Spaces:
Sleeping
Sleeping
File size: 8,955 Bytes
0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 4780d8d 0234c58 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 |
"""Performance benchmark for batch processing optimization.
This script compares the performance of:
1. Sequential single-slide processing (old method)
2. Batch processing with model caching (new method)
Usage:
python tests/benchmark_batch_performance.py --slides slide1.svs slide2.svs slide3.svs
python tests/benchmark_batch_performance.py --slide-csv test_slides.csv
"""
import argparse
import time
import pandas as pd
from pathlib import Path
import torch
from loguru import logger
from mosaic.analysis import analyze_slide
from mosaic.batch_analysis import analyze_slides_batch
from mosaic.ui.utils import load_settings, validate_settings
def benchmark_sequential_processing(
slides, settings_df, cancer_subtype_name_map, num_workers
):
"""Benchmark traditional sequential processing (models loaded per slide)."""
logger.info("=" * 80)
logger.info("BENCHMARKING: Sequential Processing (OLD METHOD)")
logger.info("=" * 80)
start_time = time.time()
start_memory = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
results = []
for idx, (slide_path, (_, row)) in enumerate(zip(slides, settings_df.iterrows())):
logger.info(f"Processing slide {idx + 1}/{len(slides)}: {slide_path}")
slide_start = time.time()
slide_mask, aeon_results, paladin_results = analyze_slide(
slide_path=slide_path,
seg_config=row["Segmentation Config"],
site_type=row["Site Type"],
sex=row.get("Sex", "Unknown"),
tissue_site=row.get("Tissue Site", "Unknown"),
cancer_subtype=row["Cancer Subtype"],
cancer_subtype_name_map=cancer_subtype_name_map,
ihc_subtype=row.get("IHC Subtype", ""),
num_workers=num_workers,
)
slide_time = time.time() - slide_start
logger.info(f"Slide {idx + 1} completed in {slide_time:.2f}s")
results.append(
{
"slide": slide_path,
"time": slide_time,
"has_mask": slide_mask is not None,
"has_aeon": aeon_results is not None,
"has_paladin": paladin_results is not None,
}
)
total_time = time.time() - start_time
peak_memory = torch.cuda.max_memory_allocated() if torch.cuda.is_available() else 0
logger.info("=" * 80)
logger.info(f"Sequential processing completed in {total_time:.2f}s")
logger.info(f"Average time per slide: {total_time / len(slides):.2f}s")
if torch.cuda.is_available():
logger.info(f"Peak GPU memory: {peak_memory / (1024**3):.2f} GB")
logger.info("=" * 80)
return {
"method": "sequential",
"total_time": total_time,
"num_slides": len(slides),
"avg_time_per_slide": total_time / len(slides),
"peak_memory_gb": peak_memory / (1024**3) if torch.cuda.is_available() else 0,
"per_slide_results": results,
}
def benchmark_batch_processing(
slides, settings_df, cancer_subtype_name_map, num_workers
):
"""Benchmark optimized batch processing (models loaded once)."""
logger.info("=" * 80)
logger.info("BENCHMARKING: Batch Processing (NEW METHOD)")
logger.info("=" * 80)
start_time = time.time()
# Reset GPU memory stats
if torch.cuda.is_available():
torch.cuda.reset_peak_memory_stats()
all_slide_masks, all_aeon_results, all_paladin_results = analyze_slides_batch(
slides=slides,
settings_df=settings_df,
cancer_subtype_name_map=cancer_subtype_name_map,
num_workers=num_workers,
aggressive_memory_mgmt=None, # Auto-detect
progress=None,
)
total_time = time.time() - start_time
peak_memory = torch.cuda.max_memory_allocated() if torch.cuda.is_available() else 0
logger.info("=" * 80)
logger.info(f"Batch processing completed in {total_time:.2f}s")
logger.info(f"Average time per slide: {total_time / len(slides):.2f}s")
if torch.cuda.is_available():
logger.info(f"Peak GPU memory: {peak_memory / (1024**3):.2f} GB")
logger.info("=" * 80)
return {
"method": "batch",
"total_time": total_time,
"num_slides": len(slides),
"avg_time_per_slide": total_time / len(slides),
"peak_memory_gb": peak_memory / (1024**3) if torch.cuda.is_available() else 0,
"num_successful": len(all_slide_masks),
}
def compare_results(sequential_stats, batch_stats):
"""Compare and report performance differences."""
logger.info("\n" + "=" * 80)
logger.info("PERFORMANCE COMPARISON")
logger.info("=" * 80)
speedup = sequential_stats["total_time"] / batch_stats["total_time"]
time_saved = sequential_stats["total_time"] - batch_stats["total_time"]
percent_faster = (
1 - (batch_stats["total_time"] / sequential_stats["total_time"])
) * 100
logger.info(f"Number of slides: {sequential_stats['num_slides']}")
logger.info(f"")
logger.info(f"Sequential processing: {sequential_stats['total_time']:.2f}s")
logger.info(f"Batch processing: {batch_stats['total_time']:.2f}s")
logger.info(f"")
logger.info(f"Time saved: {time_saved:.2f}s")
logger.info(f"Speedup: {speedup:.2f}x")
logger.info(f"Improvement: {percent_faster:.1f}% faster")
if torch.cuda.is_available():
logger.info(f"")
logger.info(
f"Sequential peak memory: {sequential_stats['peak_memory_gb']:.2f} GB"
)
logger.info(f"Batch peak memory: {batch_stats['peak_memory_gb']:.2f} GB")
memory_diff = batch_stats["peak_memory_gb"] - sequential_stats["peak_memory_gb"]
logger.info(f"Memory difference: {memory_diff:+.2f} GB")
logger.info("=" * 80)
return {
"speedup": speedup,
"time_saved_seconds": time_saved,
"percent_faster": percent_faster,
"sequential_stats": sequential_stats,
"batch_stats": batch_stats,
}
def main():
parser = argparse.ArgumentParser(
description="Benchmark batch processing performance"
)
parser.add_argument("--slides", nargs="+", help="List of slide paths to process")
parser.add_argument(
"--slide-csv", type=str, help="CSV file with slide paths and settings"
)
parser.add_argument(
"--num-workers", type=int, default=4, help="Number of workers for data loading"
)
parser.add_argument(
"--skip-sequential",
action="store_true",
help="Skip sequential benchmark (faster, only test batch mode)",
)
parser.add_argument(
"--output", type=str, help="Save benchmark results to JSON file"
)
args = parser.parse_args()
if not args.slides and not args.slide_csv:
parser.error("Must provide either --slides or --slide-csv")
# Load cancer subtype mappings
from mosaic.gradio_app import download_and_process_models
cancer_subtype_name_map, cancer_subtypes, reversed_cancer_subtype_name_map = (
download_and_process_models()
)
# Prepare slides and settings
if args.slide_csv:
settings_df = load_settings(args.slide_csv)
settings_df = validate_settings(
settings_df,
cancer_subtype_name_map,
cancer_subtypes,
reversed_cancer_subtype_name_map,
)
slides = settings_df["Slide"].tolist()
else:
slides = args.slides
# Create default settings
settings_df = pd.DataFrame(
{
"Slide": slides,
"Site Type": ["Primary"] * len(slides),
"Sex": ["Unknown"] * len(slides),
"Tissue Site": ["Unknown"] * len(slides),
"Cancer Subtype": ["Unknown"] * len(slides),
"IHC Subtype": [""] * len(slides),
"Segmentation Config": ["Biopsy"] * len(slides),
}
)
logger.info(f"Benchmarking with {len(slides)} slides")
logger.info(f"GPU available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
logger.info(f"GPU: {torch.cuda.get_device_name(0)}")
# Run benchmarks
if not args.skip_sequential:
sequential_stats = benchmark_sequential_processing(
slides, settings_df, cancer_subtype_name_map, args.num_workers
)
batch_stats = benchmark_batch_processing(
slides, settings_df, cancer_subtype_name_map, args.num_workers
)
# Compare results
if not args.skip_sequential:
comparison = compare_results(sequential_stats, batch_stats)
# Save results if requested
if args.output:
import json
output_path = Path(args.output)
with open(output_path, "w") as f:
json.dump(comparison, f, indent=2, default=str)
logger.info(f"Benchmark results saved to {output_path}")
if __name__ == "__main__":
main()
|