LION/script/train_vae_all.sh

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2023-04-01 08:20:32 +00:00
if [ -z "$1" ]
then
echo "Require NGPU input; "
exit
fi
DATA=" ddpm.input_dim 3 data.cates all "
NGPU=$1 #
num_node=1
BS=32
total_bs=$(( $NGPU * $BS ))
if (( $total_bs > 128 )); then
echo "[WARNING] total batch_size larger than 128 may lead to unstable training, please reduce the size"
exit
fi
ENT="python train_dist.py --num_process_per_node $NGPU "
kl=0.5
lr=1e-3
latent=1
skip_weight=0.01
sigma_offset=6.0
loss='l1_sum'
$ENT ddpm.num_steps 1 ddpm.ema 0 \
trainer.opt.vae_lr_warmup_epochs 0 \
latent_pts.ada_mlp_init_scale 0.1 \
sde.kl_const_coeff_vada 1e-7 \
trainer.anneal_kl 1 sde.kl_max_coeff_vada $kl \
sde.kl_anneal_portion_vada 0.5 \
shapelatent.log_sigma_offset $sigma_offset latent_pts.skip_weight $skip_weight \
trainer.opt.beta2 0.99 \
data.num_workers 4 \
ddpm.loss_weight_emd 1.0 \
trainer.epochs 8000 data.random_subsample 1 \
viz.viz_freq -400 viz.log_freq -1 viz.val_freq 200 \
data.batch_size $BS viz.save_freq 2000 \
trainer.type 'trainers.hvae_trainer' \
model_config default shapelatent.model 'models.vae_adain' \
shapelatent.decoder_type 'models.latent_points_ada.LatentPointDecPVC' \
shapelatent.encoder_type 'models.latent_points_ada.PointTransPVC' \
latent_pts.style_encoder 'models.shapelatent_modules.PointNetPlusEncoder' \
shapelatent.prior_type normal \
shapelatent.latent_dim $latent trainer.opt.lr $lr \
shapelatent.kl_weight ${kl} \
shapelatent.decoder_num_points 2048 \
data.tr_max_sample_points 2048 data.te_max_sample_points 2048 \
ddpm.loss_type $loss cmt "lion" \
$DATA viz.viz_order [2,0,1] data.recenter_per_shape False data.normalize_global False data.normalize_shape_box True #True