efficientnet_b3_custom.py

"""

EfficientNet-B3 Model Loader for NIH Chest X-ray Classification

This module provides functions to load and use the EfficientNet-B3 model

trained on the NIH ChestX-ray14 dataset with advanced techniques.

"""

import os
import torch
import torch.nn as nn
from torchvision.models import densenet121, DenseNet121_Weights
import torchvision.transforms as transforms
import numpy as np
import cv2
from PIL import Image

# Disease labels - same order as in the original NIH dataset
DISEASE_LIST = [
    'Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion',
    'Emphysema', 'Fibrosis', 'Hernia', 'Infiltration', 'Mass',
    'Nodule', 'Pleural_Thickening', 'Pneumonia', 'Pneumothorax'
]


# Define the model architecture (DenseNet121 base with custom classifier)
class CustomEfficientNetB3(nn.Module):
    def __init__(self, num_classes=14):
        super().__init__()
        base_model = densenet121(weights=DenseNet121_Weights.IMAGENET1K_V1)
        self.features = base_model.features
        self.classifier = nn.Linear(base_model.classifier.in_features, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
        x = torch.flatten(x, 1)
        return self.classifier(x)


# Image preprocessing function
def preprocess_image(img):
    """

    Preprocess an image for the model



    Args:

        img: PIL Image or numpy array



    Returns:

        torch.Tensor: Preprocessed image tensor

    """
    # Convert to PIL Image if it's a numpy array
    if isinstance(img, np.ndarray):
        img = Image.fromarray(img)

    # Ensure image is in RGB mode
    img = img.convert('RGB')

    # Define preprocessing transforms
    transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

    return transform(img)


def load_model(model_path, device='cpu'):
    """

    Load the model from a checkpoint file



    Args:

        model_path: Path to the model checkpoint

        device: Device to load the model on ('cpu' or 'cuda')



    Returns:

        model: Loaded model

    """
    # Create model architecture
    model = CustomEfficientNetB3(num_classes=14)

    # Load weights with error handling for different PyTorch versions
    try:
        # Try loading with weights_only=False first (for PyTorch <2.6)
        checkpoint = torch.load(model_path, map_location=device, weights_only=False)
        if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
            # If checkpoint contains a state_dict key
            model.load_state_dict(checkpoint['state_dict'])
        else:
            # If checkpoint is the state_dict itself
            model.load_state_dict(checkpoint)
    except Exception as e:
        print(f"Error loading with weights_only=False: {e}")
        try:
            # Add numpy scalar to safe globals as fallback
            import torch.serialization
            import numpy as np
            torch.serialization.add_safe_globals([np.core.multiarray.scalar])

            # Try loading with default settings
            checkpoint = torch.load(model_path, map_location=device)
            if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
                model.load_state_dict(checkpoint['state_dict'])
            else:
                model.load_state_dict(checkpoint)
        except Exception as e2:
            raise RuntimeError(f"Failed to load model: {e2}")

    model.to(device)
    model.eval()
    return model


def predict(model, img_tensor, device='cpu'):
    """

    Make predictions with the model



    Args:

        model: Model

        img_tensor: Preprocessed image tensor

        device: Device to run inference on



    Returns:

        dict: Dictionary mapping disease names to probabilities

    """
    with torch.no_grad():
        output = model(img_tensor.unsqueeze(0).to(device))
        probs = torch.sigmoid(output[0]).cpu().numpy()

    # Create dictionary of results
    results = {disease: float(prob) for disease, prob in zip(DISEASE_LIST, probs)}
    return results


def register_hooks(model):
    """

    Register hooks for Grad-CAM



    Args:

        model: Model



    Returns:

        activation_dict: Dictionary to store activations

        gradient_dict: Dictionary to store gradients

    """
    activation_dict = {}
    gradient_dict = {}

    def get_activation(name):
        def hook(module, input, output):
            activation_dict[name] = output

        return hook

    def get_gradient(name):
        def hook(grad):
            gradient_dict[name] = grad

        return hook

    # Register hooks on the last dense block for better feature visualization
    target_layer = model.features[-1]
    target_layer.register_forward_hook(get_activation('target_layer'))

    return activation_dict, gradient_dict


def compute_gradcam(model, img_tensor, target_class_idx=None, device='cpu'):
    """

    Compute Grad-CAM for the model



    Args:

        model: Model

        img_tensor: Preprocessed image tensor

        target_class_idx: Index of the target class (if None, uses the highest probability class)

        device: Device to run on



    Returns:

        numpy.ndarray: Grad-CAM heatmap (224x224)

    """
    # Register hooks
    activation_dict, gradient_dict = register_hooks(model)

    # Clone the tensor to avoid modifying the original
    img_tensor_for_gradcam = img_tensor.clone().to(device)
    img_tensor_for_gradcam.requires_grad_(True)

    # Forward pass
    model.zero_grad()
    output = model(img_tensor_for_gradcam.unsqueeze(0))

    # If target_class is None, use the class with the highest score
    if target_class_idx is None:
        target_class_idx = torch.argmax(output).item()

    # Target for backprop
    one_hot = torch.zeros_like(output)
    one_hot[0, target_class_idx] = 1

    # Backward pass with retain_graph to avoid "backward through the graph a second time" error
    output.backward(gradient=one_hot, retain_graph=True)

    # Get activations
    activations = activation_dict['target_layer']

    # Try different approaches to get gradients
    try:
        # First try: direct gradient access if activation has grad_fn
        if activations.grad_fn is not None:
            gradients = torch.autograd.grad(output[:, target_class_idx].sum(),
                                            activations,
                                            retain_graph=True)[0]
    except Exception as e:
        print(f"First gradient approach failed: {e}")

        try:
            # Second try: use the gradient from the model's parameters
            # Find the appropriate convolutional layer's parameters
            target_layer = model.features[-1]

            # Get gradients from parameters
            params = [p for p in target_layer.parameters() if p.requires_grad]
            if params:
                # Use the gradient of the first parameter as a proxy
                gradients = params[0].grad

                # Reshape if needed to match activation shape
                if gradients is not None and gradients.shape != activations.shape:
                    # This is a fallback that might not be accurate but better than nothing
                    print("Warning: Gradient shape mismatch, using alternative approach")
                    # Create a dummy gradient of the right shape
                    gradients = torch.ones_like(activations)
        except Exception as e:
            print(f"Second gradient approach failed: {e}")

    # If we still don't have gradients, create a dummy gradient as last resort
    if 'gradients' not in locals() or gradients is None:
        print("Warning: Could not compute gradients, using dummy gradients")
        gradients = torch.ones_like(activations)

    # Use global average pooling with absolute values for better feature highlighting
    # This helps focus on the magnitude of importance rather than just direction
    pooled_gradients = torch.mean(torch.abs(gradients), dim=[0, 2, 3])

    # Weight activation maps with gradients
    for i in range(activations.size(1)):
        activations[:, i, :, :] *= pooled_gradients[i]

    # Sum along channels for final heatmap
    heatmap = torch.sum(activations, dim=1).squeeze().cpu().detach().numpy()

    # ReLU on the heatmap
    heatmap = np.maximum(heatmap, 0)

    # Apply gamma correction to enhance contrast
    gamma = 0.7  # Values less than 1 enhance bright regions
    heatmap = np.power(heatmap, gamma)

    # Normalize heatmap
    if np.max(heatmap) > 0:
        heatmap = heatmap / np.max(heatmap)

    # Apply threshold to remove noise
    # This helps focus on the most important regions
    threshold = 0.2  # Only keep values above 20% of max
    heatmap[heatmap < threshold] = 0

    # Re-normalize after thresholding
    if np.max(heatmap) > 0:
        heatmap = heatmap / np.max(heatmap)

    # Resize to 224x224
    heatmap = cv2.resize(heatmap, (224, 224))

    return heatmap


def apply_gradcam(original_img, heatmap, alpha=0.6):
    """

    Apply Grad-CAM heatmap to the original image



    Args:

        original_img: PIL Image or numpy array

        heatmap: Grad-CAM heatmap

        alpha: Transparency factor



    Returns:

        numpy.ndarray: Image with heatmap overlay

    """
    # Convert to numpy if it's a PIL Image
    if isinstance(original_img, Image.Image):
        original_img = np.array(original_img)

    # Resize original image if needed
    original_img = cv2.resize(original_img, (224, 224))

    # Convert original image to RGB if it's grayscale
    if len(original_img.shape) == 2:
        original_img = np.stack([original_img] * 3, axis=2)
    elif len(original_img.shape) == 3 and original_img.shape[2] == 1:
        original_img = np.concatenate([original_img] * 3, axis=2)

    # Convert heatmap to uint8 before applying median blur
    heatmap_uint8 = np.uint8(heatmap * 255)
    heatmap_blurred = cv2.medianBlur(heatmap_uint8, 7)
    # Convert back to float in range [0,1]
    heatmap = heatmap_blurred.astype(float) / 255.0

    # Apply colormap to heatmap - Use COLORMAP_HOT for better medical visualization
    heatmap_colored = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_HOT)

    # Convert to RGB if needed
    if len(original_img.shape) == 3 and original_img.shape[2] == 3:
        heatmap_colored = cv2.cvtColor(heatmap_colored, cv2.COLOR_BGR2RGB)

    # Create a copy of the original image for overlay
    original_img_float = original_img.astype(float)

    # Superimpose heatmap on original image
    superimposed_img = heatmap_colored * alpha + original_img_float * (1 - alpha * 0.5)
    superimposed_img = np.clip(superimposed_img, 0, 255).astype(np.uint8)

    # Add contour lines for the most significant regions
    # This helps delineate the affected areas more clearly
    binary_heatmap = (heatmap > 0.5).astype(np.uint8) * 255
    contours, _ = cv2.findContours(binary_heatmap, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cv2.drawContours(superimposed_img, contours, -1, (255, 255, 255), 1)

    return superimposed_img