Upload gradcam_fixed.py

gradcam_fixed.py

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+"""
+Thyroid Grad-CAM Visualization (Fixed for SwinV2)
+"""
+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
+
+HF_USERNAME = "Johnyquest7"
+DATASET_NAME = "BTX24/thyroid-cancer-classification-ultrasound-dataset"
+MODEL_NAME = f"{HF_USERNAME}/ML-Inter_thyroid"
+OUTPUT_DIR = "./gradcam_outputs"
+SEED = 42
+BATCH_SIZE = 16
+
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+
+def main():
+    print("=" * 60)
+    print("Thyroid Grad-CAM Visualization")
+    print("=" * 60)
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"\nDevice: {device}")
+    processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+    model = AutoModelForImageClassification.from_pretrained(MODEL_NAME).to(device).eval()
+    id2label = model.config.id2label
+
+    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)}")
+
+    # Get predictions first
+    all_logits, all_labels = [], []
+    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") for item in batch_items]
+        inputs = processor(images, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model(pixel_values=inputs["pixel_values"].to(device))
+        all_logits.extend(outputs.logits.cpu().numpy())
+        all_labels.extend([item["label"] for item in batch_items])
+
+    y_true = np.array(all_labels)
+    y_pred = np.argmax(np.array(all_logits), axis=1)
+
+    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)...")
+
+    # Register hooks on last stage
+    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]  # shape: [H*W, C]
+            grads = gradcam_data["grad"][0]  # shape: [H*W, C]
+
+            # Compute Grad-CAM: weighted sum of channels
+            weights = grads.mean(dim=0, keepdim=True)  # [1, C]
+            cam = torch.matmul(feat, weights.t()).squeeze()  # [H*W]
+
+            # Reshape to spatial
+            H = W = int(math.sqrt(cam.shape[0]))
+            cam = cam.reshape(H, W)  # [H, W]
+
+            # Normalize
+            cam = F.relu(cam)
+            cam = cam - cam.min()
+            cam = cam / (cam.max() + 1e-8)
+
+            # Upsample to 256x256
+            cam = cam.unsqueeze(0).unsqueeze(0)  # [1, 1, H, W]
+            cam = F.interpolate(cam, size=(256, 256), mode="bilinear", align_corners=False)
+            cam = cam.squeeze().cpu().numpy()
+
+            # Get image for overlay
+            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))
+            status = "CORRECT" if y_true[idx] == y_pred[idx] else "WRONG"
+            plt.title(f"{status}: Pred={pred_name} | True={true_name}")
+            plt.axis("off")
+            fname = f"{OUTPUT_DIR}/gradcam_{status}_sample{idx}_{pred_name}_vs_{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("\nGrad-CAM complete.")
+
+if __name__ == "__main__":
+    main()