Performing inference with ResNeSt 200 returns empty tensors

[borgarpa]

Instructions To Reproduce the Issue:

I was trying to follow this tutorial Detectron2 Beginner's Tutorial using ResNeSt 200 instead of Mask RCNN to perform instance segmentation.

In order to get the configs and weights of the model I used the code proposed in the following issue #25 :

cfg = get_cfg()
cfg.merge_from_file('./configs/COCO-InstanceSegmentation/mask_cascade_rcnn_ResNeSt_200_FPN_dcn_syncBN_all_tricks_3x.yaml')
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
cfg.MODEL.WEIGHTS = "https://s3.us-west-1.wasabisys.com/resnest/detectron/resnest200_detectron-02644020.pth"
predictor = DefaultPredictor(cfg)
outputs = predictor(im)

print(outputs["instances"].pred_classes)
print(outputs["instances"].pred_boxes)

Running the code above returns empty tensors when performing inference:

tensor([], device='cuda:0', dtype=torch.int64)
Boxes(tensor([], device='cuda:0', size=(0, 4)))

Hence, there is no generated mask:

Expected behavior:

The model should return an image masked and labeled. If the model is instantiated through the model zoo the results are as expected:

cfg = get_cfg()
cfg.merge_from_file("./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")

print(outputs["instances"].pred_classes)
print(outputs["instances"].pred_boxes)

Results:

tensor([17,  0,  0,  0,  0,  0,  0,  0, 25,  0, 25, 25,  0,  0, 24],
       device='cuda:0')
Boxes(tensor([[126.6035, 244.8977, 459.8291, 480.0000],
        [251.1083, 157.8127, 338.9731, 413.6379],
        [114.8496, 268.6864, 148.2352, 398.8111],
        [  0.8217, 281.0327,  78.6072, 478.4210],
        [ 49.3954, 274.1229,  80.1545, 342.9808],
        [561.2248, 271.5816, 596.2755, 385.2552],
        [385.9072, 270.3125, 413.7130, 304.0397],
        [515.9295, 278.3744, 562.2792, 389.3802],
        [335.2409, 251.9167, 414.7491, 275.9375],
        [350.9300, 269.2060, 386.0984, 297.9081],
        [331.6292, 230.9996, 393.2759, 257.2009],
        [510.7349, 263.2656, 570.9865, 295.9194],
        [409.0841, 271.8646, 460.5582, 356.8722],
        [506.8767, 283.3257, 529.9403, 324.0392],
        [594.5663, 283.4820, 609.0577, 311.4124]], device='cuda:0'))

Masked image:

Environment:

After running the following command:

wget -nc -q https://github.com/facebookresearch/detectron2/raw/master/detectron2/utils/collect_env.py && python collect_env.py

I get environment details as follows:

------------------------  ---------------------------------------------------------------
sys.platform              linux
Python                    3.6.9 (default, Jul 17 2020, 12:50:27) [GCC 8.4.0]
numpy                     1.18.5
detectron2                0.1.1 @/content/detectron2-ResNeSt/detectron2
detectron2 compiler       GCC 7.5
detectron2 CUDA compiler  10.1
detectron2 arch flags     sm_75
DETECTRON2_ENV_MODULE     <not set>
PyTorch                   1.6.0+cu101 @/usr/local/lib/python3.6/dist-packages/torch
PyTorch debug build       False
CUDA available            True
GPU 0                     Tesla T4
CUDA_HOME                 /usr/local/cuda
NVCC                      Cuda compilation tools, release 10.1, V10.1.243
Pillow                    7.0.0
torchvision               0.7.0+cu101 @/usr/local/lib/python3.6/dist-packages/torchvision
torchvision arch flags    sm_35, sm_50, sm_60, sm_70, sm_75
cv2                       4.1.2
------------------------  ---------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, 

[AdnanMunir294]

Same problem if you find solution please let me know thanks