def test_updateTotalNumbLoops(self):
dataMetadata = dc.DefaultData_Metadata(epoch=7)
data = dc.DefaultDataMNIST(dataMetadata)
data.epochHelper = sf.EpochDataContainer()
data._updateTotalNumbLoops(dataMetadata)
ut.testCmpPandas(data.epochHelper.maxTrainTotalNumber,
"max_loops_train", 7 * 1 * len(data.trainloader))
ut.testCmpPandas(data.epochHelper.maxTestTotalNumber, "max_loops_test",
7 * 2 * len(data.testloader))
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_updateTotalNumbLoops_testMode(self):
with sf.test_mode():
dataMetadata = dc.DefaultData_Metadata(epoch=7)
data = dc.DefaultDataMNIST(dataMetadata)
data.epochHelper = sf.EpochDataContainer()
data._updateTotalNumbLoops(dataMetadata)
ut.testCmpPandas(data.epochHelper.maxTrainTotalNumber,
"max_loops_train",
7 * sf.StaticData.MAX_DEBUG_LOOPS * 1)
ut.testCmpPandas(data.epochHelper.maxTestTotalNumber,
"max_loops_test",
7 * sf.StaticData.MAX_DEBUG_LOOPS * 2)
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)