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import os
import time
from dataclasses import dataclass
from configs.mode import FaceSwapMode
from configs.singleton import Singleton
@Singleton
@dataclass
class TrainConfig:
mode = FaceSwapMode.MANY_TO_MANY
source_name: str = ""
dataset_index: str = "/data/dataset/faceswap/full.pkl"
dataset_root: str = "/data/dataset/faceswap"
batch_size: int = 8
num_threads: int = 8
same_rate: float = 0.5
lr: float = 5e-5
grad_clip: float = 1000.0
use_ddp: bool = True
mouth_mask: bool = True
eye_hm_loss: bool = False
mouth_hm_loss: bool = False
load_checkpoint = None # ("/data/checkpoints/hififace/rebuilt_discriminator_SFF_c256_1683367464544", 400000)
identity_extractor_config = {
"f_3d_checkpoint_path": "/checkpoints/Deep3DFaceRecon/epoch_20_new.pth",
"f_id_checkpoint_path": "/checkpoints/arcface/ms1mv3_arcface_r100_fp16_backbone.pth",
"bfm_folder": "/checkpoints/useful_ckpt/BFM",
"hrnet_path": "/checkpoints/useful_ckpt/face_98lmks/HR18-WFLW.pth",
}
visualize_interval: int = 100
plot_interval: int = 100
max_iters: int = 1000000
checkpoint_interval: int = 40000
exp_name: str = "exp_base"
log_basedir: str = "/data/logs/hififace/"
checkpoint_basedir = "/data/checkpoints/hififace"
def __post_init__(self):
time_stamp = int(time.time() * 1000)
self.log_dir = os.path.join(self.log_basedir, f"{self.exp_name}_{time_stamp}")
self.checkpoint_dir = os.path.join(self.checkpoint_basedir, f"{self.exp_name}_{time_stamp}")
if __name__ == "__main__":
tc = TrainConfig()
print(tc.log_dir)
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