Python modelRun Exemples

Exemple #1

Fichier : resnet18.py Projet : Izentar/inz_skrypty

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()

Exemple #2

Fichier : alexnet_MINST_disabledSmoothing.py Projet : Izentar/inz_skrypty

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()

Exemple #3

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()

Exemple #4

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()