The following error occurred when I tested my trained model.

[Freezing-hxy]

(sort2) hxy@uac516:~/Desktop/anomaly_detection/pytorch_Realtime_Multi-Person_Pose_Estimation$ python demo/picture_demo.py
Bulding VGG19
Traceback (most recent call last):
File "demo/picture_demo.py", line 46, in
model.load_state_dict(torch.load(args.weight))
File "/home/hxy/anaconda3/envs/sort2/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1224, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for rtpose_model:
Missing key(s) in state_dict: "model0.0.weight", "model0.0.bias", "model0.2.weight", "model0.2.bias", "model0.5.weight", "model0.5.bias", "model0.7.weight", "model0.7.bias", "model0.10.weight", "model0.10.bias", "model0.12.weight", "model0.12.bias", "model0.14.weight", "model0.14.bias", "model0.16.weight", "model0.16.bias", "model0.19.weight", "model0.19.bias", "model0.21.weight", "model0.21.bias", "model0.23.weight", "model0.23.bias", "model0.25.weight", "model0.25.bias", "model1_1.0.weight", "model1_1.0.bias", "model1_1.2.weight", "model1_1.2.bias", "model1_1.4.weight", "model1_1.4.bias", "model1_1.6.weight", "model1_1.6.bias", "model1_1.8.weight", "model1_1.8.bias", "model2_1.0.weight", "model2_1.0.bias", "model2_1.2.weight", "model2_1.2.bias", "model2_1.4.weight", "model2_1.4.bias", "model2_1.6.weight", "model2_1.6.bias", "model2_1.8.weight", "model2_1.8.bias", "model2_1.10.weight", "model2_1.10.bias", "model2_1.12.weight", "model2_1.12.bias", "model3_1.0.weight", "model3_1.0.bias", "model3_1.2.weight", "model3_1.2.bias", "model3_1.4.weight", "model3_1.4.bias", "model3_1.6.weight", "model3_1.6.bias", "model3_1.8.weight", "model3_1.8.bias", "model3_1.10.weight", "model3_1.10.bias", "model3_1.12.weight", "model3_1.12.bias", "model4_1.0.weight", "model4_1.0.bias", "model4_1.2.weight", "model4_1.2.bias", "model4_1.4.weight", "model4_1.4.bias", "model4_1.6.weight", "model4_1.6.bias", "model4_1.8.weight", "model4_1.8.bias", "model4_1.10.weight", "model4_1.10.bias", "model4_1.12.weight", "model4_1.12.bias", "model5_1.0.weight", "model5_1.0.bias", "model5_1.2.weight", "model5_1.2.bias", "model5_1.4.weight", "model5_1.4.bias", "model5_1.6.weight", "model5_1.6.bias", "model5_1.8.weight", "model5_1.8.bias", "model5_1.10.weight", "model5_1.10.bias", "model5_1.12.weight", "model5_1.12.bias", "model6_1.0.weight", "model6_1.0.bias", "model6_1.2.weight", "model6_1.2.bias", "model6_1.4.weight", "model6_1.4.bias", "model6_1.6.weight", "model6_1.6.bias", "model6_1.8.weight", "model6_1.8.bias", "model6_1.10.weight", "model6_1.10.bias", "model6_1.12.weight", "model6_1.12.bias", "model1_2.0.weight", "model1_2.0.bias", "model1_2.2.weight", "model1_2.2.bias", "model1_2.4.weight", "model1_2.4.bias", "model1_2.6.weight", "model1_2.6.bias", "model1_2.8.weight", "model1_2.8.bias", "model2_2.0.weight", "model2_2.0.bias", "model2_2.2.weight", "model2_2.2.bias", "model2_2.4.weight", "model2_2.4.bias", "model2_2.6.weight", "model2_2.6.bias", "model2_2.8.weight", "model2_2.8.bias", "model2_2.10.weight", "model2_2.10.bias", "model2_2.12.weight", "model2_2.12.bias", "model3_2.0.weight", "model3_2.0.bias", "model3_2.2.weight", "model3_2.2.bias", "model3_2.4.weight", "model3_2.4.bias", "model3_2.6.weight", "model3_2.6.bias", "model3_2.8.weight", "model3_2.8.bias", "model3_2.10.weight", "model3_2.10.bias", "model3_2.12.weight", "model3_2.12.bias", "model4_2.0.weight", "model4_2.0.bias", "model4_2.2.weight", "model4_2.2.bias", "model4_2.4.weight", "model4_2.4.bias", "model4_2.6.weight", "model4_2.6.bias", "model4_2.8.weight", "model4_2.8.bias", "model4_2.10.weight", "model4_2.10.bias", "model4_2.12.weight", "model4_2.12.bias", "model5_2.0.weight", "model5_2.0.bias", "model5_2.2.weight", "model5_2.2.bias", "model5_2.4.weight", "model5_2.4.bias", "model5_2.6.weight", "model5_2.6.bias", "model5_2.8.weight", "model5_2.8.bias", "model5_2.10.weight", "model5_2.10.bias", "model5_2.12.weight", "model5_2.12.bias", "model6_2.0.weight", "model6_2.0.bias", "model6_2.2.weight", "model6_2.2.bias", "model6_2.4.weight", "model6_2.4.bias", "model6_2.6.weight", "model6_2.6.bias", "model6_2.8.weight", "model6_2.8.bias", "model6_2.10.weight", "model6_2.10.bias", "model6_2.12.weight", "model6_2.12.bias".
Unexpected key(s) in state_dict: "module.model0.0.weight", "module.model0.0.bias", "module.model0.2.weight", "module.model0.2.bias", "module.model0.5.weight", "module.model0.5.bias", "module.model0.7.weight", "module.model0.7.bias", "module.model0.10.weight", "module.model0.10.bias", "module.model0.12.weight", "module.model0.12.bias", "module.model0.14.weight", "module.model0.14.bias", "module.model0.16.weight", "module.model0.16.bias", "module.model0.19.weight", "module.model0.19.bias", "module.model0.21.weight", "module.model0.21.bias", "module.model0.23.weight", "module.model0.23.bias", "module.model0.25.weight", "module.model0.25.bias", "module.model1_1.0.weight", "module.model1_1.0.bias", "module.model1_1.2.weight", "module.model1_1.2.bias", "module.model1_1.4.weight", "module.model1_1.4.bias", "module.model1_1.6.weight", "module.model1_1.6.bias", "module.model1_1.8.weight", "module.model1_1.8.bias", "module.model2_1.0.weight", "module.model2_1.0.bias", "module.model2_1.2.weight", "module.model2_1.2.bias", "module.model2_1.4.weight", "module.model2_1.4.bias", "module.model2_1.6.weight", "module.model2_1.6.bias", "module.model2_1.8.weight", "module.model2_1.8.bias", "module.model2_1.10.weight", "module.model2_1.10.bias", "module.model2_1.12.weight", "module.model2_1.12.bias", "module.model3_1.0.weight", "module.model3_1.0.bias", "module.model3_1.2.weight", "module.model3_1.2.bias", "module.model3_1.4.weight", "module.model3_1.4.bias", "module.model3_1.6.weight", "module.model3_1.6.bias", "module.model3_1.8.weight", "module.model3_1.8.bias", "module.model3_1.10.weight", "module.model3_1.10.bias", "module.model3_1.12.weight", "module.model3_1.12.bias", "module.model4_1.0.weight", "module.model4_1.0.bias", "module.model4_1.2.weight", "module.model4_1.2.bias", "module.model4_1.4.weight", "module.model4_1.4.bias", "module.model4_1.6.weight", "module.model4_1.6.bias", "module.model4_1.8.weight", "module.model4_1.8.bias", "module.model4_1.10.weight", "module.model4_1.10.bias", "module.model4_1.12.weight", "module.model4_1.12.bias", "module.model5_1.0.weight", "module.model5_1.0.bias", "module.model5_1.2.weight", "module.model5_1.2.bias", "module.model5_1.4.weight", "module.model5_1.4.bias", "module.model5_1.6.weight", "module.model5_1.6.bias", "module.model5_1.8.weight", "module.model5_1.8.bias", "module.model5_1.10.weight", "module.model5_1.10.bias", "module.model5_1.12.weight", "module.model5_1.12.bias", "module.model6_1.0.weight", "module.model6_1.0.bias", "module.model6_1.2.weight", "module.model6_1.2.bias", "module.model6_1.4.weight", "module.model6_1.4.bias", "module.model6_1.6.weight", "module.model6_1.6.bias", "module.model6_1.8.weight", "module.model6_1.8.bias", "module.model6_1.10.weight", "module.model6_1.10.bias", "module.model6_1.12.weight", "module.model6_1.12.bias", "module.model1_2.0.weight", "module.model1_2.0.bias", "module.model1_2.2.weight", "module.model1_2.2.bias", "module.model1_2.4.weight", "module.model1_2.4.bias", "module.model1_2.6.weight", "module.model1_2.6.bias", "module.model1_2.8.weight", "module.model1_2.8.bias", "module.model2_2.0.weight", "module.model2_2.0.bias", "module.model2_2.2.weight", "module.model2_2.2.bias", "module.model2_2.4.weight", "module.model2_2.4.bias", "module.model2_2.6.weight", "module.model2_2.6.bias", "module.model2_2.8.weight", "module.model2_2.8.bias", "module.model2_2.10.weight", "module.model2_2.10.bias", "module.model2_2.12.weight", "module.model2_2.12.bias", "module.model3_2.0.weight", "module.model3_2.0.bias", "module.model3_2.2.weight", "module.model3_2.2.bias", "module.model3_2.4.weight", "module.model3_2.4.bias", "module.model3_2.6.weight", "module.model3_2.6.bias", "module.model3_2.8.weight", "module.model3_2.8.bias", "module.model3_2.10.weight", "module.model3_2.10.bias", "module.model3_2.12.weight", "module.model3_2.12.bias", "module.model4_2.0.weight", "module.model4_2.0.bias", "module.model4_2.2.weight", "module.model4_2.2.bias", "module.model4_2.4.weight", "module.model4_2.4.bias", "module.model4_2.6.weight", "module.model4_2.6.bias", "module.model4_2.8.weight", "module.model4_2.8.bias", "module.model4_2.10.weight", "module.model4_2.10.bias", "module.model4_2.12.weight", "module.model4_2.12.bias", "module.model5_2.0.weight", "module.model5_2.0.bias", "module.model5_2.2.weight", "module.model5_2.2.bias", "module.model5_2.4.weight", "module.model5_2.4.bias", "module.model5_2.6.weight", "module.model5_2.6.bias", "module.model5_2.8.weight", "module.model5_2.8.bias", "module.model5_2.10.weight", "module.model5_2.10.bias", "module.model5_2.12.weight", "module.model5_2.12.bias", "module.model6_2.0.weight", "module.model6_2.0.bias", "module.model6_2.2.weight", "module.model6_2.2.bias", "module.model6_2.4.weight", "module.model6_2.4.bias", "module.model6_2.6.weight", "module.model6_2.6.bias", "module.model6_2.8.weight", "module.model6_2.8.bias", "module.model6_2.10.weight", "module.model6_2.10.bias", "module.model6_2.12.weight", "module.model6_2.12.bias".

[thisimyusername]

Hi,Sorry I dont know how to fix your problem.
But you trained success! I am also troubled with the train process.Could you give some ideas?
I train the model on a animal-pose datasets which has 20 keypoints and 15 keypoint-connection.
When I train, error log said: the shape of heatmap and vec not match to outputs of network(which is heatmap 19 and vec(paf) 38) ,I try to change the output channels of network, but got another error:RuntimeError: Given groups=1, weight of size [128, 185, 7, 7], expected input[8, 179, 46, 46] to have 185 channels, but got 179 channels instead.

[thisimyusername]

now I meet same error，

[SaraSherinThomas]

In the demo code, update the lines to load the model as below:

model = get_model('vgg19')
wts_dict = torch.load(args.weight)
wts_dict_corrected = {k.replace('module.',''): v for k, v in wts_dict.items()}
model.load_state_dict(wts_dict_corrected)

[chris2lee]

I think you downloaded a wrong weight.