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Evaluate generation quality: FID, Precision, and Recall per class.
Handles variable-size real images by resizing to a fixed resolution.
"""
import argparse
import json
import os
import sys
from pathlib import Path
import torch
import numpy as np
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
# Use torch_fidelity's Inception model for feature extraction
from torch_fidelity.helpers import vassert
from torch_fidelity.utils import create_feature_extractor
class ResizedImageDataset(Dataset):
"""Load images from a directory and resize to fixed size."""
EXTS = {'.png', '.jpg', '.jpeg', '.tif', '.tiff', '.bmp'}
def __init__(self, root, size=299):
self.root = Path(root)
self.size = size
self.files = sorted([
f for f in self.root.iterdir()
if f.suffix.lower() in self.EXTS
])
self.transform = transforms.Compose([
transforms.Resize((size, size), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
])
def __len__(self):
return len(self.files)
def __getitem__(self, idx):
img = Image.open(self.files[idx]).convert('RGB')
return self.transform(img)
def extract_features(dataset, device, batch_size=64):
"""Extract Inception features from a dataset."""
feat_extractor = create_feature_extractor('inception-v3-compat', ['2048'], cuda=(device.type == 'cuda'))
feat_extractor.eval()
loader = DataLoader(dataset, batch_size=batch_size, shuffle=False,
num_workers=4, pin_memory=True, drop_last=False)
all_features = []
with torch.no_grad():
for batch in loader:
# torch_fidelity expects uint8 [0,255] input
batch_uint8 = (batch * 255).clamp(0, 255).to(torch.uint8).to(device)
features = feat_extractor(batch_uint8)
# features is a dict, get the 2048-dim features
feat = features[0] if isinstance(features, tuple) else list(features.values())[0]
all_features.append(feat.cpu().float())
return torch.cat(all_features, dim=0).numpy()
def compute_fid(mu1, sigma1, mu2, sigma2):
"""Compute FID between two sets of statistics."""
from scipy import linalg
diff = mu1 - mu2
covmean, _ = linalg.sqrtm(sigma1 @ sigma2, disp=False)
if np.iscomplexobj(covmean):
covmean = covmean.real
fid = diff @ diff + np.trace(sigma1 + sigma2 - 2 * covmean)
return float(fid)
def compute_precision_recall(feats_gen, feats_real, k=3):
"""Compute Precision and Recall using k-nearest neighbors."""
from sklearn.metrics import pairwise_distances
# Subsample real features if too many (for efficiency)
max_real = 10000
if len(feats_real) > max_real:
idx = np.random.RandomState(42).permutation(len(feats_real))[:max_real]
feats_real_sub = feats_real[idx]
else:
feats_real_sub = feats_real
# Compute pairwise distances
# For precision: for each generated sample, check if it's in the support of real data
# For recall: for each real sample, check if it's in the support of generated data
# Get k-th nearest neighbor distance in real data (manifold radius)
dist_real = pairwise_distances(feats_real_sub, feats_real_sub)
np.fill_diagonal(dist_real, np.inf)
real_nn_dist = np.partition(dist_real, k-1, axis=1)[:, k-1]
# Precision: fraction of generated samples falling within real manifold
dist_gen_to_real = pairwise_distances(feats_gen, feats_real_sub)
gen_min_dist = dist_gen_to_real.min(axis=1)
# A generated sample is "precise" if its nearest real neighbor is within that neighbor's manifold
nearest_real_idx = dist_gen_to_real.argmin(axis=1)
precision = float(np.mean(gen_min_dist <= real_nn_dist[nearest_real_idx]))
# Recall: fraction of real samples with a generated sample nearby
dist_real_to_gen = pairwise_distances(feats_real_sub, feats_gen)
# Get k-th nearest neighbor distance in generated data
if len(feats_gen) >= k:
dist_gen = pairwise_distances(feats_gen, feats_gen)
np.fill_diagonal(dist_gen, np.inf)
gen_nn_dist = np.partition(dist_gen, k-1, axis=1)[:, k-1]
else:
gen_nn_dist = np.full(len(feats_gen), np.inf)
real_min_dist = dist_real_to_gen.min(axis=1)
nearest_gen_idx = dist_real_to_gen.argmin(axis=1)
recall = float(np.mean(real_min_dist <= gen_nn_dist[nearest_gen_idx]))
return precision, recall
def evaluate_class(gen_dir, real_dir, device, batch_size=64):
"""Evaluate a single class."""
gen_ds = ResizedImageDataset(gen_dir, size=299)
real_ds = ResizedImageDataset(real_dir, size=299)
if len(gen_ds) == 0:
return None
feats_gen = extract_features(gen_ds, device, batch_size)
feats_real = extract_features(real_ds, device, batch_size)
# FID
mu_gen, sigma_gen = feats_gen.mean(0), np.cov(feats_gen, rowvar=False)
mu_real, sigma_real = feats_real.mean(0), np.cov(feats_real, rowvar=False)
fid = compute_fid(mu_gen, sigma_gen, mu_real, sigma_real)
# Precision & Recall
precision, recall = compute_precision_recall(feats_gen, feats_real)
return {'fid': round(fid, 4), 'precision': round(precision, 4), 'recall': round(recall, 4),
'n_gen': len(gen_ds), 'n_real': len(real_ds)}
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gen-dir', type=str, required=True)
parser.add_argument('--real-dir', type=str, required=True)
parser.add_argument('--output', type=str, required=True)
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--device', type=str, default='cuda')
args = parser.parse_args()
device = torch.device(args.device)
classes = sorted([d.name for d in Path(args.real_dir).iterdir() if d.is_dir()])
gen_base = Path(args.gen_dir)
# Find CFG scale directories
cfg_dirs = sorted([d.name for d in gen_base.iterdir() if d.is_dir() and d.name.startswith('cfg_')])
results = {}
for cfg_dir in cfg_dirs:
cfg_scale = cfg_dir.replace('cfg_', '')
print(f"\n{'='*60}")
print(f"CFG Scale = {cfg_scale}")
print(f"{'='*60}")
cfg_results = {}
for cls_name in classes:
gen_cls_dir = gen_base / cfg_dir / cls_name
real_cls_dir = Path(args.real_dir) / cls_name
if not gen_cls_dir.exists():
print(f" {cls_name}: SKIPPED (no generated images)")
continue
n_real = len(list(real_cls_dir.iterdir()))
n_gen = len(list(gen_cls_dir.glob('*.png')))
warn = f" [WARNING: only {n_real} real images]" if n_real < 200 else ""
print(f" {cls_name}: {n_gen} gen vs {n_real} real{warn}")
try:
metrics = evaluate_class(gen_cls_dir, real_cls_dir, device, args.batch_size)
if metrics:
cfg_results[cls_name] = metrics
print(f" FID={metrics['fid']:.2f} Prec={metrics['precision']:.4f} Rec={metrics['recall']:.4f}")
except Exception as e:
print(f" ERROR: {e}")
cfg_results[cls_name] = {'error': str(e)}
results[f'cfg_{cfg_scale}'] = cfg_results
# Compute average (excluding errors)
valid = [v for v in cfg_results.values() if 'fid' in v]
if valid:
avg_fid = np.mean([v['fid'] for v in valid])
avg_prec = np.mean([v['precision'] for v in valid])
avg_rec = np.mean([v['recall'] for v in valid])
print(f"\n AVERAGE: FID={avg_fid:.2f} Prec={avg_prec:.4f} Rec={avg_rec:.4f}")
results[f'cfg_{cfg_scale}']['_average'] = {
'fid': round(float(avg_fid), 4),
'precision': round(float(avg_prec), 4),
'recall': round(float(avg_rec), 4)
}
# Save results
os.makedirs(os.path.dirname(args.output), exist_ok=True)
with open(args.output, 'w') as f:
json.dump(results, f, indent=2)
print(f"\nResults saved to {args.output}")
if __name__ == '__main__':
main()
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