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20d7fde | 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 | """Evaluate a checkpoint on the synthetic validation set and the real crops.
python eval.py --ckpt checkpoints/best.pt
"""
import argparse
import torch
from torch.utils.data import DataLoader
from togyz.dataset import RealCropDataset, SyntheticCellDataset
from togyz.model import auto_device, load_checkpoint
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--ckpt", default="checkpoints/best.pt")
parser.add_argument("--val-size", type=int, default=4_000)
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--workers", type=int, default=4)
parser.add_argument("--device", default=None)
args = parser.parse_args()
device = torch.device(args.device) if args.device else auto_device()
model, ckpt = load_checkpoint(args.ckpt, device)
classes = ckpt["classes"]
print(f"Loaded {args.ckpt} (epoch {ckpt['epoch'] + 1}, synth val {ckpt['val_acc']:.2%})")
# synthetic validation (same fixed seed as train.py)
val_loader = DataLoader(
SyntheticCellDataset(args.val_size, seed=1234),
batch_size=args.batch_size,
num_workers=args.workers,
)
correct = total = 0
with torch.no_grad():
for images, labels in val_loader:
preds = model(images.to(device)).argmax(dim=1).cpu()
correct += (preds == labels).sum().item()
total += labels.numel()
print(f"Synthetic val accuracy: {correct / total:.2%} ({total} samples)")
# real crops with per-file report
real = RealCropDataset()
if len(real) == 0:
print("No real crops found in data/real_crops - skipping.")
return
images, labels, names = real.batch()
with torch.no_grad():
probs = torch.softmax(model(images.to(device)).cpu(), dim=1)
topk = probs.topk(3, dim=1)
print(f"\nReal crops ({len(real)} files):")
top1 = top3 = 0
for i, name in enumerate(names):
truth = classes[labels[i]]
guesses = [
f"{classes[idx]} {p:.1%}"
for idx, p in zip(topk.indices[i].tolist(), topk.values[i].tolist())
]
hit1 = topk.indices[i, 0] == labels[i]
hit3 = (topk.indices[i] == labels[i]).any()
top1 += int(hit1)
top3 += int(hit3)
marker = "OK " if hit1 else ("~3 " if hit3 else "MISS")
print(f" [{marker}] {name:<14} truth={truth:<4} top3: {', '.join(guesses)}")
print(f"Real top-1: {top1}/{len(real)} ({top1 / len(real):.0%}) "
f"top-3: {top3}/{len(real)} ({top3 / len(real):.0%})")
if __name__ == "__main__":
main()
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