gradcam_fixed.py

"""
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()