Instructions to use MingzhenL/vessa-2d-ssl-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use MingzhenL/vessa-2d-ssl-bundle with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("MingzhenL/vessa-2d-ssl-bundle") with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained("MingzhenL/vessa-2d-ssl-bundle") with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>) # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
Download code/VESSA_data_preprocess/process_lungx.py from MingzhenL/vessa-2d-ssl-bundle: direct link, hf CLI and curl.
- Browser
- Download file 4.58 kB
-
https://huggingface.co/MingzhenL/vessa-2d-ssl-bundle/resolve/main/code/VESSA_data_preprocess/process_lungx.py
- Command line
-
hf download hf://MingzhenL/vessa-2d-ssl-bundle/code/VESSA_data_preprocess/process_lungx.py
-
curl -L -o process_lungx.py https://huggingface.co/MingzhenL/vessa-2d-ssl-bundle/resolve/main/code/VESSA_data_preprocess/process_lungx.py
4.58 kB
| #!/usr/bin/env python3 | |
| """ | |
| Process lungx BiomedParse dataset to RL_dataset format. | |
| Dataset: lungx (Montgomery + Shenzhen Chest X-Ray lung segmentation) | |
| - Classes: background(0), left_lung(1), right_lung(2) | |
| - Train: 110 images, Test: 28 images | |
| - Masks: binary per-class ({base}_left_lung.png, {base}_right_lung.png) | |
| Input: /projects/p32685/datasets/biomedparse/lungx | |
| Output: /projects/p32685/RL_dataset/lungx/ | |
| /projects/p32685/RL_dataset/lungx_10percent_processed/ | |
| """ | |
| import os | |
| import sys | |
| import subprocess | |
| from pathlib import Path | |
| SCRIPT_DIR = Path(__file__).resolve().parent | |
| sys.path.insert(0, str(SCRIPT_DIR)) | |
| DATASET_CONFIG = { | |
| "name": "lungx", | |
| "raw_dir": "/projects/p32685/datasets/biomedparse/lungx", | |
| "description": "Lung X-Ray - Left and Right Lung Segmentation (Montgomery + Shenzhen)", | |
| } | |
| OUTPUT_BASE_DIR = "/projects/p32685/RL_dataset" | |
| TEMPLATE_PERCENT = 10 | |
| DEVICE = "cuda:0" | |
| def process_dataset(dataset_config, skip_convert=False, skip_packing=False, skip_precompute=False): | |
| dataset_name = dataset_config["name"] | |
| raw_dir = dataset_config["raw_dir"] | |
| print("\n" + "=" * 80) | |
| print(f"Processing {dataset_name}: {dataset_config['description']}") | |
| print("=" * 80) | |
| print(f"Raw data: {raw_dir}") | |
| print(f"Output base: {OUTPUT_BASE_DIR}") | |
| print(f"Template percent: {TEMPLATE_PERCENT}%") | |
| if not os.path.exists(raw_dir): | |
| print(f"β Error: Raw data directory does not exist: {raw_dir}") | |
| return False | |
| cmd = [ | |
| sys.executable, | |
| os.path.join(SCRIPT_DIR, "end_to_end_preprocessing.py"), | |
| "--dataset_name", dataset_name, | |
| "--template_percent", str(TEMPLATE_PERCENT), | |
| "--raw_data_dir", raw_dir, | |
| "--output_base_dir", OUTPUT_BASE_DIR, | |
| "--device", DEVICE, | |
| ] | |
| if skip_convert: | |
| cmd.append("--skip_convert") | |
| if skip_packing: | |
| cmd.append("--skip_packing") | |
| if skip_precompute: | |
| cmd.append("--skip_precompute") | |
| print(f"\nRunning: {' '.join(cmd)}") | |
| try: | |
| result = subprocess.run(cmd, check=True, cwd=SCRIPT_DIR) | |
| if result.returncode == 0: | |
| print(f"\nβ {dataset_name} processed successfully!") | |
| return True | |
| else: | |
| print(f"\nβ {dataset_name} failed with return code {result.returncode}") | |
| return False | |
| except subprocess.CalledProcessError as e: | |
| print(f"\nβ {dataset_name} failed: {e}") | |
| return False | |
| except Exception as e: | |
| print(f"\nβ {dataset_name} failed with exception: {e}") | |
| import traceback | |
| traceback.print_exc() | |
| return False | |
| def main(): | |
| import argparse | |
| parser = argparse.ArgumentParser(description="Process lungx BiomedParse dataset to RL_dataset") | |
| parser.add_argument("--skip_convert", action="store_true", help="Skip step 1 (conversion)") | |
| parser.add_argument("--skip_packing", action="store_true", help="Skip step 2 (packing)") | |
| parser.add_argument("--skip_precompute", action="store_true", help="Skip step 3 (precompute)") | |
| parser.add_argument("--device", type=str, default="cuda:0", help="Device for template precomputation") | |
| parser.add_argument("--template_percent", type=int, default=10, help="Template percentage (default: 10)") | |
| args = parser.parse_args() | |
| global DEVICE, TEMPLATE_PERCENT | |
| DEVICE = args.device | |
| TEMPLATE_PERCENT = args.template_percent | |
| print("β" + "=" * 78 + "β") | |
| print("β" + " " * 15 + "PROCESS LUNGX BIOMEDPARSE DATASET" + " " * 30 + "β") | |
| print("β" + "=" * 78 + "β") | |
| print(f"\nDataset: lungx") | |
| print(f"Output base: {OUTPUT_BASE_DIR}") | |
| print(f"Template percent: {TEMPLATE_PERCENT}%") | |
| print(f"Device: {DEVICE}") | |
| if args.skip_convert: | |
| print("β Skipping Step 1 (conversion)") | |
| if args.skip_packing: | |
| print("β Skipping Step 2 (packing)") | |
| if args.skip_precompute: | |
| print("β Skipping Step 3 (precompute)") | |
| success = process_dataset( | |
| DATASET_CONFIG, | |
| args.skip_convert, | |
| args.skip_packing, | |
| args.skip_precompute, | |
| ) | |
| print("\n" + "=" * 80) | |
| print("PROCESSING SUMMARY") | |
| print("=" * 80) | |
| if success: | |
| print("β lungx processed successfully!") | |
| out = f"lungx_{TEMPLATE_PERCENT}percent_processed" | |
| print(f"\nOutput: {OUTPUT_BASE_DIR}/") | |
| print(f" lungx/ (converted HF format)") | |
| print(f" {out}/ (processed with templates)") | |
| else: | |
| print("β lungx processing failed!") | |
| if __name__ == "__main__": | |
| main() | |