mmsegmentation/.circleci
zoulinxin 72e20a8854
[Feature] remote sensing inference (#3131)
## Motivation

Supports inference for ultra-large-scale remote sensing images.

## Modification

Add RSImageInference.py in demo.

## Use cases

Taking the inference of Vaihingen dataset images using PSPNet as an
example, the following settings are required:

**img**: Specify the path of the image.
**model**: Provide the configuration file for the model.
**checkpoint**: Specify the weight file for the model.
**out**: Set the output path for the results.
**batch_size**: Determine the batch size used during inference.
**win_size**: Specify the width and height(512x512) of the sliding
window.
**stride**: Set the stride(400x400) for sliding the window.
**thread(default: 1)**: Specify the number of threads to be used for
inference.
**Inference device (default: cuda:0)**: Specify the device for inference
(e.g., cuda:0 for CPU).

```shell
python demo/rs_image_inference.py demo/demo.png projects/pp_mobileseg/configs/pp_mobileseg/pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_tiny.py pp_mobileseg_mobilenetv3_2xb16_3rdparty-tiny_512x512-ade20k-a351ebf5.pth --batch-size 8 --device cpu --thread 2
```

---------

Co-authored-by: xiexinch <xiexinch@outlook.com>
2023-08-31 12:44:46 +08:00
..
docker set circle ci (#1804) 2022-07-21 22:20:09 +08:00
config.yml [CI] Update ci image (#2801) 2023-04-06 11:12:14 +08:00
test.yml [Feature] remote sensing inference (#3131) 2023-08-31 12:44:46 +08:00