ValueError: The `target` has to be an integer tensor.

[mengdexing]

Hi When I do linear evaluation: Traceback (most recent call last): File "linear_evaluation.py", line 149, in trainer.fit(module, train_loader, valid_loader) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 741, in fit self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1199, in _run self._dispatch() File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1279, in _dispatch self.training_type_plugin.start_training(self) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 202, in start_training self._results = trainer.run_stage() File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1289, in run_stage return self._run_train() File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1311, in _run_train self._run_sanity_check(self.lightning_module) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 1375, in _run_sanity_check self._evaluation_loop.run() File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/loops/base.py", line 145, in run self.advance(*args, **kwargs) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 110, in advance dl_outputs = self.epoch_loop.run(dataloader, dataloader_idx, dl_max_batches, self.num_dataloaders) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/loops/base.py", line 145, in run self.advance(*args, **kwargs) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 122, in advance output = self._evaluation_step(batch, batch_idx, dataloader_idx) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 217, in _evaluation_step output = self.trainer.accelerator.validation_step(step_kwargs) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 239, in validation_step return self.training_type_plugin.validation_step(*step_kwargs.values()) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 219, in validation_step return self.model.validation_step(*args, **kwargs) File "/data1/simon/projects/musicrepresentations/clmr/modules/linear_evaluation.py", line 64, in validation_step self.log("Valid/accuracy", self.accuracy(preds, y)) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/torchmetrics/metric.py", line 205, in forward self.update(*args, **kwargs) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/torchmetrics/metric.py", line 263, in wrapped_func return update(*args, **kwargs) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/torchmetrics/classification/accuracy.py", line 228, in update mode = _mode(preds, target, self.threshold, self.top_k, self.num_classes, self.multiclass) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/torchmetrics/functional/classification/accuracy.py", line 59, in _mode preds, target, threshold=threshold, top_k=top_k, num_classes=num_classes, multiclass=multiclass File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/torchmetrics/utilities/checks.py", line 251, in _check_classification_inputs _basic_input_validation(preds, target, threshold, multiclass) File "/data1/anaconda3/envs/CLMR/lib/python3.6/site-packages/torchmetrics/utilities/checks.py", line 33, in _basic_input_validation raise ValueError("The target has to be an integer tensor.") ValueError: The target has to be an integer tensor.

[mengdexing]

@Spijkervet Looking for your reply! Thanks a lot

[Ramlinbird]

@Spijkervet Looking for your reply! Thanks a lot

I met this error also, just change the target datatype to long inside the accuracy calcation logic works.