hpanwar08 / detectron2 Goto Github PK
View Code? Open in Web Editor NEWThis project forked from facebookresearch/detectron2
Detectron2 for Document Layout Analysis
License: Apache License 2.0
This project forked from facebookresearch/detectron2
Detectron2 for Document Layout Analysis
License: Apache License 2.0
Thank you @hpanwar08 for providing with detectron2 trained publaynet models.
I wanted help with using this model to extract region features of the objects (the layout elements detected) detected
Firstly, thank you so much @hpanwar08 for your contributions!
I was trying to use Mask_RCNN but I observed that whenever data is a little bit spreaded like in a table, then it fails to record it. There is no bbox in that case.
Consider the following picture as a reference.
Can you help me with this?
Thank you
Hi. I wanted to download the pre-trained models, but the download links for all of them give a 404 error. Could you check this please ?
I think "--num-gpus 8" config is not working with train_net_dla.py when I tryed to training from scratch.
How can I set the config?
Hi,
I would like to use this implementation to segment historical newspaper pages. Should I simply use the existing model for prediction, or train the model further with additional historical newspaper ground truth?
All the best
Julian
Hi there.
Thanks so much for releasing Detectron2.
I'm trying to benchmark our own extensions from the detectron2 model, and as a part of
the process I need to define what is going on with the model trimming. Can you please explain
how the model is trimmed (and if possible where in the api you would do this)?
A pointer to documentation I have missed would be perfectly fine as well.
Thanks.
Is it possible to start training with additional categories such as: heading2, heading3, ..., image description?
Some elements are being detected twice:
first time as a different class and second time as a different class with slight variation in bounding box or addition of some extra texts.
Also, many smaller elements(like titles or small paragraphs) are being detected inside a bigger element(like a list). Mean to say, overlapping is also there in some cases.
So, it is possible to eliminate those issues or at least the overlapping issue?
Thanks in advance
Hi,
I am trying to predict titles, tables and text in an input image.
I used the command :
python demo/demo.py --config-file configs/DLA_mask_rcnn_R_101_FPN_3x.yaml --input "1.png" --output "./testt.png" --confidence-threshold 0.5 --opts MODEL.WEIGHTS "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/model_final_a3ec72.pkl" MODEL.DEVICE cpu
The command runs with the following logs and returns true at the end.
Config './configs/DLA_mask_rcnn_R_101_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
'roi_heads.box_predictor.cls_score.weight' has shape (81, 1024) in the checkpoint but (6, 1024) in the model! Skipped.
'roi_heads.box_predictor.cls_score.bias' has shape (81,) in the checkpoint but (6,) in the model! Skipped.
'roi_heads.box_predictor.bbox_pred.weight' has shape (320, 1024) in the checkpoint but (20, 1024) in the model! Skipped.
'roi_heads.box_predictor.bbox_pred.bias' has shape (320,) in the checkpoint but (20,) in the model! Skipped.
'roi_heads.mask_head.predictor.weight' has shape (80, 256, 1, 1) in the checkpoint but (5, 256, 1, 1) in the model! Skipped.
'roi_heads.mask_head.predictor.bias' has shape (80,) in the checkpoint but (5,) in the model! Skipped.
However, the output file remains unchanged with no bounding boxes or labels getting predicted. Can anyone guide why it's happening so.
Kindly help.
Thanks!
Hi, while I am trying to finetune the publaynet model in colab I keep running into errors. What would the process of this be? Should I start with this package or the normal detectron2 from facebookreserch? I have tried to manually add the publaynet model to my google drive but it does not seem to work that way. Some steps to achieve this would be of great help.
Thanks in advance
Hi,
I want to fine-tune the model with an existing class (table in my case). How should I set the category id of the new data? What should be its value, in a sense should it be the same as in the pre-trained model?
Thanks in advance!
Hey!
How can I trim model weights ?
According to the documentation of Detectron2, when registering custom dataset, you should set 'category_id' for each annotation in range [0, num_of_categories) where the value for 'num_of_categories' is reserved for the background class. link
However, the original paper states that the background's id is 0.
Please tell me how should I map the labels with the correct category_ids.
the output i got is shown below;
00:44 detectron2]: Arguments: Namespace(confidence_threshold=0.2, config_file='configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml', input=['/content/gdrive/My Drive/Untitled folder/PMC4527132_00004.jpg'], opts=['MODEL.WEIGHTS', '/content/gdrive/My Drive/Unt/model_final.pth', 'MODEL.DEVICE', 'cpu'], output='/content/gdrive/My Drive/Unt', video_input=None, webcam=False)
WARNING [03/27 12:00:44 d2.config.compat]: Config 'configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
'backbone.bottom_up.res2.0.conv1.weight' has shape (64, 64, 1, 1) in the checkpoint but (256, 64, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.weight' has shape (64, 256, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.weight' has shape (64, 256, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.2.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.weight' has shape (128, 256, 1, 1) in the checkpoint but (512, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.0.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.weight' has shape (128, 512, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.1.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.weight' has shape (128, 512, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.2.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.weight' has shape (128, 512, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv1.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.weight' has shape (128, 128, 3, 3) in the checkpoint but (512, 16, 3, 3) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.weight' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.bias' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.running_mean' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv2.norm.running_var' has shape (128,) in the checkpoint but (512,) in the model! Skipped.
'backbone.bottom_up.res3.3.conv3.weight' has shape (512, 128, 1, 1) in the checkpoint but (512, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.weight' has shape (256, 512, 1, 1) in the checkpoint but (1024, 512, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.0.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.1.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.2.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.3.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.4.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.5.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.6.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.7.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.8.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.9.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.10.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.11.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.12.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.13.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.14.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.15.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.16.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.17.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.18.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.19.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.20.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.21.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.weight' has shape (256, 1024, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv1.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.weight' has shape (256, 256, 3, 3) in the checkpoint but (1024, 32, 3, 3) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.weight' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.bias' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.running_mean' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv2.norm.running_var' has shape (256,) in the checkpoint but (1024,) in the model! Skipped.
'backbone.bottom_up.res4.22.conv3.weight' has shape (1024, 256, 1, 1) in the checkpoint but (1024, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.weight' has shape (512, 1024, 1, 1) in the checkpoint but (2048, 1024, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv1.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.weight' has shape (512, 512, 3, 3) in the checkpoint but (2048, 64, 3, 3) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv2.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.0.conv3.weight' has shape (2048, 512, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.weight' has shape (512, 2048, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv1.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.weight' has shape (512, 512, 3, 3) in the checkpoint but (2048, 64, 3, 3) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv2.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.1.conv3.weight' has shape (2048, 512, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.weight' has shape (512, 2048, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv1.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.weight' has shape (512, 512, 3, 3) in the checkpoint but (2048, 64, 3, 3) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.weight' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.bias' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.running_mean' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv2.norm.running_var' has shape (512,) in the checkpoint but (2048,) in the model! Skipped.
'backbone.bottom_up.res5.2.conv3.weight' has shape (2048, 512, 1, 1) in the checkpoint but (2048, 2048, 1, 1) in the model! Skipped.
0% 0/1 [00:00<?, ?it/s][03/27 12:01:02 detectron2]: /content/gdrive/My Drive/Untitled folder/PMC4527132_00004.jpg: detected 0 instances in 15.44s
100% 1/1 [00:15<00:00, 15.52s/it]
i use google colab and i use the pre trained weights given in repository
Can I use your custom class model to finetune for object detection or segmentation?
Basically, I should for register Detectron2 dataset?
Hi,
I prepared a custom dataset in COCO format having 3 classes like below:
But there is no samples related to 'Resumes' class. Only 'heading' and 'text' classes are present there in my sample.
"categories": [
{
"id": 0,
"name": "Resumes",
"supercategory": "none"
},
{
"id": 1,
"name": "heading",
"supercategory": "Resumes"
},
{
"id": 2,
"name": "text",
"supercategory": "Resumes"
}
],
"images":[{
"id": 159,
"license": 1,
"file_name": "outfile_Lavanya-4_10---2--docx-pdf_1.rf.bfcd3081e0b5399829ae9b1bdc1e67d4.jpg",
"height": 842,
"width": 596,
"date_captured": "2021-09-09T13:55:03+00:00"
},
],
"annotations": [
{
"id": 0,
"image_id": 0,
"category_id": 2,
"bbox": [
7,
6,
266.66666666666663,
101.28205128205127
],
"area": 27008.547008547,
"segmentation": [
[
7,
6,
266.66666666666663,
6,
266.66666666666663,
101.28205128205127,
7,
101.28205128205127
]
],
"iscrowd": 0
},
]
I'm using this config file for finetuning
_BASE_: "Base-RCNN-FPN.yaml"
MODEL:
MASK_ON: True
# WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
#WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/model_final_2d9806.pkl"
PIXEL_STD: [57.375, 57.120, 58.395]
ROI_HEADS:
NUM_CLASSES: 5
RESNETS:
STRIDE_IN_1X1: False # this is a C2 model
NUM_GROUPS: 32
WIDTH_PER_GROUP: 8
DEPTH: 101
DATASETS:
TRAIN: ("dla_train",)
TEST: ("dla_val",)
SOLVER:
STEPS: (210000, 250000)
MAX_ITER: 125500
IMS_PER_BATCH: 2
#BASE_LR: 0.0009
BASE_LR: 0.00005
DATALOADER:
NUM_WORKERS: 1
Got following error:-
WARNING [09/09 20:18:55 d2.data.datasets.coco]:
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
[09/09 20:18:55 d2.data.datasets.coco]: Loaded 638 images in COCO format from ./data/train/annotations.json
[09/09 20:18:55 d2.data.build]: Removed 320 images with no usable annotations. 318 images left.
[09/09 20:18:55 d2.data.build]: Distribution of instances among all 3 categories:
| category | #instances | category | #instances | category | #instances |
|:----------:|:-------------|:----------:|:-------------|:----------:|:-------------|
| Resumes | 0 | heading | 1028 | text | 1951 |
| | | | | | |
| total | 2979 | | | | |
[09/09 20:18:55 d2.data.detection_utils]: TransformGens used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[09/09 20:18:55 d2.data.build]: Using training sampler TrainingSampler
[09/09 20:18:56 d2.engine.train_loop]: Starting training from iteration 75500
ERROR [09/09 20:18:57 d2.engine.train_loop]: Exception during training:
Traceback (most recent call last):
File "/home/ujjawal/miniconda2/envs/caffe2/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 132, in train
self.run_step()
File "/home/ujjawal/miniconda2/envs/caffe2/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 216, in run_step
self._detect_anomaly(losses, loss_dict)
File "/home/ujjawal/miniconda2/envs/caffe2/lib/python3.7/site-packages/detectron2/engine/train_loop.py", line 239, in _detect_anomaly
self.iter, loss_dict
FloatingPointError: Loss became infinite or NaN at iteration=75501!
loss_dict = {'loss_cls': tensor(nan, device='cuda:0', grad_fn=<NllLossBackward>), 'loss_box_reg': tensor(nan, device='cuda:0', grad_fn=<DivBackward0>), 'loss_mask': tensor(0.7118, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>), 'loss_rpn_cls': tensor(0.6949, device='cuda:0', grad_fn=<MulBackward0>), 'loss_rpn_loc': tensor(0.4812, device='cuda:0', grad_fn=<MulBackward0>)}
I tried to change the NUM_CLASSES: 5 to 3 but no luck.
Some suggested to reduce the LR still no luck.
Can anyone please suggest a way to tackle this issue?
How to do we display labels with detectron2 on the output?
I checked the visualizer.py file, the logic seems to be fine within. But then again there are no labels displayed on the results, although the confidence score is displayed.
It uses the labels from the training dataset which is not available. But while training, the labels must have been encoded or dumped somewhere, can someone please point to the dump of the labels. Or if there is a way around to display the labels.
I am using Colab for training my model and as the time limit gets over, training stops. I have checkpoints saved after 500 iterations. I want to resume my training given a checkpoint in the output folder but every time it starts training from iteration 0. I have opened the train_net_dla.py
file and training the model, below is configurations and steps for training
cfg = get_cfg()
# mask rcnn resnet101
# mask rcnn resnext
cfg.merge_from_file("./configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml")
cfg.MODEL.WEIGHTS = "/content/drive/MyDrive/layout/model_final_trimmed.pth"
output_dir = "./output"
cfg.OUTPUT_DIR = output_dir
#os.makedirs(output_dir, exist_ok=True)
cfg.TEST.EVAL_PERIOD = 200
cfg.SOLVER.CHECKPOINT_PERIOD= 500
#logger.info(cfg)
# serialize the training config
cfg_str = cfg.dump()
with open(os.path.join(cfg.OUTPUT_DIR, "train_config.yaml"), "w") as f:
f.write(cfg_str)
f.close()
model = build_model(cfg)
checkpointer = DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
checkpointer = DetectionCheckpointer(model, save_dir=output_dir)
checkpointer = PeriodicCheckpointer(checkpointer, period=200,max_iter = 500)
checkpointer.save("model")
trainer = COCOTrainer(cfg)
trainer.resume_or_load(resume=True)
trainer.train()
I've gone through https://github.com/hpanwar08/detectron2/blob/master/tools/train_net_dla.py
training script. But, I'm bit confused on how to structure my custom data (both JSON and IMG-data).
If you can share a sample JSON and folder structure for placing training images, it would be great !!
Hi @hpanwar08 ,
I was trying to use your pre-trained models for prediction by following your instructions and using the below command:
python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "<path to image.jpg>" --output <path to save the predicted image> --confidence-threshold 0.5 --opts MODEL.WEIGHTS <path to model_final_trimmed.pth> MODEL.DEVICE cpu
I am using Google Colab for the work and I am getting this error:
Traceback (most recent call last):
File "demo/demo.py", line 73, in <module>
demo = VisualizationDemo(cfg)
File "/content/drive/My Drive/PublayNet/detectron2/demo/predictor.py", line 35, in __init__
self.predictor = DefaultPredictor(cfg)
File "/usr/local/lib/python3.6/dist-packages/detectron2/engine/defaults.py", line 187, in __init__
checkpointer.load(cfg.MODEL.WEIGHTS)
File "/usr/local/lib/python3.6/dist-packages/fvcore/common/checkpoint.py", line 117, in load
checkpoint = self._load_file(path)
File "/usr/local/lib/python3.6/dist-packages/detectron2/checkpoint/detection_checkpoint.py", line 42, in _load_file
loaded = super()._load_file(filename) # load native pth checkpoint
File "/usr/local/lib/python3.6/dist-packages/fvcore/common/checkpoint.py", line 213, in _load_file
return torch.load(f, map_location=torch.device("cpu"))
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 586, in load
with _open_zipfile_reader(f) as opened_zipfile:
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 246, in __init__
super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))
RuntimeError: [enforce fail at inline_container.cc:208] . file not found: /version
Can you please provide trimmed version of the weights for the model mentioned in the title as there is trimmed version of the other model (Mask R-CNN with ResNeXt-101-32x8-FPN)
@hpanwar08 hi there,
Thanks for providing the pretrained Mask R-CNN models! Just curious whether you have a plan to provide pretrained Faster R-CNN models as well? I believe it'll be helpful for fine tuning tasks where pixel-level annotation is not easy to get.
pip install -e detectron2_repo
Obtaining file:///root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo
Requirement already satisfied: termcolor>=1.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (1.1.0)
Requirement already satisfied: Pillow>=6.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (7.1.2)
Requirement already satisfied: yacs>=0.1.6 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (0.1.7)
Requirement already satisfied: tabulate in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (0.8.7)
Requirement already satisfied: cloudpickle in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (1.4.1)
Requirement already satisfied: matplotlib in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (3.2.1)
Requirement already satisfied: tqdm>4.29.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (4.46.0)
Requirement already satisfied: tensorboard in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (2.2.1)
Requirement already satisfied: fvcore in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from detectron2==0.1) (0.1.dev200506)
Requirement already satisfied: PyYAML in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from yacs>=0.1.6->detectron2==0.1) (5.3.1)
Requirement already satisfied: python-dateutil>=2.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (2.8.1)
Requirement already satisfied: numpy>=1.11 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (1.18.1)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (2.4.7)
Requirement already satisfied: kiwisolver>=1.0.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (1.2.0)
Requirement already satisfied: cycler>=0.10 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from matplotlib->detectron2==0.1) (0.10.0)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (0.4.1)
Requirement already satisfied: six>=1.10.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.14.0)
Requirement already satisfied: grpcio>=1.24.3 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.28.1)
Requirement already satisfied: google-auth<2,>=1.6.3 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.14.2)
Requirement already satisfied: wheel>=0.26; python_version >= "3" in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (0.34.2)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.6.0.post3)
Requirement already satisfied: requests<3,>=2.21.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (2.23.0)
Requirement already satisfied: werkzeug>=0.11.15 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (1.0.1)
Requirement already satisfied: setuptools>=41.0.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (46.1.3.post20200325)
Requirement already satisfied: absl-py>=0.4 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (0.9.0)
Requirement already satisfied: protobuf>=3.6.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (3.11.3)
Requirement already satisfied: markdown>=2.6.8 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from tensorboard->detectron2==0.1) (3.2.2)
Requirement already satisfied: portalocker in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from fvcore->detectron2==0.1) (1.7.0)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->detectron2==0.1) (1.3.0)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (0.2.8)
Requirement already satisfied: rsa<4.1,>=3.1.4 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (4.0)
Requirement already satisfied: cachetools<5.0,>=2.0.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (4.1.0)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (1.25.9)
Requirement already satisfied: chardet<4,>=3.0.2 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (2.9)
Requirement already satisfied: certifi>=2017.4.17 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.1) (2020.4.5.1)
Requirement already satisfied: importlib-metadata; python_version < "3.8" in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from markdown>=2.6.8->tensorboard->detectron2==0.1) (1.6.0)
Requirement already satisfied: oauthlib>=3.0.0 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->detectron2==0.1) (3.1.0)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->detectron2==0.1) (0.4.8)
Requirement already satisfied: zipp>=0.5 in /root/miniconda3/envs/dla/lib/python3.6/site-packages (from importlib-metadata; python_version < "3.8"->markdown>=2.6.8->tensorboard->detectron2==0.1) (3.1.0)
Installing collected packages: detectron2
Running setup.py develop for detectron2
ERROR: Command errored out with exit status 1:
command: /root/miniconda3/envs/dla/bin/python3.6 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"'; __file__='"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps
cwd: /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/
Complete output (581 lines):
running develop
running egg_info
writing detectron2.egg-info/PKG-INFO
writing dependency_links to detectron2.egg-info/dependency_links.txt
writing requirements to detectron2.egg-info/requires.txt
writing top-level names to detectron2.egg-info/top_level.txt
reading manifest file 'detectron2.egg-info/SOURCES.txt'
writing manifest file 'detectron2.egg-info/SOURCES.txt'
running build_ext
building 'detectron2._C' extension
Emitting ninja build file /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/build.ninja...
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
[1/4] /usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/cuda_version.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/cuda_version.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
[2/4] /usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
FAILED: /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.o
/usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(136): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(184): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(234): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(284): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h(341): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(155): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(338): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(503): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(696): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(823): error: identifier "AT_CHECK" is undefined
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv_cuda.cu(953): error: identifier "AT_CHECK" is undefined
11 errors detected in the compilation of "/tmp/tmpxft_00002fc3_00000000-6_deform_conv_cuda.cpp1.ii".
[3/4] c++ -MMD -MF /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o.d -pthread -B /root/miniconda3/envs/dla/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14
FAILED: /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o
c++ -MMD -MF /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o.d -pthread -B /root/miniconda3/envs/dla/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:4:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h: In function ‘at::Tensor detectron2::ROIAlign_forward(const at::Tensor&, const at::Tensor&, float, int, int, int, bool)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h:62:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (input.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:4:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h: In function ‘at::Tensor detectron2::ROIAlign_backward(const at::Tensor&, const at::Tensor&, float, int, int, int, int, int, int, int, bool)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign.h:98:17: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (grad.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:5:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h: In function ‘at::Tensor detectron2::ROIAlignRotated_forward(const at::Tensor&, const at::Tensor&, float, int, int, int)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h:57:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (input.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:5:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h: In function ‘at::Tensor detectron2::ROIAlignRotated_backward(const at::Tensor&, const at::Tensor&, float, int, int, int, int, int, int, int)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlignRotated/ROIAlignRotated.h:85:17: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (grad.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘int detectron2::deform_conv_forward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, int)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:134:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (input.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:136:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:136:5: error: ‘AT_CHECK’ was not declared in this scope
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^~~~~~~~
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:136:5: note: suggested alternative: ‘DCHECK’
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^~~~~~~~
DCHECK
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:137:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘int detectron2::deform_conv_backward_input(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, int)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:182:23: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (gradOutput.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:184:25: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:184:5: error: ‘AT_CHECK’ was not declared in this scope
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^~~~~~~~
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:184:5: note: suggested alternative: ‘DCHECK’
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^~~~~~~~
DCHECK
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:185:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:186:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘int detectron2::deform_conv_backward_filter(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, float, int)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:232:23: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (gradOutput.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:234:25: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:234:5: error: ‘AT_CHECK’ was not declared in this scope
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^~~~~~~~
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:234:5: note: suggested alternative: ‘DCHECK’
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^~~~~~~~
DCHECK
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:235:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘void detectron2::modulated_deform_conv_forward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, bool)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:282:18: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (input.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:284:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:284:5: error: ‘AT_CHECK’ was not declared in this scope
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^~~~~~~~
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:284:5: note: suggested alternative: ‘DCHECK’
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^~~~~~~~
DCHECK
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:285:24: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:286:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h: In function ‘void detectron2::modulated_deform_conv_backward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int, int, int, int, int, int, int, bool)’:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:339:24: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
if (grad_output.type().is_cuda()) {
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:341:25: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:341:5: error: ‘AT_CHECK’ was not declared in this scope
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^~~~~~~~
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:341:5: note: suggested alternative: ‘DCHECK’
AT_CHECK(input.type().is_cuda(), "input tensor is not on GPU!");
^~~~~~~~
DCHECK
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:342:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(weight.type().is_cuda(), "weight tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:343:24: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(bias.type().is_cuda(), "bias tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:7:0:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/deformable/deform_conv.h:344:26: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
AT_CHECK(offset.type().is_cuda(), "offset tensor is not on GPU!");
^
In file included from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:11:0,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/ATen.h:5,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/vision.cpp:3:
/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:262:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
[4/4] /usr/local/cuda-10.2/bin/nvcc -DWITH_CUDA -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/TH -I/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/include/THC -I/usr/local/cuda-10.2/include -I/root/miniconda3/envs/dla/include/python3.6m -c -c /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.cu -o /root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/build/temp.linux-x86_64-3.6/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/detectron2/layers/csrc/ROIAlign/ROIAlign_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++14
ninja: build stopped: subcommand failed.
Traceback (most recent call last):
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1400, in _run_ninja_build
check=True)
File "/root/miniconda3/envs/dla/lib/python3.6/subprocess.py", line 438, in run
output=stdout, stderr=stderr)
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py", line 138, in <module>
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/__init__.py", line 144, in setup
return distutils.core.setup(**attrs)
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/core.py", line 148, in setup
dist.run_commands()
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/dist.py", line 955, in run_commands
self.run_command(cmd)
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/develop.py", line 38, in run
self.install_for_development()
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/develop.py", line 140, in install_for_development
self.run_command('build_ext')
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/dist.py", line 974, in run_command
cmd_obj.run()
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/build_ext.py", line 87, in run
_build_ext.run(self)
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/Cython/Distutils/old_build_ext.py", line 186, in run
_build_ext.build_ext.run(self)
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 339, in run
self.build_extensions()
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 580, in build_extensions
build_ext.build_extensions(self)
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/Cython/Distutils/old_build_ext.py", line 195, in build_extensions
_build_ext.build_ext.build_extensions(self)
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 448, in build_extensions
self._build_extensions_serial()
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 473, in _build_extensions_serial
self.build_extension(ext)
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/setuptools/command/build_ext.py", line 208, in build_extension
_build_ext.build_extension(self, ext)
File "/root/miniconda3/envs/dla/lib/python3.6/distutils/command/build_ext.py", line 533, in build_extension
depends=ext.depends)
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 423, in unix_wrap_ninja_compile
with_cuda=with_cuda)
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1140, in _write_ninja_file_and_compile_objects
error_prefix='Error compiling objects for extension')
File "/root/miniconda3/envs/dla/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1413, in _run_ninja_build
raise RuntimeError(message)
RuntimeError: Error compiling objects for extension
----------------------------------------
ERROR: Command errored out with exit status 1: /root/miniconda3/envs/dla/bin/python3.6 -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"'; __file__='"'"'/root/miniconda3/envs/dla/lib/python3.6/site-packages/detectron2_repo/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' develop --no-deps Check the logs for full command output.
sys.platform linux
Python 3.6.10 | packaged by conda-forge | (default, Apr 24 2020, 16:44:11) [GCC 7.3.0]
Numpy 1.18.1
detectron2._C failed to import
DETECTRON2_ENV_MODULE
PyTorch 1.5.0
PyTorch Debug Build False
torchvision 0.6.0a0+82fd1c8
CUDA available True
GPU 0 GeForce GTX 1080
CUDA_HOME /usr/local/cuda-10.2
NVCC Cuda compilation tools, release 10.2, V10.2.89
Pillow 7.1.2
PyTorch built with:
I want to train a model from scratch. It looks like the tools directory is where I should dive into. I have the PubLayNet data, but I refined the data I want to use, and don't want to use the pre-trained models or weights(All the labels are the same format). How would I go about training a fresh model?
Thanks in advance.
Unfortunately, I always run into an error when fine-tuning on my own dataset (in coco format).
The detectron2 documentation on datasets was of no help either (maybe I overlooked something though).
After registering the data via register_coco_instances
(as seen in train_net_dla.py
) the following code
train data (train_data = DatasetCatalog.get("dla_train")
train data[0]
gives me:
{'file_name': 'cyber_train/public_imgs_sampled/file01.png',
'height': 1754,
'width': 1241,
'image_id': 0,
'annotations': [{'iscrowd': 0,
'bbox': [78, 600, 977, 33],
'category_id': 1,
'segmentation': [[83, 606, 83, 633, 1050, 633, 1050, 606]],
'bbox_mode': <BoxMode.XYWH_ABS: 1>}
...
which does look good to me telling from the COCO formatting style.
I can even visualize the data using Visualizer
:
for d in random.sample(train_data, 1):
img = cv2.imread(d["file_name"])
visualizer = Visualizer(img[:, :, :], scale=0.5)
out = visualizer.draw_dataset_dict(d)
plt.imshow(out.get_image()[:,:,::-1])
which gives me the expected output:
Creating the trainer is also possible using trainer = DefaultTrainer(cfg)
, but calling trainer.train()
gives me a stack trace for the AttributeError: Cannot find field 'gt_masks' in the given Instances!'
in DataLoader.
I am very confused about the error, because 'segmentation' is present in the train_data and the specified polygons are also valid telling from the output of Visualizer
.
Anyway, thank you very much for your great work @hpanwar08 !
Hi, when I got the error by calling
python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input “/home/jupyter/layout/detectron2/input.jpg” --output “/home/jupyter/layout/” --confidence-threshold 0.5 --opts MODEL.WEIGHTS /home/jupyter/layout/detectron2/model_final_trimmed.pth MODEL.DEVICE cpu
Here is the error, not sure why the .yaml doesn't have VERSION. Thank you
WARNING [03/26 04:43:12 d2.config.compat]: Config 'configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
Traceback (most recent call last):
File "demo/demo.py", line 76, in <module>
demo = VisualizationDemo(cfg)
File "/home/jupyter/layout/detectron2/demo/predictor.py", line 35, in __init__
self.predictor = DefaultPredictor(cfg)
File "/opt/conda/lib/python3.7/site-packages/detectron2/engine/defaults.py", line 168, in __init__
checkpointer.load(cfg.MODEL.WEIGHTS)
File "/opt/conda/lib/python3.7/site-packages/fvcore/common/checkpoint.py", line 103, in load
checkpoint = self._load_file(path)
File "/opt/conda/lib/python3.7/site-packages/detectron2/checkpoint/detection_checkpoint.py", line 42, in _load_file
loaded = super()._load_file(filename) # load native pth checkpoint
File "/opt/conda/lib/python3.7/site-packages/fvcore/common/checkpoint.py", line 189, in _load_file
return torch.load(f, map_location=torch.device("cpu"))
File "/opt/conda/lib/python3.7/site-packages/torch/serialization.py", line 529, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/opt/conda/lib/python3.7/site-packages/torch/serialization.py", line 709, in _legacy_load
deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly)
RuntimeError: unexpected EOF, expected 16489904 more bytes. The file might be corrupted.
terminate called after throwing an instance of 'c10::Error'
what(): owning_ptr == NullType::singleton() || owning_ptr->refcount_.load() > 0 INTERNAL ASSERT FAILED at /pytorch/c10/util/intrusive_ptr.h:348, please report a bug to PyTorch. intrusive_ptr: Can only intrusive_ptr::reclaim() owning pointers that were created using intrusive_ptr::release(). (reclaim at /pytorch/c10/util/intrusive_ptr.h:348)
frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x33 (0x7f545aa70193 in /opt/conda/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x186b6af (0x7f545c72e6af in /opt/conda/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: THStorage_free + 0x17 (0x7f545cef6cb7 in /opt/conda/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: <unknown function> + 0x55d23d (0x7f54a377223d in /opt/conda/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #22: __libc_start_main + 0xf1 (0x7f54c6f032e1 in /lib/x86_64-linux-gnu/libc.so.6)
Aborted
Hi, I tried the train_net_dla.py
script in the repo to finetune on custom data. Data is all fine, but I'm getting this error just before training starts: RuntimeError: Not compiled with GPU support
. I double checked everything !! not able to figure out this issue ??
Hi @hpanwar08
Can you please confirm, what is the LR (learning rate you used), also did you used same learning rate through out your training process or was it scheduled at different learning rates ?
I've setup everything and unable to see labels as text,figure,table on the output image. please help....
Hi @hpanwar08
Thank you very much for your contributions.
Currently, I trained detectron2 (use RCNN-FPN network) with PubLayNet. However, I want to use dilated convolution for this network (RCNN-FPN network, not DC5 network).
Could you please give me the guideline to training?
Thank you very much.
Hi, I'm currently using the pre-trained model weights supplied in the repo. But, I need to fine-tune the model further to suit my requirements. So, I ran a sample training with only one image and one instance/bbox in that image, but the training script says it needs 1 day 18 hrs to complete training on this minuscule training set. Is this an issue with ETA estimation or does the model really take that long to train on one image ??
Hi ,
I wanted to Know how to calculate the evaluation metrics for custom dataset which has only pdf, no xml as told by the PublayNet paper anyone tried this?
I ran this on my documents got the labels and their predictions but now I need to calculate the accuracy with ground truth but I don't have the labelled data so is there any way to calculate metrics for the results.
Why did you train using x101 fpn
, have you tested mask_rcnn_R_50_FPN_3x.yaml
?
I have custom dataset having 8 classes for layout detection and around 3000 samples in training. I have trained model for more than 15000 iterations but loss is jumping around 1.23, 1.35 etc. I tried multiple learning rate but it's not decreasing. What changes I should do to overcome this overfitting situation or may be it something else. The configuration I am using is DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml
My config file is-
CUDNN_BENCHMARK: false
DATALOADER:
ASPECT_RATIO_GROUPING: true
FILTER_EMPTY_ANNOTATIONS: true
NUM_WORKERS: 4
REPEAT_THRESHOLD: 0.0
SAMPLER_TRAIN: TrainingSampler
DATASETS:
PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
PROPOSAL_FILES_TEST: []
PROPOSAL_FILES_TRAIN: []
TEST:
- layout_valid
TRAIN:
- layout_train
GLOBAL:
HACK: 1.0
INPUT:
CROP:
ENABLED: false
SIZE:
- 0.9
- 0.9
TYPE: relative_range
FORMAT: BGR
MASK_FORMAT: polygon
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN:
- 640
- 672
- 704
- 736
- 768
- 800
MIN_SIZE_TRAIN_SAMPLING: choice
RANDOM_FLIP: horizontal
MODEL:
ANCHOR_GENERATOR:
ANGLES:
- - -90
- 0
- 90
ASPECT_RATIOS:
- - 0.5
- 1.0
- 2.0
NAME: DefaultAnchorGenerator
OFFSET: 0.0
SIZES:
- - 32
- - 64
- - 128
- - 256
- - 512
BACKBONE:
FREEZE_AT: 2
NAME: build_resnet_fpn_backbone
DEVICE: cuda
FPN:
FUSE_TYPE: sum
IN_FEATURES:
- res2
- res3
- res4
- res5
NORM: ''
OUT_CHANNELS: 256
KEYPOINT_ON: false
LOAD_PROPOSALS: false
MASK_ON: true
META_ARCHITECTURE: GeneralizedRCNN
PANOPTIC_FPN:
COMBINE:
ENABLED: true
INSTANCES_CONFIDENCE_THRESH: 0.5
OVERLAP_THRESH: 0.5
STUFF_AREA_LIMIT: 4096
INSTANCE_LOSS_WEIGHT: 1.0
PIXEL_MEAN:
- 103.53
- 116.28
- 123.675
PIXEL_STD:
- 57.375
- 57.12
- 58.395
PROPOSAL_GENERATOR:
MIN_SIZE: 0
NAME: RPN
RESNETS:
DEFORM_MODULATED: false
DEFORM_NUM_GROUPS: 1
DEFORM_ON_PER_STAGE:
- false
- false
- false
- false
DEPTH: 101
NORM: FrozenBN
NUM_GROUPS: 32
OUT_FEATURES:
- res2
- res3
- res4
- res5
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: false
WIDTH_PER_GROUP: 8
RETINANET:
BBOX_REG_LOSS_TYPE: smooth_l1
BBOX_REG_WEIGHTS: &id001
- 1.0
- 1.0
- 1.0
- 1.0
FOCAL_LOSS_ALPHA: 0.25
FOCAL_LOSS_GAMMA: 2.0
IN_FEATURES:
- p3
- p4
- p5
- p6
- p7
IOU_LABELS:
- 0
- -1
- 1
IOU_THRESHOLDS:
- 0.4
- 0.5
NMS_THRESH_TEST: 0.5
NORM: ''
NUM_CLASSES: 80
NUM_CONVS: 4
PRIOR_PROB: 0.01
SCORE_THRESH_TEST: 0.05
SMOOTH_L1_LOSS_BETA: 0.1
TOPK_CANDIDATES_TEST: 1000
ROI_BOX_CASCADE_HEAD:
BBOX_REG_WEIGHTS:
- - 10.0
- 10.0
- 5.0
- 5.0
- - 20.0
- 20.0
- 10.0
- 10.0
- - 30.0
- 30.0
- 15.0
- 15.0
IOUS:
- 0.5
- 0.6
- 0.7
ROI_BOX_HEAD:
BBOX_REG_LOSS_TYPE: smooth_l1
BBOX_REG_LOSS_WEIGHT: 1.0
BBOX_REG_WEIGHTS:
- 10.0
- 10.0
- 5.0
- 5.0
CLS_AGNOSTIC_BBOX_REG: false
CONV_DIM: 256
FC_DIM: 1024
NAME: FastRCNNConvFCHead
NORM: ''
NUM_CONV: 0
NUM_FC: 2
POOLER_RESOLUTION: 7
POOLER_SAMPLING_RATIO: 0
POOLER_TYPE: ROIAlignV2
SMOOTH_L1_BETA: 0.0
TRAIN_ON_PRED_BOXES: false
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
IN_FEATURES:
- p2
- p3
- p4
- p5
IOU_LABELS:
- 0
- 1
IOU_THRESHOLDS:
- 0.5
NAME: StandardROIHeads
NMS_THRESH_TEST: 0.5
NUM_CLASSES: 8
POSITIVE_FRACTION: 0.25
PROPOSAL_APPEND_GT: true
SCORE_THRESH_TEST: 0.05
ROI_KEYPOINT_HEAD:
CONV_DIMS:
- 512
- 512
- 512
- 512
- 512
- 512
- 512
- 512
LOSS_WEIGHT: 1.0
MIN_KEYPOINTS_PER_IMAGE: 1
NAME: KRCNNConvDeconvUpsampleHead
NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: true
NUM_KEYPOINTS: 17
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_TYPE: ROIAlignV2
ROI_MASK_HEAD:
CLS_AGNOSTIC_MASK: false
CONV_DIM: 256
NAME: MaskRCNNConvUpsampleHead
NORM: ''
NUM_CONV: 4
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_TYPE: ROIAlignV2
RPN:
BATCH_SIZE_PER_IMAGE: 256
BBOX_REG_LOSS_TYPE: smooth_l1
BBOX_REG_LOSS_WEIGHT: 1.0
BBOX_REG_WEIGHTS: *id001
BOUNDARY_THRESH: -1
CONV_DIMS:
- -1
HEAD_NAME: StandardRPNHead
IN_FEATURES:
- p2
- p3
- p4
- p5
- p6
IOU_LABELS:
- 0
- -1
- 1
IOU_THRESHOLDS:
- 0.3
- 0.7
LOSS_WEIGHT: 1.0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOPK_TEST: 1000
POST_NMS_TOPK_TRAIN: 1000
PRE_NMS_TOPK_TEST: 1000
PRE_NMS_TOPK_TRAIN: 2000
SMOOTH_L1_BETA: 0.0
SEM_SEG_HEAD:
COMMON_STRIDE: 4
CONVS_DIM: 128
IGNORE_VALUE: 255
IN_FEATURES:
- p2
- p3
- p4
- p5
LOSS_WEIGHT: 1.0
NAME: SemSegFPNHead
NORM: GN
NUM_CLASSES: 54
WEIGHTS: /content/drive/MyDrive/layout/resnext101.pth
OUTPUT_DIR: ./output
SEED: -1
SOLVER:
AMP:
ENABLED: false
BASE_LR: 8.0e-06
BIAS_LR_FACTOR: 1.0
CHECKPOINT_PERIOD: 2000
CLIP_GRADIENTS:
CLIP_TYPE: value
CLIP_VALUE: 1.0
ENABLED: false
NORM_TYPE: 2.0
GAMMA: 0.1
IMS_PER_BATCH: 2
LR_SCHEDULER_NAME: WarmupMultiStepLR
MAX_ITER: 25000
MOMENTUM: 0.9
NESTEROV: false
REFERENCE_WORLD_SIZE: 0
STEPS:
- 210000
- 250000
WARMUP_FACTOR: 0.001
WARMUP_ITERS: 1000
WARMUP_METHOD: linear
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0.0001
WEIGHT_DECAY_NORM: 0.0
TEST:
AUG:
ENABLED: false
FLIP: true
MAX_SIZE: 4000
MIN_SIZES:
- 400
- 500
- 600
- 700
- 800
- 900
- 1000
- 1100
- 1200
DETECTIONS_PER_IMAGE: 100
EVAL_PERIOD: 0
EXPECTED_RESULTS: []
KEYPOINT_OKS_SIGMAS: []
PRECISE_BN:
ENABLED: false
NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
Hello,
In PDF documents am currently working on text is coming along with tables and figures. I am only concerned with tables and figures (each one essential). Is there any parameter that I could set to avoid appearance of text?
Thanks
I did the normal training from scratch with the LR at .0009 for 125k iterations like is shown in the config file for DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml. and am only getting a 77.22 AP score. This learning rate is the last one that IBM had used so confused as to what your training process looked like, and if you changed learning rates when getting a total of 190K iterations that it looks like you did.
So the question is, what exactly did your training run look like on the DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml to get a ~90 AP score.
Thanks!
I just run
python demo/demo.py --config-file configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml --input "test001.jpg" --output "results/abc.jpg" --confidence-threshold 0.5 --opts MODEL.WEIGHTS "models/model_final_trimmed.pth" MODEL.DEVICE cp
What I get :
[05/28 17:14:21 detectron2]: Arguments: Namespace(confidence_threshold=0.5, config_file='configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml', input=['test001.jpg'], opts=['MODEL.WEIGHTS', 'models/model_final_trimmed.pth', 'MODEL.DEVICE', 'cpu'], output='results/abc.jpg', video_input=None, webcam=False)
WARNING [05/28 17:14:21 d2.config.compat]: Config 'configs/DLA_mask_rcnn_X_101_32x8d_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
'backbone.bottom_up.res2.0.conv1.weight' has shape (64, 64, 1, 1) in the checkpoint but (256, 64, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv2.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.0.conv3.weight' has shape (256, 64, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.weight' has shape (64, 256, 1, 1) in the checkpoint but (256, 256, 1, 1) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv1.norm.running_var' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.weight' has shape (64, 64, 3, 3) in the checkpoint but (256, 8, 3, 3) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.weight' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.bias' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
'backbone.bottom_up.res2.1.conv2.norm.running_mean' has shape (64,) in the checkpoint but (256,) in the model! Skipped.
At last, no box was produced.
I don't know what's happened.
Hoping get some suggestions.
Hi, is there an internal feature which lets each classed be saved as a seperate segment, or image? I am trying to identify tables, seperate and then run through a tabular data analyzer and ocr - so far am able to get the image predictions with your code, but not the actual annotations/segmented fields for further analysis/ocr.
Note that you can implement many features by extending detectron2.
See projects for some examples.
We would only consider adding new features if they are relevant to many users.
whether to consider using TensorRT to accelerate the MaskRCNN model?
Note that you can implement many features by extending detectron2.
See projects for some examples.
We would only consider adding new features if they are relevant to many users.
This is my colab file: https://gist.github.com/alex-2201/56c9cb561fb6d5e4626ba7abcfc95471
but it doesn't work :(
I can't setup and run your code, could you give me guideline please.
Thank you!
Hi, I'm planning to apply Detectron2 on more general pdf documents from all sorts of companies. Is there a feature or model already present or is my best option to start fine tuning the existing model by making my own test dataset? Thanks in advance
It would be great if you can provide script for fine-tuning.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.