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"""
Batch process six biomedical datasets.
This script processes six datasets sequentially:
- BraTS2020
- SPIDER
- STS-2D-Tooth
- STS-3D-Tooth
- TotalSegmentator
- TotalSegmentatorMRI
Each dataset goes through:
- Converts BiomedParse format to HF format
- Creates processed datasets with templates
- Output structure:
RL_dataset/
brats2020/
brats2020_10percent_processed/
spider/
spider_10percent_processed/
...
"""
import os
import sys
import subprocess
from pathlib import Path
# Add script directory to path
SCRIPT_DIR = Path(__file__).resolve().parent
sys.path.insert(0, str(SCRIPT_DIR))
# Six dataset configurations
SIX_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"
},
{
"name": "TotalSegmentator",
"raw_dir": "/projects/p32685/datasets/biomedparse/TotalSegmentator",
"description": "TotalSegmentator v2.0.1 - 117 anatomical structures segmentation"
},
{
"name": "TotalSegmentatorMRI",
"raw_dir": "/projects/p32685/datasets/biomedparse/TotalSegmentatorMRI",
"description": "TotalSegmentator MRI v2.0.0 - 50 anatomical structures segmentation"
}
]
# Configuration
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.
Args:
dataset_config: Dictionary with dataset configuration
skip_convert: Skip step 1 (conversion)
skip_packing: Skip step 2 (packing)
skip_precompute: Skip step 3 (precompute)
"""
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}%")
# Check if raw directory exists
if not os.path.exists(raw_dir):
print(f"✗ Error: Raw data directory does not exist: {raw_dir}")
return False
# Build command
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():
"""Main function to process all six datasets."""
import argparse
parser = argparse.ArgumentParser(description="Batch process six biomedical datasets")
parser.add_argument("--start_from", type=int, default=1,
help="Start from dataset number (1-6)")
parser.add_argument("--end_at", type=int, default=6,
help="End at dataset number (1-6)")
parser.add_argument("--skip_convert", action="store_true",
help="Skip step 1 (conversion) for all datasets")
parser.add_argument("--skip_packing", action="store_true",
help="Skip step 2 (packing) for all datasets")
parser.add_argument("--skip_precompute", action="store_true",
help="Skip step 3 (precompute) for all datasets")
parser.add_argument("--device", type=str, default="cuda:0",
help="Device for template precomputation")
parser.add_argument("--template_percent", type=int, default=10,
help="Percentage of training data to use as templates (default: 10)")
args = parser.parse_args()
global DEVICE, TEMPLATE_PERCENT
DEVICE = args.device
TEMPLATE_PERCENT = args.template_percent
print("â•”" + "="*78 + "â•—")
print("â•‘" + " "*20 + "BATCH PROCESS SIX DATASETS" + " "*33 + "â•‘")
print("â•”" + "="*78 + "â•—")
print(f"\nTotal datasets: {len(SIX_DATASETS)}")
print(f"Processing datasets: {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)")
# Process datasets
success_count = 0
failed_datasets = []
for i, dataset_config in enumerate(SIX_DATASETS, start=1):
if i < args.start_from or i > args.end_at:
continue
print(f"\n{'='*80}")
print(f"Dataset {i}/{len(SIX_DATASETS)}: {dataset_config['name']}")
print(f"{'='*80}")
success = process_dataset(
dataset_config,
skip_convert=args.skip_convert,
skip_packing=args.skip_packing,
skip_precompute=args.skip_precompute
)
if success:
success_count += 1
else:
failed_datasets.append(dataset_config['name'])
# Summary
print("\n" + "="*80)
print("BATCH PROCESSING SUMMARY")
print("="*80)
print(f"Successfully processed: {success_count}/{args.end_at - args.start_from + 1} datasets")
if failed_datasets:
print(f"\nFailed datasets:")
for dataset in failed_datasets:
print(f" - {dataset}")
else:
print("\n✓ All datasets processed successfully!")
print(f"\nOutput structure:")
print(f" {OUTPUT_BASE_DIR}/")
for i in range(args.start_from, args.end_at + 1):
dataset_name = SIX_DATASETS[i-1]['name']
dataset_name_lower = dataset_name.lower()
print(f" {dataset_name_lower}/")
print(f" {dataset_name_lower}_{TEMPLATE_PERCENT}percent_processed/")
if __name__ == "__main__":
main()
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