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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, hazem.fahmy@uni.lu, SNT, 2019.
# Modified by Mojtaba Bagherzadeh, m.bagherzadeh@uottawa.ca, University of Ottawa, 2019.
#
import testModule
from imports import setupTransformer, os, Image, Variable, argparse, datasets, torch, pd, np, isfile, basename, join
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 = predict.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 = predict.IEEPredictor(outputSet, outputModel, 0)
trainDataSet, _ = predictor.load_data(outputSet)
predictor2 = predict.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, 0.3) #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"]
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"]
if retrainMode != "SEDE":
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"], transform=setupTransformer(datasetName))
print("TrainingSet Size:", len(ts))
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):
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)
tsList = list()
randomID = int(np.random.randint(100, 100000))
#randomID = 70382
bagPath = join(caseFile["filesPath"], "DataSets",
retrainMode + "_" + str(randomID) + "_toBag/")
print(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
tsList.append(ts)
tsList.append(ts2)
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())
testAccuracy, resultDictNew = alexTest(bestModelPath, testSet+"_R", resultDict, 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)
improvedImages, worsenedImages, clusterDict = collectImprovedData(resultDictNew, clusterDict)
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(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 resultDict is None:
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 = []
for idx, (inputs, labels) in enumerate(data_batches):
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())
return loss_valid
def save_best(self, train_loss, test_loss, real_loss):
# 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
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 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)
def reportAvgImages(testDataPath, retrainMode, counter1, counter2, outputPath, datasetName, modelExtension, modelArch,
numClass, unimprovedList):
modelPath = join(outputPath, "DNNModels")
worsened = 0
improved = 0
maxWorsened = 0
minWorsened = 50000000
maxImproved = 0
minImproved = 50000000
#unimprovImagesX = open(unimprovedList, "r")
#unimprovedImages = list()
#for line in unimprovImagesX:
# unimprovedImages.append(line.split("\n")[0])
#for line in f:
#print(line)
#print("Total images to skip", str(len(unimprovedImages)))
ieeData, unityData, imgList = testModule.loadData(testDataPath, datasetName, 4, 128, None, None)
testResultPath = join(outputPath, "testResult.csv")
imgClasses = unityData.dataset.classes
imageList = pd.read_csv(testResultPath,
names=["image", "result", "expected", "predicted"].append(imgClasses))
cnt1 = 0
resultDict = {}
accuList = list()
expirement = int(np.random.randint(100, 100000))
textReport = join(modelPath, "Report_" + retrainMode + "_" + str(expirement) + ".txt")
for index, row in imageList.iterrows():
imagePath = row["image"]
imageFileName = basename(imagePath)
#if unimprovedImages.count(imageFileName)<1:
cnt1 = cnt1 + 1
if cnt1 % 100 == 0:
print("Image checked: " + str(cnt1) + "/" + str(len(imageList)), end="\r")
resultDict[imageFileName] = {}
resultDict[imageFileName]["Old"] = row["result"]
for i in range(counter1, counter2 + 1):
file = open(textReport, "a")
print(join(modelPath, retrainMode + "_" + str(i) + "." + modelExtension))
w, i, accu = reportImages(datasetName, join(modelPath, retrainMode + "_" + str(i) + "." + modelExtension),
resultDict, testDataPath, modelArch, numClass, None)
accuList.append(accu)
file.write(str(accuList))
file.close()
worsened += w
improved += i
if (w > maxWorsened):
maxWorsened = w
if (w < minWorsened):
minWorsened = w
if (i > maxImproved):
maxImproved = i
if (i < minImproved):
minImproved = i
total = counter2 - counter1 + 1
print("Avg worsened: ", str((worsened / total)))
print("Max worsened: ", str((maxWorsened)))
print("Min worsened: ", str((minWorsened)))
print("Avg improved: ", str((improved / total)))
print("Max improved: ", str((maxImproved)))
print("Min improved: ", str((minImproved)))