utils.py

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.cm
from PIL import Image

# Adapted from: https://colab.research.google.com/github/kevinzakka/clip_playground/blob/main/CLIP_GradCAM_Visualization.ipynb
class Hook:
    """Attaches to a module and records its activations and gradients."""

    def __init__(self, module: nn.Module):
        self.data = None
        self.hook = module.register_forward_hook(self.save_grad)
        
    def save_grad(self, module, input, output):
        self.data = output
        output.requires_grad_(True)
        output.retain_grad()
        
    def __enter__(self):
        return self
    
    def __exit__(self, exc_type, exc_value, exc_traceback):
        self.hook.remove()
        
    @property
    def activation(self) -> torch.Tensor:
        return self.data
    
    @property
    def gradient(self) -> torch.Tensor:
        return self.data.grad


# Reference: https://arxiv.org/abs/1610.02391
def gradCAM(
    model: nn.Module,
    input: torch.Tensor,
    target: torch.Tensor,
    layer: nn.Module
) -> torch.Tensor:
    # Zero out any gradients at the input.
    if input.grad is not None:
        input.grad.data.zero_()
        
    # Disable gradient settings.
    requires_grad = {}
    for name, param in model.named_parameters():
        requires_grad[name] = param.requires_grad
        param.requires_grad_(False)
        
    # Attach a hook to the model at the desired layer.
    assert isinstance(layer, nn.Module)
    with Hook(layer) as hook:        
        # Do a forward and backward pass.
        output = model(input)
        output.backward(target)

        grad = hook.gradient.float()
        act = hook.activation.float()
    
        # Global average pool gradient across spatial dimension
        # to obtain importance weights.
        alpha = grad.mean(dim=(2, 3), keepdim=True)
        # Weighted combination of activation maps over channel
        # dimension.
        gradcam = torch.sum(act * alpha, dim=1, keepdim=True)
        # We only want neurons with positive influence so we
        # clamp any negative ones.
        gradcam = torch.clamp(gradcam, min=0)

    # Resize gradcam to input resolution.
    gradcam = F.interpolate(
        gradcam,
        input.shape[2:],
        mode='bicubic',
        align_corners=False)
    
    # Restore gradient settings.
    for name, param in model.named_parameters():
        param.requires_grad_(requires_grad[name])
        
    return gradcam


# Modified from: https://github.com/salesforce/ALBEF/blob/main/visualization.ipynb
def getAttMap(img, attn_map):
    # Normalize attention map
    attn_map = attn_map - attn_map.min()
    if attn_map.max() > 0:
        attn_map = attn_map / attn_map.max()

    H = matplotlib.cm.jet(attn_map)
    H = (H * 255).astype(np.uint8)[:, :, :3]
    img_heatmap = Image.fromarray(H)
    img_heatmap = img_heatmap.resize((256, 256))
    
    return Image.blend(
        img.resize((256, 256)), img_heatmap, 0.4)