AttributeError: 'NoneType' object has no attribute 'copy'

[s5248]

I want to run the keypoints detection demo, alike to the demo, I executed the following shell code:

python2 tools/infer_simple.py \
--cfg configs/test_time_aug/keypoint_rcnn_R-50-FPN_1x.yaml \
--output-dir /tmp/detectron-visualizations \
--image-ext jpg \
--wts /tmp/detectron-download-cache/ImageNetPretrained/MSRA/R-50.pkl \
demo

then I got errors:

url= https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
cache_file_path= /tmp/detectron-download-cache/ImageNetPretrained/MSRA/R-50.pkl
url= https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl
cache_file_path= /tmp/detectron-download-cache/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_train/generalized_rcnn/rpn_proposals.pkl
url= https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl
cache_file_path= /tmp/detectron-download-cache/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_valminusminival/generalized_rcnn/rpn_proposals.pkl
url= https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl
cache_file_path= /tmp/detectron-download-cache/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/test/keypoints_coco_2014_minival/generalized_rcnn/rpn_proposals.pkl
WARNING cnn.py: 40: [====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.
WARNING model_builder.py: 444: Deprecated: use MODEL.TYPE: generalized_rcnn with MODEL.KEYPOINTS_ON: True
INFO net.py: 54: Loading from: /tmp/detectron-download-cache/ImageNetPretrained/MSRA/R-50.pkl
INFO net.py: 91: conv1_w loaded from weights file into gpu_0/conv1_w: (64, 3, 7, 7)
INFO net.py: 91: res_conv1_bn_s loaded from weights file into gpu_0/res_conv1_bn_s: (64,)
INFO net.py: 91: res_conv1_bn_b loaded from weights file into gpu_0/res_conv1_bn_b: (64,)
INFO net.py: 91: res2_0_branch2a_w loaded from weights file into gpu_0/res2_0_branch2a_w: (64, 64, 1, 1)
INFO net.py: 91: res2_0_branch2a_bn_s loaded from weights file into gpu_0/res2_0_branch2a_bn_s: (64,)
INFO net.py: 91: res2_0_branch2a_bn_b loaded from weights file into gpu_0/res2_0_branch2a_bn_b: (64,)
INFO net.py: 91: res2_0_branch2b_w loaded from weights file into gpu_0/res2_0_branch2b_w: (64, 64, 3, 3)
INFO net.py: 91: res2_0_branch2b_bn_s loaded from weights file into gpu_0/res2_0_branch2b_bn_s: (64,)
INFO net.py: 91: res2_0_branch2b_bn_b loaded from weights file into gpu_0/res2_0_branch2b_bn_b: (64,)
INFO net.py: 91: res2_0_branch2c_w loaded from weights file into gpu_0/res2_0_branch2c_w: (256, 64, 1, 1)
INFO net.py: 91: res2_0_branch2c_bn_s loaded from weights file into gpu_0/res2_0_branch2c_bn_s: (256,)
INFO net.py: 91: res2_0_branch2c_bn_b loaded from weights file into gpu_0/res2_0_branch2c_bn_b: (256,)
INFO net.py: 91: res2_0_branch1_w loaded from weights file into gpu_0/res2_0_branch1_w: (256, 64, 1, 1)
INFO net.py: 91: res2_0_branch1_bn_s loaded from weights file into gpu_0/res2_0_branch1_bn_s: (256,)
INFO net.py: 91: res2_0_branch1_bn_b loaded from weights file into gpu_0/res2_0_branch1_bn_b: (256,)
INFO net.py: 91: res2_1_branch2a_w loaded from weights file into gpu_0/res2_1_branch2a_w: (64, 256, 1, 1)
INFO net.py: 91: res2_1_branch2a_bn_s loaded from weights file into gpu_0/res2_1_branch2a_bn_s: (64,)
INFO net.py: 91: res2_1_branch2a_bn_b loaded from weights file into gpu_0/res2_1_branch2a_bn_b: (64,)
INFO net.py: 91: res2_1_branch2b_w loaded from weights file into gpu_0/res2_1_branch2b_w: (64, 64, 3, 3)
INFO net.py: 91: res2_1_branch2b_bn_s loaded from weights file into gpu_0/res2_1_branch2b_bn_s: (64,)
INFO net.py: 91: res2_1_branch2b_bn_b loaded from weights file into gpu_0/res2_1_branch2b_bn_b: (64,)
INFO net.py: 91: res2_1_branch2c_w loaded from weights file into gpu_0/res2_1_branch2c_w: (256, 64, 1, 1)
INFO net.py: 91: res2_1_branch2c_bn_s loaded from weights file into gpu_0/res2_1_branch2c_bn_s: (256,)
INFO net.py: 91: res2_1_branch2c_bn_b loaded from weights file into gpu_0/res2_1_branch2c_bn_b: (256,)
INFO net.py: 91: res2_2_branch2a_w loaded from weights file into gpu_0/res2_2_branch2a_w: (64, 256, 1, 1)
INFO net.py: 91: res2_2_branch2a_bn_s loaded from weights file into gpu_0/res2_2_branch2a_bn_s: (64,)
INFO net.py: 91: res2_2_branch2a_bn_b loaded from weights file into gpu_0/res2_2_branch2a_bn_b: (64,)
INFO net.py: 91: res2_2_branch2b_w loaded from weights file into gpu_0/res2_2_branch2b_w: (64, 64, 3, 3)
INFO net.py: 91: res2_2_branch2b_bn_s loaded from weights file into gpu_0/res2_2_branch2b_bn_s: (64,)
INFO net.py: 91: res2_2_branch2b_bn_b loaded from weights file into gpu_0/res2_2_branch2b_bn_b: (64,)
INFO net.py: 91: res2_2_branch2c_w loaded from weights file into gpu_0/res2_2_branch2c_w: (256, 64, 1, 1)
INFO net.py: 91: res2_2_branch2c_bn_s loaded from weights file into gpu_0/res2_2_branch2c_bn_s: (256,)
INFO net.py: 91: res2_2_branch2c_bn_b loaded from weights file into gpu_0/res2_2_branch2c_bn_b: (256,)
INFO net.py: 91: res3_0_branch2a_w loaded from weights file into gpu_0/res3_0_branch2a_w: (128, 256, 1, 1)
INFO net.py: 91: res3_0_branch2a_bn_s loaded from weights file into gpu_0/res3_0_branch2a_bn_s: (128,)
INFO net.py: 91: res3_0_branch2a_bn_b loaded from weights file into gpu_0/res3_0_branch2a_bn_b: (128,)
INFO net.py: 91: res3_0_branch2b_w loaded from weights file into gpu_0/res3_0_branch2b_w: (128, 128, 3, 3)
INFO net.py: 91: res3_0_branch2b_bn_s loaded from weights file into gpu_0/res3_0_branch2b_bn_s: (128,)
INFO net.py: 91: res3_0_branch2b_bn_b loaded from weights file into gpu_0/res3_0_branch2b_bn_b: (128,)
INFO net.py: 91: res3_0_branch2c_w loaded from weights file into gpu_0/res3_0_branch2c_w: (512, 128, 1, 1)
INFO net.py: 91: res3_0_branch2c_bn_s loaded from weights file into gpu_0/res3_0_branch2c_bn_s: (512,)
INFO net.py: 91: res3_0_branch2c_bn_b loaded from weights file into gpu_0/res3_0_branch2c_bn_b: (512,)
INFO net.py: 91: res3_0_branch1_w loaded from weights file into gpu_0/res3_0_branch1_w: (512, 256, 1, 1)
INFO net.py: 91: res3_0_branch1_bn_s loaded from weights file into gpu_0/res3_0_branch1_bn_s: (512,)
INFO net.py: 91: res3_0_branch1_bn_b loaded from weights file into gpu_0/res3_0_branch1_bn_b: (512,)
INFO net.py: 91: res3_1_branch2a_w loaded from weights file into gpu_0/res3_1_branch2a_w: (128, 512, 1, 1)
INFO net.py: 91: res3_1_branch2a_bn_s loaded from weights file into gpu_0/res3_1_branch2a_bn_s: (128,)
INFO net.py: 91: res3_1_branch2a_bn_b loaded from weights file into gpu_0/res3_1_branch2a_bn_b: (128,)
INFO net.py: 91: res3_1_branch2b_w loaded from weights file into gpu_0/res3_1_branch2b_w: (128, 128, 3, 3)
INFO net.py: 91: res3_1_branch2b_bn_s loaded from weights file into gpu_0/res3_1_branch2b_bn_s: (128,)
INFO net.py: 91: res3_1_branch2b_bn_b loaded from weights file into gpu_0/res3_1_branch2b_bn_b: (128,)
INFO net.py: 91: res3_1_branch2c_w loaded from weights file into gpu_0/res3_1_branch2c_w: (512, 128, 1, 1)
INFO net.py: 91: res3_1_branch2c_bn_s loaded from weights file into gpu_0/res3_1_branch2c_bn_s: (512,)
INFO net.py: 91: res3_1_branch2c_bn_b loaded from weights file into gpu_0/res3_1_branch2c_bn_b: (512,)
INFO net.py: 91: res3_2_branch2a_w loaded from weights file into gpu_0/res3_2_branch2a_w: (128, 512, 1, 1)
INFO net.py: 91: res3_2_branch2a_bn_s loaded from weights file into gpu_0/res3_2_branch2a_bn_s: (128,)
INFO net.py: 91: res3_2_branch2a_bn_b loaded from weights file into gpu_0/res3_2_branch2a_bn_b: (128,)
INFO net.py: 91: res3_2_branch2b_w loaded from weights file into gpu_0/res3_2_branch2b_w: (128, 128, 3, 3)
INFO net.py: 91: res3_2_branch2b_bn_s loaded from weights file into gpu_0/res3_2_branch2b_bn_s: (128,)
INFO net.py: 91: res3_2_branch2b_bn_b loaded from weights file into gpu_0/res3_2_branch2b_bn_b: (128,)
INFO net.py: 91: res3_2_branch2c_w loaded from weights file into gpu_0/res3_2_branch2c_w: (512, 128, 1, 1)
INFO net.py: 91: res3_2_branch2c_bn_s loaded from weights file into gpu_0/res3_2_branch2c_bn_s: (512,)
INFO net.py: 91: res3_2_branch2c_bn_b loaded from weights file into gpu_0/res3_2_branch2c_bn_b: (512,)
INFO net.py: 91: res3_3_branch2a_w loaded from weights file into gpu_0/res3_3_branch2a_w: (128, 512, 1, 1)
INFO net.py: 91: res3_3_branch2a_bn_s loaded from weights file into gpu_0/res3_3_branch2a_bn_s: (128,)
INFO net.py: 91: res3_3_branch2a_bn_b loaded from weights file into gpu_0/res3_3_branch2a_bn_b: (128,)
INFO net.py: 91: res3_3_branch2b_w loaded from weights file into gpu_0/res3_3_branch2b_w: (128, 128, 3, 3)
INFO net.py: 91: res3_3_branch2b_bn_s loaded from weights file into gpu_0/res3_3_branch2b_bn_s: (128,)
INFO net.py: 91: res3_3_branch2b_bn_b loaded from weights file into gpu_0/res3_3_branch2b_bn_b: (128,)
INFO net.py: 91: res3_3_branch2c_w loaded from weights file into gpu_0/res3_3_branch2c_w: (512, 128, 1, 1)
INFO net.py: 91: res3_3_branch2c_bn_s loaded from weights file into gpu_0/res3_3_branch2c_bn_s: (512,)
INFO net.py: 91: res3_3_branch2c_bn_b loaded from weights file into gpu_0/res3_3_branch2c_bn_b: (512,)
INFO net.py: 91: res4_0_branch2a_w loaded from weights file into gpu_0/res4_0_branch2a_w: (256, 512, 1, 1)
INFO net.py: 91: res4_0_branch2a_bn_s loaded from weights file into gpu_0/res4_0_branch2a_bn_s: (256,)
INFO net.py: 91: res4_0_branch2a_bn_b loaded from weights file into gpu_0/res4_0_branch2a_bn_b: (256,)
INFO net.py: 91: res4_0_branch2b_w loaded from weights file into gpu_0/res4_0_branch2b_w: (256, 256, 3, 3)
INFO net.py: 91: res4_0_branch2b_bn_s loaded from weights file into gpu_0/res4_0_branch2b_bn_s: (256,)
INFO net.py: 91: res4_0_branch2b_bn_b loaded from weights file into gpu_0/res4_0_branch2b_bn_b: (256,)
INFO net.py: 91: res4_0_branch2c_w loaded from weights file into gpu_0/res4_0_branch2c_w: (1024, 256, 1, 1)
INFO net.py: 91: res4_0_branch2c_bn_s loaded from weights file into gpu_0/res4_0_branch2c_bn_s: (1024,)
INFO net.py: 91: res4_0_branch2c_bn_b loaded from weights file into gpu_0/res4_0_branch2c_bn_b: (1024,)
INFO net.py: 91: res4_0_branch1_w loaded from weights file into gpu_0/res4_0_branch1_w: (1024, 512, 1, 1)
INFO net.py: 91: res4_0_branch1_bn_s loaded from weights file into gpu_0/res4_0_branch1_bn_s: (1024,)
INFO net.py: 91: res4_0_branch1_bn_b loaded from weights file into gpu_0/res4_0_branch1_bn_b: (1024,)
INFO net.py: 91: res4_1_branch2a_w loaded from weights file into gpu_0/res4_1_branch2a_w: (256, 1024, 1, 1)
INFO net.py: 91: res4_1_branch2a_bn_s loaded from weights file into gpu_0/res4_1_branch2a_bn_s: (256,)
INFO net.py: 91: res4_1_branch2a_bn_b loaded from weights file into gpu_0/res4_1_branch2a_bn_b: (256,)
INFO net.py: 91: res4_1_branch2b_w loaded from weights file into gpu_0/res4_1_branch2b_w: (256, 256, 3, 3)
INFO net.py: 91: res4_1_branch2b_bn_s loaded from weights file into gpu_0/res4_1_branch2b_bn_s: (256,)
INFO net.py: 91: res4_1_branch2b_bn_b loaded from weights file into gpu_0/res4_1_branch2b_bn_b: (256,)
INFO net.py: 91: res4_1_branch2c_w loaded from weights file into gpu_0/res4_1_branch2c_w: (1024, 256, 1, 1)
INFO net.py: 91: res4_1_branch2c_bn_s loaded from weights file into gpu_0/res4_1_branch2c_bn_s: (1024,)
INFO net.py: 91: res4_1_branch2c_bn_b loaded from weights file into gpu_0/res4_1_branch2c_bn_b: (1024,)
INFO net.py: 91: res4_2_branch2a_w loaded from weights file into gpu_0/res4_2_branch2a_w: (256, 1024, 1, 1)
INFO net.py: 91: res4_2_branch2a_bn_s loaded from weights file into gpu_0/res4_2_branch2a_bn_s: (256,)
INFO net.py: 91: res4_2_branch2a_bn_b loaded from weights file into gpu_0/res4_2_branch2a_bn_b: (256,)
INFO net.py: 91: res4_2_branch2b_w loaded from weights file into gpu_0/res4_2_branch2b_w: (256, 256, 3, 3)
INFO net.py: 91: res4_2_branch2b_bn_s loaded from weights file into gpu_0/res4_2_branch2b_bn_s: (256,)
INFO net.py: 91: res4_2_branch2b_bn_b loaded from weights file into gpu_0/res4_2_branch2b_bn_b: (256,)
INFO net.py: 91: res4_2_branch2c_w loaded from weights file into gpu_0/res4_2_branch2c_w: (1024, 256, 1, 1)
INFO net.py: 91: res4_2_branch2c_bn_s loaded from weights file into gpu_0/res4_2_branch2c_bn_s: (1024,)
INFO net.py: 91: res4_2_branch2c_bn_b loaded from weights file into gpu_0/res4_2_branch2c_bn_b: (1024,)
INFO net.py: 91: res4_3_branch2a_w loaded from weights file into gpu_0/res4_3_branch2a_w: (256, 1024, 1, 1)
INFO net.py: 91: res4_3_branch2a_bn_s loaded from weights file into gpu_0/res4_3_branch2a_bn_s: (256,)
INFO net.py: 91: res4_3_branch2a_bn_b loaded from weights file into gpu_0/res4_3_branch2a_bn_b: (256,)
INFO net.py: 91: res4_3_branch2b_w loaded from weights file into gpu_0/res4_3_branch2b_w: (256, 256, 3, 3)
INFO net.py: 91: res4_3_branch2b_bn_s loaded from weights file into gpu_0/res4_3_branch2b_bn_s: (256,)
INFO net.py: 91: res4_3_branch2b_bn_b loaded from weights file into gpu_0/res4_3_branch2b_bn_b: (256,)
INFO net.py: 91: res4_3_branch2c_w loaded from weights file into gpu_0/res4_3_branch2c_w: (1024, 256, 1, 1)
INFO net.py: 91: res4_3_branch2c_bn_s loaded from weights file into gpu_0/res4_3_branch2c_bn_s: (1024,)
INFO net.py: 91: res4_3_branch2c_bn_b loaded from weights file into gpu_0/res4_3_branch2c_bn_b: (1024,)
INFO net.py: 91: res4_4_branch2a_w loaded from weights file into gpu_0/res4_4_branch2a_w: (256, 1024, 1, 1)
INFO net.py: 91: res4_4_branch2a_bn_s loaded from weights file into gpu_0/res4_4_branch2a_bn_s: (256,)
INFO net.py: 91: res4_4_branch2a_bn_b loaded from weights file into gpu_0/res4_4_branch2a_bn_b: (256,)
INFO net.py: 91: res4_4_branch2b_w loaded from weights file into gpu_0/res4_4_branch2b_w: (256, 256, 3, 3)
INFO net.py: 91: res4_4_branch2b_bn_s loaded from weights file into gpu_0/res4_4_branch2b_bn_s: (256,)
INFO net.py: 91: res4_4_branch2b_bn_b loaded from weights file into gpu_0/res4_4_branch2b_bn_b: (256,)
INFO net.py: 91: res4_4_branch2c_w loaded from weights file into gpu_0/res4_4_branch2c_w: (1024, 256, 1, 1)
INFO net.py: 91: res4_4_branch2c_bn_s loaded from weights file into gpu_0/res4_4_branch2c_bn_s: (1024,)
INFO net.py: 91: res4_4_branch2c_bn_b loaded from weights file into gpu_0/res4_4_branch2c_bn_b: (1024,)
INFO net.py: 91: res4_5_branch2a_w loaded from weights file into gpu_0/res4_5_branch2a_w: (256, 1024, 1, 1)
INFO net.py: 91: res4_5_branch2a_bn_s loaded from weights file into gpu_0/res4_5_branch2a_bn_s: (256,)
INFO net.py: 91: res4_5_branch2a_bn_b loaded from weights file into gpu_0/res4_5_branch2a_bn_b: (256,)
INFO net.py: 91: res4_5_branch2b_w loaded from weights file into gpu_0/res4_5_branch2b_w: (256, 256, 3, 3)
INFO net.py: 91: res4_5_branch2b_bn_s loaded from weights file into gpu_0/res4_5_branch2b_bn_s: (256,)
INFO net.py: 91: res4_5_branch2b_bn_b loaded from weights file into gpu_0/res4_5_branch2b_bn_b: (256,)
INFO net.py: 91: res4_5_branch2c_w loaded from weights file into gpu_0/res4_5_branch2c_w: (1024, 256, 1, 1)
INFO net.py: 91: res4_5_branch2c_bn_s loaded from weights file into gpu_0/res4_5_branch2c_bn_s: (1024,)
INFO net.py: 91: res4_5_branch2c_bn_b loaded from weights file into gpu_0/res4_5_branch2c_bn_b: (1024,)
INFO net.py: 91: res5_0_branch2a_w loaded from weights file into gpu_0/res5_0_branch2a_w: (512, 1024, 1, 1)
INFO net.py: 91: res5_0_branch2a_bn_s loaded from weights file into gpu_0/res5_0_branch2a_bn_s: (512,)
INFO net.py: 91: res5_0_branch2a_bn_b loaded from weights file into gpu_0/res5_0_branch2a_bn_b: (512,)
INFO net.py: 91: res5_0_branch2b_w loaded from weights file into gpu_0/res5_0_branch2b_w: (512, 512, 3, 3)
INFO net.py: 91: res5_0_branch2b_bn_s loaded from weights file into gpu_0/res5_0_branch2b_bn_s: (512,)
INFO net.py: 91: res5_0_branch2b_bn_b loaded from weights file into gpu_0/res5_0_branch2b_bn_b: (512,)
INFO net.py: 91: res5_0_branch2c_w loaded from weights file into gpu_0/res5_0_branch2c_w: (2048, 512, 1, 1)
INFO net.py: 91: res5_0_branch2c_bn_s loaded from weights file into gpu_0/res5_0_branch2c_bn_s: (2048,)
INFO net.py: 91: res5_0_branch2c_bn_b loaded from weights file into gpu_0/res5_0_branch2c_bn_b: (2048,)
INFO net.py: 91: res5_0_branch1_w loaded from weights file into gpu_0/res5_0_branch1_w: (2048, 1024, 1, 1)
INFO net.py: 91: res5_0_branch1_bn_s loaded from weights file into gpu_0/res5_0_branch1_bn_s: (2048,)
INFO net.py: 91: res5_0_branch1_bn_b loaded from weights file into gpu_0/res5_0_branch1_bn_b: (2048,)
INFO net.py: 91: res5_1_branch2a_w loaded from weights file into gpu_0/res5_1_branch2a_w: (512, 2048, 1, 1)
INFO net.py: 91: res5_1_branch2a_bn_s loaded from weights file into gpu_0/res5_1_branch2a_bn_s: (512,)
INFO net.py: 91: res5_1_branch2a_bn_b loaded from weights file into gpu_0/res5_1_branch2a_bn_b: (512,)
INFO net.py: 91: res5_1_branch2b_w loaded from weights file into gpu_0/res5_1_branch2b_w: (512, 512, 3, 3)
INFO net.py: 91: res5_1_branch2b_bn_s loaded from weights file into gpu_0/res5_1_branch2b_bn_s: (512,)
INFO net.py: 91: res5_1_branch2b_bn_b loaded from weights file into gpu_0/res5_1_branch2b_bn_b: (512,)
INFO net.py: 91: res5_1_branch2c_w loaded from weights file into gpu_0/res5_1_branch2c_w: (2048, 512, 1, 1)
INFO net.py: 91: res5_1_branch2c_bn_s loaded from weights file into gpu_0/res5_1_branch2c_bn_s: (2048,)
INFO net.py: 91: res5_1_branch2c_bn_b loaded from weights file into gpu_0/res5_1_branch2c_bn_b: (2048,)
INFO net.py: 91: res5_2_branch2a_w loaded from weights file into gpu_0/res5_2_branch2a_w: (512, 2048, 1, 1)
INFO net.py: 91: res5_2_branch2a_bn_s loaded from weights file into gpu_0/res5_2_branch2a_bn_s: (512,)
INFO net.py: 91: res5_2_branch2a_bn_b loaded from weights file into gpu_0/res5_2_branch2a_bn_b: (512,)
INFO net.py: 91: res5_2_branch2b_w loaded from weights file into gpu_0/res5_2_branch2b_w: (512, 512, 3, 3)
INFO net.py: 91: res5_2_branch2b_bn_s loaded from weights file into gpu_0/res5_2_branch2b_bn_s: (512,)
INFO net.py: 91: res5_2_branch2b_bn_b loaded from weights file into gpu_0/res5_2_branch2b_bn_b: (512,)
INFO net.py: 91: res5_2_branch2c_w loaded from weights file into gpu_0/res5_2_branch2c_w: (2048, 512, 1, 1)
INFO net.py: 91: res5_2_branch2c_bn_s loaded from weights file into gpu_0/res5_2_branch2c_bn_s: (2048,)
INFO net.py: 91: res5_2_branch2c_bn_b loaded from weights file into gpu_0/res5_2_branch2c_bn_b: (2048,)
INFO net.py: 83: fpn_inner_res5_2_sum_w not found
INFO net.py: 83: fpn_inner_res5_2_sum_b not found
INFO net.py: 83: fpn_inner_res4_5_sum_lateral_w not found
INFO net.py: 83: fpn_inner_res4_5_sum_lateral_b not found
INFO net.py: 83: fpn_inner_res3_3_sum_lateral_w not found
INFO net.py: 83: fpn_inner_res3_3_sum_lateral_b not found
INFO net.py: 83: fpn_inner_res2_2_sum_lateral_w not found
INFO net.py: 83: fpn_inner_res2_2_sum_lateral_b not found
INFO net.py: 83: fpn_res5_2_sum_w not found
INFO net.py: 83: fpn_res5_2_sum_b not found
INFO net.py: 83: fpn_res4_5_sum_w not found
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INFO net.py: 83: fpn_res3_3_sum_w not found
INFO net.py: 83: fpn_res3_3_sum_b not found
INFO net.py: 83: fpn_res2_2_sum_w not found
INFO net.py: 83: fpn_res2_2_sum_b not found
INFO net.py: 83: fc6_w not found
INFO net.py: 83: fc6_b not found
INFO net.py: 83: fc7_w not found
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INFO net.py: 83: cls_score_w not found
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INFO net.py: 83: bbox_pred_w not found
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INFO net.py: 83: conv_fcn1_w not found
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INFO net.py: 83: conv_fcn4_w not found
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INFO net.py: 83: conv_fcn5_w not found
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INFO net.py: 83: kps_score_lowres_w not found
INFO net.py: 83: kps_score_lowres_b not found
INFO net.py: 83: kps_score_w not found
INFO net.py: 83: kps_score_b not found
INFO net.py: 125: res2_1_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res3_1_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_2_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res4_0_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res2_2_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res3_3_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res5_1_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res3_3_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res4_4_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_5_branch2b_b preserved in workspace (unused)
INFO net.py: 125: conv1_b preserved in workspace (unused)
INFO net.py: 125: fc1000_b preserved in workspace (unused)
INFO net.py: 125: fc1000_w preserved in workspace (unused)
INFO net.py: 125: res3_2_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res3_2_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res2_0_branch1_b preserved in workspace (unused)
INFO net.py: 125: res4_2_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res2_1_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res5_0_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_5_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res4_1_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_3_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_0_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_2_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res2_0_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res4_0_branch1_b preserved in workspace (unused)
INFO net.py: 125: res2_2_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res3_2_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res3_0_branch1_b preserved in workspace (unused)
INFO net.py: 125: res3_1_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res2_0_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res2_1_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res4_1_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res4_0_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res4_1_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res2_2_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res5_2_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_5_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res3_0_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res3_1_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res5_1_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res5_1_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res4_4_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res5_2_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res3_3_branch2b_b preserved in workspace (unused)
INFO net.py: 125: res4_4_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res4_3_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res5_0_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res5_2_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res5_0_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res3_0_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res5_0_branch1_b preserved in workspace (unused)
INFO net.py: 125: res3_0_branch2c_b preserved in workspace (unused)
INFO net.py: 125: res2_0_branch2a_b preserved in workspace (unused)
INFO net.py: 125: res4_3_branch2c_b preserved in workspace (unused)
I0124 12:11:30.604378 26977 net_dag_utils.cc:118] Operator graph pruning prior to chain compute took: 9.9979e-05 secs
I0124 12:11:30.604557 26977 net_dag.cc:61] Number of parallel execution chains 36 Number of operators = 201
I0124 12:11:30.618330 26977 net_dag_utils.cc:118] Operator graph pruning prior to chain compute took: 9.0454e-05 secs
I0124 12:11:30.618464 26977 net_dag.cc:61] Number of parallel execution chains 30 Number of operators = 188
I0124 12:11:30.620652 26977 net_dag_utils.cc:118] Operator graph pruning prior to chain compute took: 1.4322e-05 secs
I0124 12:11:30.620702 26977 net_dag.cc:61] Number of parallel execution chains 5 Number of operators = 24
INFO infer_simple.py: 113: Processing demo/66e75fd6dd47431b9be184abd3829b97_th.jpg -> /tmp/detectron-visualizations/66e75fd6dd47431b9be184abd3829b97_th.jpg
Traceback (most recent call last):
File "tools/infer_simple.py", line 150, in
main(args)
File "tools/infer_simple.py", line 120, in main
model, im, None, timers=timers
File "/export/huangzhibiao/code/Detectron/lib/core/test.py", line 57, in im_detect_all
scores, boxes, im_scales = im_detect_bbox_aug(model, im, box_proposals)
File "/export/huangzhibiao/code/Detectron/lib/core/test.py", line 216, in im_detect_bbox_aug
model, im, box_proposals
File "/export/huangzhibiao/code/Detectron/lib/core/test.py", line 289, in im_detect_bbox_hflip
box_proposals_hf = box_utils.flip_boxes(box_proposals, im_width)
File "/export/huangzhibiao/code/Detectron/lib/utils/boxes.py", line 248, in flip_boxes
boxes_flipped = boxes.copy()
AttributeError: 'NoneType' object has no attribute 'copy'

[rbgirshick]

There are two problems:

1. You are trying to use an ImageNet pretrained model as the source of model weights, but this model was never trained for keypoint prediction. You need to change it to the weights from a keypoints model file.

2. The test-time augmentation yaml config is actually only set up for inference with precomputed RPN proposals on the COCO minival set.

Here's an example for keypoint inference with an end-to-end trained keypoints Mask R-CNN model:

python2 tools/infer_simple.py \
    --cfg configs/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_s1x.yaml \
    --output-dir /tmp/detectron-visualizations \
    --image-ext jpg \
    --wts https://s3-us-west-2.amazonaws.com/detectron/37697714/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_s1x.yaml.08_44_03.qrQ0ph6M/output/train/keypoints_coco_2014_train:keypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl \
    demo