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PointMLP_VPT.yaml
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PointMLP_VPT.yaml
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optimizer_clip : {
type: sgd,
kwargs: {
momentum: 0.9,
base_lr : 0.01,
bias_multiplier : 1,
weight_decay : 0.0001
}}
scheduler: {
type: cosine,
kwargs: {
epochs: 100,
initial_epochs : 0
}}
optimizer : {
type: AdamW,
kwargs: {
lr : 0.0001,
weight_decay : 0.1,
# momentum : 0.9
}}
scheduler: {
type: CosLR,
kwargs: {
epochs: 100,
initial_epochs : 0,
min_lr : 0.000001
}}
dataset : {
train : { _base_: cfgs/dataset_configs/ShapeNet-55.yaml,
others: {subset: 'train','bs': 32}},
mn40 : { _base_: cfgs/dataset_configs/ModelNet40.yaml,
others: {subset: 'test','bs': 32}},
mn10 : { _base_: cfgs/dataset_configs/ModelNet10.yaml,
others: {subset: 'test','bs': 32}},
scan : { _base_: cfgs/dataset_configs/ScanObjectNN_hardest.yaml,
others: {subset: 'test','bs': 32}},
s3 : { _base_: cfgs/dataset_configs/S3DIS.yaml,
others: {subset: 'test','bs': 32}},
s3dis : { _base_: cfgs/dataset_configs/S3DIS.yaml,
others: {subset: 'train','bs': 32}}}
model : {
NAME: PointMLP,
trans_dim: 384,
depth: 12,
drop_path_rate: 0.1,
cls_dim: 40,
num_heads: 6,
group_size: 32,
num_group: 64,
encoder_dims: 256,
}
npoints: 1024
total_bs : 32
step_per_update : 1
max_epoch : 100
grad_norm_clip : 10
consider_metric: CDL1