
+        self.he_estimator = he_estimator
+        self.mapping = mapping
+
+        self.kp_extractor.eval()
+        self.generator.eval()
+        self.he_estimator.eval()
+        self.mapping.eval()
+         
+        self.device = device
+    
+    def load_cpk_facevid2vid_safetensor(self, checkpoint_path, generator=None, 
+                        kp_detector=None, he_estimator=None,  
+                        device="cpu"):
+
+        checkpoint = safetensors.torch.load_file(checkpoint_path)
+
+        if generator is not None:
+            x_generator = {}
+            for k,v in checkpoint.items():
+                if 'generator' in k:
+                    x_generator[k.replace('generator.', '')] = v
+            generator.load_state_dict(x_generator)
+        if kp_detector is not None:
+            x_generator = {}
+            for k,v in checkpoint.items():
+                if 'kp_extractor' in k:
+                    x_generator[k.replace('kp_extractor.', '')] = v
+            kp_detector.load_state_dict(x_generator)
+        if he_estimator is not None:
+            x_generator = {}
+            for k,v in checkpoint.items():
+                if 'he_estimator' in k:
+                    x_generator[k.replace('he_estimator.', '')] = v
+            he_estimator.load_state_dict(x_generator)
+        
+        return None
+
+    def load_cpk_facevid2vid(self, checkpoint_path, generator=None, discriminator=None, 
+                        kp_detector=None, he_estimator=None, optimizer_generator=None, 
+                        optimizer_discriminator=None, optimizer_kp_detector=None, 
+                        optimizer_he_estimator=None, device="cpu"):
+        checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
+        if generator is not None:
+            generator.load_state_dict(checkpoint['generator'])
+        if kp_detector is not None:
+            kp_detector.load_state_dict(checkpoint['kp_detector'])
+        if he_estimator is not None:
+            he_estimator.load_state_dict(checkpoint['he_estimator'])
+        if discriminator is not None:
+            try:
+               discriminator.load_state_dict(checkpoint['discriminator'])
+            except:
+               print ('No discriminator in the state-dict. Dicriminator will be randomly initialized')
+        if optimizer_generator is not None:
+            optimizer_generator.load_state_dict(checkpoint['optimizer_generator'])
+        if optimizer_discriminator is not None:
+            try:
+                optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
+            except RuntimeError as e:
+                print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized')
+        if optimizer_kp_detector is not None:
+            optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector'])
+        if optimizer_he_estimator is not None:
+            optimizer_he_estimator.load_state_dict(checkpoint['optimizer_he_estimator'])
+
+        return checkpoint['epoch']
+    
+    def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None,
+                 optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
+        checkpoint = torch.load(checkpoint_path,  map_location=torch.device(device))
+        if mapping is not None:
+            mapping.load_state_dict(checkpoint['mapping'])
+        if discriminator is not None:
+            discriminator.load_state_dict(checkpoint['discriminator'])
+        if optimizer_mapping is not None:
+            optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
+        if optimizer_discriminator is not None:
+            optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
+
+        return checkpoint['epoch']
+
+    def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, background_enhancer=None, preprocess='crop', img_size=256):
+
+        source_image=x['source_image'].type(torch.FloatTensor)
+        source_semantics=x['source_semantics'].type(torch.FloatTensor)
+        target_semantics=x['target_semantics_list'].type(torch.FloatTensor) 
+        source_image=source_image.to(self.device)
+        source_semantics=source_semantics.to(self.device)
+        target_semantics=target_semantics.to(self.device)
+        if 'yaw_c_seq' in x:
+            yaw_c_seq = x['yaw_c_seq'].type(torch.FloatTensor)
+            yaw_c_seq = x['yaw_c_seq'].to(self.device)
+        else:
+            yaw_c_seq = None
+        if 'pitch_c_seq' in x:
+            pitch_c_seq = x['pitch_c_seq'].type(torch.FloatTensor)
+            pitch_c_seq = x['pitch_c_seq'].to(self.device)
+        else:
+            pitch_c_seq = None
+        if 'roll_c_seq' in x:
+            roll_c_seq = x['roll_c_seq'].type(torch.FloatTensor) 
+            roll_c_seq = x['roll_c_seq'].to(self.device)
+        else:
+            roll_c_seq = None
+
+        frame_num = x['frame_num']
+
+        predictions_video = make_animation(source_image, source_semantics, target_semantics,
+                                        self.generator, self.kp_extractor, self.he_estimator, self.mapping, 
+                                        yaw_c_seq, pitch_c_seq, roll_c_seq, use_exp = True)
+
+        predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:])
+        predictions_video = predictions_video[:frame_num]
+
+        video = []
+        for idx in range(predictions_video.shape[0]):
+            image = predictions_video[idx]
+            image = np.transpose(image.data.cpu().numpy(), [1, 2, 0]).astype(np.float32)
+            video.append(image)
+        result = img_as_ubyte(video)
+
+        ### the generated video is 256x256, so we keep the aspect ratio, 
+        original_size = crop_info[0]
+        if original_size:
+            result = [ cv2.resize(result_i,(img_size, int(img_size * original_size[1]/original_size[0]) )) for result_i in result ]
+        
+        video_name = x['video_name']  + '.mp4'
+        path = os.path.join(video_save_dir, 'temp_'+video_name)
+        
+        imageio.mimsave(path, result,  fps=float(25))
+
+        av_path = os.path.join(video_save_dir, video_name)
+        return_path = av_path 
+        
+        audio_path =  x['audio_path'] 
+        audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0]
+        new_audio_path = os.path.join(video_save_dir, audio_name+'.wav')
+        start_time = 0
+        # cog will not keep the .mp3 filename
+        sound = AudioSegment.from_file(audio_path)
+        frames = frame_num 
+        end_time = start_time + frames*1/25*1000
+        word1=sound.set_frame_rate(16000)
+        word = word1[start_time:end_time]
+        word.export(new_audio_path, format="wav")
+
+        save_video_with_watermark(path, new_audio_path, av_path, watermark= False)
+        print(f'The generated video is named {video_save_dir}/{video_name}') 
+
+        if 'full' in preprocess.lower():
+            # only add watermark to the full image.
+            video_name_full = x['video_name']  + '_full.mp4'
+            full_video_path = os.path.join(video_save_dir, video_name_full)
+            return_path = full_video_path
+            paste_pic(path, pic_path, crop_info, new_audio_path, full_video_path, extended_crop= True if 'ext' in preprocess.lower() else False)
+            print(f'The generated video is named {video_save_dir}/{video_name_full}') 
+        else:
+            full_video_path = av_path 
+
+        #### paste back then enhancers
+        if enhancer:
+            video_name_enhancer = x['video_name']  + '_enhanced.mp4'
+            enhanced_path = os.path.join(video_save_dir, 'temp_'+video_name_enhancer)
+            av_path_enhancer = os.path.join(video_save_dir, video_name_enhancer) 
+            return_path = av_path_enhancer
+
+            try:
+                enhanced_images_gen_with_len = enhancer_generator_with_len(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
+                imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
+            except:
+                enhanced_images_gen_with_len = enhancer_list(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
+                imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
+            
+            save_video_with_watermark(enhanced_path, new_audio_path, av_path_enhancer, watermark= False)
+            print(f'The generated video is named {video_save_dir}/{video_name_enhancer}')
+            os.remove(enhanced_path)
+
+        os.remove(path)
+        os.remove(new_audio_path)
+
+        return return_path
+

animation/SadTalker/src/facerender/modules/dense_motion.py

@@ -0,0 +1,121 @@
+from torch import nn
+import torch.nn.functional as F
+import torch
+from src.facerender.modules.util import Hourglass, make_coordinate_grid, kp2gaussian
+
+from src.facerender.sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
+
+
+class DenseMotionNetwork(nn.Module):
+    """
+    Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving
+    """
+
+    def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress,
+                 estimate_occlusion_map=False):
+        super(DenseMotionNetwork, self).__init__()
+        # self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(feature_channel+1), max_features=max_features, num_blocks=num_blocks)
+        self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks)
+
+        self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3)
+
+        self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1)
+        self.norm = BatchNorm3d(compress, affine=True)
+
+        if estimate_occlusion_map:
+            # self.occlusion = nn.Conv2d(reshape_channel*reshape_depth, 1, kernel_size=7, padding=3)
+            self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3)
+        else:
+            self.occlusion = None
+
+        self.num_kp = num_kp
+
+
+    def create_sparse_motions(self, feature, kp_driving, kp_source):
+        bs, _, d, h, w = feature.shape
+        identity_grid = make_coordinate_grid((d, h, w), type=kp_source['value'].type())
+        identity_grid = identity_grid.view(1, 1, d, h, w, 3)
+        coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 1, 3)
+        
+        # if 'jacobian' in kp_driving:
+        if 'jacobian' in kp_driving and kp_driving['jacobian'] is not None:
+            jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian']))
+            jacobian = jacobian.unsqueeze(-3).unsqueeze(-3).unsqueeze(-3)
+            jacobian = jacobian.repeat(1, 1, d, h, w, 1, 1)
+            coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1))
+            coordinate_grid = coordinate_grid.squeeze(-1)                  
+
+
+        driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 1, 3)    # (bs, num_kp, d, h, w, 3)
+
+        #adding background feature
+        identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1, 1)
+        sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1)                #bs num_kp+1 d h w 3
+        
+        # sparse_motions = driving_to_source
+
+        return sparse_motions
+
+    def create_deformed_feature(self, feature, sparse_motions):
+        bs, _, d, h, w = feature.shape
+        feature_repeat = feature.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp+1, 1, 1, 1, 1, 1)      # (bs, num_kp+1, 1, c, d, h, w)
+        feature_repeat = feature_repeat.view(bs * (self.num_kp+1), -1, d, h, w)                         # (bs*(num_kp+1), c, d, h, w)
+        sparse_motions = sparse_motions.view((bs * (self.num_kp+1), d, h, w, -1))                       # (bs*(num_kp+1), d, h, w, 3) !!!!
+        sparse_deformed = F.grid_sample(feature_repeat, sparse_motions)
+        sparse_deformed = sparse_deformed.view((bs, self.num_kp+1, -1, d, h, w))                        # (bs, num_kp+1, c, d, h, w)
+        return sparse_deformed
+
+    def create_heatmap_representations(self, feature, kp_driving, kp_source):
+        spatial_size = feature.shape[3:]
+        gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01)
+        gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01)
+        heatmap = gaussian_driving - gaussian_source
+
+        # adding background feature
+        zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.type())
+        heatmap = torch.cat([zeros, heatmap], dim=1)
+        heatmap = heatmap.unsqueeze(2)         # (bs, num_kp+1, 1, d, h, w)
+        return heatmap
+
+    def forward(self, feature, kp_driving, kp_source):
+        bs, _, d, h, w = feature.shape
+
+        feature = self.compress(feature)
+        feature = self.norm(feature)
+        feature = F.relu(feature)
+
+        out_dict = dict()
+        sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source)
+        deformed_feature = self.create_deformed_feature(feature, sparse_motion)
+
+        heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source)
+
+        input_ = torch.cat([heatmap, deformed_feature], dim=2)
+        input_ = input_.view(bs, -1, d, h, w)
+
+        # input = deformed_feature.view(bs, -1, d, h, w)      # (bs, num_kp+1 * c, d, h, w)
+
+        prediction = self.hourglass(input_)
+
+
+        mask = self.mask(prediction)
+        mask = F.softmax(mask, dim=1)
+        out_dict['mask'] = mask
+        mask = mask.unsqueeze(2)                                   # (bs, num_kp+1, 1, d, h, w)
+        
+        zeros_mask = torch.zeros_like(mask)   
+        mask = torch.where(mask < 1e-3, zeros_mask, mask) 
+
+        sparse_motion = sparse_motion.permute(0, 1, 5, 2, 3, 4)    # (bs, num_kp+1, 3, d, h, w)
+        deformation = (sparse_motion * mask).sum(dim=1)            # (bs, 3, d, h, w)
+        deformation = deformation.permute(0, 2, 3, 4, 1)           # (bs, d, h, w, 3)
+
+        out_dict['deformation'] = deformation
+
+        if self.occlusion:
+            bs, c, d, h, w = prediction.shape
+            prediction = prediction.view(bs, -1, h, w)
+            occlusion_map = torch.sigmoid(self.occlusion(prediction))
+            out_dict['occlusion_map'] = occlusion_map
+
+        return out_dict

animation/SadTalker/src/facerender/modules/discriminator.py

@@ -0,0 +1,90 @@
+from torch import nn
+import torch.nn.functional as F
+from facerender.modules.util import kp2gaussian
+import torch
+
+
+class DownBlock2d(nn.Module):
+    """
+    Simple block for processing video (encoder).
+    """
+
+    def __init__(self, in_features, out_features, norm=False, kernel_size=4, pool=False, sn=False):
+        super(DownBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size)
+
+        if sn:
+            self.conv = nn.utils.spectral_norm(self.conv)
+
+        if norm:
+            self.norm = nn.InstanceNorm2d(out_features, affine=True)
+        else:
+            self.norm = None
+        self.pool = pool
+
+    def forward(self, x):
+        out = x
+        out = self.conv(out)
+        if self.norm:
+            out = self.norm(out)
+        out = F.leaky_relu(out, 0.2)
+        if self.pool:
+            out = F.avg_pool2d(out, (2, 2))
+        return out
+
+
+class Discriminator(nn.Module):
+    """
+    Discriminator similar to Pix2Pix
+    """
+
+    def __init__(self, num_channels=3, block_expansion=64, num_blocks=4, max_features=512,
+                 sn=False, **kwargs):
+        super(Discriminator, self).__init__()
+
+        down_blocks = []
+        for i in range(num_blocks):
+            down_blocks.append(
+                DownBlock2d(num_channels if i == 0 else min(max_features, block_expansion * (2 ** i)),
+                            min(max_features, block_expansion * (2 ** (i + 1))),
+                            norm=(i != 0), kernel_size=4, pool=(i != num_blocks - 1), sn=sn))
+
+        self.down_blocks = nn.ModuleList(down_blocks)
+        self.conv = nn.Conv2d(self.down_blocks[-1].conv.out_channels, out_channels=1, kernel_size=1)
+        if sn:
+            self.conv = nn.utils.spectral_norm(self.conv)
+
+    def forward(self, x):
+        feature_maps = []
+        out = x
+
+        for down_block in self.down_blocks:
+            feature_maps.append(down_block(out))
+            out = feature_maps[-1]
+        prediction_map = self.conv(out)
+
+        return feature_maps, prediction_map
+
+
+class MultiScaleDiscriminator(nn.Module):
+    """
+    Multi-scale (scale) discriminator
+    """
+
+    def __init__(self, scales=(), **kwargs):
+        super(MultiScaleDiscriminator, self).__init__()
+        self.scales = scales
+        discs = {}
+        for scale in scales:
+            discs[str(scale).replace('.', '-')] = Discriminator(**kwargs)
+        self.discs = nn.ModuleDict(discs)
+
+    def forward(self, x):
+        out_dict = {}
+        for scale, disc in self.discs.items():
+            scale = str(scale).replace('-', '.')
+            key = 'prediction_' + scale
+            feature_maps, prediction_map = disc(x[key])
+            out_dict['feature_maps_' + scale] = feature_maps
+            out_dict['prediction_map_' + scale] = prediction_map
+        return out_dict

animation/SadTalker/src/facerender/modules/generator.py

@@ -0,0 +1,255 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from src.facerender.modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d, ResBlock3d, SPADEResnetBlock
+from src.facerender.modules.dense_motion import DenseMotionNetwork
+
+
+class OcclusionAwareGenerator(nn.Module):
+    """
+    Generator follows NVIDIA architecture.
+    """
+
+    def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth,
+                 num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False):
+        super(OcclusionAwareGenerator, self).__init__()
+
+        if dense_motion_params is not None:
+            self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel,
+                                                           estimate_occlusion_map=estimate_occlusion_map,
+                                                           **dense_motion_params)
+        else:
+            self.dense_motion_network = None
+
+        self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(7, 7), padding=(3, 3))
+
+        down_blocks = []
+        for i in range(num_down_blocks):
+            in_features = min(max_features, block_expansion * (2 ** i))
+            out_features = min(max_features, block_expansion * (2 ** (i + 1)))
+            down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+        self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1)
+
+        self.reshape_channel = reshape_channel
+        self.reshape_depth = reshape_depth
+
+        self.resblocks_3d = torch.nn.Sequential()
+        for i in range(num_resblocks):
+            self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1))
+
+        out_features = block_expansion * (2 ** (num_down_blocks))
+        self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True)
+        self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1)
+
+        self.resblocks_2d = torch.nn.Sequential()
+        for i in range(num_resblocks):
+            self.resblocks_2d.add_module('2dr' + str(i), ResBlock2d(out_features, kernel_size=3, padding=1))
+
+        up_blocks = []
+        for i in range(num_down_blocks):
+            in_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i)))
+            out_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i - 1)))
+            up_blocks.append(UpBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
+        self.up_blocks = nn.ModuleList(up_blocks)
+
+        self.final = nn.Conv2d(block_expansion, image_channel, kernel_size=(7, 7), padding=(3, 3))
+        self.estimate_occlusion_map = estimate_occlusion_map
+        self.image_channel = image_channel
+
+    def deform_input(self, inp, deformation):
+        _, d_old, h_old, w_old, _ = deformation.shape
+        _, _, d, h, w = inp.shape
+        if d_old != d or h_old != h or w_old != w:
+            deformation = deformation.permute(0, 4, 1, 2, 3)
+            deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear')
+            deformation = deformation.permute(0, 2, 3, 4, 1)
+        return F.grid_sample(inp, deformation)
+
+    def forward(self, source_image, kp_driving, kp_source):
+        # Encoding (downsampling) part
+        out = self.first(source_image)
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out)
+        out = self.second(out)
+        bs, c, h, w = out.shape
+        # print(out.shape)
+        feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w) 
+        feature_3d = self.resblocks_3d(feature_3d)
+
+        # Transforming feature representation according to deformation and occlusion
+        output_dict = {}
+        if self.dense_motion_network is not None:
+            dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving,
+                                                     kp_source=kp_source)
+            output_dict['mask'] = dense_motion['mask']
+
+            if 'occlusion_map' in dense_motion:
+                occlusion_map = dense_motion['occlusion_map']
+                output_dict['occlusion_map'] = occlusion_map
+            else:
+                occlusion_map = None
+            deformation = dense_motion['deformation']
+            out = self.deform_input(feature_3d, deformation)
+
+            bs, c, d, h, w = out.shape
+            out = out.view(bs, c*d, h, w)
+            out = self.third(out)
+            out = self.fourth(out)
+
+            if occlusion_map is not None:
+                if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]:
+                    occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear')
+                out = out * occlusion_map
+
+            # output_dict["deformed"] = self.deform_input(source_image, deformation)  # 3d deformation cannot deform 2d image
+
+        # Decoding part
+        out = self.resblocks_2d(out)
+        for i in range(len(self.up_blocks)):
+            out = self.up_blocks[i](out)
+        out = self.final(out)
+        out = F.sigmoid(out)
+
+        output_dict["prediction"] = out
+
+        return output_dict
+
+
+class SPADEDecoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        ic = 256
+        oc = 64
+        norm_G = 'spadespectralinstance'
+        label_nc = 256
+        
+        self.fc = nn.Conv2d(ic, 2 * ic, 3, padding=1)
+        self.G_middle_0 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
+        self.G_middle_1 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
+        self.G_middle_2 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
+        self.G_middle_3 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
+        self.G_middle_4 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
+        self.G_middle_5 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc)
+        self.up_0 = SPADEResnetBlock(2 * ic, ic, norm_G, label_nc)
+        self.up_1 = SPADEResnetBlock(ic, oc, norm_G, label_nc)
+        self.conv_img = nn.Conv2d(oc, 3, 3, padding=1)
+        self.up = nn.Upsample(scale_factor=2)
+        
+    def forward(self, feature):
+        seg = feature
+        x = self.fc(feature)
+        x = self.G_middle_0(x, seg)
+        x = self.G_middle_1(x, seg)
+        x = self.G_middle_2(x, seg)
+        x = self.G_middle_3(x, seg)
+        x = self.G_middle_4(x, seg)
+        x = self.G_middle_5(x, seg)
+        x = self.up(x)                
+        x = self.up_0(x, seg)         # 256, 128, 128
+        x = self.up(x)                
+        x = self.up_1(x, seg)         # 64, 256, 256
+
+        x = self.conv_img(F.leaky_relu(x, 2e-1))
+        # x = torch.tanh(x)
+        x = F.sigmoid(x)
+        
+        return x
+
+
+class OcclusionAwareSPADEGenerator(nn.Module):
+
+    def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth,
+                 num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False):
+        super(OcclusionAwareSPADEGenerator, self).__init__()
+
+        if dense_motion_params is not None:
+            self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel,
+                                                           estimate_occlusion_map=estimate_occlusion_map,
+                                                           **dense_motion_params)
+        else:
+            self.dense_motion_network = None
+
+        self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(3, 3), padding=(1, 1))
+
+        down_blocks = []
+        for i in range(num_down_blocks):
+            in_features = min(max_features, block_expansion * (2 ** i))
+            out_features = min(max_features, block_expansion * (2 ** (i + 1)))
+            down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+        self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1)
+
+        self.reshape_channel = reshape_channel
+        self.reshape_depth = reshape_depth
+
+        self.resblocks_3d = torch.nn.Sequential()
+        for i in range(num_resblocks):
+            self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1))
+
+        out_features = block_expansion * (2 ** (num_down_blocks))
+        self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True)
+        self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1)
+
+        self.estimate_occlusion_map = estimate_occlusion_map
+        self.image_channel = image_channel
+
+        self.decoder = SPADEDecoder()
+
+    def deform_input(self, inp, deformation):
+        _, d_old, h_old, w_old, _ = deformation.shape
+        _, _, d, h, w = inp.shape
+        if d_old != d or h_old != h or w_old != w:
+            deformation = deformation.permute(0, 4, 1, 2, 3)
+            deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear')
+            deformation = deformation.permute(0, 2, 3, 4, 1)
+        return F.grid_sample(inp, deformation)
+
+    def forward(self, source_image, kp_driving, kp_source):
+        # Encoding (downsampling) part
+        out = self.first(source_image)
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out)
+        out = self.second(out)
+        bs, c, h, w = out.shape
+        # print(out.shape)
+        feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w) 
+        feature_3d = self.resblocks_3d(feature_3d)
+
+        # Transforming feature representation according to deformation and occlusion
+        output_dict = {}
+        if self.dense_motion_network is not None:
+            dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving,
+                                                     kp_source=kp_source)
+            output_dict['mask'] = dense_motion['mask']
+
+            # import pdb; pdb.set_trace()
+
+            if 'occlusion_map' in dense_motion:
+                occlusion_map = dense_motion['occlusion_map']
+                output_dict['occlusion_map'] = occlusion_map
+            else:
+                occlusion_map = None
+            deformation = dense_motion['deformation']
+            out = self.deform_input(feature_3d, deformation)
+
+            bs, c, d, h, w = out.shape
+            out = out.view(bs, c*d, h, w)
+            out = self.third(out)
+            out = self.fourth(out)
+
+            # occlusion_map = torch.where(occlusion_map < 0.95, 0, occlusion_map)
+            
+            if occlusion_map is not None:
+                if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]:
+                    occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear')
+                out = out * occlusion_map
+
+        # Decoding part
+        out = self.decoder(out)
+
+        output_dict["prediction"] = out
+        
+        return output_dict

animation/SadTalker/src/facerender/modules/keypoint_detector.py

@@ -0,0 +1,179 @@
+from torch import nn
+import torch
+import torch.nn.functional as F
+
+from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
+from src.facerender.modules.util import KPHourglass, make_coordinate_grid, AntiAliasInterpolation2d, ResBottleneck
+
+
+class KPDetector(nn.Module):
+    """
+    Detecting canonical keypoints. Return keypoint position and jacobian near each keypoint.
+    """
+
+    def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, reshape_channel, reshape_depth,
+                 num_blocks, temperature, estimate_jacobian=False, scale_factor=1, single_jacobian_map=False):
+        super(KPDetector, self).__init__()
+
+        self.predictor = KPHourglass(block_expansion, in_features=image_channel,
+                                     max_features=max_features,  reshape_features=reshape_channel, reshape_depth=reshape_depth, num_blocks=num_blocks)
+
+        # self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=7, padding=3)
+        self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=3, padding=1)
+
+        if estimate_jacobian:
+            self.num_jacobian_maps = 1 if single_jacobian_map else num_kp
+            # self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=7, padding=3)
+            self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=3, padding=1)
+            '''
+            initial as:
+            [[1 0 0]
+             [0 1 0]
+             [0 0 1]]
+            '''
+            self.jacobian.weight.data.zero_()
+            self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float))
+        else:
+            self.jacobian = None
+
+        self.temperature = temperature
+        self.scale_factor = scale_factor
+        if self.scale_factor != 1:
+            self.down = AntiAliasInterpolation2d(image_channel, self.scale_factor)
+
+    def gaussian2kp(self, heatmap):
+        """
+        Extract the mean from a heatmap
+        """
+        shape = heatmap.shape
+        heatmap = heatmap.unsqueeze(-1)
+        grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0)
+        value = (heatmap * grid).sum(dim=(2, 3, 4))
+        kp = {'value': value}
+
+        return kp
+
+    def forward(self, x):
+        if self.scale_factor != 1:
+            x = self.down(x)
+
+        feature_map = self.predictor(x)
+        prediction = self.kp(feature_map)
+
+        final_shape = prediction.shape
+        heatmap = prediction.view(final_shape[0], final_shape[1], -1)
+        heatmap = F.softmax(heatmap / self.temperature, dim=2)
+        heatmap = heatmap.view(*final_shape)
+
+        out = self.gaussian2kp(heatmap)
+
+        if self.jacobian is not None:
+            jacobian_map = self.jacobian(feature_map)
+            jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 9, final_shape[2],
+                                                final_shape[3], final_shape[4])
+            heatmap = heatmap.unsqueeze(2)
+
+            jacobian = heatmap * jacobian_map
+            jacobian = jacobian.view(final_shape[0], final_shape[1], 9, -1)
+            jacobian = jacobian.sum(dim=-1)
+            jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 3, 3)
+            out['jacobian'] = jacobian
+
+        return out
+
+
+class HEEstimator(nn.Module):
+    """
+    Estimating head pose and expression.
+    """
+
+    def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, num_bins=66, estimate_jacobian=True):
+        super(HEEstimator, self).__init__()
+
+        self.conv1 = nn.Conv2d(in_channels=image_channel, out_channels=block_expansion, kernel_size=7, padding=3, stride=2)
+        self.norm1 = BatchNorm2d(block_expansion, affine=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.conv2 = nn.Conv2d(in_channels=block_expansion, out_channels=256, kernel_size=1)
+        self.norm2 = BatchNorm2d(256, affine=True)
+
+        self.block1 = nn.Sequential()
+        for i in range(3):
+            self.block1.add_module('b1_'+ str(i), ResBottleneck(in_features=256, stride=1))
+
+        self.conv3 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1)
+        self.norm3 = BatchNorm2d(512, affine=True)
+        self.block2 = ResBottleneck(in_features=512, stride=2)
+
+        self.block3 = nn.Sequential()
+        for i in range(3):
+            self.block3.add_module('b3_'+ str(i), ResBottleneck(in_features=512, stride=1))
+
+        self.conv4 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1)
+        self.norm4 = BatchNorm2d(1024, affine=True)
+        self.block4 = ResBottleneck(in_features=1024, stride=2)
+
+        self.block5 = nn.Sequential()
+        for i in range(5):
+            self.block5.add_module('b5_'+ str(i), ResBottleneck(in_features=1024, stride=1))
+
+        self.conv5 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=1)
+        self.norm5 = BatchNorm2d(2048, affine=True)
+        self.block6 = ResBottleneck(in_features=2048, stride=2)
+
+        self.block7 = nn.Sequential()
+        for i in range(2):
+            self.block7.add_module('b7_'+ str(i), ResBottleneck(in_features=2048, stride=1))
+
+        self.fc_roll = nn.Linear(2048, num_bins)
+        self.fc_pitch = nn.Linear(2048, num_bins)
+        self.fc_yaw = nn.Linear(2048, num_bins)
+
+        self.fc_t = nn.Linear(2048, 3)
+
+        self.fc_exp = nn.Linear(2048, 3*num_kp)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.norm1(out)
+        out = F.relu(out)
+        out = self.maxpool(out)
+
+        out = self.conv2(out)
+        out = self.norm2(out)
+        out = F.relu(out)
+
+        out = self.block1(out)
+
+        out = self.conv3(out)
+        out = self.norm3(out)
+        out = F.relu(out)
+        out = self.block2(out)
+
+        out = self.block3(out)
+
+        out = self.conv4(out)
+        out = self.norm4(out)
+        out = F.relu(out)
+        out = self.block4(out)
+
+        out = self.block5(out)
+
+        out = self.conv5(out)
+        out = self.norm5(out)
+        out = F.relu(out)
+        out = self.block6(out)
+
+        out = self.block7(out)
+
+        out = F.adaptive_avg_pool2d(out, 1)
+        out = out.view(out.shape[0], -1)
+
+        yaw = self.fc_roll(out)
+        pitch = self.fc_pitch(out)
+        roll = self.fc_yaw(out)
+        t = self.fc_t(out)
+        exp = self.fc_exp(out)
+
+        return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp}
+

animation/SadTalker/src/facerender/modules/make_animation.py

@@ -0,0 +1,170 @@
+from scipy.spatial import ConvexHull
+import torch
+import torch.nn.functional as F
+import numpy as np
+from tqdm import tqdm 
+
+def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
+                 use_relative_movement=False, use_relative_jacobian=False):
+    if adapt_movement_scale:
+        source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
+        driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
+        adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
+    else:
+        adapt_movement_scale = 1
+
+    kp_new = {k: v for k, v in kp_driving.items()}
+
+    if use_relative_movement:
+        kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
+        kp_value_diff *= adapt_movement_scale
+        kp_new['value'] = kp_value_diff + kp_source['value']
+
+        if use_relative_jacobian:
+            jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
+            kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
+
+    return kp_new
+
+def headpose_pred_to_degree(pred):
+    device = pred.device
+    idx_tensor = [idx for idx in range(66)]
+    idx_tensor = torch.FloatTensor(idx_tensor).type_as(pred).to(device)
+    pred = F.softmax(pred)
+    degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
+    return degree
+
+def get_rotation_matrix(yaw, pitch, roll):
+    yaw = yaw / 180 * 3.14
+    pitch = pitch / 180 * 3.14
+    roll = roll / 180 * 3.14
+
+    roll = roll.unsqueeze(1)
+    pitch = pitch.unsqueeze(1)
+    yaw = yaw.unsqueeze(1)
+
+    pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch), 
+                          torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch),
+                          torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1)
+    pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
+
+    yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw), 
+                           torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw),
+                           -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1)
+    yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
+
+    roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll),  
+                         torch.sin(roll), torch.cos(roll), torch.zeros_like(roll),
+                         torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1)
+    roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
+
+    rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat)
+
+    return rot_mat
+
+def keypoint_transformation(kp_canonical, he, wo_exp=False):
+    kp = kp_canonical['value']    # (bs, k, 3) 
+    yaw, pitch, roll= he['yaw'], he['pitch'], he['roll']      
+    yaw = headpose_pred_to_degree(yaw) 
+    pitch = headpose_pred_to_degree(pitch)
+    roll = headpose_pred_to_degree(roll)
+
+    if 'yaw_in' in he:
+        yaw = he['yaw_in']
+    if 'pitch_in' in he:
+        pitch = he['pitch_in']
+    if 'roll_in' in he:
+        roll = he['roll_in']
+
+    rot_mat = get_rotation_matrix(yaw, pitch, roll)    # (bs, 3, 3)
+
+    t, exp = he['t'], he['exp']
+    if wo_exp:
+        exp =  exp*0  
+    
+    # keypoint rotation
+    kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
+
+    # keypoint translation
+    t[:, 0] = t[:, 0]*0
+    t[:, 2] = t[:, 2]*0
+    t = t.unsqueeze(1).repeat(1, kp.shape[1], 1)
+    kp_t = kp_rotated + t
+
+    # add expression deviation 
+    exp = exp.view(exp.shape[0], -1, 3)
+    kp_transformed = kp_t + exp
+
+    return {'value': kp_transformed}
+
+
+
+def make_animation(source_image, source_semantics, target_semantics,
+                            generator, kp_detector, he_estimator, mapping, 
+                            yaw_c_seq=None, pitch_c_seq=None, roll_c_seq=None,
+                            use_exp=True, use_half=False):
+    with torch.no_grad():
+        predictions = []
+
+        kp_canonical = kp_detector(source_image)
+        he_source = mapping(source_semantics)
+        kp_source = keypoint_transformation(kp_canonical, he_source)
+    
+        for frame_idx in tqdm(range(target_semantics.shape[1]), 'Face Renderer:'):
+            # still check the dimension
+            # print(target_semantics.shape, source_semantics.shape)
+            target_semantics_frame = target_semantics[:, frame_idx]
+            he_driving = mapping(target_semantics_frame)
+            if yaw_c_seq is not None:
+                he_driving['yaw_in'] = yaw_c_seq[:, frame_idx]
+            if pitch_c_seq is not None:
+                he_driving['pitch_in'] = pitch_c_seq[:, frame_idx] 
+            if roll_c_seq is not None:
+                he_driving['roll_in'] = roll_c_seq[:, frame_idx] 
+            
+            kp_driving = keypoint_transformation(kp_canonical, he_driving)
+                
+            kp_norm = kp_driving
+            out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)
+            '''
+            source_image_new = out['prediction'].squeeze(1)
+            kp_canonical_new =  kp_detector(source_image_new)
+            he_source_new = he_estimator(source_image_new) 
+            kp_source_new = keypoint_transformation(kp_canonical_new, he_source_new, wo_exp=True)
+            kp_driving_new = keypoint_transformation(kp_canonical_new, he_driving, wo_exp=True)
+            out = generator(source_image_new, kp_source=kp_source_new, kp_driving=kp_driving_new)
+            '''
+            predictions.append(out['prediction'])
+        predictions_ts = torch.stack(predictions, dim=1)
+    return predictions_ts
+
+class AnimateModel(torch.nn.Module):
+    """
+    Merge all generator related updates into single model for better multi-gpu usage
+    """
+
+    def __init__(self, generator, kp_extractor, mapping):
+        super(AnimateModel, self).__init__()
+        self.kp_extractor = kp_extractor
+        self.generator = generator
+        self.mapping = mapping
+
+        self.kp_extractor.eval()
+        self.generator.eval()
+        self.mapping.eval()
+
+    def forward(self, x):
+        
+        source_image = x['source_image']
+        source_semantics = x['source_semantics']
+        target_semantics = x['target_semantics']
+        yaw_c_seq = x['yaw_c_seq']
+        pitch_c_seq = x['pitch_c_seq']
+        roll_c_seq = x['roll_c_seq']
+
+        predictions_video = make_animation(source_image, source_semantics, target_semantics,
+                                        self.generator, self.kp_extractor,
+                                        self.mapping, use_exp = True,
+                                        yaw_c_seq=yaw_c_seq, pitch_c_seq=pitch_c_seq, roll_c_seq=roll_c_seq)
+        
+        return predictions_video

animation/SadTalker/src/facerender/modules/mapping.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class MappingNet(nn.Module):
+    def __init__(self, coeff_nc, descriptor_nc, layer, num_kp, num_bins):
+        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
+
+        self.fc_roll = nn.Linear(descriptor_nc, num_bins)
+        self.fc_pitch = nn.Linear(descriptor_nc, num_bins)
+        self.fc_yaw = nn.Linear(descriptor_nc, num_bins)
+        self.fc_t = nn.Linear(descriptor_nc, 3)
+        self.fc_exp = nn.Linear(descriptor_nc, 3*num_kp)
+
+    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)
+        out = out.view(out.shape[0], -1)
+        #print('out:', out.shape)
+
+        yaw = self.fc_yaw(out)
+        pitch = self.fc_pitch(out)
+        roll = self.fc_roll(out)
+        t = self.fc_t(out)
+        exp = self.fc_exp(out)
+
+        return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} 

animation/SadTalker/src/facerender/modules/util.py

@@ -0,0 +1,564 @@
+from torch import nn
+
+import torch.nn.functional as F
+import torch
+
+from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
+from src.facerender.sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
+
+import torch.nn.utils.spectral_norm as spectral_norm
+
+
+def kp2gaussian(kp, spatial_size, kp_variance):
+    """
+    Transform a keypoint into gaussian like representation
+    """
+    mean = kp['value']
+
+    coordinate_grid = make_coordinate_grid(spatial_size, mean.type())
+    number_of_leading_dimensions = len(mean.shape) - 1
+    shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
+    coordinate_grid = coordinate_grid.view(*shape)
+    repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1)
+    coordinate_grid = coordinate_grid.repeat(*repeats)
+
+    # Preprocess kp shape
+    shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3)
+    mean = mean.view(*shape)
+
+    mean_sub = (coordinate_grid - mean)
+
+    out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
+
+    return out
+
+def make_coordinate_grid_2d(spatial_size, type):
+    """
+    Create a meshgrid [-1,1] x [-1,1] of given spatial_size.
+    """
+    h, w = spatial_size
+    x = torch.arange(w).type(type)
+    y = torch.arange(h).type(type)
+
+    x = (2 * (x / (w - 1)) - 1)
+    y = (2 * (y / (h - 1)) - 1)
+
+    yy = y.view(-1, 1).repeat(1, w)
+    xx = x.view(1, -1).repeat(h, 1)
+
+    meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
+
+    return meshed
+
+
+def make_coordinate_grid(spatial_size, type):
+    d, h, w = spatial_size
+    x = torch.arange(w).type(type)
+    y = torch.arange(h).type(type)
+    z = torch.arange(d).type(type)
+
+    x = (2 * (x / (w - 1)) - 1)
+    y = (2 * (y / (h - 1)) - 1)
+    z = (2 * (z / (d - 1)) - 1)
+   
+    yy = y.view(1, -1, 1).repeat(d, 1, w)
+    xx = x.view(1, 1, -1).repeat(d, h, 1)
+    zz = z.view(-1, 1, 1).repeat(1, h, w)
+
+    meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3)
+
+    return meshed
+
+
+class ResBottleneck(nn.Module):
+    def __init__(self, in_features, stride):
+        super(ResBottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features//4, kernel_size=1)
+        self.conv2 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features//4, kernel_size=3, padding=1, stride=stride)
+        self.conv3 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features, kernel_size=1)
+        self.norm1 = BatchNorm2d(in_features//4, affine=True)
+        self.norm2 = BatchNorm2d(in_features//4, affine=True)
+        self.norm3 = BatchNorm2d(in_features, affine=True)
+
+        self.stride = stride
+        if self.stride != 1:
+            self.skip = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=1, stride=stride)
+            self.norm4 = BatchNorm2d(in_features, affine=True)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.norm1(out)
+        out = F.relu(out)
+        out = self.conv2(out)
+        out = self.norm2(out)
+        out = F.relu(out)
+        out = self.conv3(out)
+        out = self.norm3(out)
+        if self.stride != 1:
+            x = self.skip(x)
+            x = self.norm4(x)
+        out += x
+        out = F.relu(out)
+        return out
+
+
+class ResBlock2d(nn.Module):
+    """
+    Res block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, kernel_size, padding):
+        super(ResBlock2d, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.norm1 = BatchNorm2d(in_features, affine=True)
+        self.norm2 = BatchNorm2d(in_features, affine=True)
+
+    def forward(self, x):
+        out = self.norm1(x)
+        out = F.relu(out)
+        out = self.conv1(out)
+        out = self.norm2(out)
+        out = F.relu(out)
+        out = self.conv2(out)
+        out += x
+        return out
+
+
+class ResBlock3d(nn.Module):
+    """
+    Res block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, kernel_size, padding):
+        super(ResBlock3d, self).__init__()
+        self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.norm1 = BatchNorm3d(in_features, affine=True)
+        self.norm2 = BatchNorm3d(in_features, affine=True)
+
+    def forward(self, x):
+        out = self.norm1(x)
+        out = F.relu(out)
+        out = self.conv1(out)
+        out = self.norm2(out)
+        out = F.relu(out)
+        out = self.conv2(out)
+        out += x
+        return out
+
+
+class UpBlock2d(nn.Module):
+    """
+    Upsampling block for use in decoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(UpBlock2d, self).__init__()
+
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = BatchNorm2d(out_features, affine=True)
+
+    def forward(self, x):
+        out = F.interpolate(x, scale_factor=2)
+        out = self.conv(out)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+class UpBlock3d(nn.Module):
+    """
+    Upsampling block for use in decoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(UpBlock3d, self).__init__()
+
+        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = BatchNorm3d(out_features, affine=True)
+
+    def forward(self, x):
+        # out = F.interpolate(x, scale_factor=(1, 2, 2), mode='trilinear')
+        out = F.interpolate(x, scale_factor=(1, 2, 2))
+        out = self.conv(out)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class DownBlock2d(nn.Module):
+    """
+    Downsampling block for use in encoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(DownBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = BatchNorm2d(out_features, affine=True)
+        self.pool = nn.AvgPool2d(kernel_size=(2, 2))
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        out = self.pool(out)
+        return out
+
+
+class DownBlock3d(nn.Module):
+    """
+    Downsampling block for use in encoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(DownBlock3d, self).__init__()
+        '''
+        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups, stride=(1, 2, 2))
+        '''
+        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = BatchNorm3d(out_features, affine=True)
+        self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2))
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        out = self.pool(out)
+        return out
+
+
+class SameBlock2d(nn.Module):
+    """
+    Simple block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False):
+        super(SameBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features,
+                              kernel_size=kernel_size, padding=padding, groups=groups)
+        self.norm = BatchNorm2d(out_features, affine=True)
+        if lrelu:
+            self.ac = nn.LeakyReLU()
+        else:
+            self.ac = nn.ReLU()
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = self.ac(out)
+        return out
+
+
+class Encoder(nn.Module):
+    """
+    Hourglass Encoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Encoder, self).__init__()
+
+        down_blocks = []
+        for i in range(num_blocks):
+            down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
+                                           min(max_features, block_expansion * (2 ** (i + 1))),
+                                           kernel_size=3, padding=1))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+    def forward(self, x):
+        outs = [x]
+        for down_block in self.down_blocks:
+            outs.append(down_block(outs[-1]))
+        return outs
+
+
+class Decoder(nn.Module):
+    """
+    Hourglass Decoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Decoder, self).__init__()
+
+        up_blocks = []
+
+        for i in range(num_blocks)[::-1]:
+            in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))
+            out_filters = min(max_features, block_expansion * (2 ** i))
+            up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
+
+        self.up_blocks = nn.ModuleList(up_blocks)
+        # self.out_filters = block_expansion
+        self.out_filters = block_expansion + in_features
+
+        self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1)
+        self.norm = BatchNorm3d(self.out_filters, affine=True)
+
+    def forward(self, x):
+        out = x.pop()
+        # for up_block in self.up_blocks[:-1]:
+        for up_block in self.up_blocks:
+            out = up_block(out)
+            skip = x.pop()
+            out = torch.cat([out, skip], dim=1)
+        # out = self.up_blocks[-1](out)
+        out = self.conv(out)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class Hourglass(nn.Module):
+    """
+    Hourglass architecture.
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Hourglass, self).__init__()
+        self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features)
+        self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features)
+        self.out_filters = self.decoder.out_filters
+
+    def forward(self, x):
+        return self.decoder(self.encoder(x))
+
+
+class KPHourglass(nn.Module):
+    """
+    Hourglass architecture.
+    """ 
+
+    def __init__(self, block_expansion, in_features, reshape_features, reshape_depth, num_blocks=3, max_features=256):
+        super(KPHourglass, self).__init__()
+        
+        self.down_blocks = nn.Sequential()
+        for i in range(num_blocks):
+            self.down_blocks.add_module('down'+ str(i), DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
+                                                                   min(max_features, block_expansion * (2 ** (i + 1))),
+                                                                   kernel_size=3, padding=1))
+
+        in_filters = min(max_features, block_expansion * (2 ** num_blocks))
+        self.conv = nn.Conv2d(in_channels=in_filters, out_channels=reshape_features, kernel_size=1)
+
+        self.up_blocks = nn.Sequential()
+        for i in range(num_blocks):
+            in_filters = min(max_features, block_expansion * (2 ** (num_blocks - i)))
+            out_filters = min(max_features, block_expansion * (2 ** (num_blocks - i - 1)))
+            self.up_blocks.add_module('up'+ str(i), UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
+
+        self.reshape_depth = reshape_depth
+        self.out_filters = out_filters
+
+    def forward(self, x):
+        out = self.down_blocks(x)
+        out = self.conv(out)
+        bs, c, h, w = out.shape
+        out = out.view(bs, c//self.reshape_depth, self.reshape_depth, h, w)
+        out = self.up_blocks(out)
+
+        return out
+        
+
+
+class AntiAliasInterpolation2d(nn.Module):
+    """
+    Band-limited downsampling, for better preservation of the input signal.
+    """
+    def __init__(self, channels, scale):
+        super(AntiAliasInterpolation2d, self).__init__()
+        sigma = (1 / scale - 1) / 2
+        kernel_size = 2 * round(sigma * 4) + 1
+        self.ka = kernel_size // 2
+        self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka
+
+        kernel_size = [kernel_size, kernel_size]
+        sigma = [sigma, sigma]
+        # The gaussian kernel is the product of the
+        # gaussian function of each dimension.
+        kernel = 1
+        meshgrids = torch.meshgrid(
+            [
+                torch.arange(size, dtype=torch.float32)
+                for size in kernel_size
+                ]
+        )
+        for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
+            mean = (size - 1) / 2
+            kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))
+
+        # Make sure sum of values in gaussian kernel equals 1.
+        kernel = kernel / torch.sum(kernel)
+        # Reshape to depthwise convolutional weight
+        kernel = kernel.view(1, 1, *kernel.size())
+        kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
+
+        self.register_buffer('weight', kernel)
+        self.groups = channels
+        self.scale = scale
+        inv_scale = 1 / scale
+        self.int_inv_scale = int(inv_scale)
+
+    def forward(self, input):
+        if self.scale == 1.0:
+            return input
+
+        out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))
+        out = F.conv2d(out, weight=self.weight, groups=self.groups)
+        out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale]
+
+        return out
+
+
+class SPADE(nn.Module):
+    def __init__(self, norm_nc, label_nc):
+        super().__init__()
+
+        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
+        nhidden = 128
+
+        self.mlp_shared = nn.Sequential(
+            nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1),
+            nn.ReLU())
+        self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
+        self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
+
+    def forward(self, x, segmap):
+        normalized = self.param_free_norm(x)
+        segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
+        actv = self.mlp_shared(segmap)
+        gamma = self.mlp_gamma(actv)
+        beta = self.mlp_beta(actv)
+        out = normalized * (1 + gamma) + beta
+        return out
+    
+
+class SPADEResnetBlock(nn.Module):
+    def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1):
+        super().__init__()
+        # Attributes
+        self.learned_shortcut = (fin != fout)
+        fmiddle = min(fin, fout)
+        self.use_se = use_se
+        # create conv layers
+        self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation)
+        self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation)
+        if self.learned_shortcut:
+            self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)
+        # apply spectral norm if specified
+        if 'spectral' in norm_G:
+            self.conv_0 = spectral_norm(self.conv_0)
+            self.conv_1 = spectral_norm(self.conv_1)
+            if self.learned_shortcut:
+                self.conv_s = spectral_norm(self.conv_s)
+        # define normalization layers
+        self.norm_0 = SPADE(fin, label_nc)
+        self.norm_1 = SPADE(fmiddle, label_nc)
+        if self.learned_shortcut:
+            self.norm_s = SPADE(fin, label_nc)
+
+    def forward(self, x, seg1):
+        x_s = self.shortcut(x, seg1)
+        dx = self.conv_0(self.actvn(self.norm_0(x, seg1)))
+        dx = self.conv_1(self.actvn(self.norm_1(dx, seg1)))
+        out = x_s + dx
+        return out
+
+    def shortcut(self, x, seg1):
+        if self.learned_shortcut:
+            x_s = self.conv_s(self.norm_s(x, seg1))
+        else:
+            x_s = x
+        return x_s
+
+    def actvn(self, x):
+        return F.leaky_relu(x, 2e-1)
+
+class audio2image(nn.Module):
+    def __init__(self, generator, kp_extractor, he_estimator_video, he_estimator_audio, train_params):
+        super().__init__()
+        # Attributes
+        self.generator = generator
+        self.kp_extractor = kp_extractor
+        self.he_estimator_video = he_estimator_video
+        self.he_estimator_audio = he_estimator_audio
+        self.train_params = train_params
+
+    def headpose_pred_to_degree(self, pred):
+        device = pred.device
+        idx_tensor = [idx for idx in range(66)]
+        idx_tensor = torch.FloatTensor(idx_tensor).to(device)
+        pred = F.softmax(pred)
+        degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
+
+        return degree
+    
+    def get_rotation_matrix(self, yaw, pitch, roll):
+        yaw = yaw / 180 * 3.14
+        pitch = pitch / 180 * 3.14
+        roll = roll / 180 * 3.14
+
+        roll = roll.unsqueeze(1)
+        pitch = pitch.unsqueeze(1)
+        yaw = yaw.unsqueeze(1)
+
+        roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll), 
+                          torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll),
+                          torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1)
+        roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
+
+        pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch), 
+                           torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch),
+                           -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1)
+        pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
+
+        yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw),  
+                         torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw),
+                         torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1)
+        yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
+
+        rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat)
+
+        return rot_mat
+
+    def keypoint_transformation(self, kp_canonical, he):
+        kp = kp_canonical['value']    # (bs, k, 3)
+        yaw, pitch, roll = he['yaw'], he['pitch'], he['roll']
+        t, exp = he['t'], he['exp']
+    
+        yaw = self.headpose_pred_to_degree(yaw)
+        pitch = self.headpose_pred_to_degree(pitch)
+        roll = self.headpose_pred_to_degree(roll)
+
+        rot_mat = self.get_rotation_matrix(yaw, pitch, roll)    # (bs, 3, 3)
+    
+        # keypoint rotation
+        kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
+
+    
+
+        # keypoint translation
+        t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1)
+        kp_t = kp_rotated + t
+
+        # add expression deviation 
+        exp = exp.view(exp.shape[0], -1, 3)
+        kp_transformed = kp_t + exp
+
+        return {'value': kp_transformed}
+
+    def forward(self, source_image, target_audio):
+        pose_source = self.he_estimator_video(source_image)
+        pose_generated = self.he_estimator_audio(target_audio)
+        kp_canonical = self.kp_extractor(source_image)
+        kp_source = self.keypoint_transformation(kp_canonical, pose_source)
+        kp_transformed_generated = self.keypoint_transformation(kp_canonical, pose_generated)
+        generated = self.generator(source_image, kp_source=kp_source, kp_driving=kp_transformed_generated)
+        return generated

animation/SadTalker/src/facerender/sync_batchnorm/__init__.py

@@ -0,0 +1,12 @@
+# -*- coding: utf-8 -*-
+# File   : __init__.py
+# Author : Jiayuan Mao
+# Email  : maojiayuan@gmail.com
+# Date   : 27/01/2018
+# 
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
+from .replicate import DataParallelWithCallback, patch_replication_callback

animation/SadTalker/src/facerender/sync_batchnorm/batchnorm.py

@@ -0,0 +1,315 @@
+# -*- coding: utf-8 -*-
+# File   : batchnorm.py
+# Author : Jiayuan Mao
+# Email  : maojiayuan@gmail.com
+# Date   : 27/01/2018
+# 
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import collections
+
+import torch
+import torch.nn.functional as F
+
+from torch.nn.modules.batchnorm import _BatchNorm
+from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
+
+from .comm import SyncMaster
+
+__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
+
+
+def _sum_ft(tensor):
+    """sum over the first and last dimention"""
+    return tensor.sum(dim=0).sum(dim=-1)
+
+
+def _unsqueeze_ft(tensor):
+    """add new dementions at the front and the tail"""
+    return tensor.unsqueeze(0).unsqueeze(-1)
+
+
+_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
+_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
+
+
+class _SynchronizedBatchNorm(_BatchNorm):
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
+        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
+
+        self._sync_master = SyncMaster(self._data_parallel_master)
+
+        self._is_parallel = False
+        self._parallel_id = None
+        self._slave_pipe = None
+
+    def forward(self, input):
+        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
+        if not (self._is_parallel and self.training):
+            return F.batch_norm(
+                input, self.running_mean, self.running_var, self.weight, self.bias,
+                self.training, self.momentum, self.eps)
+
+        # Resize the input to (B, C, -1).
+        input_shape = input.size()
+        input = input.view(input.size(0), self.num_features, -1)
+
+        # Compute the sum and square-sum.
+        sum_size = input.size(0) * input.size(2)
+        input_sum = _sum_ft(input)
+        input_ssum = _sum_ft(input ** 2)
+
+        # Reduce-and-broadcast the statistics.
+        if self._parallel_id == 0:
+            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
+        else:
+            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
+
+        # Compute the output.
+        if self.affine:
+            # MJY:: Fuse the multiplication for speed.
+            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
+        else:
+            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
+
+        # Reshape it.
+        return output.view(input_shape)
+
+    def __data_parallel_replicate__(self, ctx, copy_id):
+        self._is_parallel = True
+        self._parallel_id = copy_id
+
+        # parallel_id == 0 means master device.
+        if self._parallel_id == 0:
+            ctx.sync_master = self._sync_master
+        else:
+            self._slave_pipe = ctx.sync_master.register_slave(copy_id)
+
+    def _data_parallel_master(self, intermediates):
+        """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
+
+        # Always using same "device order" makes the ReduceAdd operation faster.
+        # Thanks to:: Tete Xiao (http://tetexiao.com/)
+        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
+
+        to_reduce = [i[1][:2] for i in intermediates]
+        to_reduce = [j for i in to_reduce for j in i]  # flatten
+        target_gpus = [i[1].sum.get_device() for i in intermediates]
+
+        sum_size = sum([i[1].sum_size for i in intermediates])
+        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
+        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
+
+        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
+
+        outputs = []
+        for i, rec in enumerate(intermediates):
+            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
+
+        return outputs
+
+    def _compute_mean_std(self, sum_, ssum, size):
+        """Compute the mean and standard-deviation with sum and square-sum. This method
+        also maintains the moving average on the master device."""
+        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
+        mean = sum_ / size
+        sumvar = ssum - sum_ * mean
+        unbias_var = sumvar / (size - 1)
+        bias_var = sumvar / size
+
+        self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
+        self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
+
+        return mean, bias_var.clamp(self.eps) ** -0.5
+
+
+class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
+    r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
+    mini-batch.
+
+    .. math::
+
+        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+    This module differs from the built-in PyTorch BatchNorm1d as the mean and
+    standard-deviation are reduced across all devices during training.
+
+    For example, when one uses `nn.DataParallel` to wrap the network during
+    training, PyTorch's implementation normalize the tensor on each device using
+    the statistics only on that device, which accelerated the computation and
+    is also easy to implement, but the statistics might be inaccurate.
+    Instead, in this synchronized version, the statistics will be computed
+    over all training samples distributed on multiple devices.
+    
+    Note that, for one-GPU or CPU-only case, this module behaves exactly same
+    as the built-in PyTorch implementation.
+
+    The mean and standard-deviation are calculated per-dimension over
+    the mini-batches and gamma and beta are learnable parameter vectors
+    of size C (where C is the input size).
+
+    During training, this layer keeps a running estimate of its computed mean
+    and variance. The running sum is kept with a default momentum of 0.1.
+
+    During evaluation, this running mean/variance is used for normalization.
+
+    Because the BatchNorm is done over the `C` dimension, computing statistics
+    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
+
+    Args:
+        num_features: num_features from an expected input of size
+            `batch_size x num_features [x width]`
+        eps: a value added to the denominator for numerical stability.
+            Default: 1e-5
+        momentum: the value used for the running_mean and running_var
+            computation. Default: 0.1
+        affine: a boolean value that when set to ``True``, gives the layer learnable
+            affine parameters. Default: ``True``
+
+    Shape:
+        - Input: :math:`(N, C)` or :math:`(N, C, L)`
+        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
+
+    Examples:
+        >>> # With Learnable Parameters
+        >>> m = SynchronizedBatchNorm1d(100)
+        >>> # Without Learnable Parameters
+        >>> m = SynchronizedBatchNorm1d(100, affine=False)
+        >>> input = torch.autograd.Variable(torch.randn(20, 100))
+        >>> output = m(input)
+    """
+
+    def _check_input_dim(self, input):
+        if input.dim() != 2 and input.dim() != 3:
+            raise ValueError('expected 2D or 3D input (got {}D input)'
+                             .format(input.dim()))
+        super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
+
+
+class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
+    r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
+    of 3d inputs
+
+    .. math::
+
+        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+    This module differs from the built-in PyTorch BatchNorm2d as the mean and
+    standard-deviation are reduced across all devices during training.
+
+    For example, when one uses `nn.DataParallel` to wrap the network during
+    training, PyTorch's implementation normalize the tensor on each device using
+    the statistics only on that device, which accelerated the computation and
+    is also easy to implement, but the statistics might be inaccurate.
+    Instead, in this synchronized version, the statistics will be computed
+    over all training samples distributed on multiple devices.
+    
+    Note that, for one-GPU or CPU-only case, this module behaves exactly same
+    as the built-in PyTorch implementation.
+
+    The mean and standard-deviation are calculated per-dimension over
+    the mini-batches and gamma and beta are learnable parameter vectors
+    of size C (where C is the input size).
+
+    During training, this layer keeps a running estimate of its computed mean
+    and variance. The running sum is kept with a default momentum of 0.1.
+
+    During evaluation, this running mean/variance is used for normalization.
+
+    Because the BatchNorm is done over the `C` dimension, computing statistics
+    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
+
+    Args:
+        num_features: num_features from an expected input of
+            size batch_size x num_features x height x width
+        eps: a value added to the denominator for numerical stability.
+            Default: 1e-5
+        momentum: the value used for the running_mean and running_var
+            computation. Default: 0.1
+        affine: a boolean value that when set to ``True``, gives the layer learnable
+            affine parameters. Default: ``True``
+
+    Shape:
+        - Input: :math:`(N, C, H, W)`
+        - Output: :math:`(N, C, H, W)` (same shape as input)
+
+    Examples:
+        >>> # With Learnable Parameters
+        >>> m = SynchronizedBatchNorm2d(100)
+        >>> # Without Learnable Parameters
+        >>> m = SynchronizedBatchNorm2d(100, affine=False)
+        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
+        >>> output = m(input)
+    """
+
+    def _check_input_dim(self, input):
+        if input.dim() != 4:
+            raise ValueError('expected 4D input (got {}D input)'
+                             .format(input.dim()))
+        super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
+
+
+class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
+    r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
+    of 4d inputs
+
+    .. math::
+
+        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
+
+    This module differs from the built-in PyTorch BatchNorm3d as the mean and
+    standard-deviation are reduced across all devices during training.
+
+    For example, when one uses `nn.DataParallel` to wrap the network during
+    training, PyTorch's implementation normalize the tensor on each device using
+    the statistics only on that device, which accelerated the computation and
+    is also easy to implement, but the statistics might be inaccurate.
+    Instead, in this synchronized version, the statistics will be computed
+    over all training samples distributed on multiple devices.
+    
+    Note that, for one-GPU or CPU-only case, this module behaves exactly same
+    as the built-in PyTorch implementation.
+
+    The mean and standard-deviation are calculated per-dimension over
+    the mini-batches and gamma and beta are learnable parameter vectors
+    of size C (where C is the input size).
+
+    During training, this layer keeps a running estimate of its computed mean
+    and variance. The running sum is kept with a default momentum of 0.1.
+
+    During evaluation, this running mean/variance is used for normalization.
+
+    Because the BatchNorm is done over the `C` dimension, computing statistics
+    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
+    or Spatio-temporal BatchNorm
+
+    Args:
+        num_features: num_features from an expected input of
+            size batch_size x num_features x depth x height x width
+        eps: a value added to the denominator for numerical stability.
+            Default: 1e-5
+        momentum: the value used for the running_mean and running_var
+            computation. Default: 0.1
+        affine: a boolean value that when set to ``True``, gives the layer learnable
+            affine parameters. Default: ``True``
+
+    Shape:
+        - Input: :math:`(N, C, D, H, W)`
+        - Output: :math:`(N, C, D, H, W)` (same shape as input)
+
+    Examples:
+        >>> # With Learnable Parameters
+        >>> m = SynchronizedBatchNorm3d(100)
+        >>> # Without Learnable Parameters
+        >>> m = SynchronizedBatchNorm3d(100, affine=False)
+        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
+        >>> output = m(input)
+    """
+
+    def _check_input_dim(self, input):
+        if input.dim() != 5:
+            raise ValueError('expected 5D input (got {}D input)'
+                             .format(input.dim()))
+        super(SynchronizedBatchNorm3d, self)._check_input_dim(input)

animation/SadTalker/src/facerender/sync_batchnorm/comm.py

@@ -0,0 +1,137 @@
+# -*- coding: utf-8 -*-
+# File   : comm.py
+# Author : Jiayuan Mao
+# Email  : maojiayuan@gmail.com
+# Date   : 27/01/2018
+# 
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import queue
+import collections
+import threading
+
+__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']
+
+
+class FutureResult(object):
+    """A thread-safe future implementation. Used only as one-to-one pipe."""
+
+    def __init__(self):
+        self._result = None
+        self._lock = threading.Lock()
+        self._cond = threading.Condition(self._lock)
+
+    def put(self, result):
+        with self._lock:
+            assert self._result is None, 'Previous result has\'t been fetched.'
+            self._result = result
+            self._cond.notify()
+
+    def get(self):
+        with self._lock:
+            if self._result is None:
+                self._cond.wait()
+
+            res = self._result
+            self._result = None
+            return res
+
+
+_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
+_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])
+
+
+class SlavePipe(_SlavePipeBase):
+    """Pipe for master-slave communication."""
+
+    def run_slave(self, msg):
+        self.queue.put((self.identifier, msg))
+        ret = self.result.get()
+        self.queue.put(True)
+        return ret
+
+
+class SyncMaster(object):
+    """An abstract `SyncMaster` object.
+
+    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
+    call `register(id)` and obtain an `SlavePipe` to communicate with the master.
+    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
+    and passed to a registered callback.
+    - After receiving the messages, the master device should gather the information and determine to message passed
+    back to each slave devices.
+    """
+
+    def __init__(self, master_callback):
+        """
+
+        Args:
+            master_callback: a callback to be invoked after having collected messages from slave devices.
+        """
+        self._master_callback = master_callback
+        self._queue = queue.Queue()
+        self._registry = collections.OrderedDict()
+        self._activated = False
+
+    def __getstate__(self):
+        return {'master_callback': self._master_callback}
+
+    def __setstate__(self, state):
+        self.__init__(state['master_callback'])
+
+    def register_slave(self, identifier):
+        """
+        Register an slave device.
+
+        Args:
+            identifier: an identifier, usually is the device id.
+
+        Returns: a `SlavePipe` object which can be used to communicate with the master device.
+
+        """
+        if self._activated:
+            assert self._queue.empty(), 'Queue is not clean before next initialization.'
+            self._activated = False
+            self._registry.clear()
+        future = FutureResult()
+        self._registry[identifier] = _MasterRegistry(future)
+        return SlavePipe(identifier, self._queue, future)
+
+    def run_master(self, master_msg):
+        """
+        Main entry for the master device in each forward pass.
+        The messages were first collected from each devices (including the master device), and then
+        an callback will be invoked to compute the message to be sent back to each devices
+        (including the master device).
+
+        Args:
+            master_msg: the message that the master want to send to itself. This will be placed as the first
+            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.
+
+        Returns: the message to be sent back to the master device.
+
+        """
+        self._activated = True
+
+        intermediates = [(0, master_msg)]
+        for i in range(self.nr_slaves):
+            intermediates.append(self._queue.get())
+
+        results = self._master_callback(intermediates)
+        assert results[0][0] == 0, 'The first result should belongs to the master.'
+
+        for i, res in results:
+            if i == 0:
+                continue
+            self._registry[i].result.put(res)
+
+        for i in range(self.nr_slaves):
+            assert self._queue.get() is True
+
+        return results[0][1]
+
+    @property
+    def nr_slaves(self):
+        return len(self._registry)

animation/SadTalker/src/facerender/sync_batchnorm/replicate.py

@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+# File   : replicate.py
+# Author : Jiayuan Mao
+# Email  : maojiayuan@gmail.com
+# Date   : 27/01/2018
+# 
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import functools
+
+from torch.nn.parallel.data_parallel import DataParallel
+
+__all__ = [
+    'CallbackContext',
+    'execute_replication_callbacks',
+    'DataParallelWithCallback',
+    'patch_replication_callback'
+]
+
+
+class CallbackContext(object):
+    pass
+
+
+def execute_replication_callbacks(modules):
+    """
+    Execute an replication callback `__data_parallel_replicate__` on each module created by original replication.
+
+    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+    Note that, as all modules are isomorphism, we assign each sub-module with a context
+    (shared among multiple copies of this module on different devices).
+    Through this context, different copies can share some information.
+
+    We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback
+    of any slave copies.
+    """
+    master_copy = modules[0]
+    nr_modules = len(list(master_copy.modules()))
+    ctxs = [CallbackContext() for _ in range(nr_modules)]
+
+    for i, module in enumerate(modules):
+        for j, m in enumerate(module.modules()):
+            if hasattr(m, '__data_parallel_replicate__'):
+                m.__data_parallel_replicate__(ctxs[j], i)
+
+
+class DataParallelWithCallback(DataParallel):
+    """
+    Data Parallel with a replication callback.
+
+    An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by
+    original `replicate` function.
+    The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)`
+
+    Examples:
+        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+        # sync_bn.__data_parallel_replicate__ will be invoked.
+    """
+
+    def replicate(self, module, device_ids):
+        modules = super(DataParallelWithCallback, self).replicate(module, device_ids)
+        execute_replication_callbacks(modules)
+        return modules
+
+
+def patch_replication_callback(data_parallel):
+    """
+    Monkey-patch an existing `DataParallel` object. Add the replication callback.
+    Useful when you have customized `DataParallel` implementation.
+
+    Examples:
+        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+        > sync_bn = DataParallel(sync_bn, device_ids=[0, 1])
+        > patch_replication_callback(sync_bn)
+        # this is equivalent to
+        > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
+        > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
+    """
+
+    assert isinstance(data_parallel, DataParallel)
+
+    old_replicate = data_parallel.replicate
+
+    @functools.wraps(old_replicate)
+    def new_replicate(module, device_ids):
+        modules = old_replicate(module, device_ids)
+        execute_replication_callbacks(modules)
+        return modules
+
+    data_parallel.replicate = new_replicate

animation/SadTalker/src/facerender/sync_batchnorm/unittest.py

@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# File   : unittest.py
+# Author : Jiayuan Mao
+# Email  : maojiayuan@gmail.com
+# Date   : 27/01/2018
+# 
+# This file is part of Synchronized-BatchNorm-PyTorch.
+# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
+# Distributed under MIT License.
+
+import unittest
+
+import numpy as np
+from torch.autograd import Variable
+
+
+def as_numpy(v):
+    if isinstance(v, Variable):
+        v = v.data
+    return v.cpu().numpy()
+
+
+class TorchTestCase(unittest.TestCase):
+    def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3):
+        npa, npb = as_numpy(a), as_numpy(b)
+        self.assertTrue(
+                np.allclose(npa, npb, atol=atol),
+                'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max())
+        )