import caffe
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
import DataManager as DM
import utilities
from os.path import splitext
from multiprocessing import Process, Queue
class VNet(object):
params=None
dataManagerTrain=None
dataManagerTest=None
def __init__(self,params):
self.params=params
caffe.set_device(self.params['ModelParams']['device'])
caffe.set_mode_gpu()
def prepareDataThread(self, dataQueue, numpyImages, numpyGT):
nr_iter = self.params['ModelParams']['numIterations']
batchsize = self.params['ModelParams']['batchsize']
keysIMG = numpyImages.keys()
nr_iter_dataAug = nr_iter*batchsize
np.random.seed()
whichDataList = np.random.randint(len(keysIMG), size=int(nr_iter_dataAug/self.params['ModelParams']['nProc']))
whichDataForMatchingList = np.random.randint(len(keysIMG), size=int(nr_iter_dataAug/self.params['ModelParams']['nProc']))
for whichData,whichDataForMatching in zip(whichDataList,whichDataForMatchingList):
filename, ext = splitext(keysIMG[whichData])
currGtKey = filename + '_segmentation' + ext
currImgKey = filename + ext
# data agugumentation through hist matching across different examples...
ImgKeyMatching = keysIMG[whichDataForMatching]
defImg = numpyImages[currImgKey]
defLab = numpyGT[currGtKey]
defImg = utilities.hist_match(defImg, numpyImages[ImgKeyMatching])
if(np.random.rand(1)[0]>0.5): #do not apply deformations always, just sometimes
defImg, defLab = utilities.produceRandomlyDeformedImage(defImg, defLab,
self.params['ModelParams']['numcontrolpoints'],
self.params['ModelParams']['sigma'])
weightData = np.zeros_like(defLab,dtype=float)
weightData[defLab == 1] = np.prod(defLab.shape) / np.sum((defLab==1).astype(dtype=np.float32))
weightData[defLab == 0] = np.prod(defLab.shape) / np.sum((defLab == 0).astype(dtype=np.float32))
dataQueue.put(tuple((defImg,defLab, weightData)))
def trainThread(self,dataQueue,solver):
nr_iter = self.params['ModelParams']['numIterations']
batchsize = self.params['ModelParams']['batchsize']
batchData = np.zeros((batchsize, 1, self.params['DataManagerParams']['VolSize'][0], self.params['DataManagerParams']['VolSize'][1], self.params['DataManagerParams']['VolSize'][2]), dtype=float)
batchLabel = np.zeros((batchsize, 1, self.params['DataManagerParams']['VolSize'][0], self.params['DataManagerParams']['VolSize'][1], self.params['DataManagerParams']['VolSize'][2]), dtype=float)
#only used if you do weighted multinomial logistic regression
batchWeight = np.zeros((batchsize, 1, self.params['DataManagerParams']['VolSize'][0],
self.params['DataManagerParams']['VolSize'][1],
self.params['DataManagerParams']['VolSize'][2]), dtype=float)
train_loss = np.zeros(nr_iter)
for it in range(nr_iter):
for i in range(batchsize):
[defImg, defLab, defWeight] = dataQueue.get()
batchData[i, 0, :, :, :] = defImg.astype(dtype=np.float32)
batchLabel[i, 0, :, :, :] = (defLab > 0.5).astype(dtype=np.float32)
batchWeight[i, 0, :, :, :] = defWeight.astype(dtype=np.float32)
solver.net.blobs['data'].data[...] = batchData.astype(dtype=np.float32)
solver.net.blobs['label'].data[...] = batchLabel.astype(dtype=np.float32)
#solver.net.blobs['labelWeight'].data[...] = batchWeight.astype(dtype=np.float32)
#use only if you do softmax with loss
solver.step(1) # this does the training
train_loss[it] = solver.net.blobs['loss'].data
if (np.mod(it, 10) == 0):
plt.clf()
plt.plot(range(0, it), train_loss[0:it])
plt.pause(0.00000001)
matplotlib.pyplot.show()
def train(self):
print self.params['ModelParams']['dirTrain']
#we define here a data manage object
self.dataManagerTrain = DM.DataManager(self.params['ModelParams']['dirTrain'],
self.params['ModelParams']['dirResult'],
self.params['DataManagerParams'])
self.dataManagerTrain.loadTrainingData() #loads in sitk format
howManyImages = len(self.dataManagerTrain.sitkImages)
howManyGT = len(self.dataManagerTrain.sitkGT)
assert howManyGT == howManyImages
print "The dataset has shape: data - " + str(howManyImages) + ". labels - " + str(howManyGT)
test_interval = 50000
# Write a temporary solver text file because pycaffe is stupid
with open("solver.prototxt", 'w') as f:
f.write("net: \"" + self.params['ModelParams']['prototxtTrain'] + "\" \n")
f.write("base_lr: " + str(self.params['ModelParams']['baseLR']) + " \n")
f.write("momentum: 0.99 \n")
f.write("weight_decay: 0.0005 \n")
f.write("lr_policy: \"step\" \n")
f.write("stepsize: 20000 \n")
f.write("gamma: 0.1 \n")
f.write("display: 1 \n")
f.write("snapshot: 500 \n")
f.write("snapshot_prefix: \"" + self.params['ModelParams']['dirSnapshots'] + "\" \n")
#f.write("test_iter: 3 \n")
#f.write("test_interval: " + str(test_interval) + "\n")
f.close()
solver = caffe.SGDSolver("solver.prototxt")
os.remove("solver.prototxt")
if (self.params['ModelParams']['snapshot'] > 0):
solver.restore(self.params['ModelParams']['dirSnapshots'] + "_iter_" + str(
self.params['ModelParams']['snapshot']) + ".solverstate")
plt.ion()
numpyImages = self.dataManagerTrain.getNumpyImages()
numpyGT = self.dataManagerTrain.getNumpyGT()
#numpyImages['Case00.mhd']
#numpy images is a dictionary that you index in this way (with filenames)
for key in numpyImages:
mean = np.mean(numpyImages[key][numpyImages[key]>0])
std = np.std(numpyImages[key][numpyImages[key]>0])
numpyImages[key]-=mean
numpyImages[key]/=std
dataQueue = Queue(30) #max 50 images in queue
dataPreparation = [None] * self.params['ModelParams']['nProc']
#thread creation
for proc in range(0,self.params['ModelParams']['nProc']):
dataPreparation[proc] = Process(target=self.prepareDataThread, args=(dataQueue, numpyImages, numpyGT))
dataPreparation[proc].daemon = True
dataPreparation[proc].start()
self.trainThread(dataQueue, solver)
def test(self):
self.dataManagerTest = DM.DataManager(self.params['ModelParams']['dirTest'], self.params['ModelParams']['dirResult'], self.params['DataManagerParams'])
self.dataManagerTest.loadTestData()
net = caffe.Net(self.params['ModelParams']['prototxtTest'],
os.path.join(self.params['ModelParams']['dirSnapshots'],"_iter_" + str(self.params['ModelParams']['snapshot']) + ".caffemodel"),
caffe.TEST)
numpyImages = self.dataManagerTest.getNumpyImages()
for key in numpyImages:
mean = np.mean(numpyImages[key][numpyImages[key]>0])
std = np.std(numpyImages[key][numpyImages[key]>0])
numpyImages[key] -= mean
numpyImages[key] /= std
results = dict()
for key in numpyImages:
btch = np.reshape(numpyImages[key],[1,1,numpyImages[key].shape[0],numpyImages[key].shape[1],numpyImages[key].shape[2]])
net.blobs['data'].data[...] = btch
out = net.forward()
l = out["labelmap"]
labelmap = np.squeeze(l[0,1,:,:,:])
results[key] = np.squeeze(labelmap)
self.dataManagerTest.writeResultsFromNumpyLabel(np.squeeze(labelmap),key)
