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bb3294d | 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 | """PathoCNN demo: predict one bundled sample per class (native 28x28 test
patches, in-distribution), then print the first misclassified test images
with their confidences — the failures are part of the demo, not edited out.
This is a mechanics demo on a 28x28 benchmark model, not a clinical tool.
"""
import glob
import os
import sys
import numpy as np
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
HERE = os.path.dirname(os.path.abspath(__file__))
PKG = os.path.dirname(HERE)
from pathocnn import LABELS, load_model, predict # noqa: E402
from pathocnn.infer import TRAIN_MEAN, TRAIN_STD # noqa: E402
model = load_model(os.path.join(PKG, "model.safetensors"))
print("== bundled demo samples: first PathMNIST test patch of each class (28x28) ==")
for path in sorted(glob.glob(os.path.join(PKG, "assets", "demo_samples", "*.png"))):
cls = int(os.path.basename(path).split("_")[0])
r = predict(model, path)
mark = "OK " if r["label"] == LABELS[cls] else "MISS"
print(f"[{mark}] true={LABELS[cls]:38s} -> predicted {r['label']:38s} "
f"(confidence {r['confidence']:.1%})")
print("\n== first misclassified test patches (from assets/pathmnist_test.npz) ==")
import torch # noqa: E402
d = np.load(os.path.join(PKG, "assets", "pathmnist_test.npz"))
x_test = d["test_images"]
y_test = d["test_labels"].ravel()
mean = np.asarray(TRAIN_MEAN, dtype=np.float32)
std = np.asarray(TRAIN_STD, dtype=np.float32)
shown = 0
with torch.no_grad():
for i in range(0, len(x_test), 512):
xb = (x_test[i:i + 512].astype(np.float32) / 255.0 - mean) / std
xt = torch.from_numpy(xb.transpose(0, 3, 1, 2))
probs = torch.softmax(model(xt), 1).numpy()
for j, p in enumerate(probs):
pred = int(p.argmax())
if pred != int(y_test[i + j]) and shown < 5:
print(f"test[{i + j}] true={LABELS[int(y_test[i + j])]} -> predicted "
f"{LABELS[pred]} (confidence {p[pred]:.1%})")
shown += 1
if shown >= 5:
break
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