Official code of ECCV 2020 paper "GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision". GSNet performs joint vehicle pose estimation and vehicle shape reconstruction with single RGB image as input.
Home Page: https://arxiv.org/abs/2007.13124
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
I am trying to run bash.run, unsuccessfully.
I get an attribute Error:
AttributeError: Cannot find field 'predict_rotation' in the given Instances!
There are other fields missing as well...
Any ideas?
Thanks!
I'm trying to run the run.sh script in order to give a try to the provided demo, but I got the following error
File "/usr/local/lib/python3.7/dist-packages/detectron2/structures/instances.py", line 65, in getattr
raise AttributeError("Cannot find field '{}' in the given Instances!".format(name))
AttributeError: Cannot find field 'predict_trans' in the given Instances!
My environment :
torchvision=0.6.0
CUDA = cu10.1
torch=1.5.0
First of all, I'd like to thank you for your code.
I followed instructions in README, but I have a problem with the demo caused by an error below:
[10/29 03:45:35 detectron2]: Arguments: Namespace(confidence_threshold=0.5, config_file='configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x_apollo.yaml', input=['datasets/coco/val_apollo/'], opts=['MODEL.WEIGHTS', './output/model_final.pth'], output='medium_result_val/', video_input=None, webcam=False)
Traceback (most recent call last):
File "demo/demo.py", line 73, in <module>
cfg = setup_cfg(args)
File "demo/demo.py", line 28, in setup_cfg
cfg.MODEL.FCOS.INFERENCE_TH = args.confidence_threshold
File "/home/azureuser/.virtualenvs/py36/lib/python3.6/site-packages/yacs/config.py", line 141, in __getattr__
raise AttributeError(name)
AttributeError: FCOS
Could you help point out what I should do to make it run successfully? Thanks in advance.
Hi there. When I run the demo, I get the warnings below. Like this one:
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: Shape of roi_heads.box_predictor.bbox_pred.bias in checkpoint is torch.Size([4]), while shape of roi_heads.box_predictor.bbox_pred.bias in model is torch.Size([316]).
Can you tell me what is going on here? The predictions are empty also:
Predicitons: {'instances': Instances(num_instances=0, image_height=563, image_width=750, fields=[pred_boxes: Boxes(tensor([], device='cuda:0', size=(0, 4))), scores: tensor([], device='cuda:0'), pred_classes: tensor([], device='cuda:0', dtype=torch.int64), pred_keypoints: tensor([], device='cuda:0', size=(0, 17, 3)), pred_keypoint_heatmaps: tensor([], device='cuda:0', size=(0, 17, 56, 56))])}
[09/23 05:51:08 detectron2]: /content/example_images//licenseplates-20220825-0034-768.jpg: detected 0 instances in 1.25s
/content/gsnet/reference_code/GSNet-release
[09/23 05:51:03 detectron2]: Arguments: Namespace(confidence_threshold=0.5, config_file='configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x_apollo.yaml', input=['/content/example_images/'], opts=['MODEL.WEIGHTS', './output/model_final.pth'], output='medium_results_val/', video_input=None, webcam=False)
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: Shape of roi_heads.box_predictor.bbox_pred.bias in checkpoint is torch.Size([4]), while shape of roi_heads.box_predictor.bbox_pred.bias in model is torch.Size([316]).
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: roi_heads.box_predictor.bbox_pred.bias will not be loaded. Please double check and see if this is desired.
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: Shape of roi_heads.box_predictor.bbox_pred.weight in checkpoint is torch.Size([4, 1024]), while shape of roi_heads.box_predictor.bbox_pred.weight in model is torch.Size([316, 1024]).
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: roi_heads.box_predictor.bbox_pred.weight will not be loaded. Please double check and see if this is desired.
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: Shape of roi_heads.keypoint_head.score_lowres.bias in checkpoint is torch.Size([66]), while shape of roi_heads.keypoint_head.score_lowres.bias in model is torch.Size([17]).
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: roi_heads.keypoint_head.score_lowres.bias will not be loaded. Please double check and see if this is desired.
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: Shape of roi_heads.keypoint_head.score_lowres.weight in checkpoint is torch.Size([512, 66, 4, 4]), while shape of roi_heads.keypoint_head.score_lowres.weight in model is torch.Size([512, 17, 4, 4]).
WARNING [09/23 05:51:06 d2.checkpoint.c2_model_loading]: roi_heads.keypoint_head.score_lowres.weight will not be loaded. Please double check and see if this is desired.
[09/23 05:51:06 d2.checkpoint.c2_model_loading]: Following weights matched with model:
| Names in Model | Names in Checkpoint | Shapes |
|:------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------|
| backbone.bottom_up.res2.0.conv1.* | backbone.bottom_up.res2.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,1,1) |
| backbone.bottom_up.res2.0.conv2.* | backbone.bottom_up.res2.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |
| backbone.bottom_up.res2.0.conv3.* | backbone.bottom_up.res2.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| backbone.bottom_up.res2.0.shortcut.* | backbone.bottom_up.res2.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| backbone.bottom_up.res2.1.conv1.* | backbone.bottom_up.res2.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,256,1,1) |
| backbone.bottom_up.res2.1.conv2.* | backbone.bottom_up.res2.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |
| backbone.bottom_up.res2.1.conv3.* | backbone.bottom_up.res2.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| backbone.bottom_up.res2.2.conv1.* | backbone.bottom_up.res2.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,256,1,1) |
| backbone.bottom_up.res2.2.conv2.* | backbone.bottom_up.res2.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,64,3,3) |
| backbone.bottom_up.res2.2.conv3.* | backbone.bottom_up.res2.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1) |
| backbone.bottom_up.res3.0.conv1.* | backbone.bottom_up.res3.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,256,1,1) |
| backbone.bottom_up.res3.0.conv2.* | backbone.bottom_up.res3.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| backbone.bottom_up.res3.0.conv3.* | backbone.bottom_up.res3.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| backbone.bottom_up.res3.0.shortcut.* | backbone.bottom_up.res3.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,256,1,1) |
| backbone.bottom_up.res3.1.conv1.* | backbone.bottom_up.res3.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |
| backbone.bottom_up.res3.1.conv2.* | backbone.bottom_up.res3.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| backbone.bottom_up.res3.1.conv3.* | backbone.bottom_up.res3.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| backbone.bottom_up.res3.2.conv1.* | backbone.bottom_up.res3.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |
| backbone.bottom_up.res3.2.conv2.* | backbone.bottom_up.res3.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| backbone.bottom_up.res3.2.conv3.* | backbone.bottom_up.res3.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| backbone.bottom_up.res3.3.conv1.* | backbone.bottom_up.res3.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,512,1,1) |
| backbone.bottom_up.res3.3.conv2.* | backbone.bottom_up.res3.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (128,) (128,) (128,) (128,) (128,128,3,3) |
| backbone.bottom_up.res3.3.conv3.* | backbone.bottom_up.res3.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,128,1,1) |
| backbone.bottom_up.res4.0.conv1.* | backbone.bottom_up.res4.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,512,1,1) |
| backbone.bottom_up.res4.0.conv2.* | backbone.bottom_up.res4.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.0.conv3.* | backbone.bottom_up.res4.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.0.shortcut.* | backbone.bottom_up.res4.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1) |
| backbone.bottom_up.res4.1.conv1.* | backbone.bottom_up.res4.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.1.conv2.* | backbone.bottom_up.res4.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.1.conv3.* | backbone.bottom_up.res4.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.10.conv1.* | backbone.bottom_up.res4.10.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.10.conv2.* | backbone.bottom_up.res4.10.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.10.conv3.* | backbone.bottom_up.res4.10.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.11.conv1.* | backbone.bottom_up.res4.11.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.11.conv2.* | backbone.bottom_up.res4.11.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.11.conv3.* | backbone.bottom_up.res4.11.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.12.conv1.* | backbone.bottom_up.res4.12.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.12.conv2.* | backbone.bottom_up.res4.12.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.12.conv3.* | backbone.bottom_up.res4.12.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.13.conv1.* | backbone.bottom_up.res4.13.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.13.conv2.* | backbone.bottom_up.res4.13.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.13.conv3.* | backbone.bottom_up.res4.13.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.14.conv1.* | backbone.bottom_up.res4.14.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.14.conv2.* | backbone.bottom_up.res4.14.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.14.conv3.* | backbone.bottom_up.res4.14.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.15.conv1.* | backbone.bottom_up.res4.15.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.15.conv2.* | backbone.bottom_up.res4.15.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.15.conv3.* | backbone.bottom_up.res4.15.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.16.conv1.* | backbone.bottom_up.res4.16.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.16.conv2.* | backbone.bottom_up.res4.16.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.16.conv3.* | backbone.bottom_up.res4.16.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.17.conv1.* | backbone.bottom_up.res4.17.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.17.conv2.* | backbone.bottom_up.res4.17.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.17.conv3.* | backbone.bottom_up.res4.17.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.18.conv1.* | backbone.bottom_up.res4.18.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.18.conv2.* | backbone.bottom_up.res4.18.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.18.conv3.* | backbone.bottom_up.res4.18.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.19.conv1.* | backbone.bottom_up.res4.19.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.19.conv2.* | backbone.bottom_up.res4.19.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.19.conv3.* | backbone.bottom_up.res4.19.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.2.conv1.* | backbone.bottom_up.res4.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.2.conv2.* | backbone.bottom_up.res4.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.2.conv3.* | backbone.bottom_up.res4.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.20.conv1.* | backbone.bottom_up.res4.20.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.20.conv2.* | backbone.bottom_up.res4.20.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.20.conv3.* | backbone.bottom_up.res4.20.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.21.conv1.* | backbone.bottom_up.res4.21.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.21.conv2.* | backbone.bottom_up.res4.21.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.21.conv3.* | backbone.bottom_up.res4.21.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.22.conv1.* | backbone.bottom_up.res4.22.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.22.conv2.* | backbone.bottom_up.res4.22.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.22.conv3.* | backbone.bottom_up.res4.22.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.3.conv1.* | backbone.bottom_up.res4.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.3.conv2.* | backbone.bottom_up.res4.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.3.conv3.* | backbone.bottom_up.res4.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.4.conv1.* | backbone.bottom_up.res4.4.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.4.conv2.* | backbone.bottom_up.res4.4.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.4.conv3.* | backbone.bottom_up.res4.4.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.5.conv1.* | backbone.bottom_up.res4.5.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.5.conv2.* | backbone.bottom_up.res4.5.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.5.conv3.* | backbone.bottom_up.res4.5.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.6.conv1.* | backbone.bottom_up.res4.6.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.6.conv2.* | backbone.bottom_up.res4.6.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.6.conv3.* | backbone.bottom_up.res4.6.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.7.conv1.* | backbone.bottom_up.res4.7.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.7.conv2.* | backbone.bottom_up.res4.7.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.7.conv3.* | backbone.bottom_up.res4.7.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.8.conv1.* | backbone.bottom_up.res4.8.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.8.conv2.* | backbone.bottom_up.res4.8.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.8.conv3.* | backbone.bottom_up.res4.8.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res4.9.conv1.* | backbone.bottom_up.res4.9.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,1024,1,1) |
| backbone.bottom_up.res4.9.conv2.* | backbone.bottom_up.res4.9.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,256,3,3) |
| backbone.bottom_up.res4.9.conv3.* | backbone.bottom_up.res4.9.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,256,1,1) |
| backbone.bottom_up.res5.0.conv1.* | backbone.bottom_up.res5.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,1024,1,1) |
| backbone.bottom_up.res5.0.conv2.* | backbone.bottom_up.res5.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |
| backbone.bottom_up.res5.0.conv3.* | backbone.bottom_up.res5.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |
| backbone.bottom_up.res5.0.shortcut.* | backbone.bottom_up.res5.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |
| backbone.bottom_up.res5.1.conv1.* | backbone.bottom_up.res5.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,2048,1,1) |
| backbone.bottom_up.res5.1.conv2.* | backbone.bottom_up.res5.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |
| backbone.bottom_up.res5.1.conv3.* | backbone.bottom_up.res5.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |
| backbone.bottom_up.res5.2.conv1.* | backbone.bottom_up.res5.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,2048,1,1) |
| backbone.bottom_up.res5.2.conv2.* | backbone.bottom_up.res5.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,512,3,3) |
| backbone.bottom_up.res5.2.conv3.* | backbone.bottom_up.res5.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,512,1,1) |
| backbone.bottom_up.stem.conv1.* | backbone.bottom_up.stem.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (64,) (64,) (64,) (64,) (64,3,7,7) |
| backbone.fpn_lateral2.* | backbone.fpn_lateral2.{bias,weight} | (256,) (256,256,1,1) |
| backbone.fpn_lateral3.* | backbone.fpn_lateral3.{bias,weight} | (256,) (256,512,1,1) |
| backbone.fpn_lateral4.* | backbone.fpn_lateral4.{bias,weight} | (256,) (256,1024,1,1) |
| backbone.fpn_lateral5.* | backbone.fpn_lateral5.{bias,weight} | (256,) (256,2048,1,1) |
| backbone.fpn_output2.* | backbone.fpn_output2.{bias,weight} | (256,) (256,256,3,3) |
| backbone.fpn_output3.* | backbone.fpn_output3.{bias,weight} | (256,) (256,256,3,3) |
| backbone.fpn_output4.* | backbone.fpn_output4.{bias,weight} | (256,) (256,256,3,3) |
| backbone.fpn_output5.* | backbone.fpn_output5.{bias,weight} | (256,) (256,256,3,3) |
| proposal_generator.rpn_head.anchor_deltas.* | proposal_generator.rpn_head.anchor_deltas.{bias,weight} | (12,) (12,256,1,1) |
| proposal_generator.rpn_head.conv.* | proposal_generator.rpn_head.conv.{bias,weight} | (256,) (256,256,3,3) |
| proposal_generator.rpn_head.objectness_logits.* | proposal_generator.rpn_head.objectness_logits.{bias,weight} | (3,) (3,256,1,1) |
| roi_heads.box_head.fc1.* | roi_heads.box_head.fc1.{bias,weight} | (1024,) (1024,12544) |
| roi_heads.box_head.fc2.* | roi_heads.box_head.fc2.{bias,weight} | (1024,) (1024,1024) |
| roi_heads.box_predictor.cls_score.* | roi_heads.box_predictor.cls_score.{bias,weight} | (80,) (80,1024) |
| roi_heads.keypoint_head.conv_fcn1.* | roi_heads.keypoint_head.conv_fcn1.{bias,weight} | (512,) (512,256,3,3) |
| roi_heads.keypoint_head.conv_fcn2.* | roi_heads.keypoint_head.conv_fcn2.{bias,weight} | (512,) (512,512,3,3) |
| roi_heads.keypoint_head.conv_fcn3.* | roi_heads.keypoint_head.conv_fcn3.{bias,weight} | (512,) (512,512,3,3) |
| roi_heads.keypoint_head.conv_fcn4.* | roi_heads.keypoint_head.conv_fcn4.{bias,weight} | (512,) (512,512,3,3) |
| roi_heads.keypoint_head.conv_fcn5.* | roi_heads.keypoint_head.conv_fcn5.{bias,weight} | (512,) (512,512,3,3) |
| roi_heads.keypoint_head.conv_fcn6.* | roi_heads.keypoint_head.conv_fcn6.{bias,weight} | (512,) (512,512,3,3) |
| roi_heads.keypoint_head.conv_fcn7.* | roi_heads.keypoint_head.conv_fcn7.{bias,weight} | (512,) (512,512,3,3) |
| roi_heads.keypoint_head.conv_fcn8.* | roi_heads.keypoint_head.conv_fcn8.{bias,weight} | (512,) (512,512,3,3) |
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (4,) in the checkpoint but (316,) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (4, 1024) in the checkpoint but (316, 1024) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.keypoint_head.score_lowres.bias' to the model due to incompatible shapes: (66,) in the checkpoint but (17,) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.keypoint_head.score_lowres.weight' to the model due to incompatible shapes: (512, 66, 4, 4) in the checkpoint but (512, 17, 4, 4) in the model! You might want to double check if this is expected.
Some model parameters or buffers are not found in the checkpoint:
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.keypoint_head.score_lowres.{bias, weight}
The checkpoint state_dict contains keys that are not used by the model:
roi_heads.fuse_box_pos.{bias, weight}
roi_heads.fuse_box_pos_1.{bias, weight}
roi_heads.fuse_heatmap_conv.{bias, weight}
roi_heads.fuse_heatmap_conv_1.{bias, weight}
roi_heads.fuse_heatmap_conv_2.{bias, weight}
roi_heads.fuse_heatmap_conv_3.{bias, weight}
roi_heads.fuse_kpts_pos.{bias, weight}
roi_heads.fuse_kpts_pos_1.{bias, weight}
roi_heads.fuse_roi_feature_conv.{bias, weight}
roi_heads.fuse_roi_feature_conv_1.{bias, weight}
roi_heads.fuse_roi_feature_conv_2.{bias, weight}
roi_heads.regress_car_cls.{bias, weight}
roi_heads.regress_car_cluster_type.{bias, weight}
roi_heads.regress_car_params_0.{bias, weight}
roi_heads.regress_car_params_1.{bias, weight}
roi_heads.regress_car_params_2.{bias, weight}
roi_heads.regress_car_params_3.{bias, weight}
roi_heads.regress_rotation.{bias, weight}
roi_heads.regress_translation.{bias, weight}
Hi, I have a question about 3D semantic keypoints on the predicted mesh as you mentioned eq(2) above in your paper? How to get them? Are they generated along with model reconstruction?
I have successfully built Detectron2 under this environment. But when i build SoftRas，i meet this problem. Can you give me some advice? Thanks so much.
1 error detected in the compilation of "D:/ApolloCar3D/各种pip包/SoftRas-master/soft_renderer/cuda/soft_rasterize_cuda_kernel.cu".
soft_rasterize_cuda_kernel.cu
ninja: build stopped: subcommand failed.
Traceback (most recent call last):
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\torch\utils\cpp_extension.py", line 1673, in _run_ninja_build
env=env)
File "C:\Users\One\anaconda3\envs\detectron2\lib\subprocess.py", line 438, in run
output=stdout, stderr=stderr)
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "setup.py", line 38, in
cmdclass={'build_ext': BuildExtension}
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools_init_.py", line 153, in setup
return distutils.core.setup(**attrs)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\core.py", line 148, in setup
dist.run_commands()
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\dist.py", line 955, in run_commands
self.run_command(cmd)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\dist.py", line 974, in run_command
cmd_obj.run()
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools\command\install.py", line 67, in run
self.do_egg_install()
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools\command\install.py", line 109, in do_egg_install
self.run_command('bdist_egg')
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\dist.py", line 974, in run_command
cmd_obj.run()
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools\command\bdist_egg.py", line 164, in run
cmd = self.call_command('install_lib', warn_dir=0)
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools\command\bdist_egg.py", line 150, in call_command
self.run_command(cmdname)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\dist.py", line 974, in run_command
cmd_obj.run()
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools\command\install_lib.py", line 11, in run
self.build()
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\command\install_lib.py", line 107, in build
self.run_command('build_ext')
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\dist.py", line 974, in run_command
cmd_obj.run()
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools\command\build_ext.py", line 79, in run
_build_ext.run(self)
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\Cython\Distutils\old_build_ext.py", line 186, in run
_build_ext.build_ext.run(self)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\command\build_ext.py", line 339, in run
self.build_extensions()
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\torch\utils\cpp_extension.py", line 708, in build_extensions
build_ext.build_extensions(self)
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\Cython\Distutils\old_build_ext.py", line 195, in build_extensions
_build_ext.build_ext.build_extensions(self)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\command\build_ext.py", line 448, in build_extensions
self._build_extensions_serial()
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\command\build_ext.py", line 473, in _build_extensions_serial
self.build_extension(ext)
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\setuptools\command\build_ext.py", line 196, in build_extension
_build_ext.build_extension(self, ext)
File "C:\Users\One\anaconda3\envs\detectron2\lib\distutils\command\build_ext.py", line 533, in build_extension
depends=ext.depends)
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\torch\utils\cpp_extension.py", line 690, in win_wrap_ninja_compile
with_cuda=with_cuda)
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\torch\utils\cpp_extension.py", line 1359, in _write_ninja_file_and_compile_objects
error_prefix='Error compiling objects for extension')
File "C:\Users\One\anaconda3\envs\detectron2\lib\site-packages\torch\utils\cpp_extension.py", line 1683, in _run_ninja_build
raise RuntimeError(message) from e
RuntimeError: Error compiling objects for extension
Hello,
Firstly, thanks for the codes of your impressive research.
when i tired to the below command to to execute the demo.py ( i don't see the datasets folder )
CUDA_VISIBLE_DEVICES=0 python3 demo/demo.py --config-file configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x_apollo.yaml
--input 'datasets/coco/val_apollo/'
--output 'medium_result_val/'
--opts MODEL.WEIGHTS ./output/model_final.pth
by reading the parser arguments i learnt --input should be list of input images with this i changed the command as follows
command : CUDA_VISIBLE_DEVICES=0 python3 demo/demo.py --config-file configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x_apollo.yaml --input 'datasets/apollo/annotations/test/images/171206_034559609_Camera_5.jpg' --output 'medium_result_val/' --opts MODEL.WEIGHTS ./output/model_final.pth
Output
name: coco_2014_train
name: coco_2014_val
name: coco_2014_minival
name: coco_2014_minival_100
name: coco_2014_valminusminival
name: coco_2017_train
name: coco_2017_val
name: coco_2017_occ
name: coco_2017_test
name: coco_2017_test-dev
name: coco_2017_val_100
name: apollo_train
name: apollo_val
name: keypoints_coco_2014_train
name: keypoints_coco_2014_val
name: keypoints_coco_2014_minival
name: keypoints_coco_2014_valminusminival
name: keypoints_coco_2014_minival_100
name: keypoints_coco_2017_train
name: keypoints_coco_2017_val
name: keypoints_coco_2017_val_100
[10/13 00:22:46 detectron2]: Arguments: Namespace(config_file='configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x_apollo.yaml', webcam=False, video_input=None, input=['datasets/apollo/annotations/test/images/171206_034559609_Camera_5.jpg'], output='medium_result_val/', confidence_threshold=0.5, opts=['MODEL.WEIGHTS', './output/model_final.pth'])
Killed
can you please help me where I'm going wrong
Hello, i just need a strong key point detector for traffic task, i don't need to create a 3d scene.
What part of this project runs the inference?
Hi @lkeab ,
Thanks for the great work! I wonder if you will be able to share the training codes and some detailed training procedures so we are able to train the model ourselves. Please let us know if that's possible, thank you!
Hi, firstly thanks for your work.
I have a question about the relationship between 1352 vertices and 66 keypoints. There is a file, named kpts_mapping.npy. It's not really used in your code as I understand. What means the content of this file? And how could I understand the relationship between mesh vertices and detected keypoints?
Thanks a lot.
Hello,
I want to retrain the model using my own datasets, but I find there is no code about training, such as loss function. Could you please add this part?
Hi,
I wanted to try your method and evaluate the results for a better knowledge, can you provide the scripts and files regarding the same?
Hi, I met a problem when reading the code.
As mentioned in other issues, the kpts_mapping.npy is used to get 66 3d coordinates of 3D semmantic points in the mesh model.
But I can not understand how to use this file to get the results specifily. Would you answer this problem ?
Thanks
Could you please give us a new link to the pretrained model. The old link doesn't contain anything anymore.
I meet this error when i use run.sh, i have successfully install the detectron2
Traceback (most recent call last):
File "demo2/demo.py", line 90, in
predictions, visualized_output = demo.run_on_image(img, save_name)
File "D:\ApolloCar3D\detectron2-master\demo2\predictor.py", line 107, in run_on_image
for index in range(instances.predict_trans.shape[0]):
File "d:\apollocar3d\detectron2-master\detectron2\structures\instances.py", line 65, in getattr
raise AttributeError("Cannot find field '{}' in the given Instances!".format(name))
AttributeError: Cannot find field 'predict_trans' in the given Instances!
Hello,
Firstly, thanks for the codes of your impressive research.
I have an issue when running
CUDA_VISIBLE_DEVICES=0 python3 demo/demo.py --config-file configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x_apollo.yaml \
--input 'datasets/coco/val_apollo/' \
--output 'medium_result_val/' \
--opts MODEL.WEIGHTS ./output/model_final.pth
Traceback (most recent call last):
File "demo/demo.py", line 11, in <module>
from detectron2.data.detection_utils import read_image
File "/home/Test/code/gsnet/reference_code/GSNet-release/detectron2/data/__init__.py", line 4, in <module>
from .build import (
File "/home/Test/code/gsnet/reference_code/GSNet-release/detectron2/data/build.py", line 14, in <module>
from detectron2.structures import BoxMode
File "/home/Test/code/gsnet/reference_code/GSNet-release/detectron2/structures/__init__.py", line 2, in <module>
from .boxes import Boxes, BoxMode, pairwise_iou
File "/home/Test/code/gsnet/reference_code/GSNet-release/detectron2/structures/boxes.py", line 8, in <module>
from detectron2.layers import cat
File "/home/Test/code/gsnet/reference_code/GSNet-release/detectron2/layers/__init__.py", line 3, in <module>
from .deform_conv import DeformConv, ModulatedDeformConv
File "/home/Test/code/gsnet/reference_code/GSNet-release/detectron2/layers/deform_conv.py", line 10, in <module>
from detectron2 import _C
ImportError: /home/Test/code/gsnet/reference_code/GSNet-release/detectron2/_C.cpython-36m-x86_64-linux-gnu.so: undefined symbol: THPVariableClass
python: 3.6.2
CUDA: 10.1
pytorch: 1.5.0
ubuntu: 18.04
The existing solutions about this issue give me that the version of pytorch is incompatible with detectron2.
python -m pip install detectron2 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html
By doing this I got:
Traceback (most recent call last):
File "demo/demo.py", line 73, in <module>
cfg = setup_cfg(args)
File "demo/demo.py", line 28, in setup_cfg
cfg.MODEL.FCOS.INFERENCE_TH = args.confidence_threshold
File "/home/Test/anaconda3/envs/gsnet/lib/python3.6/site-packages/yacs/config.py", line 141, in __getattr__
raise AttributeError(name)
AttributeError: FCOS
It looks like that the the version of detectron2 provided in GSNet-release is different from that from https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html
So could you please give some hints about the version of pytorch used for the detectron2 provided in GSNet-release?
Many thanks in advance.
Hi @lkeab, First of all, thank you for such a nice work! and making it public. I am curious how you are doing the geoemtric consistency loss (because it looks like there are no 3D semantic coordinates as mentioned in the paper). Could you please point me where this is implemented in your code?
Thank you,
Srikanth
Hi,
Thank you for open sourcing your code. I have one question: how did you use SoftRas to generate the deformed car models that have the same topology? You only briefly mentioned it in the paper, but I have not found code in the repo to do this. Thanks!
Great research! Could you please tell me where to find the "66 3D vertices " mentioned below eq.(4) ? Are they included in this project?
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