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drawPlot.py
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from visualization import runExplain,runGGradCam,runGradCam,runVanillaBP,runsmoothGrad,runGBackProp
from misc_functions import get_params
from matplotlib import pyplot as plt
# for grad cam these are the outputs:
#original_image,gray,color,result,adversarial,gray2,color2,result2
# for explain:
#original_img,heat, mask,cam,adversarialpic,heat2, cam2
# for GBP:
#original_image, colorgrads,graygrads,possal, negsal, adversarial,colorgrads2,graygrads2,possal2,negsal2
# for GGCam:
# original_image, guidedgrad, grayguidedgrad, adversarial, guidedgrad2, grayguidedgrad2
# for smooth_grad:
#original_image,colorgrads,graygrads,adversarial,colorgrads2,graygrads2
# vanilla BP:
# original_image,vanilbp,grayvanilbp,adversarial,vanilbp2,grayvanilbp2
# photo index, network, visualization
def compareAttacks(vizmethod,
choose_network,
image_index,
training='',
structure=''):
isTrained = True
_,_,_,img_name,_ = get_params(image_index,choose_network,isTrained,training, structure)
attacks = ['FGSM','DeepFool','PGD','SalMap','LBFGS','RPGD']# , 'Boundary']#,'SinglePixel']
rows = 1+len(attacks)
fig = plt.figure()
fig.suptitle('Comparing Attacks:'+img_name+' - '+ vizmethod)
if vizmethod == 'Explain':
iters = 50
j = 1
for i in attacks:
original_img,heat, mask,cam,\
adversarialpic,heat2, mask2, cam2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals= runExplain(choose_network,isTrained,
training,structure,
image_index,iters,
attack_type=i)
if j == 1:
ax11 = fig.add_subplot(rows, 5, 1)
ax11.imshow(original_img)
ax11.set_title('Original Image')
ax1 = fig.add_subplot(rows, 5, 2)
ax1.imshow(heat)
ax1.set_title('Learned Mask Color')
ax2 = fig.add_subplot(rows, 5, 3)
ax2.imshow(mask)
ax2.set_title('Learned Mask Gray')
ax3 = fig.add_subplot(rows, 5, 4)
ax3.imshow(cam)
ax3.set_title('Cam Result')
ax9 = fig.add_subplot(rows, 5, 5)
ax9.bar(indices, orig_vals, align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs, rotation=45, ha="right")
ax12 = fig.add_subplot(rows, 5, 5*j+1)
ax12.imshow(adversarialpic)
ax12.set_title(i + ' Attack')
ax5 = fig.add_subplot(rows, 5, 5*j+2)
ax5.imshow(heat2)
ax6 = fig.add_subplot(rows, 5, 5*j+3)
ax6.imshow(mask2)
ax6.set_title('Adversary Mask Gray')
ax7 = fig.add_subplot(rows, 5, 5*j+4)
ax7.imshow(cam2)
ax7.set_title('Adversary Cam Result')
ax10 = fig.add_subplot(rows, 5, 5*(j+1))
ax10.bar(indices, adver_vals, align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs, rotation=45, ha="right")
j += 1
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/AttackComp' +'_' +
img_name +'_' +
vizmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
elif vizmethod == 'GradCam':
j=1
for i in attacks:
print(i)
original_image,gray,color,result,adversarial,gray2,color2,result2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGradCam(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
if j == 1:
ax0 = fig.add_subplot(rows,5,1)
ax0.imshow(original_image)
ax0.set_title('Original Image')
ax1 = fig.add_subplot(rows,5,2)
ax1.imshow(gray)
ax1.set_title('Cam Grasycale')
ax2 = fig.add_subplot(rows,5,3)
ax2.imshow(color)
ax2.set_title('Cam HeatMap')
ax3 = fig.add_subplot(rows,5,4)
ax3.imshow(result)
ax3.set_title('Cam Result')
ax9 = fig.add_subplot(rows,5,5)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,5,5*j+1)
ax12.imshow(adversarial)
ax12.set_title(i + ' Attack')
ax4 = fig.add_subplot(rows,5,5*j+2)
ax4.imshow(gray2)
ax4.set_title('Adversary Cam Grasycale')
ax5 = fig.add_subplot(rows,5,5*j+3)
ax5.imshow(color2)
ax5.set_title('Adversary Cam HeatMap')
ax6 = fig.add_subplot(rows,5,5*j+4)
ax6.imshow(result2)
ax6.set_title('Adversary Cam Result')
ax10 = fig.add_subplot(rows,5,5*j+5)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j +=1
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/AttackComp' +'_' +
img_name +'_' +
vizmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
elif vizmethod == 'GBP':
j =1
for i in attacks:
original_image, colorgrads,graygrads,possal, negsal, \
adversarial,colorgrads2,graygrads2,possal2,negsal2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGBackProp(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
if j ==1:
ax11 = fig.add_subplot(rows,6,1)
ax11.imshow(original_image)
ax11.set_title('Original Image')
ax1 = fig.add_subplot(rows, 6,2)
ax1.imshow(colorgrads)
ax1.set_title('Guided BP Color')
ax2 = fig.add_subplot(rows, 6, 3)
ax2.imshow(graygrads)
ax2.set_title( 'Guided BP Gray')
ax3 = fig.add_subplot(rows, 6, 4)
ax3.imshow(possal)
ax3.set_title('Positive Saliency')
ax4 = fig.add_subplot(rows, 6, 5)
ax4.imshow(negsal)
ax4.set_title('Negative Saliency')
ax9 = fig.add_subplot(rows, 6,6)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows, 6,6*j+1)
ax12.imshow(adversarial)
ax12.set_title(i + ' Attack')
ax5 = fig.add_subplot(rows, 6, 6*j+2)
ax5.imshow(colorgrads2)
ax5.set_title('Adversarial Guided BP Color')
ax6 = fig.add_subplot(rows, 6, 6*j+3)
ax6.imshow(graygrads2)
ax6.set_title('Adversarial'+ 'Guided BP Gray')
ax7 = fig.add_subplot(rows, 6, 6*j+4)
ax7.imshow(possal2)
ax7.set_title('Adversarial ''Positive Saliency')
ax8 = fig.add_subplot(rows, 6, 6*j+5)
ax8.imshow(negsal2)
ax8.set_title('Adversarial'+'Negative Saliency')
ax10 = fig.add_subplot(rows, 6,6*j+6)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 1
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/AttackComp' +'_' +
img_name +'_' +
vizmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
elif vizmethod == 'GGradCam':
j = 1
for i in attacks:
original_image, guidedgrad, grayguidedgrad,\
adversarial, guidedgrad2, grayguidedgrad2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGGradCam(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
if j ==1:
ax0 = fig.add_subplot(rows,4,1)
ax0.imshow(original_image)
ax0.set_title('Original Image')
ax1 = fig.add_subplot(rows,4,2)
ax1.imshow(guidedgrad)
ax1.set_title('Guided Grad Cam')
ax2 = fig.add_subplot(rows,4,3)
ax2.imshow(grayguidedgrad)
ax2.set_title('Guided Grad Cam Grasycale')
ax9 = fig.add_subplot(rows,4,4)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,4,4*j+1)
ax12.imshow(adversarial)
ax12.set_title(i + ' Attack')
ax3 = fig.add_subplot(rows,4,4*j+2)
ax3.imshow(guidedgrad2)
ax3.set_title('Adversary Guided Grad Cam')
ax4 = fig.add_subplot(rows,4,4*j+3)
ax4.imshow(grayguidedgrad2)
ax4.set_title('Adversary Guided Grad Cam Grasycale')
ax10 = fig.add_subplot(rows,4,4*j+4)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/AttackComp' +'_' +
img_name +'_' +
vizmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
elif vizmethod == 'SmoothGrad':
j=1
for i in attacks:
original_image,colorgrads,graygrads,\
adversarial,colorgrads2,graygrads2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runsmoothGrad(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
if j ==1:
ax0 = fig.add_subplot(rows,4,1)
ax0.imshow(original_image)
ax0.set_title('Original Image')
ax1 = fig.add_subplot(rows,4,2)
ax1.imshow(colorgrads)
ax1.set_title('Smooth BP')
ax2 = fig.add_subplot(rows,4, 3)
ax2.imshow(graygrads)
ax2.set_title('Smooth BP Gray')
ax9 = fig.add_subplot(rows,4,4)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,4,4*j+1)
ax12.imshow(adversarial)
ax12.set_title(i + ' Attack')
ax3 = fig.add_subplot(rows,4,4*j+2)
ax3.imshow(colorgrads2)
ax3.set_title('Adversary Smooth BP')
ax4 = fig.add_subplot(rows,4, 4*j+3)
ax4.imshow(graygrads2)
ax4.set_title('Adversary Smooth BP Gray')
ax10 = fig.add_subplot(rows,4,4*j+4)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 1
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/AttackComp' +'_' +
img_name +'_' +
vizmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
elif vizmethod == 'VanillaBP':
j = 1
for i in attacks:
original_image,vanilbp,grayvanilbp,\
adversarial,vanilbp2,grayvanilbp2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runVanillaBP(choose_network,
isTrained,training,structure,
image_index,attack_type=i)
if j==1:
ax0 = fig.add_subplot(rows,4,1)
ax0.imshow(original_image)
ax0.set_title('Original Image')
ax1 = fig.add_subplot(rows,4,2)
ax1.imshow(vanilbp)
ax1.set_title('Vanilla BackProp')
ax2 = fig.add_subplot(rows,4,3)
ax2.imshow(grayvanilbp)
ax2.set_title('Vanilla BackProp GrayScale')
ax9 = fig.add_subplot(rows,4,4)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,4,j*4+1)
ax12.imshow(adversarial)
ax12.set_title(i + ' Attack')
ax3 = fig.add_subplot(rows,4,j*4+2)
ax3.imshow(vanilbp2)
ax3.set_title('Adversary Vanilla BackProp')
ax4 = fig.add_subplot(rows,4,j*4+3)
ax4.imshow(grayvanilbp2)
ax4.set_title('Adversary Vanilla BackProp GrayScale')
ax10 = fig.add_subplot(rows,4,j*4+4)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 1
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/AttackComp' +'_' +
img_name +'_' +
vizmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
# *********************************************************************************************************
# *********************************************************************************************************
def compareNetworks(attackmethod,vizmethod, image_index, training='Normal'):
isTrained = True
i = attackmethod
_,_,_,img_name,_ = get_params(image_index,'AlexNet',isTrained) #dont mind this alexnet here.
networks = ('AlexNet','VGG19','ResNet50','Custom')
structures = ('ResNet50','VGG19')
numberofmodels = len(networks)+len(structures)-1
rows = 2*numberofmodels
fig = plt.figure()
fig.suptitle('Comparing Networks:'+img_name+' - '+ attackmethod)
j = 0
n = 0
s = 0
while n<len(networks) and s<len(structures):
choose_network = networks[n]
structure = structures[s]
if vizmethod == 'Explain':
iters = 100
original_img,heat, mask,cam,adversarialpic,heat2, mask2, cam2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals= runExplain(choose_network,isTrained,
training,structure,
image_index,iters,
attack_type=i)
ax11 = fig.add_subplot(rows,5, 5*j+1)
ax11.imshow(original_img)
ax11.set_title('Original Image. Method: '+ vizmethod)
ax1 = fig.add_subplot(rows,5, 5*j+2)
ax1.imshow(heat)
ax1.set_title('Learned Mask Color')
ax2 = fig.add_subplot(rows,5, 5*j+3)
ax2.imshow(mask)
ax2.set_title('Learned Mask Gray')
ax3 = fig.add_subplot(rows,5, 5*j+4)
ax3.imshow(cam)
ax3.set_title('Cam Result')
ax9 = fig.add_subplot(rows,5, 5*j+5)
ax9.bar(indices, orig_vals, align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs, rotation=45, ha="right")
ax12 = fig.add_subplot(rows,5, 5*j+6)
ax12.imshow(adversarialpic)
ax12.set_title('Adversarial Image')
ax5 = fig.add_subplot(rows,5, 5*j+7)
ax5.imshow(heat2)
ax6 = fig.add_subplot(rows,5, 5*j+8)
ax6.imshow(mask2)
ax6.set_title('Adversary Mask Gray')
ax7 = fig.add_subplot(rows,5, 5*j+9)
ax7.imshow(cam2)
ax7.set_title('Adversary Cam Result')
ax10 = fig.add_subplot(rows,5, 5*j+10)
ax10.bar(indices, adver_vals, align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs, rotation=45, ha="right")
j += 2
elif vizmethod == 'GradCam':
original_image,gray,color,result,adversarial,gray2,color2,result2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGradCam(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,5,5*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,5,5*j+2)
ax1.imshow(gray)
ax1.set_title('Cam Grasycale')
ax2 = fig.add_subplot(rows,5,5*j+3)
ax2.imshow(color)
ax2.set_title('Cam HeatMap')
ax3 = fig.add_subplot(rows,5,5*j+4)
ax3.imshow(result)
ax3.set_title('Cam Result')
ax9 = fig.add_subplot(rows,5,5*j+5)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,5,5*j+6)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax4 = fig.add_subplot(rows,5,5*j+7)
ax4.imshow(gray2)
ax4.set_title('Adversary Cam Grasycale')
ax5 = fig.add_subplot(rows,5,5*j+8)
ax5.imshow(color2)
ax5.set_title('Adversary Cam HeatMap')
ax6 = fig.add_subplot(rows,5,5*j+9)
ax6.imshow(result2)
ax6.set_title('Adversary Cam Result')
ax10 = fig.add_subplot(rows,5,5*j+10)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j +=2
elif vizmethod == 'GBP':
original_image, colorgrads,graygrads,possal, negsal, \
adversarial,colorgrads2,graygrads2,possal2,negsal2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGBackProp(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax11 = fig.add_subplot(rows,6,6*j+1)
ax11.imshow(original_image)
ax11.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,6,6*j+2)
ax1.imshow(colorgrads)
ax1.set_title('Guided BP Color')
ax2 = fig.add_subplot(rows,6, 6*j+3)
ax2.imshow(graygrads)
ax2.set_title( 'Guided BP Gray')
ax3 = fig.add_subplot(rows,6, 6*j+4)
ax3.imshow(possal)
ax3.set_title('Positive Saliency')
ax4 = fig.add_subplot(rows,6, 6*j+5)
ax4.imshow(negsal)
ax4.set_title('Negative Saliency')
ax9 = fig.add_subplot(rows,6,6*j+6)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,6,6*j+7)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax5 = fig.add_subplot(rows,6, 6*j+8)
ax5.imshow(colorgrads2)
ax5.set_title('Adversarial Guided BP Color')
ax6 = fig.add_subplot(rows,6, 6*j+9)
ax6.imshow(graygrads2)
ax6.set_title('Adversarial'+ 'Guided BP Gray')
ax7 = fig.add_subplot(rows,6, 6*j+10)
ax7.imshow(possal2)
ax7.set_title('Adversarial ''Positive Saliency')
ax8 = fig.add_subplot(rows,6, 6*j+11)
ax8.imshow(negsal2)
ax8.set_title('Adversarial'+'Negative Saliency')
ax10 = fig.add_subplot(rows,6,6*j+12)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
elif vizmethod == 'GGradCam':
original_image, guidedgrad, grayguidedgrad,\
adversarial, guidedgrad2, grayguidedgrad2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGGradCam(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,4,4*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,4,4*j+2)
ax1.imshow(guidedgrad)
ax1.set_title('Guided Grad Cam')
ax2 = fig.add_subplot(rows,4,4*j+3)
ax2.imshow(grayguidedgrad)
ax2.set_title('Guided Grad Cam Grasycale')
ax9 = fig.add_subplot(rows,4,4*j+4)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,4,4*j+5)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax3 = fig.add_subplot(rows,4,64*j+6)
ax3.imshow(guidedgrad2)
ax3.set_title('Adversary Guided Grad Cam')
ax4 = fig.add_subplot(rows,4,4*j+7)
ax4.imshow(grayguidedgrad2)
ax4.set_title('Adversary Guided Grad Cam Grasycale')
ax10 = fig.add_subplot(rows,4,4*j+8)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
elif vizmethod == 'SmoothGrad':
original_image,colorgrads,graygrads,\
adversarial,colorgrads2,graygrads2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runsmoothGrad(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,4,4*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,4,4*j+2)
ax1.imshow(colorgrads)
ax1.set_title('Smooth BP')
ax2 = fig.add_subplot(rows,4,4*j+3)
ax2.imshow(graygrads)
ax2.set_title('Smooth BP Gray')
ax9 = fig.add_subplot(rows,4,4*j+4)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,4,4*j+5)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax3 = fig.add_subplot(rows,4,4*j+6)
ax3.imshow(colorgrads2)
ax3.set_title('Adversary Smooth BP')
ax4 = fig.add_subplot(rows,4, 4*j+7)
ax4.imshow(graygrads2)
ax4.set_title('Adversary Smooth BP Gray')
ax10 = fig.add_subplot(rows,4,4*j+8)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
elif vizmethod == 'VanillaBP':
original_image,vanilbp,grayvanilbp,\
adversarial,vanilbp2,grayvanilbp2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runVanillaBP(choose_network,
isTrained,training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,4,4*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,4,4*j+2)
ax1.imshow(vanilbp)
ax1.set_title('Vanilla BackProp')
ax2 = fig.add_subplot(rows,4,4*j+3)
ax2.imshow(grayvanilbp)
ax2.set_title('Vanilla BackProp GrayScale')
ax9 = fig.add_subplot(rows,4,4*j+4)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,4,4*j+5)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax3 = fig.add_subplot(rows,4,4*j+6)
ax3.imshow(vanilbp2)
ax3.set_title('Adversary Vanilla BackProp')
ax4 = fig.add_subplot(rows,4,4*j+7)
ax4.imshow(grayvanilbp2)
ax4.set_title('Adversary Vanilla BackProp GrayScale')
ax10 = fig.add_subplot(rows,4,4*j+8)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
if choose_network=='Custom':
s += 1
else:
n += 1
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/NetworkComp' +'_' +
img_name +'_' +
attackmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
# *********************************************************************************************************
# *********************************************************************************************************
def compareVisualizations(attackmethod,
choose_network,
image_index,
training='',
structure=''):
isTrained = True
i = attackmethod
_,_,_,img_name,_ = get_params(image_index,choose_network,isTrained,training, structure)
vizmethods = ['GradCam','GBP','GGradCam']#,'SmoothGrad','VanillaBP',Explain']
rows = 2*len(vizmethods)
fig = plt.figure()
fig.suptitle('Comparing Visualizations:'+img_name+' - '+ attackmethod)
j = 0
for vizmethod in vizmethods:
if vizmethod == 'Explain':
iters = 100
original_img,heat, mask,cam,adversarialpic,heat2, mask2, cam2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals= runExplain(choose_network,isTrained,
training,structure,
image_index,iters,
attack_type=i)
ax11 = fig.add_subplot(rows,6, 6*j+1)
ax11.imshow(original_img)
ax11.set_title('Original Image. Method: '+ vizmethod)
ax1 = fig.add_subplot(rows,6, 6*j+2)
ax1.imshow(heat)
ax1.set_title('Learned Mask Color')
ax2 = fig.add_subplot(rows,6, 6*j+3)
ax2.imshow(mask)
ax2.set_title('Learned Mask Gray')
ax3 = fig.add_subplot(rows,6, 6*j+4)
ax3.imshow(cam)
ax3.set_title('Cam Result')
ax9 = fig.add_subplot(rows,6, 6*j+6)
ax9.bar(indices, orig_vals, align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs, rotation=45, ha="right")
ax12 = fig.add_subplot(rows,6, 6*j+7)
ax12.imshow(adversarialpic)
ax12.set_title('Adversarial Image')
ax5 = fig.add_subplot(rows,6, 6*j+8)
ax5.imshow(heat2)
ax6 = fig.add_subplot(rows,6, 6*j+9)
ax6.imshow(mask2)
ax6.set_title('Adversary Mask Gray')
ax7 = fig.add_subplot(rows,6, 6*j+10)
ax7.imshow(cam2)
ax7.set_title('Adversary Cam Result')
ax10 = fig.add_subplot(rows,6, 6*j+12)
ax10.bar(indices, adver_vals, align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs, rotation=45, ha="right")
j += 2
elif vizmethod == 'GradCam':
original_image,gray,color,result,adversarial,gray2,color2,result2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGradCam(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,6,6*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,6,6*j+2)
ax1.imshow(gray)
ax1.set_title('Cam Grasycale')
ax2 = fig.add_subplot(rows,6,6*j+3)
ax2.imshow(color)
ax2.set_title('Cam HeatMap')
ax3 = fig.add_subplot(rows,6,6*j+4)
ax3.imshow(result)
ax3.set_title('Cam Result')
ax9 = fig.add_subplot(rows,6,6*j+6)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,6,6*j+7)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax4 = fig.add_subplot(rows,6,6*j+8)
ax4.imshow(gray2)
ax4.set_title('Adversary Cam Grasycale')
ax5 = fig.add_subplot(rows,6,6*j+9)
ax5.imshow(color2)
ax5.set_title('Adversary Cam HeatMap')
ax6 = fig.add_subplot(rows,6,6*j+10)
ax6.imshow(result2)
ax6.set_title('Adversary Cam Result')
ax10 = fig.add_subplot(rows,6,6*j+12)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j +=2
elif vizmethod == 'GBP':
original_image, colorgrads,graygrads,possal, negsal, \
adversarial,colorgrads2,graygrads2,possal2,negsal2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGBackProp(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax11 = fig.add_subplot(rows,6,6*j+1)
ax11.imshow(original_image)
ax11.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,6,6*j+2)
ax1.imshow(colorgrads)
ax1.set_title('Guided BP Color')
ax2 = fig.add_subplot(rows,6, 6*j+3)
ax2.imshow(graygrads)
ax2.set_title( 'Guided BP Gray')
ax3 = fig.add_subplot(rows,6, 6*j+4)
ax3.imshow(possal)
ax3.set_title('Positive Saliency')
ax4 = fig.add_subplot(rows,6, 6*j+5)
ax4.imshow(negsal)
ax4.set_title('Negative Saliency')
ax9 = fig.add_subplot(rows,6,6*j+6)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,6,6*j+7)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax5 = fig.add_subplot(rows,6, 6*j+8)
ax5.imshow(colorgrads2)
ax5.set_title('Adversarial Guided BP Color')
ax6 = fig.add_subplot(rows,6, 6*j+9)
ax6.imshow(graygrads2)
ax6.set_title('Adversarial'+ 'Guided BP Gray')
ax7 = fig.add_subplot(rows,6, 6*j+10)
ax7.imshow(possal2)
ax7.set_title('Adversarial ''Positive Saliency')
ax8 = fig.add_subplot(rows,6, 6*j+11)
ax8.imshow(negsal2)
ax8.set_title('Adversarial'+'Negative Saliency')
ax10 = fig.add_subplot(rows,6,6*j+12)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
elif vizmethod == 'GGradCam':
original_image, guidedgrad, grayguidedgrad,\
adversarial, guidedgrad2, grayguidedgrad2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGGradCam(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,6,6*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,6,6*j+2)
ax1.imshow(guidedgrad)
ax1.set_title('Guided Grad Cam')
ax2 = fig.add_subplot(rows,6,6*j+3)
ax2.imshow(grayguidedgrad)
ax2.set_title('Guided Grad Cam Grasycale')
ax9 = fig.add_subplot(rows,6,6*j+6)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,6,6*j+7)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax3 = fig.add_subplot(rows,6,6*j+8)
ax3.imshow(guidedgrad2)
ax3.set_title('Adversary Guided Grad Cam')
ax4 = fig.add_subplot(rows,6,6*j+9)
ax4.imshow(grayguidedgrad2)
ax4.set_title('Adversary Guided Grad Cam Grasycale')
ax10 = fig.add_subplot(rows,6,6*j+12)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
elif vizmethod == 'SmoothGrad':
original_image,colorgrads,graygrads,\
adversarial,colorgrads2,graygrads2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runsmoothGrad(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,6,6*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,6,6*j+2)
ax1.imshow(colorgrads)
ax1.set_title('Smooth BP')
ax2 = fig.add_subplot(rows,6,6*j+3)
ax2.imshow(graygrads)
ax2.set_title('Smooth BP Gray')
ax9 = fig.add_subplot(rows,6,6*j+6)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,6,6*j+7)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax3 = fig.add_subplot(rows,6,6*j+8)
ax3.imshow(colorgrads2)
ax3.set_title('Adversary Smooth BP')
ax4 = fig.add_subplot(rows,6, 6*j+9)
ax4.imshow(graygrads2)
ax4.set_title('Adversary Smooth BP Gray')
ax10 = fig.add_subplot(rows,6,6*j+12)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
elif vizmethod == 'VanillaBP':
original_image,vanilbp,grayvanilbp,\
adversarial,vanilbp2,grayvanilbp2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runVanillaBP(choose_network,
isTrained,training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,6,6*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Method:' +vizmethod)
ax1 = fig.add_subplot(rows,6,6*j+2)
ax1.imshow(vanilbp)
ax1.set_title('Vanilla BackProp')
ax2 = fig.add_subplot(rows,6,6*j+3)
ax2.imshow(grayvanilbp)
ax2.set_title('Vanilla BackProp GrayScale')
ax9 = fig.add_subplot(rows,6,6*j+6)
ax9.bar(indices,orig_vals,align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs,rotation = 45,ha="right")
ax12 = fig.add_subplot(rows,6,6*j+7)
ax12.imshow(adversarial)
ax12.set_title('Adversarial Image')
ax3 = fig.add_subplot(rows,6,6*j+8)
ax3.imshow(vanilbp2)
ax3.set_title('Adversary Vanilla BackProp')
ax4 = fig.add_subplot(rows,6,6*j+9)
ax4.imshow(grayvanilbp2)
ax4.set_title('Adversary Vanilla BackProp GrayScale')
ax10 = fig.add_subplot(rows,6,6*j+12)
ax10.bar(indices,adver_vals,align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs,rotation = 45,ha="right")
j += 2
fig.set_size_inches(32, 9*rows)
fig.tight_layout()
fig.savefig('Comparing/VisualizationComp' +'_' +
img_name +'_' +
attackmethod + ' (' +
choose_network +'_' +
training + '_' +
structure + ' )', dpi=100)
# *********************************************************************************************************
# *********************************************************************************************************
def compareTraining(attackmethod,
vizmethod,
structure,
image_index):
isTrained = True
choose_network = 'Custom'
i = attackmethod
_,_,_,img_name,_ = get_params(image_index,'AlexNet',True) #dont mind this alexnet here.
numberofmodels = 4
rows = 2*numberofmodels
fig = plt.figure()
fig.suptitle('Comparing Networks:'+img_name+' - '+ attackmethod)
n = 0
j = 0
ifNoise = 'No'
while n<numberofmodels:
if n==0:
training = 'Normal'
elif n==1:
training = 'Adversarial'
elif n ==2 :
training = 'Normal'
ifNoise = 'Yes'
print('add noise to the image and then give it to the normally trained network')
elif n ==3 :
training = 'Adversarial'
ifNoise = 'Yes'
print('add noise to the image and then give it to the normally trained network')
if vizmethod == 'Explain':
iters = 100
original_img,heat, mask,cam,adversarialpic,heat2, mask2, cam2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals= runExplain(choose_network,isTrained,
training,structure,
image_index,iters,
attack_type=i)
ax11 = fig.add_subplot(rows,5, 5*j+1)
ax11.imshow(original_img)
ax11.set_title('Original Image. Training: ' + training + 'Noise:' +ifNoise+ vizmethod)
ax1 = fig.add_subplot(rows,5, 5*j+2)
ax1.imshow(heat)
ax1.set_title('Learned Mask Color')
ax2 = fig.add_subplot(rows,5, 5*j+3)
ax2.imshow(mask)
ax2.set_title('Learned Mask Gray')
ax3 = fig.add_subplot(rows,5, 5*j+4)
ax3.imshow(cam)
ax3.set_title('Cam Result')
ax9 = fig.add_subplot(rows,5, 5*j+5)
ax9.bar(indices, orig_vals, align='center', alpha=0.5)
ax9.set_title('Orignial Image Predictions')
ax9.set_xticks(indices)
ax9.set_xticklabels(orig_labs, rotation=45, ha="right")
ax12 = fig.add_subplot(rows,5, 5*j+6)
ax12.imshow(adversarialpic)
ax12.set_title('Adversarial Image. Training: ' + training + 'Noise:' +ifNoise)
ax5 = fig.add_subplot(rows,5, 5*j+7)
ax5.imshow(heat2)
ax6 = fig.add_subplot(rows,5, 5*j+8)
ax6.imshow(mask2)
ax6.set_title('Adversary Mask Gray')
ax7 = fig.add_subplot(rows,5, 5*j+9)
ax7.imshow(cam2)
ax7.set_title('Adversary Cam Result')
ax10 = fig.add_subplot(rows,5, 5*j+10)
ax10.bar(indices, adver_vals, align='center', alpha=0.5)
ax10.set_title('Adversary Image Predictions')
ax10.set_xticks(indices)
ax10.set_xticklabels(adver_labs, rotation=45, ha="right")
j += 2
elif vizmethod == 'GradCam':
original_image,gray,color,result,adversarial,gray2,color2,result2,\
indices,orig_labs,orig_vals,adver_labs,adver_vals = runGradCam(choose_network,isTrained,
training,structure,
image_index,attack_type=i)
ax0 = fig.add_subplot(rows,5,5*j+1)
ax0.imshow(original_image)
ax0.set_title('Original Image. Training: ' + training + 'Noise:' +ifNoise +vizmethod)
ax1 = fig.add_subplot(rows,5,5*j+2)
ax1.imshow(gray)