mxbonn / anchor_pruning Goto Github PK

Traceback (most recent call last):
File "tools/mmdet_train.py", line 239, in
main()
File "tools/mmdet_train.py", line 235, in main
meta=meta)
File "/home/featurize/mmdetection-2.25.0/mmdet/apis/train.py", line 244, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/environment/miniconda3/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/environment/miniconda3/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/environment/miniconda3/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter
**kwargs)
File "/environment/miniconda3/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 75, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/home/featurize/mmdetection-2.25.0/mmdet/models/detectors/base.py", line 248, in train_step
losses = self(**data)
File "/environment/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/environment/miniconda3/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func
return old_func(*args, **kwargs)
File "/home/featurize/mmdetection-2.25.0/mmdet/models/detectors/base.py", line 172, in forward
return self.forward_train(img, img_metas, **kwargs)
File "/home/featurize/mmdetection-2.25.0/mmdet/models/detectors/single_stage.py", line 84, in forward_train
gt_labels, gt_bboxes_ignore)
File "/home/featurize/mmdetection-2.25.0/mmdet/models/dense_heads/base_dense_head.py", line 335, in forward_train
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
File "/environment/miniconda3/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 186, in new_func
return old_func(*args, **kwargs)
File "/home/featurize/mmdetection-2.25.0/mmdet/models/dense_heads/anchor_head.py", line 519, in loss
num_total_samples=num_total_samples)
File "/home/featurize/mmdetection-2.25.0/mmdet/core/utils/misc.py", line 30, in multi_apply
return tuple(map(list, zip(*map_results)))
File "/home/featurize/mmdetection-2.25.0/mmdet/models/dense_heads/anchor_head.py", line 434, in loss_single
cls_score, labels, label_weights, avg_factor=num_total_samples)
File "/environment/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/featurize/mmdetection-2.25.0/mmdet/models/losses/focal_loss.py", line 240, in forward
avg_factor=avg_factor)
File "/home/featurize/mmdetection-2.25.0/mmdet/models/losses/focal_loss.py", line 140, in sigmoid_focal_loss
alpha, None, 'none')
File "/environment/miniconda3/lib/python3.7/site-packages/mmcv/ops/focal_loss.py", line 56, in forward
input, target, weight, output, gamma=ctx.gamma, alpha=ctx.alpha)
RuntimeError: max(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.

When I attempted to modify the code to retinanet-resnet18, the above information appeared, and there was only one point in the resulting graph.