| """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 |
| from pathocnn.infer import TRAIN_MEAN, TRAIN_STD |
|
|
| 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 |
|
|
| 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 |
|
|
