AttributeError: Method AffineChannelNd is not a registered operator. Did you mean: [AffineChannel]

Traceback (most recent call last): File "tools/test_net.py", line 151, in main(ind_range=args.range, multi_gpu_testing=False) File "tools/test_net.py", line 113, in main engine.test_net_on_dataset(multi_gpu=multi_gpu_testing) File "/home/chandu/workspace/tracking/DaT/lib/core/test_engine.py", line 321, in test_net_on_dataset all_boxes, all_segms, all_keyps = test_net() File "/home/chandu/workspace/tracking/DaT/lib/core/test_engine.py", line 135, in test_net model = initialize_model_from_cfg() File "/home/chandu/workspace/tracking/DaT/lib/core/test_engine.py", line 59, in initialize_model_from_cfg init_params=cfg.TEST.INIT_RANDOM_VARS_BEFORE_LOADING) File "/home/chandu/workspace/tracking/DaT/lib/modeling/model_builder.py", line 63, in create return get_func(model_name)(init_model(model_name, train, init_params)) File "/home/chandu/workspace/tracking/DaT/lib/modeling/model_builder.py", line 156, in keypoint_rcnn add_roi_keypoint_head_func=get_func(cfg.KRCNN.ROI_KEYPOINTS_HEAD)) File "/home/chandu/workspace/tracking/DaT/lib/modeling/model_builder.py", line 307, in build_generic_fast_rcnn_model build_data_parallel_model(model, _single_gpu_build_func) File "/home/chandu/workspace/tracking/DaT/lib/modeling/model_builder.py", line 953, in build_data_parallel_model single_gpu_build_func(model) File "/home/chandu/workspace/tracking/DaT/lib/modeling/model_builder.py", line 199, in _single_gpu_build_func blob_conv, dim_conv, spatial_scale_conv = add_conv_body_func(model) File "/home/chandu/workspace/tracking/DaT/lib/modeling/FPN3D.py", line 51, in add_fpn_ResNet101_conv5_body ResNet.stage_info_ResNet101_conv5) File "/home/chandu/workspace/tracking/DaT/lib/modeling/FPN3D.py", line 98, in add_fpn_generic_onto_body conv_body_func(model) File "/home/chandu/workspace/tracking/DaT/lib/modeling/ResNet3D.py", line 389, in add_ResNet101_conv5_body return add_ResNet_convX_body(model, (3, 4, 23, 3), freeze_at=2) File "/home/chandu/workspace/tracking/DaT/lib/modeling/ResNet3D.py", line 262, in add_ResNet_convX_body p = model.AffineChannelNd(p, 'res_conv1_bn', dim_out=feat_dims[0], inplace=True) File "/home/chandu/workspace/tracking/DaT/lib/modeling/detector.py", line 94, in AffineChannelNd if cfg.MODEL.USE_BN: File "/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib/python2.7/site-packages/caffe2/python/core.py", line 2082, in getattr ",".join(workspace.C.nearby_opnames(op_type)) + ']' AttributeError: Method AffineChannelNd is not a registered operator. Did you mean: [AffineChannel]

Why am I getting this error?

Please read the error message: "Method AffineChannelNd is not a registered operator". You might want to look at the readme: https://github.com/facebookresearch/DetectAndTrack#compile-some-custom-ops

@rohitgirdhar thanks for the reply I followed that step and the output is:

(gtracking) chandu@username:~/workspace/tracking/DaT/lib$ make && make ops python2 setup.py develop --user running develop running egg_info writing Detectron.egg-info/PKG-INFO writing top-level names to Detectron.egg-info/top_level.txt writing dependency_links to Detectron.egg-info/dependency_links.txt reading manifest file 'Detectron.egg-info/SOURCES.txt' writing manifest file 'Detectron.egg-info/SOURCES.txt' running build_ext building 'utils.cython_bbox' extension creating build creating build/temp.linux-x86_64-2.7 creating build/temp.linux-x86_64-2.7/utils gcc -pthread -B /home/chandu/workspace/tracking/anaconda2/envs/gtracking/compiler_compat -Wl,--sysroot=/ -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib/python2.7/site-packages/numpy/core/include -I/home/chandu/workspace/tracking/anaconda2/envs/gtracking/include/python2.7 -c utils/cython_bbox.c -o build/temp.linux-x86_64-2.7/utils/cython_bbox.o -Wno-cpp creating build/lib.linux-x86_64-2.7 creating build/lib.linux-x86_64-2.7/utils gcc -pthread -shared -B /home/chandu/workspace/tracking/anaconda2/envs/gtracking/compiler_compat -L/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib -Wl,-rpath=/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-2.7/utils/cython_bbox.o -L/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib -lpython2.7 -o build/lib.linux-x86_64-2.7/utils/cython_bbox.so building 'utils.cython_nms' extension gcc -pthread -B /home/chandu/workspace/tracking/anaconda2/envs/gtracking/compiler_compat -Wl,--sysroot=/ -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib/python2.7/site-packages/numpy/core/include -I/home/chandu/workspace/tracking/anaconda2/envs/gtracking/include/python2.7 -c utils/cython_nms.c -o build/temp.linux-x86_64-2.7/utils/cython_nms.o -Wno-cpp gcc -pthread -shared -B /home/chandu/workspace/tracking/anaconda2/envs/gtracking/compiler_compat -L/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib -Wl,-rpath=/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-2.7/utils/cython_nms.o -L/home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib -lpython2.7 -o build/lib.linux-x86_64-2.7/utils/cython_nms.so copying build/lib.linux-x86_64-2.7/utils/cython_bbox.so -> utils copying build/lib.linux-x86_64-2.7/utils/cython_nms.so -> utils Creating /home/chandu/.local/lib/python2.7/site-packages/Detectron.egg-link (link to .) Adding Detectron 0.0.0 to easy-install.pth file

Installed /home/chandu/workspace/tracking/DaT/lib Processing dependencies for Detectron==0.0.0 Finished processing dependencies for Detectron==0.0.0 mkdir -p build && cd build && cmake .. && make -j48 -- The C compiler identification is GNU 4.9.4 -- The CXX compiler identification is GNU 4.9.4 -- Check for working C compiler: /usr/bin/cc -- Check for working C compiler: /usr/bin/cc -- works -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Detecting C compile features -- Detecting C compile features - done -- Check for working CXX compiler: /usr/bin/c++ -- Check for working CXX compiler: /usr/bin/c++ -- works -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Detecting CXX compile features -- Detecting CXX compile features - done -- Looking for pthread.h -- Looking for pthread.h - found -- Looking for pthread_create -- Looking for pthread_create - not found -- Looking for pthread_create in pthreads -- Looking for pthread_create in pthreads - not found -- Looking for pthread_create in pthread -- Looking for pthread_create in pthread - found -- Found Threads: TRUE
-- Caffe2: Found gflags with new-style gflags target. -- Caffe2: Cannot find glog automatically. Using legacy find. -- Found glog: /usr/include
-- Caffe2: Found glog (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libglog.so) -- Caffe2: Found protobuf with new-style protobuf targets. -- Caffe2: Protobuf version 3.5.0 -- Found CUDA: /usr/local/cuda-9.0 (found suitable version "9.0", minimum required is "7.0") -- Caffe2: CUDA detected: 9.0 -- Caffe2: CUDA nvcc is: /usr/local/cuda-9.0/bin/nvcc -- Caffe2: CUDA toolkit directory: /usr/local/cuda-9.0 -- Caffe2: Header version is: 9.0 -- Found CUDNN: /usr/include
-- Found cuDNN: v7.0.5 (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so) -- Autodetected CUDA architecture(s): 6.1 -- Added CUDA NVCC flags for: -gencode;arch=compute_61,code=sm_61 -- CUDA detected: 9.0 -- Added CUDA NVCC flags for: sm_30 sm_35 sm_50 sm_52 sm_60 sm_61 sm_70 -- Found libcuda: /usr/lib/x86_64-linux-gnu/libcuda.so -- Found libnvrtc: /usr/lib/x86_64-linux-gnu/libnvrtc.so -- Found cuDNN: v7.0.5 (include: /usr/include, library: /usr/lib/x86_64-linux-gnu/libcudnn.so) -- Summary: -- CMake version : 3.10.3 -- CMake command : /home/chandu/workspace/tracking/anaconda2/envs/gtracking/bin/cmake -- System name : Linux -- C++ compiler : /usr/bin/c++ -- C++ compiler version : 4.9.4 -- CXX flags : -std=c++11 -O2 -fPIC -Wno-narrowing -- Caffe2 version : 0.8.2 -- Caffe2 include path : /home/chandu/workspace/tracking/anaconda2/envs/gtracking/include -- Have CUDA : TRUE -- CUDA version : 9.0 -- CuDNN version : 7.0.5 -- Configuring done CMake Warning at /home/chandu/workspace/tracking/anaconda2/envs/gtracking/share/cmake/Caffe2/Modules_CUDA_fix/upstream/FindCUDA.cmake:1836 (add_library): Cannot generate a safe runtime search path for target caffe2_detectron_custom_ops_gpu because files in some directories may conflict with libraries in implicit directories:

runtime library [libglog.so.0] in /usr/lib/x86_64-linux-gnu may be hidden by files in:
  /home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib

Some of these libraries may not be found correctly. Call Stack (most recent call first): CMakeLists.txt:41 (CUDA_ADD_LIBRARY)

CMake Warning at CMakeLists.txt:26 (add_library): Cannot generate a safe runtime search path for target caffe2_detectron_custom_ops because files in some directories may conflict with libraries in implicit directories:

runtime library [libglog.so.0] in /usr/lib/x86_64-linux-gnu may be hidden by files in:
  /home/chandu/workspace/tracking/anaconda2/envs/gtracking/lib

Some of these libraries may not be found correctly.

-- Generating done -- Build files have been written to: /home/chandu/workspace/tracking/DaT/lib/build make[1]: Entering directory '/home/chandu/workspace/tracking/DaT/lib/build' make[2]: Entering directory '/home/chandu/workspace/tracking/DaT/lib/build' make[3]: Entering directory '/home/chandu/workspace/tracking/DaT/lib/build' make[3]: Entering directory '/home/chandu/workspace/tracking/DaT/lib/build' [ 12%] Building NVCC (Device) object CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/caffe2_detectron_custom_ops_gpu_generated_zero_even_op.cu.o [ 25%] Building NVCC (Device) object CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/caffe2_detectron_custom_ops_gpu_generated_affine_channel_nd_op.cu.o Scanning dependencies of target caffe2_detectron_custom_ops nvcc fatal : redefinition of argument 'std' CMake Error at caffe2_detectron_custom_ops_gpu_generated_zero_even_op.cu.o.cmake:215 (message): Error generating /home/chandu/workspace/tracking/DaT/lib/build/CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/./caffe2_detectron_custom_ops_gpu_generated_zero_even_op.cu.o

CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/build.make:70: recipe for target 'CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/caffe2_detectron_custom_ops_gpu_generated_zero_even_op.cu.o' failed make[3]: [CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/caffe2_detectron_custom_ops_gpu_generated_zero_even_op.cu.o] Error 1 make[3]: Waiting for unfinished jobs.... make[3]: Leaving directory '/home/chandu/workspace/tracking/DaT/lib/build' nvcc fatal : redefinition of argument 'std' CMake Error at caffe2_detectron_custom_ops_gpu_generated_affine_channel_nd_op.cu.o.cmake:215 (message): Error generating /home/chandu/workspace/tracking/DaT/lib/build/CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/./caffe2_detectron_custom_ops_gpu_generated_affine_channel_nd_op.cu.o

CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/build.make:63: recipe for target 'CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/caffe2_detectron_custom_ops_gpu_generated_affine_channel_nd_op.cu.o' failed make[3]: [CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/ops/caffe2_detectron_custom_ops_gpu_generated_affine_channel_nd_op.cu.o] Error 1 make[3]: Leaving directory '/home/chandu/workspace/tracking/DaT/lib/build' CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/all' failed make[2]: [CMakeFiles/caffe2_detectron_custom_ops_gpu.dir/all] Error 2 make[2]: Waiting for unfinished jobs.... make[3]: Entering directory '/home/chandu/workspace/tracking/DaT/lib/build' [ 37%] Building CXX object CMakeFiles/caffe2_detectron_custom_ops.dir/ops/affine_channel_nd_op.cc.o [ 50%] Building CXX object CMakeFiles/caffe2_detectron_custom_ops.dir/ops/zero_even_op.cc.o [ 62%] Linking CXX shared library libcaffe2_detectron_custom_ops.so make[3]: Leaving directory '/home/chandu/workspace/tracking/DaT/lib/build' [ 62%] Built target caffe2_detectron_custom_ops make[2]: Leaving directory '/home/chandu/workspace/tracking/DaT/lib/build' Makefile:129: recipe for target 'all' failed make[1]: [all] Error 2 make[1]: Leaving directory '/home/chandu/workspace/tracking/DaT/lib/build' Makefile:13: recipe for target 'ops' failed make: *** [ops] Error 2

Me, too. I got same error. Did you solve it?

Have you tried copying the files to detectron module folder as mentioned in the README? (Also, in https://github.com/facebookresearch/DetectAndTrack/issues/27#issuecomment-409453279)

Please read the error message: "Method AffineChannelNd is not a registered operator". You might want to look at the readme: https://github.com/facebookresearch/DetectAndTrack#compile-some-custom-ops

HI ，same error，

$ python tools/test_net.py --range 0 1477 --cfg outputs/configs/video/2d_best/01_R101_best_hungarian-4GPU.yaml/test/posetrack_v1.0_val/keypoint_rcnn/detection_range_config.yaml NUM_GPUS 1 Found Detectron ops lib: /home/vivian/HelloWorld/tracker/tools/pytorch/build/lib/libcaffe2_detectron_ops_gpu.so [E init_intrinsics_check.cc:43] CPU feature avx is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU. [E init_intrinsics_check.cc:43] CPU feature avx2 is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU. [E init_intrinsics_check.cc:43] CPU feature fma is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU. INFO test_net.py: 113: Called with args: INFO test_net.py: 114: Namespace(cfg_file='outputs/configs/video/2d_best/01_R101_best_hungarian-4GPU.yaml/test/posetrack_v1.0_val/keypoint_rcnn/detection_range_config.yaml', multi_gpu_testing=False, opts=['NUM_GPUS', '1'], range=[0, 1477], vis=False, wait=True) INFO test_net.py: 120: Testing with config: INFO test_net.py: 121: {'BBOX_XFORM_CLIP': 4.135166556742356, 'CLUSTER': {'AUTO_RESUME': True, 'ON_CLUSTER': False}, 'DEBUG': {'DATA_LOADING': False, 'STOP_TRAIN_ITER': False}, 'DEDUP_BOXES': 0.0625, 'DEVSTORAGE': {'HOSTNAME': '', 'MOUNT_ENABLED': False, 'MOUNT_POINT': '/tmp/devstorage', 'REMOTE_PATH': ''}, 'EPS': 1e-14, 'EVAL': {'EVAL_MPII_DROP_DETECTION_THRESHOLD': 0.5, 'EVAL_MPII_KPT_THRESHOLD': 1.95, 'EVAL_MPII_PER_VIDEO': False}, 'EXT_PATHS': {'POSEVAL_CODE_PATH': '/home/rgirdhar/local/OpenSource/bitbucket/poseval/'}, 'FAST_RCNN': {'MLP_HEAD_DIM': 1024, 'ROI_XFORM_METHOD': 'RoIAlign', 'ROI_XFORM_RESOLUTION': 7, 'ROI_XFORM_SAMPLING_RATIO': 2}, 'FINAL_MSG': '', 'FPN': {'COARSEST_STRIDE': 32, 'DIM': 256, 'EXTRA_CONV_LEVELS': False, 'FPN_ON': True, 'INPLACE_LATERAL': False, 'MULTILEVEL_ROIS': True, 'MULTILEVEL_RPN': True, 'ROI_CANONICAL_LEVEL': 4, 'ROI_CANONICAL_SCALE': 224, 'ROI_MAX_LEVEL': 5, 'ROI_MIN_LEVEL': 2, 'RPN_ANCHOR_START_SIZE': 32, 'RPN_ASPECT_RATIOS': (0.5, 1, 2), 'RPN_MAX_LEVEL': 6, 'RPN_MIN_LEVEL': 2, 'ZERO_INIT_LATERAL': False}, 'KRCNN': {'CONV_HEAD_DIM': 512, 'CONV_HEAD_KERNEL': 3, 'CONV_INIT': 'MSRAFill', 'DECONV_DIM': 256, 'DECONV_KERNEL': 4, 'DILATION': 1, 'HEATMAP_SIZE': 56, 'INFERENCE_MIN_SIZE': 0, 'KEYPOINT_CONFIDENCE': 'bbox', 'LOSS_WEIGHT': 1.0, 'MIN_KEYPOINT_COUNT_FOR_VALID_MINIBATCH': 20, 'NMS_OKS': False, 'NO_3D_DECONV_TIME_TO_CH': False, 'NUM_KEYPOINTS': 17, 'NUM_STACKED_CONVS': 8, 'ROI_KEYPOINTS_HEAD': 'keypoint_rcnn_heads.add_roi_pose_head_v1convX', 'ROI_XFORM_METHOD': 'RoIAlign', 'ROI_XFORM_RESOLUTION': 14, 'ROI_XFORM_SAMPLING_RATIO': 2, 'UP_SCALE': 2, 'USE_3D_DECONV': False, 'USE_DECONV': False, 'USE_DECONV_OUTPUT': True}, 'MATLAB': 'matlab', 'MEMONGER': True, 'MEMONGER_SHARE_ACTIVATIONS': False, 'MODEL': {'BBOX_REG_WEIGHTS': (10.0, 10.0, 5.0, 5.0), 'BN_EPSILON': 1.0000001e-05, 'BN_MOMENTUM': 0.9, 'CLS_AGNOSTIC_BBOX_REG': False, 'CONV_BODY': 'FPN3D.add_fpn_ResNet101_conv5_body', 'DILATION': 1, 'EXECUTION_TYPE': 'dag', 'FASTER_RCNN': True, 'KEYPOINTS_ON': True, 'MASK_ON': False, 'NUM_CLASSES': 2, 'PS_GRID_SIZE': 3, 'ROI_HEAD': 'head_builder.add_roi_2mlp_head', 'RPN_ONLY': False, 'TYPE': 'keypoint_rcnn', 'USE_BN': False, 'USE_BN_TESTMODE_ONLY': False, 'VIDEO_ON': True}, 'MRCNN': {'CLS_SPECIFIC_MASK': True, 'CONV_INIT': 'GaussianFill', 'DILATION': 2, 'DIM_REDUCED': 256, 'MASK_HEAD_NAME': '', 'RESOLUTION': 14, 'ROI_XFORM_METHOD': 'RoIAlign', 'ROI_XFORM_RESOLUTION': 7, 'ROI_XFORM_SAMPLING_RATIO': 0, 'THRESH_BINARIZE': 0.5, 'UPSAMPLE_RATIO': 1, 'USE_FC_OUTPUT': False, 'WEIGHT_LOSS_MASK': 1.0}, 'NUM_GPUS': 1, 'NUM_WORKERS': 4, 'OUTPUT_DIR': 'outputs/configs/video/2d_best/01_R101_best_hungarian-4GPU.yaml', 'PIXEL_MEANS': array([[[102.9801, 115.9465, 122.7717]]]), 'RESNETS': {'NUM_GROUPS': 1, 'STRIDE_1X1': True, 'TRANS_FUNC': 'bottleneck_transformation', 'WIDTH_PER_GROUP': 64}, 'RNG_SEED': 3, 'ROIDB_SUBSET': [], 'ROOT_DIR': '/home/vivian/HelloWorld/tracker/DetectAndTrack', 'ROOT_GPU_ID': 0, 'RPN': {'ASPECT_RATIOS': (0.5, 1, 2), 'ON': True, 'SIZES': (64, 128, 256, 512), 'STRIDE': 16}, 'SOLVER': {'BASE_LR': 0.0005, 'GAMMA': 0.1, 'LOG_LR_CHANGE_THRESHOLD': 1.1, 'LRS': [], 'LR_POLICY': 'steps_with_decay', 'MAX_ITER': 26000, 'MOMENTUM': 0.9, 'SCALE_MOMENTUM': True, 'SCALE_MOMENTUM_THRESHOLD': 1.1, 'STEPS': [0, 20000, 24000], 'STEP_SIZE': 30000, 'WARM_UP_FACTOR': 0.3333333333333333, 'WARM_UP_ITERS': 500, 'WARM_UP_METHOD': u'linear', 'WEIGHT_DECAY': 0.0001}, 'TEST': {'BBOX_AUG': {'AREA_TH_HI': 32400, 'AREA_TH_LO': 2500, 'ASPECT_RATIOS': (), 'ASPECT_RATIO_H_FLIP': False, 'COORD_HEUR': 'ID', 'H_FLIP': False, 'MAX_SIZE': 4000, 'SCALES': (), 'SCALE_H_FLIP': False, 'SCALE_SIZE_DEP': False, 'SCORE_HEUR': 'ID'}, 'BBOX_REG': True, 'BBOX_VOTE': {'ENABLED': False, 'VOTE_TH': 0.8}, 'COMPETITION_MODE': False, 'DATASET': 'posetrack_v1.0_val', 'DATASETS': ('posetrack_v1.0_val',), 'DETECTIONS_PER_IM': 100, 'ENSEMBLE': {'DEVSTORAGE_CACHE': False, 'PROPOSAL_CACHE': '/tmp', 'RPN_CONFIGS': ()}, 'EXT_CNN_FEATURES': False, 'EXT_CNN_FEATURES_MODEL': 'ImNet', 'FORCE_JSON_DATASET_EVAL': False, 'INIT_RANDOM_VARS_BEFORE_LOADING': False, 'KPS_AUG': {'AREA_TH': 32400, 'ASPECT_RATIOS': (), 'ASPECT_RATIO_H_FLIP': False, 'HEUR': 'HM_AVG', 'H_FLIP': False, 'MAX_SIZE': 4000, 'SCALES': (), 'SCALE_H_FLIP': False, 'SCALE_SIZE_DEP': False}, 'MASK_AUG': {'AREA_TH': 32400, 'ASPECT_RATIOS': (), 'ASPECT_RATIO_H_FLIP': False, 'HEUR': 'SOFT_AVG', 'H_FLIP': False, 'MAX_SIZE': 4000, 'SCALES': (), 'SCALE_H_FLIP': False, 'SCALE_SIZE_DEP': False}, 'MAX_SIZE': 1333, 'NMS': 0.5, 'PROPOSAL_FILE': '', 'PROPOSAL_FILES': ('',), 'PROPOSAL_LIMIT': 2000, 'RPN_MIN_SIZE': 0, 'RPN_NMS_THRESH': 0.7, 'RPN_POST_NMS_TOP_N': 1000, 'RPN_PRE_NMS_TOP_N': 1000, 'SCALES': (800,), 'SCORE_THRESH': 0.05, 'SOFT_NMS': {'ENABLED': False, 'METHOD': 'linear', 'SIGMA': 0.5}, 'SVM': False, 'WEIGHTS': 'pretrained_models/configs/video/2d_best/01_R101_best_hungarian-4GPU.yaml/model_final.pkl'}, 'TRACKING': {'BIPARTITE_MATCHING_ALGO': 'hungarian', 'CNN_MATCHING_LAYER': 'layer3', 'CONF_FILTER_INITIAL_DETS': 0.95, 'DEBUG': {'DUMMY_TRACKS': False, 'FLOW_SMOOTHING_COMBINE': False, 'UPPER_BOUND': False, 'UPPER_BOUND_2_GT_KPS': False, 'UPPER_BOUND_2_GT_KPS_ONLY_CONF': False, 'UPPER_BOUND_3_SHOTS': False, 'UPPER_BOUND_4_EVAL_UPPER_BOUND': False, 'UPPER_BOUND_5_GT_KPS_ONLY': False}, 'DETECTIONS_FILE': '', 'DISTANCE_METRICS': ('bbox-overlap', 'cnn-cosdist'), 'DISTANCE_METRIC_WTS': (1.0, 0.0), 'FLOW_SMOOTHING': {'EXTEND_TRACKS': True, 'FLOW_SHOT_BOUNDARY_TH': 6.0, 'N_CONTEXT_FRAMES': 3}, 'FLOW_SMOOTHING_ON': False, 'KEEP_CENTER_DETS_ONLY': True, 'KP_CONF_TYPE': 'global', 'LSTM': {'BATCH_SIZE': 20, 'CONSIDER_SHORT_TRACKS_TOO': False, 'DROPOUT': 0.2, 'EMSIZE': 200, 'EPOCHS': 10, 'FEATS_TO_CONSIDER': [u'bbox', u'kpts'], 'GRAD_CLIP': 0.25, 'LOG_INTERVAL': 200, 'LOSS_LAST_PRED_ONLY': False, 'LR': 0.1, 'MODEL': 'LSTM', 'NHID': 200, 'NLAYERS': 2, 'NUM_WORKERS': 4, 'TIED_WTS': False}, 'LSTM_TEST': {'LSTM_TRACKING_ON': False, 'LSTM_WEIGHTS': ''}}, 'TRAIN': {'ASPECT_GROUPING': True, 'BATCH_SIZE_PER_IM': 512, 'BBOX_NORMALIZE_TARGETS_PRECOMPUTED_deprecated': None, 'BBOX_REG': True, 'BBOX_THRESH': 0.5, 'BG_THRESH_HI': 0.5, 'BG_THRESH_LO': 0.0, 'CROWD_FILTER_THRESH': 0.7, 'DATASET': 'posetrack_v1.0_train', 'DROPOUT': 0.0, 'FG_FRACTION': 0.25, 'FG_THRESH': 0.5, 'GT_MIN_AREA': -1, 'IMS_PER_BATCH': 1, 'MAX_SIZE': 1333, 'MINIBATCH_QUEUE_SIZE': 64, 'PROPOSAL_FILE': '', 'RPN_BATCH_SIZE_PER_IM': 256, 'RPN_FG_FRACTION': 0.5, 'RPN_MIN_SIZE': 0, 'RPN_NEGATIVE_OVERLAP': 0.3, 'RPN_NMS_THRESH': 0.7, 'RPN_POSITIVE_OVERLAP': 0.7, 'RPN_POST_NMS_TOP_N': 2000, 'RPN_PRE_NMS_TOP_N': 2000, 'RPN_STRADDLE_THRESH': 0, 'SCALES': (640, 672, 704, 736, 768, 800), 'SNAPSHOT_ITERS': 20000, 'USE_FLIPPED': True, 'WEIGHTS': 'pretrained_models/e2e_keypoint_rcnn_R-101-FPN_1x_trainedCOCO.pkl'}, 'USE_GPU_NMS_deprecated': None, 'USE_NCCL': True, 'VIDEO': {'BODY_HEAD_LINK': 'slice-center', 'DEBUG_USE_RPN_GT': False, 'DEFAULT_CLIPS_PER_VIDEO': 9999999999, 'NUM_FRAMES': 1, 'NUM_FRAMES_MID': 1, 'PREDICT_RPN_BOX_VIS': False, 'RPN_TUBE_GEN_STYLE': 'replicate', 'TIME_INTERVAL': 0, 'TIME_KERNEL_DIM': {'BODY': 1, 'HEAD_DET': 1, 'HEAD_KPS': 1, 'HEAD_RPN': 1}, 'TIME_STRIDE_ON': False, 'WEIGHTS_INFLATE_MODE': 'center-only'}, 'VIS': False, 'VIS_THR': 0.9, 'VOC_DIR': '/mnt/vol/gfsai-east/ai-group/datasets'} /home/vivian/anaconda3/envs/detect_and_track/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype fromfloattonp.floatingis deprecated. In future, it will be treated asnp.float64 == np.dtype(float).type. from ._conv import register_converters as _register_converters loading annotations into memory... Done (t=0.41s) creating index... index created! Video-fying the roidb: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 5906/5906 [00:04<00:00, 1424.40it/s] INFO video.py: 200: Video-fied roidb contains 5906 elements WARNING cnn.py: 25: [====DEPRECATE WARNING====]: you are creating an object from CNNModelHelper class which will be deprecated soon. Please use ModelHelper object with brew module. For more information, please refer to caffe2.ai and python/brew.py, python/brew_test.py for more information. FPN3D.add_fpn_ResNet101_conv5_body model <modeling.detector.DetectionModelHelper object at 0x7f9fb86d8b90> Traceback (most recent call last): File "tools/test_net.py", line 146, in <module> main(ind_range=args.range, multi_gpu_testing=args.multi_gpu_testing) File "tools/test_net.py", line 97, in main engine.test_net(ind_range=ind_range) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/core/test_engine.py", line 136, in test_net model = initialize_model_from_cfg() File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/core/test_engine.py", line 60, in initialize_model_from_cfg init_params=cfg.TEST.INIT_RANDOM_VARS_BEFORE_LOADING) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/model_builder.py", line 61, in create return get_func(model_name)(init_model(model_name, train, init_params)) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/model_builder.py", line 154, in keypoint_rcnn add_roi_keypoint_head_func=get_func(cfg.KRCNN.ROI_KEYPOINTS_HEAD)) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/model_builder.py", line 305, in build_generic_fast_rcnn_model build_data_parallel_model(model, _single_gpu_build_func) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/model_builder.py", line 951, in build_data_parallel_model single_gpu_build_func(model) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/model_builder.py", line 197, in _single_gpu_build_func blob_conv, dim_conv, spatial_scale_conv = add_conv_body_func(model) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/FPN3D.py", line 51, in add_fpn_ResNet101_conv5_body ResNet.stage_info_ResNet101_conv5) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/FPN3D.py", line 98, in add_fpn_generic_onto_body conv_body_func(model) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/ResNet3D.py", line 389, in add_ResNet101_conv5_body return add_ResNet_convX_body(model, (3, 4, 23, 3), freeze_at=2) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/ResNet3D.py", line 262, in add_ResNet_convX_body p = model.AffineChannelNd(p, 'res_conv1_bn', dim_out=feat_dims[0], inplace=True) File "/home/vivian/HelloWorld/tracker/DetectAndTrack/lib/modeling/detector.py", line 107, in AffineChannelNd return self.net.AffineChannelNd([blob_in, scale, bias], blob_in) File "/home/vivian/HelloWorld/tracker/tools/pytorch/build/caffe2/python/core.py", line 2171, in __getattr__ ",".join(workspace.C.nearby_opnames(op_type)) + ']' AttributeError: Method AffineChannelNd is not a registered operator. Did you mean: [AffineChannel]

and I tired it

$ python tests/test_zero_even_op.py[E init_intrinsics_check.cc:43] CPU feature avx is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.
[E init_intrinsics_check.cc:43] CPU feature avx2 is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.
[E init_intrinsics_check.cc:43] CPU feature fma is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU.
............
----------------------------------------------------------------------
Ran 12 tests in 1.525s

OK

can you give me some help？

I have the same problem when running "test_on_single_video.py" error is : Key already registered. Offending key: AffineChannelNd.

it was working fine, after I recompile the caffe2 with USE_LMDB=ON I got this error. I even removed all the caffe2 related installed files from anaconda, copied affine_channel_nd_op* to the ./modules/detectron and ran a clean caffe2 build did not help.

facebookresearch / DetectAndTrack

AttributeError: Method AffineChannelNd is not a registered operator. Did you mean: [AffineChannel] #20