Caffe2(3)----下载现成的模型并使用

Caffe2训练好的模型可在Model Zoo下载，下载的命令很简单，接下来以下载和使用squeezenet为例，进行简单说明。

 

1.浏览可下载的模型

已有模型都放在github上，地址：https://github.com/caffe2/caffe2/wiki/Model-Zoo，当前有caffe和caffe2两种版本的选择。

 

2.选择下载模型

注意名字为小写，有些会加下划线，我们这里选择caffe2的版本

下载并安装（安装目录为/usr/local/caffe2/python/models），命令如下：

python -m caffe2.python.models.download --install squeezenet

有时候需要sudo的权限，则要改为执行如下命令：

sudo PYTHONPATH=/usr/local python -m caffe2.python.models.download --install squeezenet

 

3.应用模型

我们尝试从网上下载一些代码，然后进行预测，在官方代码的基础上做了一些改动，代码如下：

# load up the caffe2 workspace
from caffe2.python import workspace
# choose your model here (use the downloader first)
from caffe2.python.models import squeezenet as mynet
# helper image processing functions
import caffe2.python.tutorials.helpers as helpers

import skimage.io 
from matplotlib import pyplot as plt

# load the pre-trained model
init_net = mynet.init_net
predict_net = mynet.predict_net
# you must name it something
predict_net.name = "squeezenet_predict"
workspace.RunNetOnce(init_net)
workspace.CreateNet(predict_net)
p = workspace.Predictor(init_net.SerializeToString(), predict_net.SerializeToString())

# use whatever image you want (local files or urls)
#img_pth = "https://upload.wikimedia.org/wikipedia/commons/thumb/7/7b/Orange-Whole-%26-Split.jpg/1200px-Orange-Whole-%26-Split.jpg"
#img_pth = "https://upload.wikimedia.org/wikipedia/commons/a/ac/Pretzel.jpg"
img_pth = "https://cdn.pixabay.com/photo/2015/02/10/21/28/flower-631765_1280.jpg"
# average mean to subtract from the image
mean = 128
# the size of images that the model was trained with
input_size = 227

# use the image helper to load the image and convert it to NCHW
img = helpers.loadToNCHW(img_pth, mean, input_size)

# submit the image to net and get a tensor of results

results = p.run([img])
response = helpers.parseResults(results)
# and lookup our result from the list
print response

#show result on image
img_mat = skimage.io.imread(img_pth)
skimage.io.imshow(img_mat)
plt.title(response,{'fontsize': '20'})
plt.savefig('pretzel.jpg')
plt.show()

注意别忘记把推理的文件inference_codes.txt放在程序的当前目录

 

4.结果

对三张图片分类的结果及其概率如图所示

5.参考资料

[1].Model Zoo Doc

[2].Model Zoo Github

[3].贾扬清等人撰文详解Caffe2：从强大新能力到上手教程