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mld_demo.py
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import logging
import hydra
import os
from pathlib import Path
from omegaconf import DictConfig, OmegaConf
from sinc.data.tools.collate import collate_length_and_text
import sinc.launch.prepare
# from sinc.render.mesh_viz import visualize_meshes
# from sinc.render.video import save_video_samples, stack_vids
import torch
from sinc.transforms.base import Datastruct
from sinc.utils.inference import cfg_mean_nsamples_resolution, get_path
from sinc.utils.file_io import read_json, write_json
from sinc.transforms.smpl import RotTransDatastruct
from sinc.transforms.rots2joints.smplh import SMPLH
labels = read_json('deps/inference/labels.json')
from sinc.render.mesh_viz import visualize_meshes
from sinc.render.video import save_video_samples
logger = logging.getLogger(__name__)
@hydra.main(config_path="configs", config_name="mld_demo")
def _calc_temos_score(cfg: DictConfig):
return calc_temos_score(cfg)
def fix_config_if_needed(cfg):
if 'gpt_path' not in cfg.data:
cfg.data['gpt_path'] = '${path.deps}/gpt/gpt3-labels.json'
def calc_temos_score(newcfg: DictConfig) -> None:
# Load last config
output_dir = Path(hydra.utils.to_absolute_path(newcfg.folder))
last_ckpt_path = newcfg.last_ckpt_path
# Load previous config
prevcfg = OmegaConf.load(output_dir / ".hydra/config.yaml")
fix_config_if_needed(prevcfg)
# Overload it
cfg = OmegaConf.merge(prevcfg, newcfg)
SMPL_layer = SMPLH(path=f'{cfg.path.data}/smpl_models/smplh', jointstype='vertices', gender='male')
if cfg.mean and cfg.number_of_samples > 1:
logger.error("All the samples will be the mean.. cfg.number_of_samples=1 will be forced.")
cfg.number_of_samples = 1
logger.info("Sample script. The outputs will be stored in:")
import pytorch_lightning as pl
import numpy as np
from hydra.utils import instantiate
seed_logger = logging.getLogger("pytorch_lightning.utilities.seed")
seed_logger.setLevel(logging.WARNING)
pl.seed_everything(cfg.seed)
logger.info("Loading data module")
# only pair evaluation to be fair
# keep same order
cfg.data.dtype = 'spatial_pairs+seg+seq'
data_module = instantiate(cfg.data)
logger.info(f"Data module '{cfg.data.dataname}' loaded")
dataset = getattr(data_module, f"{cfg.split}_dataset")
from tqdm import tqdm
logger.info("Loading model")
# Instantiate all modules specified in the configs
from mld_specifics import parse_args
cfg_for_mld = parse_args() # parse config file
# MLD specific changes
from sinc.model.mld import MLD
model = MLD(cfg_for_mld, cfg.transforms, cfg.path)
# state_dict = torch.load('/is/cluster/fast/nathanasiou/logs/sinc/sinc-arxiv/temos-bs64x1-scheduler/babel-amass/checkpoints/latest-epoch=599.ckpt', map_location='cpu')
# # extract encoder/decoder
# from collections import OrderedDict
# decoder_dict = OrderedDict()
# encoder_dict = OrderedDict()
# for k, v in state_dict['state_dict'].items():
# if k.split(".")[0] == "motionencoder":
# name = k.replace("motionencoder.", "")
# encoder_dict[name] = v
# if k.split(".")[0] == "motiondecoder":
# name = k.replace("motiondecoder.", "")
# decoder_dict[name] = v
# model.vae_encoder.load_state_dict(encoder_dict, strict=True)
# model.vae_decoder.load_state_dict(decoder_dict, strict=True)
logger.info(f"Model '{cfg.model.modelname}' loaded")
state_dict = torch.load(last_ckpt_path, map_location='cpu')['state_dict']
model.load_state_dict(state_dict, strict=True)
# Load the last checkpoint
# temos_model = temos_model.load_from_checkpoint(last_ckpt_path)
model.eval()
# Load the last checkpoint
# model = model.load_from_checkpoint(last_ckpt_path)
# model.eval()
logger.info("Model weights restored")
model.sample_mean = cfg.mean
model.fact = cfg.fact
# trainer = pl.Trainer(**OmegaConf.to_container(cfg.trainer, resolve=True))
logger.info("Trainer initialized")
model.transforms.rots2joints.jointstype = cfg.jointstype
# ds = model.transforms.Datastruct
if cfg.set == 'submission':
from sinc.utils.inference import sinc_eval_set
keyids = sinc_eval_set
elif cfg.set == 'small':
from sinc.utils.inference import validation_nostand_notrain
keyids = validation_nostand_notrain
elif cfg.set == 'supmat':
from sinc.utils.inference import sinc_supmat
keyids = sinc_supmat
else:
if cfg.set == 'pairs':
keyids = [k for k in dataset.keyids if k.split('-')[0] == 'spatial_pairs']
elif cfg.set == 'single':
keyids = [k for k in dataset.keyids if k.split('-')[0] in ['seq', 'seg']]
else:
keyids = dataset.keyids
motion_type = "rotsd"
outd = Path(cfg.savedir)
outd.mkdir(exist_ok=True, parents=True)
with torch.no_grad():
with tqdm(total=len(keyids), position=0, leave=True) as pbar:
for keyid in (pbar := tqdm(keyids, position=0, leave=True)):
pbar.set_description(f"Processing {keyid}")
one_data = dataset.load_keyid(keyid, mode='inference')
from sinc.data.tools import collate_text_and_length
batch = collate_text_and_length([one_data])
cur_lens = batch['length']
cur_texts = [list(batch['text'][0])]
# batch_size = 1 for reproductability
# fix the seed
pl.seed_everything(0)
dtype_sample = keyid.split('-')[0]
is_sp = dtype_sample == 'spatial_pairs'
motion = model(cur_texts,cur_lens)
# motion = datastruct.rots
# rots, transl = motion.rots, motion.trans
# from sinc.transforms.smpl import RotTransDatastruct
# final_datastruct = self.Datastruct(
# rots_=RotTransDatastruct(rots=rots, trans=transl))
ds_ = RotTransDatastruct(rots=motion.rots, trans=motion.trans)
motion_verts = SMPL_layer(ds_).numpy()
vid_ = visualize_meshes(motion_verts.squeeze())
vid_p = save_video_samples(vid_,
f'{str(outd)}/{keyid}.mp4',
cur_texts[0],
fps=30)
logger.info(f"The samples are saved under: {outd}")
return
if __name__ == '__main__':
_calc_temos_score()