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base.yaml
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base.yaml
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optimizer : {
type: AdamW,
kwargs: {
lr : 0.0005,
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}},
val : { _base_: cfgs/dataset_configs/ShapeNet-55.yaml,
others: {subset: 'test', npoints: 1024}},
test : { _base_: cfgs/dataset_configs/ShapeNet-55.yaml,
others: {subset: 'test', npoints: 1024}},
# using extra data to validate
# extra_train: { _base_: cfgs/dataset_configs/ModelNet40.yaml,
# others: {subset: 'train', npoints: 1024}},
# extra_val: { _base_: cfgs/dataset_configs/ModelNet40.yaml,
# others: {subset: 'test', npoints: 1024}}
}
model : {
NAME: ReCon,
group_size: 32,
num_group: 64,
loss: cdl2,
transformer_config: {
mask_ratio: 0.6,
mask_type: 'rand',
trans_dim: 384,
encoder_dims: 384,
depth: 12,
drop_path_rate: 0.1,
num_heads: 6,
decoder_depth: 4,
decoder_num_heads: 6,
},
img_encoder: 'vit_base_patch16_224_in21k',
# img_encoder: 'ViT-B/32', # for Zero-Shot pretraining setting
text_encoder: 'ViT-B/32',
self_contrastive: FALSE,
}
npoints: 1024
total_bs : 128
step_per_update : 1
max_epoch : 300
validate : 'none' # 'svm', 'zeroshot'