Méthode "change_num_class_for_finetuning" inexistante dans la classe "PyGrandLANet"

[AurelienChauveheid]

Bonjour, j'essaie de finetunner un modèle que j'ai déjà entrainer avec myria3d, après avoir paramétrer les fichiers de callbacks et model j'obtiens l'erreur suivante en lancant run.py :

Traceback (most recent call last): File "/home/achauveheid/Documents/LIDAR/myria3d_predict/myria3d/run.py", line 57, in launch_train return train(config) File "/home/achauveheid/Documents/LIDAR/myria3d_predict/myria3d/myria3d/train.py", line 145, in train trainer.fit(model=model, datamodule=datamodule, ckpt_path=config.model.ckpt_path) File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 544, in fit call._call_and_handle_interrupt( File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 44, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 580, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 949, in _run call._call_setup_hook(self) # allow user to set up LightningModule in accelerator environment File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 93, in _call_setup_hook _call_callback_hooks(trainer, "setup", stage=fn) File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 208, in _call_callback_hooks fn(trainer, trainer.lightning_module, *args, **kwargs) File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/pytorch_lightning/callbacks/finetuning.py", line 277, in setup self.freeze_before_training(pl_module) File "/home/achauveheid/Documents/LIDAR/myria3d_predict/myria3d/myria3d/callbacks/finetuning_callbacks.py", line 23, in freeze_before_training pl_module.model.change_num_class_for_finetuning(self._num_classes) File "/home/achauveheid/anaconda3/envs/myria3d/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1695, in __getattr__ raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") AttributeError: 'PyGRandLANet' object has no attribute 'change_num_class_for_finetuning'

Je n'ai pas vu de référence à cette méthode dans la classe 'PyGRandLANet' et non plus dans la documentation de torch.nn.Module. Est-ce que vous pouvez m'aider/éclairer ?

[leavauchier]

Bonjour @AurelienChauveheid,

cette erreur ne me parle pas, j'ai essayé de chercher aussi où elle est référencée mais je ne la trouve pas non plus.

@CharlesGaydon, à tout hasard, est-ce que ça te rappelle quelque chose ?

[AurelienChauveheid]

En modifiant la classe FinetuningFreezeUnfreeze, nous avons pu finetuner un modèle déjà entraîné par myria3D. Par contre je ne suis pas sur que nous respectons correctement les étapes de dégel.

class FinetuningFreezeUnfreeze(BaseFinetuning):
    def __init__(
        self,
        d_in: int = 9,
        num_classes: int = 6,
        unfreeze_fc_end_epoch: int = 3,
        unfreeze_decoder_train_epoch: int = 6,
    ):
        super().__init__()

        self._d_in = d_in
        self._num_classes = num_classes
        self._unfreeze_decoder_epoch = unfreeze_decoder_train_epoch
        self._unfreeze_fc_end_epoch = unfreeze_fc_end_epoch
        self.fc_end = self._unfreeze_fc_end_epoch
 
    def freeze_before_training(self, pl_module):
        """Update in and out dimensions, and freeze everything at start."""
 
        # here we could both load the model weights and update its dim afterward
        # TODO: check change_num_class_for_finetuning
        # pl_module.model.change_num_class_for_finetuning(self._num_classes)
        self.freeze(pl_module.model)
 
    def finetune_function(self, pl_module, current_epoch, optimizer, optimizer_idx: int=0):
        import copy
 
        """Unfreeze layers sequentially, starting from the end of the architecture."""
        pl_module.model.fc_end = copy.deepcopy(pl_module.model.fc_classif)
        if current_epoch == 0:
            self.unfreeze_and_add_param_group(
                modules=pl_module.model.fc_classif,
                optimizer=optimizer,
                train_bn=True,
                initial_denom_lr=100,
            )
        if current_epoch == self._unfreeze_fc_end_epoch:
            self.unfreeze_and_add_param_group(
                # TODO:
                modules=pl_module.model.mlp_classif,
                # modules=pl_module.model.fc_end,
                optimizer=optimizer,
                train_bn=True,
                initial_denom_lr=100,
            )
        if current_epoch == self._unfreeze_decoder_epoch:
            self.unfreeze_and_add_param_group(
                modules=pl_module.model.decoder,
                optimizer=optimizer,
                train_bn=True,
                initial_denom_lr=100,
            )