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refactor register_model decorator
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@ -147,7 +147,6 @@ class OptimizerConfig(BaseModel):
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class ModelConfig(BaseModel):
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checkpoint: Path | None = None
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# If None, then requires_grad will NOT be changed when loading the model
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# this can be useful if you want to train only a part of the model
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requires_grad: bool | None = None
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@ -163,7 +162,6 @@ T = TypeVar("T", bound="BaseConfig")
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class BaseConfig(BaseModel):
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models: dict[str, ModelConfig]
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training: TrainingConfig
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optimizer: OptimizerConfig
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scheduler: SchedulerConfig
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@ -430,7 +430,9 @@ class Trainer(Generic[ConfigType, Batch], ABC):
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registered_callback(config)
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def _load_models(self) -> None:
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for name, config in self.config.models.items():
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for name, config in self.config:
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if not isinstance(config, ModelConfig):
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continue
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try:
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registered_model = getattr(self, name)
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except AttributeError:
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@ -1,4 +1,4 @@
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[models.mock_model]
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[mock_model]
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requires_grad = true
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[clock]
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@ -1,8 +1,8 @@
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[models.mock_model1]
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[mock_model1]
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requires_grad = true
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learning_rate = 1e-5
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[models.mock_model2]
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[mock_model2]
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requires_grad = true
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[clock]
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@ -28,7 +28,7 @@ class MockBatch:
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class MockConfig(BaseConfig):
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pass
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mock_model: ModelConfig
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class MockModel(fl.Chain):
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@ -230,9 +230,14 @@ class MockTrainerWith2Models(MockTrainer):
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return norm(outputs - targets)
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class MockConfig_2_Models(BaseConfig):
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mock_model1: ModelConfig
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mock_model2: ModelConfig
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@pytest.fixture
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def mock_config_2_models(test_device: torch.device) -> MockConfig:
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return MockConfig.load_from_toml(Path(__file__).parent / "mock_config_2_models.toml")
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def mock_config_2_models() -> MockConfig_2_Models:
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return MockConfig_2_Models.load_from_toml(Path(__file__).parent / "mock_config_2_models.toml")
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@pytest.fixture
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