Not able to generate data.json and segmentation fault while running application

[kartik122]

I tried using the repo with changes in detectron2 and main.py suggested in issue@
And still getting the following error:

Get mask of cloth

Get openpose coordinate using posenet

Segmentation fault
Generate semantic segmentation using Graphonomy-Master library

Constructing DeepLabv3+ model...
Number of classes: 20
Output stride: 16
Number of Input Channels: 3
load model: ./inference.pth
Segmentation fault

Generate Densepose image using detectron2 library

[05/23 07:54:23 apply_net]: Loading config from detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x.yaml
[05/23 07:54:23 apply_net]: Loading model from https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl
[05/23 07:54:25 apply_net]: Loading data from origin.jpg
Segmentation fault
Traceback (most recent call last):
File "/home/deepk/Desktop/backend/virtual_tryon/TryYours-Virtual-Try-On/get_densepose.py", line 27, in
with open('./data.json', 'r') as f:
FileNotFoundError: [Errno 2] No such file or directory: './data.json'

Run HR-VITON to generate final image

Start to test! - HR-VITON
Network [SPADEGenerator] was created. Total number of parameters: 100.5 million. To see the architecture, do print(network).
Segmentation fault

[myinzack]

The issue is fixed.

1. Go to file TryYours-Virtual-Try-On\detectron2\projects\DensePose\apply_net.py

2. Go to @classmethod def postexecute
   In the existing code the output is saved as output.pkl ensure the output is saved to data.json as shown in next step

3. change the code as follows

       def postexecute(cls: type, context: Dict[str, Any]):
   
              out_fname = context["out_fname"]
              out_dir = os.path.dirname(out_fname)
              if len(out_dir) > 0 and not os.path.exists(out_dir):
                  os.makedirs(out_dir)
              context = context["results"][0]
              with open('./data.json','w') as f:
                  json.dump([context["pred_densepose"][0].labels.cpu().tolist(), 
                  context["pred_densepose"][0].uv.cpu().tolist(), 
                  context["pred_boxes_XYXY"][0].cpu().tolist()], f)
              
              logger.info(f"Output saved to data.json")