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https://github.com/nikhilweee/semantic-human-matting
A PyTorch implementation of the 2018 ACL Multimedia paper on Semantic Human Matting.
https://github.com/nikhilweee/semantic-human-matting
Last synced: about 1 month ago
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A PyTorch implementation of the 2018 ACL Multimedia paper on Semantic Human Matting.
- Host: GitHub
- URL: https://github.com/nikhilweee/semantic-human-matting
- Owner: nikhilweee
- Created: 2020-08-19T15:24:51.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-01-15T17:51:48.000Z (almost 4 years ago)
- Last Synced: 2023-08-30T12:32:19.061Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 4
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Semantic Human Matting
Yet another PyTorch implementation of the 2018 ACL Multimedia paper on [Semantic Human Matting](https://arxiv.org/abs/1809.01354).
## Getting Started
### Install Dependencies
All dependencies are listed in the Pipfile. You can install them using pipenv.
```shell
$ pipenv install
```
This repository depends on the PSPNet implementation from https://github.com/hszhao/semseg. You will need to download the resnet model `resnet50_v2.pth` from the `initmodel` directory from [this google drive link](https://drive.google.com/open?id=15wx9vOM0euyizq-M1uINgN0_wjVRf9J3) and place it in `data/models`.
### Preparing Data
This repository expects training data in the form of raw images and alpha mattes placed in `data/images` and `data/mattes` folders respectively.
#### Pre-train TNet
If you're considering pretraining the TNet separately, you will need target trimaps for training. To do so, simply run the `generate_trimap.py` script located in the `data` directory with a list of all files to be converted in `images.txt`. This will create trimaps in `data/trimaps` which can be used while pre-training the model.
```shell
# cd data
$ python3 generate_trimap.py
```
#### Pre-train MNet
This repository currently assumes that the final mattes in `data/mattes` are also the ground truths for pre-training the MNet. There is no support for using a separate ground-truth as of now.
## Training
To train the image matting pipeline end-to-end, simply run the `train.py` script.
```shell
$ python3 train.py
```
The training script also supports pre-training of TNet and MNet. This can easily be done by using the `--mode` flag.
```shell
# Pre-train TNet
$ python3 train.py --mode pretrain_tnet
# Pre-train MNet
$ python3 train.py --mode pretrain_mnet
```
For additional options such as changing hyperparameters or using a GPU, please use the `--help` flag.
## Inference
To run inference with a trained model, use the `test.py` script. This will automatically choose the best model available.
```shell
$ python3 test.py
```
For additional options, please see the `--help` flag.
## What's different?
Although there are a bunch of implementations available for this paper, here are a few key differences why you might want to consider this repository.
- **Minimal dependencies**: The only dependencies are `torch` and `torchvision`.
- **Correct loss computation**: Most other implementations use the L2 loss even when the paper specifically mentions the L1 loss.
- **Based on official repositories**: The code is based on the official implementations of [PSPNet](https://github.com/hszhao/semseg) and [DIMNet](https://github.com/foamliu/Deep-Image-Matting-PyTorch).
## Acknowledgements
This repository is primarily based on the official implementations of PSPNet and DIMNet from https://github.com/foamliu/Deep-Image-Matting-PyTorch and https://github.com/hszhao/semseg respectively. Any other attributions are commented on top of individual files.