DATA=" ddpm.input_dim 3 data.cates car "
NGPU=$1 # 
num_node=1
mem=40 
BS=32 

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 True