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
File size: 5,742 Bytes
67b0413 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | #!/usr/bin/env python3
"""
Batch process four BiomedParse datasets to RL_dataset format.
Datasets:
- BraTS2020
- SPIDER
- STS-2D-Tooth
- STS-3D-Tooth
Input: /projects/p32685/datasets/biomedparse/{BraTS2020,SPIDER,STS-2D-Tooth,STS-3D-Tooth}
Output: /projects/p32685/RL_dataset/{brats2020,spider,sts-2d-tooth,sts-3d-tooth}_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))
FOUR_DATASETS = [
{
"name": "BraTS2020",
"raw_dir": "/projects/p32685/datasets/biomedparse/BraTS2020",
"description": "BraTS 2020 - Brain Tumour Segmentation"
},
{
"name": "SPIDER",
"raw_dir": "/projects/p32685/datasets/biomedparse/SPIDER",
"description": "SPIDER - Spine Segmentation Dataset"
},
{
"name": "STS-2D-Tooth",
"raw_dir": "/projects/p32685/datasets/biomedparse/STS-2D-Tooth",
"description": "STS-2D-Tooth - 2D Tooth Segmentation Dataset"
},
{
"name": "STS-3D-Tooth",
"raw_dir": "/projects/p32685/datasets/biomedparse/STS-3D-Tooth",
"description": "STS-3D-Tooth - 3D Tooth Segmentation Dataset"
}
]
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):
"""Process a single dataset."""
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"✗ Error: Raw data directory does not exist: {raw_dir}")
return False
cmd = [
"python3",
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"\n✓ {dataset_name} processed successfully!")
return True
else:
print(f"\n✗ {dataset_name} failed with return code {result.returncode}")
return False
except subprocess.CalledProcessError as e:
print(f"\n✗ {dataset_name} failed: {e}")
return False
except Exception as e:
print(f"\n✗ {dataset_name} failed with exception: {e}")
import traceback
traceback.print_exc()
return False
def main():
import argparse
parser = argparse.ArgumentParser(description="Batch process four BiomedParse datasets to RL_dataset")
parser.add_argument("--start_from", type=int, default=1, help="Start from dataset number (1-4)")
parser.add_argument("--end_at", type=int, default=4, help="End at dataset number (1-4)")
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("â•”" + "="*78 + "â•—")
print("â•‘" + " "*15 + "BATCH PROCESS BIOMEDPARSE FOUR DATASETS" + " "*26 + "â•‘")
print("â•”" + "="*78 + "â•—")
print(f"\nDatasets: BraTS2020, SPIDER, STS-2D-Tooth, STS-3D-Tooth")
print(f"Processing: {args.start_from} to {args.end_at}")
print(f"Output base: {OUTPUT_BASE_DIR}")
print(f"Template percent: {TEMPLATE_PERCENT}%")
print(f"Device: {DEVICE}")
if args.skip_convert:
print("âš Skipping Step 1 (conversion)")
if args.skip_packing:
print("âš Skipping Step 2 (packing)")
if args.skip_precompute:
print("âš Skipping Step 3 (precompute)")
success_count = 0
failed = []
for i, cfg in enumerate(FOUR_DATASETS, start=1):
if i < args.start_from or i > args.end_at:
continue
print(f"\n{'='*80}")
print(f"Dataset {i}/4: {cfg['name']}")
print(f"{'='*80}")
if process_dataset(cfg, args.skip_convert, args.skip_packing, args.skip_precompute):
success_count += 1
else:
failed.append(cfg['name'])
print("\n" + "="*80)
print("BATCH PROCESSING SUMMARY")
print("="*80)
n_total = args.end_at - args.start_from + 1
print(f"Successfully processed: {success_count}/{n_total} datasets")
if failed:
print(f"Failed: {', '.join(failed)}")
else:
print("\n✓ All datasets processed successfully!")
print(f"\nOutput: {OUTPUT_BASE_DIR}/")
for i in range(args.start_from, args.end_at + 1):
name = FOUR_DATASETS[i-1]['name']
out = f"{name.lower()}_{TEMPLATE_PERCENT}percent_processed"
print(f" {out}/")
if __name__ == "__main__":
main()
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