CAM 和 Grad-CAM 实现
https://bindog.github.io/blog/2018/02/10/model-explanation/
推荐这个博客，感觉原理讲的比较清楚。
代码： 代码参考链接：https://github.com/jacobgil/keras-grad-cam 对其中有问题的地方进行了更改。
from keras.applications.vgg16 import (
VGG16, preprocess_input, decode_predictions)
from keras.preprocessing import image
from keras.layers.core import Lambda
from keras.models import Sequential
from tensorflow.python.framework import ops
import keras.backend as K
import tensorflow as tf
import numpy as np
import keras
import sys
import cv2
import os
from keras.models import Model
def target_category_loss(x, category_index, nb_classes):
return tf.multiply(x, K.one_hot([category_index], nb_classes))
def target_category_loss_output_shape(input_shape):
return input_shape
def normalize(x):
# utility function to normalize a tensor by its L2 norm
return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)
def load_image(path):
img_path = sys.argv[1]
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
return x
def register_gradient():
if "GuidedBackProp" not in ops._gradient_registry._registry:
@ops.RegisterGradient("GuidedBackProp")
def _GuidedBackProp(op, grad):
dtype = op.inputs[0].dtype
return grad * tf.cast(grad > 0., dtype) * \
tf.cast(op.inputs[0] > 0., dtype)
def compile_saliency_function(model, activation_layer='block5_conv3'):
input_img = model.input
layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])
layer_output = layer_dict[activation_layer].output
max_output = K.max(layer_output, axis=3)
saliency = K.gradients(K.sum(max_output), input_img)[0]
return K.function([input_img, K.learning_phase()], [saliency])
def modify_backprop(model, name):
g = tf.get_default_graph()
with g.gradient_override_map({'Relu': name}):
# get layers that have an activation
layer_dict = [layer for layer in model.layers[1:]
if hasattr(layer, 'activation')]
# replace relu activation
for layer in layer_dict:
if layer.activation == keras.activations.relu:
layer.activation = tf.nn.relu
# re-instanciate a new model
new_model = VGG16(weights='imagenet')
return new_model
def deprocess_image(x):
'''
Same normalization as in:
https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py
'''
if np.ndim(x) > 3:
x = np.squeeze(x)
# normalize tensor: center on 0., ensure std is 0.1
x -= x.mean()
x /= (x.std() + 1e-5)
x *= 0.1
# clip to [0, 1]
x += 0.5
x = np.clip(x, 0, 1)
# convert to RGB array
x *= 255
if K.image_dim_ordering() == 'th':
x = x.transpose((1, 2, 0))
x = np.clip(x, 0, 255).astype('uint8')
return x
def _compute_gradients(tensor, var_list):
grads = tf.gradients(tensor, var_list)
return [grad if grad is not None else tf.zeros_like(var)
for var, grad in zip(var_list, grads)]
def grad_cam(input_model, image, category_index, layer_name):
nb_classes = 1000
target_layer = lambda x: target_category_loss(x, category_index, nb_classes)
x = Lambda(target_layer, output_shape = target_category_loss_output_shape)(input_model.output)
model = Model(inputs=input_model.input, outputs=x)
model.summary()
loss = K.sum(model.output)
conv_output = [l for l in model.layers if l.name is layer_name][0].output
grads = normalize(_compute_gradients(loss, [conv_output])[0])
gradient_function = K.function([model.input], [conv_output, grads])
output, grads_val = gradient_function([image])
output, grads_val = output[0, :], grads_val[0, :, :, :]
weights = np.mean(grads_val, axis = (0, 1))
cam = np.ones(output.shape[0 : 2], dtype = np.float32)
for i, w in enumerate(weights):
cam += w * output[:, :, i]
cam = cv2.resize(cam, (224, 224))
cam = np.maximum(cam, 0)
heatmap = cam / np.max(cam)
#Return to BGR [0..255] from the preprocessed image
image = image[0, :]
image -= np.min(image)
image = np.minimum(image, 255)
cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
cam = np.float32(cam) + np.float32(image)
cam = 255 * cam / np.max(cam)
return np.uint8(cam), heatmap
"""
def grad_cam(input_model, image, category_index, layer_name):
model = Sequential()
model.add(input_model)
nb_classes = 1000
target_layer = lambda x: target_category_loss(x, category_index, nb_classes)
model.add(Lambda(target_layer,
output_shape = target_category_loss_output_shape))
loss = K.sum(model.layers[-1].output)
conv_output = [l for l in model.layers[0].layers if l.name is layer_name][0].output
grads = normalize(_compute_gradients(loss, [conv_output])[0])
gradient_function = K.function([model.layers[0].input], [conv_output, grads])
output, grads_val = gradient_function([image])
output, grads_val = output[0, :], grads_val[0, :, :, :]
weights = np.mean(grads_val, axis = (0, 1))
cam = np.ones(output.shape[0 : 2], dtype = np.float32)
for i, w in enumerate(weights):
cam += w * output[:, :, i]
cam = cv2.resize(cam, (224, 224))
cam = np.maximum(cam, 0)
heatmap = cam / np.max(cam)
#Return to BGR [0..255] from the preprocessed image
image = image[0, :]
image -= np.min(image)
image = np.minimum(image, 255)
cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
cam = np.float32(cam) + np.float32(image)
cam = 255 * cam / np.max(cam)
return np.uint8(cam), heatmap """
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
preprocessed_input = load_image(sys.argv[1])
model = VGG16(weights='imagenet')
predictions = model.predict(preprocessed_input)
top_1 = decode_predictions(predictions)[0][0]
print('Predicted class:')
print('%s (%s) with probability %.2f' % (top_1[1], top_1[0], top_1[2]))
predicted_class = np.argmax(predictions)
cam, heatmap = grad_cam(model, preprocessed_input, predicted_class, "block5_conv3")
cv2.imwrite("gradcam.jpg", cam)
register_gradient()
guided_model = modify_backprop(model, 'GuidedBackProp')
saliency_fn = compile_saliency_function(guided_model)
saliency = saliency_fn([preprocessed_input, 0])
gradcam = saliency[0] * heatmap[..., np.newaxis]
cv2.imwrite("guided_gradcam.jpg", deprocess_image(gradcam))
环境：keras2.1.6 python3 tf1.8
使用：python grad-cam.py
测试：
原图：
grad-cam图：
guided-gradcam图：