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retrainModule.py
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retrainModule.py
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#
# Copyright (c) University of Luxembourg 2019-2020.
# Created by Hazem FAHMY, [email protected], SNT, 2019.
# Modified by Mojtaba Bagherzadeh, [email protected], University of Ottawa, 2019.
#
import dataSupplier
import testModule
from imports import setupTransformer, os, Image, Variable, argparse, datasets, torch, pd, np, isfile, basename, join, tqdm
import dataSupplier as dataSupply
import testModule
import dnnModels
import ieepredict
from imports import sys, torch, nn, optim, Variable, np, shutil, os, time, datasets, math, pd, PathImageFolder, \
setupTransformer, exists, join, isfile, isdir, basename, dirname
from dataSupplier import DataSupplier
from imports import np, optim, torch, Variable, math
import time
learning_rate = 0.001
momentum = 0.9
log_schedule = 10
nPoints = 64
def run(caseFile_):
global caseFile
global expIndex
global BaggedUnsafeSet
caseFile = caseFile_
caseFile["DNNsPath"] = join(caseFile["outputPathOriginal"], caseFile["RCC"], "DNNModels_")
caseFile[caseFile["retrainMode"]] = {}
start = time.time()
if caseFile_["Alex"]:
avgImp, maxImp, minImp, avgWor, maxWor, minWor, avgTestSet, DNNModels, dumbDict, clusterDict = retrainAlex()
if caseFile_["KP"]:
#avgImp, maxImp, minImp, avgWor, maxWor, minWor, avgTestSet, DNNModels, dumbDict, clusterDict = retrainKP()
retrainKP()
caseFile[caseFile["retrainMode"]]["failCount"] = caseFile[caseFile["retrainMode"]]["totalFailCount"] = 0
if "retrieveAccuracy" not in caseFile:
failCount = caseFile[caseFile["retrainMode"]]["failCount"] / (caseFile["expNum2"]-caseFile["expNum1"]+1)
failCount_ = 100.00 * (failCount / BaggedUnsafeSet)
totalFailCount = caseFile[caseFile["retrainMode"]]["totalFailCount"] / (caseFile["expNum2"]-caseFile["expNum1"]+1)
totalFailCount_ = 100.00 * (totalFailCount / BaggedUnsafeSet)
else:
dumbDict = torch.load(join(DNNModels, caseFile["retrainMode"] + "_resultDict.pt"))
failCount = dumbDict["failCount"]
failCount_ = dumbDict["fail%"]
caseFile[caseFile["retrainMode"]]["AvgImproved"] = avgImp
caseFile[caseFile["retrainMode"]]["MaxImproved"] = maxImp
caseFile[caseFile["retrainMode"]]["MinImproved"] = minImp
caseFile[caseFile["retrainMode"]]["AvgWorsened"] = avgWor
caseFile[caseFile["retrainMode"]]["MaxWorsened"] = maxWor
caseFile[caseFile["retrainMode"]]["MinWorsened"] = minWor
caseFile[caseFile["retrainMode"]]["AvgTestAccuracy"] = avgTestSet
caseFile[caseFile["retrainMode"]]["failCount"] = failCount
caseFile[caseFile["retrainMode"]]["totalFailCount"] = totalFailCount
print(caseFile["retrainMode"], str(caseFile["expNum2"]-caseFile["expNum1"]+1) + " exp.", str(avgTestSet) + "%")
print("Improved (avg/min/max):", str(avgImp) + "/" + str(minImp) + "/" + str(maxImp))
print("Worsened (avg/min/max):", str(avgWor) + "/" + str(minWor) + "/" + str(maxWor))
#print("Failing% (avg):", str(failCount_))
#print("Total Failing% (avg):", str(totalFailCount_))
clsWithAssImages = torch.load(caseFile["clsPath"])
for clusterID in clusterDict:
if clusterID in clsWithAssImages['clusters']:
clusterDict[clusterID]["Imp"] /= (caseFile["expNum2"]-caseFile["expNum1"]+1)
clusterDict[clusterID]["Imp"] /= len(clsWithAssImages['clusters'][clusterID]['members'])
clusterDict[clusterID]["Imp"] *= 100.00
clusterDict[clusterID]["Wor"] /= (caseFile["expNum2"]-caseFile["expNum1"]+1)
clusterDict[clusterID]["Wor"] /= len(clsWithAssImages['clusters'][clusterID]['members'])
clusterDict[clusterID]["Wor"] *= 100.00
#print("Avg % of Improved Images per Cluster:")
avgImpPerCls = list()
for i in range(1, len(clusterDict)+1):
#print(i, str(clusterDict[i]["Imp"])[0:5])
avgImpPerCls.append(clusterDict[i]["Imp"])
#print("Avg % of Improved Images per Cluster:", sum(avgImpPerCls)/len(avgImpPerCls))
dumbDict["Improved"] = str(avgImp) + "/" + str(minImp) + "/" + str(maxImp)
dumbDict["Worsened"] = str(avgWor) + "/" + str(minWor) + "/" + str(maxWor)
dumbDict["AvgTestAccuracy"] = avgTestSet
dumbDict["failCount"] = failCount
dumbDict["fail%"] = failCount_
torch.save(dumbDict, join(DNNModels, caseFile["retrainMode"] + "_resultDict.pt"))
torch.save(caseFile, caseFile["caseFile"])
newName = str(caseFile["retrainMode"]) + "_" + str(caseFile[caseFile["retrainMode"]]["AvgTestAccuracy"])[0:6]
oldPath = caseFile["DNNsPath"] + str(caseFile["retrainMode"]) + "_" + expIndex
newPath = caseFile["DNNsPath"] + newName
if "retrieveAccuracy" not in caseFile:
shutil.copytree(oldPath, newPath)
shutil.rmtree(oldPath)
torch.save(caseFile, join(newPath, "caseFile.pt"))
print("Total time of batch job is " + str((time.time() - start) / 60.0) + " minutes.")
print("*****************************")
print("*****************************")
print("*****************************")
def retrainKP():
expNumber = caseFile["expNum1"]
expNumber2 = caseFile["expNum2"]
outputPathOriginal = caseFile["outputPathOriginal"]
retrainMode = caseFile["retrainMode"]
batchSize = caseFile["batchSize"]
Epochs = caseFile["Epochs"]
components = caseFile["components"]
modelsPath = join(outputPathOriginal, "DNNModels")
realDataNpy = caseFile["realDataNpy"]
testDataNpy = caseFile["testDataNpy"]
accuList = list()
#if "retrainSet" in caseFile:
# outputSet = join(outputPathOriginal, caseFile["retrainSet"])
#else:
# outputSet = join(outputPathOriginal, str(retrainMode) + str(0) + ".npy")
#dataSupply.loadIEETrainData(caseFile, outputSet)
if "expIndex" not in caseFile:
expIndex = str(int(np.random.randint(100, 100000)))
else:
expIndex = caseFile["expIndex"]
expDir = join(modelsPath, str(retrainMode) + "_" + expIndex)
if not exists(expDir):
os.makedirs(expDir)
errPath = join(caseFile["outputPathOriginal"], "errList.pt")
if exists(errPath):
errList = torch.load(errPath)
else:
improvePredict = ieepredict.IEEPredictor(caseFile["improveDataNpy"], caseFile["modelPath"], 0)
dst2 = join(caseFile["outputPathOriginal"], "improveerror")
improveDataSet, _ = improvePredict.load_data(caseFile["improveDataNpy"])
_, errList = improvePredict.predict(improveDataSet, dst2, caseFile["improveDataPath"], True,
caseFile["improveCSV"], 1, False)
torch.save(errList, errPath)
model = testModule.loadDNN(caseFile["modelPath"], "KPNet", None, False)
model.eval()
for exp in range(expNumber, expNumber2 + 1):
DNN = loadDNN(caseFile, None)
outputModel = join(modelsPath, str(retrainMode) + str(exp) + "_kpmodel.pt")
outputLoss = join(modelsPath, str(retrainMode) + str(exp) + "_loss.npy")
if "retrainSet" in caseFile:
outputSet = join(outputPathOriginal, caseFile["retrainSet"])
else:
outputSet = join(outputPathOriginal, str(retrainMode) + str(exp) + ".npy")
dataSupply.loadIEETrainData(caseFile, outputSet, errList)
trainer = Trainer(outputSet, testDataNpy, realDataNpy, batchSize, False, 0, 0, 0
, False, True, True, 0, outputModel, outputLoss, DNN, Epochs)
trainer.train()
print("Using model:", outputModel)
predictor = ieepredict.IEEPredictor(outputSet, outputModel, 0)
trainDataSet, _ = predictor.load_data(outputSet)
predictor2 = ieepredict.IEEPredictor(testDataNpy, outputModel, 0)
testDataSet, _ = predictor.load_data(testDataNpy)
outputTrainCSV = join(outputPathOriginal,
retrainMode + str(exp) + "_trainResult.csv")
outputTestCSV = join(outputPathOriginal,
retrainMode + str(exp) + "_testResult.csv")
dst = join(outputPathOriginal, "trainerror")
dst2 = join(outputPathOriginal, "testerror")
#counter = predictor.predict(trainDataSet, dst, None, True, outputTrainCSV, 1, False)
predictor2.model = dnnModels.KPNet()
if torch.cuda.is_available():
weights = torch.load(outputModel, map_location=torch.device('cuda:0'))
else:
weights = torch.load(outputModel, map_location=torch.device('cpu'))
predictor2.model.load_state_dict(weights.state_dict())
counter2 = predictor2.predict(testDataSet, dst2, None, True, outputTestCSV, 0, False)
imageList = pd.read_csv(outputTrainCSV)
imageList2 = pd.read_csv(outputTestCSV)
dictResult = {}
cntTot = 0
cntMC = 0
for component in components:
cntComp = 0
cntTot = 0
cntMC = 0
for index, row in imageList.iterrows():
cntTot += 1
if row["result"] == "Wrong":
cntMC += 1
if row["worst_component"] == component:
cntComp += 1
dictResult[component] = cntComp
accuList.append(cntMC / cntTot)
testAccuracy = 100.0*((1 - cntMC) / cntTot)
if "retrainSet" not in caseFile:
shutil.move(outputSet, join(expDir, str(retrainMode) + str(exp) + "_" +
str(testAccuracy)[0:6] + ".npy"))
shutil.move(outputModel, join(expDir, str(retrainMode) + str(exp) + "_" +
str(testAccuracy)[0:6] + "_kpmodel.pt"))
shutil.move(outputLoss, join(expDir, str(retrainMode) + str(exp) + "_" +
str(testAccuracy)[0:6] + "_loss.npy"))
return
def retrainAlex():
global caseFile, eta, etaT, expIndex, BaggedUnsafeSet
if caseFile["retrainMode"] != "SEDE":
clusterUCs, totalAssigned, totalUc, totalUb, Ub = dataSupply.getUCs(caseFile, 1) #U4/U5
print("Total Assigned Images:", totalAssigned)
print("US:", math.ceil(totalUc))
print("BLUS:", math.ceil(totalUb))
BaggedUnsafeSet = math.ceil(totalUb)
retrainMode = caseFile["retrainMode"]
if "retrieveAccuracy" in caseFile:
retrieveAccuracy = caseFile["retrieveAccuracy"]
newName = str(retrainMode) + "_" + str(caseFile["retrieveAccuracy"])
DNNModels = caseFile["DNNsPath"] + newName
else:
retrieveAccuracy = None
if "expIndex" not in caseFile:
expIndex = str(int(np.random.randint(100, 100000)))
else:
expIndex = caseFile["expIndex"]
print("Experiment Number", expIndex)
DNNModels = join(caseFile["filesPath"], "DNNModels_" + retrainMode + "_" + expIndex)
eta = etaT = "N/A"
outputPath = caseFile["outputPath"]
modelPath = caseFile["modelPath"]
epochNum = caseFile["Epochs"]
datasetName = caseFile["datasetName"]
expNum = caseFile["expNum1"]
expNum2 = caseFile["expNum2"]
clsPath = caseFile["assignPTFile"]
DataSets = join(outputPath, "DataSets")
testSet = join(DataSets, "TestSet")
if not exists(DNNModels):
os.makedirs(DNNModels)
imgClasses = caseFile["trainDataSet"].dataset.classes
imageList = pd.read_csv(caseFile["testCSV"], names=["image", "result", "expected", "predicted"].append(imgClasses))
cnt1 = 0
resultDict = {}
for index, row in imageList.iterrows():
imagePath = row["image"]
cnt1 += 1
resultDict[imagePath] = {}
resultDict[imagePath]["Old"] = row["result"]
clsWithAssImages = torch.load(join(caseFile["filesPath"], "ClusterAnalysis_" + str(caseFile["clustMode"]),
caseFile["selectedLayer"] + ".pt"))
##clsWithAssImages = torch.load(clsPath)
clusterDistrib = list()
clusterDict = {}
for clusterID in clsWithAssImages['clusters']:
clusterDict[clusterID] = {}
clusterDict[clusterID]["Imp"] = clusterDict[clusterID]["Wor"] = 0
if 'assigned' in clsWithAssImages['clusters'][clusterID]:
clustLen = len(clsWithAssImages['clusters'][clusterID]['assigned'])
if clustLen > 0:
clusterDistrib.append(clustLen)
print("UnsafeSet Distribution", clusterDistrib)
print("Total:", sum(clusterDistrib))
#print("Avg:", sum(clusterDistrib) / len(clusterDistrib))
print("Total Clusters:", len(clsWithAssImages['clusters']))
print("Assigned Clusters:", len(clusterDistrib))
#print("UnsafeSet Size:", math.ceil(totalUb))
#caseFile[retrainMode]["BaggedUnsafeSetSize"] = math.ceil(totalUb)
print("RetrainMode:", retrainMode)
ts = datasets.ImageFolder(root=caseFile["trainDataPath"]+"_R", transform=setupTransformer(datasetName))
print("TrainingSet Size:", len(ts), caseFile["trainDataPath"]+"_R")
caseFile[retrainMode]["failCount"] = caseFile[retrainMode]["totalFailCount"] = caseFile[retrainMode]["dupCount"] = 0
#if retrainMode == "HUDD":
# bagPath = join(caseFile["filesPath"], "DataSets", retrainMode + "_" + str(expIndex) + "_toBag/")
# ts2, imagesList, caseFile = dataSupply.loadTrainData(bagPath, caseFile) # UnsafeSet
test = test2 = improvedList = worsenedList = improvedClustersList = list()
expCounter = x = loadBar = 0
dumbDict = {}
numExps = expNum2 + 1 - expNum
start = time.time()
for exp in range(expNum, expNum2 + 1):
clusterDistrib = list()
clusterDict = {}
for clusterID in clsWithAssImages['clusters']:
clusterDict[clusterID] = {}
clusterDict[clusterID]["Imp"] = clusterDict[clusterID]["Wor"] = 0
if 'assigned' in clsWithAssImages['clusters'][clusterID]:
clustLen = len(clsWithAssImages['clusters'][clusterID]['assigned'])
if clustLen > 0:
clusterDistrib.append(clustLen)
bestModelPath = join(DNNModels, retrainMode + "_" + str(exp) + "." +
str(basename(modelPath).split(".")[1]))
if not (retrieveAccuracy is None):
#DNN = loadDNN(caseFile, bestModelPath)
for b in os.listdir(DNNModels):
if b.startswith(join(retrainMode + "_" + str(exp))):
bestModelPath = join(DNNModels, b)
else:
DNN = loadDNN(caseFile, None)
testAccuracy, resultDictNew = alexTest(bestModelPath, testSet, resultDict, datasetName, DNN, False, None)
print("R:", testAccuracy.item())
test.append(testAccuracy.item())
tsList = list()
randomID = int(np.random.randint(100, 100000))
randomID = 54207
#randomID = 84304
bagPath = join(caseFile["filesPath"], "DataSets",
retrainMode + "_" + str(randomID) + "_toBag/")
print(bagPath)
if not exists(bagPath):
if "retrainSet" in caseFile:
dumbDict = torch.load(join(caseFile["DNNsPath"] + str(retrainMode) + "_" +
str(caseFile["retrainSet"]).split("_")[1], caseFile["retrainMode"]
+ "_resultDict.pt"))
imagesList = dumbDict["UnsafeSet_" + str(caseFile["retrainSet"]).split("_")[0]]
ts2, imagesList, caseFile = dataSupply.loadTrainData(bagPath, caseFile, imagesList)
else:
ts2, imagesList, caseFile = dataSupply.loadTrainData(bagPath, caseFile, None) # UnsafeSet
dumbDict["UnsafeSet_" + retrainMode + str(exp)] = imagesList
else:
ts2 = datasets.ImageFolder(root=bagPath, transform=setupTransformer(datasetName))
tsList.append(ts)
tsList.append(ts2)
ts3 = datasets.ImageFolder(root=caseFile["trainDataPath"]+"_S", transform=setupTransformer(datasetName))
dataset_subset = torch.utils.data.Subset(ts3, np.random.choice(len(ts3), int(len(ts3)/20), replace=False))
print("Total Size:", len(ts3), " -- Selected Size:", int(len(ts3)/20), caseFile["trainDataPath"]+"_S")
print("Final TrainingSet Size:", len(ts) + len(ts2) + int(len(ts3)/20))
tsList.append(dataset_subset)
concatList = torch.utils.data.ConcatDataset(tsList)
newTrainDataSet = torch.utils.data.DataLoader(concatList, batch_size=caseFile["batchSize"], shuffle=True,
num_workers=caseFile["workersCount"])
_, DNN = alexTrain(caseFile, epochNum, newTrainDataSet, bestModelPath, DNN, None)
#shutil.rmtree(bagPath)
DNN = loadDNN(caseFile, bestModelPath)
testAccuracy, resultDictNew = alexTest(bestModelPath, testSet, resultDict, datasetName, DNN, False, None)
print("R:",testAccuracy.item())
test.append(testAccuracy.item())
improvedImages, worsenedImages, clusterDict = collectImprovedData(resultDictNew, clusterDict)
#testAccuracy, _ = alexTest(bestModelPath, testSet+"_S", None, datasetName, DNN, False, None)
#print("S:",testAccuracy.item())
#bagPath = join(caseFile["filesPath"], "DataSets", retrainMode + "_" +
# str(int(np.random.randint(100, 100000))) + "_toBag/")
#dataSupply.generateTestSet(caseFile, bagPath)
#tsList2 = list()
#ts3 = PathImageFolder(root=bagPath, transform=setupTransformer(datasetName))
#ts4 = PathImageFolder(root=caseFile["testDataPath"], transform=setupTransformer(datasetName))
#tsList2.append(ts3)
#tsList2.append(ts4)
#concatList = torch.utils.data.ConcatDataset(tsList2)
#newTestDataSet = torch.utils.data.DataLoader(concatList, batch_size=caseFile["batchSize"], shuffle=True,
# num_workers=caseFile["workersCount"])
#testAccuracy2, _ = alexTest(bestModelPath, newTestDataSet, None, datasetName, DNN, False, None)
#test2.append(testAccuracy2.item())
#print(testAccuracy2.item())
#shutil.rmtree(bagPath)
improvedList.append(improvedImages)
worsenedList.append(worsenedImages)
expCounter += 1
x += 1
if int(x / (numExps * 0.1)) == 1:
loadBar += 10.0
spentTime = ((time.time() - start) / 60.0)
timePerLoadBar = spentTime/loadBar
spentTime = timePerLoadBar * loadBar
fullTime = timePerLoadBar * 100
remTime = math.ceil(fullTime - spentTime)
if remTime > 60:
etaT = str(remTime/60)[0:4] + "hs."
else:
etaT = str(remTime) + " mins."
x = 0
eta = str(int(100.0 * exp / (numExps)))
if retrieveAccuracy is None:
shutil.move(join(DNNModels, retrainMode + "_" + str(exp) + "." +
str(basename(modelPath).split(".")[1])),
join(DNNModels, retrainMode + "_" + str(exp) + "_" + str(testAccuracy.item())[0:6] +
"." + str(basename(modelPath).split(".")[1])))
print(test)
print(sum(test)/len(test))
print(test2)
print(sum(test2)/len(test2))
return sum(improvedList) / len(improvedList), max(improvedList), min(improvedList), \
sum(worsenedList) / len(worsenedList), max(worsenedList), min(worsenedList), sum(test)/len(test), \
DNNModels, dumbDict, clusterDict
def loadAlexRetrainDataSet():
return
def updateCaseFile():
return
def collectImprovedData(resultDictNew, clusterDict):
global clsWithAssImages
global caseFile
clsWithAssImages = torch.load(caseFile["clsPath"])
worsenedImages = 0
improvedImages = 0
for img in resultDictNew:
if resultDictNew[img]["Old"] == "Correct":
if resultDictNew[img]["New"] == "Wrong":
worsenedImages += 1
if resultDictNew[img]["Old"] == "Wrong":
if resultDictNew[img]["New"] == "Correct":
imgClass = basename(dirname(img))
imgName = "Test_" + str(basename(img)).split(".")[0] + "_" + imgClass
improvedImages += 1
for clusterID in clsWithAssImages['clusters']:
if clsWithAssImages['clusters'][clusterID]['members'].count(imgName) > 0:
clusterDict[clusterID]["Imp"] += 1
for clusterID in clusterDict:
print(clusterID, clusterDict[clusterID]["Imp"])
print("Worsened:", worsenedImages)
return improvedImages, worsenedImages, clusterDict
def writeFile(textPath, input, text):
file = open(textPath, "a")
if text is not None:
file.write(text + "\n")
for i in input:
file.write(str(i) + "\n")
file.close()
def loadDNN(caseFile, modelPath):
if modelPath is None:
modelPath = caseFile["modelPath"]
Alex = caseFile["Alex"]
KP = caseFile["KP"]
datasetName = caseFile["datasetName"]
numClass = caseFile["numClass"]
scratchFlag = caseFile["scratchFlag"]
if Alex:
if datasetName.startswith("HPD"):
net = dnnModels.AlexNetIEE(numClass)
else:
net = dnnModels.AlexNet(numClass)
elif KP:
net = dnnModels.KPNet()
if torch.cuda.is_available():
if not scratchFlag:
weights = torch.load(modelPath)
if Alex:
net.load_state_dict(weights)
elif KP:
net.load_state_dict(weights.state_dict())
net = net.to('cuda')
net.cuda()
net.eval()
DNN = net
else:
if not scratchFlag:
weights = torch.load(modelPath, map_location=torch.device('cpu'))
if Alex:
net.load_state_dict(weights)
elif KP:
net.load_state_dict(weights.state_dict())
net.eval()
DNN = net
return DNN
def alexTrain(caseFile, epochNum, trainData, bestModelPath, net, validationSet):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=momentum, weight_decay=5e-4)
best_loss = 0
x = 0
ETA1 = 0
ETA2 = 0
x = 0
trainAccuracy = list()
print("TrainingSet Size:", len(trainData.dataset))
torch.save(net.state_dict(), bestModelPath)
for i in range(1, epochNum + 1):
totalCounter = 0
start1 = time.time()
correct = 0
net.train()
# print(trainData)
retrainLength = len(trainData)
for b_idx, (data, classes) in enumerate(trainData):
start1 = time.time()
totalCounter += 1
if torch.cuda.is_available():
net.cuda()
data, classes = data.cuda(), classes.cuda()
else:
data = data.cpu()
classes = classes.cpu()
data, classes = Variable(data), Variable(classes)
optimizer.zero_grad()
scores = net.forward(data)
scores = scores.view(data.size()[0], caseFile["numClass"])
_, prediction = torch.max(scores.data, 1)
correct += torch.sum(prediction == classes.data).float()
loss = criterion(scores, classes)
loss.backward()
optimizer.step()
# if b_idx % log_schedule == 0:
# print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
# i, (b_idx + 1) * len(data), len(trainData.dataset),
# 100. * (b_idx + 1) * len(data) / len(trainData.dataset), loss.item()), end="\r")
if x == 0:
end = time.time()
ETA1 = int((((end - start1) / 60.0) * retrainLength))
ETA2 = int((ETA1 * epochNum))
x += 1
#if eta is not None:
# print("Checked:", str(totalCounter) + "/" + str(retrainLength) + " " +
# str(int(100.0 * totalCounter / retrainLength)) + "%",
# str(int(100.0 * i / epochNum)) + "%",
# eta + "%", "ETA:" + str(etaT), end="\r")
#else:
print("Checked:", str(totalCounter) + "/" + str(retrainLength) + " " +
str(int(100.0 * totalCounter / retrainLength)) + "%",
str(int(100.0 * i / epochNum)) + "%", end="\r")
#print(i)
if validationSet is None:
avg_loss = correct / len(trainData.dataset) * 100
else:
avg_loss, _ = alexTest(bestModelPath, validationSet, None, caseFile["datasetName"], net, False, None)
#print("loss:", avg_loss)
if (avg_loss > best_loss):
torch.save(net.state_dict(), bestModelPath)
best_loss = avg_loss
dataTransform = setupTransformer(caseFile["datasetName"])
transformedData2 = PathImageFolder(root=caseFile["testDataPath"] , transform=dataTransform)
testData2 = torch.utils.data.DataLoader(transformedData2, batch_size=caseFile["batchSize"], shuffle=True,
num_workers=caseFile["workersCount"])
testAccuracy2, resultDictNew = alexTest(bestModelPath, testData2, None, caseFile["datasetName"],
net, False, None)
print(testAccuracy2.item())
# print("training accuracy ({:.2f}%)".format(avg_loss))
trainAccuracy.append(avg_loss)
return trainAccuracy, net
def alexTrain_Original(caseFile, epochNum, trainData, bestModelPath, net, validationSet):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=momentum, weight_decay=5e-4)
best_loss = 0
x = 0
ETA1 = 0
ETA2 = 0
bestNet = net
best_t_loss = 0
test_loss = 0
x = 0
trainAccuracy = list()
print(len(trainData.dataset))
torch.save(net.state_dict(), bestModelPath)
for i in range(1, epochNum + 1):
totalCounter = 0
start1 = time.time()
correct = 0
bestNet.train()
# print(trainData)
retrainLength = len(trainData)
for b_idx, (data, classes) in enumerate(trainData):
start1 = time.time()
totalCounter += 1
if torch.cuda.is_available():
bestNet.cuda()
data, classes = data.cuda(), classes.cuda()
else:
data = data.cpu()
classes = classes.cpu()
data, classes = Variable(data), Variable(classes)
optimizer.zero_grad()
scores = bestNet.forward(data)
scores = scores.view(data.size()[0], caseFile["numClass"])
_, prediction = torch.max(scores.data, 1)
correct += torch.sum(prediction == classes.data).float()
loss = criterion(scores, classes)
loss.backward()
optimizer.step()
# if b_idx % log_schedule == 0:
# print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
# i, (b_idx + 1) * len(data), len(trainData.dataset),
# 100. * (b_idx + 1) * len(data) / len(trainData.dataset), loss.item()), end="\r")
if x == 0:
end = time.time()
ETA1 = int((((end - start1) / 60.0) * retrainLength))
ETA2 = int((ETA1 * epochNum))
x += 1
#if eta is not None:
# print("Checked:", str(totalCounter) + "/" + str(retrainLength) + " " +
# str(int(100.0 * totalCounter / retrainLength)) + "%",
# str(int(100.0 * i / epochNum)) + "%",
# eta + "%", "ETA:" + str(etaT), end="\r")
#else:
print("Checked:", str(totalCounter) + "/" + str(retrainLength) + " " +
str(int(100.0 * totalCounter / retrainLength)) + "%",
str(int(100.0 * i / epochNum)) + "%", end="\r")
train_loss = correct / len(trainData.dataset) * 100
print("train loss:", train_loss)
if (train_loss > best_loss):
#if test_loss > best_t_loss:
torch.save(net.state_dict(), bestModelPath)
bestNet = net
best_loss = train_loss
print("model saved")
if validationSet is not None:
test_loss, _ = alexTest(bestModelPath, validationSet, None, caseFile["datasetName"], bestNet, False, None)
# print("training accuracy ({:.2f}%)".format(avg_loss))
trainAccuracy.append(train_loss)
print("test loss:", test_loss)
return trainAccuracy, net
def alexTest(bestModelPath, testSet, resultDict, datasetName, net, saveFlag, outPutFile):
global best_accuracy
global exp
global epoch
global caseFile
correct = 0
if exists(bestModelPath):
if torch.cuda.is_available():
weights = torch.load(bestModelPath)
else:
weights = torch.load(bestModelPath, map_location=torch.device('cpu'))
net.load_state_dict(weights)
net.eval()
if not isinstance(testSet, str):
testData = testSet
else:
dataTransformer = setupTransformer(datasetName)
transformedData = PathImageFolder(root=testSet, transform=dataTransformer)
testData = torch.utils.data.DataLoader(transformedData, batch_size=caseFile["batchSize"], shuffle=True,
num_workers=caseFile["workersCount"])
classesStr = ','.join(str(class_) for class_ in testData.dataset.classes)
if saveFlag:
outFile = open(outPutFile, 'w')
outFile.writelines("image,result,expected,predicted," + classesStr + "\r\n")
totalInputs = 0
for idx, (data, classes, paths) in enumerate(testData):
if torch.cuda.is_available():
data, classes = data.cuda(), classes.cuda()
totalInputs += len(data)
data, classes = Variable(data), Variable(classes)
scores = net.forward(data)
pred = scores.data.max(1)[1]
correct += torch.sum(pred == classes.data).float()
for i in range(len(data)):
if (classes.data[i].eq(pred[i])):
outcome = "Correct"
else:
outcome = "Wrong"
# imageFileName = basename(paths[i])
if resultDict is not None:
resultDict[paths[i]]["New"] = outcome
strExpected = testData.dataset.classes[classes[i]]
strPred = testData.dataset.classes[pred[i].item()]
scoreStr = ','.join([str(score) for score in scores[i].data.tolist()])
if saveFlag:
outFile.writelines(paths[i] + "," + outcome + "," + strExpected + "," + strPred + "," + scoreStr[1:len(
scoreStr) -
2] +
"\r\n")
#print("Predicted {} out of {} correctly".format(correct, totalInputs))
#print("The average accuracy is: {} %".format(100.0 * correct / (float(totalInputs))))
#print("Total erronous" + str(totalInputs - correct))
if saveFlag:
outFile.close()
#print("predicted {} out of {}".format(correct, len(testData.dataset)))
val_accuracy = (correct / float(totalInputs)) * 100
#print(val_accuracy)
# now save the model if it has better accuracy than the best model seen so forward
return val_accuracy, resultDict
def getSize(outputPath, bagSize, clsPath):
labelDataSize = 0
numClusters = 0
bagSizeperCluster = bagSize
clusterwithAssignedImages = torch.load(clsPath)
for clusterID in clusterwithAssignedImages['clusters']:
numClusters = numClusters + 1
for image in clusterwithAssignedImages['clusters'][clusterID]['assigned']:
labelDataSize = labelDataSize + 1
toBag = (bagSizeperCluster * numClusters) - labelDataSize
totalUnbaggedSize = labelDataSize
totalBaggedSize = bagSizeperCluster * numClusters
return numClusters, toBag, totalUnbaggedSize, totalBaggedSize
def testImage(model, inputs, labels, thresholdPixels, area):
if torch.cuda.is_available():
model = model.cuda()
inputs = Variable(inputs.cuda())
else:
inputs = Variable(inputs)
predict = model(inputs)
predict_cpu = predict.cpu()
predict_cpu = predict_cpu.detach().numpy()
errorList = testModule.ieeError(predict_cpu, labels, area, thresholdPixels)
return errorList[0]
from imports import os, torch, datasets, transforms, Variable, nn, optim, setupTransformer
import dnnModels
import testModule
learning_rate = 0.001
momentum = 0.9
log_schedule = 10
def genericTrain(outputPath, datasetName, epochNum):
# outputPath = "/home/users/hfahmy/DEEP/HPC/FR"
# outputPath = "/scratch/users/fpastore/DEEP/gazedetectionandanalysisdnn/Learning/HUDD/OD/"
testData = join(outputPath, "DataSets", "TestSet")
trainData = join(outputPath, "DataSets", "TrainingSet")
validationData = join(outputPath, "DataSets", "ValidationSet")
improvementData = join(outputPath, "DataSets", "ImprovementSet")
_, unityData, _ = loadData(trainData, datasetName, 4, 128, None, None)
_, testData, _ = loadData(testData, datasetName, 4, 128, None, None)
print(len(unityData.dataset.classes))
net = loadDNNX(None, "AlexNet", len(unityData.dataset.classes), scratchFlag=True)
# print(unityData.dataset)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=momentum, weight_decay=5e-4)
best_loss = 0
trainAccuracy = list()
validAccuracy = list()
# classes = torch.FloatTensor(unityData.dataset.classes)
for i in range(1, epochNum + 1):
print("Epoch", i)
##TRAIN
correct = 0
net = net.train()
for b_idx, (data, classes, imgs) in enumerate(unityData):
if torch.cuda.is_available():
net.cuda()
data, classes = data.cuda(), classes.cuda()
# print(data.shape)
data, classes = Variable(data), Variable(classes)
optimizer.zero_grad()
scores = net.forward(data)
scores = scores.view(data.size()[0], len(unityData.dataset.classes))
_, prediction = torch.max(scores.data, 1)
correct += torch.sum(prediction == classes.data).float()
loss = criterion(scores, classes)
loss.backward()
optimizer.step()
if b_idx % log_schedule == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
i, (b_idx + 1) * len(data), len(unityData.dataset),
100. * (b_idx + 1) * len(data) / len(unityData.dataset), loss.item()), end='\r')
avg_loss = correct / len(unityData.dataset) * 100
print("training accuracy ({:.2f}%)".format(avg_loss))
if (avg_loss > best_loss):
net = net.eval()
torch.save(net.state_dict(), join(outputPath, str(i) + "_pretrainedModel.pth"))
best_loss = avg_loss
##TEST
model = loadDNNX(join(outputPath, str(i) + "_pretrainedModel.pth"), "AlexNet",
len(unityData.dataset.classes), scratchFlag=False)
model = model.eval()
testModule.testErrorAlexNet(model, testData, False, None)
def loadDNNX(modelPath, modelArch: str, numClasses, scratchFlag):
if modelArch == "AlexNet":
net = dnnModels.AlexNetIEE(numClasses) ### HPD
#net = dnnModels.AlexNet(numClasses) ### GD - OC - ASL - TS - AC - OD
if torch.cuda.is_available():
if not scratchFlag:
weights = torch.load(modelPath)
net.load_state_dict(weights)
net = net.to('cuda')
net.cuda()
else:
if not scratchFlag:
weights = torch.load(modelPath, map_location=torch.device('cpu'))
net.load_state_dict(weights)
net.eval()
elif modelArch == "KPNet":
print(modelArch)
net = dnnModels.KPNet()
if torch.cuda.is_available():
if not scratchFlag:
weights = torch.load(modelPath)
net.load_state_dict(weights.state_dict())
net = net.to('cuda')
net.cuda()
else:
if not scratchFlag:
weights = torch.load(modelPath, map_location=torch.device('cpu'))
net.load_state_dict(weights.state_dict())
net.eval()
else:
net = dnnModels.AlexNet(8) # Default is GD
return net
def loadData(dataPath: str, dataSetName: str, workersCount: int, batchSize: int, outputPath, weightPath):
dataSet = 0
train_di = 0
imagesList = 0
if dataSetName == "IEETEST":
x=0
#ds = DataSupply.DataSupplier(using_gm=False)
#if not isfile(outputPath):
# DataSupply.createData(dataPath, outputPath, weightPath)
#train_di, valid_di, imagesList = ds.get_test_iter(outputPath) # for test data
#elif dataSetName == "IEETRAIN":
# ds = DataSupply.DataSupplier(using_gm=False)
# if not isfile(outputPath):
# DataSupply.createData(dataPath, outputPath)
# train_di = ds.get_train_iter(outputPath) # for test data
else:
dataTransformer = setupTransformer(dataSetName)
transformedData = PathImageFolder(root=dataPath, transform=dataTransformer)
dataSet = torch.utils.data.DataLoader(transformedData, batch_size=batchSize, shuffle=True,
num_workers=workersCount)
return train_di, dataSet, imagesList
class PathImageFolder(datasets.ImageFolder):
def __getitem__(self, index):
# this is what ImageFolder normally returns
original_tuple = super(PathImageFolder, self).__getitem__(index)
# the image file path
path = self.imgs[index][0]
# make a new tuple that includes original and the path
tuple_with_path = (original_tuple + (path,))
return tuple_with_path
class Trainer(object):
def __init__(self, iee_train_data, iee_test_data, iee_real_data, batch_size, pin_memory, train_max_num,
test_max_num, real_max_num, multi_gpu, use_gpu_test, use_gpu_train, gpu_id, best_model_path,
loss_file_path, DNN, total_epoch):
self.model = DNN
self.iee_train_data = iee_train_data
self.iee_test_data = iee_test_data
self.iee_real_data = iee_real_data
self.batch_size = batch_size
self.pin_memory = pin_memory
self.train_max_num = train_max_num
self.test_max_num = test_max_num
self.real_max_num = real_max_num
self.multi_gpu = multi_gpu
self.use_gpu_test = use_gpu_test
self.gpu_id = gpu_id
self.use_gpu_train = use_gpu_train
self.best_model_path = best_model_path
self.loss_file_path = loss_file_path
self.total_epoch = total_epoch
if self.multi_gpu:
self.model = torch.nn.DataParallel(self.model)
print("switch to DataParallel mode")
self.optimizer = optim.RMSprop(self.model.parameters(), lr=1e-3)
self.lowest_mse = np.inf
def get_train_data(self):
data_supplier = DataSupplier(self.iee_train_data, self.batch_size, True, self.pin_memory, self.train_max_num)
return data_supplier.get_data_iters()
def get_test_data(self):
data_supplier = DataSupplier(self.iee_test_data, self.batch_size, True, self.pin_memory, self.test_max_num)
return data_supplier.get_data_iters()
def get_real_data(self):
data_supplier = DataSupplier(self.iee_real_data, self.batch_size, True, self.pin_memory, self.real_max_num)
return data_supplier.get_data_iters()
def loss_fn(self, predict, label):
loss = torch.nn.MSELoss()
# if weight:
# predict = predict*weight
return loss(predict, label)
def validate(self, data_batches):
with torch.no_grad():
loss_valid = []
correctInputs = 0
worst_error = 0
best_error = 1e9
for idx, (inputs, labels) in enumerate(data_batches):
labels_gt = labels["kps"]
labels = labels["gm"]
if self.use_gpu_test:
if not self.multi_gpu:
self.model = self.model.cuda(self.gpu_id)
inputs, labels = Variable(inputs.cuda(self.gpu_id)), Variable(labels.cuda(self.gpu_id))
else:
self.model = self.model.cuda()
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
predict = self.model(inputs.float())
loss = self.loss_fn(predict, labels.float())
loss_valid.append(loss.data.item())
#for (inputs, labels) in tqdm(data_batches):
#labels = labels["gm"]
labels_msk = np.ones(labels_gt.numpy().shape)
labels_msk[labels_gt.numpy() <= 1e-5] = 0
#if self.use_gpu_test:
# if not self.multi_gpu:
# self.model = self.model.cuda(self.gpu_id)
# inputs, labels = Variable(inputs.cuda(self.gpu_id)), Variable(labels.cuda(self.gpu_id))
# else:
# self.model = self.model.cuda()
# inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
#else:
# inputs, labels = Variable(inputs), Variable(labels)
#predict = self.model(inputs.float())
predict_cpu = predict.cpu()
predict_cpu = predict_cpu.detach().numpy()
predict_xy1 = dataSupplier.transfer_target(predict_cpu, n_points=dataSupplier.n_points)
predict_xy = np.multiply(predict_xy1, labels_msk)
diff = np.square(labels_gt.numpy() - predict_xy)
sum_diff = np.sqrt(diff[:, :, 0] + diff[:, :, 1])
avg_error = np.sum(sum_diff) / len(sum_diff)
if avg_error < 4:
correctInputs += 1
if avg_error > worst_error:
worst_error = avg_error
if avg_error < best_error:
best_error = avg_error
print("Avg. MSE loss", np.mean(loss_valid))
print("Worst image error", worst_error)
print("Best image error", best_error)
print("Avg. Accuracy", 100 * (correctInputs/len(data_batches)), "%")
return loss_valid
def save_best(self, train_loss, test_loss, real_loss=None):
# v_mse = np.mean(test_loss)
v_mse = np.mean(train_loss)
if v_mse < self.lowest_mse:
self.lowest_mse = v_mse
torch.save(self.model, self.best_model_path)
loss = {"train": train_loss, "test": test_loss, "real": real_loss}
np.save(self.loss_file_path, loss)
# print("INFO: save model to: ", self.best_model_path, "save loss data to: ", self.loss_file_path)
def train(self):
print("INFO: loading training data...")
train_data, _ = self.get_train_data()
print("INFO: loading test data...")
test_data, _ = self.get_test_data()
print("INFO: loading kaggle data...")
real_data, _ = self.get_real_data()
print("INFO: starting training ...")
start = time.time()
ETA1 = 0
ETA2 = 0
x = 0
self.model.modifyToTrain()
for epoch in range(self.total_epoch):
loss_train = []
totalCounter = 0
retrainLength = len(train_data)
t_s = time.time()
for idx, (inputs, labels) in enumerate(train_data):
start1 = time.time()
totalCounter += 1
labels = labels["gm"]
# print("input shape: ", inputs.shape, "labels shape: ", labels.shape)
if torch.cuda.is_available():
self.use_gpu_train = True
self.use_gpu_test = True
else:
self.use_gpu_train = False
self.use_gpu_test = False
if self.use_gpu_train:
if not self.multi_gpu:
self.model = self.model.cuda(self.gpu_id)
inputs, labels = Variable(inputs.cuda(self.gpu_id)), Variable(labels.cuda(self.gpu_id))
else:
self.model = self.model.cuda()
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
predict = self.model(inputs.float())
loss = self.loss_fn(predict, labels.float())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
loss_train.append(loss.data.item())
if x == 0:
end = time.time()
ETA1 = int((((end - start1) / 60.0) * retrainLength))
ETA2 = int((ETA1 * self.total_epoch))
x += 1
print("Checked:", str(totalCounter) + "/" + str(retrainLength) + " " +
str(int(100.0 * totalCounter / retrainLength)) + "%", " ETA1: <" +
str(ETA1 - math.ceil(ETA1 * (totalCounter / retrainLength))) + "m. " + " ETA2: <" +
str(math.ceil((ETA2 - int(ETA2 * (epoch / self.total_epoch))) / 60.0)) + "h. " +
str(int(100.0 * epoch / self.total_epoch)) + "%", end="\r")
loss_test = self.validate(test_data)
#loss_real = self.validate(real_data)
t_e = time.time()
# print('\nEpoch: {} Loss Train: {}, Loss Test: {}, Loss Real: {}, time: {} seconds '.format(epoch, np.mean(loss_train),
# np.mean(loss_test), np.mean(loss_real), int(t_e-t_s)), end='\n')
#self.save_best(loss_train, loss_test, loss_real)
self.save_best(loss_train, loss_test)