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import os
import pickle
import sys
import datetime
import logging
import os.path as osp
from omegaconf import OmegaConf
import torch
from mld.config import parse_args
from mld.data.get_data import get_datasets
from mld.models.modeltype.mld import MLD
from mld.utils.utils import set_seed, move_batch_to_device
from mld.data.humanml.utils.plot_script import plot_3d_motion
from mld.utils.temos_utils import remove_padding
def load_example_input(text_path: str) -> tuple:
with open(text_path, "r") as f:
lines = f.readlines()
count = 0
texts, lens = [], []
# Strips the newline character
for line in lines:
count += 1
s = line.strip()
s_l = s.split(" ")[0]
s_t = s[(len(s_l) + 1):]
lens.append(int(s_l))
texts.append(s_t)
return texts, lens
def main():
cfg = parse_args()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
set_seed(cfg.TRAIN.SEED_VALUE)
name_time_str = osp.join(cfg.NAME, "demo_" + datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
output_dir = osp.join(cfg.TEST_FOLDER, name_time_str)
vis_dir = osp.join(output_dir, 'samples')
os.makedirs(output_dir, exist_ok=False)
os.makedirs(vis_dir, exist_ok=False)
steam_handler = logging.StreamHandler(sys.stdout)
file_handler = logging.FileHandler(osp.join(output_dir, 'output.log'))
logging.basicConfig(level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[steam_handler, file_handler])
logger = logging.getLogger(__name__)
OmegaConf.save(cfg, osp.join(output_dir, 'config.yaml'))
state_dict = torch.load(cfg.TEST.CHECKPOINTS, map_location="cpu")["state_dict"]
logger.info("Loading checkpoints from {}".format(cfg.TEST.CHECKPOINTS))
lcm_key = 'denoiser.time_embedding.cond_proj.weight'
is_lcm = False
if lcm_key in state_dict:
is_lcm = True
time_cond_proj_dim = state_dict[lcm_key].shape[1]
cfg.model.denoiser.params.time_cond_proj_dim = time_cond_proj_dim
logger.info(f'Is LCM: {is_lcm}')
cn_key = "controlnet.controlnet_cond_embedding.0.weight"
is_controlnet = True if cn_key in state_dict else False
cfg.model.is_controlnet = is_controlnet
logger.info(f'Is Controlnet: {is_controlnet}')
datasets = get_datasets(cfg, phase="test")[0]
model = MLD(cfg, datasets)
model.to(device)
model.eval()
model.load_state_dict(state_dict)
# example only support text-to-motion
if cfg.example is not None and not is_controlnet:
text, length = load_example_input(cfg.example)
for t, l in zip(text, length):
logger.info(f"{l}: {t}")
batch = {"length": length, "text": text}
for rep_i in range(cfg.replication):
with torch.no_grad():
joints, _ = model(batch)
num_samples = len(joints)
batch_id = 0
for i in range(num_samples):
res = dict()
pkl_path = osp.join(vis_dir, f"batch_id_{batch_id}_sample_id_{i}_length_{length[i]}_rep_{rep_i}.pkl")
res['joints'] = joints[i].detach().cpu().numpy()
res['text'] = text[i]
res['length'] = length[i]
res['hint'] = None
with open(pkl_path, 'wb') as f:
pickle.dump(res, f)
logger.info(f"Motions are generated here:\n{pkl_path}")
if not cfg.no_plot:
plot_3d_motion(pkl_path.replace('.pkl', '.mp4'), joints[i].detach().cpu().numpy(), text[i], fps=20)
else:
test_dataloader = datasets.test_dataloader()
for rep_i in range(cfg.replication):
for batch_id, batch in enumerate(test_dataloader):
batch = move_batch_to_device(batch, device)
with torch.no_grad():
joints, joints_ref = model(batch)
num_samples = len(joints)
text = batch['text']
length = batch['length']
if 'hint' in batch:
hint = batch['hint']
mask_hint = hint.view(hint.shape[0], hint.shape[1], model.njoints, 3).sum(dim=-1, keepdim=True) != 0
hint = model.datamodule.denorm_spatial(hint)
hint = hint.view(hint.shape[0], hint.shape[1], model.njoints, 3) * mask_hint
hint = remove_padding(hint, lengths=length)
else:
hint = None
for i in range(num_samples):
res = dict()
pkl_path = osp.join(vis_dir, f"batch_id_{batch_id}_sample_id_{i}_length_{length[i]}_rep_{rep_i}.pkl")
res['joints'] = joints[i].detach().cpu().numpy()
res['text'] = text[i]
res['length'] = length[i]
res['hint'] = hint[i].detach().cpu().numpy() if hint is not None else None
with open(pkl_path, 'wb') as f:
pickle.dump(res, f)
logger.info(f"Motions are generated here:\n{pkl_path}")
if not cfg.no_plot:
plot_3d_motion(pkl_path.replace('.pkl', '.mp4'), joints[i].detach().cpu().numpy(),
text[i], fps=20, hint=hint[i].detach().cpu().numpy() if hint is not None else None)
if rep_i == 0:
res['joints'] = joints_ref[i].detach().cpu().numpy()
with open(pkl_path.replace('.pkl', '_ref.pkl'), 'wb') as f:
pickle.dump(res, f)
logger.info(f"Motions are generated here:\n{pkl_path.replace('.pkl', '_ref.pkl')}")
if not cfg.no_plot:
plot_3d_motion(pkl_path.replace('.pkl', '_ref.mp4'), joints_ref[i].detach().cpu().numpy(),
text[i], fps=20, hint=hint[i].detach().cpu().numpy() if hint is not None else None)
if __name__ == "__main__":
main()
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