File size: 3,959 Bytes
9a85a36 | 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 | #!/usr/bin/env python3
"""
Example script for evaluating FID and IS scores of generated images.
"""
import torch
from eval import (
clear_evaluation_cache,
run_evaluation_only,
create_image_paths_file
)
from metric import (
print_memory_usage,
force_memory_cleanup,
)
def main():
# Configuration
real_cache_dir = "/lustre/scratch/client/movian/research/users/phuclh12/Code/minHiWave/output_invsr/eval_cache"
output_dir = "/lustre/scratch/client/movian/research/users/phuclh12/Code/minHiWave/eccv_2026/sdxlbase_pixelrushsdxl_sparse_2k_2patches"
output_dir = "/lustre/scratch/client/movian/research/users/phuclh12/Code/FreeScale/nips_release"
output_dir = "/lustre/scratch/client/movian/research/users/phuclh12/Code/minHiWave/ducanh_reproduce_pixrush/sdxl_reproduce_pixrush"
# Option 1: Use directory containing real images
real_image_dir = "/lustre/scratch/client/movian/research/users/ngannh11/data/laion/train"
# Option 2: Use text file containing image paths (uncomment to use)
# real_image_dir = "real_image_paths.txt" # Text file with one image path per line
# Option 3: Create image paths file from directory (uncomment to use)
# create_image_paths_file(
# image_dir="/lustre/scratch/client/movian/research/users/ngannh11/data/laion/train",
# output_file="real_image_paths.txt"
# )
# real_image_dir = "real_image_paths.txt"
# Check if CUDA is available
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}")
# Memory management tips
print_memory_usage("Initial")
if device == 'cuda':
print("Memory optimization tips:")
print("- Use smaller batch_size (8-16) if you encounter memory issues")
print("- Adjust chunk_size (1000-5000) for large datasets")
print("- Each chunk is cached separately for efficient reprocessing")
print("- The cache will significantly speed up subsequent runs")
print("- Memory is automatically cleared during processing")
# Option 1: Run evaluation with chunked processing (recommended for large datasets)
print("Running evaluation with chunked processing...")
results = run_evaluation_only(
output_dir=output_dir,
real_image_dir=real_image_dir, # Can be directory or text file
batch_size=32, # Reduced batch size for memory efficiency
device=device,
use_cache=False, # Use cached features if available
chunk_size=3000, # Process 3k images per chunk to manage memory,
use_patch=False, # patch-FID,
real_cache_dir=real_cache_dir
)
# Option 2: Run evaluation without cache (force recomputation)
# print("Running evaluation without cache...")
# results = run_evaluation_only(
# output_dir=output_dir,
# real_image_dir=real_image_dir,
# batch_size=16, # Reduced batch size for memory efficiency
# device=device,
# use_cache=False, # Force recomputation
# chunk_size=3000 # Process 3k images per chunk to manage memory
# )
# Option 3: Clear cache if needed
# print("Clearing evaluation cache...")
# clear_evaluation_cache(output_dir)
# Option 4: Force memory cleanup if needed
# print("Forcing memory cleanup...")
# force_memory_cleanup()
# Option 2: If you want to run evaluation with real images, uncomment and modify:
# results = run_evaluation_only(
# output_dir="output",
# real_image_dir="/lustre/scratch/client/movian/research/users/ngannh11/data/laion/train",
# batch_size=32,
# device=device
# )
if results:
print("\nEvaluation completed successfully!")
print(f"Results have been saved to '{output_dir}/evaluation_results.json'")
else:
print("Evaluation failed or no images found.")
if __name__ == "__main__":
main() |