利用FCN-8s网络训练自己数据集（NYUD为例）

作者: 李亚鑫 | 来源:发表于2017-08-31 16:33 被阅读890次

FCN 官方Github 地址：shelhamer/fcn.berkeleyvision.org
我修改后的Gitbub 地址： yxliwhu/NYUD-FCN8s

Papers:
Fully Convolutional Models for Semantic Segmentation
Evan Shelhamer, Jonathan Long, Trevor Darrell
PAMI 2016
arXiv:1605.06211
Fully Convolutional Models for Semantic Segmentation
Jonathan Long, Evan Shelhamer, Trevor Darrell
CVPR 2015
arXiv:1411.4038

官方的Code提供了PASCAL VOC models，SIFT Flow models，PASCAL-Context models的完整（32s，16s，8s）的代码，但对于NYUD只提供了32s的代码，这里我们就以NYUD作为例子说明一下FCN-8s训练的完整过程（网上有很多教程，但不是不完整，就是存在错误）。

源代码下载和数据集预处理

下载官方源代码：

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

下载VGG16的预训练模型并放在FCN源码文件夹中的ilsvrc-nets文件夹下：

cd /fcn.berkeleyvision.org/ilsvrc-nets
wget http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel

获取与其相对应的deploy文件：

wget https://gist.githubusercontent.com/ksimonyan/211839e770f7b538e2d8/raw/0067c9b32f60362c74f4c445a080beed06b07eb3/VGG_ILSVRC_16_layers_deploy.prototxt

下载数据集：

cd data/nyud/
wget http://people.eecs.berkeley.edu/~sgupta/cvpr13/data.tgz
tar -xvf data.tgz

这个时候，nyud的文件夹应该是这个样子：

1.png

其中data文件夹中有三个子文件夹：benchmarkData, colorImage, pointCloud. 其中benchmarkData/groundTruth中储存这所有我们需要的分割的真值，colorImage文件夹储存着原始的RGB文件．由于源代码设置的groundTruth路径和现有的路径不一样，所以我们要把groundTruth文件copy到指定路径：

mkdir segmentation
cp data/benchmarkData/groundTruth/*.mat segmentation/

同时，我们要合并train.txt和val.txt: 在nyud文件夹中新建一个空白的.txt文件并命名为trainval.txt，然后将train.txt和val.txt中的内容Copy过去．这个时候nyud文件夹应为是这个样子：

image.png

数据准备完毕，现在开始训练FCN-32s网络．

FCN-32s网络训练：

把要用到的.py文件Copy到nyud-fcn32s-color文件夹：

cd fcn.berkeleyvision.org
cp *.py nyud-fcn32s-color/
cd nyud-fcn32s-color
rm pascalcontext_layers.py
rm voc_helper.py
rm voc_layer.py
rm siftflow_layers.py

修改solver.prototxt文件,下面是我个人的文件内容，关于相关参数的含义请参考：Caffe学习系列(7)：solver及其配置 - denny402 - 博客园**

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-10
# high momentum
momentum: 0.99
# no gradient accumulation
iter_size: 1
max_iter: 300000
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "snapshot/train"
test_initialization: false

solve.py文件修改：
在这里郑重声明一下：如果训练fcn32s的网络模型，一定要修改solve.py，利用transplant的方式获取vgg16的网络权重．具体操作为：

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

vgg_weights = '../ilsvrc-nets/VGG_ILSVRC_16_layers.caffemodel'
vgg_proto = '../ilsvrc-nets/VGG_ILSVRC_16_layers_deploy.prototxt'

# init
#caffe.set_device(int(sys.argv[1]))
#caffe.set_device(0)
#caffe.set_mode_gpu()
caffe.set_mode_cpu()

# solver = caffe.SGDSolver('solver.prototxt')
# solver.net.copy_from(weights)
solver = caffe.SGDSolver('solver.prototxt')
vgg_net = caffe.Net(vgg_proto, vgg_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/nyud/test.txt', dtype=str)

for _ in range(50):
    solver.step(2000)
    score.seg_tests(solver, False, test, layer='score')

可以看到我注释了：

#weights = '../ilsvrc-nets/vgg16-fcn.caffemodel
...
...
# solver = caffe.SGDSolver('solver.prototxt')
# solver.net.copy_from(weights)

添加了：

vgg_weights = '../ilsvrc-nets/VGG_ILSVRC_16_layers.caffemodel'
vgg_proto = '../ilsvrc-nets/VGG_ILSVRC_16_layers_deploy.prototxt'
...
...
solver = caffe.SGDSolver('solver.prototxt')
vgg_net = caffe.Net(vgg_proto, vgg_weights, caffe.TRAIN)
surgery.transplant(solver.net, vgg_net)
del vgg_net

关于transplant函数的解释可以再surgery.py文件中找到：

2.png
同时由于路径原因，需要修改nyud_layers.py中laod_label function 的内容：

#label = scipy.io.loadmat('{}/segmentation/img_{}.mat'.format(self.nyud_dir, idx))['segmentation'].astype(np.uint8)
label = scipy.io.loadmat('{}/segmentation/img_{}.mat'.format(self.nyud_dir, idx))['groundTruth'][0,0][0,0]['SegmentationClass'].astype(np.uint16)
for (x,y), value in np.ndenumerate(label):
label[x,y] = self.class_map[0][value-1]
label = label.astype(np.uint8)

以上配置全部结束，开始进行模型训练：

cd nyud-fcn32s-color
mkdir snapshot
python solve.py

大概迭代150000次以后，就可以达到论文描述的精度．
测试单张图片
在fcn源码文件夹，找到infer.py，重命名为test.py 并修改：

im = Image.open('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn32s-color/test.png')
...
...
net = caffe.Net('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn32s-color/deploy.prototxt', '/home/li/Downloads/nyud-fcn32s-color-heavy.caffemodel', caffe.TEST)

其中：nyud-fcn32s-color-heavy.caffemodel　为训练得到的model文件, test.png 为测试文件．

我附上个人完整的test.py的代码：

import numpy as np  
from PIL import Image  
import matplotlib.pyplot as plt  
import sys     
import caffe  
import cv
import scipy.io
# import pydensecrf.densecrf as dcrf 
# from pydensecrf.utils import compute_unary, create_pairwise_bilateral,create_pairwise_gaussian, softmax_to_unary 
import pdb
# matplotlib inline  
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe  
im = Image.open('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn32s-color/test.png')  
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('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn32s-color/deploy.prototxt', '/home/li/Downloads/nyud-fcn32s-color-heavy.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()  
# pdb.set_trace()
out = net.blobs['score'].data[0].argmax(axis=0) 
scipy.io.savemat('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn32s-color/out.mat',{'X':out}) 
#print "hello,python!"  
  
#plt.imshow(out,cmap='gray');  
plt.imshow(out)  
plt.axis('off')  
plt.savefig('testout_32s.png')

如果没有deploy文件，可以参考如下方法：
首先，根据你利用的模型，例如模型是nyud-fcn32s-color的，那么你就去nyud-fcn32s-color的文件夹，里面有trainval.prototxt文件，将文件打开，全选，复制，新建一个名为deploy.prototxt文件，粘贴进去，然后ctrl+F 寻找所有名为loss的layer 将这个layer统统删除
然后在文件顶部加上

layer {
name: "input"
type: "Input"
top: "data"
input_param {
# These dimensions are purely for sake of example;
# see infer.py for how to reshape the net to the given input size.
shape { dim: 1 dim: 3 dim: 425 dim: 540 }
}
}

其中shape{dim:1 dim:3 dim:425 dim:540}, 这里425 和540代表测试文件的维度．

4.png

FCN-16s网络训练：

对于FCN-16s网络的训练，由于没有对应的源代码，所以一切的东西都要我们自己来做，还好官方提供了其他dataset的源代码，我们可以依照这些内容生成相应的训练文件．我们可以先比较一下voc-fcn16s和voc-fcn32s 相对应的net.py(用来生成.prototxt文件)代码：

3.png

红色框是两个文件的不同的地方，对比两者的network结构可以清楚的看到区别．
想要获取network，运行/caffe/python 文件夹下的draw_net.py文件，这里就不展开了．
所以我们可以根据上图对比的结构从nyud-fcn32s-color/net.py 修改得到新的net.py文件：

cd fcn.berkeleyvision.org
mkdir nyud-fcn16s-color
cp nyud-fcn32s-color/net.py nyud-fcn16s-color/net.py

修改后的net.py文件为：

import caffe
from caffe import layers as L, params as P
from caffe.coord_map import crop

def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
        num_output=nout, pad=pad,
        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
    return conv, L.ReLU(conv, in_place=True)

def max_pool(bottom, ks=2, stride=2):
    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)

def fcn(split, tops):
    n = caffe.NetSpec()
    n.data, n.label = L.Python(module='nyud_layers',
            layer='NYUDSegDataLayer', ntop=2,
            param_str=str(dict(nyud_dir='../data/nyud', split=split,
                tops=tops, seed=1337)))

    # the base net
    n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100)
    n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64)
    n.pool1 = max_pool(n.relu1_2)

    n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128)
    n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128)
    n.pool2 = max_pool(n.relu2_2)

    n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256)
    n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256)
    n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256)
    n.pool3 = max_pool(n.relu3_3)

    n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512)
    n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512)
    n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512)
    n.pool4 = max_pool(n.relu4_3)

    n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512)
    n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512)
    n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512)
    n.pool5 = max_pool(n.relu5_3)

    # fully conv
    n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0)
    n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
    n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0)
    n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)

    n.score_fr = L.Convolution(n.drop7, num_output=40, kernel_size=1, pad=0,
        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
    n.upscore2 = L.Deconvolution(n.score_fr,
        convolution_param=dict(num_output=40, kernel_size=4, stride=2,
            bias_term=False),
        param=[dict(lr_mult=0)])

    n.score_pool4 = L.Convolution(n.pool4, num_output=40, kernel_size=1, pad=0,
        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
    n.score_pool4c = crop(n.score_pool4, n.upscore2)
    n.fuse_pool4 = L.Eltwise(n.upscore2, n.score_pool4c,
            operation=P.Eltwise.SUM)
    n.upscore16 = L.Deconvolution(n.fuse_pool4,
        convolution_param=dict(num_output=40, kernel_size=32, stride=16,
            bias_term=False),
        param=[dict(lr_mult=0)])

    n.score = crop(n.upscore16, n.data)
    n.loss = L.SoftmaxWithLoss(n.score, n.label,
            loss_param=dict(normalize=False, ignore_label=255))

    return n.to_proto()

def make_net():
    tops = ['color', 'label']
    with open('trainval.prototxt', 'w') as f:
        f.write(str(fcn('trainval', tops)))

    with open('test.prototxt', 'w') as f:
        f.write(str(fcn('test', tops)))

if __name__ == '__main__':
    make_net()

运行net.py文件来生成.prototxt 文件：

cd nyud-fcn16s-color/
python net.py

Copy并修改solve.py文件：

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 = '../nyud-fcn32s-color/nyud-fcn32s-color-heavy.caffemodel'
#vgg_weights = '../ilsvrc-nets/VGG_ILSVRC_16_layers.caffemodel'
#vgg_proto = '../ilsvrc-nets/VGG_ILSVRC_16_layers_deploy.prototxt'

# init
#caffe.set_device(int(sys.argv[1]))
#caffe.set_device(0)
#caffe.set_mode_gpu()
caffe.set_mode_cpu()

solver = caffe.SGDSolver('solver.prototxt')
solver.net.copy_from(weights)
#solver = caffe.SGDSolver('solver.prototxt')
#vgg_net = caffe.Net(vgg_proto, vgg_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/nyud/test.txt', dtype=str)

for _ in range(50):
    solver.step(5000)
    score.seg_tests(solver, False, test, layer='score')

Copy并修改solver.prototxt文件（主要是修改base_lr的值，也就是Learning rate）:

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: 300000
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "snapshot/train"
test_initialization: false

然后把要用到的.py文件拷贝到nyud-fcn16s-color文件夹：

cd fcn.berkeleyvision.org
cp *.py nyud-fcn16s-color/
cd nyud-fcn32s-color
rm pascalcontext_layers.py
rm voc_helper.py
rm voc_layer.py
rm siftflow_layers.py

别忘修改nyud_layer.py文件．
运行solve.py开始训练：

cd nyud-fcn32s-color
mkdir snapshot
python solve.py

测试的过程和FCN-32s相同，对应的test.py文件为：

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import sys
import caffe
import cv
import scipy.io
# import pydensecrf.densecrf as dcrf
# from pydensecrf.utils import compute_unary, create_pairwise_bilateral,create_pairwise_gaussian, softmax_to_unary
import pdb
# matplotlib inline
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
im = Image.open('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn16s-color/test2.png')
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('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn16s-color/deploy.prototxt', '/home/li/Downloads/16_170000.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()
# pdb.set_trace()
out = net.blobs['score'].data[0].argmax(axis=0)
scipy.io.savemat('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn16s-color/out.mat',{'X':out})
#print "hello,python!"
#plt.imshow(out,cmap='gray');
plt.imshow(out)
plt.axis('off')
plt.savefig('testout2_170000.png')

5.png

FCN-8s网络训练：

代码修改和FCN-16s相似，对应的net.py文件为：

import caffe
from caffe import layers as L, params as P
from caffe.coord_map import crop

def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
        num_output=nout, pad=pad,
        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
    return conv, L.ReLU(conv, in_place=True)

def max_pool(bottom, ks=2, stride=2):
    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)

def fcn(split, tops):
    n = caffe.NetSpec()
    n.data, n.label = L.Python(module='nyud_layers',
            layer='NYUDSegDataLayer', ntop=2,
            param_str=str(dict(nyud_dir='../data/nyud', split=split,
                tops=tops, seed=1337)))

    # the base net
    n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100)
    n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64)
    n.pool1 = max_pool(n.relu1_2)

    n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128)
    n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128)
    n.pool2 = max_pool(n.relu2_2)

    n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256)
    n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256)
    n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256)
    n.pool3 = max_pool(n.relu3_3)

    n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512)
    n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512)
    n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512)
    n.pool4 = max_pool(n.relu4_3)

    n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512)
    n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512)
    n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512)
    n.pool5 = max_pool(n.relu5_3)

    # fully conv
    n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0)
    n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
    n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0)
    n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)

    n.score_fr = L.Convolution(n.drop7, num_output=40, kernel_size=1, pad=0,
        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
    n.upscore2 = L.Deconvolution(n.score_fr,
        convolution_param=dict(num_output=40, kernel_size=4, stride=2,
            bias_term=False),
        param=[dict(lr_mult=0)])

    n.score_pool4 = L.Convolution(n.pool4, num_output=40, kernel_size=1, pad=0,
        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
    n.score_pool4c = crop(n.score_pool4, n.upscore2)
    n.fuse_pool4 = L.Eltwise(n.upscore2, n.score_pool4c,
            operation=P.Eltwise.SUM)
    n.upscore_pool4 = L.Deconvolution(n.fuse_pool4,
        convolution_param=dict(num_output=40, kernel_size=4, stride=2,
            bias_term=False),
        param=[dict(lr_mult=0)])

    n.score_pool3 = L.Convolution(n.pool3, num_output=40, kernel_size=1, pad=0,
        param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
    n.score_pool3c = crop(n.score_pool3, n.upscore_pool4)
    n.fuse_pool3 = L.Eltwise(n.upscore_pool4, n.score_pool3c,
            operation=P.Eltwise.SUM)
    n.upscore8 = L.Deconvolution(n.fuse_pool3,
        convolution_param=dict(num_output=40, kernel_size=16, stride=8,
            bias_term=False),
        param=[dict(lr_mult=0)])

    n.score = crop(n.upscore8, n.data)
    n.loss = L.SoftmaxWithLoss(n.score, n.label,
            loss_param=dict(normalize=False, ignore_label=255))

    return n.to_proto()

def make_net():
    tops = ['color', 'label']
    with open('trainval.prototxt', 'w') as f:
        f.write(str(fcn('trainval', tops)))

    with open('test.prototxt', 'w') as f:
        f.write(str(fcn('test', tops)))

if __name__ == '__main__':
    make_net()

solve.py文件为：

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 = '../nyud-fcn16s-color/snapshot/train_iter_170000.caffemodel'
#vgg_weights = '../ilsvrc-nets/VGG_ILSVRC_16_layers.caffemodel'
#vgg_proto = '../ilsvrc-nets/VGG_ILSVRC_16_layers_deploy.prototxt'

# init
#caffe.set_device(int(sys.argv[1]))
caffe.set_device(0)
caffe.set_mode_gpu()
#caffe.set_mode_cpu()

solver = caffe.SGDSolver('solver.prototxt')
solver.net.copy_from(weights)
#solver = caffe.SGDSolver('solver.prototxt')
#vgg_net = caffe.Net(vgg_proto, vgg_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/nyud/test.txt', dtype=str)

for _ in range(50):
    solver.step(5000)
    score.seg_tests(solver, False, test, layer='score')

test.py文件为：

import numpy as np  
from PIL import Image  
import matplotlib.pyplot as plt  
import sys     
import caffe  
import cv
import scipy.io
# import pydensecrf.densecrf as dcrf 
# from pydensecrf.utils import compute_unary, create_pairwise_bilateral,create_pairwise_gaussian, softmax_to_unary 
import pdb
# matplotlib inline  
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe  
im = Image.open('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn8s-color/door/RGB_69388896_000000.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('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn8s-color/deploy.prototxt', '/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn8s-color/snapshot/train_iter_130000.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()  
# pdb.set_trace()
out = net.blobs['score'].data[0].argmax(axis=0) 
scipy.io.savemat('/home/li/Documents/fcn.berkeleyvision.org/nyud-fcn8s-color/out.mat',{'X':out}) 
#print "hello,python!"  
  
#plt.imshow(out,cmap='gray');  
plt.imshow(out)  
plt.axis('off')  
plt.savefig('./door/out/RGB_69388896_000000.png')

Reference:
FCN网络训练 SIFTFLOW数据集**
shelhamer/fcn.berkeleyvision.org**
Caffe学习系列(7)：solver及其配置 - denny402 - 博客园**
https://gist.githubusercontent.com/ksimonyan/211839e770f7b538e2d8/raw/0067c9b32f60362c74f4c445a080beed06b07eb3/VGG_ILSVRC_16_layers_deploy.prototxt**

利用FCN-8s网络训练自己数据集（NYUD为例）

源代码下载和数据集预处理

FCN-32s网络训练：

FCN-16s网络训练：

FCN-8s网络训练：

相关文章

网友评论

延伸阅读

深度阅读

栏目导航

热点阅读

Semantic Segmentation

深度学习·神经网络·计算机视觉

机器学习与数据挖掘

机器学习