Grad-CAM from Scratch with PyTorch Hooks

automobile stops immediately. Worryingly, there is no such thing as a cease register sight. The engineers can solely make guesses as to why the automobile’s neural community turned confused. It might be a tumbleweed rolling throughout the road, a automobile coming down the opposite lane or the pink billboard within the background. To seek out the true cause, they flip to Grad-CAM [1].

Grad-CAM is an explainable AI (XAI) method that helps reveal why a convolutional neural community (CNN) made a specific determination. The tactic produces a heatmap that highlights the areas in a picture which can be an important for a prediction. For our self-driving automobile instance, this might present if the pixels from the weed, automobile or billboard brought on the automobile to cease.

Now, Grad-CAM is one among many XAI methods for Computer Vision. Resulting from its pace, flexibility and reliability, it has rapidly develop into some of the in style. It has additionally impressed many associated strategies. So, in case you are excited about XAI, it’s value understanding precisely how this methodology works. To do this, we can be implementing Grad-CAM from scratch utilizing Python.

Particularly, we can be counting on PyTorch Hooks. As you will note, these enable us to dynamically extract gradients and activations from a community throughout ahead and backwards passes. These are sensible expertise that won’t solely will let you implement Grad-CAM but in addition any gradient-based XAI methodology. See the total challenge on GitHub.

The speculation behind Grad-CAM

Earlier than we get to the code, it’s value bearing on the speculation behind Grad-CAM. If you’d like a deep dive, then try the video under. If you wish to study different strategies, then see this free XAI for Computer Vision course.

To summarise, when creating Grad-CAM heatmaps, we begin with a skilled CNN. We then do a ahead move by means of this community with a single pattern picture. This can activate all convolutional layers within the community. We name these function maps ($A^ok$). They are going to be a group of 2D matrices that comprise completely different options detected within the pattern picture.

With Grad-CAM, we’re sometimes within the maps from the final convolutional layer of the community. Once we apply the tactic to VGG16, you will note that its last layer has 512 function maps. We use these as they comprise options with probably the most detailed semantic info whereas nonetheless retaining spatial info. In different phrases, they inform us what was used for a prediction and the place within the picture it was taken from.

The issue is that these maps additionally comprise options which can be necessary for different lessons. To mitigate this, we observe the method proven in Determine 1. As soon as we now have the function maps ($A^ok$), we weight them by how necessary they’re to the category of curiosity ($y_c$). We do that utilizing $a_k^c$ — the common gradient of the rating for $y_c$ w.r.t. to the weather within the function map. We then do element-wise summation. For VGG16, you will note we go from 512 maps of 14×14 pixels to a single 14×14 map.

The gradients for a person ingredient ($frac{partial y^c}{partial A_{ij}^ok}$) inform us how a lot the rating will change with a small change within the ingredient. Which means giant common gradients point out that your entire function map was necessary and will contribute extra to the ultimate heatmap. So, after we weight and sum the maps, those that comprise options for different lessons will seemingly contribute much less.

The ultimate steps are to use the ReLU activation operate to make sure all adverse parts may have a price of zero. Then we upsample with interpolation so the heatmap has the identical dimensions because the pattern picture. The ultimate map is summarised by the method under. You may recognise it from the Grad-CAM paper [1].

$$ L_{Grad-CAM}^c = ReLUleft( sum_{ok} a_k^c A^ok proper) $$

Grad-CAM from Scratch

Don’t fear if the speculation just isn’t utterly clear. We are going to stroll by means of it step-by-step as we apply the tactic from scratch. Yow will discover the total challenge on GitHub. To begin, we now have our imports under. These are all frequent imports for laptop imaginative and prescient issues.

import matplotlib.pyplot as plt
import numpy as np

import cv2
from PIL import Picture

import torch
import torch.nn.purposeful as F
from torchvision import fashions, transforms

import urllib.request

Load pretrained mannequin from PyTorch

We’ll be making use of Grad-CAM to VGG16 pretrained on ImageNet. To assist, we now have the 2 capabilities under. The primary will format a picture within the right approach for enter into the mannequin. The normalisation values used are the imply and commonplace deviation of the pictures in ImageNet. The 224×224 dimension can also be commonplace for ImageNet fashions.

def preprocess_image(img_path):

    """Load and preprocess pictures for PyTorch fashions."""

    img = Picture.open(img_path).convert("RGB")

    #Transforms utilized by imagenet fashions
    remodel = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])

    return remodel(img).unsqueeze(0)

ImageNet has many lessons. The second operate will format the output of the mannequin so we show the lessons with the best predicted chances.

def display_output(output,n=5):

    """Show the highest n classes predicted by the mannequin."""
    
    # Obtain the classes
    url = "https://uncooked.githubusercontent.com/pytorch/hub/grasp/imagenet_classes.txt"
    urllib.request.urlretrieve(url, "imagenet_classes.txt")

    with open("imagenet_classes.txt", "r") as f:
        classes = [s.strip() for s in f.readlines()]

    # Present prime classes per picture
    chances = torch.nn.purposeful.softmax(output[0], dim=0)
    top_prob, top_catid = torch.topk(chances, n)

    for i in vary(top_prob.dimension(0)):
        print(classes[top_catid[i]], top_prob[i].merchandise())

    return top_catid[0]

We now load the pretrained VGG16 model (line 2), transfer it to a GPU (strains 5-8) and set it to analysis mode (line 11). You may see a snippet of the mannequin output in Determine 2. VGG16 is made from 16 weighted layers. Right here, you possibly can see the final 2 of 13 convolutional layers and the three absolutely related layers.

# Load the pre-trained mannequin (e.g., VGG16)
mannequin = fashions.vgg16(pretrained=True)

# Set the mannequin to gpu
machine = torch.machine('mps' if torch.backends.mps.is_built() 
                      else 'cuda' if torch.cuda.is_available() 
                      else 'cpu')
mannequin.to(machine)

# Set the mannequin to analysis mode
mannequin.eval()

The names you see in Determine 2 are necessary. Later, we are going to use them to reference a selected layer within the community to entry its activations and gradients. Particularly, we are going to use mannequin.options[28]. That is the ultimate convolutional layer within the community. As you possibly can see within the snapshot, this layer accommodates 512 function maps.

Ahead move with pattern picture

We can be explaining a prediction from this mannequin. To do that, we want a pattern picture that can be fed into the mannequin. We downloaded one from Wikipedia Commons (strains 2-3). We then load it (strains 5-6), crop it to have equal top and width (line 7) and show it (strains 9-10). In Determine 3, you possibly can see we’re utilizing a picture of a whale shark in an aquarium.

# Load a pattern picture from the online
img_url = "https://add.wikimedia.org/wikipedia/commons/thumb/a/a1/Male_whale_shark_at_Georgia_Aquarium.jpg/960px-Male_whale_shark_at_Georgia_Aquarium.jpg"
urllib.request.urlretrieve(img_url, "sample_image.jpg")[0]

img_path = "sample_image.jpg"
img = Picture.open(img_path).convert("RGB")
img = img.crop((320, 0, 960, 640))  # Crop to 640x640

plt.imshow(img)
plt.axis("off")

ImageNet has no devoted class for whale sharks, so will probably be attention-grabbing to see what the mannequin predicts. To do that, we begin by processing our picture (line 2) and shifting it to the GPU (line 3). We then do a ahead move to get a prediction (line 6) and show the highest 5 chances (line 7). You may see these in Determine 4.

# Preprocess the picture
img_tensor = preprocess_image(img_path)
img_tensor = img_tensor.to(machine)

# Ahead move
predictions = mannequin(img_tensor)
display_output(predictions,n=5)

Given the obtainable lessons, these appear cheap. They’re all marine life and the highest two are sharks. Now, let’s see how we will clarify this prediction. We wish to perceive what areas of the picture contribute probably the most to the best predicted class — hammerhead.

PyTorch hooks naming conventions

Grad-CAM heatmaps are created utilizing each activations from a ahead move and gradients from a backwards move. To entry these, we are going to use PyTorch hooks. These are capabilities that will let you save the inputs and outputs of a layer. We gained’t do it right here, however they even will let you alter these features. For instance, Guided Backpropagation may be utilized by guaranteeing solely constructive gradients are propagated utilizing a backwards hook.

You may see some examples of those capabilities under. A forwards_hook can be referred to as throughout a ahead move. It is going to be registered on a given module (i.e. layer). By default, the operate receives three arguments — the module, its enter and its output. Equally, a backwards_hook is triggered throughout a backwards move with the module and gradients of the enter and output.

# Instance of a forwards hook operate
def fowards_hook(module, enter, output):
    """Parameters:
            module (nn.Module): The module the place the hook is utilized.
            enter (tuple of Tensors): Enter to the module.
            output (Tensor): Output of the module."""
    ...

# Instance of a backwards hook operate 
def backwards_hook(module, grad_in, grad_out):
    """Parameters:
            module (nn.Module): The module the place the hook is utilized.
            grad_in (tuple of Tensors): Gradients w.r.t. the enter of the module.
            grad_out (tuple of Tensors): Gradients w.r.t. the output of the module."""
    ...

To keep away from confusion, let’s make clear the parameter names utilized by these capabilities. Check out the overview of the usual backpropagation process for a convolutional layer in Determine 5. This layer consists of a set of kernels, $Ok$, and biases, $b$. The opposite components are the:

• enter – a set of function maps or a picture
• output – set of function maps
• grad_in is the gradient of the loss w.r.t. the layer’s enter.
• grad_out is the gradient of the loss w.r.t. the layer’s output.

We now have labelled these utilizing the identical names of the arguments used to name the hook capabilities that we apply later.

Bear in mind, we gained’t use the gradients in the identical approach as backpropagation. Often, we use the gradients of a batch of pictures to replace $Ok$ and $b$. Now, we’re solely excited about grad_out of a single pattern picture. This can give us the gradients of the weather within the layer’s function maps. In different phrases, the gradients we use to weight the function maps.

Activations with PyTorch ahead hook

Our VGG16 community has been created utilizing ReLU with inplace=True. These modify tensors in reminiscence, so the unique values are misplaced. That’s, tensors used as enter are overwritten by the ReLU operate. This could result in issues when making use of hooks, as we may have the unique enter. So we use the code under to switch all ReLU capabilities with inplace=False ones. This is not going to affect the output of the mannequin, however it’ll improve its reminiscence utilization.

# Substitute all in-place ReLU activations with out-of-place ones
def replace_relu(mannequin):

    for identify, little one in mannequin.named_children():
        if isinstance(little one, torch.nn.ReLU):
            setattr(mannequin, identify, torch.nn.ReLU(inplace=False))
            print(f"Changing ReLU activation in layer: {identify}")
        else:
            replace_relu(little one)  # Recursively apply to submodules

# Apply the modification to the VGG16 mannequin
replace_relu(mannequin)

Beneath we now have our first hook operate — save_activations. This can append the output from a module (line 6) to an inventory of activations (line 2). In our case, we are going to solely register the hook onto one module (i.e. the final convolutional layer), so this record will solely comprise one ingredient. Discover how we format the output (line 6). We detach it from the computational graph so the community just isn’t affected. We additionally format them as a numpy array and squeeze the batch dimension.

# Record to retailer activations
activations = []

# Operate to save lots of activations
def save_activations(module, enter, output):
    activations.append(output.detach().cpu().numpy().squeeze())

To make use of the hook operate, we register it on the final convolutional layer — mannequin.options[28]. That is achieved utilizing the register_forward_hook operate.

# Register the hook to the final convolutional layer
hook = mannequin.options[28].register_forward_hook(save_activations)

Now, after we do a ahead move (line 2), the save_activations hook operate can be referred to as for this layer. In different phrases, its output can be saved to the activations record.

# Ahead move by means of the mannequin to get activations
prediction = mannequin(img_tensor)

Lastly, it’s good follow to take away the hook operate when it’s now not wanted (line 2). This implies the ahead hook operate is not going to be triggered if we do one other ahead move.

# Take away the hook after use
hook.take away()  

The form of those activations is (512, 14, 14). In different phrases, we now have 512 function maps and every map is 14×14 pixels. You may see some examples of those in Determine 6. A few of these maps might comprise options necessary for different lessons or those who lower the likelihood of the expected class. So let’s see how we will discover gradients to assist establish an important maps.

act_shape = np.form(activations[0])
print(f"Form of activations: {act_shape}") # (512, 14, 14)

Gradients with PyTorch backwards hooks

To get gradients, we observe an analogous course of to earlier than. The important thing distinction is that we now use the register_full_backward_hook to register the save_gradients operate (line 7). This can make sure that it’s referred to as throughout a backwards move. Importantly, we do the backwards move (line 16) from the output for the category with the best rating (line 13). This successfully units the rating for this class to 1 and all different scores to 0. In different phrases, we get the gradients of the hammerhead class w.r.t. to the weather of the function maps.

gradients = []

def save_gradient(module, grad_in, grad_out):
    gradients.append(grad_out[0].cpu().numpy().squeeze())

# Register the backward hook on a convolutional layer
hook = mannequin.options[28].register_full_backward_hook(save_gradient)

# Ahead move
output = mannequin(img_tensor)

# Decide the category with highest rating
rating = output[0].max()

# Backward move from the rating
rating.backward()

# Take away the hook after use
hook.take away()

We may have a gradient for each ingredient of the function maps. So, once more, the form is (512, 14, 14). Determine 7 visualises a few of these. You may see some are inclined to have increased values. Nevertheless, we aren’t so involved with the person gradients. Once we create a Grad-CAM heatmap, we are going to use the common gradient of every function map.

grad_shape = np.form(gradients[0])
print(f"Form of gradients: {grad_shape}") # (512, 14, 14)

Lastly, earlier than we transfer on, it’s good follow to reset the mannequin’s gradients (line 2). That is notably necessary if you happen to plan to run the code for a number of pictures, as gradients may be gathered with every backwards move.

# Reset gradients
mannequin.zero_grad() 

Creating Grad-CAM heatmaps

First, we discover the imply gradients for every function map. There can be 512 of those common gradients. Plotting a histogram of them, you possibly can see most are usually round 0. In different phrases, these don’t have a lot affect on the expected rating. There are just a few that are inclined to have a adverse affect and a constructive affect. It’s these function maps we wish to give extra weight to.

# Step 1: mixture the gradients
gradients_aggregated = np.imply(gradients[0], axis=(1, 2))

We mix all of the activations by doing element-wise summation (strains 2-4). Once we do that, we weight every function map by its common gradient (line 3). In the long run, we may have one 14×14 array.

# Step 2: weight the activations by the aggregated gradients and sum them up
weighted_activations = np.sum(activations[0] * 
                              gradients_aggregated[:, np.newaxis, np.newaxis], 
                              axis=0)

These weighted activations will comprise each constructive and adverse pixels. We are able to contemplate the adverse pixels to be suppressing the expected rating. In different phrases, a rise within the worth of those areas tends to lower the rating. Since we’re solely within the constructive contributions—areas that assist the category prediction—we apply a ReLU activation to the ultimate heatmap (line 2). You may see the distinction within the heatmaps in Determine 9.

# Step 3: ReLU summed activations
relu_weighted_activations = np.most(weighted_activations, 0)

You may see the heatmap in Determine 9 is sort of coarse. It might be extra helpful if it had the size of the unique picture. That is why the final step for creating Grad-CAM heatmaps is to upsample to the dimension of the enter picture (strains 2-4). On this case, we now have a 224×224 picture.

#Step 4: Upsample the heatmap to the unique picture dimension
upsampled_heatmap = cv2.resize(relu_weighted_activations, 
                               (img_tensor.dimension(3), img_tensor.dimension(2)), 
                               interpolation=cv2.INTER_LINEAR)

print(np.form(upsampled_heatmap))  # Ought to be (224, 224)

Determine 10 provides us our last visualisation. We show the pattern picture (strains 5-7) subsequent to the heatmap (strains 10-15). For the latter, we create a transparent visualisation with the assistance of Canny Edge detection (line 10). This offers us an edge map (i.e. define) of the pattern picture. We are able to then overlay the heatmap on prime of this (line 14).

# Step 5: visualise the heatmap
fig, ax = plt.subplots(1, 2, figsize=(8, 8))

# Enter picture
resized_img = img.resize((224, 224))
ax[0].imshow(resized_img)
ax[0].axis("off")

# Edge map for the enter picture
edge_img = cv2.Canny(np.array(resized_img), 100, 200)
ax[1].imshow(255-edge_img, alpha=0.5, cmap='grey')

# Overlay the heatmap 
ax[1].imshow(upsampled_heatmap, alpha=0.5, cmap='coolwarm')
ax[1].axis("off")

our Grad-CAM heatmap, there’s some noise. Nevertheless, it seems the mannequin is counting on the tail fin and, to a lesser extent, the pectoral fin to make its predictions. It’s beginning to make sense why the mannequin categorized this shark as a hammerhead. Maybe each animals share these traits.

For some additional investigation, we apply the identical course of however now utilizing an precise picture of a hammerhead. On this case, the mannequin seems to be counting on the identical options. It is a bit regarding. Would we not anticipate the mannequin to make use of one of many shark’s defining options— the hammerhead? Finally, this will lead VGG16 to confuse various kinds of sharks.

With this instance, we see how Grad-CAM can spotlight potential flaws in our mannequin. We cannot solely get their predictions but in addition perceive how they made them. We are able to perceive if the options used will result in unexpected predictions down the road. This could probably save us a variety of time, cash and within the case of extra consequential purposes, lives!

If you wish to study extra about XAI for CV try one among these articles. Or see this Free XAI for CV course.