liveportrait

src/config/crop_config.py

+# coding: utf-8
+
+"""
+parameters used for crop faces
+"""
+
+from dataclasses import dataclass
+
+from .base_config import PrintableConfig
+
+
+@dataclass(repr=False)  # use repr from PrintableConfig
+class CropConfig(PrintableConfig):
+    insightface_root: str = "../../pretrained_weights/insightface"
+    landmark_ckpt_path: str = "../../pretrained_weights/liveportrait/landmark.onnx"
+    device_id: int = 0  # gpu device id
+    flag_force_cpu: bool = False  # force cpu inference, WIP
+    ########## source image or video cropping option ##########
+    dsize: int = 512  # crop size
+    scale: float = 2.8  # scale factor
+    vx_ratio: float = 0  # vx ratio
+    vy_ratio: float = -0.125  # vy ratio +up, -down
+    max_face_num: int = 0  # max face number, 0 mean no limit
+    flag_do_rot: bool = True # whether to conduct the rotation when flag_do_crop is True
+
+    ########## driving video auto cropping option ##########
+    scale_crop_driving_video: float = 2.2  # 2.0 # scale factor for cropping driving video
+    vx_ratio_crop_driving_video: float = 0.0  # adjust y offset
+    vy_ratio_crop_driving_video: float = -0.1  # adjust x offset
+    direction: str = "large-small"  # direction of cropping
\ No newline at end of file

src/config/inference_config.py

+# coding: utf-8
+
+"""
+config dataclass used for inference
+"""
+
+import cv2
+from numpy import ndarray
+from dataclasses import dataclass, field
+from typing import Literal, Tuple
+from .base_config import PrintableConfig, make_abs_path
+
+
+@dataclass(repr=False)  # use repr from PrintableConfig
+class InferenceConfig(PrintableConfig):
+    # MODEL CONFIG, NOT EXPORTED PARAMS
+    models_config: str = make_abs_path('./models.yaml')  # portrait animation config
+    checkpoint_F: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/appearance_feature_extractor.pth')  # path to checkpoint of F
+    checkpoint_M: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/motion_extractor.pth')  # path to checkpoint pf M
+    checkpoint_G: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/spade_generator.pth')  # path to checkpoint of G
+    checkpoint_W: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/warping_module.pth')  # path to checkpoint of W
+    checkpoint_S: str = make_abs_path('../../pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth')  # path to checkpoint to S and R_eyes, R_lip
+
+    # EXPORTED PARAMS
+    flag_use_half_precision: bool = True
+    flag_crop_driving_video: bool = False
+    device_id: int = 0
+    flag_normalize_lip: bool = True
+    flag_source_video_eye_retargeting: bool = False
+    flag_video_editing_head_rotation: bool = False
+    flag_eye_retargeting: bool = False
+    flag_lip_retargeting: bool = False
+    flag_stitching: bool = True
+    flag_relative_motion: bool = True
+    flag_pasteback: bool = True
+    flag_do_crop: bool = True
+    flag_do_rot: bool = True
+    flag_force_cpu: bool = False
+    flag_do_torch_compile: bool = False
+
+    # NOT EXPORTED PARAMS
+    lip_normalize_threshold: float = 0.03 # threshold for flag_normalize_lip
+    source_video_eye_retargeting_threshold: float = 0.18 # threshold for eyes retargeting if the input is a source video
+    driving_smooth_observation_variance: float = 1e-7 # smooth strength scalar for the animated video when the input is a source video, the larger the number, the smoother the animated video; too much smoothness would result in loss of motion accuracy
+    anchor_frame: int = 0 # TO IMPLEMENT
+
+    input_shape: Tuple[int, int] = (256, 256)  # input shape
+    output_format: Literal['mp4', 'gif'] = 'mp4'  # output video format
+    crf: int = 15  # crf for output video
+    output_fps: int = 25 # default output fps
+
+    mask_crop: ndarray = field(default_factory=lambda: cv2.imread(make_abs_path('../utils/resources/mask_template.png'), cv2.IMREAD_COLOR))
+    size_gif: int = 256 # default gif size, TO IMPLEMENT
+    source_max_dim: int = 1280 # the max dim of height and width of source image or video
+    source_division: int = 2 # make sure the height and width of source image or video can be divided by this number
\ No newline at end of file

src/config/models.yaml

+model_params:
+  appearance_feature_extractor_params: # the F in the paper
+    image_channel: 3
+    block_expansion: 64
+    num_down_blocks: 2
+    max_features: 512
+    reshape_channel: 32
+    reshape_depth: 16
+    num_resblocks: 6
+  motion_extractor_params: # the M in the paper
+    num_kp: 21
+    backbone: convnextv2_tiny
+  warping_module_params: # the W in the paper
+    num_kp: 21
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 2
+    reshape_channel: 32
+    estimate_occlusion_map: True
+    dense_motion_params:
+      block_expansion: 32
+      max_features: 1024
+      num_blocks: 5
+      reshape_depth: 16
+      compress: 4
+  spade_generator_params: # the G in the paper
+    upscale: 2 # represents upsample factor 256x256 -> 512x512
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 2
+  stitching_retargeting_module_params: # the S in the paper
+    stitching:
+      input_size: 126 # (21*3)*2
+      hidden_sizes: [128, 128, 64]
+      output_size: 65 # (21*3)+2(tx,ty)
+    lip:
+      input_size: 65 # (21*3)+2
+      hidden_sizes: [128, 128, 64]
+      output_size: 63 # (21*3)
+    eye:
+      input_size: 66 # (21*3)+3
+      hidden_sizes: [256, 256, 128, 128, 64]
+      output_size: 63 # (21*3)

src/gradio_pipeline.py

+# coding: utf-8
+
+"""
+Pipeline for gradio
+"""
+
+import os.path as osp
+import gradio as gr
+
+from .config.argument_config import ArgumentConfig
+from .live_portrait_pipeline import LivePortraitPipeline
+from .utils.io import load_img_online
+from .utils.rprint import rlog as log
+from .utils.crop import prepare_paste_back, paste_back
+from .utils.camera import get_rotation_matrix
+from .utils.helper import is_square_video
+
+
+def update_args(args, user_args):
+    """update the args according to user inputs
+    """
+    for k, v in user_args.items():
+        if hasattr(args, k):
+            setattr(args, k, v)
+    return args
+
+
+class GradioPipeline(LivePortraitPipeline):
+
+    def __init__(self, inference_cfg, crop_cfg, args: ArgumentConfig):
+        super().__init__(inference_cfg, crop_cfg)
+        # self.live_portrait_wrapper = self.live_portrait_wrapper
+        self.args = args
+
+    def execute_video(
+        self,
+        input_source_image_path=None,
+        input_source_video_path=None,
+        input_driving_video_path=None,
+        flag_relative_input=True,
+        flag_do_crop_input=True,
+        flag_remap_input=True,
+        flag_crop_driving_video_input=True,
+        flag_video_editing_head_rotation=False,
+        scale=2.3,
+        vx_ratio=0.0,
+        vy_ratio=-0.125,
+        scale_crop_driving_video=2.2,
+        vx_ratio_crop_driving_video=0.0,
+        vy_ratio_crop_driving_video=-0.1,
+        driving_smooth_observation_variance=1e-7,
+        tab_selection=None,
+    ):
+        """ for video-driven potrait animation or video editing
+        """
+        if tab_selection == 'Image':
+            input_source_path = input_source_image_path
+        elif tab_selection == 'Video':
+            input_source_path = input_source_video_path
+        else:
+            input_source_path = input_source_image_path
+
+        if input_source_path is not None and input_driving_video_path is not None:
+            if osp.exists(input_driving_video_path) and is_square_video(input_driving_video_path) is False:
+                flag_crop_driving_video_input = True
+                log("The source video is not square, the driving video will be cropped to square automatically.")
+                gr.Info("The source video is not square, the driving video will be cropped to square automatically.", duration=2)
+
+            args_user = {
+                'source': input_source_path,
+                'driving': input_driving_video_path,
+                'flag_relative_motion': flag_relative_input,
+                'flag_do_crop': flag_do_crop_input,
+                'flag_pasteback': flag_remap_input,
+                'flag_crop_driving_video': flag_crop_driving_video_input,
+                'flag_video_editing_head_rotation': flag_video_editing_head_rotation,
+                'scale': scale,
+                'vx_ratio': vx_ratio,
+                'vy_ratio': vy_ratio,
+                'scale_crop_driving_video': scale_crop_driving_video,
+                'vx_ratio_crop_driving_video': vx_ratio_crop_driving_video,
+                'vy_ratio_crop_driving_video': vy_ratio_crop_driving_video,
+                'driving_smooth_observation_variance': driving_smooth_observation_variance,
+            }
+            # update config from user input
+            self.args = update_args(self.args, args_user)
+            self.live_portrait_wrapper.update_config(self.args.__dict__)
+            self.cropper.update_config(self.args.__dict__)
+            # video driven animation
+            video_path, video_path_concat = self.execute(self.args)
+            gr.Info("Run successfully!", duration=2)
+            return video_path, video_path_concat,
+        else:
+            raise gr.Error("Please upload the source portrait or source video, and driving video 🤗🤗🤗", duration=5)
+
+    def execute_image(self, input_eye_ratio: float, input_lip_ratio: float, input_image, flag_do_crop=True):
+        """ for single image retargeting
+        """
+        # disposable feature
+        f_s_user, x_s_user, source_lmk_user, crop_M_c2o, mask_ori, img_rgb = \
+            self.prepare_retargeting(input_image, flag_do_crop)
+
+        if input_eye_ratio is None or input_lip_ratio is None:
+            raise gr.Error("Invalid ratio input 💥!", duration=5)
+        else:
+            inference_cfg = self.live_portrait_wrapper.inference_cfg
+            x_s_user = x_s_user.to(self.live_portrait_wrapper.device)
+            f_s_user = f_s_user.to(self.live_portrait_wrapper.device)
+            # ∆_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i)
+            combined_eye_ratio_tensor = self.live_portrait_wrapper.calc_combined_eye_ratio([[input_eye_ratio]], source_lmk_user)
+            eyes_delta = self.live_portrait_wrapper.retarget_eye(x_s_user, combined_eye_ratio_tensor)
+            # ∆_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i)
+            combined_lip_ratio_tensor = self.live_portrait_wrapper.calc_combined_lip_ratio([[input_lip_ratio]], source_lmk_user)
+            lip_delta = self.live_portrait_wrapper.retarget_lip(x_s_user, combined_lip_ratio_tensor)
+            # default: use x_s
+            x_d_new = x_s_user + eyes_delta + lip_delta
+            # D(W(f_s; x_s, x′_d))
+            out = self.live_portrait_wrapper.warp_decode(f_s_user, x_s_user, x_d_new)
+            out = self.live_portrait_wrapper.parse_output(out['out'])[0]
+            out_to_ori_blend = paste_back(out, crop_M_c2o, img_rgb, mask_ori)
+            gr.Info("Run successfully!", duration=2)
+            return out, out_to_ori_blend
+
+    def prepare_retargeting(self, input_image, flag_do_crop=True):
+        """ for single image retargeting
+        """
+        if input_image is not None:
+            # gr.Info("Upload successfully!", duration=2)
+            inference_cfg = self.live_portrait_wrapper.inference_cfg
+            ######## process source portrait ########
+            img_rgb = load_img_online(input_image, mode='rgb', max_dim=1280, n=16)
+            log(f"Load source image from {input_image}.")
+            crop_info = self.cropper.crop_source_image(img_rgb, self.cropper.crop_cfg)
+            if flag_do_crop:
+                I_s = self.live_portrait_wrapper.prepare_source(crop_info['img_crop_256x256'])
+            else:
+                I_s = self.live_portrait_wrapper.prepare_source(img_rgb)
+            x_s_info = self.live_portrait_wrapper.get_kp_info(I_s)
+            R_s = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll'])
+            ############################################
+            f_s_user = self.live_portrait_wrapper.extract_feature_3d(I_s)
+            x_s_user = self.live_portrait_wrapper.transform_keypoint(x_s_info)
+            source_lmk_user = crop_info['lmk_crop']
+            crop_M_c2o = crop_info['M_c2o']
+            mask_ori = prepare_paste_back(inference_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
+            return f_s_user, x_s_user, source_lmk_user, crop_M_c2o, mask_ori, img_rgb
+        else:
+            # when press the clear button, go here
+            raise gr.Error("Please upload a source portrait as the retargeting input 🤗🤗🤗", duration=5)
\ No newline at end of file

src/live_portrait_wrapper.py

+# coding: utf-8
+
+"""
+Wrapper for LivePortrait core functions
+"""
+
+import contextlib
+import os.path as osp
+import numpy as np
+import cv2
+import torch
+import yaml
+
+from .utils.timer import Timer
+from .utils.helper import load_model, concat_feat
+from .utils.camera import headpose_pred_to_degree, get_rotation_matrix
+from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio
+from .config.inference_config import InferenceConfig
+from .utils.rprint import rlog as log
+
+
+class LivePortraitWrapper(object):
+
+    def __init__(self, inference_cfg: InferenceConfig):
+
+        self.inference_cfg = inference_cfg
+        self.device_id = inference_cfg.device_id
+        self.compile = inference_cfg.flag_do_torch_compile
+        if inference_cfg.flag_force_cpu:
+            self.device = 'cpu'
+        else:
+            if torch.backends.mps.is_available():
+                self.device = 'mps'
+            else:
+                self.device = 'cuda:' + str(self.device_id)
+
+        model_config = yaml.load(open(inference_cfg.models_config, 'r'), Loader=yaml.SafeLoader)
+        # init F
+        self.appearance_feature_extractor = load_model(inference_cfg.checkpoint_F, model_config, self.device, 'appearance_feature_extractor')
+        log(f'Load appearance_feature_extractor done.')
+        # init M
+        self.motion_extractor = load_model(inference_cfg.checkpoint_M, model_config, self.device, 'motion_extractor')
+        log(f'Load motion_extractor done.')
+        # init W
+        self.warping_module = load_model(inference_cfg.checkpoint_W, model_config, self.device, 'warping_module')
+        log(f'Load warping_module done.')
+        # init G
+        self.spade_generator = load_model(inference_cfg.checkpoint_G, model_config, self.device, 'spade_generator')
+        log(f'Load spade_generator done.')
+        # init S and R
+        if inference_cfg.checkpoint_S is not None and osp.exists(inference_cfg.checkpoint_S):
+            self.stitching_retargeting_module = load_model(inference_cfg.checkpoint_S, model_config, self.device, 'stitching_retargeting_module')
+            log(f'Load stitching_retargeting_module done.')
+        else:
+            self.stitching_retargeting_module = None
+        # Optimize for inference
+        if self.compile:
+            torch._dynamo.config.suppress_errors = True  # Suppress errors and fall back to eager execution
+            self.warping_module = torch.compile(self.warping_module, mode='max-autotune')
+            self.spade_generator = torch.compile(self.spade_generator, mode='max-autotune')
+
+        self.timer = Timer()
+
+    def inference_ctx(self):
+        if self.device == "mps":
+            ctx = contextlib.nullcontext()
+        else:
+            ctx = torch.autocast(device_type=self.device[:4], dtype=torch.float16,
+                                 enabled=self.inference_cfg.flag_use_half_precision)
+        return ctx
+
+    def update_config(self, user_args):
+        for k, v in user_args.items():
+            if hasattr(self.inference_cfg, k):
+                setattr(self.inference_cfg, k, v)
+
+    def prepare_source(self, img: np.ndarray) -> torch.Tensor:
+        """ construct the input as standard
+        img: HxWx3, uint8, 256x256
+        """
+        h, w = img.shape[:2]
+        if h != self.inference_cfg.input_shape[0] or w != self.inference_cfg.input_shape[1]:
+            x = cv2.resize(img, (self.inference_cfg.input_shape[0], self.inference_cfg.input_shape[1]))
+        else:
+            x = img.copy()
+
+        if x.ndim == 3:
+            x = x[np.newaxis].astype(np.float32) / 255.  # HxWx3 -> 1xHxWx3, normalized to 0~1
+        elif x.ndim == 4:
+            x = x.astype(np.float32) / 255.  # BxHxWx3, normalized to 0~1
+        else:
+            raise ValueError(f'img ndim should be 3 or 4: {x.ndim}')
+        x = np.clip(x, 0, 1)  # clip to 0~1
+        x = torch.from_numpy(x).permute(0, 3, 1, 2)  # 1xHxWx3 -> 1x3xHxW
+        x = x.to(self.device)
+        return x
+
+    def prepare_videos(self, imgs) -> torch.Tensor:
+        """ construct the input as standard
+        imgs: NxBxHxWx3, uint8
+        """
+        if isinstance(imgs, list):
+            _imgs = np.array(imgs)[..., np.newaxis]  # TxHxWx3x1
+        elif isinstance(imgs, np.ndarray):
+            _imgs = imgs
+        else:
+            raise ValueError(f'imgs type error: {type(imgs)}')
+
+        y = _imgs.astype(np.float32) / 255.
+        y = np.clip(y, 0, 1)  # clip to 0~1
+        y = torch.from_numpy(y).permute(0, 4, 3, 1, 2)  # TxHxWx3x1 -> Tx1x3xHxW
+        y = y.to(self.device)
+
+        return y
+
+    def extract_feature_3d(self, x: torch.Tensor) -> torch.Tensor:
+        """ get the appearance feature of the image by F
+        x: Bx3xHxW, normalized to 0~1
+        """
+        with torch.no_grad(), self.inference_ctx():
+            feature_3d = self.appearance_feature_extractor(x)
+
+        return feature_3d.float()
+
+    def get_kp_info(self, x: torch.Tensor, **kwargs) -> dict:
+        """ get the implicit keypoint information
+        x: Bx3xHxW, normalized to 0~1
+        flag_refine_info: whether to trandform the pose to degrees and the dimention of the reshape
+        return: A dict contains keys: 'pitch', 'yaw', 'roll', 't', 'exp', 'scale', 'kp'
+        """
+        with torch.no_grad(), self.inference_ctx():
+            kp_info = self.motion_extractor(x)
+
+            if self.inference_cfg.flag_use_half_precision:
+                # float the dict
+                for k, v in kp_info.items():
+                    if isinstance(v, torch.Tensor):
+                        kp_info[k] = v.float()
+
+        flag_refine_info: bool = kwargs.get('flag_refine_info', True)
+        if flag_refine_info:
+            bs = kp_info['kp'].shape[0]
+            kp_info['pitch'] = headpose_pred_to_degree(kp_info['pitch'])[:, None]  # Bx1
+            kp_info['yaw'] = headpose_pred_to_degree(kp_info['yaw'])[:, None]  # Bx1
+            kp_info['roll'] = headpose_pred_to_degree(kp_info['roll'])[:, None]  # Bx1
+            kp_info['kp'] = kp_info['kp'].reshape(bs, -1, 3)  # BxNx3
+            kp_info['exp'] = kp_info['exp'].reshape(bs, -1, 3)  # BxNx3
+
+        return kp_info
+
+    def get_pose_dct(self, kp_info: dict) -> dict:
+        pose_dct = dict(
+            pitch=headpose_pred_to_degree(kp_info['pitch']).item(),
+            yaw=headpose_pred_to_degree(kp_info['yaw']).item(),
+            roll=headpose_pred_to_degree(kp_info['roll']).item(),
+        )
+        return pose_dct
+
+    def get_fs_and_kp_info(self, source_prepared, driving_first_frame):
+
+        # get the canonical keypoints of source image by M
+        source_kp_info = self.get_kp_info(source_prepared, flag_refine_info=True)
+        source_rotation = get_rotation_matrix(source_kp_info['pitch'], source_kp_info['yaw'], source_kp_info['roll'])
+
+        # get the canonical keypoints of first driving frame by M
+        driving_first_frame_kp_info = self.get_kp_info(driving_first_frame, flag_refine_info=True)
+        driving_first_frame_rotation = get_rotation_matrix(
+            driving_first_frame_kp_info['pitch'],
+            driving_first_frame_kp_info['yaw'],
+            driving_first_frame_kp_info['roll']
+        )
+
+        # get feature volume by F
+        source_feature_3d = self.extract_feature_3d(source_prepared)
+
+        return source_kp_info, source_rotation, source_feature_3d, driving_first_frame_kp_info, driving_first_frame_rotation
+
+    def transform_keypoint(self, kp_info: dict):
+        """
+        transform the implicit keypoints with the pose, shift, and expression deformation
+        kp: BxNx3
+        """
+        kp = kp_info['kp']    # (bs, k, 3)
+        pitch, yaw, roll = kp_info['pitch'], kp_info['yaw'], kp_info['roll']
+
+        t, exp = kp_info['t'], kp_info['exp']
+        scale = kp_info['scale']
+
+        pitch = headpose_pred_to_degree(pitch)
+        yaw = headpose_pred_to_degree(yaw)
+        roll = headpose_pred_to_degree(roll)
+
+        bs = kp.shape[0]
+        if kp.ndim == 2:
+            num_kp = kp.shape[1] // 3  # Bx(num_kpx3)
+        else:
+            num_kp = kp.shape[1]  # Bxnum_kpx3
+
+        rot_mat = get_rotation_matrix(pitch, yaw, roll)    # (bs, 3, 3)
+
+        # Eqn.2: s * (R * x_c,s + exp) + t
+        kp_transformed = kp.view(bs, num_kp, 3) @ rot_mat + exp.view(bs, num_kp, 3)
+        kp_transformed *= scale[..., None]  # (bs, k, 3) * (bs, 1, 1) = (bs, k, 3)
+        kp_transformed[:, :, 0:2] += t[:, None, 0:2]  # remove z, only apply tx ty
+
+        return kp_transformed
+
+    def retarget_eye(self, kp_source: torch.Tensor, eye_close_ratio: torch.Tensor) -> torch.Tensor:
+        """
+        kp_source: BxNx3
+        eye_close_ratio: Bx3
+        Return: Bx(3*num_kp)
+        """
+        feat_eye = concat_feat(kp_source, eye_close_ratio)
+
+        with torch.no_grad():
+            delta = self.stitching_retargeting_module['eye'](feat_eye)
+
+        return delta.reshape(-1, kp_source.shape[1], 3)
+
+    def retarget_lip(self, kp_source: torch.Tensor, lip_close_ratio: torch.Tensor) -> torch.Tensor:
+        """
+        kp_source: BxNx3
+        lip_close_ratio: Bx2
+        Return: Bx(3*num_kp)
+        """
+        feat_lip = concat_feat(kp_source, lip_close_ratio)
+
+        with torch.no_grad():
+            delta = self.stitching_retargeting_module['lip'](feat_lip)
+
+        return delta.reshape(-1, kp_source.shape[1], 3)
+
+    def stitch(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
+        """
+        kp_source: BxNx3
+        kp_driving: BxNx3
+        Return: Bx(3*num_kp+2)
+        """
+        feat_stiching = concat_feat(kp_source, kp_driving)
+
+        with torch.no_grad():
+            delta = self.stitching_retargeting_module['stitching'](feat_stiching)
+
+        return delta
+
+    def stitching(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
+        """ conduct the stitching
+        kp_source: Bxnum_kpx3
+        kp_driving: Bxnum_kpx3
+        """
+
+        if self.stitching_retargeting_module is not None:
+
+            bs, num_kp = kp_source.shape[:2]
+
+            kp_driving_new = kp_driving.clone()
+            delta = self.stitch(kp_source, kp_driving_new)
+
+            delta_exp = delta[..., :3*num_kp].reshape(bs, num_kp, 3)  # 1x20x3
+            delta_tx_ty = delta[..., 3*num_kp:3*num_kp+2].reshape(bs, 1, 2)  # 1x1x2
+
+            kp_driving_new += delta_exp
+            kp_driving_new[..., :2] += delta_tx_ty
+
+            return kp_driving_new
+
+        return kp_driving
+
+    def warp_decode(self, feature_3d: torch.Tensor, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
+        """ get the image after the warping of the implicit keypoints
+        feature_3d: Bx32x16x64x64, feature volume
+        kp_source: BxNx3
+        kp_driving: BxNx3
+        """
+        # The line 18 in Algorithm 1: D(W(f_s; x_s, x′_d,i)）
+        with torch.no_grad(), self.inference_ctx():
+            if self.compile:
+                # Mark the beginning of a new CUDA Graph step
+                torch.compiler.cudagraph_mark_step_begin()
+            # get decoder input
+            ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving)
+            # decode
+            ret_dct['out'] = self.spade_generator(feature=ret_dct['out'])
+
+            # float the dict
+            if self.inference_cfg.flag_use_half_precision:
+                for k, v in ret_dct.items():
+                    if isinstance(v, torch.Tensor):
+                        ret_dct[k] = v.float()
+
+        return ret_dct
+
+    def parse_output(self, out: torch.Tensor) -> np.ndarray:
+        """ construct the output as standard
+        return: 1xHxWx3, uint8
+        """
+        out = np.transpose(out.data.cpu().numpy(), [0, 2, 3, 1])  # 1x3xHxW -> 1xHxWx3
+        out = np.clip(out, 0, 1)  # clip to 0~1
+        out = np.clip(out * 255, 0, 255).astype(np.uint8)  # 0~1 -> 0~255
+
+        return out
+
+    def calc_ratio(self, lmk_lst):
+        input_eye_ratio_lst = []
+        input_lip_ratio_lst = []
+        for lmk in lmk_lst:
+            # for eyes retargeting
+            input_eye_ratio_lst.append(calc_eye_close_ratio(lmk[None]))
+            # for lip retargeting
+            input_lip_ratio_lst.append(calc_lip_close_ratio(lmk[None]))
+        return input_eye_ratio_lst, input_lip_ratio_lst
+
+    def calc_combined_eye_ratio(self, c_d_eyes_i, source_lmk):
+        c_s_eyes = calc_eye_close_ratio(source_lmk[None])
+        c_s_eyes_tensor = torch.from_numpy(c_s_eyes).float().to(self.device)
+        c_d_eyes_i_tensor = torch.Tensor([c_d_eyes_i[0][0]]).reshape(1, 1).to(self.device)
+        # [c_s,eyes, c_d,eyes,i]
+        combined_eye_ratio_tensor = torch.cat([c_s_eyes_tensor, c_d_eyes_i_tensor], dim=1)
+        return combined_eye_ratio_tensor
+
+    def calc_combined_lip_ratio(self, c_d_lip_i, source_lmk):
+        c_s_lip = calc_lip_close_ratio(source_lmk[None])
+        c_s_lip_tensor = torch.from_numpy(c_s_lip).float().to(self.device)
+        c_d_lip_i_tensor = torch.Tensor([c_d_lip_i[0]]).to(self.device).reshape(1, 1) # 1x1
+        # [c_s,lip, c_d,lip,i]
+        combined_lip_ratio_tensor = torch.cat([c_s_lip_tensor, c_d_lip_i_tensor], dim=1) # 1x2
+        return combined_lip_ratio_tensor
\ No newline at end of file

src/modules/appearance_feature_extractor.py

+# coding: utf-8
+
+"""
+Appearance extractor(F) defined in paper, which maps the source image s to a 3D appearance feature volume.
+"""
+
+import torch
+from torch import nn
+from .util import SameBlock2d, DownBlock2d, ResBlock3d
+
+
+class AppearanceFeatureExtractor(nn.Module):
+
+    def __init__(self, image_channel, block_expansion, num_down_blocks, max_features, reshape_channel, reshape_depth, num_resblocks):
+        super(AppearanceFeatureExtractor, self).__init__()
+        self.image_channel = image_channel
+        self.block_expansion = block_expansion
+        self.num_down_blocks = num_down_blocks
+        self.max_features = max_features
+        self.reshape_channel = reshape_channel
+        self.reshape_depth = reshape_depth
+
+        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.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))
+
+    def forward(self, source_image):
+        out = self.first(source_image)  # Bx3x256x256 -> Bx64x256x256
+
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out)
+        out = self.second(out)
+        bs, c, h, w = out.shape  # ->Bx512x64x64
+
+        f_s = out.view(bs, self.reshape_channel, self.reshape_depth, h, w)  # ->Bx32x16x64x64
+        f_s = self.resblocks_3d(f_s)  # ->Bx32x16x64x64
+        return f_s

src/modules/convnextv2.py

+# coding: utf-8
+
+"""
+This moudle is adapted to the ConvNeXtV2 version for the extraction of implicit keypoints, poses, and expression deformation.
+"""
+
+import torch
+import torch.nn as nn
+# from timm.models.layers import trunc_normal_, DropPath
+from .util import LayerNorm, DropPath, trunc_normal_, GRN
+
+__all__ = ['convnextv2_tiny']
+
+
+class Block(nn.Module):
+    """ ConvNeXtV2 Block.
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path (float): Stochastic depth rate. Default: 0.0
+    """
+
+    def __init__(self, dim, drop_path=0.):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
+        self.norm = LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, 4 * dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(4 * dim)
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        x = self.dwconv(x)
+        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = input + self.drop_path(x)
+        return x
+
+
+class ConvNeXtV2(nn.Module):
+    """ ConvNeXt V2
+
+    Args:
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
+        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
+        drop_path_rate (float): Stochastic depth rate. Default: 0.
+        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
+    """
+
+    def __init__(
+        self,
+        in_chans=3,
+        depths=[3, 3, 9, 3],
+        dims=[96, 192, 384, 768],
+        drop_path_rate=0.,
+        **kwargs
+    ):
+        super().__init__()
+        self.depths = depths
+        self.downsample_layers = nn.ModuleList()  # stem and 3 intermediate downsampling conv layers
+        stem = nn.Sequential(
+            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
+            LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
+        )
+        self.downsample_layers.append(stem)
+        for i in range(3):
+            downsample_layer = nn.Sequential(
+                LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
+                nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
+            )
+            self.downsample_layers.append(downsample_layer)
+
+        self.stages = nn.ModuleList()  # 4 feature resolution stages, each consisting of multiple residual blocks
+        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        cur = 0
+        for i in range(4):
+            stage = nn.Sequential(
+                *[Block(dim=dims[i], drop_path=dp_rates[cur + j]) for j in range(depths[i])]
+            )
+            self.stages.append(stage)
+            cur += depths[i]
+
+        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer
+
+        # NOTE: the output semantic items
+        num_bins = kwargs.get('num_bins', 66)
+        num_kp = kwargs.get('num_kp', 24)  # the number of implicit keypoints
+        self.fc_kp = nn.Linear(dims[-1], 3 * num_kp)  # implicit keypoints
+
+        # print('dims[-1]: ', dims[-1])
+        self.fc_scale = nn.Linear(dims[-1], 1)  # scale
+        self.fc_pitch = nn.Linear(dims[-1], num_bins)  # pitch bins
+        self.fc_yaw = nn.Linear(dims[-1], num_bins)  # yaw bins
+        self.fc_roll = nn.Linear(dims[-1], num_bins)  # roll bins
+        self.fc_t = nn.Linear(dims[-1], 3)  # translation
+        self.fc_exp = nn.Linear(dims[-1], 3 * num_kp)  # expression / delta
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv2d, nn.Linear)):
+            trunc_normal_(m.weight, std=.02)
+            nn.init.constant_(m.bias, 0)
+
+    def forward_features(self, x):
+        for i in range(4):
+            x = self.downsample_layers[i](x)
+            x = self.stages[i](x)
+        return self.norm(x.mean([-2, -1]))  # global average pooling, (N, C, H, W) -> (N, C)
+
+    def forward(self, x):
+        x = self.forward_features(x)
+
+        # implicit keypoints
+        kp = self.fc_kp(x)
+
+        # pose and expression deformation
+        pitch = self.fc_pitch(x)
+        yaw = self.fc_yaw(x)
+        roll = self.fc_roll(x)
+        t = self.fc_t(x)
+        exp = self.fc_exp(x)
+        scale = self.fc_scale(x)
+
+        ret_dct = {
+            'pitch': pitch,
+            'yaw': yaw,
+            'roll': roll,
+            't': t,
+            'exp': exp,
+            'scale': scale,
+
+            'kp': kp,  # canonical keypoint
+        }
+
+        return ret_dct
+
+
+def convnextv2_tiny(**kwargs):
+    model = ConvNeXtV2(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
+    return model

src/modules/dense_motion.py

+# coding: utf-8
+
+"""
+The module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving
+"""
+
+from torch import nn
+import torch.nn.functional as F
+import torch
+from .util import Hourglass, make_coordinate_grid, kp2gaussian
+
+
+class DenseMotionNetwork(nn.Module):
+    def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress, estimate_occlusion_map=True):
+        super(DenseMotionNetwork, self).__init__()
+        self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks)  # ~60+G
+
+        self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3)  # 65G! NOTE: computation cost is large
+        self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1)  # 0.8G
+        self.norm = nn.BatchNorm3d(compress, affine=True)
+        self.num_kp = num_kp
+        self.flag_estimate_occlusion_map = estimate_occlusion_map
+
+        if self.flag_estimate_occlusion_map:
+            self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3)
+        else:
+            self.occlusion = None
+
+    def create_sparse_motions(self, feature, kp_driving, kp_source):
+        bs, _, d, h, w = feature.shape  # (bs, 4, 16, 64, 64)
+        identity_grid = make_coordinate_grid((d, h, w), ref=kp_source)  # (16, 64, 64, 3)
+        identity_grid = identity_grid.view(1, 1, d, h, w, 3)  # (1, 1, d=16, h=64, w=64, 3)
+        coordinate_grid = identity_grid - kp_driving.view(bs, self.num_kp, 1, 1, 1, 3)
+
+        k = coordinate_grid.shape[1]
+
+        # NOTE: there lacks an one-order flow
+        driving_to_source = coordinate_grid + kp_source.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, 1+num_kp, d, h, w, 3)
+        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, align_corners=False)
+        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:]  # (d=16, h=64, w=64)
+        gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01)  # (bs, num_kp, d, h, w)
+        gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01)  # (bs, num_kp, d, h, w)
+        heatmap = gaussian_driving - gaussian_source  # (bs, num_kp, d, h, w)
+
+        # adding background feature
+        zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.dtype).to(heatmap.device)
+        heatmap = torch.cat([zeros, heatmap], dim=1)
+        heatmap = heatmap.unsqueeze(2)         # (bs, 1+num_kp, 1, d, h, w)
+        return heatmap
+
+    def forward(self, feature, kp_driving, kp_source):
+        bs, _, d, h, w = feature.shape  # (bs, 32, 16, 64, 64)
+
+        feature = self.compress(feature)  # (bs, 4, 16, 64, 64)
+        feature = self.norm(feature)  # (bs, 4, 16, 64, 64)
+        feature = F.relu(feature)  # (bs, 4, 16, 64, 64)
+
+        out_dict = dict()
+
+        # 1. deform 3d feature
+        sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source)  # (bs, 1+num_kp, d, h, w, 3)
+        deformed_feature = self.create_deformed_feature(feature, sparse_motion)  # (bs, 1+num_kp, c=4, d=16, h=64, w=64)
+
+        # 2. (bs, 1+num_kp, d, h, w)
+        heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source)  # (bs, 1+num_kp, 1, d, h, w)
+
+        input = torch.cat([heatmap, deformed_feature], dim=2)  # (bs, 1+num_kp, c=5, d=16, h=64, w=64)
+        input = input.view(bs, -1, d, h, w)  # (bs, (1+num_kp)*c=105, d=16, h=64, w=64)
+
+        prediction = self.hourglass(input)
+
+        mask = self.mask(prediction)
+        mask = F.softmax(mask, dim=1)  # (bs, 1+num_kp, d=16, h=64, w=64)
+        out_dict['mask'] = mask
+        mask = mask.unsqueeze(2)                                   # (bs, num_kp+1, 1, d, h, w)
+        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)  mask take effect in this place
+        deformation = deformation.permute(0, 2, 3, 4, 1)           # (bs, d, h, w, 3)
+
+        out_dict['deformation'] = deformation
+
+        if self.flag_estimate_occlusion_map:
+            bs, _, d, h, w = prediction.shape
+            prediction_reshape = prediction.view(bs, -1, h, w)
+            occlusion_map = torch.sigmoid(self.occlusion(prediction_reshape))  # Bx1x64x64
+            out_dict['occlusion_map'] = occlusion_map
+
+        return out_dict

src/modules/motion_extractor.py

+# coding: utf-8
+
+"""
+Motion extractor(M), which directly predicts the canonical keypoints, head pose and expression deformation of the input image
+"""
+
+from torch import nn
+import torch
+
+from .convnextv2 import convnextv2_tiny
+from .util import filter_state_dict
+
+model_dict = {
+    'convnextv2_tiny': convnextv2_tiny,
+}
+
+
+class MotionExtractor(nn.Module):
+    def __init__(self, **kwargs):
+        super(MotionExtractor, self).__init__()
+
+        # default is convnextv2_base
+        backbone = kwargs.get('backbone', 'convnextv2_tiny')
+        self.detector = model_dict.get(backbone)(**kwargs)
+
+    def load_pretrained(self, init_path: str):
+        if init_path not in (None, ''):
+            state_dict = torch.load(init_path, map_location=lambda storage, loc: storage)['model']
+            state_dict = filter_state_dict(state_dict, remove_name='head')
+            ret = self.detector.load_state_dict(state_dict, strict=False)
+            print(f'Load pretrained model from {init_path}, ret: {ret}')
+
+    def forward(self, x):
+        out = self.detector(x)
+        return out

src/modules/spade_generator.py

+# coding: utf-8
+
+"""
+Spade decoder(G) defined in the paper, which input the warped feature to generate the animated image.
+"""
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from .util import SPADEResnetBlock
+
+
+class SPADEDecoder(nn.Module):
+    def __init__(self, upscale=1, max_features=256, block_expansion=64, out_channels=64, num_down_blocks=2):
+        for i in range(num_down_blocks):
+            input_channels = min(max_features, block_expansion * (2 ** (i + 1)))
+        self.upscale = upscale
+        super().__init__()
+        norm_G = 'spadespectralinstance'
+        label_num_channels = input_channels  # 256
+
+        self.fc = nn.Conv2d(input_channels, 2 * input_channels, 3, padding=1)
+        self.G_middle_0 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_1 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_2 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_3 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_4 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_5 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.up_0 = SPADEResnetBlock(2 * input_channels, input_channels, norm_G, label_num_channels)
+        self.up_1 = SPADEResnetBlock(input_channels, out_channels, norm_G, label_num_channels)
+        self.up = nn.Upsample(scale_factor=2)
+
+        if self.upscale is None or self.upscale <= 1:
+            self.conv_img = nn.Conv2d(out_channels, 3, 3, padding=1)
+        else:
+            self.conv_img = nn.Sequential(
+                nn.Conv2d(out_channels, 3 * (2 * 2), kernel_size=3, padding=1),
+                nn.PixelShuffle(upscale_factor=2)
+            )
+
+    def forward(self, feature):
+        seg = feature  # Bx256x64x64
+        x = self.fc(feature)  # Bx512x64x64
+        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)  # Bx512x64x64 -> Bx512x128x128
+        x = self.up_0(x, seg)  # Bx512x128x128 -> Bx256x128x128
+        x = self.up(x)  # Bx256x128x128 -> Bx256x256x256
+        x = self.up_1(x, seg)  # Bx256x256x256 -> Bx64x256x256
+
+        x = self.conv_img(F.leaky_relu(x, 2e-1))  # Bx64x256x256 -> Bx3xHxW
+        x = torch.sigmoid(x)  # Bx3xHxW
+
+        return x
\ No newline at end of file

src/modules/stitching_retargeting_network.py

+# coding: utf-8
+
+"""
+Stitching module(S) and two retargeting modules(R) defined in the paper.
+
+- The stitching module pastes the animated portrait back into the original image space without pixel misalignment, such as in
+the stitching region.
+
+- The eyes retargeting module is designed to address the issue of incomplete eye closure during cross-id reenactment, especially
+when a person with small eyes drives a person with larger eyes.
+
+- The lip retargeting module is designed similarly to the eye retargeting module, and can also normalize the input by ensuring that
+the lips are in a closed state, which facilitates better animation driving.
+"""
+from torch import nn
+
+
+class StitchingRetargetingNetwork(nn.Module):
+    def __init__(self, input_size, hidden_sizes, output_size):
+        super(StitchingRetargetingNetwork, self).__init__()
+        layers = []
+        for i in range(len(hidden_sizes)):
+            if i == 0:
+                layers.append(nn.Linear(input_size, hidden_sizes[i]))
+            else:
+                layers.append(nn.Linear(hidden_sizes[i - 1], hidden_sizes[i]))
+            layers.append(nn.ReLU(inplace=True))
+        layers.append(nn.Linear(hidden_sizes[-1], output_size))
+        self.mlp = nn.Sequential(*layers)
+
+    def initialize_weights_to_zero(self):
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.zeros_(m.weight)
+                nn.init.zeros_(m.bias)
+
+    def forward(self, x):
+        return self.mlp(x)

src/modules/util.py

+# coding: utf-8
+
+"""
+This file defines various neural network modules and utility functions, including convolutional and residual blocks,
+normalizations, and functions for spatial transformation and tensor manipulation.
+"""
+
+from torch import nn
+import torch.nn.functional as F
+import torch
+import torch.nn.utils.spectral_norm as spectral_norm
+import math
+import warnings
+
+
+def kp2gaussian(kp, spatial_size, kp_variance):
+    """
+    Transform a keypoint into gaussian like representation
+    """
+    mean = kp
+
+    coordinate_grid = make_coordinate_grid(spatial_size, mean)
+    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(spatial_size, ref, **kwargs):
+    d, h, w = spatial_size
+    x = torch.arange(w).type(ref.dtype).to(ref.device)
+    y = torch.arange(h).type(ref.dtype).to(ref.device)
+    z = torch.arange(d).type(ref.dtype).to(ref.device)
+
+    # NOTE: must be right-down-in
+    x = (2 * (x / (w - 1)) - 1)  # the x axis faces to the right
+    y = (2 * (y / (h - 1)) - 1)  # the y axis faces to the bottom
+    z = (2 * (z / (d - 1)) - 1)  # the z axis faces to the inner
+
+    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 ConvT2d(nn.Module):
+    """
+    Upsampling block for use in decoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, stride=2, padding=1, output_padding=1):
+        super(ConvT2d, self).__init__()
+
+        self.convT = nn.ConvTranspose2d(in_features, out_features, kernel_size=kernel_size, stride=stride,
+                                        padding=padding, output_padding=output_padding)
+        self.norm = nn.InstanceNorm2d(out_features)
+
+    def forward(self, x):
+        out = self.convT(x)
+        out = self.norm(out)
+        out = F.leaky_relu(out)
+        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 = nn.BatchNorm3d(in_features, affine=True)
+        self.norm2 = nn.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 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 = nn.BatchNorm3d(out_features, affine=True)
+
+    def forward(self, x):
+        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 = nn.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 = nn.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 = nn.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 + in_features
+
+        self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1)
+        self.norm = nn.BatchNorm3d(self.out_filters, affine=True)
+
+    def forward(self, x):
+        out = x.pop()
+        for up_block in self.up_blocks:
+            out = up_block(out)
+            skip = x.pop()
+            out = torch.cat([out, skip], dim=1)
+        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 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)
+
+
+def filter_state_dict(state_dict, remove_name='fc'):
+    new_state_dict = {}
+    for key in state_dict:
+        if remove_name in key:
+            continue
+        new_state_dict[key] = state_dict[key]
+    return new_state_dict
+
+
+class GRN(nn.Module):
+    """ GRN (Global Response Normalization) layer
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+class LayerNorm(nn.Module):
+    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
+    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
+    with shape (batch_size, channels, height, width).
+    """
+
+    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.bias = nn.Parameter(torch.zeros(normalized_shape))
+        self.eps = eps
+        self.data_format = data_format
+        if self.data_format not in ["channels_last", "channels_first"]:
+            raise NotImplementedError
+        self.normalized_shape = (normalized_shape, )
+
+    def forward(self, x):
+        if self.data_format == "channels_last":
+            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+        elif self.data_format == "channels_first":
+            u = x.mean(1, keepdim=True)
+            s = (x - u).pow(2).mean(1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.eps)
+            x = self.weight[:, None, None] * x + self.bias[:, None, None]
+            return x
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def drop_path(x, drop_prob=0., training=False, scale_by_keep=True):
+    """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class DropPath(nn.Module):
+    """ Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None, scale_by_keep=True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)

src/modules/warping_network.py

+# coding: utf-8
+
+"""
+Warping field estimator(W) defined in the paper, which generates a warping field using the implicit
+keypoint representations x_s and x_d, and employs this flow field to warp the source feature volume f_s.
+"""
+
+from torch import nn
+import torch.nn.functional as F
+from .util import SameBlock2d
+from .dense_motion import DenseMotionNetwork
+
+
+class WarpingNetwork(nn.Module):
+    def __init__(
+        self,
+        num_kp,
+        block_expansion,
+        max_features,
+        num_down_blocks,
+        reshape_channel,
+        estimate_occlusion_map=False,
+        dense_motion_params=None,
+        **kwargs
+    ):
+        super(WarpingNetwork, self).__init__()
+
+        self.upscale = kwargs.get('upscale', 1)
+        self.flag_use_occlusion_map = kwargs.get('flag_use_occlusion_map', True)
+
+        if dense_motion_params is not None:
+            self.dense_motion_network = DenseMotionNetwork(
+                num_kp=num_kp,
+                feature_channel=reshape_channel,
+                estimate_occlusion_map=estimate_occlusion_map,
+                **dense_motion_params
+            )
+        else:
+            self.dense_motion_network = None
+
+        self.third = SameBlock2d(max_features, block_expansion * (2 ** num_down_blocks), kernel_size=(3, 3), padding=(1, 1), lrelu=True)
+        self.fourth = nn.Conv2d(in_channels=block_expansion * (2 ** num_down_blocks), out_channels=block_expansion * (2 ** num_down_blocks), kernel_size=1, stride=1)
+
+        self.estimate_occlusion_map = estimate_occlusion_map
+
+    def deform_input(self, inp, deformation):
+        return F.grid_sample(inp, deformation, align_corners=False)
+
+    def forward(self, feature_3d, kp_driving, kp_source):
+        if self.dense_motion_network is not None:
+            # Feature warper, Transforming feature representation according to deformation and occlusion
+            dense_motion = self.dense_motion_network(
+                feature=feature_3d, kp_driving=kp_driving, kp_source=kp_source
+            )
+            if 'occlusion_map' in dense_motion:
+                occlusion_map = dense_motion['occlusion_map']  # Bx1x64x64
+            else:
+                occlusion_map = None
+
+            deformation = dense_motion['deformation']  # Bx16x64x64x3
+            out = self.deform_input(feature_3d, deformation)  # Bx32x16x64x64
+
+            bs, c, d, h, w = out.shape  # Bx32x16x64x64
+            out = out.view(bs, c * d, h, w)  # -> Bx512x64x64
+            out = self.third(out)  # -> Bx256x64x64
+            out = self.fourth(out)  # -> Bx256x64x64
+
+            if self.flag_use_occlusion_map and (occlusion_map is not None):
+                out = out * occlusion_map
+
+        ret_dct = {
+            'occlusion_map': occlusion_map,
+            'deformation': deformation,
+            'out': out,
+        }
+
+        return ret_dct

src/utils/camera.py

+# coding: utf-8
+
+"""
+functions for processing and transforming 3D facial keypoints
+"""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+PI = np.pi
+
+
+def headpose_pred_to_degree(pred):
+    """
+    pred: (bs, 66) or (bs, 1) or others
+    """
+    if pred.ndim > 1 and pred.shape[1] == 66:
+        # NOTE: note that the average is modified to 97.5
+        device = pred.device
+        idx_tensor = [idx for idx in range(0, 66)]
+        idx_tensor = torch.FloatTensor(idx_tensor).to(device)
+        pred = F.softmax(pred, dim=1)
+        degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 97.5
+
+        return degree
+
+    return pred
+
+
+def get_rotation_matrix(pitch_, yaw_, roll_):
+    """ the input is in degree
+    """
+    # transform to radian
+    pitch = pitch_ / 180 * PI
+    yaw = yaw_ / 180 * PI
+    roll = roll_ / 180 * PI
+
+    device = pitch.device
+
+    if pitch.ndim == 1:
+        pitch = pitch.unsqueeze(1)
+    if yaw.ndim == 1:
+        yaw = yaw.unsqueeze(1)
+    if roll.ndim == 1:
+        roll = roll.unsqueeze(1)
+
+    # calculate the euler matrix
+    bs = pitch.shape[0]
+    ones = torch.ones([bs, 1]).to(device)
+    zeros = torch.zeros([bs, 1]).to(device)
+    x, y, z = pitch, yaw, roll
+
+    rot_x = torch.cat([
+        ones, zeros, zeros,
+        zeros, torch.cos(x), -torch.sin(x),
+        zeros, torch.sin(x), torch.cos(x)
+    ], dim=1).reshape([bs, 3, 3])
+
+    rot_y = torch.cat([
+        torch.cos(y), zeros, torch.sin(y),
+        zeros, ones, zeros,
+        -torch.sin(y), zeros, torch.cos(y)
+    ], dim=1).reshape([bs, 3, 3])
+
+    rot_z = torch.cat([
+        torch.cos(z), -torch.sin(z), zeros,
+        torch.sin(z), torch.cos(z), zeros,
+        zeros, zeros, ones
+    ], dim=1).reshape([bs, 3, 3])
+
+    rot = rot_z @ rot_y @ rot_x
+    return rot.permute(0, 2, 1)  # transpose

src/utils/crop.py

+# coding: utf-8
+
+"""
+cropping function and the related preprocess functions for cropping
+"""
+
+import numpy as np
+import os.path as osp
+from math import sin, cos, acos, degrees
+import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) # NOTE: enforce single thread
+from .rprint import rprint as print
+
+DTYPE = np.float32
+CV2_INTERP = cv2.INTER_LINEAR
+
+def make_abs_path(fn):
+    return osp.join(osp.dirname(osp.realpath(__file__)), fn)
+
+def _transform_img(img, M, dsize, flags=CV2_INTERP, borderMode=None):
+    """ conduct similarity or affine transformation to the image, do not do border operation!
+    img:
+    M: 2x3 matrix or 3x3 matrix
+    dsize: target shape (width, height)
+    """
+    if isinstance(dsize, tuple) or isinstance(dsize, list):
+        _dsize = tuple(dsize)
+    else:
+        _dsize = (dsize, dsize)
+
+    if borderMode is not None:
+        return cv2.warpAffine(img, M[:2, :], dsize=_dsize, flags=flags, borderMode=borderMode, borderValue=(0, 0, 0))
+    else:
+        return cv2.warpAffine(img, M[:2, :], dsize=_dsize, flags=flags)
+
+
+def _transform_pts(pts, M):
+    """ conduct similarity or affine transformation to the pts
+    pts: Nx2 ndarray
+    M: 2x3 matrix or 3x3 matrix
+    return: Nx2
+    """
+    return pts @ M[:2, :2].T + M[:2, 2]
+
+
+def parse_pt2_from_pt101(pt101, use_lip=True):
+    """
+    parsing the 2 points according to the 101 points, which cancels the roll
+    """
+    # the former version use the eye center, but it is not robust, now use interpolation
+    pt_left_eye = np.mean(pt101[[39, 42, 45, 48]], axis=0)  # left eye center
+    pt_right_eye = np.mean(pt101[[51, 54, 57, 60]], axis=0)  # right eye center
+
+    if use_lip:
+        # use lip
+        pt_center_eye = (pt_left_eye + pt_right_eye) / 2
+        pt_center_lip = (pt101[75] + pt101[81]) / 2
+        pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0)
+    else:
+        pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt106(pt106, use_lip=True):
+    """
+    parsing the 2 points according to the 106 points, which cancels the roll
+    """
+    pt_left_eye = np.mean(pt106[[33, 35, 40, 39]], axis=0)  # left eye center
+    pt_right_eye = np.mean(pt106[[87, 89, 94, 93]], axis=0)  # right eye center
+
+    if use_lip:
+        # use lip
+        pt_center_eye = (pt_left_eye + pt_right_eye) / 2
+        pt_center_lip = (pt106[52] + pt106[61]) / 2
+        pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0)
+    else:
+        pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt203(pt203, use_lip=True):
+    """
+    parsing the 2 points according to the 203 points, which cancels the roll
+    """
+    pt_left_eye = np.mean(pt203[[0, 6, 12, 18]], axis=0)  # left eye center
+    pt_right_eye = np.mean(pt203[[24, 30, 36, 42]], axis=0)  # right eye center
+    if use_lip:
+        # use lip
+        pt_center_eye = (pt_left_eye + pt_right_eye) / 2
+        pt_center_lip = (pt203[48] + pt203[66]) / 2
+        pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0)
+    else:
+        pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt68(pt68, use_lip=True):
+    """
+    parsing the 2 points according to the 68 points, which cancels the roll
+    """
+    lm_idx = np.array([31, 37, 40, 43, 46, 49, 55], dtype=np.int32) - 1
+    if use_lip:
+        pt5 = np.stack([
+            np.mean(pt68[lm_idx[[1, 2]], :], 0),  # left eye
+            np.mean(pt68[lm_idx[[3, 4]], :], 0),  # right eye
+            pt68[lm_idx[0], :],  # nose
+            pt68[lm_idx[5], :],  # lip
+            pt68[lm_idx[6], :]   # lip
+        ], axis=0)
+
+        pt2 = np.stack([
+            (pt5[0] + pt5[1]) / 2,
+            (pt5[3] + pt5[4]) / 2
+        ], axis=0)
+    else:
+        pt2 = np.stack([
+            np.mean(pt68[lm_idx[[1, 2]], :], 0),  # left eye
+            np.mean(pt68[lm_idx[[3, 4]], :], 0),  # right eye
+        ], axis=0)
+
+    return pt2
+
+
+def parse_pt2_from_pt5(pt5, use_lip=True):
+    """
+    parsing the 2 points according to the 5 points, which cancels the roll
+    """
+    if use_lip:
+        pt2 = np.stack([
+            (pt5[0] + pt5[1]) / 2,
+            (pt5[3] + pt5[4]) / 2
+        ], axis=0)
+    else:
+        pt2 = np.stack([
+            pt5[0],
+            pt5[1]
+        ], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt_x(pts, use_lip=True):
+    if pts.shape[0] == 101:
+        pt2 = parse_pt2_from_pt101(pts, use_lip=use_lip)
+    elif pts.shape[0] == 106:
+        pt2 = parse_pt2_from_pt106(pts, use_lip=use_lip)
+    elif pts.shape[0] == 68:
+        pt2 = parse_pt2_from_pt68(pts, use_lip=use_lip)
+    elif pts.shape[0] == 5:
+        pt2 = parse_pt2_from_pt5(pts, use_lip=use_lip)
+    elif pts.shape[0] == 203:
+        pt2 = parse_pt2_from_pt203(pts, use_lip=use_lip)
+    elif pts.shape[0] > 101:
+        # take the first 101 points
+        pt2 = parse_pt2_from_pt101(pts[:101], use_lip=use_lip)
+    else:
+        raise Exception(f'Unknow shape: {pts.shape}')
+
+    if not use_lip:
+        # NOTE: to compile with the latter code, need to rotate the pt2 90 degrees clockwise manually
+        v = pt2[1] - pt2[0]
+        pt2[1, 0] = pt2[0, 0] - v[1]
+        pt2[1, 1] = pt2[0, 1] + v[0]
+
+    return pt2
+
+
+def parse_rect_from_landmark(
+    pts,
+    scale=1.5,
+    need_square=True,
+    vx_ratio=0,
+    vy_ratio=0,
+    use_deg_flag=False,
+    **kwargs
+):
+    """parsing center, size, angle from 101/68/5/x landmarks
+    vx_ratio: the offset ratio along the pupil axis x-axis, multiplied by size
+    vy_ratio: the offset ratio along the pupil axis y-axis, multiplied by size, which is used to contain more forehead area
+
+    judge with pts.shape
+    """
+    pt2 = parse_pt2_from_pt_x(pts, use_lip=kwargs.get('use_lip', True))
+
+    uy = pt2[1] - pt2[0]
+    l = np.linalg.norm(uy)
+    if l <= 1e-3:
+        uy = np.array([0, 1], dtype=DTYPE)
+    else:
+        uy /= l
+    ux = np.array((uy[1], -uy[0]), dtype=DTYPE)
+
+    # the rotation degree of the x-axis, the clockwise is positive, the counterclockwise is negative (image coordinate system)
+    # print(uy)
+    # print(ux)
+    angle = acos(ux[0])
+    if ux[1] < 0:
+        angle = -angle
+
+    # rotation matrix
+    M = np.array([ux, uy])
+
+    # calculate the size which contains the angle degree of the bbox, and the center
+    center0 = np.mean(pts, axis=0)
+    rpts = (pts - center0) @ M.T  # (M @ P.T).T = P @ M.T
+    lt_pt = np.min(rpts, axis=0)
+    rb_pt = np.max(rpts, axis=0)
+    center1 = (lt_pt + rb_pt) / 2
+
+    size = rb_pt - lt_pt
+    if need_square:
+        m = max(size[0], size[1])
+        size[0] = m
+        size[1] = m
+
+    size *= scale  # scale size
+    center = center0 + ux * center1[0] + uy * center1[1]  # counterclockwise rotation, equivalent to M.T @ center1.T
+    center = center + ux * (vx_ratio * size) + uy * \
+        (vy_ratio * size)  # considering the offset in vx and vy direction
+
+    if use_deg_flag:
+        angle = degrees(angle)
+
+    return center, size, angle
+
+
+def parse_bbox_from_landmark(pts, **kwargs):
+    center, size, angle = parse_rect_from_landmark(pts, **kwargs)
+    cx, cy = center
+    w, h = size
+
+    # calculate the vertex positions before rotation
+    bbox = np.array([
+        [cx-w/2, cy-h/2],  # left, top
+        [cx+w/2, cy-h/2],
+        [cx+w/2, cy+h/2],  # right, bottom
+        [cx-w/2, cy+h/2]
+    ], dtype=DTYPE)
+
+    # construct rotation matrix
+    bbox_rot = bbox.copy()
+    R = np.array([
+        [np.cos(angle), -np.sin(angle)],
+        [np.sin(angle),  np.cos(angle)]
+    ], dtype=DTYPE)
+
+    # calculate the relative position of each vertex from the rotation center, then rotate these positions, and finally add the coordinates of the rotation center
+    bbox_rot = (bbox_rot - center) @ R.T + center
+
+    return {
+        'center': center,  # 2x1
+        'size': size,  # scalar
+        'angle': angle,  # rad, counterclockwise
+        'bbox': bbox,  # 4x2
+        'bbox_rot': bbox_rot,  # 4x2
+    }
+
+
+def crop_image_by_bbox(img, bbox, lmk=None, dsize=512, angle=None, flag_rot=False, **kwargs):
+    left, top, right, bot = bbox
+    if int(right - left) != int(bot - top):
+        print(f'right-left {right-left} != bot-top {bot-top}')
+    size = right - left
+
+    src_center = np.array([(left + right) / 2, (top + bot) / 2], dtype=DTYPE)
+    tgt_center = np.array([dsize / 2, dsize / 2], dtype=DTYPE)
+
+    s = dsize / size  # scale
+    if flag_rot and angle is not None:
+        costheta, sintheta = cos(angle), sin(angle)
+        cx, cy = src_center[0], src_center[1]  # ori center
+        tcx, tcy = tgt_center[0], tgt_center[1]  # target center
+        # need to infer
+        M_o2c = np.array(
+            [[s * costheta, s * sintheta, tcx - s * (costheta * cx + sintheta * cy)],
+             [-s * sintheta, s * costheta, tcy - s * (-sintheta * cx + costheta * cy)]],
+            dtype=DTYPE
+        )
+    else:
+        M_o2c = np.array(
+            [[s, 0, tgt_center[0] - s * src_center[0]],
+             [0, s, tgt_center[1] - s * src_center[1]]],
+            dtype=DTYPE
+        )
+
+    # if flag_rot and angle is None:
+        # print('angle is None, but flag_rotate is True', style="bold yellow")
+
+    img_crop = _transform_img(img, M_o2c, dsize=dsize, borderMode=kwargs.get('borderMode', None))
+    lmk_crop = _transform_pts(lmk, M_o2c) if lmk is not None else None
+
+    M_o2c = np.vstack([M_o2c, np.array([0, 0, 1], dtype=DTYPE)])
+    M_c2o = np.linalg.inv(M_o2c)
+
+    # cv2.imwrite('crop.jpg', img_crop)
+
+    return {
+        'img_crop': img_crop,
+        'lmk_crop': lmk_crop,
+        'M_o2c': M_o2c,
+        'M_c2o': M_c2o,
+    }
+
+
+def _estimate_similar_transform_from_pts(
+    pts,
+    dsize,
+    scale=1.5,
+    vx_ratio=0,
+    vy_ratio=-0.1,
+    flag_do_rot=True,
+    **kwargs
+):
+    """ calculate the affine matrix of the cropped image from sparse points, the original image to the cropped image, the inverse is the cropped image to the original image
+    pts: landmark, 101 or 68 points or other points, Nx2
+    scale: the larger scale factor, the smaller face ratio
+    vx_ratio: x shift
+    vy_ratio: y shift, the smaller the y shift, the lower the face region
+    rot_flag: if it is true, conduct correction
+    """
+    center, size, angle = parse_rect_from_landmark(
+        pts, scale=scale, vx_ratio=vx_ratio, vy_ratio=vy_ratio,
+        use_lip=kwargs.get('use_lip', True)
+    )
+
+    s = dsize / size[0]  # scale
+    tgt_center = np.array([dsize / 2, dsize / 2], dtype=DTYPE)  # center of dsize
+
+    if flag_do_rot:
+        costheta, sintheta = cos(angle), sin(angle)
+        cx, cy = center[0], center[1]  # ori center
+        tcx, tcy = tgt_center[0], tgt_center[1]  # target center
+        # need to infer
+        M_INV = np.array(
+            [[s * costheta, s * sintheta, tcx - s * (costheta * cx + sintheta * cy)],
+             [-s * sintheta, s * costheta, tcy - s * (-sintheta * cx + costheta * cy)]],
+            dtype=DTYPE
+        )
+    else:
+        M_INV = np.array(
+            [[s, 0, tgt_center[0] - s * center[0]],
+             [0, s, tgt_center[1] - s * center[1]]],
+            dtype=DTYPE
+        )
+
+    M_INV_H = np.vstack([M_INV, np.array([0, 0, 1])])
+    M = np.linalg.inv(M_INV_H)
+
+    # M_INV is from the original image to the cropped image, M is from the cropped image to the original image
+    return M_INV, M[:2, ...]
+
+
+def crop_image(img, pts: np.ndarray, **kwargs):
+    dsize = kwargs.get('dsize', 224)
+    scale = kwargs.get('scale', 1.5)  # 1.5 | 1.6
+    vy_ratio = kwargs.get('vy_ratio', -0.1)  # -0.0625 | -0.1
+
+    M_INV, _ = _estimate_similar_transform_from_pts(
+        pts,
+        dsize=dsize,
+        scale=scale,
+        vy_ratio=vy_ratio,
+        flag_do_rot=kwargs.get('flag_do_rot', True),
+    )
+
+    img_crop = _transform_img(img, M_INV, dsize)  # origin to crop
+    pt_crop = _transform_pts(pts, M_INV)
+
+    M_o2c = np.vstack([M_INV, np.array([0, 0, 1], dtype=DTYPE)])
+    M_c2o = np.linalg.inv(M_o2c)
+
+    ret_dct = {
+        'M_o2c': M_o2c,  # from the original image to the cropped image 3x3
+        'M_c2o': M_c2o,  # from the cropped image to the original image 3x3
+        'img_crop': img_crop,  # the cropped image
+        'pt_crop': pt_crop,  # the landmarks of the cropped image
+    }
+
+    return ret_dct
+
+def average_bbox_lst(bbox_lst):
+    if len(bbox_lst) == 0:
+        return None
+    bbox_arr = np.array(bbox_lst)
+    return np.mean(bbox_arr, axis=0).tolist()
+
+def prepare_paste_back(mask_crop, crop_M_c2o, dsize):
+    """prepare mask for later image paste back
+    """
+    mask_ori = _transform_img(mask_crop, crop_M_c2o, dsize)
+    mask_ori = mask_ori.astype(np.float32) / 255.
+    return mask_ori
+
+def paste_back(img_crop, M_c2o, img_ori, mask_ori):
+    """paste back the image
+    """
+    dsize = (img_ori.shape[1], img_ori.shape[0])
+    result = _transform_img(img_crop, M_c2o, dsize=dsize)
+    result = np.clip(mask_ori * result + (1 - mask_ori) * img_ori, 0, 255).astype(np.uint8)
+    return result

src/utils/cropper.py

+# coding: utf-8
+
+import os.path as osp
+from dataclasses import dataclass, field
+from typing import List, Tuple, Union
+
+import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
+import numpy as np
+import torch
+
+from ..config.crop_config import CropConfig
+from .crop import (
+    average_bbox_lst,
+    crop_image,
+    crop_image_by_bbox,
+    parse_bbox_from_landmark,
+)
+from .io import contiguous
+from .rprint import rlog as log
+from .face_analysis_diy import FaceAnalysisDIY
+from .landmark_runner import LandmarkRunner
+
+
+def make_abs_path(fn):
+    return osp.join(osp.dirname(osp.realpath(__file__)), fn)
+
+
+@dataclass
+class Trajectory:
+    start: int = -1  # start frame
+    end: int = -1  # end frame
+    lmk_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # lmk list
+    bbox_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # bbox list
+    M_c2o_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # M_c2o list
+
+    frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # frame list
+    lmk_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # lmk list
+    frame_rgb_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # frame crop list
+
+
+class Cropper(object):
+    def __init__(self, **kwargs) -> None:
+        self.crop_cfg: CropConfig = kwargs.get("crop_cfg", None)
+        device_id = kwargs.get("device_id", 0)
+        flag_force_cpu = kwargs.get("flag_force_cpu", False)
+        if flag_force_cpu:
+            device = "cpu"
+            face_analysis_wrapper_provider = ["CPUExecutionProvider"]
+        else:
+            if torch.backends.mps.is_available():
+                # Shape inference currently fails with CoreMLExecutionProvider
+                # for the retinaface model
+                device = "mps"
+                face_analysis_wrapper_provider = ["CPUExecutionProvider"]
+            else:
+                device = "cuda"
+                face_analysis_wrapper_provider = ["CUDAExecutionProvider"]
+        self.landmark_runner = LandmarkRunner(
+            ckpt_path=make_abs_path(self.crop_cfg.landmark_ckpt_path),
+            onnx_provider=device,
+            device_id=device_id,
+        )
+        self.landmark_runner.warmup()
+
+        self.face_analysis_wrapper = FaceAnalysisDIY(
+            name="buffalo_l",
+            root=make_abs_path(self.crop_cfg.insightface_root),
+            providers=face_analysis_wrapper_provider,
+        )
+        self.face_analysis_wrapper.prepare(ctx_id=device_id, det_size=(512, 512))
+        self.face_analysis_wrapper.warmup()
+
+    def update_config(self, user_args):
+        for k, v in user_args.items():
+            if hasattr(self.crop_cfg, k):
+                setattr(self.crop_cfg, k, v)
+
+    def crop_source_image(self, img_rgb_: np.ndarray, crop_cfg: CropConfig):
+        # crop a source image and get neccessary information
+        img_rgb = img_rgb_.copy()  # copy it
+
+        img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
+        src_face = self.face_analysis_wrapper.get(
+            img_bgr,
+            flag_do_landmark_2d_106=True,
+            direction=crop_cfg.direction,
+            max_face_num=crop_cfg.max_face_num,
+        )
+
+        if len(src_face) == 0:
+            log("No face detected in the source image.")
+            return None
+        elif len(src_face) > 1:
+            log(f"More than one face detected in the image, only pick one face by rule {crop_cfg.direction}.")
+
+        # NOTE: temporarily only pick the first face, to support multiple face in the future
+        src_face = src_face[0]
+        lmk = src_face.landmark_2d_106  # this is the 106 landmarks from insightface
+
+        # crop the face
+        ret_dct = crop_image(
+            img_rgb,  # ndarray
+            lmk,  # 106x2 or Nx2
+            dsize=crop_cfg.dsize,
+            scale=crop_cfg.scale,
+            vx_ratio=crop_cfg.vx_ratio,
+            vy_ratio=crop_cfg.vy_ratio,
+            flag_do_rot=crop_cfg.flag_do_rot,
+        )
+
+        lmk = self.landmark_runner.run(img_rgb, lmk)
+        ret_dct["lmk_crop"] = lmk
+
+        # update a 256x256 version for network input
+        ret_dct["img_crop_256x256"] = cv2.resize(ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA)
+        ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / crop_cfg.dsize
+
+        return ret_dct
+
+    def crop_source_video(self, source_rgb_lst, crop_cfg: CropConfig, **kwargs):
+        """Tracking based landmarks/alignment and cropping"""
+        trajectory = Trajectory()
+        direction = kwargs.get("direction", "large-small")
+        for idx, frame_rgb in enumerate(source_rgb_lst):
+            if idx == 0 or trajectory.start == -1:
+                src_face = self.face_analysis_wrapper.get(
+                    contiguous(frame_rgb[..., ::-1]),
+                    flag_do_landmark_2d_106=True,
+                    direction=crop_cfg.direction,
+                    max_face_num=crop_cfg.max_face_num,
+                )
+                if len(src_face) == 0:
+                    log(f"No face detected in the frame #{idx}")
+                    continue
+                elif len(src_face) > 1:
+                    log(f"More than one face detected in the source frame_{idx}, only pick one face by rule {direction}.")
+                src_face = src_face[0]
+                lmk = src_face.landmark_2d_106
+                lmk = self.landmark_runner.run(frame_rgb, lmk)
+                trajectory.start, trajectory.end = idx, idx
+            else:
+                lmk = self.landmark_runner.run(frame_rgb, trajectory.lmk_lst[-1])
+                trajectory.end = idx
+            trajectory.lmk_lst.append(lmk)
+
+            # crop the face
+            ret_dct = crop_image(
+                frame_rgb,  # ndarray
+                lmk,  # 106x2 or Nx2
+                dsize=crop_cfg.dsize,
+                scale=crop_cfg.scale,
+                vx_ratio=crop_cfg.vx_ratio,
+                vy_ratio=crop_cfg.vy_ratio,
+                flag_do_rot=crop_cfg.flag_do_rot,
+            )
+            lmk = self.landmark_runner.run(frame_rgb, lmk)
+            ret_dct["lmk_crop"] = lmk
+
+            # update a 256x256 version for network input
+            ret_dct["img_crop_256x256"] = cv2.resize(ret_dct["img_crop"], (256, 256), interpolation=cv2.INTER_AREA)
+            ret_dct["lmk_crop_256x256"] = ret_dct["lmk_crop"] * 256 / crop_cfg.dsize
+
+            trajectory.frame_rgb_crop_lst.append(ret_dct["img_crop_256x256"])
+            trajectory.lmk_crop_lst.append(ret_dct["lmk_crop_256x256"])
+            trajectory.M_c2o_lst.append(ret_dct['M_c2o'])
+
+        return {
+            "frame_crop_lst": trajectory.frame_rgb_crop_lst,
+            "lmk_crop_lst": trajectory.lmk_crop_lst,
+            "M_c2o_lst": trajectory.M_c2o_lst,
+        }
+
+    def crop_driving_video(self, driving_rgb_lst, **kwargs):
+        """Tracking based landmarks/alignment and cropping"""
+        trajectory = Trajectory()
+        direction = kwargs.get("direction", "large-small")
+        for idx, frame_rgb in enumerate(driving_rgb_lst):
+            if idx == 0 or trajectory.start == -1:
+                src_face = self.face_analysis_wrapper.get(
+                    contiguous(frame_rgb[..., ::-1]),
+                    flag_do_landmark_2d_106=True,
+                    direction=direction,
+                )
+                if len(src_face) == 0:
+                    log(f"No face detected in the frame #{idx}")
+                    continue
+                elif len(src_face) > 1:
+                    log(f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.")
+                src_face = src_face[0]
+                lmk = src_face.landmark_2d_106
+                lmk = self.landmark_runner.run(frame_rgb, lmk)
+                trajectory.start, trajectory.end = idx, idx
+            else:
+                lmk = self.landmark_runner.run(frame_rgb, trajectory.lmk_lst[-1])
+                trajectory.end = idx
+
+            trajectory.lmk_lst.append(lmk)
+            ret_bbox = parse_bbox_from_landmark(
+                lmk,
+                scale=self.crop_cfg.scale_crop_driving_video,
+                vx_ratio_crop_driving_video=self.crop_cfg.vx_ratio_crop_driving_video,
+                vy_ratio=self.crop_cfg.vy_ratio_crop_driving_video,
+            )["bbox"]
+            bbox = [
+                ret_bbox[0, 0],
+                ret_bbox[0, 1],
+                ret_bbox[2, 0],
+                ret_bbox[2, 1],
+            ]  # 4,
+            trajectory.bbox_lst.append(bbox)  # bbox
+            trajectory.frame_rgb_lst.append(frame_rgb)
+
+        global_bbox = average_bbox_lst(trajectory.bbox_lst)
+
+        for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)):
+            ret_dct = crop_image_by_bbox(
+                frame_rgb,
+                global_bbox,
+                lmk=lmk,
+                dsize=kwargs.get("dsize", 512),
+                flag_rot=False,
+                borderValue=(0, 0, 0),
+            )
+            trajectory.frame_rgb_crop_lst.append(ret_dct["img_crop"])
+            trajectory.lmk_crop_lst.append(ret_dct["lmk_crop"])
+
+        return {
+            "frame_crop_lst": trajectory.frame_rgb_crop_lst,
+            "lmk_crop_lst": trajectory.lmk_crop_lst,
+        }
+
+
+    def calc_lmks_from_cropped_video(self, driving_rgb_crop_lst, **kwargs):
+        """Tracking based landmarks/alignment"""
+        trajectory = Trajectory()
+        direction = kwargs.get("direction", "large-small")
+
+        for idx, frame_rgb_crop in enumerate(driving_rgb_crop_lst):
+            if idx == 0 or trajectory.start == -1:
+                src_face = self.face_analysis_wrapper.get(
+                    contiguous(frame_rgb_crop[..., ::-1]),  # convert to BGR
+                    flag_do_landmark_2d_106=True,
+                    direction=direction,
+                )
+                if len(src_face) == 0:
+                    log(f"No face detected in the frame #{idx}")
+                    raise Exception(f"No face detected in the frame #{idx}")
+                elif len(src_face) > 1:
+                    log(f"More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.")
+                src_face = src_face[0]
+                lmk = src_face.landmark_2d_106
+                lmk = self.landmark_runner.run(frame_rgb_crop, lmk)
+                trajectory.start, trajectory.end = idx, idx
+            else:
+                lmk = self.landmark_runner.run(frame_rgb_crop, trajectory.lmk_lst[-1])
+                trajectory.end = idx
+
+            trajectory.lmk_lst.append(lmk)
+        return trajectory.lmk_lst
\ No newline at end of file

src/utils/dependencies/insightface/__init__.py

+# coding: utf-8
+# pylint: disable=wrong-import-position
+"""InsightFace: A Face Analysis Toolkit."""
+from __future__ import absolute_import
+
+try:
+    #import mxnet as mx
+    import onnxruntime
+except ImportError:
+    raise ImportError(
+        "Unable to import dependency onnxruntime. "
+    )
+
+__version__ = '0.7.3'
+
+from . import model_zoo
+from . import utils
+from . import app
+from . import data
+