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pretrain_act_distill.yaml
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pretrain_act_distill.yaml
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optimizer :
type: AdamW
kwargs:
lr: 0.001
weight_decay: 0.05
scheduler:
type: CosLR
kwargs:
epochs: 300
initial_epochs : 10
dataset:
train:
_base_: cfgs/dataset_configs/ShapeNet-55.yaml
others:
subset: train
npoints: 1024
whole: True
val:
_base_: cfgs/dataset_configs/ModelNet40.yaml
others:
subset: test
extra_train:
_base_: cfgs/dataset_configs/ModelNet40.yaml
others:
subset: train
model:
NAME: ACT_PointDistillation
loss: cosine
transformer_config:
mask_ratio: 0.8
mask_type: 'rand'
proj: linear
embed_dim: 384
encoder_dims: 384
depth: 12
drop_path_rate: 0.1
cls_dim: 512
replace_pob: 0.
num_heads: 6
decoder_depth: 2
decoder_num_heads: 6
return_all_tokens: False
cls_loss: False
register_shallow_hook: 9
dvae_config:
# ViT
visual_embed_type: vit_base_patch16_384
# visual_embed_type: vit_small_patch16_384
# visual_embed_type: vit_base_r50_s16_224_in21k
# visual_embed_type: vit_base_r50_s16_384
# DEiT
# visual_embed_type: deit_small_distilled_patch16_224
# visual_embed_type: deit_base_distilled_patch16_384
# BEiT
# visual_embed_type: beit_base_patch16_384
# visual_embed_type: beit_large_patch16_512
# CLIP
# visual_embed_type: CLIP:ViT-B/16
# MLP-Mixer
# visual_embed_type: mixer_b16_224_in21k
# visual_embed_type: mixer_b16_224_miil
# visual_embed_type: resmlp_24_distilled_224
# BERT:
# visual_embed_type:
# visual_embed_dim: 384
visual_embed_dim: 768
visual_embed_pos: after_dgcnn1
freeze_visual_embed: true
num_prompt_token: 64
# num_prompt_token: 132 # MLP-Mixer
use_deep_prompt: true
num_group: 64
group_size: 32
encoder_dims: 384
num_tokens: 8192
tokens_dims: 384
decoder_dims: 384
ckpt: 'model_zoo/ckpt_act_dvae.pth' # set the dVAE weight here
total_bs: 128
step_per_update: 1
max_epoch: 300
consider_metric: CDL1