superpoint_transformer/configs/experiment/panoptic/kitti360.yaml at master · drprojects/superpoint_transformer · GitHub

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# @package _global_

# to execute this experiment run:
# python train.py experiment=panoptic/kitti360

defaults:
  - override /datamodule: panoptic/kitti360.yaml
  - override /model: panoptic/spt-2.yaml
  - override /trainer: gpu.yaml

# all parameters below will be merged with parameters from default configurations set above
# this allows you to overwrite only specified parameters

trainer:
  max_epochs: 200

model:
  optimizer:
    lr: 0.01
    weight_decay: 1e-4

  _down_dim: [ 128, 128, 128, 128 ]
  _up_dim: [ 128, 128, 128 ]

  net:
    no_ffn: False
    down_ffn_ratio: 1


  partitioner:
    regularization: 10
    x_weight: 5e-2
    cutoff: 1

  partition_every_n_epoch: 10

logger:
  wandb:
    project: "spt_kitti360"
    name: "SPT-128"

# metric based on which models will be selected
optimized_metric: "val/pq"

# modify checkpointing callbacks to adapt to partition_every_n_epoch
# being potentially different
callbacks:
  model_checkpoint:
    every_n_epochs: ${eval:'max(${trainer.check_val_every_n_epoch}, ${model.partition_every_n_epoch})'}

  early_stopping:
    strict: False