def test_grad_cam(self):
model = keras.applications.MobileNetV2()
cam = grad_cam(model,
layer_cut='Conv_1',
inputs=np.random.random((3, 224, 224, 3)),
target_class=0)
self.assertEqual((3, 7, 7), cam.shape)
def test_grad_cam_pp(self):
model = keras.applications.MobileNetV2()
model = replace_layers(model, activation_mapping={'softmax': 'linear'})
cam = grad_cam(model,
layer_cut='Conv_1',
inputs=np.random.random((3, 224, 224, 3)),
target_class=0,
plus=True)
self.assertEqual((3, 7, 7), cam.shape)
def process(target_class,
cmap='jet',
alpha=0.5):
model = keras.applications.MobileNetV2()
cam = grad_cam(model=model, layer_cut='Conv_1', inputs=inputs, target_class=target_class)[0]
# Visualization
heatmap = plt.get_cmap(cmap)(cam, bytes=True)
heatmap = Image.fromarray(heatmap[..., :3], mode='RGB')
heatmap = heatmap.resize((original_image.width, original_image.height), resample=Image.BILINEAR)
return Image.blend(original_image, heatmap, alpha=alpha)