Documented to reduce the level of fire danger lower than other products currently on the market today. FX Lumber Guard XT is tested beyond the standard requirements of fire retardant wood treatments. Ideas of projects / materials that can be treated are Preservative Treated (green treatment), Re-Claimed Woods, and Wooden Furniture, Bridges, Fencing, Decks, Shake Shingles, Car Ports, Landscape Beds and similar projects. Cedar, Oak, OSB, Red Wood and other similar species are treatable for FX Lumber Guard XT. > 219 return Class A fire retardant wood treatment for unfinished, unpainted lumber and plywood, the following species have certified testing SYP, SPF, Doug Fir and Hem Fir lumber and SYP plywood. usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in validation_step(self, *args, **kwargs)Ģ18 def validation_step(self, *args, **kwargs): > 239 return aining_type_plugin.validation_step(*step_kwargs.values())Ģ41 def test_step(self, step_kwargs: Dict]) -> Optional: usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py in validation_step(self, step_kwargs)Ģ38 with self.precision_plugin.val_step_context(): usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py in _evaluation_step(self, batch, batch_idx, dataloader_idx)Ģ15 _module._current_fx_name = "validation_step"Ģ16 with ("validation_step"): > 122 output = self._evaluation_step(batch, batch_idx, dataloader_idx)ġ23 output = self._evaluation_step_end(output) usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py in advance(self, data_fetcher, dataloader_idx, dl_max_batches, num_dataloaders)ġ21 with ("evaluation_step_and_end"): > 110 dl_outputs = self.epoch_n(dataloader, dataloader_idx, dl_max_batches, self.num_dataloaders)ġ12 # store batch level output per dataloader usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py in advance(self, *args, **kwargs)ġ08 dl_max_batches = self._max_batches usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py in run(self, *args, **kwargs)ġ44 self.on_advance_start(*args, **kwargs) usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _run_sanity_check(self, ref_model)ġ377 self.call_hook("on_sanity_check_end") > 1311 self._run_sanity_check(self.lightning_module) usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _run_train(self)ġ309 self.progress_bar_callback.disable() usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in run_stage(self) > 202 self._results = n_stage()Ģ04 def start_evaluating(self, trainer: "pl.Trainer") -> None: usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)Ģ00 def start_training(self, trainer: "pl.Trainer") -> None:Ģ01 # double dispatch to initiate the training loop > 1279 aining_type_plugin.start_training(self) usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _dispatch(self)ġ277 aining_type_plugin.start_predicting(self) usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _run(self, model, ckpt_path)ġ198 # dispatch `start_training` or `start_evaluating` or `start_predicting`ġ201 # plugin will finalized fitting (e.g. > 777 self._run(model, ckpt_path=ckpt_path) usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)ħ76 ckpt_path = ckpt_path or self.resume_from_checkpoint > 685 return trainer_fn(*args, **kwargs)Ħ86 # TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7Ħ87 except KeyboardInterrupt as exception: usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _call_and_handle_interrupt(self, trainer_fn, *args, **kwargs) > 741 self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloaders, val_dataloaders, datamodule, train_dataloader, ckpt_path) > 2 trainer.fit(model, train_dl, valid_dl) Optimizer = (self.parameters(), lr=1e-3)ĭef training_step(self, train_batch, batch_idx):ĭef validation_step(self, val_batch, batch_idx):Įrror AttributeError Traceback (most recent call last) Model class AutoEncoder(pl.LightningModule): Train_dl, valid_dl = DataLoader(train_ds), DataLoader(valid_ds) Sample data data = np.random.rand(400, 46, 55, 46)ĭs = TensorDataset(om_numpy(data)) I have a pytorch which i am trying to train but i am getting this error AttributeError: 'list' object has no attribute 'view'.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |