File "//python3.7/site-packages/detectron2/engine/defaults.py", line 674, in auto_scale_workers ), "Invalid REFERENCE_WORLD_SIZE in config!" AssertionError: Invalid REFERENCE_WORLD_SIZE in config!

Hi, Thanks for this repo :-D

When I run the demo.py I get the following error:

python3 demo.py --output /home/ws/Desktop/ --config-file configs/coco-instance/yolomask.yaml --input /home/ws/data/dataset/nets_kinneret_only24_2/images/0_2.jpg --opts MODEL.WEIGHTS output/coco_yolomask/model_final.pth
Install mish-cuda to speed up training and inference. More importantly, replace the naive Mish with MishCuda will give a ~1.5G memory saving during training.
[05/08 10:45:50 detectron2]: Arguments: Namespace(confidence_threshold=0.21, config_file='configs/coco-instance/yolomask.yaml', input='/home/ws/data/dataset/nets_kinneret_only24_2/images/0_2.jpg', nms_threshold=0.6, opts=['MODEL.WEIGHTS', 'output/coco_yolomask/model_final.pth'], output='/home/ws/Desktop/', video_input=None, webcam=False)
10:45:50 05.08 INFO yolomask.py:86]: YOLO.ANCHORS: [[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]]
10:45:50 05.08 INFO yolomask.py:90]: backboneshape: [64, 128, 256, 512], size_divisibility: 32
[[142, 110], [192, 243], [459, 401], [36, 75], [76, 55], [72, 146], [12, 16], [19, 36], [40, 28]]
/home/ws/.local/lib/python3.6/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[05/08 10:45:52 fvcore.common.checkpoint]: [Checkpointer] Loading from output/coco_yolomask/model_final.pth ...
[05/08 10:45:53 d2.checkpoint.c2_model_loading]: Following weights matched with model:
| Names in Model                                    | Names in Checkpoint
                                    | Shapes                         |
|:--------------------------------------------------|:------------------------------------------------------------------------------------------------------|:-------------------------------|
| backbone.dark2.0.bn.*                             | backbone.dark2.0.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (64,) () (64,) (64,) (64,)     |
| backbone.dark2.0.conv.weight                      | backbone.dark2.0.conv.weight
                                    | (64, 32, 3, 3)                 |
| backbone.dark2.1.conv1.bn.*                       | backbone.dark2.1.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (32,) () (32,) (32,) (32,)     |
| backbone.dark2.1.conv1.conv.weight                | backbone.dark2.1.conv1.conv.weight
                                    | (32, 64, 1, 1)                 |
| backbone.dark2.1.conv2.bn.*                       | backbone.dark2.1.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (32,) () (32,) (32,) (32,)     |
| backbone.dark2.1.conv2.conv.weight                | backbone.dark2.1.conv2.conv.weight
                                    | (32, 64, 1, 1)                 |
| backbone.dark2.1.conv3.bn.*                       | backbone.dark2.1.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (64,) () (64,) (64,) (64,)     |
| backbone.dark2.1.conv3.conv.weight                | backbone.dark2.1.conv3.conv.weight
                                    | (64, 64, 1, 1)                 |
| backbone.dark2.1.m.0.conv1.bn.*                   | backbone.dark2.1.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (32,) () (32,) (32,) (32,)     |
| backbone.dark2.1.m.0.conv1.conv.weight            | backbone.dark2.1.m.0.conv1.conv.weight
                                    | (32, 32, 1, 1)                 |
| backbone.dark2.1.m.0.conv2.bn.*                   | backbone.dark2.1.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (32,) () (32,) (32,) (32,)     |
| backbone.dark2.1.m.0.conv2.conv.weight            | backbone.dark2.1.m.0.conv2.conv.weight
                                    | (32, 32, 3, 3)                 |
| backbone.dark3.0.bn.*                             | backbone.dark3.0.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (128,) () (128,) (128,) (128,) |
| backbone.dark3.0.conv.weight                      | backbone.dark3.0.conv.weight
                                    | (128, 64, 3, 3)                |
| backbone.dark3.1.conv1.bn.*                       | backbone.dark3.1.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.conv1.conv.weight                | backbone.dark3.1.conv1.conv.weight
                                    | (64, 128, 1, 1)                |
| backbone.dark3.1.conv2.bn.*                       | backbone.dark3.1.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.conv2.conv.weight                | backbone.dark3.1.conv2.conv.weight
                                    | (64, 128, 1, 1)                |
| backbone.dark3.1.conv3.bn.*                       | backbone.dark3.1.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (128,) () (128,) (128,) (128,) |
| backbone.dark3.1.conv3.conv.weight                | backbone.dark3.1.conv3.conv.weight
                                    | (128, 128, 1, 1)               |
| backbone.dark3.1.m.0.conv1.bn.*                   | backbone.dark3.1.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.m.0.conv1.conv.weight            | backbone.dark3.1.m.0.conv1.conv.weight
                                    | (64, 64, 1, 1)                 |
| backbone.dark3.1.m.0.conv2.bn.*                   | backbone.dark3.1.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.m.0.conv2.conv.weight            | backbone.dark3.1.m.0.conv2.conv.weight
                                    | (64, 64, 3, 3)                 |
| backbone.dark3.1.m.1.conv1.bn.*                   | backbone.dark3.1.m.1.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.m.1.conv1.conv.weight            | backbone.dark3.1.m.1.conv1.conv.weight
                                    | (64, 64, 1, 1)                 |
| backbone.dark3.1.m.1.conv2.bn.*                   | backbone.dark3.1.m.1.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.m.1.conv2.conv.weight            | backbone.dark3.1.m.1.conv2.conv.weight
                                    | (64, 64, 3, 3)                 |
| backbone.dark3.1.m.2.conv1.bn.*                   | backbone.dark3.1.m.2.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.m.2.conv1.conv.weight            | backbone.dark3.1.m.2.conv1.conv.weight
                                    | (64, 64, 1, 1)                 |
| backbone.dark3.1.m.2.conv2.bn.*                   | backbone.dark3.1.m.2.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (64,) () (64,) (64,) (64,)     |
| backbone.dark3.1.m.2.conv2.conv.weight            | backbone.dark3.1.m.2.conv2.conv.weight
                                    | (64, 64, 3, 3)                 |
| backbone.dark4.0.bn.*                             | backbone.dark4.0.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (256,) () (256,) (256,) (256,) |
| backbone.dark4.0.conv.weight                      | backbone.dark4.0.conv.weight
                                    | (256, 128, 3, 3)               |
| backbone.dark4.1.conv1.bn.*                       | backbone.dark4.1.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.conv1.conv.weight                | backbone.dark4.1.conv1.conv.weight
                                    | (128, 256, 1, 1)               |
| backbone.dark4.1.conv2.bn.*                       | backbone.dark4.1.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.conv2.conv.weight                | backbone.dark4.1.conv2.conv.weight
                                    | (128, 256, 1, 1)               |
| backbone.dark4.1.conv3.bn.*                       | backbone.dark4.1.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (256,) () (256,) (256,) (256,) |
| backbone.dark4.1.conv3.conv.weight                | backbone.dark4.1.conv3.conv.weight
                                    | (256, 256, 1, 1)               |
| backbone.dark4.1.m.0.conv1.bn.*                   | backbone.dark4.1.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.m.0.conv1.conv.weight            | backbone.dark4.1.m.0.conv1.conv.weight
                                    | (128, 128, 1, 1)               |
| backbone.dark4.1.m.0.conv2.bn.*                   | backbone.dark4.1.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.m.0.conv2.conv.weight            | backbone.dark4.1.m.0.conv2.conv.weight
                                    | (128, 128, 3, 3)               |
| backbone.dark4.1.m.1.conv1.bn.*                   | backbone.dark4.1.m.1.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.m.1.conv1.conv.weight            | backbone.dark4.1.m.1.conv1.conv.weight
                                    | (128, 128, 1, 1)               |
| backbone.dark4.1.m.1.conv2.bn.*                   | backbone.dark4.1.m.1.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.m.1.conv2.conv.weight            | backbone.dark4.1.m.1.conv2.conv.weight
                                    | (128, 128, 3, 3)               |
| backbone.dark4.1.m.2.conv1.bn.*                   | backbone.dark4.1.m.2.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.m.2.conv1.conv.weight            | backbone.dark4.1.m.2.conv1.conv.weight
                                    | (128, 128, 1, 1)               |
| backbone.dark4.1.m.2.conv2.bn.*                   | backbone.dark4.1.m.2.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (128,) () (128,) (128,) (128,) |
| backbone.dark4.1.m.2.conv2.conv.weight            | backbone.dark4.1.m.2.conv2.conv.weight
                                    | (128, 128, 3, 3)               |
| backbone.dark5.0.bn.*                             | backbone.dark5.0.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (512,) () (512,) (512,) (512,) |
| backbone.dark5.0.conv.weight                      | backbone.dark5.0.conv.weight
                                    | (512, 256, 3, 3)               |
| backbone.dark5.1.conv1.bn.*                       | backbone.dark5.1.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (256,) () (256,) (256,) (256,) |
| backbone.dark5.1.conv1.conv.weight                | backbone.dark5.1.conv1.conv.weight
                                    | (256, 512, 1, 1)               |
| backbone.dark5.1.conv2.bn.*                       | backbone.dark5.1.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (512,) () (512,) (512,) (512,) |
| backbone.dark5.1.conv2.conv.weight                | backbone.dark5.1.conv2.conv.weight
                                    | (512, 1024, 1, 1)              |
| backbone.dark5.2.conv1.bn.*                       | backbone.dark5.2.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (256,) () (256,) (256,) (256,) |
| backbone.dark5.2.conv1.conv.weight                | backbone.dark5.2.conv1.conv.weight
                                    | (256, 512, 1, 1)               |
| backbone.dark5.2.conv2.bn.*                       | backbone.dark5.2.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (256,) () (256,) (256,) (256,) |
| backbone.dark5.2.conv2.conv.weight                | backbone.dark5.2.conv2.conv.weight
                                    | (256, 512, 1, 1)               |
| backbone.dark5.2.conv3.bn.*                       | backbone.dark5.2.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                  | (512,) () (512,) (512,) (512,) |
| backbone.dark5.2.conv3.conv.weight                | backbone.dark5.2.conv3.conv.weight
                                    | (512, 512, 1, 1)               |
| backbone.dark5.2.m.0.conv1.bn.*                   | backbone.dark5.2.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (256,) () (256,) (256,) (256,) |
| backbone.dark5.2.m.0.conv1.conv.weight            | backbone.dark5.2.m.0.conv1.conv.weight
                                    | (256, 256, 1, 1)               |
| backbone.dark5.2.m.0.conv2.bn.*                   | backbone.dark5.2.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}              | (256,) () (256,) (256,) (256,) |
| backbone.dark5.2.m.0.conv2.conv.weight            | backbone.dark5.2.m.0.conv2.conv.weight
                                    | (256, 256, 3, 3)               |
| backbone.stem.conv.bn.*                           | backbone.stem.conv.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                      | (32,) () (32,) (32,) (32,)     |
| backbone.stem.conv.conv.weight                    | backbone.stem.conv.conv.weight
                                    | (32, 12, 3, 3)                 |
| m.0.*                                             | m.0.{bias,weight}
                                    | (255,) (255,512,1,1)           |
| m.1.*                                             | m.1.{bias,weight}
                                    | (255,) (255,256,1,1)           |
| m.2.*                                             | m.2.{bias,weight}
                                    | (255,) (255,128,1,1)           |
| neck.C3_n3.conv1.bn.*                             | neck.C3_n3.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (128,) () (128,) (128,) (128,) |
| neck.C3_n3.conv1.conv.weight                      | neck.C3_n3.conv1.conv.weight
                                    | (128, 256, 1, 1)               |
| neck.C3_n3.conv2.bn.*                             | neck.C3_n3.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (128,) () (128,) (128,) (128,) |
| neck.C3_n3.conv2.conv.weight                      | neck.C3_n3.conv2.conv.weight
                                    | (128, 256, 1, 1)               |
| neck.C3_n3.conv3.bn.*                             | neck.C3_n3.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (256,) () (256,) (256,) (256,) |
| neck.C3_n3.conv3.conv.weight                      | neck.C3_n3.conv3.conv.weight
                                    | (256, 256, 1, 1)               |
| neck.C3_n3.m.0.conv1.bn.*                         | neck.C3_n3.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (128,) () (128,) (128,) (128,) |
| neck.C3_n3.m.0.conv1.conv.weight                  | neck.C3_n3.m.0.conv1.conv.weight
                                    | (128, 128, 1, 1)               |
| neck.C3_n3.m.0.conv2.bn.*                         | neck.C3_n3.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (128,) () (128,) (128,) (128,) |
| neck.C3_n3.m.0.conv2.conv.weight                  | neck.C3_n3.m.0.conv2.conv.weight
                                    | (128, 128, 3, 3)               |
| neck.C3_n4.conv1.bn.*                             | neck.C3_n4.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (256,) () (256,) (256,) (256,) |
| neck.C3_n4.conv1.conv.weight                      | neck.C3_n4.conv1.conv.weight
                                    | (256, 512, 1, 1)               |
| neck.C3_n4.conv2.bn.*                             | neck.C3_n4.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (256,) () (256,) (256,) (256,) |
| neck.C3_n4.conv2.conv.weight                      | neck.C3_n4.conv2.conv.weight
                                    | (256, 512, 1, 1)               |
| neck.C3_n4.conv3.bn.*                             | neck.C3_n4.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (512,) () (512,) (512,) (512,) |
| neck.C3_n4.conv3.conv.weight                      | neck.C3_n4.conv3.conv.weight
                                    | (512, 512, 1, 1)               |
| neck.C3_n4.m.0.conv1.bn.*                         | neck.C3_n4.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (256,) () (256,) (256,) (256,) |
| neck.C3_n4.m.0.conv1.conv.weight                  | neck.C3_n4.m.0.conv1.conv.weight
                                    | (256, 256, 1, 1)               |
| neck.C3_n4.m.0.conv2.bn.*                         | neck.C3_n4.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (256,) () (256,) (256,) (256,) |
| neck.C3_n4.m.0.conv2.conv.weight                  | neck.C3_n4.m.0.conv2.conv.weight
                                    | (256, 256, 3, 3)               |
| neck.C3_p3.conv1.bn.*                             | neck.C3_p3.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (64,) () (64,) (64,) (64,)     |
| neck.C3_p3.conv1.conv.weight                      | neck.C3_p3.conv1.conv.weight
                                    | (64, 256, 1, 1)                |
| neck.C3_p3.conv2.bn.*                             | neck.C3_p3.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (64,) () (64,) (64,) (64,)     |
| neck.C3_p3.conv2.conv.weight                      | neck.C3_p3.conv2.conv.weight
                                    | (64, 256, 1, 1)                |
| neck.C3_p3.conv3.bn.*                             | neck.C3_p3.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (128,) () (128,) (128,) (128,) |
| neck.C3_p3.conv3.conv.weight                      | neck.C3_p3.conv3.conv.weight
                                    | (128, 128, 1, 1)               |
| neck.C3_p3.m.0.conv1.bn.*                         | neck.C3_p3.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (64,) () (64,) (64,) (64,)     |
| neck.C3_p3.m.0.conv1.conv.weight                  | neck.C3_p3.m.0.conv1.conv.weight
                                    | (64, 64, 1, 1)                 |
| neck.C3_p3.m.0.conv2.bn.*                         | neck.C3_p3.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (64,) () (64,) (64,) (64,)     |
| neck.C3_p3.m.0.conv2.conv.weight                  | neck.C3_p3.m.0.conv2.conv.weight
                                    | (64, 64, 3, 3)                 |
| neck.C3_p4.conv1.bn.*                             | neck.C3_p4.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (128,) () (128,) (128,) (128,) |
| neck.C3_p4.conv1.conv.weight                      | neck.C3_p4.conv1.conv.weight
                                    | (128, 512, 1, 1)               |
| neck.C3_p4.conv2.bn.*                             | neck.C3_p4.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (128,) () (128,) (128,) (128,) |
| neck.C3_p4.conv2.conv.weight                      | neck.C3_p4.conv2.conv.weight
                                    | (128, 512, 1, 1)               |
| neck.C3_p4.conv3.bn.*                             | neck.C3_p4.conv3.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                        | (256,) () (256,) (256,) (256,) |
| neck.C3_p4.conv3.conv.weight                      | neck.C3_p4.conv3.conv.weight
                                    | (256, 256, 1, 1)               |
| neck.C3_p4.m.0.conv1.bn.*                         | neck.C3_p4.m.0.conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (128,) () (128,) (128,) (128,) |
| neck.C3_p4.m.0.conv1.conv.weight                  | neck.C3_p4.m.0.conv1.conv.weight
                                    | (128, 128, 1, 1)               |
| neck.C3_p4.m.0.conv2.bn.*                         | neck.C3_p4.m.0.conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                    | (128,) () (128,) (128,) (128,) |
| neck.C3_p4.m.0.conv2.conv.weight                  | neck.C3_p4.m.0.conv2.conv.weight
                                    | (128, 128, 3, 3)               |
| neck.bu_conv1.bn.*                                | neck.bu_conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                           | (256,) () (256,) (256,) (256,) |
| neck.bu_conv1.conv.weight                         | neck.bu_conv1.conv.weight
                                    | (256, 256, 3, 3)               |
| neck.bu_conv2.bn.*                                | neck.bu_conv2.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                           | (128,) () (128,) (128,) (128,) |
| neck.bu_conv2.conv.weight                         | neck.bu_conv2.conv.weight
                                    | (128, 128, 3, 3)               |
| neck.lateral_conv0.bn.*                           | neck.lateral_conv0.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                      | (256,) () (256,) (256,) (256,) |
| neck.lateral_conv0.conv.weight                    | neck.lateral_conv0.conv.weight
                                    | (256, 512, 1, 1)               |
| neck.reduce_conv1.bn.*                            | neck.reduce_conv1.bn.{bias,num_batches_tracked,running_mean,running_var,weight}                       | (128,) () (128,) (128,) (128,) |
| neck.reduce_conv1.conv.weight                     | neck.reduce_conv1.conv.weight
                                    | (128, 256, 1, 1)               |
| orien_head.neck_orien.0.conv_block.0.weight       | orien_head.neck_orien.0.conv_block.0.weight
                                    | (128, 256, 1, 1)               |
| orien_head.neck_orien.0.conv_block.1.*            | orien_head.neck_orien.0.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}       | (128,) () (128,) (128,) (128,) |
| orien_head.neck_orien.1.conv_block.0.weight       | orien_head.neck_orien.1.conv_block.0.weight
                                    | (256, 128, 3, 3)               |
| orien_head.neck_orien.1.conv_block.1.*            | orien_head.neck_orien.1.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}       | (256,) () (256,) (256,) (256,) |
| orien_head.neck_orien.2.conv_block.0.weight       | orien_head.neck_orien.2.conv_block.0.weight
                                    | (128, 256, 1, 1)               |
| orien_head.neck_orien.2.conv_block.1.*            | orien_head.neck_orien.2.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}       | (128,) () (128,) (128,) (128,) |
| orien_head.neck_orien.3.conv_block.0.weight       | orien_head.neck_orien.3.conv_block.0.weight
                                    | (256, 128, 3, 3)               |
| orien_head.neck_orien.3.conv_block.1.*            | orien_head.neck_orien.3.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}       | (256,) () (256,) (256,) (256,) |
| orien_head.neck_orien.4.conv_block.0.weight       | orien_head.neck_orien.4.conv_block.0.weight
                                    | (128, 256, 1, 1)               |
| orien_head.neck_orien.4.conv_block.1.*            | orien_head.neck_orien.4.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}       | (128,) () (128,) (128,) (128,) |
| orien_head.orien_m.0.conv_block.0.weight          | orien_head.orien_m.0.conv_block.0.weight
                                    | (256, 128, 3, 3)               |
| orien_head.orien_m.0.conv_block.1.*               | orien_head.orien_m.0.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}          | (256,) () (256,) (256,) (256,) |
| orien_head.orien_m.1.conv_block.0.weight          | orien_head.orien_m.1.conv_block.0.weight
                                    | (128, 256, 1, 1)               |
| orien_head.orien_m.1.conv_block.1.*               | orien_head.orien_m.1.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}          | (128,) () (128,) (128,) (128,) |
| orien_head.orien_m.2.conv_block.0.weight          | orien_head.orien_m.2.conv_block.0.weight
                                    | (256, 128, 3, 3)               |
| orien_head.orien_m.2.conv_block.1.*               | orien_head.orien_m.2.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}          | (256,) () (256,) (256,) (256,) |
| orien_head.orien_m.3.conv_block.0.weight          | orien_head.orien_m.3.conv_block.0.weight
                                    | (128, 256, 1, 1)               |
| orien_head.orien_m.3.conv_block.1.*               | orien_head.orien_m.3.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}          | (128,) () (128,) (128,) (128,) |
| orien_head.orien_m.4.conv_block.0.weight          | orien_head.orien_m.4.conv_block.0.weight
                                    | (256, 128, 3, 3)               |
| orien_head.orien_m.4.conv_block.1.*               | orien_head.orien_m.4.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}          | (256,) () (256,) (256,) (256,) |
| orien_head.orien_m.5.*                            | orien_head.orien_m.5.{bias,weight}
                                    | (18,) (18,256,1,1)             |
| orien_head.up_levels_2to5.0.conv_block.0.weight   | orien_head.up_levels_2to5.0.conv_block.0.weight
                                    | (64, 64, 1, 1)                 |
| orien_head.up_levels_2to5.0.conv_block.1.*        | orien_head.up_levels_2to5.0.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight}   | (64,) () (64,) (64,) (64,)     |
| orien_head.up_levels_2to5.1.0.conv_block.0.weight | orien_head.up_levels_2to5.1.0.conv_block.0.weight
                                    | (64, 128, 1, 1)                |
| orien_head.up_levels_2to5.1.0.conv_block.1.*      | orien_head.up_levels_2to5.1.0.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight} | (64,) () (64,) (64,) (64,)     |
| orien_head.up_levels_2to5.2.0.conv_block.0.weight | orien_head.up_levels_2to5.2.0.conv_block.0.weight
                                    | (64, 256, 1, 1)                |
| orien_head.up_levels_2to5.2.0.conv_block.1.*      | orien_head.up_levels_2to5.2.0.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight} | (64,) () (64,) (64,) (64,)     |
| orien_head.up_levels_2to5.3.0.conv_block.0.weight | orien_head.up_levels_2to5.3.0.conv_block.0.weight
                                    | (64, 512, 1, 1)                |
| orien_head.up_levels_2to5.3.0.conv_block.1.*      | orien_head.up_levels_2to5.3.0.conv_block.1.{bias,num_batches_tracked,running_mean,running_var,weight} | (64,) () (64,) (64,) (64,)     |
416 416 600
confidence thresh:  0.21
image after transform:  (416, 416, 3)
cost: 0.02672123908996582, fps: 37.42341425983922
Traceback (most recent call last):
  File "demo.py", line 211, in <module>
    res = vis_res_fast(res, img, class_names, colors, conf_thresh)
  File "demo.py", line 146, in vis_res_fast
    img, clss, bit_masks, force_colors=None, draw_contours=False
  File "/home/ws/.local/lib/python3.6/site-packages/alfred/vis/image/mask.py", line 302, in vis_bitmasks_with_classes
    txt = f'{class_names[classes[i]]}'
TypeError: list indices must be integers or slices, not numpy.float32

I think this is happened because this line: scores = ins.scores.cpu().numpy() clss = ins.pred_classes.cpu().numpy()

This cuse clss to by a numpy array and not a list of int. Am I right. How it's works for you?

Also, in line (157): if bboxes: bboxes is not bool Maybe it's should by: if bboxes is not None:

Thanks

i do traning on cutstom dataset and i got this error

`[07/11 22:24:10 detectron2]: Running with full config was omitted.
[07/11 22:24:10 detectron2]: Full config saved to ./output/config.yaml
[07/11 22:24:10 d2.utils.env]: Using a generated random seed 10422596
[07/11 22:24:10 d2.engine.defaults]: Auto-scaling the config to batch_size=2, learning_rate=0.0025, max_iter=720000, warmup=8000.
Traceback (most recent call last):
  File "train_ahmed.py", line 61, in <module>
    args=(args,),
  File "/content/detectron2/detectron2/engine/launch.py", line 82, in launch
    main_func(*args)
  File "train_ahmed.py", line 48, in main
    trainer = Trainer(cfg)
  File "/content/detectron2/detectron2/engine/defaults.py", line 376, in __init__
    model = self.build_model(cfg)
  File "/content/yolov7/train_det.py", line 37, in build_model
    model = build_model(cfg)
  File "/content/detectron2/detectron2/modeling/meta_arch/build.py", line 22, in build_model
    model = META_ARCH_REGISTRY.get(meta_arch)(cfg)
  File "/content/yolov7/yolov7/modeling/meta_arch/yolov7.py", line 95, in __init__
    backbone_shape = [backbone_shape[i].channels for i in self.in_features]
  File "/content/yolov7/yolov7/modeling/meta_arch/yolov7.py", line 95, in <listcomp>
    backbone_shape = [backbone_shape[i].channels for i in self.in_features]
KeyError: 'dark3'`

Hello!

Has anyone successfully exported the sparseinst model to onnx and run inference on it using onnxruntime? I'm struggling getting reasonable output when running inference in C++ onnxruntime. I'm running the same frame through the network pre-export and the output looks well. But post-export the model outputs scores size (1, 50) with all zeros but a few scores ~0.04. For comparison the model pre-export output two person labels and one sports ball all over 80% confidence.

I exported the model using the provided export_onnx.py as per readme instruction. The onnx-model looks, from what I can tell, fine when looking at it Netron.

Just wondering if anyone has successfully used the exported model and could share some examples? =)

Thank you so much for your work @jinfagang. I have tested yolov7 and I realized that SparseInst models cannot be converted to ONNX. Is the export onnx code compatible with exporting SparseInst models?

While trying to train a SparseInst on a custom dataset for instance segmentation, I came across the following error:

AttributeError: Cannot find field 'pred_boxes' in the given Instances!

The error is thrown from within the detectron2library. When I print one Instance, there are indeed no pred_boxesin there, only pred_masks. But as far as I understand, SparseInst does not output bounding boxes, it predicts directly seg. masks, as they highlight in their paper: https://arxiv.org/abs/2203.12827 Then I am wondering why the code is looking for predicted boxes in this case... o_0

Hi, I tried exporting the weights of SparseInst (GIAM) that are in this repository to ONNX format using export.py with the following command (I assume I need to use this command? The documentation reads 'use export_onnx.py', but there's no export_onnx.py in the current branch of this repository).

python export.py --config-file configs/coco/sparseinst/sparse_inst_r50_giam_aug.yaml --opts MODEL.WEIGHTS weights/base_giam.pth INPUT.MIN_SIZE_TEST 512

This leads to the following issue:

  File ".../yolov7/yolov7/modeling/meta_arch/sparseinst.py", line 95, in <listcomp>
    images = [x["image"].to(self.device) for x in batched_inputs]
IndexError: too many indices for tensor of dimension 3

Any ideas on how to fix this? Thank you.

I try the demo of sparseinst like this

python demo.py --config-file configs/coco/sparseinst/sparse_inst_r50vd_giam_aug.yaml --video-input ~/Movies/Videos/86277963_nb2-1-80.flv -c 0.4 --opts MODEL.WEIGHTS weights/sparse_inst_r50vd_giam_aug_8bc5b3.pth

but I get error

Traceback (most recent call last):
  File "/home/dreamdeck/Documents/MJJ/code/Seg/yolov7-main/demo.py", line 230, in <module>
    res = vis_res_fast(res, frame, class_names, colors, conf_thresh)
  File "/home/dreamdeck/Documents/MJJ/code/Seg/yolov7-main/demo.py", line 155, in vis_res_fast
    img, clss, bit_masks, force_colors=None, draw_contours=False
  File "/home/dreamdeck/anaconda3/envs/detectron2/lib/python3.6/site-packages/alfred/vis/image/mask.py", line 285, in vis_bitmasks_with_classes
    thickness=-1, lineType=cv2.LINE_AA)
cv2.error: OpenCV(4.5.5) /io/opencv/modules/imgproc/src/drawing.cpp:2599: error: (-215:Assertion failed) reader.ptr != NULL in function 'cvDrawContours'

I debuged and found that there is no masks when run predictions = self.model([inputs]). All values in pred_masks are False.

Do I need modify the config file or download other files?( I have dolwnloaded the sparse_inst_r50vd_giam_aug_8bc5b3.pth) What is the full steps to test demo?

@jinfagang ,hello, thank you for your work! when i convert SparseInst onnx model to tensorrt model using trtexec , the error is In node -1(importRange):UNSUPPORTED_NODE:Assertion failed:inputs.at(0).isInt32() && "For range operator with dynamic inputs,this version og TensorRT only supports INT32!

tensorrt version:7.2.3 onnx version : v7 pytorch version:1.10

This PR adds the ability to log training metrics for object detection into Weights & Biases. Here's an example dashboard of fine-tuning the faster_rcnn_R_101_FPN_3x detection model from detectron2 model zoo.

您好，很感谢您提供代码，我在使用代码时遇到两个问题，希望您能帮忙解答一下

1. 当我使用yolomask_8gpu.yaml在coco上做evaluation时，到第27张图片进入model得到输出后显存会增加300多M，到第47张图片又会同样增加，然后就报错cuda out of memory。

2. coco-instance中包含两个config，其中yolomask.yaml中IMS_PER_BATCH:3，但是默认的REFERENCE_WORLD_SIZE: 8，运行时会报错提示IMS_PER_BATCH是无效的，查看源码提示IMS_PER_BATCH%REFERENCE_WORLD_SIZE==0

Hi, @jinfagang

According to darknet53.yaml, I downloaded yolov3.pt as the backbone for initializing the yolov7.

However, I met the following problem, do you have any suggestions to provide accurate parameters for yolov7?

RuntimeError: Error(s) in loading state_dict for Darknet: Missing key(s) in state_dict: "stem.conv.weight", "stem.bn.weight", "stem.bn.bias", "stem.bn.running_mean", "stem.bn.running_var", "dark1.0.conv.weight", "dark1.0.bn.weight", "dark1.0.bn.bias", "dark1.0.bn.running_mean", "dark1.0.bn.running_var", "dark1.1.layer1.conv.weight", "dark1.1.layer1.bn.weight", "dark1.1.layer1.bn.bias", "dark1.1.layer1.bn.running_mean", "dark1.1.layer1.bn.running_var", "dark1.1.layer2.conv.weight", "dark1.1.layer2.bn.weight", ....... .......

Hello, I appreciate your novel work. I have a question about your implementation. From official yolo v7 github, there are no public backbone network application around yolo v7 variants. Therefore, I can't find the certain name of yolov7 backbone or head network. Then, Could you give me an exact combination for each yolov7 variant from your implementation? For example, darknet53 + yolofpn + yoloxhead = yolo_v7_xxx.

Best regards.

Hi,

• in docker container from pytorch/pytorch:1.11.0-cuda11.3-cudnn8-devel
• torch 1.11.0
• torchvision 0.12.0
• latest detectron2 main branch
• python3 export.py failed while exporting model sparse_inst_r50vd_dcn_giam_aug.yaml

Error log:

Traceback (most recent call last):
  File "export.py", line 285, in <module>
    torch.onnx.export(
  File "/opt/conda/lib/python3.8/site-packages/torch/onnx/__init__.py", line 305, in export
    return utils.export(model, args, f, export_params, verbose, training,
  File "/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py", line 118, in export
    _export(model, args, f, export_params, verbose, training, input_names, output_names,
  File "/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py", line 738, in _export
    proto, export_map, val_use_external_data_format = graph._export_onnx(
RuntimeError: ONNX export failed: Couldn't export Python operator _DeformConv


Defined at:
/yolov7_d2/detectron2/detectron2/layers/deform_conv.py(394): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1098): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1110): _call_impl
/yolov7_d2/yolov7/modeling/backbone/resnetvd.py(105): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1098): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1110): _call_impl
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/container.py(141): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1098): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1110): _call_impl
/yolov7_d2/yolov7/modeling/backbone/resnetvd.py(509): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1098): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1110): _call_impl
/yolov7_d2/yolov7/modeling/meta_arch/sparseinst.py(147): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1098): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1110): _call_impl
/opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py(118): wrapper
/opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py(127): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1110): _call_impl
/opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py(1166): _get_trace_graph
/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py(391): _trace_and_get_graph_from_model
/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py(440): _create_jit_graph
/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py(499): _model_to_graph
/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py(719): _export
/opt/conda/lib/python3.8/site-packages/torch/onnx/utils.py(118): export
/opt/conda/lib/python3.8/site-packages/torch/onnx/__init__.py(305): export
export.py(285): <module>