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()