outDir = "/home/sheng/mountData/unaryDepthInference/" runDir = outDir + "/testRun/" plotDir = runDir + "plots/" if not os.path.exists(runDir): os.makedirs(runDir) if not os.path.exists(plotDir): os.makedirs(plotDir) load = True loadFile = outDir + "/saved/saved.ckpt" #Get object from which tensorflow will pull data from testDataObj = kittiObj(imageList, depthList) #Allocate obj to calc mean/std trainDataObj = kittiObj(trainImageList, trainDepthList) #Set mean/std on test set testDataObj.setMeanVar(trainDataObj.mean, trainDataObj.std) vggFile = "/home/sheng/mountData/pretrain/imagenet-vgg-f.mat" #Allocate tf obj with test data tfObj = unaryDepthInference(testDataObj, vggFile) #Load weights if(load): tfObj.loadModel(loadFile) else:
outDir = "/home/sheng/mountData/unaryDepthInference/" runDir = outDir + "/run0/" plotDir = runDir + "plots/" if not os.path.exists(runDir): os.makedirs(runDir) if not os.path.exists(plotDir): os.makedirs(plotDir) load = False loadFile = outDir + "/saved/saved.ckpt" #Get object from which tensorflow will pull data from trainDataObj = kittiObj(trainImageList, trainDepthList) testDataObj = kittiObj(testImageList, testDepthList) testDataObj.setMeanVar(trainDataObj.mean, trainDataObj.std) ##Get all segments #(drop, gt) = dataObj.allSegments() #plotImg(dataObj.currSegments, dataObj.segLabels, gt) #plotImg(dataObj.currSegments, dataObj.segLabels, np.log(gt)) # #plt.hist(gt) #plt.show() # #plt.hist(np.log(gt)) #plt.show() #
outDir = "/home/sheng/mountData/unaryDepthInference/"
runDir = outDir + "/testRun/"
plotDir = runDir + "plots/"
if not os.path.exists(runDir):
os.makedirs(runDir)
if not os.path.exists(plotDir):
os.makedirs(plotDir)
load = True
loadFile = outDir + "/saved/saved.ckpt"
#Get object from which tensorflow will pull data from
testDataObj = kittiObj(imageList, depthList)
#Allocate obj to calc mean/std
trainDataObj = kittiObj(trainImageList, trainDepthList)
#Set mean/std on test set
testDataObj.setMeanVar(trainDataObj.mean, trainDataObj.std)
vggFile = "/home/sheng/mountData/pretrain/imagenet-vgg-f.mat"
#Allocate tf obj with test data
tfObj = unaryDepthInference(testDataObj, vggFile)
#Load weights
if (load):
tfObj.loadModel(loadFile)
else:
