From 223f2c6abb433142bb3fb18fb95377127b8c168c Mon Sep 17 00:00:00 2001 From: aidengu Date: Fri, 19 Jul 2024 15:44:47 -0700 Subject: [PATCH] improve readme --- README.md | 127 ++++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 118 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 426c940..8b0ebae 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,13 @@ -# *BiomedParse* -:grapes: \[[Read Our arXiv Paper](https://arxiv.org/abs/2405.12971)\]   :apple: \[[Check Our Demo](https://microsoft.github.io/BiomedParse/)\] +# **BiomedParse** +:grapes: \[[Read Our arXiv Paper](https://arxiv.org/abs/2405.12971)\]   :apple: \[[Check Our Demo](https://microsoft.github.io/BiomedParse/)\] -install docker -``` +## Installation + +### Install Docker + +Follow these commands to install Docker on Ubuntu: + +```sh sudo apt update sudo apt install apt-transport-https ca-certificates curl software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - @@ -12,8 +17,12 @@ apt-cache policy docker-ce sudo apt install docker-ce ``` -Prepare docker environment: -Specify project directories in docker/README.md +## Prepare Docker Environment + +Specify the project directories in `docker/README.md`. + +Run the following commands to set up the Docker environment: + ```sh bash docker/docker_build.sh bash docker/docker_run.sh @@ -21,14 +30,114 @@ bash docker/setup_inside_docker.sh source docker/data_env.sh ``` -Training using example BioParseData: +## Data Description and Preparation + +### Data Description + +@IceBubble217 Provide a detailed description of the dataset used. Include information such as the source, structure, and any preprocessing steps required. + +### Data Preparation +Preprocessing scripts or commands that need to be run. + + +## Training + +To train the model using the example BioParseData, run: + ```sh bash assets/scripts/train.sh ``` -Evaluation on example BioParseData: +### Customizing Training Settings + +**Placeholder:** +- Changing Parameters: [Describe how to change parameters, e.g., learning rate, batch size, etc.] +- Customizing Training Settings: [Provide examples of how to customize the training settings] + +## Evaluation + +To evaluate the model on the example BioParseData, run: + ```sh bash assets/scripts/eval.sh ``` -Detection and Recognition inference code are provided in inference_utils/output_processing.py. check_mask_stats() outputs p value for model predicted mask for detection. combine_masks() combines predictions for non-overlapping masks. See the Method section in our paper for details. +## Inference + +Detection and recognition inference code are provided in `inference_utils/output_processing.py`. + +- `check_mask_stats()`: Outputs p-value for model-predicted mask for detection. +- `combine_masks()`: Combines predictions for non-overlapping masks. + +@IceBubble217 details/example for usage. + + +## Reproducing Results +To reproduce the exact results presented in the paper, use the following table of parameters and configurations: + +| Configuration Parameter | Description | Value | +|-----------------------------|------------------------------------------|------------------------------------| +| Data Directory | Path to the dataset | `/path/to/data/` | +| Pre-trained Model Checkpoint| Path to the pre-trained model checkpoint | `/path/to/checkpoint/model.pth` | +| Training Script | Script used for training | `assets/scripts/train.sh` | +| Evaluation Script | Script used for evaluation | `assets/scripts/eval.sh` | +| Inference Script | Script for running inference | `inference_utils/output_processing.py` | +| Environment Variables | Required environment variables | See below | | +| Configuration File | Configuration file for the model | `configs/biomedseg/biomed_seg_lang_v1.yaml` | +| Training Parameters | Additional training parameters | See below | +| Output Directory | Directory to save outputs | `outputs/` | + +### Environment Variables +```sh +export DETECTRON2_DATASETS=data/ +export DATASET=data/ +export DATASET2=data/ +export VLDATASET=data/ +export PATH=$PATH:data/coco_caption/jre1.8.0_321/bin/ +export PYTHONPATH=$PYTHONPATH:data/coco_caption/ +export OMPI_ALLOW_RUN_AS_ROOT=1 +export OMPI_ALLOW_RUN_AS_ROOT_CONFIRM=1 +export WANDB_KEY=YOUR_WANDB_KEY # Provide your wandb key here +``` + +### Training Parameters +```sh +CUDA_VISIBLE_DEVICES=0,1,2,3 mpirun -n 4 python entry.py train \ + --conf_files configs/biomedseg/biomed_seg_lang_v1.yaml \ + --overrides \ + FP16 True \ + RANDOM_SEED 2024 \ + BioMed.INPUT.IMAGE_SIZE 1024 \ + MODEL.DECODER.HIDDEN_DIM 512 \ + MODEL.ENCODER.CONVS_DIM 512 \ + MODEL.ENCODER.MASK_DIM 512 \ + TEST.BATCH_SIZE_TOTAL 4 \ + TRAIN.BATCH_SIZE_TOTAL 4 \ + TRAIN.BATCH_SIZE_PER_GPU 1 \ + SOLVER.MAX_NUM_EPOCHS 10 \ + SOLVER.BASE_LR 0.00005 \ + SOLVER.FIX_PARAM.backbone False \ + SOLVER.FIX_PARAM.lang_encoder False \ + SOLVER.FIX_PARAM.pixel_decoder False \ + MODEL.DECODER.COST_SPATIAL.CLASS_WEIGHT 1.0 \ + MODEL.DECODER.COST_SPATIAL.MASK_WEIGHT 1.0 \ + MODEL.DECODER.COST_SPATIAL.DICE_WEIGHT 1.0 \ + MODEL.DECODER.TOP_SPATIAL_LAYERS 10 \ + MODEL.DECODER.SPATIAL.ENABLED True \ + MODEL.DECODER.GROUNDING.ENABLED True \ + LOADER.SAMPLE_PROB prop \ + FIND_UNUSED_PARAMETERS True \ + ATTENTION_ARCH.SPATIAL_MEMORIES 32 \ + MODEL.DECODER.SPATIAL.MAX_ITER 0 \ + ATTENTION_ARCH.QUERY_NUMBER 3 \ + STROKE_SAMPLER.MAX_CANDIDATE 10 \ + WEIGHT True \ + RESUME_FROM pretrained/biomed_parse.pt +``` + + +## Additional Notes +- Refer to the Method section in our paper for more details on the algorithms and implementation. + + +