RuntimeError: The size of tensor a (3) must match the size of tensor b (128) at non-singleton dimension 1

[nasim-ahmed]

I am trying to run "Dynamic LSTM-GNN" as mentioned in the training using the follwing command "python3 -m train_dynamic --bilstm --random_g --ts_mask --add_flat --class_weights --gnn_name mpnn --read_bes " Here is the stack trace of the error.. File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/usr/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/content/drive/MyDrive/eICU/train_dynamic.py", line 286, in main(config) File "/content/drive/MyDrive/eICU/train_dynamic.py", line 205, in main trainer.fit(model) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 771, in fit self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 723, in _call_and_handle_interrupt return trainer_fn(*args, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl results = self._run(model, ckpt_path=self.ckpt_path) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1236, in _run results = self._run_stage() File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage return self._run_train() File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1353, in _run_train self.fit_loop.run() File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run self.advance(*args, *kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/fit_loop.py", line 269, in advance self._outputs = self.epoch_loop.run(self._data_fetcher) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run self.advance(args, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 208, in advance batch_output = self.batch_loop.run(batch, batch_idx) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run self.advance(*args, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 88, in advance outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/base.py", line 204, in run self.advance(*args, *kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 207, in advance self.optimizer_idx, File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 256, in _run_optimization self._optimizer_step(optimizer, opt_idx, batch_idx, closure) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 378, in _optimizer_step using_lbfgs=is_lbfgs, File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1595, in _call_lightning_module_hook output = fn(args, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/lightning.py", line 1646, in optimizer_step optimizer.step(closure=optimizer_closure) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/optimizer.py", line 168, in step step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/strategies/strategy.py", line 193, in optimizer_step return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 155, in optimizer_step return optimizer.step(closure=closure, kwargs) File "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py", line 65, in wrapper return wrapped(*args, *kwargs) File "/usr/local/lib/python3.7/dist-packages/torch/optim/optimizer.py", line 88, in wrapper return func(args, kwargs) File "/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, kwargs) File "/usr/local/lib/python3.7/dist-packages/torch/optim/adam.py", line 100, in step loss = closure() File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 140, in _wrap_closure closure_result = closure() File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 148, in call self._result = self.closure(*args, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 134, in closure step_output = self._step_fn() File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loops/optimization/optimizer_loop.py", line 427, in _training_step training_step_output = self.trainer._call_strategy_hook("training_step", step_kwargs.values()) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 1765, in _call_strategy_hook output = fn(args, kwargs) File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/strategies/strategy.py", line 333, in training_step return self.model.training_step(*args, kwargs) File "/content/drive/MyDrive/eICU/train_dynamic.py", line 59, in training_step pred, pred_lstm = self(seq, flat) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(*input, *kwargs) File "/content/drive/MyDrive/eICU/train_dynamic.py", line 47, in forward out = self.net(seq, flat=flat) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1110, in _call_impl return forward_call(input, **kwargs) File "/content/drive/MyDrive/eICU/src/models/dgnn.py", line 91, in forward edge_index, edge_weights = self.knn_to_graph(out) File "/content/drive/MyDrive/eICU/src/models/dgnn.py", line 59, in knn_to_graph us, vs, vals = self.get_u_v(k_closest[1], k_closest[0]) File "/content/drive/MyDrive/eICU/src/models/dgnn.py", line 74, in get_u_v reflected_int = edges_int + edges_int.transpose(1, 0)

[EmmaRocheteau]

Hi! I have some exams this week but will get back to you when I can :)

[EmmaRocheteau]

Hello! Did you manage to solve this in the end? I will look into it if not

[nasim-ahmed]

Hi, sorry for the late response. I couldn't solve the issue.Could you please take a look into the issue? Thanks