train.fit_sequence_module(
in_memory=True,
train_var="train_val",
... model=model,
... sdata=sd_train,
... seq_var="ohe_seq",
... target_vars= "id_x",
... in_memory=True,
... train_var="train_val",
... epochs=25,
... gpus=1,
... batch_size=9,
... num_workers=4,
... prefetch_factor=2,
... drop_last=False,
... name="LTRidentity",
... version="0.75",
... transforms={"ohe_seq": lambda x: x.swapaxes(1, 2)}
... )
Dropping 0 sequences with NaN targets.
Loading ohe_seq and ['id_x'] into memory
No seed set
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
-------------------------------------------------
0 | arch | SmallCNN | 2.8 K
1 | train_metric | MulticlassAUROC | 0
2 | val_metric | MulticlassAUROC | 0
3 | test_metric | MulticlassAUROC | 0
-------------------------------------------------
2.8 K Trainable params
0 Non-trainable params
2.8 K Total params
0.011 Total estimated model params size (MB)
Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s]/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/eugene/models/_SequenceModule.py:203: UserWarning: Using a target size (torch.Size([9])) that is different to the input size (torch.Size([9, 9])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
loss = self.loss_fxn(outs, y) # train
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/eugene/train/_fit.py", line 273, in fit_sequence_module
trainer = fit(
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/eugene/train/_fit.py", line 123, in fit
trainer.fit(
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 544, in fit
call._call_and_handle_interrupt(
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 44, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 580, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 989, in _run
results = self._run_stage()
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1033, in _run_stage
self._run_sanity_check()
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1062, in _run_sanity_check
val_loop.run()
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/loops/utilities.py", line 182, in _decorator
return loop_run(self, *args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 134, in run
self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 391, in _evaluation_step
output = call._call_strategy_hook(trainer, hook_name, *step_args)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 309, in _call_strategy_hook
output = fn(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/pytorch_lightning/strategies/strategy.py", line 403, in validation_step
return self.lightning_module.validation_step(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/eugene/models/_SequenceModule.py", line 228, in validation_step
calculate_metric(
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/eugene/models/base/_metrics.py", line 53, in calculate_metric
metric(outs, y)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/torchmetrics/metric.py", line 298, in forward
self._forward_cache = self._forward_reduce_state_update(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/torchmetrics/metric.py", line 367, in _forward_reduce_state_update
self.update(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/torchmetrics/metric.py", line 460, in wrapped_func
update(*args, **kwargs)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/torchmetrics/classification/precision_recall_curve.py", line 345, in update
_multiclass_precision_recall_curve_tensor_validation(preds, target, self.num_classes, self.ignore_index)
File "/home/xiongzx/.conda/envs/eugene/lib/python3.9/site-packages/torchmetrics/functional/classification/precision_recall_curve.py", line 394, in _multiclass_precision_recall_curve_tensor_validation
raise ValueError(
**ValueError: Expected argument `target` to be an int or long tensor, but got tensor with dtype torch.float32**sd_train
<xarray.Dataset>
Dimensions: (_sequence: 20466, _length: 300, length: 300, _ohe: 4)
Dimensions without coordinates: _sequence, _length, length, _ohe
Data variables:
qseqid_x (_sequence) object dask.array<chunksize=(91,), meta=np.ndarray>
seq (_sequence, _length) |S1 dask.array<chunksize=(91, 300), meta=np.ndarray>
set (_sequence) object dask.array<chunksize=(91,), meta=np.ndarray>
spe (_sequence) object dask.array<chunksize=(91,), meta=np.ndarray>
sseqid (_sequence) object dask.array<chunksize=(91,), meta=np.ndarray>
id (_sequence) <U8 'seq00000' 'seq00001' ... 'seq22738' 'seq22739'
ohe_seq (_sequence, length, _ohe) uint8 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0
id_x (_sequence) int8 0 1 1 0 1 0 0 0 1 0 0 ... 1 1 1 1 1 1 1 0 1 0 1
train_val (_sequence) bool False True True False ... True False True False
**target (_sequence) int8 0 1 1 0 1 0 0 0 1 0 0 ... 1 1 1 1 1 1 1 0 1 0 1**
>>> **sd_train['target']
<xarray.DataArray 'target' (_sequence: 20466)>
array([0, 1, 1, ..., 1, 0, 1], dtype=int8)
Dimensions without coordinates: _sequence**
but
My target var is int ，why this error happen？