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

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# @package _global_

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

defaults:
  - override /datamodule: panoptic/scannet_nano.yaml
  - override /model: panoptic/nano-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: 100

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

  scheduler:
    num_warmup: 2

  _node_mlp_out: 32
  _h_edge_mlp_out: 32
  _down_dim: [ 32, 32, 32, 32 ]
  _up_dim: [ 32, 32, 32 ]
  net:
    no_ffn: False
    down_ffn_ratio: 1

  partitioner:
    regularization: 20
    x_weight: 5e-2
    cutoff: 300

  edge_affinity_loss_lambda: 10

  partition_every_n_epoch: 4

logger:
  wandb:
    project: "spt_scannet"
    name: "NANO"

# 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