* Various built-in models including generic ones. The cartoons can be exported as video and shared online. Another important feature is an interactive timeline element that allows you easily remake unsuccessful moves and pieces of narration The app employs a unique technology of deforming objects that enables authors to make naturally-looking animations with just a few gestures. Come up with stories about adventures in space, underwater or on your back yard. After selecting the setting and music just press the record button and start moving characters around the screen while speaking a narrative.Īuthors can choose among numerous embedded characters including generic ones which can be customized with camera. > 219 return, Animator provides an easy way to create your own animated movies. 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) Also included youll find a few words of wisdom as well as some general information for the A/UX community. 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) This FAQ list is intended to cut down on the number of 'often asked questions' that make the rounds on and to bring the official FAQ into the 21st century. 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'.