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Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
https://detectron2.readthedocs.io/en/latest/
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Some model parameters or buffers are not found in the checkpoint #4090

Closed RichardcLee closed 2 years ago

RichardcLee commented 2 years ago

Expected behavior:

It should load all my model weights.

SOS！Why detectron2 version==0.4，can not load my whole model weights?

image

Instructions To Reproduce the 🐛 Bug:

  1. What exact command you run: python projects/CenterNet2/train_net.py --config-file projects/CenterNet2/configs/OurNet_R101_DCN.yaml --eval-only MODEL.WEIGHTS D:\MyFile\dataset\model\processed+aerial_processes\OurNet_R101_DCN\model_0069999.pth

  2. Full logs or other relevant observations:

    
Command Line Args: Namespace(config_file='projects/CenterNet2/configs/OurNet_R101_DCN.yaml', dist_url='tcp://127.0.0.1:46263', eval_only=True, machine_rank=0, manual_device='', num_gpus=1
, num_machines=1, opts=['MODEL.WEIGHTS', 'D:\\MyFile\\dataset\\model\\processed+aerial_processes\\OurNet_R101_DCN\\model_0069499.pth'], resume=False)
Config 'projects/CenterNet2/configs/OurNet_R101_DCN.yaml' has no VERSION. Assuming it to be compatible with latest v2.
[03/19 20:45:12 detectron2]: Rank of current process: 0. World size: 1
[03/19 20:45:13 detectron2]: Environment info:
----------------------  --------------------------------------------------------------------------------------------------
sys.platform            win32
Python                  3.7.11 (default, Jul 27 2021, 09:42:29) [MSC v.1916 64 bit (AMD64)]
numpy                   1.17.0
detectron2              0.6 @d:\myfile\projects\git projects\detectron2\detectron2
Compiler                MSVC 192628806
CUDA compiler           CUDA 10.2
detectron2 arch flags   d:\myfile\projects\git projects\detectron2\detectron2\_C.cp37-win_amd64.pyd; cannot find cuobjdump
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.10.2+cu102 @D:\MyFile\tool\Anaconda\lib\site-packages\torch
PyTorch debug build     False
GPU available           Yes
GPU 0                   GeForce GTX 1660 Ti (arch=7.5)
Driver version          457.49
CUDA_HOME               C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
Pillow                  8.3.1
torchvision             0.11.3+cu102 @D:\MyFile\tool\Anaconda\lib\site-packages\torchvision
torchvision arch flags  D:\MyFile\tool\Anaconda\lib\site-packages\torchvision\_C.pyd; cannot find cuobjdump
fvcore                  0.1.5
iopath                  0.1.8
cv2                     4.5.4-dev
----------------------  --------------------------------------------------------------------------------------------------
PyTorch built with:
- C++ Version: 199711
- MSVC 192829337
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 2019
- LAPACK is enabled (usually provided by MKL)
- CPU capability usage: AVX2
- CUDA Runtime 10.2
- 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_61,code=sm_61;-gencode;arch=
compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
- CuDNN 7.6.5
- Magma 2.5.4
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=C:/w/b/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /
w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/w/b/windows/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE
_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLO
G=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON,

[03/19 20:45:13 detectron2]: Command line arguments: Namespace(config_file='projects/CenterNet2/configs/OurNet_R101_DCN.yaml', dist_url='tcp://127.0.0.1:46263', eval_only=True, machine_ra nk=0, manual_device='', num_gpus=1, num_machines=1, opts=['MODEL.WEIGHTS', 'D:\MyFile\dataset\model\processed+aerial_processes\OurNet_R101_DCN\model_0069499.pth'], resume=False) [03/19 20:45:13 detectron2]: Contents of args.config_file=projects/CenterNet2/configs/OurNet_R101_DCN.yaml: BASE: "Base-OurNet.yaml" MODEL: OURNET: USE_DEFORMABLE: True RESNETS: DEPTH: 101 DEFORM_ON_PER_STAGE: [False, False, True, True] # on Res4, Res5 DEFORM_MODULATED: True SOLVER: IMS_PER_BATCH: 1

[03/19 20:45:13 detectron2]: Running with full config: 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:

[03/19 20:45:13 detectron2]: Full config saved to ./output/OurNet/OurNet_R101_DCN\config.yaml [03/19 20:45:13 d2.utils.env]: Using a generated random seed 13956036

GeneralizedRCNN( (backbone): FPN( (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (top_block): LastLevelP6P7_P5( (p6): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (p7): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (bottom_up): ResNet( (stem): BasicStem( (conv1): Conv2d( 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) ) (res2): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv1): Conv2d( 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) ) (res3): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv1): Conv2d( 256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (3): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) ) (res4): Sequential( (0): DeformBottleneckBlock( (shortcut): Conv2d( 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv1): Conv2d( 512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (1): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (2): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (3): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (4): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (5): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (6): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (7): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (8): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (9): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (10): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (11): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (12): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (13): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (14): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (15): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (16): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (17): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (18): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (19): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (20): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (21): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (22): DeformBottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2_offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) ) (res5): Sequential( (0): DeformBottleneckBlock( (shortcut): Conv2d( 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv1): Conv2d( 1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (1): DeformBottleneckBlock( (conv1): Conv2d( 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (2): DeformBottleneckBlock( (conv1): Conv2d( 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2_offset): Conv2d(512, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): ModulatedDeformConv( in_channels=512, out_channels=512, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) ) ) ) (proposal_generator): OurNet( (iou_loss): IOULoss() (ournet_head): OurNetHead( (cls_tower): Sequential() (bbox_tower): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): GroupNorm(32, 256, eps=1e-05, affine=True) (2): ReLU(inplace=True) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): GroupNorm(32, 256, eps=1e-05, affine=True) (5): ReLU(inplace=True) (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (7): GroupNorm(32, 256, eps=1e-05, affine=True) (8): ReLU(inplace=True) (9): DFConv2d( (offset): Conv2d(256, 27, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv): ModulatedDeformConv(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=1, padding=1, dilation=1, groups=1, deformable_groups=1, bias=True) ) (10): GroupNorm(32, 256, eps=1e-05, affine=True) (11): ReLU(inplace=True) ) (share_tower): Sequential() (offset): Conv2d(256, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (reppoints_dconv): DeformConv(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=1, deformable_groups=1, bias=False) (bbox_pred): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (scales): ModuleList( (0): Scale() (1): Scale() (2): Scale() (3): Scale() (4): Scale() ) (relu): ReLU(inplace=True) (agn_hm): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (roi_heads): CustomCascadeROIHeads( (box_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True) (3): ROIAlign(output_size=(7, 7), spatial_scale=0.015625, sampling_ratio=0, aligned=True) (4): ROIAlign(output_size=(7, 7), spatial_scale=0.0078125, sampling_ratio=0, aligned=True) ) ) (box_head): ModuleList( (0): FastRCNNConvFCHead( (flatten): Flatten(start_dim=1, end_dim=-1) (fc1): Linear(in_features=12544, out_features=1024, bias=True) (fc_relu1): ReLU() (fc2): Linear(in_features=1024, out_features=1024, bias=True) (fc_relu2): ReLU() ) (1): FastRCNNConvFCHead( (flatten): Flatten(start_dim=1, end_dim=-1) (fc1): Linear(in_features=12544, out_features=1024, bias=True) (fc_relu1): ReLU() (fc2): Linear(in_features=1024, out_features=1024, bias=True) (fc_relu2): ReLU() ) (2): FastRCNNConvFCHead( (flatten): Flatten(start_dim=1, end_dim=-1) (fc1): Linear(in_features=12544, out_features=1024, bias=True) (fc_relu1): ReLU() (fc2): Linear(in_features=1024, out_features=1024, bias=True) (fc_relu2): ReLU() ) ) (box_predictor): ModuleList( (0): CustomFastRCNNOutputLayers( (cls_score): Linear(in_features=1024, out_features=4, bias=True) (bbox_pred): Linear(in_features=1024, out_features=4, bias=True) ) (1): CustomFastRCNNOutputLayers( (cls_score): Linear(in_features=1024, out_features=4, bias=True) (bbox_pred): Linear(in_features=1024, out_features=4, bias=True) ) (2): CustomFastRCNNOutputLayers( (cls_score): Linear(in_features=1024, out_features=4, bias=True) (bbox_pred): Linear(in_features=1024, out_features=4, bias=True) ) ) ) ) [03/19 20:45:15 fvcore.common.checkpoint]: [Checkpointer] Loading from D:\MyFile\dataset\model\processed+aerial_processes\OurNet_R101_DCN\model_0069499.pth ... WARNING [03/19 20:45:15 fvcore.common.checkpoint]: Some model parameters or buffers are not found in the checkpoint: proposal_generator.ournet_head.bbox_tower.9.conv.{bias, weight} proposal_generator.ournet_head.bbox_tower.9.offset.{bias, weight} WARNING [03/19 20:45:15 fvcore.common.checkpoint]: The checkpoint state_dict contains keys that are not used by the model: proposal_generator.ournet_head.bbox_tower.9.{bias, weight} [03/19 20:45:15 d2.data.datasets.coco]: Loaded 150 images in COCO format from D:\MyFile\dataset\detection\datasets\test\annotations_ori.json [03/19 20:45:15 d2.data.build]: Distribution of instances among all 3 categories: category #instances category #instances category #instances
digger 41 motocrane 196 towercrane 49
total 286
[03/19 20:45:15 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(1200, 1200), max_size=3200, sample_style='choice')] [03/19 20:45:15 d2.data.common]: Serializing 150 elements to byte tensors and concatenating them all ... [03/19 20:45:15 d2.data.common]: Serialized dataset takes 0.05 MiB WARNING [03/19 20:45:15 d2.evaluation.coco_evaluation]: COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead. [03/19 20:45:15 d2.evaluation.evaluator]: Start inference on 150 batches D:\MyFile\tool\Anaconda\lib\site-packages\torch\functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered inter nally at ..\aten\src\ATen\native\TensorShape.cpp:2157.) return _VF.meshgrid(tensors, *kwargs) # type: ignore[attr-defined] [03/19 20:45:30 d2.evaluation.evaluator]: Inference done 11/150. Dataloading: 0.0007 s/iter. Inference: 0.3719 s/iter. Eval: 0.0003 s/iter. Total: 0.3729 s/iter. ETA=0:00:51 [03/19 20:45:36 d2.evaluation.evaluator]: Inference done 26/150. Dataloading: 0.0008 s/iter. Inference: 0.3555 s/iter. Eval: 0.0003 s/iter. Total: 0.3568 s/iter. ETA=0:00:44 [03/19 20:45:41 d2.evaluation.evaluator]: Inference done 41/150. Dataloading: 0.0008 s/iter. Inference: 0.3552 s/iter. Eval: 0.0003 s/iter. Total: 0.3564 s/iter. ETA=0:00:38 [03/19 20:45:46 d2.evaluation.evaluator]: Inference done 55/150. Dataloading: 0.0009 s/iter. Inference: 0.3556 s/iter. Eval: 0.0003 s/iter. Total: 0.3569 s/iter. ETA=0:00:33 [03/19 20:45:51 d2.evaluation.evaluator]: Inference done 70/150. Dataloading: 0.0008 s/iter. Inference: 0.3544 s/iter. Eval: 0.0003 s/iter. Total: 0.3557 s/iter. ETA=0:00:28 [03/19 20:45:56 d2.evaluation.evaluator]: Inference done 84/150. Dataloading: 0.0008 s/iter. Inference: 0.3553 s/iter. Eval: 0.0003 s/iter. Total: 0.3565 s/iter. ETA=0:00:23 [03/19 20:46:01 d2.evaluation.evaluator]: Inference done 96/150. Dataloading: 0.0009 s/iter. Inference: 0.3643 s/iter. Eval: 0.0003 s/iter. Total: 0.3656 s/iter. ETA=0:00:19 [03/19 20:46:07 d2.evaluation.evaluator]: Inference done 110/150. Dataloading: 0.0010 s/iter. Inference: 0.3653 s/iter. Eval: 0.0003 s/iter. Total: 0.3670 s/iter. ETA=0:00:14 [03/19 20:46:12 d2.evaluation.evaluator]: Inference done 124/150. Dataloading: 0.0010 s/iter. Inference: 0.3659 s/iter. Eval: 0.0003 s/iter. Total: 0.3675 s/iter. ETA=0:00:09 [03/19 20:46:17 d2.evaluation.evaluator]: Inference done 138/150. Dataloading: 0.0010 s/iter. Inference: 0.3665 s/iter. Eval: 0.0003 s/iter. Total: 0.3681 s/iter. ETA=0:00:04 [03/19 20:46:22 d2.evaluation.evaluator]: Total inference time: 0:00:54.190565 (0.373728 s / iter per device, on 1 devices) [03/19 20:46:22 d2.evaluation.evaluator]: Total inference pure compute time: 0:00:53 (0.369442 s / iter per device, on 1 devices) [03/19 20:46:22 d2.evaluation.coco_evaluation]: Preparing results for COCO format ... [03/19 20:46:22 d2.evaluation.coco_evaluation]: Saving results to ./output/OurNet/OurNet_R101_DCN\inference_test\coco_instances_results.json [03/19 20:46:22 d2.evaluation.coco_evaluation]: Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.00s) creating index... index created! [03/19 20:46:22 d2.evaluation.fast_eval_api]: Evaluate annotation type bbox* [03/19 20:46:22 d2.evaluation.fast_eval_api]: COCOeval_opt.evaluate() finished in 0.05 seconds. [03/19 20:46:22 d2.evaluation.fast_eval_api]: Accumulating evaluation results... [03/19 20:46:22 d2.evaluation.fast_eval_api]: COCOeval_opt.accumulate() finished in 0.01 seconds. Average Precision (AP) @[ IoU=0.50:0.95 area= all maxDets=100 ] = 0.010 Average Precision (AP) @[ IoU=0.50 area= all maxDets=100 ] = 0.021 Average Precision (AP) @[ IoU=0.75 area= all maxDets=100 ] = 0.010 Average Precision (AP) @[ IoU=0.50:0.95 area= small maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 area=medium maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 area= large maxDets=100 ] = 0.012 Average Recall (AR) @[ IoU=0.50:0.95 area= all maxDets= 1 ] = 0.013 Average Recall (AR) @[ IoU=0.50:0.95 area= all maxDets= 10 ] = 0.015 Average Recall (AR) @[ IoU=0.50:0.95 area= all maxDets=100 ] = 0.015 Average Recall (AR) @[ IoU=0.50:0.95 area= small maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 area=medium maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 area= large maxDets=100 ] = 0.020 [03/19 20:46:22 d2.evaluation.coco_evaluation]: Evaluation results for bbox: AP AP50 AP75 APs APm APl
1.039 2.132 0.990 0.000 0.000 1.226
[03/19 20:46:22 d2.evaluation.coco_evaluation]: Per-category bbox AP: category AP category AP category AP
digger 0.000 motocrane 2.673 towercrane 0.446

[03/19 20:46:22 detectron2]: Evaluation results for test in csv format: [03/19 20:46:22 d2.evaluation.testing]: copypaste: Task: bbox [03/19 20:46:22 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl [03/19 20:46:22 d2.evaluation.testing]: copypaste: 1.0395,2.1322,0.9901,0.0000,0.0000,1.2261

mycfhs commented 2 years ago

Hello, I met this problem too, have you solved it?

an99990 commented 2 years ago

i have the same issues, did anyone find anything ?

Some model parameters or buffers are not found in the checkpoint:
basis_module.seg_head.0.weight
basis_module.seg_head.1.{bias, running_mean, running_var, weight}
basis_module.seg_head.3.weight
basis_module.seg_head.4.{bias, running_mean, running_var, weight}
basis_module.seg_head.6.{bias, weight}
zxf29 commented 1 year ago

Hello, I met this problem too, have you solved it?

zaharaddeen11 commented 1 year ago

Hello, I encountered the same issue any hint? Please.

116022017144 commented 1 year ago

您好，我遇到了同样的问题有什么提示吗？请。

zxf29 commented 1 year ago

您好，不好意思，我也没有找到解决的方法