Add grad-cam example

Author: koki0702

dezero/functions.py

@@ -245,6 +245,15 @@ def broadcast_to(x, shape):
     return BroadcastTo(shape)(x)
 
 
+def average(x, axis=None, keepdims=False):
+    x = as_variable(x)
+    y = sum(x, axis, keepdims)
+    return y * (y.data.size / x.data.size)
+
+
+mean = average
+
+
 class MatMul(Function):
     def forward(self, x, W):
         y = x.dot(W)

examples/grad_cam.py

@@ -0,0 +1,42 @@
+"""
+Simple implementation of Grad-CAM (https://arxiv.org/pdf/1610.02391.pdf)
+"""
+import numpy as np
+from PIL import Image
+import cv2
+import dezero
+import dezero.functions as F
+from dezero.models import VGG16
+
+
+url = 'https://github.com/oreilly-japan/deep-learning-from-scratch-3/raw/images/zebra.jpg'
+img_path = dezero.utils.get_file(url)
+img = Image.open(img_path)
+img_size = img.size
+
+model = VGG16(pretrained=True)
+x = VGG16.preprocess(img)[np.newaxis]  # preprocess for VGG
+y = model(x)
+last_conv_output = model.conv5_3.outputs[0]()
+predict_id = np.argmax(y.data)
+predict_output = y[0, predict_id]
+
+predict_output.backward(retain_grad=True)
+grads = last_conv_output.grad
+pooled_grads = F.average(grads, axis=(0, 2, 3))
+
+heatmap = last_conv_output.data[0]
+for c in range(heatmap.shape[0]):
+    heatmap[c] *= pooled_grads[c].data
+
+heatmap = np.mean(heatmap, axis=0)
+heatmap = np.maximum(heatmap, 0)
+heatmap /= np.max(heatmap)
+
+# visualize the heatmap on image
+img = cv2.imread(img_path)
+heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
+heatmap = np.uint8(255 * heatmap)
+heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
+heatmap_on_img = heatmap * 0.4 + img
+cv2.imwrite('grad_cam.png', heatmap_on_img)