Comments (9)
Hi,
Did you tried to train the model on any of the given dataset with vgg backbone? I think it would be better if you try that once just to ensure things are working on your system as I have not faced this problem till now.
Apart from that we have provided the direction for training on new dataset -> Just one addition in readme update both lib/model/utils/parser_func.py as well as lib/model/utils/parser_func_multi.py. I will integrate the code so from now ownwards there is just one update required.
I hope above thing help, however if it doesn't let me know.
Thanks
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I have changed the corresponding path in those functions and ensure they are right. May be it is too slow to train so it didn't appear anything. Since I have not downloaded the given dataset, could you give the link of the typical dataset so I can have a try.
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I continue to debug this project and find the image is not righted loaded. Then I encountered another problem when do RCNN_roi_crop in the forward process of faster_rcnn_SCL in this line:
pooled_feat = self.RCNN_roi_crop(base_feat, Variable(grid_yx).detach())
The error is:
torch.FatalError: aborting at /data/ztc/jinke/faster-rcnn.pytorch/lib/model/roi_crop/src/roi_crop_cuda.c:49
May i did not compile a right C file, could you help ? @ harsh-99
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I believe that you have not compiled all the files. Please make sure you have correct cuda and pytorch version and then follow -:
cd lib
sh make.sh
If you get some error while compiling, let me know.
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Now I can train this model, but the loss of rpn_cls becomes nan when epoch 1 iter 100/10000. By the way, i have decreased the learning rate to 0.0002. So what can the problem be? @harsh-99
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That usually occurs when the labelled dataset have some bounding box which have few indices in negative. There are few threads who have faced same problem while training object detection module.
Please refer to this -:
jwyang/faster-rcnn.pytorch#136
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May be in lib/dataset/pascal_voc.py, the corresponding code should be added.
if x1 < 0 or y1 < 0:
continue
if abs(x1 - x2) <= 100 or abs(y1-y2) <= 100:
continue
Moreover, should the learning rate be adjusted?
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Hi,
I have never faced any problems because of the learning rate and I have initialiesed lr in range of 1e-2 to 1e-4. About adding the given lines in the code, since that is dependent on dataset I don't think that's necessay to add for the dataset I have written code since I have not faced any such problems and if one follows the same instructions they also won't have any issues.
In case if you have any other issue please let me know else it would be great if you can close the issue.
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When I have trained a model and begin to test, the checkpoint can't be loaded rightly like this:
RuntimeError: Error(s) in loading state_dict for vgg16:
Missing key(s) in state_dict: "netD.conv1.weight", "netD.bn1.weight", "netD.bn1.bias", "netD.bn1.running_mean", "netD.bn1.running_var", "netD.conv2.weight", "netD.bn2.weight", "netD.bn2.bias", "netD.bn2.running_mean", "netD.bn2.running_var", "netD.conv3.weight", "netD.bn3.weight", "netD.bn3.bias", "netD.bn3.running_mean", "netD.bn3.running_var", "netD.fc.weight", "netD.fc.bias", "netD_pixel.conv1.weight", "netD_pixel.conv2.weight", "netD_pixel.conv3.weight".
Unexpected key(s) in state_dict: "netD_img.conv_image.weight", "netD_img.conv_image.bias", "netD_img.bn_image.weight", "netD_img.bn_image.bias", "netD_img.bn_image.running_mean", "netD_img.bn_image.running_var", "netD_img.bn_image.num_batches_tracked", "netD_img.fc_1_image.weight", "netD_img.fc_1_image.bias", "netD_img.bn_2.weight", "netD_img.bn_2.bias", "netD_img.bn_2.running_mean", "netD_img.bn_2.running_var", "netD_img.bn_2.num_batches_tracked", "netD_inst.fc_1_inst.weight", "netD_inst.fc_1_inst.bias", "netD_inst.fc_2_inst.weight", "netD_inst.fc_2_inst.bias", "netD_inst.bn.weight", "netD_inst.bn.bias", "netD_inst.bn.running_mean", "netD_inst.bn.running_var", "netD_inst.bn.num_batches_tracked".
What may be the reason for this phenomenon @harsh-99
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Related Issues (20)
- Experimental details HOT 1
- visualization of features from PASCAL to Clipart HOT 1
- Applying SCL on SSD HOT 1
- Can this method be used as unsupervised object detection, if yes then how to do object detection on unlabelled target domain data HOT 4
- How to do Object detection on target domain(ex: Clipart) that don't have annotation.xml label HOT 2
- Can I run this Solution in real time or without the need to train the target data HOT 3
- About cityscape and foggy dataset HOT 3
- Can i generate or train model using this method and run it on different dataset/images that is similar to target domain data but this new dataset don't exist in target domain data when it was trained
- Run trainval_net_SCL.py with custom dataset raises RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. HOT 4
- How can I add custom new class labels, lets say-x classes to a SCL trained model( which is already trained on y classes). So I do not replace those y classes and in total I have x+y class labels. HOT 1
- About sim10k dataset HOT 2
- Your implementation of domain adaptive faster rcnn performs better than paper values, what might be the reason? HOT 1
- About the trained model. HOT 1
- after filtering, there are 0 images HOT 1
- question of t-sne
- Heat Map
- About cityscape-->foggy cityscape test set
- Using multilple GPUs to accomplish distributed training HOT 1
- ImportError: No module named cython_bbox HOT 1
- File "/home/lty/anaconda3/envs/SWDA/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 301, in forward self.padding, self.dilation, self.groups) RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/THC/THCBlas.cu:249
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