File size: 1,224 Bytes
cfeea40 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from dataclasses import dataclass, field
from typing import Any
@dataclass
class Config:
# This is for CLI applications that need to reuse a CLI parameter in multiple places
# in the config file. The idea is that you use `my_cli params.output_dir=foobar`
# and in other places in the config file `output_dir: ${params.output_dir}`
params: dict[str, Any] = field(default_factory=dict)
checkpoint_path: str | None = None # Required if train == False
# if load_original is True then we load original weights in validation mode instead of EMA
load_original: bool = False
# When auto_resume is set to `True` the trainer saves a copy of each checkpoint in
# {trainer.default_root_dir}/checkpoints. Before starting training, we look in this
# directory for a checkpoint from which to resume training.
auto_resume: bool = False
# DiffusionLightningModule
lightning_module: dict[str, Any] = field(default_factory=dict)
# pytorch_lightning.Trainer
trainer: dict[str, Any] = field(default_factory=dict)
# LightningDataModule
data_module: dict[str, Any] = field(default_factory=dict)
|