LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
┏━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃   ┃ Name                   ┃ Type                   ┃ Params ┃ Mode  ┃ FLOPs ┃
┡━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ 0 │ embedding              │ MultiInputVectorEmbed… │  220 K │ train │     0 │
│ 1 │ encoder                │ JetEncoderWithPairwise │  332 K │ train │     0 │
│ 2 │ branch_decoders        │ ModuleList             │  4.8 M │ train │     0 │
│ 3 │ regression_decoder     │ RegressionDecoder      │      0 │ train │     0 │
│ 4 │ classification_decoder │ ClassificationDecoder  │      0 │ train │     0 │
│   │ other params           │ n/a                    │     12 │ n/a   │   n/a │
└───┴────────────────────────┴────────────────────────┴────────┴───────┴───────┘
Trainable params: 5.3 M
Non-trainable params: 50
Total params: 5.3 M
Total estimated model params size (MB): 21.374
Modules in train mode: 2326
Modules in eval mode: 0
Total FLOPs: 0
/opt/conda/lib/python3.12/site-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:434: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.
/opt/conda/lib/python3.12/site-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:434: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=7` in the `DataLoader` to improve performance.
Epoch 0/9  ━━━━━━━━━━━        18015/28497 2:13:29 • 1:18:57 2.21it/s v_num: quy8
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "/opt/conda/lib/python3.12/site-packages/spanet/train.py", line 277, in <module>
    main(**parser.parse_args().__dict__)
  File "/opt/conda/lib/python3.12/site-packages/spanet/train.py", line 207, in main
    trainer.fit(model, ckpt_path=checkpoint)
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 584, in fit
    call._call_and_handle_interrupt(
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py", line 49, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 630, in _fit_impl
    self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 1079, in _run
    results = self._run_stage()
              ^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/trainer.py", line 1123, in _run_stage
    self.fit_loop.run()
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py", line 217, in run
    self.advance()
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/fit_loop.py", line 469, in advance
    self.epoch_loop.run(self._data_fetcher)
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 153, in run
    self.advance(data_fetcher)
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 352, in advance
    batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 192, in run
    self._optimizer_step(batch_idx, closure)
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 270, in _optimizer_step
    call._call_lightning_module_hook(
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py", line 177, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/core/module.py", line 1368, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/core/optimizer.py", line 154, in step
    step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 239, in optimizer_step
    return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/plugins/precision/precision.py", line 123, in optimizer_step
    return optimizer.step(closure=closure, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/torch/optim/lr_scheduler.py", line 166, in wrapper
    return func.__get__(opt, opt.__class__)(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/torch/optim/optimizer.py", line 530, in wrapper
    out = func(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/torch/optim/optimizer.py", line 80, in _use_grad
    ret = func(*args, **kwargs)
          ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/torch/optim/adam.py", line 227, in step
    loss = closure()
           ^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/plugins/precision/precision.py", line 109, in _wrap_closure
    closure_result = closure()
                     ^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 146, in __call__
    self._result = self.closure(*args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 131, in closure
    step_output = self._step_fn()
                  ^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 319, in _training_step
    training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/trainer/call.py", line 329, in _call_strategy_hook
    output = fn(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/pytorch_lightning/strategies/strategy.py", line 391, in training_step
    return self.lightning_module.training_step(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/conda/lib/python3.12/site-packages/spanet/network/jet_reconstruction/jet_reconstruction_training.py", line 265, in training_step
    raise ValueError("Assignment targets contain a collision.")
ValueError: Assignment targets contain a collision.