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()