|
|
import torch |
|
|
from omegaconf import OmegaConf |
|
|
from safetensors.torch import load_model |
|
|
from diffusers.models import AutoencoderKL |
|
|
from pipeline.utils import RecEvalDataset |
|
|
from pipeline.rec_pipeline import Rec_Pipeline |
|
|
from model.model_AMD import AMDModel |
|
|
from typing import Optional |
|
|
from torch.utils.data import DataLoader |
|
|
from omegaconf import OmegaConf |
|
|
import os |
|
|
import argparse |
|
|
|
|
|
class rec_inferencer: |
|
|
def __init__( |
|
|
self, |
|
|
config, |
|
|
device, |
|
|
dtype |
|
|
): |
|
|
self.config = config |
|
|
self.device = device |
|
|
self.dtype = dtype |
|
|
self.setup() |
|
|
def setup(self): |
|
|
vae_model = AutoencoderKL.from_pretrained(self.config.vae_path, subfolder="vae").to(self.device, self.dtype).requires_grad_(False) |
|
|
amd_model = AMDModel.from_config(AMDModel.load_config(self.config.amd_config_path)).to(self.device, self.dtype).requires_grad_(False) |
|
|
load_model(amd_model, self.config.amd_ckpt_path) |
|
|
self.pipeline = Rec_Pipeline( |
|
|
amd_model, |
|
|
vae_model, |
|
|
amd_sample_steps=self.config.amd_sample_steps, |
|
|
output_dir=self.config.output_dir, |
|
|
) |
|
|
def infer(self, video_path:str, refimg_path:Optional[str]=None, output_path:Optional[str] = None): |
|
|
video = self.pipeline.run(video_path, refimg_path, output_path, config = self.config) |
|
|
return video |
|
|
|
|
|
def eval(self, video_dir:str, num_frames:int = 96): |
|
|
evalset = RecEvalDataset( |
|
|
video_dir, |
|
|
num_frames, |
|
|
) |
|
|
evalloader = DataLoader( |
|
|
evalset, 12, shuffle=False,drop_last=False,collate_fn=evalset.collate,num_workers=8 |
|
|
) |
|
|
self.pipeline.eval(evalloader, config = self.config) |
|
|
|
|
|
if __name__ == "__main__": |
|
|
|
|
|
parser = argparse.ArgumentParser() |
|
|
parser.add_argument("--config_path", type=str, default="/mnt/pfs-gv8sxa/tts/dhg/zqy/code/AMD2/config/inference/rec_spatial.yaml") |
|
|
parser.add_argument("--video_dir", type=str, default="/mnt/pfs-gv8sxa/tts/dhg/zqy/code/test/test_frame2frame_reconstruction/data/facevid/test") |
|
|
args = parser.parse_args() |
|
|
config_path = args.config_path |
|
|
video_dir = args.video_dir |
|
|
config = OmegaConf.load(config_path) |
|
|
inferencer = rec_inferencer(config, torch.device("cuda:0"), torch.float32) |
|
|
|
|
|
|
|
|
|
|
|
inferencer.eval( |
|
|
video_dir, |
|
|
96 |
|
|
) |
