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__'): torch.backends.cudnn.benchmark = True obj = models.resnet18() #sf.useDeterministic() #sf.modelDetermTest(sf.Metadata, DefaultData_Metadata, DefaultModel_Metadata, DefaultData, VGG16Model, DefaultSmoothing) stat = sf.modelRun(sf.Metadata, dc.DefaultData_Metadata, dc.DefaultModel_Metadata, dc.DefaultDataMNIST, dc.DefaultModelSimpleConv, dc.DefaultSmoothingOscilationGeneralizedMean, obj, load=False) #plt.plot(stat.trainLossArray) #plt.xlabel('Train index') #plt.ylabel('Loss') #plt.show()
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 torch.backends.cudnn.benchmark = True obj = models.alexnet() #sf.useDeterministic() #sf.modelDetermTest(sf.Metadata, DefaultData_Metadata, DefaultModel_Metadata, DefaultData, VGG16Model, DefaultSmoothing) stat = sf.modelRun(sf.Metadata, dc.DefaultData_Metadata, dc.DefaultModel_Metadata, dc.DefaultDataMNIST, dc.DefaultModelPredef, dc.DisabledSmoothing, obj, load=False ) #sf.plot([stat.logFolder + '/statLossTest.csv', stat.logFolder + '/statLossTestSmoothing.csv']) #plt.plot(stat.trainLossArray) #plt.xlabel('Train index') #plt.ylabel('Loss') #plt.show()
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 torch.backends.cudnn.benchmark = True obj = models.alexnet() #sf.useDeterministic() #sf.modelDetermTest(sf.Metadata, DefaultData_Metadata, DefaultModel_Metadata, DefaultData, VGG16Model, DefaultSmoothing) stat = sf.modelRun(sf.Metadata, dc.DefaultData_Metadata, dc.DefaultModel_Metadata, dc.DefaultDataMNIST, dc.DefaultModelPredef, dc.DefaultSmoothingOscilationWeightedMean, obj, load=False ) #sf.plot([stat.logFolder + '/statLossTest.csv', stat.logFolder + '/statLossTestSmoothing.csv']) #plt.plot(stat.trainLossArray) #plt.xlabel('Train index') #plt.ylabel('Loss') #plt.show()
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 torch.backends.cudnn.benchmark = True obj = models.alexnet() #sf.useDeterministic() #sf.modelDetermTest(sf.Metadata, DefaultData_Metadata, DefaultModel_Metadata, DefaultData, VGG16Model, DefaultSmoothing) stat = sf.modelRun( Metadata_Class=sf.Metadata, Data_Metadata_Class=dc.DefaultData_Metadata, Model_Metadata_Class=dc.DefaultModel_Metadata, Smoothing_Metadata_Class=dc. DefaultSmoothingOscilationWeightedMean_Metadata, Data_Class=dc.DefaultDataMNIST, Model_Class=dc.DefaultModelPredef, Smoothing_Class=dc.DefaultSmoothingOscilationWeightedMean, modelObj=obj, load=False) stat.printPlots(startAt=-10) #sf.plot([stat.logFolder + '/statLossTest.csv', stat.logFolder + '/statLossTestSmoothing.csv']) #plt.plot(stat.trainLossArray) #plt.xlabel('Train index') #plt.ylabel('Loss') #plt.show()