## Getting Started with FrozenSeg

This document provides a brief intro of the usage of FrozenSeg.

Please see [Getting Started with Detectron2](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for full usage.


### Inference Demo with Pre-trained Models

We provide `demo.py` that is able to demo builtin configs. Run it with:
```
python demo.py \
  --input input1.jpg input2.jpg \
  [--other-options]
  --opts MODEL.WEIGHTS /path/to/checkpoint_file
```
The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
This command will run the inference and show visualizations in an OpenCV window.

For details of the command line arguments, see `demo.py -h` or look at its source code
to understand its behavior. Some common arguments are:
* To run on your __webcam__, replace --input files with --webcam
* To run on a __video__, replace --input files with --video-input video.mp4.
* To run on __cpu__, add `MODEL.DEVICE cpu` after `--opts`.
* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.


### Training & Evaluation in Command Line

We provide a script `train_net.py`, that is made to train all the configs provided in FrozenSeg.

To train a model with "train_net.py", first setup the corresponding datasets following [datasets/README.md](./datasets/README.md), download [SAM checkpoints](https://github.com/facebookresearch/segment-anything?tab=readme-ov-file#model-checkpoints) and save it under `pretrained_checkpoint/`.
then run:
```
python train_net.py --num-gpus 4\
  --config-file configs/coco/frozenseg/convnext_large_eval_ade20k.yaml
```

The configs are made for 4-GPU training.
Since we use ADAMW optimizer, it is not clear how to scale learning rate with batch size.
To train on 1 GPU, you need to figure out learning rate and batch size by yourself:
```
python train_net.py \
  --config-file configs/coco/frozenseg/convnext_large_eval_ade20k.yaml \
  --num-gpus 1 SOLVER.IMS_PER_BATCH SET_TO_SOME_REASONABLE_VALUE SOLVER.BASE_LR SET_TO_SOME_REASONABLE_VALUE
```

To evaluate a model's performance without `OpenSeg Ensemble`:
```
python train_net.py \
  --config-file configs/coco/frozenseg/convnext_large_eval_ade20k.yaml \
  --eval-only MODEL.WEIGHTS /path/to/checkpoint_file \
  TEST.USE_SAM_MASKS False 
```

For using `OpenSeg Ensemble`:
1. generate SAM mask predictions (default saveing under `output/SAM_masks_pred`):
```
python save_sam_masks.py --data_name pc_val --sam_model vit_h
```
2. run with:
```
python train_net.py \
  --config-file configs/coco/frozenseg/convnext_large_eval_ade20k.yaml \
  --eval-only MODEL.WEIGHTS /path/to/checkpoint_file \
  TEST.USE_SAM_MASKS True 
```