/
plotter.py
600 lines (530 loc) · 27.4 KB
/
plotter.py
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import torch
import os
# dir_list=os.listdir("./total/")
# print(dir_list)
import math
import numpy as np
import glob
import argparse
# import matplotlib as mpl
from matplotlib.legend_handler import HandlerLine2D, HandlerTuple
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
mode='ImageNet'
save_suffix=''
parser = argparse.ArgumentParser(description='Runs GADA')
parser.add_argument('-c', '--config', type=str, default='_3DDFA_V2/configs/mb1_120x120.yml')
parser.add_argument('-f', '--img_fp', type=str, default='_3DDFA_V2/examples/inputs/trump_hillary.jpg')
parser.add_argument('-m', '--mode', type=str, default='cpu', help='gpu or cpu mode')
parser.add_argument('-o', '--opt', type=str, default='pncc',
choices=['2d_sparse', '2d_dense', '3d', 'depth', 'pncc', 'uv_tex', 'pose', 'ply', 'obj'])
parser.add_argument('--onnx', action='store_true', default=True)
parser.add_argument('--model', type=int, default=2, help='index of configurations')
parser.add_argument('--dict_model', type=int, default=3, help='index of configurations')
parser.add_argument('--attack', type=str, default='HSJA', help='attack method')
parser.add_argument('--defense', type=str, default='gaussian', help='defense method')
parser.add_argument('--dataset', type=str, default='LFW', help='dataset')
parser.add_argument('--result_dir', type=str, default='results', help='directory for saving results')
parser.add_argument('--sampled_image_dir', type=str, default='save', help='directory to cache sampled images')
parser.add_argument('--batch_size', type=int, default=1, help='number of image samples')
parser.add_argument('--max_num_queries', type=int, default=10000, help='number of image samples')
parser.add_argument('--log_interval', type=int, default=1000, help='number of image samples')
parser.add_argument('--num_runs', type=int, default=500, help='number of image samples')
parser.add_argument('--seed', type=int, default=1234, help='number of image samples')
parser.add_argument('--attack_batch_size', type=int, default=100, help='number of image samples')
parser.add_argument('--resume', type=bool, default=False, help='resume attack')
parser.add_argument('--targeted', type=bool, default=True, help='perform targeted attack')
parser.add_argument('--epsilon', type=float, default=0.2, help='step size per iteration')
parser.add_argument('--save_suffix', type=str, default='', help='suffix appended to save file')
args = parser.parse_args()
def get_pth_file_all(directory_path,ext=".pth"):
return glob.glob(os.path.abspath(directory_path)+"/*"+ext)
def mean(l, logarithm=False):
if logarithm:
l_lg = [np.log(x) for x in l]
return np.exp(sum(l_lg) / len(l_lg))
else:
return sum(l) / len(l)
if __name__=='__main__':
file_list = get_pth_file_all('./') # full path
name_list = [os.path.split(x)[1] for x in file_list]
print(name_list)
def load_and_plot_linf(file_name, start_img_idx=0, end_img_idx=500, start_iter=100, end_iter=10000, threshold=0.05,
logarithm=False):
log_data = torch.load(file_name)
total_num_queries = log_data['total_num_queries']
total_log_l_2 = log_data['total_log_l_2']
total_log_l_inf = log_data['total_log_l_inf']
total_log_is_adversarial = log_data['total_log_is_adversarial']
total_log_dist = log_data['total_log_dist']
total_log_last_adv_imgs = log_data['total_log_last_adv_imgs']
total_log_best_l_2_adv_imgs = log_data['total_log_best_l_2_adv_imgs']
total_log_best_l_inf_adv_imgs = log_data['total_log_best_l_inf_adv_imgs']
total_log_best_l_2 = log_data['total_log_best_l_2']
total_log_best_l_inf = log_data['total_log_best_l_inf']
end_idx = log_data['upper_idx'] // args.batch_size
print(file_name, end_idx)
end_idx = min(end_idx, end_img_idx)
best_log_l_inf = torch.zeros_like(total_log_l_inf[start_img_idx:end_idx])
best_val = torch.ones(end_idx - start_img_idx) * 5e1
for i in range(end_iter):
best_val = torch.min(best_val, 5e1 * (
1 - total_log_is_adversarial[start_img_idx:end_idx, i]) + total_log_is_adversarial[
start_img_idx:end_idx,
i] * total_log_l_inf[
start_img_idx:end_idx, i])
best_log_l_inf[:, i] = best_val
avg_X = torch.arange(start_iter, end_iter)
if logarithm == True:
avg_Y = torch.exp(torch.mean(torch.log(best_log_l_inf[:, start_iter:end_iter]), 0))
else:
avg_Y = torch.mean(best_log_l_inf[:, start_iter:end_iter], 0)
target_idx = [1000, 2000, 5000, 10000]
norms = torch.mean(best_log_l_inf, 0)[torch.LongTensor(target_idx) - 1].numpy()
thresholds = [0.05, 0.03]
Q = np.zeros(len(thresholds))
for i, t in enumerate(thresholds):
best_log_l_inf_t = best_log_l_inf.clone()
best_log_l_inf_t[best_log_l_inf <= t] = 0
best_log_l_inf_t[best_log_l_inf > t] = 1
Q[i] = ((torch.sum(best_log_l_inf_t) / (end_idx - start_img_idx)).item())
return avg_X, avg_Y, norms, Q
def load_and_plot(file_name,start_img_idx=0,end_img_idx=500,start_iter=100,end_iter=10000, threshold=4,logarithm=True):
log_data=torch.load(file_name)
total_num_queries = log_data['total_num_queries']
total_log_l_2 = log_data['total_log_l_2']
total_log_l_inf = log_data['total_log_l_inf']
total_log_is_adversarial = log_data['total_log_is_adversarial']
total_log_dist = log_data['total_log_dist']
total_log_last_adv_imgs = log_data['total_log_last_adv_imgs']
total_log_best_l_2_adv_imgs = log_data['total_log_best_l_2_adv_imgs']
total_log_best_l_inf_adv_imgs = log_data['total_log_best_l_inf_adv_imgs']
total_log_best_l_2 = log_data['total_log_best_l_2']
total_log_best_l_inf = log_data['total_log_best_l_inf']
end_idx = log_data['upper_idx'] // args.batch_size
print(file_name,end_idx)
end_idx=min(end_idx,end_img_idx)
best_log_l2=torch.zeros_like(total_log_l_2[start_img_idx:end_idx])
best_val=torch.ones(end_idx-start_img_idx)*5e1
for i in range(end_iter):
best_val=torch.min(best_val,5e1*(1-total_log_is_adversarial[start_img_idx:end_idx,i])+total_log_is_adversarial[start_img_idx:end_idx,i]*total_log_l_2[start_img_idx:end_idx,i])
best_log_l2[:,i]=best_val
avg_X=torch.arange(start_iter,end_iter)
if logarithm==True:
avg_Y=torch.exp(torch.mean(torch.log(best_log_l2[:,start_iter:end_iter]),0))
else:
avg_Y=torch.mean(best_log_l2[:,start_iter:end_iter],0)
target_idx=[1000,2000,5000,10000]
norms=torch.mean(best_log_l2,0)[torch.LongTensor(target_idx)-1].numpy()
thresholds=[4,2]
Q=np.zeros(len(thresholds))
for i,t in enumerate(thresholds):
best_log_l2_t=best_log_l2.clone()
best_log_l2_t[best_log_l2<=t]=0
best_log_l2_t[best_log_l2>t]=1
Q[i]=((torch.sum(best_log_l2_t)/(end_idx-start_img_idx)).item())
return avg_X,avg_Y,norms,Q
def load_and_plot_SD(file_name, start_img_idx=0, end_img_idx=500, start_iter=100, end_iter=10000, threshold=4,
logarithm=True):
log_data = torch.load(file_name)
total_num_queries = log_data['total_num_queries']
total_log_l_2 = log_data['total_log_l_2']
total_log_l_inf = log_data['total_log_l_inf']
total_log_is_adversarial = log_data['total_log_is_adversarial']
total_log_dist = log_data['total_log_dist']
total_log_last_adv_imgs = log_data['total_log_last_adv_imgs']
total_log_best_l_2_adv_imgs = log_data['total_log_best_l_2_adv_imgs']
total_log_best_l_inf_adv_imgs = log_data['total_log_best_l_inf_adv_imgs']
total_log_k_avg_dist = log_data['total_log_k_avg_dist']
total_log_best_l_inf = log_data['total_log_best_l_inf']
end_idx = log_data['upper_idx'] // args.batch_size
print(file_name, end_idx)
end_idx = min(end_idx, end_img_idx)
best_log_l2 = torch.zeros_like(total_log_l_2[start_img_idx:end_idx])
best_val = torch.ones(end_idx - start_img_idx) * 5e1
for i in range(end_iter):
best_val = torch.min(best_val, 5e1 * (
1 - total_log_is_adversarial[start_img_idx:end_idx, i]) + total_log_is_adversarial[
start_img_idx:end_idx,
i] * total_log_l_2[
start_img_idx:end_idx, i])
best_log_l2[:, i] = best_val
avg_X = torch.arange(start_iter, end_iter)
avg_Y = best_log_l2[10, start_iter:end_iter]
target_idx = [2000, 5000, 10000]
norms = torch.mean(best_log_l2, 0)[torch.LongTensor(target_idx) - 1].numpy()
total_log_k_avg_dist_t=total_log_k_avg_dist.clone()
total_log_k_avg_dist_t[total_log_k_avg_dist < 0.002] = 1
total_log_k_avg_dist_t[total_log_k_avg_dist >= 0.002] = 0
avg_k_avg_dist=total_log_k_avg_dist[10,start_iter:end_iter]
Q=torch.mean(torch.sum(total_log_k_avg_dist_t,1)).item()
return avg_X, avg_Y, avg_k_avg_dist,norms, Q
def load_and_plot_Dist(file_name, start_img_idx=0, end_img_idx=500, start_iter=100, end_iter=10000, threshold=4,
logarithm=True):
log_data = torch.load(file_name)
total_num_queries = log_data['total_num_queries']
total_log_l_2 = log_data['total_log_l_2']
total_log_l_inf = log_data['total_log_l_inf']
total_log_is_adversarial = log_data['total_log_is_adversarial']
total_log_dist = log_data['total_log_dist']
total_log_last_adv_imgs = log_data['total_log_last_adv_imgs']
total_log_best_l_2_adv_imgs = log_data['total_log_best_l_2_adv_imgs']
total_log_best_l_inf_adv_imgs = log_data['total_log_best_l_inf_adv_imgs']
total_log_best_l_inf = log_data['total_log_best_l_inf']
end_idx=log_data['upper_idx']
end_idx = min(end_idx, end_img_idx)
print(file_name, end_idx)
best_log_l2 = torch.zeros_like(total_log_l_2[start_img_idx:end_idx,:end_iter])
best_val = torch.ones(end_idx - start_img_idx) * 5e1
for i in range(end_iter):
best_val = torch.min(best_val, 5e1 * (
1 - total_log_is_adversarial[start_img_idx:end_idx, i]) + total_log_is_adversarial[
start_img_idx:end_idx,
i] * total_log_l_2[
start_img_idx:end_idx, i])
best_log_l2[:, i] = best_val
return best_log_l2
def load_and_plot_IMG(file_name, start_img_idx=0, end_img_idx=500, start_iter=100, end_iter=10000, idx=0, title=False,first=0):
log_data = torch.load(file_name)
total_num_queries = log_data['total_num_queries']
total_log_l_2 = log_data['total_log_l_2']
total_log_l_inf = log_data['total_log_l_inf']
total_log_is_adversarial = log_data['total_log_is_adversarial']
total_log_dist = log_data['total_log_dist']
total_log_last_adv_imgs = log_data['total_log_last_adv_imgs']
total_log_best_l_2_adv_imgs = log_data['total_log_best_l_2_adv_imgs']
total_log_best_l_inf_adv_imgs = log_data['total_log_best_l_inf_adv_imgs']
total_log_best_l_inf = log_data['total_log_best_l_inf']
end_idx = log_data['upper_idx'] // args.batch_size
print(file_name, end_idx)
end_idx = min(end_idx, end_img_idx)
best_log_l2 = torch.zeros_like(total_log_l_2[idx])
best_val = torch.ones(1) * 5e1
for i in range(end_iter):
best_val = torch.min(best_val, 5e1 * (
1 - total_log_is_adversarial[idx, i]) + total_log_is_adversarial[
idx,
i] * total_log_l_2[
idx, i])
best_log_l2[i] = best_val
left, width = .25, .5
bottom, height = .25, .5
right = left + width
top = bottom + height
#fig, ax = plt.subplots(figsize=(10, 1), tight_layout=True)
im_idx=[0,1,2,5,9]
fig, axs = plt.subplots(nrows=1, ncols=len(im_idx), gridspec_kw={'wspace': 0, 'hspace': 0},
squeeze=True, tight_layout=True)
for i,k in enumerate(im_idx):
if first==0:
axs[i].text(0.5 * (left + right), 1+0.1, '%dK'%(k+1),
horizontalalignment='center',
verticalalignment='center',
fontsize=11, color='black',
transform=axs[i].transAxes)
batch = total_log_best_l_2_adv_imgs[idx, k]
x_adv_i = np.transpose(batch.numpy(), [1, 2, 0])
#axs[i].set_title('%.2f'%best_log_l2[(k+1)*1000-1].item())
axs[i].text(0.5 * (left + right), -0.1, '%.2f'%best_log_l2[(k+1)*1000-1].item(),
horizontalalignment='center',
verticalalignment='center',
fontsize=11, color='black',
transform=axs[i].transAxes)
axs[i].axis("off")
axs[i].imshow(x_adv_i)
# build a rectangle in axes coords
axs[0].text(-0.1, 0.5 * (bottom + top), title,
horizontalalignment='right',
verticalalignment='center',
rotation='vertical',
fontsize=11, color='black',
transform=axs[0].transAxes)
if False:
img_indices=[190,258,298,430,455]
for img_idx in img_indices:
dt='CPLFW'
targeted='1'
attacks = ['SO','EA']
model = 'IR_50'
defense = 'gaussian'
colors = ['#1f77b4',
'#ff7f0e',
'#2ca02c',
'#d62728',
'#9467bd',
'#8c564b',
'#e377c2',
'#7f7f7f',
'#bcbd22',
'#17becf',
'#1a55FF']
# colors=['tab:blue','tab:red','g','tab:purple','tab:red','tab:cyan','tab:brown','tab:pink']
PLOT_INFO = []
for i, at in enumerate(attacks):
fname = dt + '_500_' + targeted + '_' + at + '_' + model + '_' + defense + '_.pth'
if os.path.exists(fname):
PLOT_INFO.append((fname, colors[i], at, [0, 500, 1000, 10000], ''))
fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)
avg_X = []
avg_y = []
plot_info = []
labels = []
for i, logs in enumerate(PLOT_INFO):
at = logs[2]
load_and_plot_IMG(logs[0], start_img_idx=logs[3][0], end_img_idx=logs[3][1],
start_iter=logs[3][2], end_iter=logs[3][3], idx=img_idx, title=at, first=i)
plt.savefig(dt + '_500_' + targeted + '_' + model + '_IMG_' + str(img_idx) + '_'+ at + '.pdf', dpi=300, bbox_inches='tight')
plt.close()
#
PLOT_INFO = [
('CPLFW_500_0_EA_IR_50_gaussian_.pth', 'm', 'EA',[0,500,1000,10000],''),
('CPLFW_500_0_EAGD_IR_50_gaussian_.pth', 'r', 'EAGD',[0,500,1000,10000],''),
]
fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)
avg_X = []
avg_y = []
labels = []
for i, logs in enumerate(PLOT_INFO):
l2_norm = load_and_plot_Dist(logs[0], start_img_idx=logs[3][0], end_img_idx=logs[3][1],
start_iter=logs[3][2], end_iter=logs[3][3], logarithm=False)
if i==1:
print(torch.topk(l2_norm_o[:,-1]-l2_norm[:,-1],30,0))
#print(torch.sum((l2_norm_o[:,-1]-l2_norm[:,-1])>0))
l2_norm_o=l2_norm.clone()
##############################################
if False:
print('SD Graph')
datasets = ['LFW'] # 'LFW',
attacks = ['EA','EAR','EAG','EAGR'] #'SO', 'HSJA','GD',
model = 'IR_50'
defense = 'SD'
colors = ['#1f77b4',
'#ff7f0e',
'#2ca02c',
'#d62728',
'#9467bd',
'#8c564b',
'#e377c2',
'#7f7f7f',
'#bcbd22',
'#17becf',
'#1a55FF']
PLOT_INFO = []
for i, at in enumerate(attacks):
fname = 'LFW_100_0_' + at + '_' + model + '_' + defense + '_.pth'
if os.path.exists(fname):
PLOT_INFO.append((fname, colors[i], at, [0, 100, 500, 10000], ''))
fig, ax1 = plt.subplots(figsize=(6, 4), tight_layout=True)
ax1.set_xlabel('# Queries', fontsize=12)
ax1.set_ylabel('$\ell_2$ norm of perturbation', fontsize=12)
ax1.tick_params(axis='y')
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
ax2.set_ylabel('k-NN dist in stateful detection', fontsize=12)
ax2.tick_params(axis='y')
avg_X = []
avg_y = []
plot_info = []
labels = []
legends=[]
legends_labels=[]
for i, logs in enumerate(PLOT_INFO):
x, y, k_avg_dist,norms, detections = load_and_plot_SD(logs[0], start_img_idx=logs[3][0], end_img_idx=logs[3][1],
start_iter=logs[3][2], end_iter=logs[3][3], logarithm=False)
x = x[::2].numpy()
y = y[::2].numpy()
k_avg_dist = k_avg_dist[::2]
for n_item in norms:
print("%.2f " % (n_item), end='')
print("%.1f" %detections, end='')
print()
l=ax1.plot(x, y, logs[1], label=logs[2])
r=ax2.plot(x, k_avg_dist, logs[1],label=logs[2], alpha=0.5) #linestyle='dashed',
legends.append((l[0],r[0]))
legends_labels.append(logs[2])
threshold=0.002*np.ones_like(k_avg_dist)
r = ax2.plot(x, threshold, 'k', label='Detection threshold', alpha=1) # linestyle='dashed',
legends.append((r[0]))
legends_labels.append('Detection threshold')
plt.legend(handles=legends,labels=legends_labels,numpoints=1, handler_map={tuple: HandlerTuple(ndivide=None)}) #
#ax2.set_ylim(0,1.5)
ax2.set_yscale('log')
#legend = ax.legend(loc='upper center', shadow=True, fontsize='x-large')
#legend.get_frame().set_facecolor('white')
#ax2.ylim(0,0.1)
plt.xticks([1000, 2000, 5000, 10000], ['1K', '2K', '5K', '10K'])
plt.savefig('SD_Graph.pdf', dpi=300, bbox_inches='tight')
plt.show()
##############################################
if False:
datasets = ['LFW'] # 'LFW',
attacks = ['EAGDR', 'EAGDO', 'EAGD'] #'EV', 'EVD', 'EVG','EV', 'EVD', 'EVG','EAGD'
model = 'IR_50'
defense = 'gaussian'
colors = ['#1f77b4',
'#ff7f0e',
'#2ca02c',
'#d62728',
'#9467bd',
'#8c564b',
'#e377c2',
'#7f7f7f',
'#bcbd22',
'#17becf',
'#1a55FF']
# colors=['tab:blue','tab:red','g','tab:purple','tab:red','tab:cyan','tab:brown','tab:pink']
for targeted in ['0', '1']:
for dt in datasets:
PLOT_INFO = []
for i, at in enumerate(attacks):
fname = dt + '_500_' + targeted + '_' + at + '_' + model + '_' + defense + '_.pth'
at=at.replace('EAGDO','EAGD1')
if os.path.exists(fname):
PLOT_INFO.append((fname, colors[i], at, [0, 500, 1000,3000], ''))
fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True)
avg_X = []
avg_y = []
plot_info = []
labels = []
for i, logs in enumerate(PLOT_INFO):
x, y, norms, queries = load_and_plot(logs[0], start_img_idx=logs[3][0], end_img_idx=logs[3][1],
start_iter=logs[3][2], end_iter=logs[3][3], logarithm=False)
x = x[::2].numpy()
y = y[::2].numpy()
for n_item in norms:
print("%.2f " % (n_item), end='')
for n_item in queries:
print("%d " % (n_item), end='')
print()
plt.plot(x, y, logs[1], label=logs[2])
plt.legend()
plt.title('Dodging attacks on the LFW dataset', fontsize=12)
#plt.xticks([1000, 2000, 5000, 10000], ['1K', '2K', '5K', '10K'])
plt.xticks([1000, 2000, 3000], ['1K', '2K', '3K'])
ax.set_xlabel('# Queries', fontsize=12)
#ax.set_yscale('log')
ax.set_ylabel('$\ell_2$ norm of perturbation', fontsize=12)
plt.savefig('Dodging_LFW.pdf', dpi=300, bbox_inches='tight')
plt.show()
# Main Table Results
# LFW CPLFW
# Impersonation Attack
# Dogging Attack
if True:
datasets=['LFW','CPLFW'] #'LFW',
attacks=['SO','HSJA','EA','EAD','EAG','EAGD']
model='IR_101'
defense='gaussian'
colors = ['#1f77b4',
'#ff7f0e',
'#2ca02c',
'#d62728',
'#9467bd',
'#8c564b',
'#e377c2',
'#7f7f7f',
'#bcbd22',
'#17becf',
'#1a55FF']
# colors=['tab:blue','tab:red','g','tab:purple','tab:red','tab:cyan','tab:brown','tab:pink']
for targeted in ['0','1']:#
for dt in datasets:
if targeted == '1':
if dt=='LFW':
attacks = ['SO', 'HSJA', 'EA', 'EAG'] # 'GD'
else:
attacks = ['SO', 'HSJA', 'EA', 'EAG'] # 'GD'
else:
attacks = ['SO', 'HSJA', 'EA', 'EAD', 'EAG', 'EAGD'] # 'GD'
PLOT_INFO=[]
for i, at in enumerate(attacks):
fname=dt+'_500_'+targeted+'_'+at+'_'+model+'_'+defense+'_.pth'
if os.path.exists(fname):
PLOT_INFO.append((fname,colors[i],at,[0,500,1000,10000],''))
fig, ax = plt.subplots(figsize=(6,4), tight_layout=True)
avg_X = []
avg_y = []
plot_info = []
labels = []
for i, logs in enumerate(PLOT_INFO):
x, y ,norms,queries= load_and_plot(logs[0], start_img_idx=logs[3][0], end_img_idx=logs[3][1],
start_iter=logs[3][2], end_iter=logs[3][3], logarithm=False)
x = x[::10].numpy()
y = y[::10].numpy()
for n_item in norms:
print(" %.2f &"%(n_item),end='')
for n_item in queries:
print(" %d &"%( n_item), end='')
print()
at=logs[2]
plt.plot(x, y, logs[1], label=at)
plt.legend()
#plt.grid(alpha=0.3)
plt.xticks([1000, 2000, 5000, 10000], ['1K', '2K', '5K', '10K'])
if targeted=='1':
plt.title('Impersonation attacks on the '+dt+' dataset')
else:
plt.title('Dodging attacks on the '+dt+' dataset')
ax.set_xlabel('# Queries', fontsize=12)
ax.set_ylabel('$\ell_2$ norm of perturbation', fontsize=12)
plt.savefig(dt+'_'+targeted+'_'+model+'.pdf', dpi=300, bbox_inches='tight')
plt.show()
if False:
datasets = ['LFW', 'CPLFW'] # 'LFW',
attacks = ['SFA', 'SFAD', 'SFAG', 'SFAGD'] #
model = 'IR_50'
defense = 'gaussian'
colors = ['#1f77b4',
'#ff7f0e',
'#2ca02c',
'#d62728',
'#9467bd',
'#8c564b',
'#e377c2',
'#7f7f7f',
'#bcbd22',
'#17becf',
'#1a55FF']
for targeted in ['0', '1']:
for dt in datasets:
PLOT_INFO = []
for i, at in enumerate(attacks):
fname = dt + '_500_' + targeted + '_' + at + '_' + model + '_' + defense + '_.pth'
if os.path.exists(fname):
PLOT_INFO.append((fname, colors[i], at, [0, 500, 1000, 10000], ''))
fig, ax1 = plt.subplots(figsize=(6, 4), tight_layout=True)
ax1.set_xlabel('# Queries', fontsize=12)
ax1.set_ylabel('$\ell_\infty$ norm of perturbation', fontsize=12)
ax1.tick_params(axis='y')
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
ax2.set_ylabel('$\ell_2$ norm of perturbation', fontsize=12)
ax2.tick_params(axis='y')
avg_X = []
avg_y = []
plot_info = []
labels = []
legends = []
legends_labels = []
for i, logs in enumerate(PLOT_INFO):
x_2, y_2, norms, queries = load_and_plot(logs[0], start_img_idx=logs[3][0], end_img_idx=logs[3][1],
start_iter=logs[3][2], end_iter=logs[3][3],
logarithm=False)
x, y, norms, queries = load_and_plot_linf(logs[0], start_img_idx=logs[3][0], end_img_idx=logs[3][1],
start_iter=logs[3][2], end_iter=logs[3][3],
logarithm=False)
x = x[::10].numpy()
y = y[::10].numpy()
for n_item in norms:
print("%.4f " % (n_item), end='')
for n_item in queries:
print("%d " % (n_item), end='')
print()
l = ax1.plot(x, y, logs[1], label=logs[2])
r = ax2.plot(x_2, y_2, logs[1], label=logs[2], linestyle='dashed',alpha=0.6) # linestyle='dashed',
legends.append((l[0], r[0]))
legends_labels.append(logs[2])
plt.legend(handles=legends, labels=legends_labels, numpoints=1, handlelength=3,
handler_map={tuple: HandlerTuple(ndivide=None)}) #
plt.xticks([1000, 2000, 5000, 10000], ['1K', '2K', '5K', '10K'])
plt.savefig('L_infty_Graph.pdf', dpi=300, bbox_inches='tight')
plt.show()