A PyTorch implementation of PointRend: Image Segmentation as Rendering

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Deep Learning pytorch segmentation instance detectron2 pointrend
Overview

PointRend

A PyTorch implementation of PointRend: Image Segmentation as Rendering

title

[arxiv] [Official Implementation: Detectron2]

This repo for Only Semantic Segmentation on the PascalVOC dataset.

Many details differ from the paper for feasibilty check.

Reproduce Fig 5.

Sampled Points showing from different strategies on A Dog image.

See test_point_sampling.ipynb

Original Figure

fig5

Reference : Pytorch Deeplab Tutorial

How to use:

First, fix data path in default.yaml

Multi GPU Training See details in Single GPU Training

➜ python3 -m torch.distributed.launch --nproc_per_node={your_gpus} main.py -h

Sinle GPU Training

➜ python3 main.py -h
usage: main.py [-h] config save

PyTorch Object Detection Training

positional arguments:
  config      It must be config/*.yaml
  save        Save path in out directory

optional arguments:
  -h, --help  show this help message and exit

e.g.)

python3 main.py config/default.yaml test_codes
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Comments
  • hello Here is my suggestion! Using

    hello Here is my suggestion! Using " torch_scatter()" rather than "for loop" during inference .

    Example:
    B, D, H, W = pred.shape rend_pred = pred.view(B, C, -1).scatter_(2, rend_result['points'].unsqueeze(1).expand(-1, C, -1), rend_result['rend'].float()) rend_pred = rend_pred.reshape((B, C, H, W))

    opened by lihaoming45 2
  • question:loss=0,seg=0,point=0

    question:loss=0,seg=0,point=0

    Thank you for your contribution, I in the use of the network to train their own data set, data format shall be carried out in accordance with the format of cityscapes only, use the command: python3. Main py configs/default yaml/output, the code can run, but the training of the output loss value, seg, point from the first epoch start all is 0 (I got the five categories, semantic segmentation), want to excuse me, what should be the problem?

    opened by gp458742574 0
  • What does

    What does "topk uncertainty points" mean?

    When running test_point_sampling.ipny in the test file, the "topk uncertainty points" in the file do not know what it means. Can you tell me？Thank you!!!

    opened by kangyisheng123456 0
  • In Class-PointHead: “During inference, subdivision uses N=8096” Why is 8096？

    In Class-PointHead: “During inference, subdivision uses N=8096” Why is 8096？

    Thanks for your contribution. Here are the code-comments in pointrend.py class: PointHead-inference: """ During inference, subdivision uses N=8096 (i.e., the number of points in the stride 16 map of a 1024×2048 image) """ i found this N=8096 in paper: "5.Experiments: SemanticSegmentation" and as same as your code variable N. I don't understand how to get this N. Is it related to the size of the input image？or maybe something else?

    opened by JintuZheng 1
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