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import os | |
import torch | |
import argparse | |
from config.config import config_train | |
from lib.dataset.Dataset import MeshDataset | |
from lib.dataset.DataLoaderX import DataLoaderX | |
from lib.module.MeshHeadModule import MeshHeadModule | |
from lib.module.CameraModule import CameraModule | |
from lib.recorder.Recorder import MeshHeadTrainRecorder | |
from lib.trainer.MeshHeadTrainer import MeshHeadTrainer | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--config', type=str, default='config/train_s1_N031.yaml') | |
arg = parser.parse_args() | |
cfg = config_train() | |
cfg.load(arg.config) | |
cfg = cfg.get_cfg() | |
dataset = MeshDataset(cfg.dataset) | |
dataloader = DataLoaderX(dataset, batch_size=cfg.batch_size, shuffle=True, pin_memory=True, drop_last=True, num_workers=8) | |
device = torch.device('cuda:%d' % cfg.gpu_id) | |
torch.cuda.set_device(cfg.gpu_id) | |
meshhead = MeshHeadModule(cfg.meshheadmodule, dataset.init_landmarks_3d_neutral).to(device) | |
if os.path.exists(cfg.load_meshhead_checkpoint): | |
meshhead.load_state_dict(torch.load(cfg.load_meshhead_checkpoint, map_location=lambda storage, loc: storage)) | |
else: | |
meshhead.pre_train_sphere(300, device) | |
camera = CameraModule() | |
recorder = MeshHeadTrainRecorder(cfg.recorder) | |
optimizer = torch.optim.Adam([{'params' : meshhead.landmarks_3d_neutral, 'lr' : cfg.lr_lmk}, | |
{'params' : meshhead.geo_mlp.parameters(), 'lr' : cfg.lr_net}, | |
{'params' : meshhead.exp_color_mlp.parameters(), 'lr' : cfg.lr_net}, | |
{'params' : meshhead.pose_color_mlp.parameters(), 'lr' : cfg.lr_net}, | |
{'params' : meshhead.exp_deform_mlp.parameters(), 'lr' : cfg.lr_net}, | |
{'params' : meshhead.pose_deform_mlp.parameters(), 'lr' : cfg.lr_net}]) | |
trainer = MeshHeadTrainer(dataloader, meshhead, camera, optimizer, recorder, cfg.gpu_id) | |
trainer.train(0, 50) | |