remain ckpt

aniportrait/audio2ldmk.py

@@ -1,310 +0,0 @@
-import argparse
-import os
-# import ffmpeg
-import random
-import numpy as np
-import cv2
-import torch
-import torchvision
-from omegaconf import OmegaConf
-from PIL import Image
-
-from src.audio_models.model import Audio2MeshModel
-from src.audio_models.pose_model import Audio2PoseModel
-from src.utils.audio_util import prepare_audio_feature
-from src.utils.mp_utils  import LMKExtractor
-from src.utils.pose_util import project_points, smooth_pose_seq
-
-
-PARTS = [
-    ('FACE', [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], (10, 200, 10)),
-    ('LEFT_EYE', [43, 44, 45, 46, 47, 48, 43], (180, 200, 10)),
-    ('LEFT_EYEBROW', [23, 24, 25, 26, 27], (180, 220, 10)),
-    ('RIGHT_EYE', [37, 38, 39, 40, 41, 42, 37], (10, 200, 180)),
-    ('RIGHT_EYEBROW', [18, 19, 20, 21, 22], (10, 220, 180)),
-    ('NOSE_UP', [28, 29, 30, 31], (10, 200, 250)),
-    ('NOSE_DOWN', [32, 33, 34, 35, 36], (250, 200, 10)),
-    ('LIPS_OUTER_BOTTOM_LEFT', [55, 56, 57, 58], (10, 180, 20)),
-    ('LIPS_OUTER_BOTTOM_RIGHT', [49, 60, 59, 58], (20, 10, 180)),
-    ('LIPS_INNER_BOTTOM_LEFT', [65, 66, 67], (100, 100, 30)),
-    ('LIPS_INNER_BOTTOM_RIGHT', [61, 68, 67], (100, 150, 50)),
-    ('LIPS_OUTER_TOP_LEFT', [52, 53, 54, 55], (20, 80, 100)),
-    ('LIPS_OUTER_TOP_RIGHT', [52, 51, 50, 49], (80, 100, 20)),
-    ('LIPS_INNER_TOP_LEFT', [63, 64, 65], (120, 100, 200)),
-    ('LIPS_INNER_TOP_RIGHT', [63, 62, 61], (150, 120, 100)),
-]
-
-
-def draw_landmarks(keypoints, h, w):
-        
-    image = np.zeros((h, w, 3))
-
-    for name, indices, color in PARTS:
-        # 选择当前部分的关键点
-        indices = np.array(indices) - 1
-        current_part_keypoints = keypoints[indices]
-
-        # 绘制关键点
-        # for point in current_part_keypoints:
-        #     x, y = point
-        #     image[y, x, :] = color
-
-        # 绘制连接线
-        for i in range(len(indices) - 1):
-            x1, y1 = current_part_keypoints[i]
-            x2, y2 = current_part_keypoints[i + 1]
-            cv2.line(image, (int(x1), int(y1)), (int(x2), int(y2)), color, thickness=2)
-        
-    return image
-
-
-
-def convert_ldmk_to_68(mediapipe_ldmk):
-    return np.stack([
-    # face coutour
-    mediapipe_ldmk[:, 234], 
-    mediapipe_ldmk[:, 93], 
-    mediapipe_ldmk[:, 132], 
-    mediapipe_ldmk[:, 58], 
-    mediapipe_ldmk[:, 172], 
-    mediapipe_ldmk[:, 136], 
-    mediapipe_ldmk[:, 150], 
-    mediapipe_ldmk[:, 176], 
-    mediapipe_ldmk[:, 152], 
-    mediapipe_ldmk[:, 400], 
-    mediapipe_ldmk[:, 379], 
-    mediapipe_ldmk[:, 365], 
-    mediapipe_ldmk[:, 397], 
-    mediapipe_ldmk[:, 288], 
-    mediapipe_ldmk[:, 361], 
-    mediapipe_ldmk[:, 323], 
-    mediapipe_ldmk[:, 454], 
-    # right eyebrow
-    mediapipe_ldmk[:, 70], 
-    mediapipe_ldmk[:, 63], 
-    mediapipe_ldmk[:, 105], 
-    mediapipe_ldmk[:, 66], 
-    mediapipe_ldmk[:, 107], 
-    # left eyebrow
-    mediapipe_ldmk[:, 336], 
-    mediapipe_ldmk[:, 296], 
-    mediapipe_ldmk[:, 334], 
-    mediapipe_ldmk[:, 293], 
-    mediapipe_ldmk[:, 300], 
-    # nose
-    mediapipe_ldmk[:, 168], 
-    mediapipe_ldmk[:, 6], 
-    mediapipe_ldmk[:, 195], 
-    mediapipe_ldmk[:, 4], 
-    # nose down 
-    mediapipe_ldmk[:, 239], 
-    mediapipe_ldmk[:, 241], 
-    mediapipe_ldmk[:, 19], 
-    mediapipe_ldmk[:, 461], 
-    mediapipe_ldmk[:, 459], 
-    # right eye
-    mediapipe_ldmk[:, 33], 
-    mediapipe_ldmk[:, 160], 
-    mediapipe_ldmk[:, 158], 
-    mediapipe_ldmk[:, 133], 
-    mediapipe_ldmk[:, 153], 
-    mediapipe_ldmk[:, 144], 
-    # left eye
-    mediapipe_ldmk[:, 362], 
-    mediapipe_ldmk[:, 385], 
-    mediapipe_ldmk[:, 387], 
-    mediapipe_ldmk[:, 263], 
-    mediapipe_ldmk[:, 373], 
-    mediapipe_ldmk[:, 380],
-    # outer lips
-    mediapipe_ldmk[:, 61], 
-    mediapipe_ldmk[:, 40], 
-    mediapipe_ldmk[:, 37], 
-    mediapipe_ldmk[:, 0], 
-    mediapipe_ldmk[:, 267], 
-    mediapipe_ldmk[:, 270], 
-    mediapipe_ldmk[:, 291], 
-    mediapipe_ldmk[:, 321], 
-    mediapipe_ldmk[:, 314], 
-    mediapipe_ldmk[:, 17], 
-    mediapipe_ldmk[:, 84], 
-    mediapipe_ldmk[:, 91], 
-    # inner lips
-    mediapipe_ldmk[:, 78], 
-    mediapipe_ldmk[:, 81], 
-    mediapipe_ldmk[:, 13], 
-    mediapipe_ldmk[:, 311], 
-    mediapipe_ldmk[:, 308], 
-    mediapipe_ldmk[:, 402], 
-    mediapipe_ldmk[:, 14], 
-    mediapipe_ldmk[:, 178],
-], axis=1)
-
-
-
-# def parse_args():
-#     parser = argparse.ArgumentParser()
-#     parser.add_argument("--config", type=str, default='./configs/prompts/animation_audio.yaml')
-#     parser.add_argument("-W", type=int, default=512)
-#     parser.add_argument("-H", type=int, default=512)
-#     parser.add_argument("-L", type=int)
-#     parser.add_argument("--seed", type=int, default=42)
-#     parser.add_argument("--cfg", type=float, default=3.5)
-#     parser.add_argument("--steps", type=int, default=25)
-#     parser.add_argument("--fps", type=int, default=30)
-#     parser.add_argument("-acc", "--accelerate", action='store_true')
-#     parser.add_argument("--fi_step", type=int, default=3)
-#     args = parser.parse_args()
-
-#     return args
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ref_image_path", type=str, required=True)
-    parser.add_argument("--audio_path", type=str, required=True)
-    parser.add_argument("--save_dir", type=str, required=True)
-    parser.add_argument("--fps", type=int, default=25)
-    parser.add_argument("--sr", type=int, default=16000)
-    args = parser.parse_args()
-
-    return args
-
-
-def set_seed(seed):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-        torch.backends.cudnn.deterministic = True
-
-
-def main():
-    args = parse_args()
-
-    config = OmegaConf.load('aniportrait/configs/config.yaml')
-
-    set_seed(42)
-
-    # if config.weight_dtype == "fp16":
-    #     weight_dtype = torch.float16
-    # else:
-    #     weight_dtype = torch.float32
-        
-    audio_infer_config = OmegaConf.load(config.audio_inference_config)
-    # prepare model
-    a2m_model = Audio2MeshModel(audio_infer_config['a2m_model'])
-    a2m_model.load_state_dict(torch.load(audio_infer_config['pretrained_model']['a2m_ckpt']), strict=False)
-    a2m_model.cuda().eval()
-
-    a2p_model = Audio2PoseModel(audio_infer_config['a2p_model'])
-    a2p_model.load_state_dict(torch.load(audio_infer_config['pretrained_model']['a2p_ckpt']), strict=False)
-    a2p_model.cuda().eval()
-
-    lmk_extractor = LMKExtractor()
-
-    ref_image_path = args.ref_image_path
-    audio_path = args.audio_path
-    save_dir = args.save_dir
-
-    ref_image_pil = Image.open(ref_image_path).convert("RGB")
-    ref_image_np = cv2.cvtColor(np.array(ref_image_pil), cv2.COLOR_RGB2BGR)
-    height, width, _ = ref_image_np.shape
-    
-    face_result = lmk_extractor(ref_image_np)
-    assert face_result is not None, "No face detected."
-    lmks = face_result['lmks'].astype(np.float32)
-    lmks[:, 0] *= width
-    lmks[:, 1] *= height
-
-    # print(lmks.shape)
-
-    # assert False
-    
-    sample = prepare_audio_feature(audio_path, fps=args.fps, wav2vec_model_path=audio_infer_config['a2m_model']['model_path'])
-    sample['audio_feature'] = torch.from_numpy(sample['audio_feature']).float().cuda()
-    sample['audio_feature'] = sample['audio_feature'].unsqueeze(0)
-
-    # print(sample['audio_feature'].shape)
-
-    # inference
-    pred = a2m_model.infer(sample['audio_feature'], sample['seq_len'])
-    pred = pred.squeeze().detach().cpu().numpy()
-    pred = pred.reshape(pred.shape[0], -1, 3)
-
-    pred = pred + face_result['lmks3d']
-
-    # print(pred.shape)
-    
-    # assert False
-
-    id_seed = 42
-    id_seed = torch.LongTensor([id_seed]).cuda()
-
-    # Currently, only inference up to a maximum length of 10 seconds is supported.
-    chunk_duration = 5  # 5 seconds
-    chunk_size = args.sr * chunk_duration 
-
-
-    audio_chunks = list(sample['audio_feature'].split(chunk_size, dim=1))
-    seq_len_list = [chunk_duration*args.fps] * (len(audio_chunks) - 1) + [sample['seq_len'] % (chunk_duration*args.fps)]
-    audio_chunks[-2] = torch.cat((audio_chunks[-2], audio_chunks[-1]), dim=1)
-    seq_len_list[-2] = seq_len_list[-2] + seq_len_list[-1]
-    del audio_chunks[-1]
-    del seq_len_list[-1]
-
-    # assert False
-
-    pose_seq = []
-    for audio, seq_len in zip(audio_chunks, seq_len_list):
-        pose_seq_chunk = a2p_model.infer(audio, seq_len, id_seed)
-        pose_seq_chunk = pose_seq_chunk.squeeze().detach().cpu().numpy()
-        pose_seq_chunk[:, :3] *= 0.5
-        pose_seq.append(pose_seq_chunk)
-    
-    pose_seq = np.concatenate(pose_seq, 0)
-    pose_seq = smooth_pose_seq(pose_seq, 7)
-
-    # project 3D mesh to 2D landmark
-    projected_vertices = project_points(pred, face_result['trans_mat'], pose_seq, [height, width])
-    projected_vertices = np.concatenate([lmks[:468, :2][None, :], projected_vertices], axis=0)
-    projected_vertices = convert_ldmk_to_68(projected_vertices)
-
-    # print(projected_vertices.shape)
-
-    pose_images = []
-    for i in range(projected_vertices.shape[0]):
-        pose_img = draw_landmarks(projected_vertices[i], height, width)
-        pose_images.append(pose_img)
-    pose_images = np.array(pose_images)
-
-    # print(pose_images.shape)
-
-    ref_image_np = cv2.cvtColor(ref_image_np, cv2.COLOR_BGR2RGB)
-    ref_imgs = np.stack([ref_image_np]*(pose_images.shape[0]), axis=0)
-
-    all_np = np.concatenate([ref_imgs, pose_images], axis=2)
-
-    # print(projected_vertices.shape)
-
-    os.makedirs(save_dir, exist_ok=True)
-
-    np.save(os.path.join(save_dir, 'landmarks.npy'), projected_vertices)
-
-    torchvision.io.write_video(os.path.join(save_dir, 'landmarks.mp4'), all_np, fps=args.fps, video_codec='h264', options={'crf': '10'})
-
-    # stream = ffmpeg.input(os.path.join(save_dir, 'landmarks.mp4'))
-    # audio = ffmpeg.input(args.audio_path)
-    # ffmpeg.output(stream.video, audio.audio, os.path.join(save_dir, 'landmarks_audio.mp4'), vcodec='copy', acodec='aac').run()
-
-
-
-
-
-            
-
-if __name__ == "__main__":
-    main()
-    

aniportrait/configs/config.yaml

@@ -1,12 +0,0 @@
-pretrained_base_model_path: 'ckpts/aniportrait/stable-diffusion-v1-5'
-pretrained_vae_path: 'ckpts/aniportrait/sd-vae-ft-mse'
-image_encoder_path: 'ckpts/aniportrait/image_encoder'
-
-denoising_unet_path: "ckpts/aniportrait/denoising_unet.pth"
-reference_unet_path: "ckpts/aniportrait/reference_unet.pth"
-pose_guider_path: "ckpts/aniportrait/pose_guider.pth"
-motion_module_path: "ckpts/aniportrait/motion_module.pth"
-
-audio_inference_config: "aniportrait/configs/inference_audio.yaml"
-inference_config: "aniportrait/configs/inference_v2.yaml"
-weight_dtype: 'fp16'

aniportrait/configs/inference_audio.yaml

@@ -1,17 +0,0 @@
-a2m_model:
-  out_dim: 1404
-  latent_dim: 512
-  model_path: ckpts/aniportrait/wav2vec2-base-960h
-  only_last_fetures: True
-  from_pretrained: True
-
-a2p_model:
-  out_dim: 6 
-  latent_dim: 512
-  model_path: ckpts/aniportrait/wav2vec2-base-960h
-  only_last_fetures: True
-  from_pretrained: True
-
-pretrained_model:
-  a2m_ckpt: ckpts/aniportrait/audio2mesh.pt
-  a2p_ckpt: ckpts/aniportrait/audio2pose.pt

aniportrait/configs/inference_v2.yaml

@@ -1,35 +0,0 @@
-unet_additional_kwargs:
-  use_inflated_groupnorm: true
-  unet_use_cross_frame_attention: false 
-  unet_use_temporal_attention: false
-  use_motion_module: true
-  motion_module_resolutions:
-  - 1
-  - 2
-  - 4
-  - 8
-  motion_module_mid_block: true 
-  motion_module_decoder_only: false
-  motion_module_type: Vanilla
-  motion_module_kwargs:
-    num_attention_heads: 8
-    num_transformer_block: 1
-    attention_block_types:
-    - Temporal_Self
-    - Temporal_Self
-    temporal_position_encoding: true
-    temporal_position_encoding_max_len: 32
-    temporal_attention_dim_div: 1
-
-noise_scheduler_kwargs:
-  beta_start: 0.00085
-  beta_end: 0.012
-  beta_schedule: "linear"
-  clip_sample: false
-  steps_offset: 1
-  ### Zero-SNR params
-  prediction_type: "v_prediction"
-  rescale_betas_zero_snr: True
-  timestep_spacing: "trailing"
-
-sampler: DDIM 

aniportrait/src/audio_models/mish.py

@@ -1,51 +0,0 @@
-"""
-Applies the mish function element-wise:
-mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
-"""
-
-# import pytorch
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-@torch.jit.script
-def mish(input):
-    """
-    Applies the mish function element-wise:
-    mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
-    See additional documentation for mish class.
-    """
-    return input * torch.tanh(F.softplus(input))
-
-class Mish(nn.Module):
-    """
-    Applies the mish function element-wise:
-    mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
-
-    Shape:
-        - Input: (N, *) where * means, any number of additional
-          dimensions
-        - Output: (N, *), same shape as the input
-
-    Examples:
-        >>> m = Mish()
-        >>> input = torch.randn(2)
-        >>> output = m(input)
-
-    Reference: https://pytorch.org/docs/stable/generated/torch.nn.Mish.html
-    """
-
-    def __init__(self):
-        """
-        Init method.
-        """
-        super().__init__()
-
-    def forward(self, input):
-        """
-        Forward pass of the function.
-        """
-        if torch.__version__ >= "1.9":
-            return F.mish(input)
-        else:
-            return mish(input)

aniportrait/src/audio_models/model.py

@@ -1,71 +0,0 @@
-import os
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import Wav2Vec2Config
-
-from .torch_utils import get_mask_from_lengths
-from .wav2vec2 import Wav2Vec2Model
-
-
-class Audio2MeshModel(nn.Module):
-    def __init__(
-        self,
-        config
-    ):  
-        super().__init__()
-        out_dim = config['out_dim']
-        latent_dim = config['latent_dim']
-        model_path = config['model_path']
-        only_last_fetures = config['only_last_fetures']
-        from_pretrained = config['from_pretrained']
-
-        self._only_last_features = only_last_fetures
-
-        self.audio_encoder_config = Wav2Vec2Config.from_pretrained(model_path, local_files_only=True)
-        if from_pretrained:
-            self.audio_encoder = Wav2Vec2Model.from_pretrained(model_path, local_files_only=True)
-        else:
-            self.audio_encoder = Wav2Vec2Model(self.audio_encoder_config)
-        self.audio_encoder.feature_extractor._freeze_parameters()
-
-        hidden_size = self.audio_encoder_config.hidden_size
-
-        self.in_fn = nn.Linear(hidden_size, latent_dim)
-       
-        self.out_fn = nn.Linear(latent_dim, out_dim)
-        nn.init.constant_(self.out_fn.weight, 0)
-        nn.init.constant_(self.out_fn.bias, 0)
-
-    def forward(self, audio, label, audio_len=None):
-        attention_mask = ~get_mask_from_lengths(audio_len) if audio_len else None
-        
-        seq_len = label.shape[1]
-
-        embeddings = self.audio_encoder(audio, seq_len=seq_len, output_hidden_states=True,
-                                        attention_mask=attention_mask)
-
-        if self._only_last_features:
-            hidden_states = embeddings.last_hidden_state
-        else:
-            hidden_states = sum(embeddings.hidden_states) / len(embeddings.hidden_states)
-
-        layer_in = self.in_fn(hidden_states)
-        out = self.out_fn(layer_in)
-
-        return out, None
-
-    def infer(self, input_value, seq_len):
-        embeddings = self.audio_encoder(input_value, seq_len=seq_len, output_hidden_states=True)
-
-        if self._only_last_features:
-            hidden_states = embeddings.last_hidden_state
-        else:
-            hidden_states = sum(embeddings.hidden_states) / len(embeddings.hidden_states)
-
-        layer_in = self.in_fn(hidden_states)
-        out = self.out_fn(layer_in)
-
-        return out
-    
-

aniportrait/src/audio_models/pose_model.py

@@ -1,125 +0,0 @@
-import os
-import math
-import torch
-import torch.nn as nn
-from transformers import Wav2Vec2Config
-
-from .torch_utils import get_mask_from_lengths
-from .wav2vec2 import Wav2Vec2Model
-
-
-def init_biased_mask(n_head, max_seq_len, period):
-    def get_slopes(n):
-        def get_slopes_power_of_2(n):
-            start = (2**(-2**-(math.log2(n)-3)))
-            ratio = start
-            return [start*ratio**i for i in range(n)]
-        if math.log2(n).is_integer():
-            return get_slopes_power_of_2(n)                   
-        else:                                                 
-            closest_power_of_2 = 2**math.floor(math.log2(n)) 
-            return get_slopes_power_of_2(closest_power_of_2) + get_slopes(2*closest_power_of_2)[0::2][:n-closest_power_of_2]
-    slopes = torch.Tensor(get_slopes(n_head))
-    bias = torch.arange(start=0, end=max_seq_len, step=period).unsqueeze(1).repeat(1,period).view(-1)//(period)
-    bias = - torch.flip(bias,dims=[0])
-    alibi = torch.zeros(max_seq_len, max_seq_len)
-    for i in range(max_seq_len):
-        alibi[i, :i+1] = bias[-(i+1):]
-    alibi = slopes.unsqueeze(1).unsqueeze(1) * alibi.unsqueeze(0)
-    mask = (torch.triu(torch.ones(max_seq_len, max_seq_len)) == 1).transpose(0, 1)
-    mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
-    mask = mask.unsqueeze(0) + alibi
-    return mask
-
-
-def enc_dec_mask(device, T, S):
-    mask = torch.ones(T, S)
-    for i in range(T):
-        mask[i, i] = 0
-    return (mask==1).to(device=device)
-
-
-class PositionalEncoding(nn.Module):
-    def __init__(self, d_model, max_len=600):
-        super(PositionalEncoding, self).__init__()
-        pe = torch.zeros(max_len, d_model)
-        position = torch.arange(0, max_len).unsqueeze(1).float()
-        div_term = torch.exp(torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model))
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0)
-        self.register_buffer('pe', pe)
-
-    def forward(self, x):
-        x = x + self.pe[:, :x.size(1)]
-        return x
-
-
-class Audio2PoseModel(nn.Module):
-    def __init__(
-        self,
-        config
-    ):  
-        
-        super().__init__()
-        
-        latent_dim = config['latent_dim']
-        model_path = config['model_path']
-        only_last_fetures = config['only_last_fetures']
-        from_pretrained = config['from_pretrained']
-        out_dim = config['out_dim']
-
-        self.out_dim = out_dim
-
-        self._only_last_features = only_last_fetures
-
-        self.audio_encoder_config = Wav2Vec2Config.from_pretrained(model_path, local_files_only=True)
-        if from_pretrained:
-            self.audio_encoder = Wav2Vec2Model.from_pretrained(model_path, local_files_only=True)
-        else:
-            self.audio_encoder = Wav2Vec2Model(self.audio_encoder_config)
-        self.audio_encoder.feature_extractor._freeze_parameters()
-
-        hidden_size = self.audio_encoder_config.hidden_size
-
-        self.pose_map = nn.Linear(out_dim, latent_dim)
-        self.in_fn = nn.Linear(hidden_size, latent_dim)
-
-        self.PPE = PositionalEncoding(latent_dim)
-        self.biased_mask = init_biased_mask(n_head = 8, max_seq_len = 600, period=1)
-        decoder_layer = nn.TransformerDecoderLayer(d_model=latent_dim, nhead=8, dim_feedforward=2*latent_dim, batch_first=True)        
-        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=8)
-        self.pose_map_r = nn.Linear(latent_dim, out_dim)
-
-        self.id_embed = nn.Embedding(100, latent_dim) # 100 ids
-
-
-    def infer(self, input_value, seq_len, id_seed=None):
-        embeddings = self.audio_encoder(input_value, seq_len=seq_len, output_hidden_states=True)
-
-        if self._only_last_features:
-            hidden_states = embeddings.last_hidden_state
-        else:
-            hidden_states = sum(embeddings.hidden_states) / len(embeddings.hidden_states)
-
-        hidden_states = self.in_fn(hidden_states)
-
-        id_embedding = self.id_embed(id_seed).unsqueeze(1)
-        
-        init_pose = torch.zeros([hidden_states.shape[0], 1, self.out_dim]).to(hidden_states.device)
-        for i in range(seq_len):
-            if i==0:
-                pose_emb = self.pose_map(init_pose)
-                pose_input = self.PPE(pose_emb)
-            else:
-                pose_input = self.PPE(pose_emb)
-
-            pose_input = pose_input + id_embedding
-            tgt_mask = self.biased_mask[:, :pose_input.shape[1], :pose_input.shape[1]].clone().detach().to(hidden_states.device)
-            memory_mask = enc_dec_mask(hidden_states.device,  pose_input.shape[1], hidden_states.shape[1])
-            pose_out = self.transformer_decoder(pose_input, hidden_states, tgt_mask=tgt_mask, memory_mask=memory_mask)
-            pose_out = self.pose_map_r(pose_out)
-            new_output = self.pose_map(pose_out[:,-1,:]).unsqueeze(1)
-            pose_emb = torch.cat((pose_emb, new_output), 1)
-        return pose_out
-    

aniportrait/src/audio_models/torch_utils.py

@@ -1,25 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-
-def get_mask_from_lengths(lengths, max_len=None):
-    lengths = lengths.to(torch.long)
-    if max_len is None:
-        max_len = torch.max(lengths).item()
-
-    ids = torch.arange(0, max_len).unsqueeze(0).expand(lengths.shape[0], -1).to(lengths.device)
-    mask = ids < lengths.unsqueeze(1).expand(-1, max_len)
-
-    return mask
-
-
-def linear_interpolation(features, seq_len):
-    features = features.transpose(1, 2)
-    output_features = F.interpolate(features, size=seq_len, align_corners=True, mode='linear')
-    return output_features.transpose(1, 2)
-
-
-if __name__ == "__main__":
-    import numpy as np
-    mask = ~get_mask_from_lengths(torch.from_numpy(np.array([4,6])))
-    import pdb; pdb.set_trace()

aniportrait/src/audio_models/wav2vec2.py

@@ -1,125 +0,0 @@
-from transformers import Wav2Vec2Config, Wav2Vec2Model
-from transformers.modeling_outputs import BaseModelOutput
-
-from .torch_utils import linear_interpolation
-
-# the implementation of Wav2Vec2Model is borrowed from
-# https://github.com/huggingface/transformers/blob/HEAD/src/transformers/models/wav2vec2/modeling_wav2vec2.py
-# initialize our encoder with the pre-trained wav2vec 2.0 weights.
-class Wav2Vec2Model(Wav2Vec2Model):
-    def __init__(self, config: Wav2Vec2Config):
-        super().__init__(config)
-
-    def forward(
-        self,
-        input_values,
-        seq_len,
-        attention_mask=None,
-        mask_time_indices=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        self.config.output_attentions = True
-
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        extract_features = self.feature_extractor(input_values)
-        extract_features = extract_features.transpose(1, 2)
-        extract_features = linear_interpolation(extract_features, seq_len=seq_len)
-
-        if attention_mask is not None:
-            # compute reduced attention_mask corresponding to feature vectors
-            attention_mask = self._get_feature_vector_attention_mask(
-                extract_features.shape[1], attention_mask, add_adapter=False
-            )
-
-        hidden_states, extract_features = self.feature_projection(extract_features)
-        hidden_states = self._mask_hidden_states(
-            hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
-        )
-
-        encoder_outputs = self.encoder(
-            hidden_states,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = encoder_outputs[0]
-
-        if self.adapter is not None:
-            hidden_states = self.adapter(hidden_states)
-
-        if not return_dict:
-            return (hidden_states, ) + encoder_outputs[1:]
-        return BaseModelOutput(
-            last_hidden_state=hidden_states,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-    def feature_extract(
-        self,
-        input_values,
-        seq_len,
-    ):
-        extract_features = self.feature_extractor(input_values)
-        extract_features = extract_features.transpose(1, 2)
-        extract_features = linear_interpolation(extract_features, seq_len=seq_len)
-
-        return extract_features
-
-    def encode(
-        self,
-        extract_features,
-        attention_mask=None,
-        mask_time_indices=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
-        self.config.output_attentions = True
-
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if attention_mask is not None:
-            # compute reduced attention_mask corresponding to feature vectors
-            attention_mask = self._get_feature_vector_attention_mask(
-                extract_features.shape[1], attention_mask, add_adapter=False
-            )
-            
-
-        hidden_states, extract_features = self.feature_projection(extract_features)
-        hidden_states = self._mask_hidden_states(
-            hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
-        )
-
-        encoder_outputs = self.encoder(
-            hidden_states,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = encoder_outputs[0]
-
-        if self.adapter is not None:
-            hidden_states = self.adapter(hidden_states)
-
-        if not return_dict:
-            return (hidden_states, ) + encoder_outputs[1:]
-        return BaseModelOutput(
-            last_hidden_state=hidden_states,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )

aniportrait/src/utils/audio_util.py

@@ -1,30 +0,0 @@
-import os
-import math
-
-import librosa
-import numpy as np
-from transformers import Wav2Vec2FeatureExtractor
-
-
-class DataProcessor:
-    def __init__(self, sampling_rate, wav2vec_model_path):
-        self._processor = Wav2Vec2FeatureExtractor.from_pretrained(wav2vec_model_path, local_files_only=True)
-        self._sampling_rate = sampling_rate
-
-    def extract_feature(self, audio_path):
-        speech_array, sampling_rate = librosa.load(audio_path, sr=self._sampling_rate)
-        input_value = np.squeeze(self._processor(speech_array, sampling_rate=sampling_rate).input_values)
-        return input_value
-
-
-def prepare_audio_feature(wav_file, fps=25, sampling_rate=16000, wav2vec_model_path=None):
-    data_preprocessor = DataProcessor(sampling_rate, wav2vec_model_path)
-
-    input_value = data_preprocessor.extract_feature(wav_file)
-    seq_len = math.ceil(len(input_value)/sampling_rate*fps)
-    return {
-        "audio_feature": input_value,
-        "seq_len": seq_len
-    }
-
-

aniportrait/src/utils/draw_util.py

@@ -1,149 +0,0 @@
-import cv2
-import mediapipe as mp
-import numpy as np
-from mediapipe.framework.formats import landmark_pb2
-
-class FaceMeshVisualizer:
-    def __init__(self, forehead_edge=False):
-        self.mp_drawing = mp.solutions.drawing_utils
-        mp_face_mesh = mp.solutions.face_mesh
-        self.mp_face_mesh = mp_face_mesh
-        self.forehead_edge = forehead_edge
-
-        DrawingSpec = mp.solutions.drawing_styles.DrawingSpec
-        f_thick = 2
-        f_rad = 1
-        right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
-        right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
-        right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
-        left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
-        left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
-        left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
-        head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
-        
-        mouth_draw_obl = DrawingSpec(color=(10, 180, 20), thickness=f_thick, circle_radius=f_rad)
-        mouth_draw_obr = DrawingSpec(color=(20, 10, 180), thickness=f_thick, circle_radius=f_rad)
-        
-        mouth_draw_ibl = DrawingSpec(color=(100, 100, 30), thickness=f_thick, circle_radius=f_rad)
-        mouth_draw_ibr = DrawingSpec(color=(100, 150, 50), thickness=f_thick, circle_radius=f_rad)
-        
-        mouth_draw_otl = DrawingSpec(color=(20, 80, 100), thickness=f_thick, circle_radius=f_rad)
-        mouth_draw_otr = DrawingSpec(color=(80, 100, 20), thickness=f_thick, circle_radius=f_rad)
-        
-        mouth_draw_itl = DrawingSpec(color=(120, 100, 200), thickness=f_thick, circle_radius=f_rad)
-        mouth_draw_itr = DrawingSpec(color=(150 ,120, 100), thickness=f_thick, circle_radius=f_rad)
-        
-        FACEMESH_LIPS_OUTER_BOTTOM_LEFT = [(61,146),(146,91),(91,181),(181,84),(84,17)]
-        FACEMESH_LIPS_OUTER_BOTTOM_RIGHT = [(17,314),(314,405),(405,321),(321,375),(375,291)]
-        
-        FACEMESH_LIPS_INNER_BOTTOM_LEFT = [(78,95),(95,88),(88,178),(178,87),(87,14)]
-        FACEMESH_LIPS_INNER_BOTTOM_RIGHT = [(14,317),(317,402),(402,318),(318,324),(324,308)]
-        
-        FACEMESH_LIPS_OUTER_TOP_LEFT = [(61,185),(185,40),(40,39),(39,37),(37,0)]
-        FACEMESH_LIPS_OUTER_TOP_RIGHT = [(0,267),(267,269),(269,270),(270,409),(409,291)]
-        
-        FACEMESH_LIPS_INNER_TOP_LEFT = [(78,191),(191,80),(80,81),(81,82),(82,13)]
-        FACEMESH_LIPS_INNER_TOP_RIGHT = [(13,312),(312,311),(311,310),(310,415),(415,308)]
-        
-        FACEMESH_CUSTOM_FACE_OVAL = [(176, 149), (150, 136), (356, 454), (58, 132), (152, 148), (361, 288), (251, 389), (132, 93), (389, 356), (400, 377), (136, 172), (377, 152), (323, 361), (172, 58), (454, 323), (365, 379), (379, 378), (148, 176), (93, 234), (397, 365), (149, 150), (288, 397), (234, 127), (378, 400), (127, 162), (162, 21)]
-
-        # mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
-        face_connection_spec = {}
-        if self.forehead_edge:
-            for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
-                face_connection_spec[edge] = head_draw
-        else:
-            for edge in FACEMESH_CUSTOM_FACE_OVAL:
-                face_connection_spec[edge] = head_draw
-        for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
-            face_connection_spec[edge] = left_eye_draw
-        for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
-            face_connection_spec[edge] = left_eyebrow_draw
-        # for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
-        #    face_connection_spec[edge] = left_iris_draw
-        for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
-            face_connection_spec[edge] = right_eye_draw
-        for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
-            face_connection_spec[edge] = right_eyebrow_draw
-        # for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
-        #    face_connection_spec[edge] = right_iris_draw
-        # for edge in mp_face_mesh.FACEMESH_LIPS:
-        #     face_connection_spec[edge] = mouth_draw
-        
-        for edge in FACEMESH_LIPS_OUTER_BOTTOM_LEFT:
-            face_connection_spec[edge] = mouth_draw_obl
-        for edge in FACEMESH_LIPS_OUTER_BOTTOM_RIGHT:
-            face_connection_spec[edge] = mouth_draw_obr
-        for edge in FACEMESH_LIPS_INNER_BOTTOM_LEFT:
-            face_connection_spec[edge] = mouth_draw_ibl
-        for edge in FACEMESH_LIPS_INNER_BOTTOM_RIGHT:
-            face_connection_spec[edge] = mouth_draw_ibr
-        for edge in FACEMESH_LIPS_OUTER_TOP_LEFT:
-            face_connection_spec[edge] = mouth_draw_otl
-        for edge in FACEMESH_LIPS_OUTER_TOP_RIGHT:
-            face_connection_spec[edge] = mouth_draw_otr
-        for edge in FACEMESH_LIPS_INNER_TOP_LEFT:
-            face_connection_spec[edge] = mouth_draw_itl
-        for edge in FACEMESH_LIPS_INNER_TOP_RIGHT:
-            face_connection_spec[edge] = mouth_draw_itr
-
-        
-        iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
-        
-        self.face_connection_spec = face_connection_spec
-    def draw_pupils(self, image, landmark_list, drawing_spec, halfwidth: int = 2):
-        """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
-        landmarks.  Until our PR is merged into mediapipe, we need this separate method."""
-        if len(image.shape) != 3:
-            raise ValueError("Input image must be H,W,C.")
-        image_rows, image_cols, image_channels = image.shape
-        if image_channels != 3:  # BGR channels
-            raise ValueError('Input image must contain three channel bgr data.')
-        for idx, landmark in enumerate(landmark_list.landmark):
-            if (
-                    (landmark.HasField('visibility') and landmark.visibility < 0.9) or
-                    (landmark.HasField('presence') and landmark.presence < 0.5)
-            ):
-                continue
-            if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
-                continue
-            image_x = int(image_cols*landmark.x)
-            image_y = int(image_rows*landmark.y)
-            draw_color = None
-            if isinstance(drawing_spec, Mapping):
-                if drawing_spec.get(idx) is None:
-                    continue
-                else:
-                    draw_color = drawing_spec[idx].color
-            elif isinstance(drawing_spec, DrawingSpec):
-                draw_color = drawing_spec.color
-            image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color
-        
-
-
-    def draw_landmarks(self, image_size, keypoints, normed=False):
-        ini_size = [512, 512]
-        image = np.zeros([ini_size[1], ini_size[0], 3], dtype=np.uint8)
-        new_landmarks = landmark_pb2.NormalizedLandmarkList()
-        for i in range(keypoints.shape[0]):
-            landmark = new_landmarks.landmark.add()
-            if normed:
-                landmark.x = keypoints[i, 0]
-                landmark.y = keypoints[i, 1]
-            else:
-                landmark.x = keypoints[i, 0] / image_size[0]
-                landmark.y = keypoints[i, 1] / image_size[1]
-            landmark.z = 1.0
-
-        self.mp_drawing.draw_landmarks(
-            image=image,
-            landmark_list=new_landmarks,
-            connections=self.face_connection_spec.keys(),
-            landmark_drawing_spec=None,
-            connection_drawing_spec=self.face_connection_spec
-        )
-        # draw_pupils(image, face_landmarks, iris_landmark_spec, 2)
-        image = cv2.resize(image, (image_size[0], image_size[1]))
-        
-        return image
-

aniportrait/src/utils/face_landmark.py

@@ -1,3305 +0,0 @@
-# Copyright 2023 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""MediaPipe face landmarker task."""
-
-import dataclasses
-import enum
-from typing import Callable, Mapping, Optional, List
-
-import numpy as np
-
-from mediapipe.framework.formats import classification_pb2
-from mediapipe.framework.formats import landmark_pb2
-from mediapipe.framework.formats import matrix_data_pb2
-from mediapipe.python import packet_creator
-from mediapipe.python import packet_getter
-from mediapipe.python._framework_bindings import image as image_module
-from mediapipe.python._framework_bindings import packet as packet_module
-# pylint: disable=unused-import
-from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2
-# pylint: enable=unused-import
-from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_pb2
-from mediapipe.tasks.python.components.containers import category as category_module
-from mediapipe.tasks.python.components.containers import landmark as landmark_module
-from mediapipe.tasks.python.core import base_options as base_options_module
-from mediapipe.tasks.python.core import task_info as task_info_module
-from mediapipe.tasks.python.core.optional_dependencies import doc_controls
-from mediapipe.tasks.python.vision.core import base_vision_task_api
-from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
-from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
-
-_BaseOptions = base_options_module.BaseOptions
-_FaceLandmarkerGraphOptionsProto = (
-    face_landmarker_graph_options_pb2.FaceLandmarkerGraphOptions
-)
-_LayoutEnum = matrix_data_pb2.MatrixData.Layout
-_RunningMode = running_mode_module.VisionTaskRunningMode
-_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
-_TaskInfo = task_info_module.TaskInfo
-
-_IMAGE_IN_STREAM_NAME = 'image_in'
-_IMAGE_OUT_STREAM_NAME = 'image_out'
-_IMAGE_TAG = 'IMAGE'
-_NORM_RECT_STREAM_NAME = 'norm_rect_in'
-_NORM_RECT_TAG = 'NORM_RECT'
-_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
-_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
-_BLENDSHAPES_STREAM_NAME = 'blendshapes'
-_BLENDSHAPES_TAG = 'BLENDSHAPES'
-_FACE_GEOMETRY_STREAM_NAME = 'face_geometry'
-_FACE_GEOMETRY_TAG = 'FACE_GEOMETRY'
-_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph'
-_MICRO_SECONDS_PER_MILLISECOND = 1000
-
-
-class Blendshapes(enum.IntEnum):
-  """The 52 blendshape coefficients."""
-
-  NEUTRAL = 0
-  BROW_DOWN_LEFT = 1
-  BROW_DOWN_RIGHT = 2
-  BROW_INNER_UP = 3
-  BROW_OUTER_UP_LEFT = 4
-  BROW_OUTER_UP_RIGHT = 5
-  CHEEK_PUFF = 6
-  CHEEK_SQUINT_LEFT = 7
-  CHEEK_SQUINT_RIGHT = 8
-  EYE_BLINK_LEFT = 9
-  EYE_BLINK_RIGHT = 10
-  EYE_LOOK_DOWN_LEFT = 11
-  EYE_LOOK_DOWN_RIGHT = 12
-  EYE_LOOK_IN_LEFT = 13
-  EYE_LOOK_IN_RIGHT = 14
-  EYE_LOOK_OUT_LEFT = 15
-  EYE_LOOK_OUT_RIGHT = 16
-  EYE_LOOK_UP_LEFT = 17
-  EYE_LOOK_UP_RIGHT = 18
-  EYE_SQUINT_LEFT = 19
-  EYE_SQUINT_RIGHT = 20
-  EYE_WIDE_LEFT = 21
-  EYE_WIDE_RIGHT = 22
-  JAW_FORWARD = 23
-  JAW_LEFT = 24
-  JAW_OPEN = 25
-  JAW_RIGHT = 26
-  MOUTH_CLOSE = 27
-  MOUTH_DIMPLE_LEFT = 28
-  MOUTH_DIMPLE_RIGHT = 29
-  MOUTH_FROWN_LEFT = 30
-  MOUTH_FROWN_RIGHT = 31
-  MOUTH_FUNNEL = 32
-  MOUTH_LEFT = 33
-  MOUTH_LOWER_DOWN_LEFT = 34
-  MOUTH_LOWER_DOWN_RIGHT = 35
-  MOUTH_PRESS_LEFT = 36
-  MOUTH_PRESS_RIGHT = 37
-  MOUTH_PUCKER = 38
-  MOUTH_RIGHT = 39
-  MOUTH_ROLL_LOWER = 40
-  MOUTH_ROLL_UPPER = 41
-  MOUTH_SHRUG_LOWER = 42
-  MOUTH_SHRUG_UPPER = 43
-  MOUTH_SMILE_LEFT = 44
-  MOUTH_SMILE_RIGHT = 45
-  MOUTH_STRETCH_LEFT = 46
-  MOUTH_STRETCH_RIGHT = 47
-  MOUTH_UPPER_UP_LEFT = 48
-  MOUTH_UPPER_UP_RIGHT = 49
-  NOSE_SNEER_LEFT = 50
-  NOSE_SNEER_RIGHT = 51
-
-
-class FaceLandmarksConnections:
-  """The connections between face landmarks."""
-
-  @dataclasses.dataclass
-  class Connection:
-    """The connection class for face landmarks."""
-
-    start: int
-    end: int
-
-  FACE_LANDMARKS_LIPS: List[Connection] = [
-      Connection(61, 146),
-      Connection(146, 91),
-      Connection(91, 181),
-      Connection(181, 84),
-      Connection(84, 17),
-      Connection(17, 314),
-      Connection(314, 405),
-      Connection(405, 321),
-      Connection(321, 375),
-      Connection(375, 291),
-      Connection(61, 185),
-      Connection(185, 40),
-      Connection(40, 39),
-      Connection(39, 37),
-      Connection(37, 0),
-      Connection(0, 267),
-      Connection(267, 269),
-      Connection(269, 270),
-      Connection(270, 409),
-      Connection(409, 291),
-      Connection(78, 95),
-      Connection(95, 88),
-      Connection(88, 178),
-      Connection(178, 87),
-      Connection(87, 14),
-      Connection(14, 317),
-      Connection(317, 402),
-      Connection(402, 318),
-      Connection(318, 324),
-      Connection(324, 308),
-      Connection(78, 191),
-      Connection(191, 80),
-      Connection(80, 81),
-      Connection(81, 82),
-      Connection(82, 13),
-      Connection(13, 312),
-      Connection(312, 311),
-      Connection(311, 310),
-      Connection(310, 415),
-      Connection(415, 308),
-  ]
-
-  FACE_LANDMARKS_LEFT_EYE: List[Connection] = [
-      Connection(263, 249),
-      Connection(249, 390),
-      Connection(390, 373),
-      Connection(373, 374),
-      Connection(374, 380),
-      Connection(380, 381),
-      Connection(381, 382),
-      Connection(382, 362),
-      Connection(263, 466),
-      Connection(466, 388),
-      Connection(388, 387),
-      Connection(387, 386),
-      Connection(386, 385),
-      Connection(385, 384),
-      Connection(384, 398),
-      Connection(398, 362),
-  ]
-
-  FACE_LANDMARKS_LEFT_EYEBROW: List[Connection] = [
-      Connection(276, 283),
-      Connection(283, 282),
-      Connection(282, 295),
-      Connection(295, 285),
-      Connection(300, 293),
-      Connection(293, 334),
-      Connection(334, 296),
-      Connection(296, 336),
-  ]
-
-  FACE_LANDMARKS_LEFT_IRIS: List[Connection] = [
-      Connection(474, 475),
-      Connection(475, 476),
-      Connection(476, 477),
-      Connection(477, 474),
-  ]
-
-  FACE_LANDMARKS_RIGHT_EYE: List[Connection] = [
-      Connection(33, 7),
-      Connection(7, 163),
-      Connection(163, 144),
-      Connection(144, 145),
-      Connection(145, 153),
-      Connection(153, 154),
-      Connection(154, 155),
-      Connection(155, 133),
-      Connection(33, 246),
-      Connection(246, 161),
-      Connection(161, 160),
-      Connection(160, 159),
-      Connection(159, 158),
-      Connection(158, 157),
-      Connection(157, 173),
-      Connection(173, 133),
-  ]
-
-  FACE_LANDMARKS_RIGHT_EYEBROW: List[Connection] = [
-      Connection(46, 53),
-      Connection(53, 52),
-      Connection(52, 65),
-      Connection(65, 55),
-      Connection(70, 63),
-      Connection(63, 105),
-      Connection(105, 66),
-      Connection(66, 107),
-  ]
-
-  FACE_LANDMARKS_RIGHT_IRIS: List[Connection] = [
-      Connection(469, 470),
-      Connection(470, 471),
-      Connection(471, 472),
-      Connection(472, 469),
-  ]
-
-  FACE_LANDMARKS_FACE_OVAL: List[Connection] = [
-      Connection(10, 338),
-      Connection(338, 297),
-      Connection(297, 332),
-      Connection(332, 284),
-      Connection(284, 251),
-      Connection(251, 389),
-      Connection(389, 356),
-      Connection(356, 454),
-      Connection(454, 323),
-      Connection(323, 361),
-      Connection(361, 288),
-      Connection(288, 397),
-      Connection(397, 365),
-      Connection(365, 379),
-      Connection(379, 378),
-      Connection(378, 400),
-      Connection(400, 377),
-      Connection(377, 152),
-      Connection(152, 148),
-      Connection(148, 176),
-      Connection(176, 149),
-      Connection(149, 150),
-      Connection(150, 136),
-      Connection(136, 172),
-      Connection(172, 58),
-      Connection(58, 132),
-      Connection(132, 93),
-      Connection(93, 234),
-      Connection(234, 127),
-      Connection(127, 162),
-      Connection(162, 21),
-      Connection(21, 54),
-      Connection(54, 103),
-      Connection(103, 67),
-      Connection(67, 109),
-      Connection(109, 10),
-  ]
-
-  FACE_LANDMARKS_CONTOURS: List[Connection] = (
-      FACE_LANDMARKS_LIPS
-      + FACE_LANDMARKS_LEFT_EYE
-      + FACE_LANDMARKS_LEFT_EYEBROW
-      + FACE_LANDMARKS_RIGHT_EYE
-      + FACE_LANDMARKS_RIGHT_EYEBROW
-      + FACE_LANDMARKS_FACE_OVAL
-  )
-
-  FACE_LANDMARKS_TESSELATION: List[Connection] = [
-      Connection(127, 34),
-      Connection(34, 139),
-      Connection(139, 127),
-      Connection(11, 0),
-      Connection(0, 37),
-      Connection(37, 11),
-      Connection(232, 231),
-      Connection(231, 120),
-      Connection(120, 232),
-      Connection(72, 37),
-      Connection(37, 39),
-      Connection(39, 72),
-      Connection(128, 121),
-      Connection(121, 47),
-      Connection(47, 128),
-      Connection(232, 121),
-      Connection(121, 128),
-      Connection(128, 232),
-      Connection(104, 69),
-      Connection(69, 67),
-      Connection(67, 104),
-      Connection(175, 171),
-      Connection(171, 148),
-      Connection(148, 175),
-      Connection(118, 50),
-      Connection(50, 101),
-      Connection(101, 118),
-      Connection(73, 39),
-      Connection(39, 40),
-      Connection(40, 73),
-      Connection(9, 151),
-      Connection(151, 108),
-      Connection(108, 9),
-      Connection(48, 115),
-      Connection(115, 131),
-      Connection(131, 48),
-      Connection(194, 204),
-      Connection(204, 211),
-      Connection(211, 194),
-      Connection(74, 40),
-      Connection(40, 185),
-      Connection(185, 74),
-      Connection(80, 42),
-      Connection(42, 183),
-      Connection(183, 80),
-      Connection(40, 92),
-      Connection(92, 186),
-      Connection(186, 40),
-      Connection(230, 229),
-      Connection(229, 118),
-      Connection(118, 230),
-      Connection(202, 212),
-      Connection(212, 214),
-      Connection(214, 202),
-      Connection(83, 18),
-      Connection(18, 17),
-      Connection(17, 83),
-      Connection(76, 61),
-      Connection(61, 146),
-      Connection(146, 76),
-      Connection(160, 29),
-      Connection(29, 30),
-      Connection(30, 160),
-      Connection(56, 157),
-      Connection(157, 173),
-      Connection(173, 56),
-      Connection(106, 204),
-      Connection(204, 194),
-      Connection(194, 106),
-      Connection(135, 214),
-      Connection(214, 192),
-      Connection(192, 135),
-      Connection(203, 165),
-      Connection(165, 98),
-      Connection(98, 203),
-      Connection(21, 71),
-      Connection(71, 68),
-      Connection(68, 21),
-      Connection(51, 45),
-      Connection(45, 4),
-      Connection(4, 51),
-      Connection(144, 24),
-      Connection(24, 23),
-      Connection(23, 144),
-      Connection(77, 146),
-      Connection(146, 91),
-      Connection(91, 77),
-      Connection(205, 50),
-      Connection(50, 187),
-      Connection(187, 205),
-      Connection(201, 200),
-      Connection(200, 18),
-      Connection(18, 201),
-      Connection(91, 106),
-      Connection(106, 182),
-      Connection(182, 91),
-      Connection(90, 91),
-      Connection(91, 181),
-      Connection(181, 90),
-      Connection(85, 84),
-      Connection(84, 17),
-      Connection(17, 85),
-      Connection(206, 203),
-      Connection(203, 36),
-      Connection(36, 206),
-      Connection(148, 171),
-      Connection(171, 140),
-      Connection(140, 148),
-      Connection(92, 40),
-      Connection(40, 39),
-      Connection(39, 92),
-      Connection(193, 189),
-      Connection(189, 244),
-      Connection(244, 193),
-      Connection(159, 158),
-      Connection(158, 28),
-      Connection(28, 159),
-      Connection(247, 246),
-      Connection(246, 161),
-      Connection(161, 247),
-      Connection(236, 3),
-      Connection(3, 196),
-      Connection(196, 236),
-      Connection(54, 68),
-      Connection(68, 104),
-      Connection(104, 54),
-      Connection(193, 168),
-      Connection(168, 8),
-      Connection(8, 193),
-      Connection(117, 228),
-      Connection(228, 31),
-      Connection(31, 117),
-      Connection(189, 193),
-      Connection(193, 55),
-      Connection(55, 189),
-      Connection(98, 97),
-      Connection(97, 99),
-      Connection(99, 98),
-      Connection(126, 47),
-      Connection(47, 100),
-      Connection(100, 126),
-      Connection(166, 79),
-      Connection(79, 218),
-      Connection(218, 166),
-      Connection(155, 154),
-      Connection(154, 26),
-      Connection(26, 155),
-      Connection(209, 49),
-      Connection(49, 131),
-      Connection(131, 209),
-      Connection(135, 136),
-      Connection(136, 150),
-      Connection(150, 135),
-      Connection(47, 126),
-      Connection(126, 217),
-      Connection(217, 47),
-      Connection(223, 52),
-      Connection(52, 53),
-      Connection(53, 223),
-      Connection(45, 51),
-      Connection(51, 134),
-      Connection(134, 45),
-      Connection(211, 170),
-      Connection(170, 140),
-      Connection(140, 211),
-      Connection(67, 69),
-      Connection(69, 108),
-      Connection(108, 67),
-      Connection(43, 106),
-      Connection(106, 91),
-      Connection(91, 43),
-      Connection(230, 119),
-      Connection(119, 120),
-      Connection(120, 230),
-      Connection(226, 130),
-      Connection(130, 247),
-      Connection(247, 226),
-      Connection(63, 53),
-      Connection(53, 52),
-      Connection(52, 63),
-      Connection(238, 20),
-      Connection(20, 242),
-      Connection(242, 238),
-      Connection(46, 70),
-      Connection(70, 156),
-      Connection(156, 46),
-      Connection(78, 62),
-      Connection(62, 96),
-      Connection(96, 78),
-      Connection(46, 53),
-      Connection(53, 63),
-      Connection(63, 46),
-      Connection(143, 34),
-      Connection(34, 227),
-      Connection(227, 143),
-      Connection(123, 117),
-      Connection(117, 111),
-      Connection(111, 123),
-      Connection(44, 125),
-      Connection(125, 19),
-      Connection(19, 44),
-      Connection(236, 134),
-      Connection(134, 51),
-      Connection(51, 236),
-      Connection(216, 206),
-      Connection(206, 205),
-      Connection(205, 216),
-      Connection(154, 153),
-      Connection(153, 22),
-      Connection(22, 154),
-      Connection(39, 37),
-      Connection(37, 167),
-      Connection(167, 39),
-      Connection(200, 201),
-      Connection(201, 208),
-      Connection(208, 200),
-      Connection(36, 142),
-      Connection(142, 100),
-      Connection(100, 36),
-      Connection(57, 212),
-      Connection(212, 202),
-      Connection(202, 57),
-      Connection(20, 60),
-      Connection(60, 99),
-      Connection(99, 20),
-      Connection(28, 158),
-      Connection(158, 157),
-      Connection(157, 28),
-      Connection(35, 226),
-      Connection(226, 113),
-      Connection(113, 35),
-      Connection(160, 159),
-      Connection(159, 27),
-      Connection(27, 160),
-      Connection(204, 202),
-      Connection(202, 210),
-      Connection(210, 204),
-      Connection(113, 225),
-      Connection(225, 46),
-      Connection(46, 113),
-      Connection(43, 202),
-      Connection(202, 204),
-      Connection(204, 43),
-      Connection(62, 76),
-      Connection(76, 77),
-      Connection(77, 62),
-      Connection(137, 123),
-      Connection(123, 116),
-      Connection(116, 137),
-      Connection(41, 38),
-      Connection(38, 72),
-      Connection(72, 41),
-      Connection(203, 129),
-      Connection(129, 142),
-      Connection(142, 203),
-      Connection(64, 98),
-      Connection(98, 240),
-      Connection(240, 64),
-      Connection(49, 102),
-      Connection(102, 64),
-      Connection(64, 49),
-      Connection(41, 73),
-      Connection(73, 74),
-      Connection(74, 41),
-      Connection(212, 216),
-      Connection(216, 207),
-      Connection(207, 212),
-      Connection(42, 74),
-      Connection(74, 184),
-      Connection(184, 42),
-      Connection(169, 170),
-      Connection(170, 211),
-      Connection(211, 169),
-      Connection(170, 149),
-      Connection(149, 176),
-      Connection(176, 170),
-      Connection(105, 66),
-      Connection(66, 69),
-      Connection(69, 105),
-      Connection(122, 6),
-      Connection(6, 168),
-      Connection(168, 122),
-      Connection(123, 147),
-      Connection(147, 187),
-      Connection(187, 123),
-      Connection(96, 77),
-      Connection(77, 90),
-      Connection(90, 96),
-      Connection(65, 55),
-      Connection(55, 107),
-      Connection(107, 65),
-      Connection(89, 90),
-      Connection(90, 180),
-      Connection(180, 89),
-      Connection(101, 100),
-      Connection(100, 120),
-      Connection(120, 101),
-      Connection(63, 105),
-      Connection(105, 104),
-      Connection(104, 63),
-      Connection(93, 137),
-      Connection(137, 227),
-      Connection(227, 93),
-      Connection(15, 86),
-      Connection(86, 85),
-      Connection(85, 15),
-      Connection(129, 102),
-      Connection(102, 49),
-      Connection(49, 129),
-      Connection(14, 87),
-      Connection(87, 86),
-      Connection(86, 14),
-      Connection(55, 8),
-      Connection(8, 9),
-      Connection(9, 55),
-      Connection(100, 47),
-      Connection(47, 121),
-      Connection(121, 100),
-      Connection(145, 23),
-      Connection(23, 22),
-      Connection(22, 145),
-      Connection(88, 89),
-      Connection(89, 179),
-      Connection(179, 88),
-      Connection(6, 122),
-      Connection(122, 196),
-      Connection(196, 6),
-      Connection(88, 95),
-      Connection(95, 96),
-      Connection(96, 88),
-      Connection(138, 172),
-      Connection(172, 136),
-      Connection(136, 138),
-      Connection(215, 58),
-      Connection(58, 172),
-      Connection(172, 215),
-      Connection(115, 48),
-      Connection(48, 219),
-      Connection(219, 115),
-      Connection(42, 80),
-      Connection(80, 81),
-      Connection(81, 42),
-      Connection(195, 3),
-      Connection(3, 51),
-      Connection(51, 195),
-      Connection(43, 146),
-      Connection(146, 61),
-      Connection(61, 43),
-      Connection(171, 175),
-      Connection(175, 199),
-      Connection(199, 171),
-      Connection(81, 82),
-      Connection(82, 38),
-      Connection(38, 81),
-      Connection(53, 46),
-      Connection(46, 225),
-      Connection(225, 53),
-      Connection(144, 163),
-      Connection(163, 110),
-      Connection(110, 144),
-      Connection(52, 65),
-      Connection(65, 66),
-      Connection(66, 52),
-      Connection(229, 228),
-      Connection(228, 117),
-      Connection(117, 229),
-      Connection(34, 127),
-      Connection(127, 234),
-      Connection(234, 34),
-      Connection(107, 108),
-      Connection(108, 69),
-      Connection(69, 107),
-      Connection(109, 108),
-      Connection(108, 151),
-      Connection(151, 109),
-      Connection(48, 64),
-      Connection(64, 235),
-      Connection(235, 48),
-      Connection(62, 78),
-      Connection(78, 191),
-      Connection(191, 62),
-      Connection(129, 209),
-      Connection(209, 126),
-      Connection(126, 129),
-      Connection(111, 35),
-      Connection(35, 143),
-      Connection(143, 111),
-      Connection(117, 123),
-      Connection(123, 50),
-      Connection(50, 117),
-      Connection(222, 65),
-      Connection(65, 52),
-      Connection(52, 222),
-      Connection(19, 125),
-      Connection(125, 141),
-      Connection(141, 19),
-      Connection(221, 55),
-      Connection(55, 65),
-      Connection(65, 221),
-      Connection(3, 195),
-      Connection(195, 197),
-      Connection(197, 3),
-      Connection(25, 7),
-      Connection(7, 33),
-      Connection(33, 25),
-      Connection(220, 237),
-      Connection(237, 44),
-      Connection(44, 220),
-      Connection(70, 71),
-      Connection(71, 139),
-      Connection(139, 70),
-      Connection(122, 193),
-      Connection(193, 245),
-      Connection(245, 122),
-      Connection(247, 130),
-      Connection(130, 33),
-      Connection(33, 247),
-      Connection(71, 21),
-      Connection(21, 162),
-      Connection(162, 71),
-      Connection(170, 169),
-      Connection(169, 150),
-      Connection(150, 170),
-      Connection(188, 174),
-      Connection(174, 196),
-      Connection(196, 188),
-      Connection(216, 186),
-      Connection(186, 92),
-      Connection(92, 216),
-      Connection(2, 97),
-      Connection(97, 167),
-      Connection(167, 2),
-      Connection(141, 125),
-      Connection(125, 241),
-      Connection(241, 141),
-      Connection(164, 167),
-      Connection(167, 37),
-      Connection(37, 164),
-      Connection(72, 38),
-      Connection(38, 12),
-      Connection(12, 72),
-      Connection(38, 82),
-      Connection(82, 13),
-      Connection(13, 38),
-      Connection(63, 68),
-      Connection(68, 71),
-      Connection(71, 63),
-      Connection(226, 35),
-      Connection(35, 111),
-      Connection(111, 226),
-      Connection(101, 50),
-      Connection(50, 205),
-      Connection(205, 101),
-      Connection(206, 92),
-      Connection(92, 165),
-      Connection(165, 206),
-      Connection(209, 198),
-      Connection(198, 217),
-      Connection(217, 209),
-      Connection(165, 167),
-      Connection(167, 97),
-      Connection(97, 165),
-      Connection(220, 115),
-      Connection(115, 218),
-      Connection(218, 220),
-      Connection(133, 112),
-      Connection(112, 243),
-      Connection(243, 133),
-      Connection(239, 238),
-      Connection(238, 241),
-      Connection(241, 239),
-      Connection(214, 135),
-      Connection(135, 169),
-      Connection(169, 214),
-      Connection(190, 173),
-      Connection(173, 133),
-      Connection(133, 190),
-      Connection(171, 208),
-      Connection(208, 32),
-      Connection(32, 171),
-      Connection(125, 44),
-      Connection(44, 237),
-      Connection(237, 125),
-      Connection(86, 87),
-      Connection(87, 178),
-      Connection(178, 86),
-      Connection(85, 86),
-      Connection(86, 179),
-      Connection(179, 85),
-      Connection(84, 85),
-      Connection(85, 180),
-      Connection(180, 84),
-      Connection(83, 84),
-      Connection(84, 181),
-      Connection(181, 83),
-      Connection(201, 83),
-      Connection(83, 182),
-      Connection(182, 201),
-      Connection(137, 93),
-      Connection(93, 132),
-      Connection(132, 137),
-      Connection(76, 62),
-      Connection(62, 183),
-      Connection(183, 76),
-      Connection(61, 76),
-      Connection(76, 184),
-      Connection(184, 61),
-      Connection(57, 61),
-      Connection(61, 185),
-      Connection(185, 57),
-      Connection(212, 57),
-      Connection(57, 186),
-      Connection(186, 212),
-      Connection(214, 207),
-      Connection(207, 187),
-      Connection(187, 214),
-      Connection(34, 143),
-      Connection(143, 156),
-      Connection(156, 34),
-      Connection(79, 239),
-      Connection(239, 237),
-      Connection(237, 79),
-      Connection(123, 137),
-      Connection(137, 177),
-      Connection(177, 123),
-      Connection(44, 1),
-      Connection(1, 4),
-      Connection(4, 44),
-      Connection(201, 194),
-      Connection(194, 32),
-      Connection(32, 201),
-      Connection(64, 102),
-      Connection(102, 129),
-      Connection(129, 64),
-      Connection(213, 215),
-      Connection(215, 138),
-      Connection(138, 213),
-      Connection(59, 166),
-      Connection(166, 219),
-      Connection(219, 59),
-      Connection(242, 99),
-      Connection(99, 97),
-      Connection(97, 242),
-      Connection(2, 94),
-      Connection(94, 141),
-      Connection(141, 2),
-      Connection(75, 59),
-      Connection(59, 235),
-      Connection(235, 75),
-      Connection(24, 110),
-      Connection(110, 228),
-      Connection(228, 24),
-      Connection(25, 130),
-      Connection(130, 226),
-      Connection(226, 25),
-      Connection(23, 24),
-      Connection(24, 229),
-      Connection(229, 23),
-      Connection(22, 23),
-      Connection(23, 230),
-      Connection(230, 22),
-      Connection(26, 22),
-      Connection(22, 231),
-      Connection(231, 26),
-      Connection(112, 26),
-      Connection(26, 232),
-      Connection(232, 112),
-      Connection(189, 190),
-      Connection(190, 243),
-      Connection(243, 189),
-      Connection(221, 56),
-      Connection(56, 190),
-      Connection(190, 221),
-      Connection(28, 56),
-      Connection(56, 221),
-      Connection(221, 28),
-      Connection(27, 28),
-      Connection(28, 222),
-      Connection(222, 27),
-      Connection(29, 27),
-      Connection(27, 223),
-      Connection(223, 29),
-      Connection(30, 29),
-      Connection(29, 224),
-      Connection(224, 30),
-      Connection(247, 30),
-      Connection(30, 225),
-      Connection(225, 247),
-      Connection(238, 79),
-      Connection(79, 20),
-      Connection(20, 238),
-      Connection(166, 59),
-      Connection(59, 75),
-      Connection(75, 166),
-      Connection(60, 75),
-      Connection(75, 240),
-      Connection(240, 60),
-      Connection(147, 177),
-      Connection(177, 215),
-      Connection(215, 147),
-      Connection(20, 79),
-      Connection(79, 166),
-      Connection(166, 20),
-      Connection(187, 147),
-      Connection(147, 213),
-      Connection(213, 187),
-      Connection(112, 233),
-      Connection(233, 244),
-      Connection(244, 112),
-      Connection(233, 128),
-      Connection(128, 245),
-      Connection(245, 233),
-      Connection(128, 114),
-      Connection(114, 188),
-      Connection(188, 128),
-      Connection(114, 217),
-      Connection(217, 174),
-      Connection(174, 114),
-      Connection(131, 115),
-      Connection(115, 220),
-      Connection(220, 131),
-      Connection(217, 198),
-      Connection(198, 236),
-      Connection(236, 217),
-      Connection(198, 131),
-      Connection(131, 134),
-      Connection(134, 198),
-      Connection(177, 132),
-      Connection(132, 58),
-      Connection(58, 177),
-      Connection(143, 35),
-      Connection(35, 124),
-      Connection(124, 143),
-      Connection(110, 163),
-      Connection(163, 7),
-      Connection(7, 110),
-      Connection(228, 110),
-      Connection(110, 25),
-      Connection(25, 228),
-      Connection(356, 389),
-      Connection(389, 368),
-      Connection(368, 356),
-      Connection(11, 302),
-      Connection(302, 267),
-      Connection(267, 11),
-      Connection(452, 350),
-      Connection(350, 349),
-      Connection(349, 452),
-      Connection(302, 303),
-      Connection(303, 269),
-      Connection(269, 302),
-      Connection(357, 343),
-      Connection(343, 277),
-      Connection(277, 357),
-      Connection(452, 453),
-      Connection(453, 357),
-      Connection(357, 452),
-      Connection(333, 332),
-      Connection(332, 297),
-      Connection(297, 333),
-      Connection(175, 152),
-      Connection(152, 377),
-      Connection(377, 175),
-      Connection(347, 348),
-      Connection(348, 330),
-      Connection(330, 347),
-      Connection(303, 304),
-      Connection(304, 270),
-      Connection(270, 303),
-      Connection(9, 336),
-      Connection(336, 337),
-      Connection(337, 9),
-      Connection(278, 279),
-      Connection(279, 360),
-      Connection(360, 278),
-      Connection(418, 262),
-      Connection(262, 431),
-      Connection(431, 418),
-      Connection(304, 408),
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-      Connection(456, 420),
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-      Connection(363, 360),
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-      Connection(401, 288),
-      Connection(288, 361),
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-      Connection(339, 249),
-      Connection(249, 390),
-      Connection(339, 448),
-      Connection(448, 255),
-      Connection(255, 339),
-  ]
-
-
-@dataclasses.dataclass
-class FaceLandmarkerResult:
-  """The face landmarks detection result from FaceLandmarker, where each vector element represents a single face detected in the image.
-
-  Attributes:
-    face_landmarks: Detected face landmarks in normalized image coordinates.
-    face_blendshapes: Optional face blendshapes results.
-    facial_transformation_matrixes: Optional facial transformation matrix.
-  """
-
-  face_landmarks: List[List[landmark_module.NormalizedLandmark]]
-  face_blendshapes: List[List[category_module.Category]]
-  facial_transformation_matrixes: List[np.ndarray]
-
-
-def _build_landmarker_result(
-    output_packets: Mapping[str, packet_module.Packet]
-) -> FaceLandmarkerResult:
-  """Constructs a `FaceLandmarkerResult` from output packets."""
-  face_landmarks_proto_list = packet_getter.get_proto_list(
-      output_packets[_NORM_LANDMARKS_STREAM_NAME]
-  )
-
-  face_landmarks_results = []
-  for proto in face_landmarks_proto_list:
-    face_landmarks = landmark_pb2.NormalizedLandmarkList()
-    face_landmarks.MergeFrom(proto)
-    face_landmarks_list = []
-    for face_landmark in face_landmarks.landmark:
-      face_landmarks_list.append(
-          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
-      )
-    face_landmarks_results.append(face_landmarks_list)
-
-  face_blendshapes_results = []
-  if _BLENDSHAPES_STREAM_NAME in output_packets:
-    face_blendshapes_proto_list = packet_getter.get_proto_list(
-        output_packets[_BLENDSHAPES_STREAM_NAME]
-    )
-    for proto in face_blendshapes_proto_list:
-      face_blendshapes_categories = []
-      face_blendshapes_classifications = classification_pb2.ClassificationList()
-      face_blendshapes_classifications.MergeFrom(proto)
-      for face_blendshapes in face_blendshapes_classifications.classification:
-        face_blendshapes_categories.append(
-            category_module.Category(
-                index=face_blendshapes.index,
-                score=face_blendshapes.score,
-                display_name=face_blendshapes.display_name,
-                category_name=face_blendshapes.label,
-            )
-        )
-      face_blendshapes_results.append(face_blendshapes_categories)
-
-  facial_transformation_matrixes_results = []
-  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
-    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
-        output_packets[_FACE_GEOMETRY_STREAM_NAME]
-    )
-    for proto in facial_transformation_matrixes_proto_list:
-      if hasattr(proto, 'pose_transform_matrix'):
-        matrix_data = matrix_data_pb2.MatrixData()
-        matrix_data.MergeFrom(proto.pose_transform_matrix)
-        matrix = np.array(matrix_data.packed_data)
-        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
-        matrix = (
-            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
-        )
-        facial_transformation_matrixes_results.append(matrix)
-
-  return FaceLandmarkerResult(
-      face_landmarks_results,
-      face_blendshapes_results,
-      facial_transformation_matrixes_results,
-  )
-
-def _build_landmarker_result2(
-    output_packets: Mapping[str, packet_module.Packet]
-) -> FaceLandmarkerResult:
-  """Constructs a `FaceLandmarkerResult` from output packets."""
-  face_landmarks_proto_list = packet_getter.get_proto_list(
-      output_packets[_NORM_LANDMARKS_STREAM_NAME]
-  )
-
-  face_landmarks_results = []
-  for proto in face_landmarks_proto_list:
-    face_landmarks = landmark_pb2.NormalizedLandmarkList()
-    face_landmarks.MergeFrom(proto)
-    face_landmarks_list = []
-    for face_landmark in face_landmarks.landmark:
-      face_landmarks_list.append(
-          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
-      )
-    face_landmarks_results.append(face_landmarks_list)
-
-  face_blendshapes_results = []
-  if _BLENDSHAPES_STREAM_NAME in output_packets:
-    face_blendshapes_proto_list = packet_getter.get_proto_list(
-        output_packets[_BLENDSHAPES_STREAM_NAME]
-    )
-    for proto in face_blendshapes_proto_list:
-      face_blendshapes_categories = []
-      face_blendshapes_classifications = classification_pb2.ClassificationList()
-      face_blendshapes_classifications.MergeFrom(proto)
-      for face_blendshapes in face_blendshapes_classifications.classification:
-        face_blendshapes_categories.append(
-            category_module.Category(
-                index=face_blendshapes.index,
-                score=face_blendshapes.score,
-                display_name=face_blendshapes.display_name,
-                category_name=face_blendshapes.label,
-            )
-        )
-      face_blendshapes_results.append(face_blendshapes_categories)
-
-  facial_transformation_matrixes_results = []
-  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
-    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
-        output_packets[_FACE_GEOMETRY_STREAM_NAME]
-    )
-    for proto in facial_transformation_matrixes_proto_list:
-      if hasattr(proto, 'pose_transform_matrix'):
-        matrix_data = matrix_data_pb2.MatrixData()
-        matrix_data.MergeFrom(proto.pose_transform_matrix)
-        matrix = np.array(matrix_data.packed_data)
-        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
-        matrix = (
-            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
-        )
-        facial_transformation_matrixes_results.append(matrix)
-
-  return FaceLandmarkerResult(
-      face_landmarks_results,
-      face_blendshapes_results,
-      facial_transformation_matrixes_results,
-  ), facial_transformation_matrixes_proto_list[0].mesh
-
-@dataclasses.dataclass
-class FaceLandmarkerOptions:
-  """Options for the face landmarker task.
-
-  Attributes:
-    base_options: Base options for the face landmarker task.
-    running_mode: The running mode of the task. Default to the image mode.
-      FaceLandmarker has three running modes: 1) The image mode for detecting
-      face landmarks on single image inputs. 2) The video mode for detecting
-      face landmarks on the decoded frames of a video. 3) The live stream mode
-      for detecting face landmarks on the live stream of input data, such as
-      from camera. In this mode, the "result_callback" below must be specified
-      to receive the detection results asynchronously.
-    num_faces: The maximum number of faces that can be detected by the
-      FaceLandmarker.
-    min_face_detection_confidence: The minimum confidence score for the face
-      detection to be considered successful.
-    min_face_presence_confidence: The minimum confidence score of face presence
-      score in the face landmark detection.
-    min_tracking_confidence: The minimum confidence score for the face tracking
-      to be considered successful.
-    output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes
-      classification. Face blendshapes are used for rendering the 3D face model.
-    output_facial_transformation_matrixes: Whether FaceLandmarker outputs facial
-      transformation_matrix. Facial transformation matrix is used to transform
-      the face landmarks in canonical face to the detected face, so that users
-      can apply face effects on the detected landmarks.
-    result_callback: The user-defined result callback for processing live stream
-      data. The result callback should only be specified when the running mode
-      is set to the live stream mode.
-  """
-
-  base_options: _BaseOptions
-  running_mode: _RunningMode = _RunningMode.IMAGE
-  num_faces: int = 1
-  min_face_detection_confidence: float = 0.5
-  min_face_presence_confidence: float = 0.5
-  min_tracking_confidence: float = 0.5
-  output_face_blendshapes: bool = False
-  output_facial_transformation_matrixes: bool = False
-  result_callback: Optional[
-      Callable[[FaceLandmarkerResult, image_module.Image, int], None]
-  ] = None
-
-  @doc_controls.do_not_generate_docs
-  def to_pb2(self) -> _FaceLandmarkerGraphOptionsProto:
-    """Generates an FaceLandmarkerGraphOptions protobuf object."""
-    base_options_proto = self.base_options.to_pb2()
-    base_options_proto.use_stream_mode = (
-        False if self.running_mode == _RunningMode.IMAGE else True
-    )
-
-    # Initialize the face landmarker options from base options.
-    face_landmarker_options_proto = _FaceLandmarkerGraphOptionsProto(
-        base_options=base_options_proto
-    )
-
-    # Configure face detector options.
-    face_landmarker_options_proto.face_detector_graph_options.num_faces = (
-        self.num_faces
-    )
-    face_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
-        self.min_face_detection_confidence
-    )
-
-    # Configure face landmark detector options.
-    face_landmarker_options_proto.min_tracking_confidence = (
-        self.min_tracking_confidence
-    )
-    face_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
-        self.min_face_detection_confidence
-    )
-    return face_landmarker_options_proto
-
-
-class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
-  """Class that performs face landmarks detection on images."""
-
-  @classmethod
-  def create_from_model_path(cls, model_path: str) -> 'FaceLandmarker':
-    """Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`.
-
-    Note that the created `FaceLandmarker` instance is in image mode, for
-    detecting face landmarks on single image inputs.
-
-    Args:
-      model_path: Path to the model.
-
-    Returns:
-      `FaceLandmarker` object that's created from the model file and the
-      default `FaceLandmarkerOptions`.
-
-    Raises:
-      ValueError: If failed to create `FaceLandmarker` object from the
-        provided file such as invalid file path.
-      RuntimeError: If other types of error occurred.
-    """
-    base_options = _BaseOptions(model_asset_path=model_path)
-    options = FaceLandmarkerOptions(
-        base_options=base_options, running_mode=_RunningMode.IMAGE
-    )
-    return cls.create_from_options(options)
-
-  @classmethod
-  def create_from_options(
-      cls, options: FaceLandmarkerOptions
-  ) -> 'FaceLandmarker':
-    """Creates the `FaceLandmarker` object from face landmarker options.
-
-    Args:
-      options: Options for the face landmarker task.
-
-    Returns:
-      `FaceLandmarker` object that's created from `options`.
-
-    Raises:
-      ValueError: If failed to create `FaceLandmarker` object from
-        `FaceLandmarkerOptions` such as missing the model.
-      RuntimeError: If other types of error occurred.
-    """
-
-    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
-      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
-        return
-
-      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
-      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
-        return
-
-      if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
-        empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
-        options.result_callback(
-            FaceLandmarkerResult([], [], []),
-            image,
-            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
-        )
-        return
-
-      face_landmarks_result = _build_landmarker_result(output_packets)
-      timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
-      options.result_callback(
-          face_landmarks_result,
-          image,
-          timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
-      )
-
-    output_streams = [
-        ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
-        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
-    ]
-
-    if options.output_face_blendshapes:
-      output_streams.append(
-          ':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME])
-      )
-    if options.output_facial_transformation_matrixes:
-      output_streams.append(
-          ':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME])
-      )
-
-    task_info = _TaskInfo(
-        task_graph=_TASK_GRAPH_NAME,
-        input_streams=[
-            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
-            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
-        ],
-        output_streams=output_streams,
-        task_options=options,
-    )
-    return cls(
-        task_info.generate_graph_config(
-            enable_flow_limiting=options.running_mode
-            == _RunningMode.LIVE_STREAM
-        ),
-        options.running_mode,
-        packets_callback if options.result_callback else None,
-    )
-
-  def detect(
-      self,
-      image: image_module.Image,
-      image_processing_options: Optional[_ImageProcessingOptions] = None,
-  ) -> FaceLandmarkerResult:
-    """Performs face landmarks detection on the given image.
-
-    Only use this method when the FaceLandmarker is created with the image
-    running mode.
-
-    The image can be of any size with format RGB or RGBA.
-    TODO: Describes how the input image will be preprocessed after the yuv
-    support is implemented.
-
-    Args:
-      image: MediaPipe Image.
-      image_processing_options: Options for image processing.
-
-    Returns:
-      The face landmarks detection results.
-
-    Raises:
-      ValueError: If any of the input arguments is invalid.
-      RuntimeError: If face landmarker detection failed to run.
-    """
-
-    normalized_rect = self.convert_to_normalized_rect(
-        image_processing_options, image, roi_allowed=False
-    )
-    output_packets = self._process_image_data({
-        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
-        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
-            normalized_rect.to_pb2()
-        ),
-    })
-
-    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
-      return FaceLandmarkerResult([], [], [])
-
-    return _build_landmarker_result2(output_packets)
-
-  def detect_for_video(
-      self,
-      image: image_module.Image,
-      timestamp_ms: int,
-      image_processing_options: Optional[_ImageProcessingOptions] = None,
-  ):
-    """Performs face landmarks detection on the provided video frame.
-
-    Only use this method when the FaceLandmarker is created with the video
-    running mode.
-
-    Only use this method when the FaceLandmarker is created with the video
-    running mode. It's required to provide the video frame's timestamp (in
-    milliseconds) along with the video frame. The input timestamps should be
-    monotonically increasing for adjacent calls of this method.
-
-    Args:
-      image: MediaPipe Image.
-      timestamp_ms: The timestamp of the input video frame in milliseconds.
-      image_processing_options: Options for image processing.
-
-    Returns:
-      The face landmarks detection results.
-
-    Raises:
-      ValueError: If any of the input arguments is invalid.
-      RuntimeError: If face landmarker detection failed to run.
-    """
-    normalized_rect = self.convert_to_normalized_rect(
-        image_processing_options, image, roi_allowed=False
-    )
-    output_packets = self._process_video_data({
-        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
-            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
-        ),
-        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
-            normalized_rect.to_pb2()
-        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
-    })
-
-    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
-      return FaceLandmarkerResult([], [], [])
-
-    return _build_landmarker_result2(output_packets)
-
-  def detect_async(
-      self,
-      image: image_module.Image,
-      timestamp_ms: int,
-      image_processing_options: Optional[_ImageProcessingOptions] = None,
-  ) -> None:
-    """Sends live image data to perform face landmarks detection.
-
-    The results will be available via the "result_callback" provided in the
-    FaceLandmarkerOptions. Only use this method when the FaceLandmarker is
-    created with the live stream running mode.
-
-    Only use this method when the FaceLandmarker is created with the live
-    stream running mode. The input timestamps should be monotonically increasing
-    for adjacent calls of this method. This method will return immediately after
-    the input image is accepted. The results will be available via the
-    `result_callback` provided in the `FaceLandmarkerOptions`. The
-    `detect_async` method is designed to process live stream data such as
-    camera input. To lower the overall latency, face landmarker may drop the
-    input images if needed. In other words, it's not guaranteed to have output
-    per input image.
-
-    The `result_callback` provides:
-      - The face landmarks detection results.
-      - The input image that the face landmarker runs on.
-      - The input timestamp in milliseconds.
-
-    Args:
-      image: MediaPipe Image.
-      timestamp_ms: The timestamp of the input image in milliseconds.
-      image_processing_options: Options for image processing.
-
-    Raises:
-      ValueError: If the current input timestamp is smaller than what the
-      face landmarker has already processed.
-    """
-    normalized_rect = self.convert_to_normalized_rect(
-        image_processing_options, image, roi_allowed=False
-    )
-    self._send_live_stream_data({
-        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
-            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
-        ),
-        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
-            normalized_rect.to_pb2()
-        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
-    })

aniportrait/src/utils/frame_interpolation.py

@@ -1,69 +0,0 @@
-# Adapted from https://github.com/dajes/frame-interpolation-pytorch
-import os
-import cv2
-import numpy as np
-import torch
-import bisect
-import shutil
-import pdb
-from tqdm import tqdm
-
-def init_frame_interpolation_model():
-    print("Initializing frame interpolation model")
-    checkpoint_name = os.path.join("./pretrained_model/film_net_fp16.pt")
-
-    model = torch.jit.load(checkpoint_name, map_location='cpu')
-    model.eval()
-    model = model.half()
-    model = model.to(device="cuda")
-    return model
-
-
-def batch_images_interpolation_tool(input_tensor, model, inter_frames=1):
-
-    video_tensor = []
-    frame_num = input_tensor.shape[2]  # bs, channel, frame, height, width
-    
-    for idx in tqdm(range(frame_num-1)):
-        image1 = input_tensor[:,:,idx]
-        image2 = input_tensor[:,:,idx+1]
-
-        results = [image1, image2]
-
-        inter_frames = int(inter_frames)
-        idxes = [0, inter_frames + 1]
-        remains = list(range(1, inter_frames + 1))
-
-        splits = torch.linspace(0, 1, inter_frames + 2)
-
-        for _ in range(len(remains)):
-            starts = splits[idxes[:-1]]
-            ends = splits[idxes[1:]]
-            distances = ((splits[None, remains] - starts[:, None]) / (ends[:, None] - starts[:, None]) - .5).abs()
-            matrix = torch.argmin(distances).item()
-            start_i, step = np.unravel_index(matrix, distances.shape)
-            end_i = start_i + 1
-
-            x0 = results[start_i]
-            x1 = results[end_i]
-
-            x0 = x0.half()
-            x1 = x1.half()
-            x0 = x0.cuda()
-            x1 = x1.cuda()
-
-            dt = x0.new_full((1, 1), (splits[remains[step]] - splits[idxes[start_i]])) / (splits[idxes[end_i]] - splits[idxes[start_i]])
-
-            with torch.no_grad():
-                prediction = model(x0, x1, dt)
-            insert_position = bisect.bisect_left(idxes, remains[step])
-            idxes.insert(insert_position, remains[step])
-            results.insert(insert_position, prediction.clamp(0, 1).cpu().float())
-            del remains[step]
-
-        for sub_idx in range(len(results)-1):
-            video_tensor.append(results[sub_idx].unsqueeze(2))
-
-    video_tensor.append(input_tensor[:,:,-1].unsqueeze(2))
-    video_tensor = torch.cat(video_tensor, dim=2)
-    return video_tensor

aniportrait/src/utils/mp_models/blaze_face_short_range.tflite

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b4578f35940bf5a1a655214a1cce5cab13eba73c1297cd78e1a04c2380b0152f
-size 229746

aniportrait/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:64184e229b263107bc2b804c6625db1341ff2bb731874b0bcc2fe6544e0bc9ff
-size 3758596

aniportrait/src/utils/mp_models/pose_landmarker_heavy.task

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:64437af838a65d18e5ba7a0d39b465540069bc8aae8308de3e318aad31fcbc7b
-size 30664242

aniportrait/src/utils/mp_utils.py

@@ -1,95 +0,0 @@
-import os
-import numpy as np
-import cv2
-import time
-from tqdm import tqdm
-import multiprocessing
-import glob
-
-import mediapipe as mp
-from mediapipe import solutions
-from mediapipe.framework.formats import landmark_pb2
-from mediapipe.tasks import python
-from mediapipe.tasks.python import vision
-from . import face_landmark
-
-CUR_DIR = os.path.dirname(__file__)
-
-
-class LMKExtractor():
-    def __init__(self, FPS=25):
-        # Create an FaceLandmarker object.
-        self.mode = mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE
-        base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/face_landmarker_v2_with_blendshapes.task'))
-        base_options.delegate = mp.tasks.BaseOptions.Delegate.CPU
-        options = vision.FaceLandmarkerOptions(base_options=base_options,
-                                            running_mode=self.mode,
-                                            output_face_blendshapes=True,
-                                            output_facial_transformation_matrixes=True,
-                                            num_faces=1)
-        self.detector = face_landmark.FaceLandmarker.create_from_options(options)
-        self.last_ts = 0
-        self.frame_ms = int(1000 / FPS)
-
-        det_base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/blaze_face_short_range.tflite'))
-        det_options = vision.FaceDetectorOptions(base_options=det_base_options)
-        self.det_detector = vision.FaceDetector.create_from_options(det_options)
-                
-
-    def __call__(self, img):
-        frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
-        t0 = time.time()
-        if self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.VIDEO:
-            det_result = self.det_detector.detect(image)
-            if len(det_result.detections) != 1:
-                return None
-            self.last_ts += self.frame_ms
-            try:
-                detection_result, mesh3d = self.detector.detect_for_video(image, timestamp_ms=self.last_ts)
-            except:
-                return None
-        elif self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE:
-            # det_result = self.det_detector.detect(image)
-
-            # if len(det_result.detections) != 1:
-            #     return None
-            try:
-                detection_result, mesh3d = self.detector.detect(image)
-            except:
-                return None
-            
-        
-        bs_list = detection_result.face_blendshapes
-        if len(bs_list) == 1:
-            bs = bs_list[0]
-            bs_values = []
-            for index in range(len(bs)):
-                bs_values.append(bs[index].score)
-            bs_values = bs_values[1:] # remove neutral
-            trans_mat = detection_result.facial_transformation_matrixes[0]
-            face_landmarks_list = detection_result.face_landmarks
-            face_landmarks = face_landmarks_list[0]
-            lmks = []
-            for index in range(len(face_landmarks)):
-                x = face_landmarks[index].x
-                y = face_landmarks[index].y
-                z = face_landmarks[index].z
-                lmks.append([x, y, z])
-            lmks = np.array(lmks)
-            
-            lmks3d = np.array(mesh3d.vertex_buffer)
-            lmks3d = lmks3d.reshape(-1, 5)[:, :3]
-            mp_tris = np.array(mesh3d.index_buffer).reshape(-1, 3) + 1
-
-            return {
-                "lmks": lmks,
-                'lmks3d': lmks3d,
-                "trans_mat": trans_mat,
-                'faces': mp_tris,
-                "bs": bs_values
-            }
-        else:
-            # print('multiple faces in the image: {}'.format(img_path))
-            return None
-        

aniportrait/src/utils/pose_util.py

@@ -1,89 +0,0 @@
-import math
-
-import numpy as np
-from scipy.spatial.transform import Rotation as R
-
-
-def create_perspective_matrix(aspect_ratio):
-    kDegreesToRadians = np.pi / 180.
-    near = 1
-    far = 10000
-    perspective_matrix = np.zeros(16, dtype=np.float32)
-
-    # Standard perspective projection matrix calculations.
-    f = 1.0 / np.tan(kDegreesToRadians * 63 / 2.)
-
-    denom = 1.0 / (near - far)
-    perspective_matrix[0] = f / aspect_ratio
-    perspective_matrix[5] = f
-    perspective_matrix[10] = (near + far) * denom
-    perspective_matrix[11] = -1.
-    perspective_matrix[14] = 1. * far * near * denom
-
-    # If the environment's origin point location is in the top left corner,
-    # then skip additional flip along Y-axis is required to render correctly.
-
-    perspective_matrix[5] *= -1.
-    return perspective_matrix
-
-
-def project_points(points_3d, transformation_matrix, pose_vectors, image_shape):
-    P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T
-    L, N, _ = points_3d.shape
-    projected_points = np.zeros((L, N, 2))
-    for i in range(L):
-        points_3d_frame = points_3d[i]
-        ones = np.ones((points_3d_frame.shape[0], 1))
-        points_3d_homogeneous = np.hstack([points_3d_frame, ones])  
-        transformed_points = points_3d_homogeneous @ (transformation_matrix @ euler_and_translation_to_matrix(pose_vectors[i][:3], pose_vectors[i][3:])).T @ P
-        projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1
-        projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1] 
-        projected_points_frame[:, 1]  = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0]
-        projected_points[i] = projected_points_frame
-    return projected_points
-
-
-def project_points_with_trans(points_3d, transformation_matrix, image_shape):
-    P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T
-    L, N, _ = points_3d.shape
-    projected_points = np.zeros((L, N, 2))
-    for i in range(L):
-        points_3d_frame = points_3d[i]
-        ones = np.ones((points_3d_frame.shape[0], 1))
-        points_3d_homogeneous = np.hstack([points_3d_frame, ones])  
-        transformed_points = points_3d_homogeneous @ transformation_matrix[i].T @ P
-        projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1
-        projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1] 
-        projected_points_frame[:, 1]  = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0]
-        projected_points[i] = projected_points_frame
-    return projected_points
-
-
-def euler_and_translation_to_matrix(euler_angles, translation_vector):
-    rotation = R.from_euler('xyz', euler_angles, degrees=True)
-    rotation_matrix = rotation.as_matrix()
-    
-    matrix = np.eye(4)
-    matrix[:3, :3] = rotation_matrix
-    matrix[:3, 3] = translation_vector
-    
-    return matrix
-
-
-def matrix_to_euler_and_translation(matrix):
-    rotation_matrix = matrix[:3, :3]
-    translation_vector = matrix[:3, 3]
-    rotation = R.from_matrix(rotation_matrix)
-    euler_angles = rotation.as_euler('xyz', degrees=True)
-    return euler_angles, translation_vector
-
-
-def smooth_pose_seq(pose_seq, window_size=5):
-    smoothed_pose_seq = np.zeros_like(pose_seq)
-
-    for i in range(len(pose_seq)):
-        start = max(0, i - window_size // 2)
-        end = min(len(pose_seq), i + window_size // 2 + 1)
-        smoothed_pose_seq[i] = np.mean(pose_seq[start:end], axis=0)
-
-    return smoothed_pose_seq

aniportrait/src/utils/util.py

@@ -1,181 +0,0 @@
-import importlib
-import os
-import os.path as osp
-import shutil
-import sys
-import cv2
-from pathlib import Path
-
-import av
-import numpy as np
-import torch
-import torchvision
-from einops import rearrange
-from PIL import Image
-
-
-def seed_everything(seed):
-    import random
-
-    import numpy as np
-
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    np.random.seed(seed % (2**32))
-    random.seed(seed)
-
-
-def import_filename(filename):
-    spec = importlib.util.spec_from_file_location("mymodule", filename)
-    module = importlib.util.module_from_spec(spec)
-    sys.modules[spec.name] = module
-    spec.loader.exec_module(module)
-    return module
-
-
-def delete_additional_ckpt(base_path, num_keep):
-    dirs = []
-    for d in os.listdir(base_path):
-        if d.startswith("checkpoint-"):
-            dirs.append(d)
-    num_tot = len(dirs)
-    if num_tot <= num_keep:
-        return
-    # ensure ckpt is sorted and delete the ealier!
-    del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep]
-    for d in del_dirs:
-        path_to_dir = osp.join(base_path, d)
-        if osp.exists(path_to_dir):
-            shutil.rmtree(path_to_dir)
-
-
-def save_videos_from_pil(pil_images, path, fps=8):
-    import av
-
-    save_fmt = Path(path).suffix
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-    width, height = pil_images[0].size
-
-    if save_fmt == ".mp4":
-        codec = "libx264"
-        container = av.open(path, "w")
-        stream = container.add_stream(codec, rate=fps)
-
-        stream.width = width
-        stream.height = height
-
-        for pil_image in pil_images:
-            # pil_image = Image.fromarray(image_arr).convert("RGB")
-            av_frame = av.VideoFrame.from_image(pil_image)
-            container.mux(stream.encode(av_frame))
-        container.mux(stream.encode())
-        container.close()
-
-    elif save_fmt == ".gif":
-        pil_images[0].save(
-            fp=path,
-            format="GIF",
-            append_images=pil_images[1:],
-            save_all=True,
-            duration=(1 / fps * 1000),
-            loop=0,
-        )
-    else:
-        raise ValueError("Unsupported file type. Use .mp4 or .gif.")
-
-
-def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
-    videos = rearrange(videos, "b c t h w -> t b c h w")
-    height, width = videos.shape[-2:]
-    outputs = []
-
-    for x in videos:
-        x = torchvision.utils.make_grid(x, nrow=n_rows)  # (c h w)
-        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)  # (h w c)
-        if rescale:
-            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
-        x = (x * 255).numpy().astype(np.uint8)
-        x = Image.fromarray(x)
-
-        outputs.append(x)
-
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-
-    save_videos_from_pil(outputs, path, fps)
-
-
-def read_frames(video_path):
-    container = av.open(video_path)
-
-    video_stream = next(s for s in container.streams if s.type == "video")
-    frames = []
-    for packet in container.demux(video_stream):
-        for frame in packet.decode():
-            image = Image.frombytes(
-                "RGB",
-                (frame.width, frame.height),
-                frame.to_rgb().to_ndarray(),
-            )
-            frames.append(image)
-
-    return frames
-
-
-def get_fps(video_path):
-    container = av.open(video_path)
-    video_stream = next(s for s in container.streams if s.type == "video")
-    fps = video_stream.average_rate
-    container.close()
-    return fps
-
-def crop_face(img, lmk_extractor, expand=1.5):
-    result = lmk_extractor(img)  # cv2 BGR
-
-    if result is None:
-        return None
-    
-    H, W, _ = img.shape
-    lmks = result['lmks']
-    lmks[:, 0] *= W
-    lmks[:, 1] *= H
-
-    x_min = np.min(lmks[:, 0])
-    x_max = np.max(lmks[:, 0])
-    y_min = np.min(lmks[:, 1])
-    y_max = np.max(lmks[:, 1])
-
-    width = x_max - x_min
-    height = y_max - y_min
-    
-    if width*height >= W*H*0.15:
-        if W == H:
-            return img
-        size = min(H, W)
-        offset = int((max(H, W) - size)/2)
-        if size == H:
-            return img[:, offset:-offset]
-        else:
-            return img[offset:-offset, :]
-    else:
-        center_x = x_min + width / 2
-        center_y = y_min + height / 2
-
-        width *= expand
-        height *= expand
-
-        size = max(width, height)
-
-        x_min = int(center_x - size / 2)
-        x_max = int(center_x + size / 2)
-        y_min = int(center_y - size / 2)
-        y_max = int(center_y + size / 2)
-
-        top = max(0, -y_min)
-        bottom = max(0, y_max - img.shape[0])
-        left = max(0, -x_min)
-        right = max(0, x_max - img.shape[1])
-        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=0)
-
-        cropped_img = img[y_min + top:y_max + top, x_min + left:x_max + left]
-
-    return cropped_img

ckpt_tree.md

@@ -1,108 +0,0 @@
-
-```
-|-- ckpts
-|   |-- aniportrait
-|   |   `-- motion_module.pth
-|   |   `-- audio2mesh.pt
-|   |   `-- film_net_fp16.pt
-|   |   |-- sd-vae-ft-mse
-|   |   |   `-- diffusion_pytorch_model.safetensors
-|   |   |   `-- config.json
-|   |   |   `-- diffusion_pytorch_model.bin
-|   |   `-- denoising_unet.pth
-|   |   `-- audio2pose.pt
-|   |   `-- pose_guider.pth
-|   |   |-- sd-image-variations-diffusers
-|   |   |   `-- v1-montage.jpg
-|   |   |   |-- scheduler
-|   |   |   |   `-- scheduler_config.json
-|   |   |   `-- README.md
-|   |   |   `-- model_index.json
-|   |   |   |-- unet
-|   |   |   |   `-- config.json
-|   |   |   |   `-- diffusion_pytorch_model.bin
-|   |   |   |-- feature_extractor
-|   |   |   |   `-- preprocessor_config.json
-|   |   |   `-- v2-montage.jpg
-|   |   |   |-- vae
-|   |   |   |   `-- config.json
-|   |   |   |   `-- diffusion_pytorch_model.bin
-|   |   |   `-- alias-montage.jpg
-|   |   |   `-- inputs.jpg
-|   |   |   |-- safety_checker
-|   |   |   |   `-- pytorch_model.bin
-|   |   |   |   `-- config.json
-|   |   |   `-- earring.jpg
-|   |   |   `-- default-montage.jpg
-|   |   |-- image_encoder
-|   |   |   `-- pytorch_model.bin
-|   |   |   `-- config.json
-|   |   |-- stable-diffusion-v1-5
-|   |   |   `-- model_index.json
-|   |   |   `-- v1-inference.yaml
-|   |   |   |-- unet
-|   |   |   |   `-- config.json
-|   |   |   |   `-- diffusion_pytorch_model.bin
-|   |   |   |-- feature_extractor
-|   |   |   |   `-- preprocessor_config.json
-|   |   `-- reference_unet.pth
-|   |   |-- wav2vec2-base-960h
-|   |   |   `-- pytorch_model.bin
-|   |   |   `-- README.md
-|   |   |   `-- vocab.json
-|   |   |   `-- config.json
-|   |   |   `-- tf_model.h5
-|   |   |   `-- tokenizer_config.json
-|   |   |   `-- model.safetensors
-|   |   |   `-- special_tokens_map.json
-|   |   |   `-- preprocessor_config.json
-|   |   |   `-- feature_extractor_config.json
-|   |-- mofa
-|   |   |-- traj_controlnet
-|   |   |   `-- diffusion_pytorch_model.safetensors
-|   |   |   `-- config.json
-|   |   |-- stable-video-diffusion-img2vid-xt-1-1
-|   |   |   |-- scheduler
-|   |   |   |   `-- scheduler_config.json
-|   |   |   `-- README.md
-|   |   |   `-- model_index.json
-|   |   |   |-- unet
-|   |   |   |   `-- diffusion_pytorch_model.fp16.safetensors
-|   |   |   |   `-- config.json
-|   |   |   |-- feature_extractor
-|   |   |   |   `-- preprocessor_config.json
-|   |   |   |-- vae
-|   |   |   |   `-- diffusion_pytorch_model.fp16.safetensors
-|   |   |   |   `-- config.json
-|   |   |   `-- LICENSE
-|   |   |   `-- svd11.webp
-|   |   |   |-- image_encoder
-|   |   |   |   `-- config.json
-|   |   |   |   `-- model.fp16.safetensors
-|   |   |-- ldmk_controlnet
-|   |   |   `-- diffusion_pytorch_model.safetensors
-|   |   |   `-- config.json
-|   |-- sad_talker
-|   |   `-- SadTalker_V0.0.2_256.safetensors
-|   |   |-- hub
-|   |   `-- mapping_00229-model.pth.tar
-|   |   |-- BFM_Fitting
-|   |   |   `-- select_vertex_id.mat
-|   |   |   `-- facemodel_info.mat
-|   |   |   `-- BFM_exp_idx.mat
-|   |   |   `-- BFM_model_front.mat
-|   |   |   `-- 01_MorphableModel.mat
-|   |   |   `-- similarity_Lm3D_all.mat
-|   |   |   `-- BFM_front_idx.mat
-|   |   |   `-- Exp_Pca.bin
-|   |   |   `-- std_exp.txt
-|   |   `-- SadTalker_V0.0.2_512.safetensors
-|   |   `-- similarity_Lm3D_all.mat
-|   |   `-- epoch_00190_iteration_000400000_checkpoint.pt
-|   |   `-- mapping_00109-model.pth.tar
-|   |-- gfpgan
-|   |   `-- alignment_WFLW_4HG.pth
-|   |   `-- parsing_parsenet.pth
-|   |   `-- detection_Resnet50_Final.pth
-
-```

expression.mat

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:93e9d69eb46e866ed5cbb569ed2bdb3813254720fb0cb745d5b56181faf9aec5
-size 1456

models/cmp/experiments/rep_learning/alexnet_yfcc+youtube_voc_16gpu_140k/config.yaml

@@ -1,59 +0,0 @@
-model:
-    arch: CMP
-    total_iter: 140000
-    lr_steps: [80000, 120000]
-    lr_mults: [0.1, 0.1]
-    lr: 0.1
-    optim: SGD
-    warmup_lr: []
-    warmup_steps: []
-    module:
-        arch: CMP
-        image_encoder: alexnet_fcn_32x
-        sparse_encoder: shallownet32x
-        flow_decoder: MotionDecoderPlain
-        skip_layer: False
-        img_enc_dim: 256
-        sparse_enc_dim: 16
-        output_dim: 198
-        decoder_combo: [1,2,4]
-        pretrained_image_encoder: False
-        flow_criterion: "DiscreteLoss"
-        nbins: 99
-        fmax: 50
-data:
-    workers: 2
-    batch_size: 12
-    batch_size_test: 1
-    data_mean: [123.675, 116.28, 103.53] # RGB
-    data_div: [58.395, 57.12, 57.375]
-    short_size: 416
-    crop_size: [384, 384]
-    sample_strategy: ['grid', 'watershed']
-    sample_bg_ratio: 0.000025
-    nms_ks: 81
-    max_num_guide: 150
-
-    flow_file_type: "jpg"
-    image_flow_aug:
-        flip: False
-    flow_aug:
-        reverse: False
-        scale: False
-        rotate: False
-    train_source:
-        - data/yfcc/lists/train.txt
-        - data/youtube9000/lists/train.txt
-    val_source:
-        - data/yfcc/lists/val.txt
-    memcached: False
-trainer:
-    initial_val: True
-    print_freq: 100
-    val_freq: 10000
-    save_freq: 10000
-    val_iter: -1
-    val_disp_start_iter: 0
-    val_disp_end_iter: 16
-    loss_record: ['loss_flow']
-    tensorboard: False

models/cmp/experiments/rep_learning/alexnet_yfcc+youtube_voc_16gpu_140k/resume.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 \
-    --nnodes=2 --node_rank=$1 \
-    --master_addr="192.168.1.1" main.py \
-    --config $work_path/config.yaml --launcher pytorch \
-    --load-iter 10000 \
-    --resume

models/cmp/experiments/rep_learning/alexnet_yfcc+youtube_voc_16gpu_140k/resume_slurm.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n16 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py \
-        --config $work_path/config.yaml --launcher slurm \
-        --load-iter 10000 \
-        --resume

models/cmp/experiments/rep_learning/alexnet_yfcc+youtube_voc_16gpu_140k/train.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 \
-    --nnodes=2 --node_rank=$1 \
-    --master_addr="192.168.1.1" main.py \
-    --config $work_path/config.yaml --launcher pytorch

models/cmp/experiments/rep_learning/alexnet_yfcc+youtube_voc_16gpu_140k/train_slurm.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n16 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py \
-        --config $work_path/config.yaml --launcher slurm

models/cmp/experiments/rep_learning/alexnet_yfcc+youtube_voc_16gpu_140k/validate.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 main.py \
-    --config $work_path/config.yaml --launcher pytorch \
-    --load-iter 70000 \
-    --validate

models/cmp/experiments/rep_learning/alexnet_yfcc+youtube_voc_16gpu_140k/validate_slurm.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n8 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py --config $work_path/config.yaml --launcher slurm \
-        --load-iter 70000 \
-        --validate

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_16gpu_70k/config.yaml

@@ -1,58 +0,0 @@
-model:
-    arch: CMP
-    total_iter: 70000
-    lr_steps: [40000, 60000]
-    lr_mults: [0.1, 0.1]
-    lr: 0.1
-    optim: SGD
-    warmup_lr: []
-    warmup_steps: []
-    module:
-        arch: CMP
-        image_encoder: alexnet_fcn_32x
-        sparse_encoder: shallownet32x
-        flow_decoder: MotionDecoderPlain
-        skip_layer: False
-        img_enc_dim: 256
-        sparse_enc_dim: 16
-        output_dim: 198
-        decoder_combo: [1,2,4]
-        pretrained_image_encoder: False
-        flow_criterion: "DiscreteLoss"
-        nbins: 99
-        fmax: 50
-data:
-    workers: 2
-    batch_size: 12
-    batch_size_test: 1
-    data_mean: [123.675, 116.28, 103.53] # RGB
-    data_div: [58.395, 57.12, 57.375]
-    short_size: 416
-    crop_size: [384, 384]
-    sample_strategy: ['grid', 'watershed']
-    sample_bg_ratio: 0.00015625
-    nms_ks: 41
-    max_num_guide: 150
-
-    flow_file_type: "jpg"
-    image_flow_aug:
-        flip: False
-    flow_aug:
-        reverse: False
-        scale: False
-        rotate: False
-    train_source:
-        - data/yfcc/lists/train.txt
-    val_source:
-        - data/yfcc/lists/val.txt
-    memcached: False
-trainer:
-    initial_val: True
-    print_freq: 100
-    val_freq: 10000
-    save_freq: 10000
-    val_iter: -1
-    val_disp_start_iter: 0
-    val_disp_end_iter: 16
-    loss_record: ['loss_flow']
-    tensorboard: False

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_16gpu_70k/resume.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 \
-    --nnodes=2 --node_rank=$1 \
-    --master_addr="192.168.1.1" main.py \
-    --config $work_path/config.yaml --launcher pytorch \
-    --load-iter 10000 \
-    --resume

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_16gpu_70k/resume_slurm.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n16 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py \
-        --config $work_path/config.yaml --launcher slurm \
-        --load-iter 10000 \
-        --resume

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_16gpu_70k/train.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 \
-    --nnodes=2 --node_rank=$1 \
-    --master_addr="192.168.1.1" main.py \
-    --config $work_path/config.yaml --launcher pytorch

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_16gpu_70k/train_slurm.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n16 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py \
-        --config $work_path/config.yaml --launcher slurm

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_16gpu_70k/validate.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 main.py \
-    --config $work_path/config.yaml --launcher pytorch \
-    --load-iter 70000 \
-    --validate

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_16gpu_70k/validate_slurm.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n8 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py --config $work_path/config.yaml --launcher slurm \
-        --load-iter 70000 \
-        --validate

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_8gpu_140k/config.yaml

@@ -1,58 +0,0 @@
-model:
-    arch: CMP
-    total_iter: 140000
-    lr_steps: [80000, 120000]
-    lr_mults: [0.1, 0.1]
-    lr: 0.1
-    optim: SGD
-    warmup_lr: []
-    warmup_steps: []
-    module:
-        arch: CMP
-        image_encoder: alexnet_fcn_32x
-        sparse_encoder: shallownet32x
-        flow_decoder: MotionDecoderPlain
-        skip_layer: False
-        img_enc_dim: 256
-        sparse_enc_dim: 16
-        output_dim: 198
-        decoder_combo: [1,2,4]
-        pretrained_image_encoder: False
-        flow_criterion: "DiscreteLoss"
-        nbins: 99
-        fmax: 50
-data:
-    workers: 2
-    batch_size: 12
-    batch_size_test: 1
-    data_mean: [123.675, 116.28, 103.53] # RGB
-    data_div: [58.395, 57.12, 57.375]
-    short_size: 416
-    crop_size: [384, 384]
-    sample_strategy: ['grid', 'watershed']
-    sample_bg_ratio: 0.00015625
-    nms_ks: 41
-    max_num_guide: 150
-
-    flow_file_type: "jpg"
-    image_flow_aug:
-        flip: False
-    flow_aug:
-        reverse: False
-        scale: False
-        rotate: False
-    train_source:
-        - data/yfcc/lists/train.txt
-    val_source:
-        - data/yfcc/lists/val.txt
-    memcached: False
-trainer:
-    initial_val: True
-    print_freq: 100
-    val_freq: 10000
-    save_freq: 10000
-    val_iter: -1
-    val_disp_start_iter: 0
-    val_disp_end_iter: 16
-    loss_record: ['loss_flow']
-    tensorboard: False

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_8gpu_140k/resume.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 main.py \
-    --config $work_path/config.yaml --launcher pytorch \
-    --load-iter 10000 \
-    --resume

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_8gpu_140k/resume_slurm.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n8 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py \
-        --config $work_path/config.yaml --launcher slurm \
-        --load-iter 10000 \
-        --resume

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_8gpu_140k/train.sh

@@ -1,4 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 main.py \
-    --config $work_path/config.yaml --launcher pytorch

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_8gpu_140k/train_slurm.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n8 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py \
-        --config $work_path/config.yaml --launcher slurm

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_8gpu_140k/validate.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 main.py \
-    --config $work_path/config.yaml --launcher pytorch \
-    --load-iter 70000 \
-    --validate

models/cmp/experiments/rep_learning/alexnet_yfcc_voc_8gpu_140k/validate_slurm.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-partition=$1
-GLOG_vmodule=MemcachedClient=-1 srun --mpi=pmi2 -p $partition -n8 \
-    --gres=gpu:8 --ntasks-per-node=8 \
-    python -u main.py --config $work_path/config.yaml --launcher slurm \
-        --load-iter 70000 \
-        --validate

models/cmp/experiments/rep_learning/resnet50_yfcc+youtube+vip+mpii_lip_16gpu_70k/config.yaml

@@ -1,61 +0,0 @@
-model:
-    arch: CMP
-    total_iter: 70000
-    lr_steps: [40000, 60000]
-    lr_mults: [0.1, 0.1]
-    lr: 0.1
-    optim: SGD
-    warmup_lr: []
-    warmup_steps: []
-    module:
-        arch: CMP
-        image_encoder: resnet50
-        sparse_encoder: shallownet8x
-        flow_decoder: MotionDecoderPlain
-        skip_layer: False
-        img_enc_dim: 256
-        sparse_enc_dim: 16
-        output_dim: 198
-        decoder_combo: [1,2,4]
-        pretrained_image_encoder: False
-        flow_criterion: "DiscreteLoss"
-        nbins: 99
-        fmax: 50
-data:
-    workers: 2
-    batch_size: 10
-    batch_size_test: 1
-    data_mean: [123.675, 116.28, 103.53] # RGB
-    data_div: [58.395, 57.12, 57.375]
-    short_size: 416
-    crop_size: [320, 320]
-    sample_strategy: ['grid', 'watershed']
-    sample_bg_ratio: 0.00015625
-    nms_ks: 15
-    max_num_guide: -1
-
-    flow_file_type: "jpg"
-    image_flow_aug:
-        flip: False
-    flow_aug:
-        reverse: False
-        scale: False
-        rotate: False
-    train_source:
-        - data/yfcc/lists/train.txt
-        - data/youtube9000/lists/train.txt
-        - data/VIP/lists/train.txt
-        - data/MPII/lists/train.txt
-    val_source:
-        - data/yfcc/lists/val.txt
-    memcached: False
-trainer:
-    initial_val: True
-    print_freq: 100
-    val_freq: 10000
-    save_freq: 10000
-    val_iter: -1
-    val_disp_start_iter: 0
-    val_disp_end_iter: 16
-    loss_record: ['loss_flow']
-    tensorboard: False

models/cmp/experiments/rep_learning/resnet50_yfcc+youtube+vip+mpii_lip_16gpu_70k/resume.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-work_path=$(dirname $0)
-python -m torch.distributed.launch --nproc_per_node=8 \
-    --nnodes=2 --node_rank=$1 \
-    --master_addr="192.168.1.1" main.py \
-    --config $work_path/config.yaml --launcher pytorch \
-    --load-iter 10000 \
-    --resume