TypeError: relu(): argument 'input' (position 1) must be Tensor, not float

[xiaoyu20010808]

Hi, thanks for the great work，I had some problems replicating the paper, when i run eval_dtu.sh, main.py has an error . on line 183, trainer.validate(volrecon, dataloader_test1).
[rank0]: Traceback (most recent call last):
[rank0]: File "/data/tx/volrecon/VolRecon-main/main.py", line 183, in
[rank0]: trainer.validate(volrecon, dataloader_test1)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 697, in validate
[rank0]: return call._call_and_handle_interrupt(
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 36, in _call_and_handle_interrupt
[rank0]: return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 88, in launch
[rank0]: return function(*args, **kwargs)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 745, in _validate_impl
[rank0]: results = self._run(model, ckpt_path=self.ckpt_path)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1112, in _run
[rank0]: results = self._run_stage()
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1188, in _run_stage
[rank0]: return self._run_evaluate()
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1228, in _run_evaluate
[rank0]: eval_loop_results = self._evaluation_loop.run()
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
[rank0]: self.advance(*args, **kwargs)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 152, in advance
[rank0]: dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
[rank0]: self.advance(*args, **kwargs)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 137, in advance
[rank0]: output = self._evaluation_step(**kwargs)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 234, in _evaluation_step
[rank0]: output = self.trainer._call_strategy_hook(hook_name, *kwargs.values())
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1494, in _call_strategy_hook
[rank0]: output = fn(*args, **kwargs)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 363, in validation_step
[rank0]: return self.model.validation_step(*args, **kwargs)
[rank0]: File "/data/tx/volrecon/VolRecon-main/code/model.py", line 265, in validation_step
[rank0]: self.extract_geometry(batch, batch_idx)
[rank0]: File "/data/tx/volrecon/VolRecon-main/code/model.py", line 375, in extract_geometry
[rank0]: srdf, points_x, depth, rgb = self.infer(batch=batch, ray_idx=ray_idx, source_imgs_feat=source_imgs_feat,
[rank0]: File "/data/tx/volrecon/VolRecon-main/code/model.py", line 143, in infer
[rank0]: rgb, depth, srdf, opacity, weight, points_in_pixel, _ = self.sample2rgb(batch, points_x, z_val, ray_d, ray_idx,
[rank0]: File "/data/tx/volrecon/VolRecon-main/code/model.py", line 85, in sample2rgb
[rank0]: rgb, depth, opacity, weight, variance = self.renderer.render(rearrange(z_val, "B RN SN -> (B RN) SN"),
[rank0]: File "/data/tx/volrecon/VolRecon-main/code/utils/renderer.py", line 32, in render
[rank0]: iter_cos = -(F.relu(-true_cos * 0.5 + 0.5) * (1.0 - cos_anneal_ratio) + F.relu(-true_cos) * cos_anneal_ratio)
[rank0]: File "/data/tx/Anaconda3/envs/UFORecon/lib/python3.10/site-packages/torch/nn/functional.py", line 1500, in relu
[rank0]: result = torch.relu(input)
[rank0]: TypeError: relu(): argument 'input' (position 1) must be Tensor, not float
I can't solve the problem right now, and I'd appreciate your help