Upload evaluate_simple.py

evaluate_simple.py

@@ -0,0 +1,171 @@
+"""
+Thyroid Ultrasound Evaluation + Grad-CAM (Pure PyTorch, no Trainer)
+"""
+import os, sys, math, json, random, warnings, traceback
+warnings.filterwarnings("ignore")
+
+import numpy as np
+from PIL import Image
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+import torch
+import torch.nn.functional as F
+from datasets import load_dataset
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_auc_score, confusion_matrix
+
+HF_USERNAME = "Johnyquest7"
+DATASET_NAME = "BTX24/thyroid-cancer-classification-ultrasound-dataset"
+MODEL_NAME = f"{HF_USERNAME}/ML-Inter_thyroid"
+OUTPUT_DIR = "./eval_outputs"
+SEED = 42
+BATCH_SIZE = 16
+
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+
+def main():
+    print("=" * 60)
+    print("Thyroid Ultrasound Evaluation + Grad-CAM")
+    print("=" * 60)
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"\nDevice: {device}")
+    print(f"Loading model: {MODEL_NAME}")
+    processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+    model = AutoModelForImageClassification.from_pretrained(MODEL_NAME).to(device).eval()
+    print(f"Model loaded: {sum(p.numel() for p in model.parameters())/1e6:.1f}M params")
+
+    print(f"\nLoading dataset: {DATASET_NAME}")
+    ds = load_dataset(DATASET_NAME, split="train")
+    ds = ds.shuffle(seed=SEED)
+    train_test = ds.train_test_split(test_size=0.2, stratify_by_column="label", seed=SEED)
+    test_ds = train_test["test"]
+    print(f"Test samples: {len(test_ds)} (Benign: {sum(1 for x in test_ds if x['label']==0)}, Malignant: {sum(1 for x in test_ds if x['label']==1)})")
+
+    id2label = model.config.id2label
+
+    # Simple inference loop
+    all_logits, all_labels = [], []
+    print("\nRunning inference...")
+    for i in range(0, len(test_ds), BATCH_SIZE):
+        batch_items = [test_ds[j] for j in range(i, min(i+BATCH_SIZE, len(test_ds)))]
+        images = [item["image"].convert("RGB") if item["image"].mode != "RGB" else item["image"] for item in batch_items]
+        inputs = processor(images, return_tensors="pt")
+        pixel_values = inputs["pixel_values"].to(device)
+        with torch.no_grad():
+            outputs = model(pixel_values=pixel_values)
+        all_logits.extend(outputs.logits.cpu().numpy())
+        all_labels.extend([item["label"] for item in batch_items])
+        if (i // BATCH_SIZE) % 5 == 0:
+            print(f"  Batch {i//BATCH_SIZE + 1}/{(len(test_ds)+BATCH_SIZE-1)//BATCH_SIZE}")
+
+    y_true = np.array(all_labels)
+    y_logits = np.array(all_logits)
+    y_pred = np.argmax(y_logits, axis=1)
+    probs = F.softmax(torch.from_numpy(y_logits), dim=1).numpy()
+
+    acc = accuracy_score(y_true, y_pred)
+    prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average="weighted", zero_division=0)
+    try:
+        auc = roc_auc_score(y_true, probs[:, 1])
+    except Exception:
+        auc = roc_auc_score(y_true, probs[:, 0])
+    cm = confusion_matrix(y_true, y_pred)
+    sens = cm[1,1] / (cm[1,1] + cm[1,0]) if (cm[1,1] + cm[1,0]) > 0 else 0
+    spec = cm[0,0] / (cm[0,0] + cm[0,1]) if (cm[0,0] + cm[0,1]) > 0 else 0
+
+    final = {
+        "test_accuracy": float(acc),
+        "test_weighted_f1": float(f1),
+        "test_weighted_precision": float(prec),
+        "test_weighted_recall": float(rec),
+        "test_roc_auc": float(auc),
+        "test_sensitivity": float(sens),
+        "test_specificity": float(spec),
+        "test_confusion_matrix": cm.tolist(),
+    }
+    print(f"\n{'='*60}")
+    print("FINAL TEST METRICS")
+    print(f"{'='*60}")
+    for k, v in final.items():
+        print(f"  {k}: {v}")
+    with open(f"{OUTPUT_DIR}/test_metrics.json", "w") as f:
+        json.dump(final, f, indent=2)
+    print(f"\nSaved to {OUTPUT_DIR}/test_metrics.json")
+
+    # Grad-CAM
+    correct_idx = [i for i in range(len(y_true)) if y_true[i] == y_pred[i]]
+    incorrect_idx = [i for i in range(len(y_true)) if y_true[i] != y_pred[i]]
+    random.shuffle(correct_idx)
+    random.shuffle(incorrect_idx)
+    selected = correct_idx[:5] + incorrect_idx[:5]
+    print(f"\nGenerating Grad-CAM for {len(selected)} samples ({len(correct_idx[:5])} correct, {len(incorrect_idx[:5])} incorrect)...")
+
+    gradcam_data = {}
+    def fwd_hook(module, input, output):
+        gradcam_data["feat"] = output.detach()
+    def bwd_hook(module, grad_input, grad_output):
+        gradcam_data["grad"] = grad_output[0].detach()
+
+    target_layer = model.swinv2.encoder.layers[-1].blocks[-1].layernorm_after
+    fwd_handle = target_layer.register_forward_hook(fwd_hook)
+    bwd_handle = target_layer.register_full_backward_hook(bwd_hook)
+
+    for idx in selected:
+        try:
+            item = test_ds[idx]
+            img = item["image"].convert("RGB")
+            label = item["label"]
+            inputs = processor(img, return_tensors="pt")
+            img_tensor = inputs["pixel_values"].to(device).requires_grad_(True)
+            model.zero_grad()
+            outputs = model(pixel_values=img_tensor)
+            target_class = int(y_pred[idx])
+            score = outputs.logits[0, target_class]
+            score.backward()
+
+            feat = gradcam_data["feat"][0]
+            grads = gradcam_data["grad"][0]
+            if feat.dim() == 3:
+                weights = grads.mean(dim=0, keepdim=True)
+                cam = torch.matmul(feat, weights.t()).squeeze()
+                H = W = int(math.sqrt(cam.shape[0]))
+                cam = cam.reshape(H, W)
+            else:
+                weights = grads.mean(dim=(0,1), keepdim=True)
+                cam = (feat * weights).sum(dim=-1).squeeze()
+
+            cam = F.relu(cam)
+            cam = cam - cam.min()
+            cam = cam / (cam.max() + 1e-8)
+            cam = F.interpolate(cam.unsqueeze(0).unsqueeze(0), size=(256,256), mode="bilinear", align_corners=False)
+            cam = cam.squeeze().cpu().numpy()
+
+            img_np = img_tensor.squeeze().detach().cpu().permute(1,2,0).numpy()
+            img_np = (img_np - img_np.min()) / (img_np.max() - img_np.min() + 1e-8)
+            plt.figure(figsize=(6,6))
+            plt.imshow(img_np)
+            plt.imshow(cam, cmap="jet", alpha=0.5)
+            pred_name = id2label.get(target_class, str(target_class))
+            true_name = id2label.get(label, str(label))
+            plt.title(f"Pred: {pred_name} | True: {true_name}")
+            plt.axis("off")
+            fname = f"{OUTPUT_DIR}/gradcam_sample_{idx}_pred{pred_name}_true{true_name}.png"
+            plt.savefig(fname, bbox_inches="tight", dpi=150)
+            plt.close()
+            print(f"  Saved {fname}")
+        except Exception as e:
+            print(f"  Skipped sample {idx}: {e}")
+            traceback.print_exc()
+
+    fwd_handle.remove()
+    bwd_handle.remove()
+    print("\nEvaluation complete.")
+
+if __name__ == "__main__":
+    main()