fcn+caffe+siftflow实验记录

环境搭建：

vs2013，编译caffe工程，cuda8.0，cudnn5.1，python2.7。

还需要安装python的一些包。Numpy+mkl scipy matplotlib scikit-image protobuf

Pip install scikit-image的时候，需要注意，下载该模块

fcn+caffe+siftflow实验记录

Windows版python模块的下载地址：

https://www.lfd.uci.edu/~gohlke/pythonlibs/

跑实验：

下载fcn的caffe源代码：

https://github.com/shelhamer/fcn.berkeleyvision.org

下载siftflow数据集：

http://www.cs.unc.edu/~jtighe/Papers/ECCV10/siftflow/SiftFlowDataset.zip

将下载的数据放在fcn文件夹下的data目录下，将其重新命名为sift-flow并将原data文件夹下的sift-flow中的文件拷贝到其中，删除原来的。

fcn+caffe+siftflow实验记录

官方预训练好的模型下载链接：

http://dl.caffe.berkeleyvision.org/

网络比较大，预加载模型，用sift-flow中的图片调整参数。

下载caffemodel，在siftflow-fcn32s目录下根据caffemodel-url 下载预训练好的模型。将下载好的模型放在fcn目录下的siftflow-fcn32s文件夹下。将fcn目录下的score.py siftflow_layer.py和surgery.py 复制到siftflow-fcn32s的文件夹下。

修改solve.prototxt文件

train_net: "trainval.prototxt"
test_net: "test.prototxt"
test_iter: 200
# make test net, but don't invoke it from the solver itself
test_interval: 999999999
display: 20
average_loss: 20
lr_policy: "fixed"
# lr for unnormalized softmax
base_lr: 1e-12
# high momentum
momentum: 0.99
# no gradient accumulation
iter_size: 1
max_iter: 100000
weight_decay: 0.0005
snapshot:10000    #训练10000次，保存一次模型
snapshot_prefix:"G:/caffe171101/caffe-master/fcn-master/siftflow-fcn16s/train"  
test_initialization: false

书写启动文件solve.py

import caffe
import surgery, score

import numpy as np
import os
import sys

try:
    import setproctitle
    setproctitle.setproctitle(os.path.basename(os.getcwd()))
except:
    pass

weights = 'siftflow-fcn8s-heavy.caffemodel'
deploy_proto = 'deploy8.prototxt'

# init
caffe.set_device(int(0))
caffe.set_mode_gpu()

solver = caffe.SGDSolver('solver.prototxt')
#solver.net.copy_from(weights)
vgg_net=caffe.Net(deploy_proto,weights,caffe.TRAIN)  
surgery.transplant(solver.net,vgg_net)  
del vgg_net  

# surgeries
interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
surgery.interp(solver.net, interp_layers)

# scoring
test = np.loadtxt('../data/sift-flow/test.txt', dtype=str)

for _ in range(50):
    solver.step(2000)
    # N.B. metrics on the semantic labels are off b.c. of missing classes;
    # score manually from the histogram instead for proper evaluation
    score.seg_tests(solver, False, test, layer='score_sem', gt='sem')
score.seg_tests(solver, False, test, layer='score_geo', gt='geo')

通过transplant的方式加载网络，会同时加载网络，和copy_from方式的区别在于拷贝参数的同时，改造原来的网络。

进入该目录，运行python solve.py，即可跑该网络。

如果，遇到错误，按照提示，修改路径或其他的一些东西。

对单张图片的预测：testSingleImg.py

import numpy as np  
from PIL import Image  
import matplotlib.pyplot as plt  
import sys    
sys.path.append('G:/caffe171101/caffe-master/python')  
import caffe  
import cv2  
#matplotlib inline  
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe  
im = Image.open('test_img/coast_arnat59.jpg')  
in_ = np.array(im, dtype=np.float32)  
in_ = in_[:,:,::-1]  
in_ -= np.array((104.00698793,116.66876762,122.67891434))  
in_ = in_.transpose((2,0,1))  
  
# load net  
net = caffe.Net('siftflow-fcn16s/deploy16.prototxt', 'siftflow-fcn16s/train_iter_100000.caffemodel', caffe.TEST)  
# shape for input (data blob is N x C x H x W), set data  
net.blobs['data'].reshape(1, *in_.shape)  
net.blobs['data'].data[...] = in_  
# run net and take argmax for prediction  
net.forward()  
out = net.blobs['score_sem'].data[0].argmax(axis=0)  
#print "hello,python!"  
  
#plt.imshow(out,cmap='gray');  
plt.imshow(out)  
plt.axis('off')  
plt.savefig('test_img/coast_arnat59_infer_16s_100000iters.png')

预测：这是用fcn32s的网络，迭代10万次的结果，并不理想

fcn+caffe+siftflow实验记录