How can I get predictions from these pretrained models?
I've been trying to generate human pose estimations, I came across many pretrained models (ex. Pose2Seg, deep-high-resolution-net ), however these models only include scripts for training and testing, this seems to be the norm in code written to implement models from research papers ,in deep-high-resolution-net I have tried to write a script to load the pretrained model and feed it my images, but the output I got was a bunch of tensors and I have no idea how to convert them to the .json annotations that I need.

total newbie here, sorry for my poor English in advance, ANY tips are appreciated.

I would include my script but its over 100 lines.

PS: is it polite to contact the authors and ask them if they can help?

because it seems a little distasteful.

Im not doing skeleton detection research, but your problem seems to be general. 

(1) I dont think other people should teaching you from begining on how to load data and run their code from begining.  

(2) For running other peoples code,  just modify their test script which is provided e.g

https://github.com/leoxiaobin/deep-high-resolution-net.pytorch/blob/master/tools/test.py

They already helps you loaded the model

 model = eval('models.'+cfg.MODEL.NAME+'.get_pose_net')(
        cfg, is_train=False
    )

    if cfg.TEST.MODEL_FILE:
        logger.info('=&gt; loading model from {}'.format(cfg.TEST.MODEL_FILE))
        model.load_state_dict(torch.load(cfg.TEST.MODEL_FILE), strict=False)
    else:
        model_state_file = os.path.join(
            final_output_dir, 'final_state.pth'
        )
        logger.info('=&gt; loading model from {}'.format(model_state_file))
        model.load_state_dict(torch.load(model_state_file))

    model = torch.nn.DataParallel(model, device_ids=cfg.GPUS).cuda()


Just call 

# evaluate on Variable x with testing data
y = model(x)
# access Variable's tensor, copy back to CPU, convert to numpy
arr = y.data.cpu().numpy()
# write CSV
np.savetxt('output.csv', arr)


You should be able to open it in excel

(3) "convert them to the .json annotations that I need". 

That's the problem nobody can help. We don't know what format you want. For their format, it can be obtained either by their paper. Or looking at their training data by

X, y = torch.load('some_training_set_with_labels.pt')


By correlating the x and y. Then you should have a pretty good idea.
