Traceback (most recent call last):


 "dpbug.py", line 195, in <module>

run_test(args)


 "dpbug.py", line 185, in run_test

trainer.fit(model, train_data, val_data)


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 440, in fit

results = self.accelerator_backend.train()


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/accelerators/dp_accelerator.py", line 97, in train

results = self.train_or_test()


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 68, in train_or_test

results = self.trainer.train()


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 485, in train

self.train_loop.run_training_epoch()


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 544, in run_training_epoch

batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 713, in run_training_batch

self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 452, in optimizer_step

self.trainer.accelerator_backend.optimizer_step(


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 115, in optimizer_step

model_ref.optimizer_step(


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/core/lightning.py", line 1216, in optimizer_step

optimizer.step(closure=optimizer_closure)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/optim/lr_scheduler.py", line 67, in wrapper

return wrapped(*args, **kwargs)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 15, in decorate_context

return func(*args, **kwargs)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/optim/sgd.py", line 86, in step

loss = closure()


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 703, in train_step_and_backward_closure

result = self.training_step_and_backward(


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 798, in training_step_and_backward

result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 305, in training_step

training_step_output = self.trainer.accelerator_backend.training_step(args)


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/accelerators/dp_accelerator.py", line 111, in training_step

output = self.trainer.model(*args)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/overrides/data_parallel.py", line 87, in forward

outputs = self.parallel_apply(replicas, inputs, kwargs)


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/overrides/data_parallel.py", line 151, in parallel_apply

return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/overrides/data_parallel.py", line 310, in parallel_apply

raise output


 "/home/catalys1/pylt/lib/python3.8/site-packages/pytorch_lightning/overrides/data_parallel.py", line 263, in _worker

output = module.training_step(*input, **kwargs)


 "dpbug.py", line 147, in training_step

output = self(x)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "dpbug.py", line 138, in forward

x = self.net(x)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "dpbug.py", line 85, in forward

x = self.conv1(x)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 419, in forward

return self._conv_forward(input, self.weight)


 "/home/catalys1/pylt/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 415, in _conv_forward

return F.conv2d(input, weight, self.bias, self.stride,
RuntimeError: Expected tensor for argument #1 'input' to have the same device as tensor for argument #2 'weight'; but device 2 does not equal 1 (while checking arguments for cudnn_convolution)

Traceback (most recent call last):


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/demo/demo.py", line 142, in <module>

for vis_frame, boxes, labels, scores, masks, sem_seg in demo.run_on_video(video):


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/demo/predictor.py", line 131, in run_on_video

yield process_predictions(frame, self.predictor(frame))


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/engine/defaults.py", line 192, in __call__

predictions = self.model([inputs])[0]


 "/home/sarim/PycharmProjects/trajectory_pred/venv3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/modeling/meta_arch/rcnn.py", line 107, in forward

return self.inference(batched_inputs)


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/modeling/meta_arch/rcnn.py", line 169, in inference

results, _ = self.roi_heads(images, features, proposals, None)


 "/home/sarim/PycharmProjects/trajectory_pred/venv3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/modeling/roi_heads/cascade_rcnn.py", line 98, in forward

pred_instances = self.forward_with_given_boxes(features, pred_instances)


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/modeling/roi_heads/roi_heads.py", line 600, in forward_with_given_boxes

instances = self._forward_mask(features, instances)


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/modeling/roi_heads/roi_heads.py", line 666, in _forward_mask

mask_logits = self.mask_head(mask_features)


 "/home/sarim/PycharmProjects/trajectory_pred/venv3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/modeling/roi_heads/mask_head.py", line 196, in forward

x = layer(x)


 "/home/sarim/PycharmProjects/trajectory_pred/venv3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/sarim/PycharmProjects/trajectory_pred/detectron2-master/detectron2/layers/wrappers.py", line 69, in forward

x = self.norm(x)


 "/home/sarim/PycharmProjects/trajectory_pred/venv3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__

result = self.forward(*input, **kwargs)


 "/home/sarim/PycharmProjects/trajectory_pred/venv3.7/lib/python3.7/site-packages/torch/nn/modules/normalization.py", line 226, in forward

input, self.num_groups, self.weight, self.bias, self.eps)


 "/home/sarim/PycharmProjects/trajectory_pred/venv3.7/lib/python3.7/site-packages/torch/nn/functional.py", line 1706, in group_norm

torch.backends.cudnn.enabled)
RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
