caffe如何自定义网络以及自定义层（python）（六）
loss层用python改写。
官网里面是有一个做欧式距离的例子。我把我做的流程贴一下。
首先是配置文件euclid.prototxt
name: 'EuclideanExample'
layer {
type: 'DummyData'
name: 'x'
top: 'x'
dummy_data_param {
shape: { dim: 10 dim: 3 dim: 2 }
data_filler: { type: 'gaussian' }
}
}
layer {
type: 'DummyData'
name: 'y'
top: 'y'
dummy_data_param {
shape: { dim: 10 dim: 3 dim: 2 }
data_filler: { type: 'gaussian' }
}
}
# include InnerProduct layers for parameters
# so the net will need backward
layer {
type: 'InnerProduct'
name: 'ipx'
top: 'ipx'
bottom: 'x'
inner_product_param {
num_output: 10
weight_filler { type: 'xavier' }
}
}
layer {
type: 'InnerProduct'
name: 'ipy'
top: 'ipy'
bottom: 'y'
inner_product_param {
num_output: 10
weight_filler { type: 'xavier' }
}
}
layer {
type: 'Python'
name: 'loss'
top: 'loss'
bottom: 'ipx'
bottom: 'ipy'
python_param {
# the module name -- usually the filename -- that needs to be in $PYTHONPATH
module: 'pyloss'
# the layer name -- the class name in the module
layer: 'EuclideanLossLayer'
}
# set loss weight so Caffe knows this is a loss layer
loss_weight: 1
}DummyData这个是用来开发和测试的层。
然后是pyloss.py
# -*- coding: utf-8 -*-
import sys
sys.path.insert(0, '/home/x/git/caffe/python/')
import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
def setup(self, bottom, top):
print('setup()')
if len(bottom) != 2:
raise Exception("Need two inputs to compute distance.")
def reshape(self, bottom, top):
print('reshape()')
if bottom[0].count != bottom[1].count:
raise Exception("Inputs must have the same dimension.")
# difference is shape of inputs
self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
# loss output is scalar
top[0].reshape(1)
def forward(self, bottom, top):
print('forward()')
self.diff[...] = bottom[0].data - bottom[1].data
top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
def backward(self, top, propagate_down, bottom):
print('backward()')
for i in range(2):
if not propagate_down[i]:
continue
if i == 0:
sign = 1
else:
sign = -1
bottom[i].diff[...] = sign * self.diff / bottom[i].numsurgery.py
from __future__ import division
import caffe
import numpy as np
def transplant(new_net, net, suffix=''):
"""
Transfer weights by copying matching parameters, coercing parameters of
incompatible shape, and dropping unmatched parameters.
The coercion is useful to convert fully connected layers to their
equivalent convolutional layers, since the weights are the same and only
the shapes are different. In particular, equivalent fully connected and
convolution layers have shapes O x I and O x I x H x W respectively for O
outputs channels, I input channels, H kernel height, and W kernel width.
Both `net` to `new_net` arguments must be instantiated `caffe.Net`s.
"""
for p in net.params:
p_new = p + suffix
if p_new not in new_net.params:
print 'dropping', p
continue
for i in range(len(net.params[p])):
if i > (len(new_net.params[p_new]) - 1):
print 'dropping', p, i
break
if net.params[p][i].data.shape != new_net.params[p_new][i].data.shape:
print 'coercing', p, i, 'from', net.params[p][i].data.shape, 'to', new_net.params[p_new][i].data.shape
else:
print 'copying', p, ' -> ', p_new, i
new_net.params[p_new][i].data.flat = net.params[p][i].data.flat
def upsample_filt(size):
"""
Make a 2D bilinear kernel suitable for upsampling of the given (h, w) size.
"""
factor = (size + 1) // 2
if size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:size, :size]
return (1 - abs(og[0] - center) / factor) * \
(1 - abs(og[1] - center) / factor)
def interp(net, layers):
"""
Set weights of each layer in layers to bilinear kernels for interpolation.
"""
for l in layers:
m, k, h, w = net.params[l][0].data.shape
if m != k and k != 1:
print 'input + output channels need to be the same or |output| == 1'
raise
if h != w:
print 'filters need to be square'
raise
filt = upsample_filt(h)
net.params[l][0].data[range(m), range(k), :, :] = filt
def expand_score(new_net, new_layer, net, layer):
"""
Transplant an old score layer's parameters, with k < k' classes, into a new
score layer with k classes s.t. the first k' are the old classes.
"""
old_cl = net.params[layer][0].num
new_net.params[new_layer][0].data[:old_cl][...] = net.params[layer][0].data
new_net.params[new_layer][1].data[0,0,0,:old_cl][...] = net.params[layer][1].datafrom __future__ import division
import caffe
import numpy as np
import os
import sys
from datetime import datetime
from PIL import Image
def fast_hist(a, b, n):
k = (a >= 0) & (a < n)
return np.bincount(n * a[k].astype(int) + b[k], minlength=n**2).reshape(n, n)
def compute_hist(net, save_dir, dataset, layer='score', gt='label'):
n_cl = net.blobs[layer].channels
if save_dir:
os.mkdir(save_dir)
hist = np.zeros((n_cl, n_cl))
loss = 0
for idx in dataset:
net.forward()
hist += fast_hist(net.blobs[gt].data[0, 0].flatten(),
net.blobs[layer].data[0].argmax(0).flatten(),
n_cl)
if save_dir:
im = Image.fromarray(net.blobs[layer].data[0].argmax(0).astype(np.uint8), mode='P')
im.save(os.path.join(save_dir, idx + '.png'))
# compute the loss as well
loss += net.blobs['loss'].data.flat[0]
return hist, loss / len(dataset)
def seg_tests(solver, save_format, dataset, layer='score', gt='label'):
print '>>>', datetime.now(), 'Begin seg tests'
solver.test_nets[0].share_with(solver.net)
do_seg_tests(solver.test_nets[0], solver.iter, save_format, dataset, layer, gt)
def do_seg_tests(net, iter, save_format, dataset, layer='score', gt='label'):
n_cl = net.blobs[layer].channels
if save_format:
save_format = save_format.format(iter)
hist, loss = compute_hist(net, save_format, dataset, layer, gt)
# mean loss
print '>>>', datetime.now(), 'Iteration', iter, 'loss', loss
# overall accuracy
acc = np.diag(hist).sum() / hist.sum()
print '>>>', datetime.now(), 'Iteration', iter, 'overall accuracy', acc
# per-class accuracy
acc = np.diag(hist) / hist.sum(1)
print '>>>', datetime.now(), 'Iteration', iter, 'mean accuracy', np.nanmean(acc)
# per-class IU
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print '>>>', datetime.now(), 'Iteration', iter, 'mean IU', np.nanmean(iu)
freq = hist.sum(1) / hist.sum()
print '>>>', datetime.now(), 'Iteration', iter, 'fwavacc', \
(freq[freq > 0] * iu[freq > 0]).sum()
return histimport caffe
import numpy as np
from PIL import Image
import random
class VOCSegDataLayer(caffe.Layer):
"""
Load (input image, label image) pairs from PASCAL VOC
one-at-a-time while reshaping the net to preserve dimensions.
Use this to feed data to a fully convolutional network.
"""
def setup(self, bottom, top):
"""
Setup data layer according to parameters:
- voc_dir: path to PASCAL VOC year dir
- split: train / val / test
- mean: tuple of mean values to subtract
- randomize: load in random order (default: True)
- seed: seed for randomization (default: None / current time)
for PASCAL VOC semantic segmentation.
example
params = dict(voc_dir="/path/to/PASCAL/VOC2011",
mean=(104.00698793, 116.66876762, 122.67891434),
split="val")
"""
# config
params = eval(self.param_str)
self.voc_dir = params['voc_dir']
self.split = params['split']
self.mean = np.array(params['mean'])
self.random = params.get('randomize', True)
self.seed = params.get('seed', None)
# two tops: data and label
if len(top) != 2:
raise Exception("Need to define two tops: data and label.")
# data layers have no bottoms
if len(bottom) != 0:
raise Exception("Do not define a bottom.")
# load indices for images and labels
split_f = '{}/ImageSets/Segmentation/{}.txt'.format(self.voc_dir,
self.split)
self.indices = open(split_f, 'r').read().splitlines()
self.idx = 0
# make eval deterministic
if 'train' not in self.split:
self.random = False
# randomization: seed and pick
if self.random:
random.seed(self.seed)
self.idx = random.randint(0, len(self.indices)-1)
def reshape(self, bottom, top):
# load image + label image pair
self.data = self.load_image(self.indices[self.idx])
self.label = self.load_label(self.indices[self.idx])
# reshape tops to fit (leading 1 is for batch dimension)
top[0].reshape(1, *self.data.shape)
top[1].reshape(1, *self.label.shape)
def forward(self, bottom, top):
# assign output
top[0].data[...] = self.data
top[1].data[...] = self.label
# pick next input
if self.random:
self.idx = random.randint(0, len(self.indices)-1)
else:
self.idx += 1
if self.idx == len(self.indices):
self.idx = 0
def backward(self, top, propagate_down, bottom):
pass
def load_image(self, idx):
"""
Load input image and preprocess for Caffe:
- cast to float
- switch channels RGB -> BGR
- subtract mean
- transpose to channel x height x width order
"""
im = Image.open('{}/JPEGImages/{}.jpg'.format(self.voc_dir, idx))
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= self.mean
in_ = in_.transpose((2,0,1))
return in_
def load_label(self, idx):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
"""
im = Image.open('{}/SegmentationClass/{}.png'.format(self.voc_dir, idx))
label = np.array(im, dtype=np.uint8)
label = label[np.newaxis, ...]
return label
class SBDDSegDataLayer(caffe.Layer):
"""
Load (input image, label image) pairs from the SBDD extended labeling
of PASCAL VOC for semantic segmentation
one-at-a-time while reshaping the net to preserve dimensions.
Use this to feed data to a fully convolutional network.
"""
def setup(self, bottom, top):
"""
Setup data layer according to parameters:
- sbdd_dir: path to SBDD `dataset` dir
- split: train / seg11valid
- mean: tuple of mean values to subtract
- randomize: load in random order (default: True)
- seed: seed for randomization (default: None / current time)
for SBDD semantic segmentation.
N.B.segv11alid is the set of segval11 that does not intersect with SBDD.
Find it here: https://gist.github.com/shelhamer/edb330760338892d511e.
example
params = dict(sbdd_dir="/path/to/SBDD/dataset",
mean=(104.00698793, 116.66876762, 122.67891434),
split="valid")
"""
# config
params = eval(self.param_str)
self.sbdd_dir = params['sbdd_dir']
self.split = params['split']
self.mean = np.array(params['mean'])
self.random = params.get('randomize', True)
self.seed = params.get('seed', None)
# two tops: data and label
if len(top) != 2:
raise Exception("Need to define two tops: data and label.")
# data layers have no bottoms
if len(bottom) != 0:
raise Exception("Do not define a bottom.")
# load indices for images and labels
split_f = '{}/{}.txt'.format(self.sbdd_dir,
self.split)
self.indices = open(split_f, 'r').read().splitlines()
self.idx = 0
# make eval deterministic
if 'train' not in self.split:
self.random = False
# randomization: seed and pick
if self.random:
random.seed(self.seed)
self.idx = random.randint(0, len(self.indices)-1)
def reshape(self, bottom, top):
# load image + label image pair
self.data = self.load_image(self.indices[self.idx])
self.label = self.load_label(self.indices[self.idx])
# reshape tops to fit (leading 1 is for batch dimension)
top[0].reshape(1, *self.data.shape)
top[1].reshape(1, *self.label.shape)
def forward(self, bottom, top):
# assign output
top[0].data[...] = self.data
top[1].data[...] = self.label
# pick next input
if self.random:
self.idx = random.randint(0, len(self.indices)-1)
else:
self.idx += 1
if self.idx == len(self.indices):
self.idx = 0
def backward(self, top, propagate_down, bottom):
pass
def load_image(self, idx):
"""
Load input image and preprocess for Caffe:
- cast to float
- switch channels RGB -> BGR
- subtract mean
- transpose to channel x height x width order
"""
im = Image.open('{}/img/{}.jpg'.format(self.sbdd_dir, idx))
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= self.mean
in_ = in_.transpose((2,0,1))
return in_
def load_label(self, idx):
"""
Load label image as 1 x height x width integer array of label indices.
The leading singleton dimension is required by the loss.
"""
import scipy.io
mat = scipy.io.loadmat('{}/cls/{}.mat'.format(self.sbdd_dir, idx))
label = mat['GTcls'][0]['Segmentation'][0].astype(np.uint8)
label = label[np.newaxis, ...]
return labelsolver.protxt
train_net: "/home/x/git/caffe/examples/test_layer_lb/PythonLayerLoss/euclid.prototxt"
display: 1
average_loss: 1
lr_policy: "fixed"
# lr for unnormalized softmax
base_lr: 1e-1
# high momentum
momentum: 0.99
# no gradient accumulation
iter_size: 1
max_iter: 100
weight_decay: 0.0005
snapshot: 50
snapshot_prefix: "train"
test_initialization: false
#!/usr/bin/env python
#coding=utf-8
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os
caffe_root = '/home/x/git/caffe/'
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
import pyloss
sys.path.append("/home/x/git/caffe/examples/test_layer_lb/PythonLayerLoss")
caffe.set_device(0)
caffe.set_mode_gpu()
import surgery, score, layers
# import setproctitle
# setproctitle.setproctitle(os.path.basename(os.getcwd()))
weights = '/home/x/git/caffe/examples/test_layer_lb/PythonLayerLoss/VGG_ILSVRC_16_layers.caffemodel'
# init
# caffe.set_device(int(sys.argv[1]))
caffe.set_device(0)
caffe.set_mode_gpu()
solver = caffe.SGDSolver('/home/x/git/caffe/examples/test_layer_lb/PythonLayerLoss/solver.prototxt')
solver.net.copy_from(weights)
# surgeries
interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
surgery.interp(solver.net, interp_layers)
for epoch in range(10):
solver.step(4000)
solver.net.save('epoch_'+str(epoch)+'.caffemodel')
'''
# scoring
val = np.loadtxt('../data/segvalid11.txt', dtype=str)
for _ in range(25):
solver.step(4000)
score.seg_tests(solver, False, val, layer='score')
'''结果图就不截了，这个相当简单了，只是实现自己定义的loss层。
工具小插曲，python对caffe的简单封装utils.py
#-.-encoding=utf-8-.-``
''' My caffe helper'''
import caffe
from caffe import layers as L
import os
import warnings
import cv2
import numpy as np
import pandas as pd
import PIL.Image as Image
import sys
import lmdb
import random
from easydict import EasyDict as edict
class CaffeSolver:
"""
Caffesolver is a class for creating a solver.prototxt file. It sets default
values and can export a solver parameter file.
Note that all parameters are stored as strings. Strings variables are
stored as strings in strings.
"""
def __init__(self, debug=False):
self.sp = {}
self.sp['test_net']="testnet.prototxt"
self.sp['train_net']="trainnet.prototxt"
# critical:
self.sp['base_lr'] = 0.001
self.sp['momentum'] = 0.9
# speed:
self.sp['test_iter'] = 100
self.sp['test_interval'] = 250
# looks:
self.sp['display'] = 25
self.sp['snapshot'] = 2500
self.sp['snapshot_prefix'] = 'snapshot' # string withing a string!
# learning rate policy
self.sp['lr_policy'] = 'fixed' # see caffe proto
# // - fixed: always return base_lr.
# // - step: return base_lr * gamma ^ (floor(iter / step))
# // - exp: return base_lr * gamma ^ iter
# // - inv: return base_lr * (1 + gamma * iter) ^ (- power)
# // - multistep: similar to step but it allows non uniform steps defined by
# // stepvalue
# // - poly: the effective learning rate follows a polynomial decay, to be
# // zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
# // - sigmoid: the effective learning rate follows a sigmod decay
# // return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
self.sp['gamma'] = 1
self.sp['weight_decay'] = 0.0005
# pretty much never change these.
self.sp['max_iter'] = 100000
self.sp['test_initialization'] = False
self.sp['average_loss'] = 25 # this has to do with the display.
self.sp['iter_size'] = 1 # this is for accumulating gradients
if (debug):
self.sp['max_iter'] = 12
self.sp['test_iter'] = 1
self.sp['test_interval'] = 4
self.sp['display'] = 1
def add_from_file_notavailablenow(self, filepath):
"""
Reads a caffe solver prototxt file and updates the Caffesolver
instance parameters.
"""
with open(filepath, 'r') as f:
for line in f:
if line[0] == '#':
continue
splitLine = line.split(':')
self.sp[splitLine[0].strip()] = splitLine[1].strip()
def write(self, filepath):
"""
Export solver parameters to INPUT "filepath". Sorted alphabetically.
"""
f = open(filepath, 'w')
for key, value in sorted(self.param2str(self.sp).items()):
if not(type(value) is str):
raise TypeError('All solver parameters must be strings')
f.write('%s: %s\n' % (key, value))
def param2str(self,sp):
for i in sp:
if isinstance(sp[i],str):
sp[i]='"'+sp[i]+'"'
elif isinstance(sp[i],bool):
if sp[i]==True:
sp[i]='true'
else:
sp[i]='false'
else:
sp[i]=str(sp[i])
return sp
class Data:
"""Helper for dealing with DIY data"""
def __init__(self):
pass
@staticmethod
def folder_opt(folder, func, *args):
"""A func operate on each image then return a list"""
all_list=[]
for img in os.listdir(folder):
if len(img) == 0:
raise ValueError("invalid name")
if len(img.replace(' ','')) != len(img):
warnings.warn("whitespace in name")
filename = os.path.join(folder, img)
ret=func(filename, *args)
if ret is None:
continue
all_list.append(ret)
return all_list
@staticmethod
def imFromFile(path,dtype=np.float32):
"""load image as array from path"""
return np.array(Image.open(path),dtype=dtype)
@classmethod
def showIm(cls,im,wait=-1,name='image'):
"""show arr image or image from directory and wait"""
if isinstance(im,str):
im=cls.imFromFile(im)
if im.max()>1:
im=im/255.0
cv2.imshow(name,im)
cv2.waitKey(wait)
@classmethod
def saveIm(cls,im,path=''):
"""save im to path"""
pass
@classmethod
def splitPath(cls,file):
base=os.path.basename(file)
dir=os.path.dirname(file)
name,ext=os.path.splitext(base)
return dir,name,ext
@classmethod
def splitDataset(cls,folder,train_ratio=.9, save_dir=None, seed=0):
"""split DIY data in a folder into train and val"""
if isinstance(folder, str):
folders=[folder]
else:
folders=folder
if save_dir is None:
save_dir=os.path.join(os.path.dirname(folder),os.path.basename(folder)+"_list")
if not os.path.exists(save_dir):
os.mkdir(save_dir)
all_list = []
train_list = []
val_list = []
if seed is not None:
random.seed(seed)
for folder in folders:
print folder
for img in os.listdir(folder):
if len(img) == 0:
raise ValueError("invalid name")
if len(img.replace(' ','')) != len(img):
warnings.warn("whitespace in name")
filename = os.path.join(folder, img)
all_list.append(filename)
random.shuffle(all_list)
data_len=len(all_list)
print 'length of name list',data_len
train_list=all_list[: int(data_len*train_ratio)]
print 'len_train_list', len(train_list)
val_list=all_list[int(data_len*train_ratio) :]
print 'len_val_list', len(val_list)
fid_train = open(os.path.join(save_dir,'train.txt'), 'w')
fid_val = open(os.path.join(save_dir,'val.txt'), 'w')
for line in train_list:
fid_train.write(line + '\n')
fid_train.close()
for line in val_list:
fid_val.write(line + '\n')
fid_val.close()
@classmethod
def im2lmdb():
"""lmdb from DIY images"""
pass
class NetHelper:
"""Helper for dealing with net"""
def __init__(self, net=None,deploy=None,model=None):
if net is not None:
self.net=net
else:
self.netFromFile(deploy,model)
def netFromFile(self,deploy_file,model_file,mode=caffe.TEST):
self.net=caffe.Net(deploy_file,model_file,mode)
@classmethod
def gpu(cls,id=0):
"""open GPU"""
caffe.set_device(id)
caffe.set_mode_gpu()
def prediction(self,c_img):
"""make prediction on single img"""
if len(c_img.shape)==3:
# color img
depth=c_img.shape[2]
pass
elif len(c_img.shape)==2:
# grey img
tmp = np.zeros(c_img[:,:,np.newaxis].shape)
tmp[:,:,0]=c_img
c_img=tmp
depth=1
else:
raise ValueError("abnormal image")
c_img = c_img.swapaxes(1,2).swapaxes(0,1)
# in Channel x Height x Width order (switch from H x W x C)
c_img = c_img.reshape((1,depth,c_img.shape[1],c_img.shape[2]))
# 1 means batch size one
prediction = self.net.forward_all(**{self.net.inputs[0]: c_img})
return prediction
def bin_pred_map(self,c_img, last_layer='prob',prediction_map=0):
"""get binary probability map prediction"""
pred=self.prediction(c_img)
prob_map=np.single(pred[last_layer][0,prediction_map,:,:])
return prob_map
def value_counts(self,layer):
print pd.value_counts(self.net.blobs[layer].data.flatten())
def hist(self,layer, filters=None, bins=4, attr="blobs"):
"""
inspect network response
Args:
filters: True will draw hist of every depth
"""
if attr=="params" or attr=="param":
response=self.net.params[layer][0].data
elif attr=="diff":
response=self.net.blobs[layer].diff
else:
response=self.net.blobs[layer].data
if filters is None:
# show response of this layer together
cnts,boundary = np.histogram(response.flatten(),bins=bins)
ret=pd.DataFrame(cnts,index=boundary[1:],columns=[layer])
print ret.T
else:
# print every filter
response=response.swapaxes(0,1)
for filter in range(np.minimum(filters, response.shape[0])):
cnts, boundary = np.histogram(response[filter,:,:,:].flatten(),bins=bins)
ret=pd.DataFrame(cnts,index=boundary[1:],columns=[layer])
print ret.T
def layerShape(self,layer):
"""inspect network params shape"""
response=self.net.blobs[layer].data
print response.shape
def showFilter(self,layer,filter=0,wait=-1,name='image'):
"""imshow filter"""
response=self.net.blobs[layer].data[0,filter,:,:]
Data.showIm(response,wait=wait,name=name)
class segmentation:
"""package for image segmentation"""
def __init__(self):
self.dices=[]
self.recall=[]
self.precision=[]
self.neg_recall=[]
self.cnt=0
def update(self,pred,label):
"""
update evaluation info using new pred and label, they must be the same size
"""
if pred.shape!=label.shape:
raise ValueError("pred and label not the same shape")
intersection=np.sum(pred & label)
pixelSum=np.sum(pred)+np.sum(label)
if pixelSum==0:
Dice=1.0
else:
Dice=2.0*intersection/pixelSum
self.dices.append(Dice)
if label.sum()==0:
self.recall.append(1.0)
else:
self.recall.append(intersection*1.0/label.sum())
if pred.sum()==0:
self.recall.append(1.0)
else:
self.recall.append(intersection*1.0/pred.sum())
self.neg_recall.append(np.sum((~pred)&(~label))*1.0/(~label).sum())
# bug: base on no all positive label
self.cnt+=1
def show(self):
"""show histogram and metrics"""
self.__print(self.dices,"dice")
self.__print(self.recall,"recall")
self.__print(self.precision,"precision")
self.__print(self.neg_recall,"neg recall")
def __print(self,metric,name='metric'):
"""print metric and hist"""
print name,np.array(metric).mean()
print np.histogram(metric)
class factory:
"""Helper for building network"""
def __init__(self, name='network'):
self.proto='name: "'+name+'"\n'
self.bottom=name
self.string_field=set(['name','type','bottom','top','source','mean_file','module','layer'])
def Data(self,
source,
mean_file=None,
mean_value=[],
name='data',
scale=1,
batch_size=32,
backend='LMDB',
new_height=None,
new_width=None,
is_color=False,
label="nothing",
phase='TRAIN'):
p=[('name',name)]
if backend=="LMDB":
p+=[('type','Data'),
('top',name)]
elif backend=="image":
p+=[('type',"ImageData"),
('top',name),
('top',label)]
if phase is not None:
p+=[('include',[('phase',phase)])]
transform_param=[('scale',scale)]
if mean_file is not None:
transform_param+=[('mean_file',mean_file)]
for i in mean_value:
transform_param+=[('mean_value',i)]
p+=[('transform_param',transform_param)]
if backend=="LMDB":
p+=[
('data_param',[
('source',source),
('batch_size',batch_size),
('backend',backend)
])]
elif backend=='image' or backend=='images':
image_data_param=[
('source',source),
('batch_size',batch_size),
('is_color',is_color),
('shuffle',False)
]
if new_height is not None and new_width is not None:
image_data_param+=[('new_height',new_height),('new_width',new_width)]
p+=[('image_data_param',image_data_param)]
else:
raise Exception("no implementation")
self.__end(p,name)
def Input(self,height,width,scale=1,name="data"):
p=[('name',name),('type','Input'),('top',name),]
self.__end(p,name)
#-------------------------Core----------------------------------
def Convolution(self,
name,
num_output,
bottom=None,
kernel_size=3,
pad=1,
weight_filler="msra",
dilation=None,
stride=1):
name=self.__start(name,'conv')
if bottom is None:
bottom=self.bottom
conv_param=[
('num_output',num_output),
('pad',pad),
('kernel_size',kernel_size),
('stride',stride),
('weight_filler',[
('type',weight_filler)
])
]
if dilation is not None:
conv_param+=[('dilation',dilation)]
p=[('name',name),
('type',"Convolution"),
('bottom',bottom),
('top',name),
('param',[
('lr_mult',1),
('decay_mult',1)
]),
('param',[
('lr_mult',2),
('decay_mult',0)
]),
('convolution_param',conv_param)
]
self.proto+=self.__printList(p)
self.bottom=name
def Deconvolution(self, name, num_output, bottom=None, stride=2,kernel_size=2, weight_filler="msra"):
if bottom is None:
bottom=self.bottom
name=self.__start(name,'up')
conv_param=[
('num_output',num_output),
('kernel_size',kernel_size),
('stride',stride),
('bias_filler',[
('type','constant')
])
]
weight_filler_list=[('type',weight_filler)]
if weight_filler=='gaussian':
weight_filler_list.append(('std',0.0001))
conv_param.append(('weight_filler',weight_filler_list))
p=[('name',name),
('type',"Deconvolution"),
('bottom',bottom),
('top',name),
('param',[
('lr_mult',1),
('decay_mult',1)
]),
('param',[
('lr_mult',2),
('decay_mult',0)
]),
('convolution_param',conv_param)
]
self.__end(p,name)
def InnerProduct(self,name, num_output, bottom=None, weight_filler="xavier"):
if bottom is None:
bottom=self.bottom
name=self.__start(name,"dense")
p=[('name',name),
('type',"InnerProduct"),
('bottom',bottom),
('top',name),
('param',[
('lr_mult',1),
('decay_mult',1)
]),
('param',[
('lr_mult',2),
('decay_mult',0)
]),
('inner_product_param',[
('num_output',num_output),
('weight_filler',[
('type',weight_filler)
]),
('bias_filler',[
('type',"constant"),
('value',0)
])
])]
self.__end(p,name)
def ReLU(self,name):
name=self.__start(name,'relu')
p=[('name',name),
('type',"ReLU"),
('bottom',self.bottom),
('top',self.bottom)]
self.proto+=self.__printList(p)
def Pooling(self,name,pool="MAX",bottom=None, kernel_size=2,stride=2, global_pooling=False):
"""pooling and global_pooling
:param pool: MAX,AVE,STOCHASTIC
"""
if bottom is None:
bottom=self.bottom
name=self.__start(name,'pool')
p=[('name',name),
('type','Pooling'),
('bottom',bottom),
('top',name)]
pooling_param=[('pool',pool)]
if global_pooling:
pooling_param+=[('global_pooling',True)]
else:
pooling_param+=[
('kernel_size',kernel_size),
('stride',stride)
]
p+=[('pooling_param',pooling_param)]
self.__end(p,name)
def Dropout(self,name,omit=0.5):
name=self.__start(name,'drop')
p=[('name',name),
('type',"Dropout"),
('bottom',self.bottom),
('top',self.bottom)]
p+=[('dropout_param',[
('dropout_ratio',omit)
])]
p+=[('include',[
('phase','TRAIN')
])]
self.proto+=self.__printList(p)
def Python(self,module,layer, name=None,bottom=[],top=[], other_params=[]):
if name is None:
name=module
p=[('name',name),
('type','Python')]
for i in bottom:
p+=[('bottom',i)]
for i in top:
p+=[('top',i)]
p+=[('python_param',[
('module',module),
('layer',layer)
])]
for param in other_params:
p+=[(param[0],param[1])]
self.__end(p,name)
#-----------------------------combinations----------------------------
def conv_relu(self,conv,relu,
num_output,
bottom=None,
kernel_size=3,
pad=1,
weight_filler="msra",
dilation=None,
stride=1):
self.Convolution(conv,num_output,bottom,kernel_size,pad,weight_filler,dilation, stride)
self.ReLU(relu)
def fc_relu(self, name, relu, num_output, bottom=None, weight_filler="xavier"):
self.InnerProduct(name, num_output, bottom, weight_filler)
self.ReLU(relu)
def fc_relu_dropout(self, name, relu, dropout, num_output, bottom=None, weight_filler="xavier", keep=.5):
self.fc_relu(name, relu, num_output, bottom, weight_filler)
self.Dropout(dropout,keep)
#-------------------------Loss----------------------------------
def Sigmoid(self,name="prob",bottom=None):
if bottom is None:
bottom=self.bottom
p=[('name',name),
('type','Sigmoid'),
('bottom',bottom),
('top',name)]
self.__end(p,name)
def diceLoss(self,pred,label):
name="loss"
p=[('name',name),
('type',"Python"),
('top',"loss"),
('bottom',pred),
('bottom',label),
('python_param',[
('module',"perClassLoss"),
('layer',"perClassLossLayer")
]),
('loss_weight',1)]
self.__end(p,name)
def SoftmaxWithLoss(self,name="softmaxwithloss",bottom2='label',bottom1=None, loss_weight=1):
if bottom1 is None:
bottom=self.bottom
p=[('name',name),
('type','SoftmaxWithLoss'),
('bottom',bottom1),
('bottom',bottom2),
('top',name),
('loss_weight',loss_weight)]
self.__end(p,name)
#-------------------------operation----------------------------------
def Concat(self,name,bottom1,bottom2, top=None,axis=1):
name=self.__start(name,'concat')
if top is None:
top=name
p=[('name',name),
('type','Concat'),
('bottom',bottom1),
('bottom',bottom2),
('top',top),
('concat_param',[
('axis',axis)
])]
self.__end(p,name)
def silence(self,*bottom):
p=[('name','silence'),('type','Silence')]
for i in bottom:
p+=[('bottom',i)]
self.proto+=self.__printList(p)
#-------------------------private----------------------------------
def __start(self,name,Type):
if isinstance(name,int):
name=Type+str(name)
elif isinstance(name,list):
tmp=Type
for i in name:
tmp+='_'+str(i)
name=tmp
return name
def __end(self,p,name):
self.proto+=self.__printList(p)
self.bottom=name
def totxt(self, filename):
with open(filename,"w") as f:
f.write(self.proto)
def __printList(self,List,depth=0):
if depth==0:
return 'layer {\n'+self.__printList(List,depth=1)+'}\n\n'
else:
ret=''
for i in List:
if isinstance(i[1], list):
ret+=' '*2*depth+i[0]+' {\n'+self.__printList(i[1],depth=depth+1)+' '*2*depth+'}\n'
else:
field=i[1]
if isinstance(field,bool):
if field==True:
field='true'
elif field==False:
field='false'
if isinstance(field, str):
if i[0] in self.string_field:
ret+=' '*2*depth+i[0]+': "'+field+'"\n'
else:
ret+=' '*2*depth+i[0]+': '+field+'\n'
else:
ret+=' '*2*depth+i[0]+': '+str(field)+'\n'
return ret收工.......................................