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