caffe如何自定义网络以及自定义层（python）（四）

刚刚看了一下官方的demo还有两三个，一起学习一下python的使用吧。经过前面的学习，后面就比较简单了。

官网这个代码看似简单了点，但是里面其实已经将自己写好的东西采用python调用了。

进入到pycaffe目录下查看一下代码结构布局：

终端运行

tree  > tree.txt

如果没有tree，先安装一下。

.
├── caffenet.py
├── layers
│   ├── pascal_multilabel_datalayers.py
│   ├── pascal_multilabel_datalayers.pyc
│   ├── pascal_multilabel_with_datalayer
│   │   ├── solver.prototxt
│   │   ├── trainnet.prototxt
│   │   └── valnet.prototxt
│   └── pyloss.py
├── linreg.prototxt
├── multilabel.py
├── multilabel.py~
├── tools.py
├── tools.pyc
└── tree.txt

2 directories, 13 files

再看看怎么运行这个demo，先下载好VOC2012的数据集。

multilabel.py是自己的脚本

import sys
import os

import numpy as np
import os.path as osp
import matplotlib.pyplot as plt
import pylab

from copy import copy
caffe_root = '/home/x/git/caffe/'
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe

caffe.set_device(0)
caffe.set_mode_gpu()
plt.rcParams['figure.figsize'] = (6, 6)

import caffe # If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.

from caffe import layers as L, params as P # Shortcuts to define the net prototxt.

sys.path.append("/home/x/git/caffe/examples/pycaffe/layers") # the datalayers we will use are in this directory.
sys.path.append("/home/x/git/caffe/examples/pycaffe") # the tools file is in this folder

import tools #this contains some tools that we need
# set data root directory, e.g:
pascal_root = osp.join(caffe_root, 'data/VOC2012')

# these are the PASCAL classes, we'll need them later.
classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])

# initialize caffe for gpu mode
caffe.set_mode_gpu()
caffe.set_device(0)
###############################Define network prototxts
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, group=group)
return conv, L.ReLU(conv, in_place=True)

# another helper function
def fc_relu(bottom, nout):
fc = L.InnerProduct(bottom, num_output=nout)
return fc, L.ReLU(fc, in_place=True)

# yet another helper function
def max_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)

# main netspec wrapper
def caffenet_multilabel(data_layer_params, datalayer):
# setup the python data layer
n = caffe.NetSpec()
n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer,
ntop = 2, param_str=str(data_layer_params))

# the net itself
n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)
n.pool1 = max_pool(n.relu1, 3, stride=2)
n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)
n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)
n.pool2 = max_pool(n.relu2, 3, stride=2)
n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)
n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)
n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)
n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)
n.pool5 = max_pool(n.relu5, 3, stride=2)
n.fc6, n.relu6 = fc_relu(n.pool5, 4096)
n.drop6 = L.Dropout(n.relu6, in_place=True)
n.fc7, n.relu7 = fc_relu(n.drop6, 4096)
n.drop7 = L.Dropout(n.relu7, in_place=True)
n.score = L.InnerProduct(n.drop7, num_output=20)
n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)

return str(n.to_proto())
workdir = '/home/x/git/caffe/examples/pycaffe/layers/pascal_multilabel_with_datalayer'
if not os.path.isdir(workdir):
os.makedirs(workdir)

solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, "trainnet.prototxt"), testnet_prototxt_path = osp.join(workdir, "valnet.prototxt"))
solverprototxt.sp['display'] = "1"
solverprototxt.sp['base_lr'] = "0.0001"
solverprototxt.write(osp.join(workdir, 'solver.prototxt'))

# write train net.
with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:
# provide parameters to the data layer as a python dictionary. Easy as pie!
data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)
f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))

# write validation net.
with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:
data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)
f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))

solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))
solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')
solver.test_nets[0].share_with(solver.net)
solver.step(1)

transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...
image_index = 0 # First image in the batch.
plt.figure()
plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))
gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)
plt.title('GT: {}'.format(classes[np.where(gtlist)]))
plt.axis('off');
pylab.show()
#train net
def hamming_distance(gt, est):
return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))

def check_accuracy(net, num_batches, batch_size = 128):
acc = 0.0
for t in range(num_batches):
net.forward()
gts = net.blobs['label'].data
ests = net.blobs['score'].data > 0
for gt, est in zip(gts, ests): #for each ground truth and estimated label vector
acc += hamming_distance(gt, est)
return acc / (num_batches * batch_size)

for itt in range(6):
solver.step(100)
print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))

def check_baseline_accuracy(net, num_batches, batch_size = 128):
acc = 0.0
for t in range(num_batches):
net.forward()
gts = net.blobs['label'].data
ests = np.zeros((batch_size, len(gts)))
for gt, est in zip(gts, ests): #for each ground truth and estimated label vector
acc += hamming_distance(gt, est)
return acc / (num_batches * batch_size)

print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))

test_net = solver.test_nets[0]
for image_index in range(5):
plt.figure()
plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))
gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)
estlist = test_net.blobs['score'].data[image_index, ...] > 0
plt.title('GT: {} \n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))
plt.axis('off')
pylab.show()

中间如果出现

File “examples/fcn/voc-fcn8s/solve.py”, line 17, in
solver = caffe.SGDSolver(‘examples/fcn/voc-fcn8s/solver.prototxt’)
SystemError: NULL result without error in PyObject_Call错误原因在于创建train.protxt中的python层时,找不到layers module, 也就是没有找到layers.py那个文件.
我的append路径没有有添加进去，后来直接给绝对路径就没错了。