40 lines
1.4 KiB
Bash
40 lines
1.4 KiB
Bash
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DATA=" ddpm.input_dim 3 data.cates car "
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NGPU=$1 #
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num_node=1
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mem=40
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BS=32
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ENT="python train_dist.py --num_process_per_node $NGPU "
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kl=0.5
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lr=1e-3
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latent=1
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skip_weight=0.01
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sigma_offset=6.0
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loss='l1_sum'
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$ENT ddpm.num_steps 1 ddpm.ema 0 \
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trainer.opt.vae_lr_warmup_epochs 0 \
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latent_pts.ada_mlp_init_scale 0.1 \
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sde.kl_const_coeff_vada 1e-7 \
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trainer.anneal_kl 1 sde.kl_max_coeff_vada $kl \
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sde.kl_anneal_portion_vada 0.5 \
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shapelatent.log_sigma_offset $sigma_offset latent_pts.skip_weight $skip_weight \
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trainer.opt.beta2 0.99 \
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data.num_workers 4 \
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ddpm.loss_weight_emd 1.0 \
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trainer.epochs 8000 data.random_subsample 1 \
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viz.viz_freq -400 viz.log_freq -1 viz.val_freq 200 \
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data.batch_size $BS viz.save_freq 2000 \
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trainer.type 'trainers.hvae_trainer' \
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model_config default shapelatent.model 'models.vae_adain' \
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shapelatent.decoder_type 'models.latent_points_ada.LatentPointDecPVC' \
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shapelatent.encoder_type 'models.latent_points_ada.PointTransPVC' \
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latent_pts.style_encoder 'models.shapelatent_modules.PointNetPlusEncoder' \
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shapelatent.prior_type normal \
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shapelatent.latent_dim $latent trainer.opt.lr $lr \
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shapelatent.kl_weight ${kl} \
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shapelatent.decoder_num_points 2048 \
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data.tr_max_sample_points 2048 data.te_max_sample_points 2048 \
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ddpm.loss_type $loss cmt "lion" \
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$DATA viz.viz_order [2,0,1] data.recenter_per_shape False data.normalize_global True
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