def test_experiment_weightedMean_MNIST_predefModel_alexnet(self):
with sf.test_mode():
modelName = "alexnet"
metadata = sf.Metadata(testFlag=True,
trainFlag=True,
debugInfo=True)
dataMetadata = dc.DefaultData_Metadata(
pin_memoryTest=True,
pin_memoryTrain=True,
epoch=1,
test_howOftenPrintTrain=2,
howOftenPrintTrain=3,
resizeTo=Test_RunExperiment.MNIST_RESIZE)
optimizerDataDict = {"learning_rate": 1e-3, "momentum": 0.9}
obj = models.alexnet()
smoothingMetadata = dc.Test_DefaultSmoothingOscilationWeightedMean_Metadata(
test_weightIter=dc.DefaultWeightDecay(1.05),
test_device='cpu',
test_epsilon=1e-5,
test_hardEpsilon=1e-7,
test_weightsEpsilon=1e-6,
test_weightSumContainerSize=3,
test_weightSumContainerSizeStartAt=1,
test_lossContainer=20,
test_lossContainerDelayedStartAt=10)
modelMetadata = dc.DefaultModel_Metadata(
lossFuncDataDict={},
optimizerDataDict=optimizerDataDict,
device='cuda:0')
data = dc.DefaultDataMNIST(dataMetadata)
smoothing = dc.DefaultSmoothingOscilationWeightedMean(
smoothingMetadata)
model = dc.DefaultModelPredef(obj=obj,
modelMetadata=modelMetadata,
name=modelName)
optimizer = optim.SGD(model.getNNModelModule().parameters(),
lr=optimizerDataDict['learning_rate'],
momentum=optimizerDataDict['momentum'])
loss_fn = nn.CrossEntropyLoss()
stat = dc.run(metadataObj=metadata,
data=data,
model=model,
smoothing=smoothing,
optimizer=optimizer,
lossFunc=loss_fn,
modelMetadata=modelMetadata,
dataMetadata=dataMetadata,
smoothingMetadata=smoothingMetadata)
def test_experiment_borderline_MNIST_predefModel_wide_resnet(self):
with sf.test_mode():
modelName = "wide_resnet"
metadata = sf.Metadata(testFlag=True,
trainFlag=True,
debugInfo=True)
dataMetadata = dc.DefaultData_Metadata(
pin_memoryTest=True,
pin_memoryTrain=True,
epoch=1,
test_howOftenPrintTrain=2,
howOftenPrintTrain=3,
resizeTo=Test_RunExperiment.MNIST_RESIZE)
optimizerDataDict = {"learning_rate": 1e-3, "momentum": 0.9}
obj = models.wide_resnet50_2()
smoothingMetadata = dc.Test_DefaultSmoothingBorderline_Metadata(
test_numbOfBatchAfterSwitchOn=5, test_device='cuda:0')
modelMetadata = dc.DefaultModel_Metadata(
lossFuncDataDict={},
optimizerDataDict=optimizerDataDict,
device='cuda:0')
data = dc.DefaultDataMNIST(dataMetadata)
smoothing = dc.DefaultSmoothingBorderline(smoothingMetadata)
model = dc.DefaultModelPredef(obj=obj,
modelMetadata=modelMetadata,
name=modelName)
optimizer = optim.SGD(model.getNNModelModule().parameters(),
lr=optimizerDataDict['learning_rate'],
momentum=optimizerDataDict['momentum'])
loss_fn = nn.CrossEntropyLoss()
stat = dc.run(metadataObj=metadata,
data=data,
model=model,
smoothing=smoothing,
optimizer=optimizer,
lossFunc=loss_fn,
modelMetadata=modelMetadata,
dataMetadata=dataMetadata,
smoothingMetadata=smoothingMetadata)
def test_experiment_pytorchSWA_CIFAR10_predefModel_alexnet(self):
with sf.test_mode():
modelName = "simpleConv"
metadata = sf.Metadata(testFlag=True,
trainFlag=True,
debugInfo=True)
dataMetadata = dc.DefaultData_Metadata(pin_memoryTest=True,
pin_memoryTrain=True,
epoch=1,
test_howOftenPrintTrain=2,
howOftenPrintTrain=3)
optimizerDataDict = {"learning_rate": 1e-3, "momentum": 0.9}
smoothingMetadata = dc.Test_DefaultPytorchAveragedSmoothing_Metadata(
test_device='cuda:0')
modelMetadata = dc.DefaultModel_Metadata(
lossFuncDataDict={},
optimizerDataDict=optimizerDataDict,
device='cuda:0')
data = dc.DefaultDataMNIST(dataMetadata)
model = dc.DefaultModelSimpleConv(modelMetadata=modelMetadata)
smoothing = dc.DefaultPytorchAveragedSmoothing(smoothingMetadata,
model=model)
optimizer = optim.SGD(model.getNNModelModule().parameters(),
lr=optimizerDataDict['learning_rate'],
momentum=optimizerDataDict['momentum'])
loss_fn = nn.CrossEntropyLoss()
stat = dc.run(metadataObj=metadata,
data=data,
model=model,
smoothing=smoothing,
optimizer=optimizer,
lossFunc=loss_fn,
modelMetadata=modelMetadata,
dataMetadata=dataMetadata,
smoothingMetadata=smoothingMetadata)
loop = 5
#####################
types = ('predefModel', 'CIFAR10', 'borderline')
try:
stats = []
rootFolder = sf.Output.getTimeStr() + ''.join(x + "_" for x in types) + "set"
for r in range(loop):
obj = models.alexnet()
metadata.resetOutput()
smoothingMetadata = dc.DefaultSmoothingBorderline_Metadata(numbOfBatchAfterSwitchOn=2000)
modelMetadata = dc.DefaultModel_Metadata()
stat=dc.run(numbOfRepetition=1, modelType=types[0], dataType=types[1], smoothingType=types[2], metadataObj=metadata,
modelMetadata=modelMetadata, dataMetadata=dataMetadata, smoothingMetadata=smoothingMetadata, modelPredefObj=obj,
rootFolder=rootFolder)
stats.append(stat)
experiments.printStats(stats, metadata)
except Exception as ex:
experiments.printException(ex, types)
#####################
types = ('predefModel', 'CIFAR10', 'borderline')
try:
stats = []
rootFolder = sf.Output.getTimeStr() + ''.join(x + "_" for x in types) + "set"
for r in range(loop):
obj = models.alexnet()
metadata.resetOutput()
modelMetadata=modelMetadata,
name=modelPredefObjName,
optimizer=optimizer,
lossFunc=lossFunc)
optimizer = optim.SGD(self.getNNModelModule().parameters(),
lr=1e-3,
momentum=0.9)
loss_fn = nn.CrossEntropyLoss()
stat = dc.run(modelType=types[0],
dataType=types[1],
smoothingType=types[2],
metadataObj=metadata,
modelMetadata=modelMetadata,
dataMetadata=dataMetadata,
smoothingMetadata=smoothingMetadata,
modelPredefObj=obj,
modelPredefObjName=modelName,
rootFolder=rootFolder,
runningAvgSize=runningAvgSize,
optimizer=optimizer,
lossFunc=loss_fn)
for idx, s in enumerate(stat):
s.saveSelf(name="stat" + str(idx))
stats.append(stat.pop(
)) # weź pod uwagę tylko ostatni wynik (najlepiej wyćwiczony)
experiments.printAvgStats(stats,
metadata,
runningAvgSize=runningAvgSize)
except Exception as ex:
experiments.printException(ex, types)
types = ('predefModel', 'CIFAR10', 'disabled')
try:
stats = []
rootFolder = prefix + sf.Output.getTimeStr() + ''.join(x + "_" for x in types)
smoothingMetadata = dc.DisabledSmoothing_Metadata()
for r in range(loop):
obj = models.ResNet(block, layers, num_classes=num_classes)
data = dc.DefaultDataCIFAR10(dataMetadata)
model = dc.DefaultModelPredef(obj=obj, modelMetadata=modelMetadata, name=modelName)
smoothing = dc.DisabledSmoothing(smoothingMetadata)
optimizer = optim.SGD(model.getNNModelModule().parameters(), lr=optimizerDataDict['learning_rate'],
weight_decay=optimizerDataDict['weight_decay'], momentum=optimizerDataDict['momentum'])
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
loss_fn = nn.CrossEntropyLoss()
stat=dc.run(metadataObj=metadata, data=data, model=model, smoothing=smoothing, optimizer=optimizer, lossFunc=loss_fn,
modelMetadata=modelMetadata, dataMetadata=dataMetadata, smoothingMetadata=smoothingMetadata, rootFolder=rootFolder,
schedulers=[([30, 60, 90, 120, 150, 180], scheduler)])
stat.saveSelf(name="stat")
stats.append(stat)
experiments.printAvgStats(stats, metadata, runningAvgSize=runningAvgSize)
except Exception as ex:
experiments.printException(ex, types)
