【Grad-Cam】pycaffe实现

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 python获取梯度脚本

# -*- coding: UTF-8 -*-
import sys
import shutil
import os
sys.path.insert(0, "caffe/python")
import caffe
import numpy as np
import dicom
import cv2
from scipy.misc import bytescale
from matplotlib import pyplot as plt
from PIL import Image
import matplotlib.cm as cmdef process(source, IMAGE_SIZE=227):ds = dicom.read_file(source)pixel_array = ds.pixel_arrayheight, width = pixel_array.shapeif height < width:pixel_array = pixel_array[:, int((width - height) / 2):int((width + height) / 2)]else:pixel_array = pixel_array[int((height - width) / 2):int((width + height) / 2), :]im = cv2.resize(pixel_array, (IMAGE_SIZE, IMAGE_SIZE))im = bytescale(im)# im = im / 256im = np.dstack((im, im, im))im = im[:, :, [2, 1, 0]]input_im = im.transpose((2, 0, 1))return im, input_imcaffe.set_mode_cpu()net = caffe.Net("bone/alexnet_deploy.prototxt", "bone/all_alexnet_train_iter_8000.caffemodel", caffe.TEST)
net.blobs['data'].reshape(1, 3, 227, 227)## output layer
final_layer = 'my-fc8' #这是你的输出层，比如最后一层经过softmax一般喜欢叫prob，就改为prob即可
### the last conv layer or any else you want to visualize
layer_name = 'conv1'def visualize(input_im):net.blobs['data'].data[...] = input_imoutput = net.forward()predict_age = output['my-fc8'][0][0]label = np.zeros(net.blobs[final_layer].shape)label[0, 0] = predict_ageimdiff = net.backward(diffs=['data', layer_name], **{net.outputs[0]: label})gradients = imdiff[layer_name]vis_grad = np.squeeze(gradients)mean_grads = np.mean(vis_grad, axis=(1, 2))activations = net.blobs[layer_name].dataactivations = np.squeeze(activations)n_nodes = activations.shape[0] # number of nodelsvis_size = activations.shape[1:] #visualization shapevis = np.zeros(vis_size, dtype=np.float32)#generating saliency imagefor i in xrange(n_nodes):activation = activations[i, :, :]weight = mean_grads[i]weighted_activation = activation*weightvis += weighted_activation# We select only those activation which has positively contributed in prediction of given classvis = np.maximum(vis, 0)   # reluvis_img = Image.fromarray(vis, None)vis_img = vis_img.resize((227,227),Image.BICUBIC)vis_img = vis_img / np.max(vis_img)vis_img = Image.fromarray(np.uint8(cm.jet(vis_img) * 255))vis_img = vis_img.convert('RGB') # dropping alpha channelreturn vis_img### for one image
# im, input_im = process('/Users/hzzone/Downloads/data/male/11.00-11.99/15696275')
# vis_img = visualize(input_im)
# im = Image.fromarray(im)
#
# heat_map = Image.blend(im, vis_img, 0.3)
# heat_map = np.array(heat_map)
#
# plt.imsave('h1.jpg', heat_map)
# plt.imshow(heat_map)
# plt.axis('off')
# plt.show()### For a foldersave_dir = './data'
data_dir = u'/Volumes/Seagate Backup Plus Drive/深度学习数据集/盆骨'for root, dirs, files in os.walk(data_dir):for file_name in files:dicom_file = os.path.join(root, file_name)im, input_im = process(dicom_file)vis_img = visualize(input_im)im = Image.fromarray(im)heat_map = Image.blend(im, vis_img, 0.3)heat_map = np.array(heat_map)save_path = os.path.join(save_dir, root.strip(data_dir))if not os.path.exists(save_path):os.makedirs(save_path)save_path = os.path.join(save_path, file_name)print(save_path)plt.imsave(save_path, heat_map)

prototxt文件

关键：force_backward: true

name: "AlexNet"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 227
input_dim: 227
force_backward: true

 参考

GitHub - Hzzone/grad-CAM-pycaffe: grad-CAM visulization technique of pycaffe, regression task of Medical Image.
https://github.com/gautamMalu/caffe-gradCAM

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