Comments (3)
Hi, I'm trying to run the following command in prepare.sh, CUDA_VISIBLE_DEVICES=6,7 ./tools/dist_train.sh configs/lvis/prompt_save_train_reuse.py 2 --work-dir workdirs/prompt_save_train and meet some errors like
Traceback (most recent call last): File "./tools/train.py", line 199, in <module> main() File "./tools/train.py", line 188, in main train_detector( File "/home/ubuntu/work/code/detpro/mmdet/apis/train.py", line 151, in train_detector runner.run(data_loaders, cfg.workflow, cfg.total_epochs) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 125, in run epoch_runner(data_loaders[i], **kwargs) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train self.run_iter(data_batch, train_mode=True) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter outputs = self.model.train_step(data_batch, self.optimizer, File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/mmcv/parallel/distributed.py", line 46, in train_step output = self.module.train_step(*inputs[0], **kwargs[0]) File "/home/ubuntu/work/code/detpro/mmdet/models/detectors/base.py", line 246, in train_step losses = self(**data) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 84, in new_func return old_func(*args, **kwargs) File "/home/ubuntu/work/code/detpro/mmdet/models/detectors/base.py", line 180, in forward return self.forward_train(img,img_no_normalize, img_metas, **kwargs) File "/home/ubuntu/work/code/detpro/mmdet/models/detectors/mask_rcnn.py", line 83, in forward_train roi_losses = self.roi_head.forward_train(x, img, img_no_normalize, img_metas, proposal_list,proposals, File "/home/ubuntu/work/code/detpro/mmdet/models/roi_heads/standard_roi_head_collect_reuse.py", line 269, in forward_train bbox_results = self._bbox_forward_train(x,img,sampling_results,proposals_pre_computed, File "/home/ubuntu/work/code/detpro/mmdet/models/roi_heads/standard_roi_head_collect_reuse.py", line 364, in _bbox_forward_train bbox_results, region_embeddings = self._bbox_forward(x, rois) File "/home/ubuntu/work/code/detpro/mmdet/models/roi_heads/standard_roi_head_collect_reuse.py", line 305, in _bbox_forward bbox_pred = self.bbox_head(bbox_feats) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/ubuntu/work/code/detpro/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py", line 214, in forward bbox_pred = self.fc_reg(x_reg) if self.with_reg else None File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 93, in forward return F.linear(input, self.weight, self.bias) File "/home/ubuntu/anaconda3/envs/detpro/lib/python3.8/site-packages/torch/nn/functional.py", line 1690, in linear ret = torch.addmm(bias, input, weight.t()) RuntimeError: mat1 dim 1 must match mat2 dim 0
It looks like the dimension of feature does not match the dimension of weight in the BBoxHead module. I guess because the shared layer is commented out in forward pass of the 'ConvFCBBoxHead' module. Can you help check the code?
I have fixed, please check.
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I have fixed, please check.
Ok, I'll check it. BTW, I already run the prepare.sh and generated the 'lvis_clip_image_proposal_embedding' folder as below. Is this right? And will this folder be used if I just want to reproduce the results of ViLD?
lvis_clip_image_proposal_embedding
├── train
│ └── train2017
└── val
├── train2017
└── val2017
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I have fixed, please check.
Ok, I'll check it. BTW, I already run the prepare.sh and generated the 'lvis_clip_image_proposal_embedding' folder as below. Is this right? And will this folder be used if I just want to reproduce the results of ViLD?
lvis_clip_image_proposal_embedding ├── train │ └── train2017 └── val ├── train2017 └── val2017
- It is correct. 2) The zip file of this folder will be used.
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