from torch_geometric.graphgym.register import register_config
[docs]
@register_config('overwrite_defaults')
def overwrite_defaults_cfg(cfg):
"""Overwrite the default config values that are first set by GraphGym in
torch_geometric.graphgym.config.set_cfg
WARNING: At the time of writing, the order in which custom config-setting
functions like this one are executed is random; see the referenced `set_cfg`
Therefore never reset here config options that are custom added, only change
those that exist in core GraphGym.
"""
# Training (and validation) pipeline mode
cfg.train.mode = 'custom' # 'standard' uses PyTorch-Lightning since PyG 2.1
# Overwrite default dataset name
cfg.dataset.name = 'none'
# Overwrite default rounding precision
cfg.round = 5
@register_config('extended_cfg')
def extended_cfg(cfg):
"""General extended config options.
"""
# Additional name tag used in `run_dir` and `wandb_name` auto generation.
cfg.name_tag = ""
# In training, if True (and also cfg.train.enable_ckpt is True) then
# always checkpoint the current best model based on validation performance,
# instead, when False, follow cfg.train.eval_period checkpointing frequency.
cfg.train.ckpt_best = False