smp.Unet(               encoder_name='resnet18',                  encoder_weights=None,                 in_channels=3,                      classes=59,                 activation='softmax2d' )

---------------------------------------------------------------------------RuntimeError                              Traceback (most recent call last)<timed eval> in <module>/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)    458         )    459 --> 460         self._run(model)    461     462         assert self.state.stopped/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in _run(self, model)    756     757         # dispatch `start_training` or `start_evaluating` or `start_predicting`--> 758         self.dispatch()    759     760         # plugin will finalized fitting (e.g. ddp_spawn will load trained model)/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)    797             self.accelerator.start_predicting(self)    798         else:--> 799             self.accelerator.start_training(self)    800     801     def run_stage(self):/opt/conda/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)     94      95     def start_training(self, trainer: 'pl.Trainer') -> None:---> 96         self.training_type_plugin.start_training(trainer)     97      98     def start_evaluating(self, trainer: 'pl.Trainer') -> None:/opt/conda/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)    142     def start_training(self, trainer: 'pl.Trainer') -> None:    143         # double dispatch to initiate the training loop--> 144         self._results = trainer.run_stage()    145     146     def start_evaluating(self, trainer: 'pl.Trainer') -> None:/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_stage(self)    807         if self.predicting:    808             return self.run_predict()--> 809         return self.run_train()    810     811     def _pre_training_routine(self):/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)    842             self.progress_bar_callback.disable()    843 --> 844         self.run_sanity_check(self.lightning_module)    845     846         self.checkpoint_connector.has_trained = False/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_sanity_check(self, ref_model)   1110    1111             # run eval step-> 1112             self.run_evaluation()   1113    1114             self.on_sanity_check_end()/opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, on_epoch)    965                 # lightning module methods    966                 with self.profiler.profile("evaluation_step_and_end"):--> 967                     output = self.evaluation_loop.evaluation_step(batch, batch_idx, dataloader_idx)    968                     output = self.evaluation_loop.evaluation_step_end(output)    969 /opt/conda/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py in evaluation_step(self, batch, batch_idx, dataloader_idx)    172             model_ref._current_fx_name = "validation_step"    173             with self.trainer.profiler.profile("validation_step"):--> 174                 output = self.trainer.accelerator.validation_step(args)    175     176         # capture any logged information/opt/conda/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in validation_step(self, args)    224     225         with self.precision_plugin.val_step_context(), self.training_type_plugin.val_step_context():--> 226             return self.training_type_plugin.validation_step(*args)    227     228     def test_step(self, args: List[Union[Any, int]]) -> Optional[STEP_OUTPUT]:/opt/conda/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in validation_step(self, *args, **kwargs)    159     160     def validation_step(self, *args, **kwargs):--> 161         return self.lightning_module.validation_step(*args, **kwargs)    162     163     def test_step(self, *args, **kwargs):<ipython-input-148-9c5092280cd4> in validation_step(self, batch, batch_idx)     54     outputs=self(image)     55     print('before',outputs.shape,segment.shape,segment.max())---> 56     loss=self.criterion(outputs,segment)     57     print('loss',loss)     58 #     dice=dice_coef_multilabel(labels,segment)/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)    725             result = self._slow_forward(*input, **kwargs)    726         else:--> 727             result = self.forward(*input, **kwargs)    728         for hook in itertools.chain(    729                 _global_forward_hooks.values(),/opt/conda/lib/python3.7/site-packages/segmentation_models_pytorch/losses/dice.py in forward(self, y_pred, y_true)     93                 y_true = y_true.permute(0, 2, 1) * mask.unsqueeze(1)  # H, C, H*W     94             else:---> 95                 y_true = F.one_hot(y_true, num_classes)  # N,H*W -> N,H*W, C     96                 y_true = y_true.permute(0, 2, 1)  # H, C, H*W     97 RuntimeError: one_hot is only applicable to index tensor.