» Code examples / Computer Vision / Grad-CAM class activation visualization

Grad-CAM class activation visualization

Author: fchollet
Date created: 2020/04/26
Last modified: 2021/03/07
Description: How to obtain a class activation heatmap for an image classification model.

View in Colab GitHub source

Adapted from Deep Learning with Python (2017).

Setup

import numpy as np
import tensorflow as tf
from tensorflow import keras

# Display
from IPython.display import Image, display
import matplotlib.pyplot as plt
import matplotlib.cm as cm

Configurable parameters

You can change these to another model.

To get the values for last_conv_layer_name use model.summary() to see the names of all layers in the model.

model_builder = keras.applications.xception.Xception
img_size = (299, 299)
preprocess_input = keras.applications.xception.preprocess_input
decode_predictions = keras.applications.xception.decode_predictions

last_conv_layer_name = "block14_sepconv2_act"

# The local path to our target image
img_path = keras.utils.get_file(
    "african_elephant.jpg", "https://i.imgur.com/Bvro0YD.png"
)

display(Image(img_path))

jpeg


The Grad-CAM algorithm

def get_img_array(img_path, size):
    # `img` is a PIL image of size 299x299
    img = keras.preprocessing.image.load_img(img_path, target_size=size)
    # `array` is a float32 Numpy array of shape (299, 299, 3)
    array = keras.preprocessing.image.img_to_array(img)
    # We add a dimension to transform our array into a "batch"
    # of size (1, 299, 299, 3)
    array = np.expand_dims(array, axis=0)
    return array


def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
    # First, we create a model that maps the input image to the activations
    # of the last conv layer as well as the output predictions
    grad_model = tf.keras.models.Model(
        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
    )

    # Then, we compute the gradient of the top predicted class for our input image
    # with respect to the activations of the last conv layer
    with tf.GradientTape() as tape:
        last_conv_layer_output, preds = grad_model(img_array)
        if pred_index is None:
            pred_index = tf.argmax(preds[0])
        class_channel = preds[:, pred_index]

    # This is the gradient of the output neuron (top predicted or chosen)
    # with regard to the output feature map of the last conv layer
    grads = tape.gradient(class_channel, last_conv_layer_output)

    # This is a vector where each entry is the mean intensity of the gradient
    # over a specific feature map channel
    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))

    # We multiply each channel in the feature map array
    # by "how important this channel is" with regard to the top predicted class
    # then sum all the channels to obtain the heatmap class activation
    last_conv_layer_output = last_conv_layer_output[0]
    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
    heatmap = tf.squeeze(heatmap)

    # For visualization purpose, we will also normalize the heatmap between 0 & 1
    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
    return heatmap.numpy()

Let's test-drive it

# Prepare image
img_array = preprocess_input(get_img_array(img_path, size=img_size))

# Make model
model = model_builder(weights="imagenet")

# Remove last layer's softmax
model.layers[-1].activation = None

# Print what the top predicted class is
preds = model.predict(img_array)
print("Predicted:", decode_predictions(preds, top=1)[0])

# Generate class activation heatmap
heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name)

# Display heatmap
plt.matshow(heatmap)
plt.show()
Predicted: [('n02504458', 'African_elephant', 9.862388)]

png


Create a superimposed visualization

def save_and_display_gradcam(img_path, heatmap, cam_path="cam.jpg", alpha=0.4):
    # Load the original image
    img = keras.preprocessing.image.load_img(img_path)
    img = keras.preprocessing.image.img_to_array(img)

    # Rescale heatmap to a range 0-255
    heatmap = np.uint8(255 * heatmap)

    # Use jet colormap to colorize heatmap
    jet = cm.get_cmap("jet")

    # Use RGB values of the colormap
    jet_colors = jet(np.arange(256))[:, :3]
    jet_heatmap = jet_colors[heatmap]

    # Create an image with RGB colorized heatmap
    jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)
    jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
    jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)

    # Superimpose the heatmap on original image
    superimposed_img = jet_heatmap * alpha + img
    superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)

    # Save the superimposed image
    superimposed_img.save(cam_path)

    # Display Grad CAM
    display(Image(cam_path))


save_and_display_gradcam(img_path, heatmap)

jpeg


Let's try another image

We will see how the grad cam explains the model's outputs for a multi-label image. Let's try an image with a cat and a dog together, and see how the grad cam behaves.

img_path = keras.utils.get_file(
    "cat_and_dog.jpg",
    "https://storage.googleapis.com/petbacker/images/blog/2017/dog-and-cat-cover.jpg",
)

display(Image(img_path))

# Prepare image
img_array = preprocess_input(get_img_array(img_path, size=img_size))

# Print what the two top predicted classes are
preds = model.predict(img_array)
print("Predicted:", decode_predictions(preds, top=2)[0])

jpeg

Predicted: [('n02112137', 'chow', 4.611241), ('n02124075', 'Egyptian_cat', 4.3817368)]

We generate class activation heatmap for "chow," the class index is 260

heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=260)

save_and_display_gradcam(img_path, heatmap)

jpeg

We generate class activation heatmap for "egyptian cat," the class index is 285

heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=285)

save_and_display_gradcam(img_path, heatmap)

jpeg