def setup_net(self):
caffe.set_mode_gpu()
caffe.set_device(0)
netFiles = cfg.get_caffe_net_files(self.prms_.netName)
self.net_ = caffe.Net(netFiles.deployFile, netFiles.netFile,
caffe.TEST)
self.cls_ = cfg.dataset2classnames(self.prms_.trainDataSet)
def setup_net(self): caffe.set_mode_gpu() caffe.set_device(0) netFiles = cfg.get_caffe_net_files(self.prms_.netName) self.net_ = caffe.Net(netFiles.deployFile, netFiles.netFile, caffe.TEST) self.cls_ = cfg.dataset2classnames(self.prms_.trainDataSet)
def __init__(self, prms={}):
# define location for output file and image folder
try:
self.outFile = prms['outFile']
except KeyError:
outFolder = '/mnt/Ext/training_data/rcnn'
dateExt = cuu.get_datetime_ext()
self.outFile = osp.join(outFolder, dateExt, 'rcnn_' + dateExt + '.txt')
os.system('mkdir -p ' + osp.dirname(self.outFile))
prms['outFile'] = self.outFile
try:
self.imageFolder = prms['imageFolder']
except KeyError:
self.imageFolder = osp.join(osp.dirname(self.outFile), 'imageFolder')
os.system('mkdir -p ' + self.imageFolder)
prms['imageFolder'] = self.imageFolder
# define dataset used for pretraining
self.pretrainDataSet = cfg.get_rcnn_prms(**prms)['trainDataSet']
# match available labels with available classes from the pretraining dataset
clsNames = cfg.dataset2classnames(self.pretrainDataSet)
labeledObjs = cfg.get_label_types(category='object')
self.clsLookup = {}
for obj in labeledObjs:
try:
self.clsLookup[obj] = clsNames.index(cfg.label2dataset(obj, dataset=self.pretrainDataSet))
except ValueError as e:
print e
ch.ChainObject.__init__(self, prms)