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facebookresearch / Detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
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AttributeError: 'NoneType' object has no attribute 'copy' #18

Closed s5248 closed 6 years ago

s5248 commented 6 years ago

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: 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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 commented 6 years ago

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