451 lines
19 KiB
Python
451 lines
19 KiB
Python
# ---------------------------------------------------------------
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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# ---------------------------------------------------------------
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from third_party.yacs_config import CfgNode as CN
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cfg = CN()
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cfg.dpm_ckpt = ''
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cfg.clipforge = CN()
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cfg.clipforge.clip_model = "ViT-B/32"
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cfg.clipforge.enable = 0
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cfg.clipforge.feat_dim = 512
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cfg.eval_trainnll = 0
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cfg.exp_name = ''
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cfg.cmt = ''
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cfg.hash = ''
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cfg.ngpu = 1
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cfg.snapshot_min = 30 # snapshot every 30 min
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cfg.bash_name = ''
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cfg.set_detect_anomaly = 0
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cfg.weight_recont = 1.0
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# vae ckpt
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# lns
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cfg.use_checkpoint = 0
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cfg.num_val_samples = 16 # 24 #12
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# config for pointtransformer
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cfg.eval = CN()
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cfg.eval.need_denoise = 0
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cfg.eval.load_other_vae_ckpt = 0
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cfg.register_deprecated_key('eval.other_vae_ckpt_path')
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cfg.vis_latent_point = 0
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cfg.latent_pts = CN()
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#cfg.latent_pts.class_embed_layer = ''
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cfg.register_deprecated_key('latent_pts.class_embed_layer')
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cfg.latent_pts.style_dim = 128 # dim of global style latent variable
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cfg.register_deprecated_key('latent_pts.perturb_input')
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cfg.register_deprecated_key('latent_pts.perturb_input_scale')
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cfg.register_deprecated_key('latent_pts.outlier_input')
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# scale of init weights for the mlp in adaGN layer
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cfg.latent_pts.ada_mlp_init_scale = 1.0
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# models.latent_points_ada.StyleMLP' # style mlp layers
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cfg.latent_pts.style_mlp = ''
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cfg.latent_pts.pts_sigma_offset = 0.0
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cfg.latent_pts.skip_weight = 0.1
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cfg.latent_pts.encoder_layer_out_dim = 32
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cfg.latent_pts.decoder_layer_out_dim = 32
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cfg.register_deprecated_key('latent_pts.encoder_nneighbor')
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cfg.register_deprecated_key('latent_pts.decoder_nneighbor')
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cfg.latent_pts.style_prior = 'models.score_sde.resnet.PriorSEDrop'
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cfg.latent_pts.mask_out_extra_latent = 0 # use only latent coordinates
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# latent coordinates directly same as input (not using the decoder and encoder)
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cfg.register_deprecated_key('latent_pts.latent_as_pts')
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cfg.latent_pts.normalization = 'bn' # BatchNorm or LayerNorm
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cfg.latent_pts.pvd_mse_loss = 0
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cfg.latent_pts.hid = 64
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cfg.register_deprecated_key('latent_pts.knn')
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cfg.register_deprecated_key('latent_pts.n5layer')
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cfg.register_deprecated_key('latent_pts.dgcnn_last_hid')
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cfg.latent_pts.latent_dim_ext = [64] # the global latent dim
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cfg.latent_pts.weight_kl_pt = 1.0 # kl ratio of the pts
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cfg.latent_pts.weight_kl_feat = 1.0 # kl ratio of the latent feat
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cfg.latent_pts.weight_kl_glb = 1.0 # kl ratio of the latent feat
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# kl ratio of the latent feat
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cfg.latent_pts.style_encoder = 'models.shapelatent_modules.PointNetPlusEncoder'
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cfg.latent_pts.use_linear_for_adagn = 0
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# cfg.latent_pts.weight_kl_glb = 1.0 # kl ratio of the global latent
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# shapelatent:
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cfg.has_shapelatent = 1
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cfg.shapelatent = CN()
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cfg.shapelatent.local_emb_agg = 'mean'
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cfg.shapelatent.freeze_vae = 0 # learn vae
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cfg.shapelatent.eps_z_global_only = 1
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cfg.shapelatent.model = 'flow'
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cfg.shapelatent.residual = 1
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cfg.shapelatent.encoder_type = 'pointnet'
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cfg.shapelatent.prior_type = 'flow'
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cfg.shapelatent.decoder_type = 'PointwiseNet'
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cfg.shapelatent.loss0_weight = 1.0
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cfg.shapelatent.latent_dim = 256
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cfg.shapelatent.kl_weight = 1e-3
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cfg.shapelatent.decoder_num_points = -1
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# offset the sigma towards zero for better init, will use the log_sigma - offset value, better to be positive s.t. - offset < 0 since we'd like to push it towards 0; exp(-0.1)=0.9, exp(-0.8)=0.44, exp(-1)=0.3, exp(-10)=4e-5
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cfg.shapelatent.log_sigma_offset = 0.0
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cfg.sde = CN()
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cfg.sde.ode_sample = 0 #1
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# train the prior or not, default is 1, only when we do voxel2pts, will freeze prior
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cfg.sde.train_dae = 1
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cfg.sde.init_t = 1.0 # start from time = 1.0
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cfg.sde.nhead = 4 # number of head in transformder: multi-head attention layer
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cfg.sde.local_prior = 'same_as_global' # architecture for local prior
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cfg.sde.drop_inactive_var = 0
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cfg.sde.learn_mixing_logit = 1 # freeze it
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cfg.sde.regularize_mlogit_margin = 0.0
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cfg.sde.share_mlogit = 0 # use same mlogit for all latent variables
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cfg.sde.hypara_mixing_logit = 0 # set as hyper-parameter and freeze it?
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cfg.sde.bound_mlogit = 0 # clamp or not
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cfg.sde.bound_mlogit_value = -5.42 # clamp the max value
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cfg.sde.regularize_mlogit = 0 # set the sum of sigmoid(mlogit) as one loss
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cfg.sde.attn_mhead = 0 # use multi-head attention in prior model
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cfg.sde.attn_mhead_local = -1 # use multi-head attention in prior model
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cfg.sde.pos_embed = 'none'
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cfg.sde.hier_prior = 0
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cfg.sde.is_continues = 0
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cfg.sde.time_emb_scales = 1.0 # -> 1k?
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cfg.sde.time_eps = 1e-2
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cfg.sde.ode_eps = 1e-5 # cut off for ode sampling
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cfg.sde.sde_type = 'vpsde' # vada
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cfg.sde.sigma2_0 = 0.0
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cfg.sde.sigma2_max = 0.99
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cfg.sde.sigma2_min = 1e-4
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cfg.sde.beta_start = 0.1 # 1e-4 * 1e3
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cfg.sde.beta_end = 20.0 # 1e-2 * 1e3
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# sampling, always iw # ll: small times; 'll_uniform' # -> ll_iw
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cfg.sde.iw_sample_p = 'll_iw'
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# drop_all_iw / drop_sigma2t_iw
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cfg.sde.iw_subvp_like_vp_sde = False
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cfg.sde.prior_model = 'models.latent_points_ada_localprior.PVCNN2Prior'
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# -- to train diffusion in latent space -- #
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cfg.sde.update_q_ema = False
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cfg.sde.iw_sample_q = 'reweight_p_samples'
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# ll_iw / reweight_p_samples
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cfg.sde.kl_anneal_portion_vada = 0.1
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cfg.sde.kl_const_portion_vada = 0.0
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cfg.sde.kl_const_coeff_vada = 0.7
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cfg.sde.kl_balance_vada = False
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cfg.sde.grad_clip_max_norm = 0.0
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cfg.sde.cont_kl_anneal = True
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# False
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cfg.sde.mixing_logit_init = -6
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cfg.sde.weight_decay_norm_vae = 0.0 #1e-2
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cfg.sde.weight_decay_norm_dae = 0.0 #1e-2
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# -> 0, for sn calculator
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cfg.sde.train_vae = True
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cfg.sde.jac_reg_coeff = 0
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cfg.sde.jac_reg_freq = 1
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cfg.sde.kin_reg_coeff = 0
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cfg.sde.learning_rate_mlogit = -1.0
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cfg.sde.learning_rate_dae_local = 3e-4
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cfg.sde.learning_rate_min_dae_local = 3e-4
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cfg.sde.learning_rate_dae = 3e-4
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cfg.sde.learning_rate_min_dae = 3e-4
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cfg.sde.learning_rate_min_vae = 1e-5
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cfg.sde.learning_rate_vae = 1e-4
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cfg.sde.epochs = 800
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cfg.sde.warmup_epochs = 20
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cfg.sde.weight_decay = 3e-4
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cfg.sde.use_adamax = False
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cfg.sde.use_adam = True # False
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cfg.sde.mixed_prediction = False # True
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cfg.sde.vae_checkpoint = ''
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cfg.sde.dae_checkpoint = ''
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# will be used to multiply with the t value, if ode solver, use 1k, if discrete solver, use 1.0
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cfg.sde.embedding_scale = 1.0 # 1000.0
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cfg.sde.embedding_type = 'positional'
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cfg.sde.train_ode_solver_tol = 1e-5
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cfg.sde.num_scales_dae = 2
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cfg.sde.autocast_train = False
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cfg.sde.diffusion_steps = 1000
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cfg.sde.embedding_dim = 128
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cfg.sde.num_channels_dae = 256
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cfg.sde.num_cell_per_scale_dae = 8
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cfg.sde.num_cell_per_scale_dae_local = 0
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cfg.sde.dropout = 0.2
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cfg.sde.num_preprocess_blocks = 2
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cfg.sde.num_latent_scales = 1
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cfg.sde.fir = False
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cfg.sde.progressive = 'none'
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cfg.sde.progressive_input = 'none'
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cfg.sde.progressive_combine = 'sum'
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cfg.sde.dataset = 'shape'
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cfg.sde.denoising_stddevs = 'beta'
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cfg.sde.ema_decay = 0.9999
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# cfg.sde.is_train_vae=True
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cfg.register_deprecated_key("sde.is_train_vae")
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cfg.sde.kl_max_coeff_vada = 1.0
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# conditional prior input
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cfg.sde.condition_add = 1
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cfg.sde.condition_cat = 0
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cfg.sde.global_prior_ckpt = '' # checkpoint for global prior component
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cfg.sde.pool_feat_cat = 0 # the local prior aggregate the feat as extra input channels
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# hyperparameter of ddim sampling
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cfg.sde.ddim_skip_type = 'uniform'
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cfg.sde.ddim_kappa = 1.0 # 1.0: fully ddpm sampling; 0: ode style sampling
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cfg.ddpm = CN()
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cfg.ddpm.use_p2_weight = 0
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cfg.ddpm.p2_k = 1.0
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cfg.ddpm.p2_gamma = 1.0
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cfg.ddpm.use_new_timeemb = 0
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cfg.ddpm.input_dim = 3
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cfg.ddpm.dropout = 0.1
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cfg.ddpm.num_layers_classifier = 3
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cfg.ddpm.use_bn = True
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cfg.ddpm.add_point_feat = True
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cfg.ddpm.use_gn = False
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cfg.ddpm.time_dim = 64
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cfg.ddpm.ema = 1
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cfg.ddpm.with_se = 0
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cfg.ddpm.use_global_attn = 0
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cfg.ddpm.num_steps = 1000
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cfg.ddpm.beta_1 = 1e-4
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cfg.ddpm.beta_T = 2e-2
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# ['linear', 'customer'] 'customer' for airplane in PVD
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cfg.ddpm.sched_mode = 'linear'
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cfg.ddpm.model_var_type = 'fixedlarge'
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# define architecture:
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cfg.register_deprecated_key("ddpm.pointnet_plus")
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cfg.register_deprecated_key("ddpm.pointnet_pp")
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cfg.register_deprecated_key("ddpm.pointnet_luo")
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# end define architecture
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#cfg.ddpm.use_pvc = 1
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cfg.register_deprecated_key("ddpm.use_pvc")
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cfg.ddpm.clip_denoised = 0
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cfg.ddpm.model_mean_type = 'eps'
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cfg.ddpm.loss_type = 'mse'
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cfg.ddpm.loss_type_0 = ''
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cfg.ddpm.loss_weight_emd = 0.02
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cfg.ddpm.loss_weight_cdnorm = 1.0
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cfg.ddpm.attn = [0, 1, 0, 0]
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cfg.ddpm.ncenter = [1024, 256, 64, 16]
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#cfg.ddpm.pvc = CN()
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#cfg.ddpm.pvc.use_small_model = 0
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#cfg.ddpm.pvc.mlp_after_pvc = 0
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cfg.register_deprecated_key("ddpm.pvc")
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cfg.register_deprecated_key("ddpm.pvc.use_small_model")
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cfg.register_deprecated_key("ddpm.pvc.mlp_after_pvc")
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cfg.ddpm.ddim_step = 200
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cfg.data = CN()
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cfg.data.nclass = 55
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cfg.data.cond_on_cat = 0
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cfg.data.cond_on_voxel = 0
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cfg.data.eval_test_split = 0 # eval loader will be using test split
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cfg.data.voxel_size = 0.1 # size of voxel for voxel_datasets.py
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cfg.data.noise_std = 0.1 # std for the noise added to the input data
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cfg.data.noise_type = 'normal' # std for the noise added to the input data
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cfg.data.noise_std_min = -1.0 # for range of noise std
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cfg.data.clip_forge_enable = 0
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cfg.data.clip_model = 'ViT-B/32'
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cfg.data.type = "datasets.pointflow_datasets"
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# datasets/neuralspline_datasets datasets/shape_curvature
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cfg.data.dataset_type = "shapenet15k"
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cfg.data.num_workers = 12 # 8
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cfg.data.train_drop_last = 1 # drop_last for train data loader
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cfg.data.cates = 'chair' # data category
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cfg.data.tr_max_sample_points = 2048
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cfg.data.te_max_sample_points = 2048
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cfg.data.data_dir = "data/ShapeNetCore.v2.PC15k" # depreciated
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cfg.data.batch_size = 12
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cfg.data.batch_size_test = 10
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cfg.data.dataset_scale = 1
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# -- the following option in terms of normalization should turn into string -- #
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cfg.data.normalize_per_shape = False
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cfg.data.normalize_shape_box = False
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cfg.data.normalize_global = False
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cfg.data.normalize_std_per_axis = False
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cfg.data.normalize_range = False # not used
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cfg.data.recenter_per_shape = True
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# -- for the normal prediction model, used in folder_datasets
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cfg.register_deprecated_key('data.load_point_stat')
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cfg.register_deprecated_key('data.is_load_pointflow2NS')
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cfg.register_deprecated_key('data.data_path')
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#
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cfg.data.sample_with_replacement = 1
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# fixed the data.tr_max_sample_points $np data.te_max_sample_points $np2048 points of the first 15k points
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cfg.data.random_subsample = 1
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# the data dim, used in dataset worker, if -1, it will be the same as ddpm.input_dim
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cfg.data.input_dim = -1
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cfg.data.is_encode_whole_dataset_trainer = 0
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cfg.register_deprecated_key('data.augment')
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cfg.register_deprecated_key('data.aug_translate')
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cfg.register_deprecated_key('data.aug_scale')
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cfg.register_deprecated_key('data.sub_train_set')
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cfg.test_size = 660
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cfg.viz = CN()
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cfg.viz.log_freq = 10
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cfg.viz.viz_freq = 400
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cfg.viz.save_freq = 200
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cfg.viz.val_freq = -1
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cfg.viz.viz_order = [2, 0, 1]
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cfg.viz.vis_sample_ddim_step = 0
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cfg.trainer = CN()
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# when loss 1 is weighted, also weight the kl terms
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cfg.trainer.apply_loss_weight_1_kl = 0
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cfg.trainer.kl_free = [0, 0] # the value for the threshold
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# not back ward kl loss if KL value is smaller than the threshold
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cfg.trainer.use_kl_free = 0
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cfg.trainer.type = "trainers.ddpm_trainer" # it means dist trainer
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cfg.trainer.epochs = 10000
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cfg.trainer.warmup_epochs = 0
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cfg.trainer.seed = 1
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cfg.trainer.use_grad_scalar = 0
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cfg.trainer.opt = CN()
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cfg.trainer.opt.type = 'adam'
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cfg.trainer.opt.lr = 1e-4 # use bs*1e-5/8
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cfg.trainer.opt.lr_min = 1e-4 # use bs*1e-5/8
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# lr start to anneal after ratio of epochs; used in cosine and lambda lr scheduler
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cfg.trainer.opt.start_ratio = 0.6
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cfg.trainer.opt.beta1 = 0.9
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cfg.trainer.opt.beta2 = 0.999
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cfg.trainer.opt.momentum = 0.9 # for SGD
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cfg.trainer.opt.weight_decay = 0.
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cfg.trainer.opt.ema_decay = 0.9999
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cfg.trainer.opt.grad_clip = -1.
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cfg.trainer.opt.scheduler = ''
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cfg.trainer.opt.step_decay = 0.998
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cfg.trainer.opt.vae_lr_warmup_epochs = 0
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cfg.trainer.anneal_kl = 0
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cfg.trainer.kl_balance = 0
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cfg.trainer.rec_balance = 0
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cfg.trainer.loss1_weight_anneal_v = 'quad'
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cfg.trainer.kl_ratio = [1.0, 1.0]
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cfg.trainer.kl_ratio_apply = 0 # apply the fixed kl ratio in the kl_ratio list
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# using spectral norm regularization on vae training or not (used in hvae_trainer)
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cfg.trainer.sn_reg_vae = 0
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cfg.trainer.sn_reg_vae_weight = 0.0 # loss weight for the sn regulatrization
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# [start] set in runtime
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cfg.log_name = ''
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cfg.save_dir = ''
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cfg.log_dir = ''
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cfg.comet_key = ''
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# [end]
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cfg.voxel2pts = CN()
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cfg.voxel2pts.init_weight = ''
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cfg.voxel2pts.diffusion_steps = [0]
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cfg.dpm = CN()
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cfg.dpm.train_encoder_only = 0
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cfg.num_ref = 0 # manully set the number of reference
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cfg.eval_ddim_step = 0 # ddim sampling for the model evaluation
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cfg.model_config = '' # used for model control, without ading new flag
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## --- depreciated --- #
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cfg.register_deprecated_key('cls') # CN()
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cfg.register_deprecated_key('cls.classifier_type') # 'models.classifier.OneLayer'
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cfg.register_deprecated_key('cls.train_on_eps') # 1
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cfg.register_deprecated_key('cond_prior') # CN()
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cfg.register_deprecated_key('cond_prior.grid_emb_resolution') # 32
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cfg.register_deprecated_key('cond_prior.emb_dim') # 64
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cfg.register_deprecated_key('cond_prior.use_voxel_feat') # 1
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cfg.register_deprecated_key('cond_encoder_prior') # 'models.shapelatent_modules.VoxelGridEncoder'
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cfg.register_deprecated_key('cond_prior.pvcconv_concat_3d_feat_input') # 0
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cfg.register_deprecated_key('generate_mode_global') # 'interpolate'
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cfg.register_deprecated_key('generate_mode_local') # 'freeze'
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cfg.register_deprecated_key('normals') # CN()
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cfg.register_deprecated_key('normals.model_type') # ''
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cfg.register_deprecated_key('save_sample_seq_and_quit') # 0
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cfg.register_deprecated_key('lns_loss_weight') # 1.0
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cfg.register_deprecated_key('normal_pred_checkpoint') # ''
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cfg.register_deprecated_key('lns') # CN()
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cfg.register_deprecated_key('lns.override_config') # ''
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cfg.register_deprecated_key('lns.wandb_checkpoint') # 'nvidia-toronto/generative_chairs/3m3gc6sz/checkpoint-171.pth'
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cfg.register_deprecated_key('lns.num_input_points') # 1000
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cfg.register_deprecated_key('lns.num_simulate') # 20
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cfg.register_deprecated_key('lns.split_simulate') # 'train'
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# use mesh-trainer or not
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cfg.register_deprecated_key('with_lns') # 0
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cfg.register_deprecated_key('normal_predictor_yaml') # ''
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cfg.register_deprecated_key('pointtransformer') # CN()
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# number of attention layer in each block
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cfg.register_deprecated_key('pointtransformer.blocks') # [2, 3, 4, 6, 3]
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cfg.register_deprecated_key('shapelatent.refiner_bp') # 1 # bp gradient to the local-decoder or not
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cfg.register_deprecated_key('shapelatent.loss_weight_refiner') # 1.0 # weighted loss for the refiner
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cfg.register_deprecated_key('shapelatent.refiner_type') # 'models.pvcnn2.PVCNN2BaseAPI' # mode for the refiner
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cfg.register_deprecated_key('shapelatent.encoder_weight_std') # 0.1
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cfg.register_deprecated_key('shapelatent.encoder_weight_norm') # 0
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cfg.register_deprecated_key('shapelatent.encoder_weight_uniform') # 1
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cfg.register_deprecated_key('shapelatent.key_point_gen') # 'mlps'
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cfg.register_deprecated_key('shapelatent.add_sub_loss') # 1 # not used
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cfg.register_deprecated_key('shapelatent.local_decoder_type') # ''
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cfg.register_deprecated_key('shapelatent.local_decoder_type_1') # ''
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cfg.register_deprecated_key('shapelatent.local_encoder_ball_radius') # 0.8
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cfg.register_deprecated_key('shapelatent.local_encoder_ap_ball_radius') # 1.0
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cfg.register_deprecated_key('shapelatent.local_encoder_type') # ''
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cfg.register_deprecated_key('shapelatent.local_encoder_type_1') # ''
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cfg.register_deprecated_key('shapelatent.local_loss_weight_max') # 50
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cfg.register_deprecated_key('shapelatent.num_neighbors') # 0
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cfg.register_deprecated_key('shapelatent.extra_centers') # []
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# for latent model is flow
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cfg.register_deprecated_key('shapelatent.latent_flow_depth') # 14
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cfg.register_deprecated_key('shapelatent.latent_flow_hidden_dim') # 256
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cfg.register_deprecated_key('shapelatent.bp_to_l0') # True
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cfg.register_deprecated_key('shapelatent.global_only_epochs') # 0
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cfg.register_deprecated_key('shapelatent.center_local_points') # 1
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cfg.register_deprecated_key('shapelatent.hvae') # CN()
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# alternatively way to compute the local loss
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cfg.register_deprecated_key('shapelatent.hvae.loss_wrt_ori') # 0
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# add voxel feature to the latent space; the decoder require pvc conv or query
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cfg.register_deprecated_key('shapelatent.add_voxel2z_global') # 0
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# reuse the encoder to get local latent
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cfg.register_deprecated_key('shapelatent.query_output_local_from_enc') # 0
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# check models/shapelatent_modules where the feature will be saved as a dict
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cfg.register_deprecated_key('shapelatent.query_local_feat_layer') # 'inter_voxelfeat_0'
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# need to check the sa_blocks of the global encoder
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cfg.register_deprecated_key('shapelatent.query_local_feat_dim') # 32
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# reuse the encoder to get local latent
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cfg.register_deprecated_key('shapelatent.query_center_emd_from_enc') # 0 # reuse the encoder for center emd
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cfg.register_deprecated_key('shapelatent.prog_dec_gf') # 8 # grow_factor in VaniDecoderProg
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cfg.register_deprecated_key('shapelatent.prog_dec_gf_list') # [0, 0] # grow_factor in VaniDecoderProg
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cfg.register_deprecated_key('shapelatent.prog_dec_ne') # 2 # num_expand in VaniDecoderProg
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# increase number hirach, used by hvaemul model
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cfg.register_deprecated_key('shapelatent.num_neighbors_per_level') # [64] # number of neighbors for each level
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cfg.register_deprecated_key('shapelatent.num_level') # 1 # number of hierarchi latent space (local)
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cfg.register_deprecated_key('shapelatent.x0_target_fps') # 0 # let the target of global output as the
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cfg.register_deprecated_key('shapelatent.downsample_input_ratio') # 1.0
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|
# whether taking other tensor as input to local-encoder of not
|
|
cfg.register_deprecated_key('shapelatent.local_enc_input') # 'sim'
|
|
# local encoder take z0 as input at which location
|
|
cfg.register_deprecated_key('shapelatent.local_encoder_condition_z0') # ''
|
|
# output the absolution coordinates or the offset w.r.t centers
|
|
cfg.register_deprecated_key('shapelatent.local_decoder_output_offset') # 0
|
|
# feed coords of keypoints to the local prior model
|
|
cfg.register_deprecated_key('shapelatent.local_prior_need_coords') # 0
|
|
|
|
# add the time embedding tensor to each encoder layer instead of add to first layer only
|
|
cfg.register_deprecated_key('sde.transformer_temb2interlayer') # 0
|
|
# normalization used in transformer encoder;
|
|
cfg.register_deprecated_key('sde.transformer_norm_type') # 'layer_norm'
|
|
cfg.register_deprecated_key('data.has_normal') # 0 # for datasets/pointflow_rgb.py only
|
|
cfg.register_deprecated_key('data.has_color') # 0 # for datasets/pointflow_rgb.py only
|
|
cfg.register_deprecated_key('data.cls_data_ratio') # 1.0 # ratio of the training data
|
|
cfg.register_deprecated_key('data.sample_curvature') # 0 # only for datasets/shape_curvature
|
|
cfg.register_deprecated_key('data.ratio_c') # 1.0 # only for datasets/shape_curvature
|