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Zero
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# coding: utf-8
"""
utility functions and classes to handle feature extraction and model loading
"""
import os
import os.path as osp
import torch
from collections import OrderedDict
from ..modules.spade_generator import SPADEDecoder
from ..modules.warping_network import WarpingNetwork
from ..modules.motion_extractor import MotionExtractor
from ..modules.appearance_feature_extractor import AppearanceFeatureExtractor
from ..modules.stitching_retargeting_network import StitchingRetargetingNetwork
def suffix(filename):
"""a.jpg -> jpg"""
pos = filename.rfind(".")
if pos == -1:
return ""
return filename[pos + 1:]
def prefix(filename):
"""a.jpg -> a"""
pos = filename.rfind(".")
if pos == -1:
return filename
return filename[:pos]
def basename(filename):
"""a/b/c.jpg -> c"""
return prefix(osp.basename(filename))
def is_video(file_path):
if file_path.lower().endswith((".mp4", ".mov", ".avi", ".webm")) or osp.isdir(file_path):
return True
return False
def is_template(file_path):
if file_path.endswith(".pkl"):
return True
return False
def mkdir(d, log=False):
# return self-assined `d`, for one line code
if not osp.exists(d):
os.makedirs(d, exist_ok=True)
if log:
print(f"Make dir: {d}")
return d
def squeeze_tensor_to_numpy(tensor):
out = tensor.data.squeeze(0).cpu().numpy()
return out
def dct2cuda(dct: dict, device_id: int):
for key in dct:
dct[key] = torch.tensor(dct[key]).cuda(device_id)
return dct
def concat_feat(kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
"""
kp_source: (bs, k, 3)
kp_driving: (bs, k, 3)
Return: (bs, 2k*3)
"""
bs_src = kp_source.shape[0]
bs_dri = kp_driving.shape[0]
assert bs_src == bs_dri, 'batch size must be equal'
feat = torch.cat([kp_source.view(bs_src, -1), kp_driving.view(bs_dri, -1)], dim=1)
return feat
def remove_ddp_dumplicate_key(state_dict):
state_dict_new = OrderedDict()
for key in state_dict.keys():
state_dict_new[key.replace('module.', '')] = state_dict[key]
return state_dict_new
def load_model(ckpt_path, model_config, device, model_type):
model_params = model_config['model_params'][f'{model_type}_params']
if model_type == 'appearance_feature_extractor':
model = AppearanceFeatureExtractor(**model_params).cuda(device)
elif model_type == 'motion_extractor':
model = MotionExtractor(**model_params).cuda(device)
elif model_type == 'warping_module':
model = WarpingNetwork(**model_params).cuda(device)
elif model_type == 'spade_generator':
model = SPADEDecoder(**model_params).cuda(device)
elif model_type == 'stitching_retargeting_module':
# Special handling for stitching and retargeting module
config = model_config['model_params']['stitching_retargeting_module_params']
checkpoint = torch.load(ckpt_path, map_location=lambda storage, loc: storage)
stitcher = StitchingRetargetingNetwork(**config.get('stitching'))
stitcher.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_shoulder']))
stitcher = stitcher.cuda(device)
stitcher.eval()
retargetor_lip = StitchingRetargetingNetwork(**config.get('lip'))
retargetor_lip.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_mouth']))
retargetor_lip = retargetor_lip.cuda(device)
retargetor_lip.eval()
retargetor_eye = StitchingRetargetingNetwork(**config.get('eye'))
retargetor_eye.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_eye']))
retargetor_eye = retargetor_eye.cuda(device)
retargetor_eye.eval()
return {
'stitching': stitcher,
'lip': retargetor_lip,
'eye': retargetor_eye
}
else:
raise ValueError(f"Unknown model type: {model_type}")
model.load_state_dict(torch.load(ckpt_path, map_location=lambda storage, loc: storage))
model.eval()
return model
# get coefficients of Eqn. 7
def calculate_transformation(config, s_kp_info, t_0_kp_info, t_i_kp_info, R_s, R_t_0, R_t_i):
if config.relative:
new_rotation = (R_t_i @ R_t_0.permute(0, 2, 1)) @ R_s
new_expression = s_kp_info['exp'] + (t_i_kp_info['exp'] - t_0_kp_info['exp'])
else:
new_rotation = R_t_i
new_expression = t_i_kp_info['exp']
new_translation = s_kp_info['t'] + (t_i_kp_info['t'] - t_0_kp_info['t'])
new_translation[..., 2].fill_(0) # Keep the z-axis unchanged
new_scale = s_kp_info['scale'] * (t_i_kp_info['scale'] / t_0_kp_info['scale'])
return new_rotation, new_expression, new_translation, new_scale
def load_description(fp):
with open(fp, 'r', encoding='utf-8') as f:
content = f.read()
return content
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