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train_posenet.py
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train_posenet.py
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import random
import configargparse
from torch.utils.data import DataLoader
from utils import dist_util
from tensorboardX import SummaryWriter
from train.training_loop_posenet import TrainLoopPoseNet
from data_loaders.dataloader_amass import DataloaderAMASS
from model.posenet import PoseNet
from diffusion import gaussian_diffusion_posenet
from diffusion.respace import SpacedDiffusionPoseNet
from utils.model_util import create_gaussian_diffusion
from utils.other_utils import *
arg_formatter = configargparse.ArgumentDefaultsHelpFormatter
cfg_parser = configargparse.YAMLConfigFileParser
description = 'RoHM code'
group = configargparse.ArgParser(formatter_class=arg_formatter,
config_file_parser_class=cfg_parser,
description=description,
prog='')
group.add_argument('--config', is_config_file=True, default='', help='config file path')
group.add_argument("--device", default=0, type=int, help="Device id to use.")
# group.add_argument("--seed", default=0, type=int, help="For fixing random seed.")
######################## diffusion setups
group.add_argument("--diffusion_steps", default=1000, type=int, help='diffusion time steps')
group.add_argument("--noise_schedule", default='cosine', choices=['linear', 'cosine'], type=str, help="Noise schedule type")
group.add_argument("--timestep_respacing_eval", default='', type=str) # if use ddim, set to 'ddimN', where N denotes ddim sampling steps
group.add_argument("--sigma_small", default='True', type=lambda x: x.lower() in ['true', '1'], help="Use smaller sigma values.")
######################## path to AMASS and body model
group.add_argument('--body_model_path', type=str, default='body_models/smplx_model', help='path to smplx model')
group.add_argument('--dataset_root', type=str, default='/mnt/hdd/diffusion_mocap_datasets/AMASS_smplx_preprocessed', help='path to datas')
####################### model setups
group.add_argument('--task', default='pose', type=str, choices=['traj', 'pose'])
group.add_argument("--clip_len", default=145, type=int, help="sequence length for each clip")
### load pretrained checkpoints
group.add_argument('--load_pretrained_model', default='False', type=lambda x: x.lower() in ['true', '1'], help='if load pretrained checkpoint')
group.add_argument('--pretrained_model_path', type=str, default='', help='')
######################## input noise scaling setups
group.add_argument('--input_noise', default='True', type=lambda x: x.lower() in ['true', '1'], help='if add nosie to input conditions')
group.add_argument("--noise_std_smplx_global_rot", default=3, type=float, help="noise ratio for smplx global orientation (unit: degree)")
group.add_argument("--noise_std_smplx_body_rot", default=2, type=float, help="noise ratio for smplx body pose (unit: degree)")
group.add_argument("--noise_std_smplx_trans", default=0.01, type=float, help="noise ratio for smplx global translation (unit: m)")
group.add_argument("--noise_std_smplx_betas", default=0.2, type=float, help="noise ratio for smplx shape param")
######################## loss weight setups
group.add_argument("--weight_loss_rec_repr_full_body", default=1.0, type=float)
group.add_argument("--weight_loss_repr_foot_contact_mse", default=1.0, type=float)
group.add_argument("--weight_loss_joint_pos_global", default=100.0, type=float)
group.add_argument("--weight_loss_joint_vel_global", default=1000.0, type=float) # 1/1e1/1e2
group.add_argument("--weight_loss_joint_smooth", default=0.0, type=float)
group.add_argument("--start_skating_loss_epoch", default=1000, type=int, help="")
group.add_argument("--weight_loss_foot_skating", default=0.0, type=float) # 0.1
####################### training setups
group.add_argument("--batch_size", default=32, type=int, help="Batch size during training.")
group.add_argument('--debug', default='False', type=lambda x: x.lower() in ['true', '1'], help='')
group.add_argument("--start_prox_mask_epoch", default=500, type=int, help="which epoch to start to apply prox masks")
group.add_argument("--mask_scheme", default='lower', type=str,
choices=['lower', 'lower+upper', 'lower+full', 'lower+upper+full'])
group.add_argument("--save_dir", default='runs', type=str, help="Path to save checkpoints and results.")
group.add_argument("--lr", default=1e-4, type=float, help="Learning rate.")
group.add_argument("--weight_decay", default=0.0, type=float, help="Optimizer weight decay.")
group.add_argument("--log_interval", default=25000, type=int)
group.add_argument("--save_interval", default=25000, type=int)
group.add_argument("--num_steps", default=1000000_000, type=int)
args = group.parse_args()
def main(args, writer, logdir, logger):
dist_util.setup_dist(args.device)
print("creating data loader...")
amass_train_datasets = ['HumanEva', 'HDM05', 'MoSh', 'Transitions',
'ACCAD', 'BMLhandball', 'BMLmovi', 'BMLrub', 'CMU',
'DFaust', 'Eyes_Japan_Dataset', 'PosePrior',
'SSM', 'GRAB', 'SOMA']
amass_test_datasets = ['TCDHands', 'TotalCapture', 'SFU']
if args.debug:
amass_train_datasets = ['HumanEva']
amass_test_datasets = ['TCDHands']
train_dataset = DataloaderAMASS(preprocessed_amass_root=args.dataset_root, split='train',
amass_datasets=amass_train_datasets,
body_model_path=args.body_model_path,
repr_abs_only=False,
input_noise=args.input_noise,
noise_std_smplx_global_rot=args.noise_std_smplx_global_rot,
noise_std_smplx_body_rot=args.noise_std_smplx_body_rot,
noise_std_smplx_trans=args.noise_std_smplx_trans,
noise_std_smplx_betas=args.noise_std_smplx_betas,
task=args.task,
clip_len=args.clip_len,
logdir=logdir,
device=dist_util.dev())
train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True,num_workers=8, drop_last=False)
test_dataset = DataloaderAMASS(preprocessed_amass_root=args.dataset_root, split='test', spacing=2,
amass_datasets=amass_test_datasets,
body_model_path=args.body_model_path,
repr_abs_only=False,
input_noise=args.input_noise,
noise_std_smplx_global_rot=args.noise_std_smplx_global_rot,
noise_std_smplx_body_rot=args.noise_std_smplx_body_rot,
noise_std_smplx_trans=args.noise_std_smplx_trans,
noise_std_smplx_betas=args.noise_std_smplx_betas,
task=args.task,
clip_len=args.clip_len,
logdir=logdir,
device=dist_util.dev())
test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, drop_last=False)
print("creating model and diffusion...")
model = PoseNet(dataset=train_dataset, body_feat_dim=train_dataset.body_feat_dim,
latent_dim=512, ff_size=1024, num_layers=8, num_heads=4, dropout=0.1, activation="gelu",
body_model_path=args.body_model_path,
device=dist_util.dev(),
traj_feat_dim=train_dataset.traj_feat_dim,
weight_loss_rec_repr_full_body=args.weight_loss_rec_repr_full_body,
weight_loss_repr_foot_contact_mse=args.weight_loss_repr_foot_contact_mse,
weight_loss_joint_pos_global=args.weight_loss_joint_pos_global,
weight_loss_joint_vel_global=args.weight_loss_joint_vel_global,
weight_loss_joint_smooth=args.weight_loss_joint_smooth,
weight_loss_foot_skating=args.weight_loss_foot_skating,
start_skating_loss_epoch=args.start_skating_loss_epoch,
).to(dist_util.dev())
if args.load_pretrained_model:
weights = torch.load(args.pretrained_model_path, map_location=lambda storage, loc: storage)
model.load_state_dict(weights)
print('loaded checkpoint from {}'.format(args.pretrained_model_path))
diffusion_train = create_gaussian_diffusion(args, gd=gaussian_diffusion_posenet,
return_class=SpacedDiffusionPoseNet,
num_diffusion_timesteps=args.diffusion_steps,
timestep_respacing=args.timestep_respacing_eval,
device=dist_util.dev())
diffusion_eval = create_gaussian_diffusion(args, gd=gaussian_diffusion_posenet,
return_class=SpacedDiffusionPoseNet,
num_diffusion_timesteps=args.diffusion_steps,
timestep_respacing=args.timestep_respacing_eval,
device=dist_util.dev())
print("Training...")
TrainLoopPoseNet(args, writer=writer, model=model,
diffusion_train=diffusion_train, diffusion_eval=diffusion_eval,
timestep_respacing_eval=args.timestep_respacing_eval,
train_dataloader=train_dataloader, test_dataloader=test_dataloader,
logdir=logdir, logger=logger,
start_prox_mask_epoch=args.start_prox_mask_epoch, mask_scheme=args.mask_scheme,
input_noise=args.input_noise, device=dist_util.dev(),
).run_loop()
if __name__ == "__main__":
run_id = random.randint(1, 100000)
logdir = os.path.join(args.save_dir, str(run_id)) # create new path
writer = SummaryWriter(log_dir=logdir)
print('RUNDIR: {}'.format(logdir))
sys.stdout.flush()
logger = get_logger(logdir)
logger.info('Let the games begin') # write in log file
save_config(logdir, args)
main(args, writer, logdir, logger)