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 setUp(self):
self.metadata = sf.Metadata()
self.metadata.debugInfo = True
self.metadata.logFolderSuffix = str(time.time())
self.metadata.debugOutput = 'debug'
self.metadata.prepareOutput()
self.modelMetadata = TestModel_Metadata()
self.model = TestModel(self.modelMetadata)
self.helper = sf.TrainDataContainer()
self.dataMetadata = dc.DefaultData_Metadata()
self.helperEpoch = sf.EpochDataContainer()
self.helperEpoch.trainTotalNumber = 3
self.helperEpoch.maxTrainTotalNumber = 1000
def setUp(self):
self.metadata = sf.Metadata()
self.metadata.debugInfo = True
self.metadata.logFolderSuffix = str(time.time())
self.metadata.debugOutput = 'debug'
self.metadata.prepareOutput()
self.modelMetadata = TestModel_Metadata()
self.model = TestModel(self.modelMetadata)
self.helper = sf.TrainDataContainer()
self.smoothingMetadata = dc.Test_DefaultSmoothingBorderline_Metadata(
test_numbOfBatchAfterSwitchOn=2)
self.dataMetadata = dc.DefaultData_Metadata()
self.helperEpoch = sf.EpochDataContainer()
self.helperEpoch.trainTotalNumber = 3
def test_pinMemory(self):
ok = False
if(torch.cuda.is_available()):
ok = True
metadata = sf.Metadata(debugInfo=False)
model_metadata = sf.Model_Metadata()
data_metadata = sf.Data_Metadata()
ut.testCmpPandas(data_metadata.pin_memoryTrain, "pin_memory_train", False)
ut.testCmpPandas(data_metadata.pin_memoryTest, "pin_memory_test", False)
data_metadata.tryPinMemoryTrain(metadata, model_metadata)
ut.testCmpPandas(data_metadata.pin_memoryTrain, "pin_memory_train", ok)
data_metadata.tryPinMemoryTest(metadata, model_metadata)
ut.testCmpPandas(data_metadata.pin_memoryTest, "pin_memory_test", ok)
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)
import torch
import torchvision
import torch.optim as optim
from framework import smoothingFramework as sf
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from framework import defaultClasses as dc
if(__name__ == '__main__'):
#sf.StaticData.TEST_MODE = True
metadata = sf.Metadata(testFlag=True, trainFlag=True, debugInfo=True)
dataMetadata = dc.DefaultData_Metadata(pin_memoryTest=True, pin_memoryTrain=True, fromGrayToRGB=False)
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()
