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How to use _cdiff method in avocado
Best Python code snippet using avocado_python
attack.py
Source:attack.py 
1#!/usr/bin/env python32from matplotlib.patches import Circle3import matplotlib.pyplot as plt4import argparse5import os6import copy7import pandas as pd8import numpy as np9import tensorflow as tf1011os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'12from tensorflow.keras.models import load_model13#from keras.datasets import cifar1014import pickle15from scipy.optimize import dual_annealing # for local search 16# Helper functions17from differential_evolution import differential_evolution18#from scipy.optimize import differential_evolution19import helper202122class PixelAttacker:23 def __init__(self, models, data, class_names, dimensions=(224, 224)):24 # Load data and model25 self.models = models26 self.x_test, self.y_test = data27 self.class_names = class_names28 self.dimensions = dimensions29 # The below line is revised to keep the probabilities30 network_stats, correct_imgs, self.original_probability = helper.evaluate_models(self.models, self.x_test, self.y_test)31 self.correct_imgs = pd.DataFrame(correct_imgs, columns=['name', 'img', 'label', 'confidence', 'pred'])32 self.network_stats = pd.DataFrame(network_stats, columns=['name', 'accuracy', 'param_count'])3334 def predict_classes(self, xs, img, target_class, model, minimize=True):35 # Perturb the image with the given pixel(s) x and get the prediction of the model36 imgs_perturbed = helper.perturb_image(xs, img)37 predictions = model.predict(imgs_perturbed)[:, target_class]38 # This function should always be minimized, so return its complement if needed39 return predictions if minimize else 1 - predictions4041 def attack_success(self, x, img, target_class, model, targeted_attack=False, verbose=False, epsilon=0.5, no_stop=False):42 # Perturb the image with the given pixel(s) and get the prediction of the model43 attack_image = helper.perturb_image(x, img)4445 confidence = model.predict(attack_image)[0]46 predicted_class = np.argmax(confidence)4748 # If the prediction is what we want (misclassification or 49 # targeted classification), return True50 if verbose:51 print('Confidence:', confidence[target_class])52 if (confidence[target_class] <= epsilon and not no_stop and53 ((targeted_attack and predicted_class == target_class) or54 (not targeted_attack and predicted_class != target_class))):55 return True5657 def attack(self, img_id, model, target=None, pixel_count=1,58 maxiter=75, popsize=400, verbose=False, plot=False, DE='DE', epsilon=0.5, LS=0, no_stop=False):59 # Change the target class based on whether this is a targeted attack or not60 targeted_attack = target is not None61 target_class = target if targeted_attack else self.y_test[img_id, 0]6263 # Define bounds for a flat vector of x,y,r,g,b values64 # For more pixels, repeat this layout65 dim_x, dim_y = self.dimensions66 bounds = [(0, dim_x), (0, dim_y), (-1, 1), (-1, 1),(-1, 1)] * pixel_count6768 # Population multiplier, in terms of the size of the perturbation vector x69 popmul = max(1, popsize // len(bounds))7071 # Format the predict/callback functions for the differential evolution algorithm72 def predict_fn(xs):73 return self.predict_classes(xs, self.x_test[img_id], target_class, model, target is None)7475 def callback_fn(x, convergence):76 return self.attack_success(x, self.x_test[img_id], target_class, model, targeted_attack, verbose, epsilon=epsilon, no_stop=no_stop)7778 # Call Scipy's Implementation of Differential Evolution79 attack_result = differential_evolution(80 predict_fn, bounds, maxiter=maxiter, popsize=popmul,81 recombination=1, atol=-1, callback=callback_fn, polish=False, disp=True, DE=DE, LS=LS)82 83 # Calculate some useful statistics to return from this function84 attack_image = helper.perturb_image(attack_result.x, self.x_test[img_id])[0]85 prior_probs = model.predict(np.array([self.x_test[img_id]]))[0]86 predicted_probs = model.predict(np.array([attack_image]))[0]87 predicted_class = np.argmax(predicted_probs)88 actual_class = self.y_test[img_id, 0]89 success = predicted_probs[actual_class] < epsilon and (predicted_class != actual_class)90 cdiff = prior_probs[actual_class] - predicted_probs[actual_class]9192 # Show the best attempt at a solution (successful or not)93 if plot:94 helper.plot_image(attack_image, actual_class, self.class_names, predicted_class)9596 return [model.name, pixel_count, img_id, actual_class, predicted_class, success, cdiff, prior_probs,97 predicted_probs, attack_result.x]9899 def attack_all_least(self, models, samples=500, pixels=(64,), targeted=False,100 maxiter=75, popsize=400, verbose=False, DE='DE', epsilon=0.5, LS=0, no_stop=False):101 results = []102 target = None103 for model in models:104 model_results = []105 valid_imgs = self.correct_imgs[self.correct_imgs.name == model.name].img106 #img_samples = np.random.choice(valid_imgs, samples)107 img_samples = valid_imgs#[0:samples] ## revised by Yisong 2020.09.06, choose the first samples of images to attack108 #img_samples = valid_imgs * samples ## revised by Yisong 2020.08.29109110 for i, img in enumerate(img_samples):111 print(model.name, '- image', img, '-', i + 1, '/', len(img_samples))112 print('The original confidence: %f'%self.original_probability[i][self.y_test[i]])113 old_result = [] 114 pixel_count = pixels[0] # the up_pixel_count115 low_pixel_count = 0116 up_pixel_count = pixel_count117 fail_pixel_count = 0118 while(up_pixel_count - low_pixel_count > 1):119 print('The number of pixels is:', pixel_count)120 result = self.attack(img, model, target, pixel_count,121 maxiter=maxiter, popsize=popsize,122 verbose=verbose, DE=DE, epsilon=epsilon, LS=LS, no_stop=no_stop)123 if len(old_result)==0: 124 old_result = copy.copy(result)125 if not result[5]: break # fail with the most pixels126127 if result[5]: # success attack128 old_result = copy.copy(result)129 up_pixel_count = pixel_count130 else:131 low_pixel_count = pixel_count132133 pixel_count = int(low_pixel_count + (up_pixel_count - low_pixel_count)/2)134135 model_results.append(old_result)136 results += model_results137 helper.checkpoint(results, targeted)138 return results139140 def attack_all(self, models, samples=500, pixels=(1, 3, 5), targeted=False,141 maxiter=75, popsize=400, verbose=False, DE='DE', epsilon=0.5, LS=0, no_stop=False):142 results = []143 for model in models:144 model_results = []145 valid_imgs = self.correct_imgs[self.correct_imgs.name == model.name].img146 #img_samples = np.random.choice(valid_imgs, samples)147 img_samples = valid_imgs#[0:samples] ## revised by Yisong 2020.09.06, choose the first samples of images to attack148 #img_samples = valid_imgs * samples ## revised by Yisong 2020.08.29149150 for pixel_count in pixels:151 for i, img in enumerate(img_samples):152 print(model.name, '- image', img, '-', i + 1, '/', len(img_samples))153 targets = [None] if not targeted else range(10)154 print('The original confidence: %f'%self.original_probability[i][self.y_test[i]])155 for target in targets:156 if targeted:157 print('Attacking with target', self.class_names[target])158 if target == self.y_test[img, 0]:159 continue160 result = self.attack(img, model, target, pixel_count,161 maxiter=maxiter, popsize=popsize,162 verbose=verbose, DE=DE, epsilon=epsilon, LS=LS, no_stop=no_stop)163 model_results.append(result)164165 results += model_results166 helper.checkpoint(results, targeted)167 return results168169def ultra_attack(model, samples, target):170 ''' one-pixel attacking each sample in samples by reversing the point171 '''172 bounds = [(-1, 1), (-1, 1),(-1, 1)]173 limits = np.array(bounds, dtype='float').T 174 __scale_arg1 = 0.5 * (limits[0] + limits[1])175 __scale_arg2 = np.fabs(limits[0] - limits[1])176177 def _scale_parameters(trial):178 """179 scale from a number between 0 and 1 to parameters.180 """181 return __scale_arg1 + (trial - 0.5) * __scale_arg2182183 def _unscale_parameters(parameters):184 """185 scale from parameters to a number between 0 and 1.186 """187 return (parameters - __scale_arg1) / __scale_arg2 + 0.5188189 def _farmost(parameters):190 t = np.zeros((3))191 for i in range(3):192 if parameters[i] < 0: 193 t[i] = 1.194 else:195 t[i] = -1.196 return t197198 for i in range(len(samples)):199 print('starting attack the %d sample'%i)200 best_prob = 1.201 type = target[i]202 203 for x in range(224):204 for y in range(224):205 rgb = samples[i][x,y]206 samples[i][x,y] = _farmost(rgb) #_scale_parameters( 1.0 - _unscale_parameters(samples[i][x,y]) )207 prob = model.predict(samples[i].reshape(1,224,224,3))[0][type] 208 samples[i][x,y] = rgb209 if best_prob > prob: 210 best_prob, best_x, best_y, = prob, x, y 211 if best_prob < 0.5: break212 if best_prob < 0.5: break 213 print('best solution x=%d, y=%d with probability=%f'%(best_x,best_y, best_prob))214 print(samples[i][best_x,best_y])215216def get_IDs_RA(up, low, pdata,type=1, top_k=10):217# get the ids to do repeat attack218 _f = open(pdata,'rb')219 _data = pickle.load(_f)220 _f.close()221 IDs = [] 222 ## choise the top k cdiff 223 dlen = len(_data)224 t_data = np.zeros((dlen,4)) # to keep ID prior-conf attacked-conf cdiff225 for i in range(dlen):226 t_data[i][0], t_data[i][1], t_data[i][2], t_data[i][3] = _data[i][2], _data[i][7][type], _data[i][8][type], _data[i][6] 227 T_data = t_data[np.argsort(t_data[:,3])][ dlen - top_k:] 228 return T_data229230if __name__ == '__main__': 231 parser = argparse.ArgumentParser(description='Attack models on DR, CXR, DERM') 232 parser.add_argument('--source', default='dr', help='The source of being attacked[cxr/dr/derm].')233 parser.add_argument('--model', default='./model/dr/wb_model.h5', help='The trained model to be attacked.')234 parser.add_argument('--pixels', nargs='+', default=(1,), type=int,235 help='The number of pixels that can be perturbed.')236 parser.add_argument('--maxiter', default=35, type=int,237 help='The maximum number of iterations in the differential evolution algorithm before giving up and failing the attack.')238 parser.add_argument('--popsize', default=400, type=int,239 help='The number of adversarial images generated each iteration in the differential evolution algorithm. Increasing this number requires more computation.')240 parser.add_argument('--samples', default=8, type=int, 241 help='The number of image samples to attack. Images are sampled randomly from the dataset.')242 parser.add_argument('--targeted', action='store_true', help='Set this switch to test for targeted attacks.')243 parser.add_argument('--data', default='dr_pos_data_10.npy', help='The data file.')244 parser.add_argument('--type', default=1, type=int, help='The type of attacked samples, 0: negative, 1: positive.')245 parser.add_argument('--LP', action='store_true', help='Compute the least pixels to sucessfully attack.')246 parser.add_argument('--save', default='results.pkl', help='Save location for the results (pickle)')247 parser.add_argument('--DE', default='DE', help='The differential evolution algorithm: DE, SHADE, EBLSHADE.')248 parser.add_argument('--epsilon', default=0.5, type=float, help='The confidence threshold.')249 parser.add_argument('--no_stop', action='store_true', help='Do not stop when find a solution.')250 parser.add_argument('--LS', default=0, type=int, help='Do local searching: (0: no local search; 1: local search at the end; 2: local search at each better solution; 4: local search at each better solution and replace the best with the the new one if it is better than the last best one.).')251 parser.add_argument('--verbose', action='store_true', help='Print out additional information every iteration.')252253 # The following paremeters are for repeating attack 254 parser.add_argument('--RA', default=1, type=int, help='The repeating times of repeating attacks')255 parser.add_argument('--RAn', default=10, type=int, help='The number samples of repeating attack')256 parser.add_argument('--low', default=0.1, type=float, help='The confidence after first attack')257 parser.add_argument('--up', default=0.9, type=float, help='The confidence before first attack')258 parser.add_argument('--pickle', type=str, help='The results pickle file')259260 args = parser.parse_args() 261262 class_names = ['positive','negative']263 model = load_model('./model/'+args.source + '/wb_model.h5')264 true_label = np.load('./data/'+args.source + '/val_test_y.npy')265 predictions = np.load('./data/'+args.source +'/winning_model_preds.npy')266 data = np.load('./data/'+args.source + '/val_test_x_preprocess.npy')267 both_true = (true_label[:,args.type] >= 0.5) & (predictions[:,args.type] >= 0.5)268 if args.samples == -1 or args.samples > sum(both_true):269 args.samples = sum(both_true)270 test = data[both_true][0:args.samples], np.array([[args.type]]*args.samples)271272 if args.RA > 1:273 _ids = get_IDs_RA(args.up, args.low, 'results/' + args.source + '/' + args.pickle, args.type, top_k=args.RAn)274 _test = np.zeros((len(_ids),224,224,3)) 275 for i in range(min(len(_ids), args.RAn)): 276 print(_ids[i])277 _test[i] = test[0][ int(_ids[i][0]) ]278 test = _test, np.array([[args.type]] * len(_ids))279 RA_results = []280 attacker = PixelAttacker([model], test, class_names)281 for i in range(args.RA):282 results = attacker.attack_all([model], samples=len(_ids), pixels=args.pixels, targeted=args.targeted, \283 maxiter=args.maxiter, popsize=args.popsize, verbose=args.verbose, DE=args.DE, \284 epsilon=args.epsilon, LS=args.LS, no_stop=args.no_stop)285 RA_results += results286 columns = ['model', 'pixels', 'image', 'true', 'predicted', 'success', 'cdiff', 'prior_probs', 'predicted_probs', 'perturbation']287 results_table = pd.DataFrame(RA_results, columns=columns)288 print(results_table[['model', 'pixels', 'image', 'true', 'predicted', 'success']])289 print('Saving to', args.save)290 with open(args.save, 'wb') as file:291 pickle.dump(RA_results, file)292 293 ## draw the figures with annotated attack points294 # for each sample 295 for i in range(min(len(_ids), args.RAn)):296 _path = './results/' + args.source + '/'297 _file_name = _path + 'RA-' +str(i) + '-' + str( int(_ids[i][0]) ) + '-' + str(args.type) + '.png'298299 # the original image300 fix,ax = plt.subplots(1)301 ax.imshow(_test[i], origin='lower')302 plt.title(' Prior conf=' + format(RA_results[i][7][args.type],'.4f'))303 plt.xlabel('ID='+str(int(_ids[i][0])))304 plt.savefig(_path + 'RA-ori-' + str(i) + '-' + str( int(_ids[i][0]) ) + '-' + str(args.type) + '.png')305 plt.close('all')306307 # the attacked image308 fig,ax = plt.subplots(1)309 ax.set_aspect('equal')310 _cdiff = 0.0311 312 # get the attacking pixels 313 ## draw circles of repeated attacks 314 for j in range(args.RA):315 _px, _py = int(RA_results[j * args.RAn + i][9][0]), int(RA_results[j * args.RAn + i][9][1])316 print(j, _px, _py)317 _cdiff += RA_results[j * args.RAn + i][6] 318 plt.scatter(_px, _py, color='', edgecolor='b', marker='o', alpha=.5, s=150)319 _test[i][_py,_px] = RA_results[j * args.RAn + i][9][2], RA_results[j * args.RAn + i][9][3], RA_results[j * args.RAn + i][9][4] 320 321 _title = ' cdiff=' + format(_cdiff/args.RA,'.4f')322 plt.title(_title)323 plt.xlabel('ID='+str(int(_ids[i][0])))324 ax.imshow(_test[i], origin='lower')325 plt.savefig(_file_name)326 plt.close('all')327 328 os._exit(0) 329 330331 attacker = PixelAttacker([model], test, class_names)332333 print('Starting attack')334 335 if args.LP:336 results = attacker.attack_all_least([model], samples=args.samples, pixels=args.pixels, targeted=args.targeted,337 maxiter=args.maxiter, popsize=args.popsize, verbose=args.verbose, DE=args.DE, 338 epsilon=args.epsilon, LS=args.LS, no_stop=args.no_stop)339 else:340 results = attacker.attack_all([model], samples=args.samples, pixels=args.pixels, targeted=args.targeted,341 maxiter=args.maxiter, popsize=args.popsize, verbose=args.verbose, DE=args.DE, 342 epsilon=args.epsilon, LS=args.LS, no_stop=args.no_stop)343344 columns = ['model', 'pixels', 'image', 'true', 'predicted', 'success', 'cdiff', 'prior_probs', 'predicted_probs', 'perturbation']345 results_table = pd.DataFrame(results, columns=columns)346347 print(results_table[['model', 'pixels', 'image', 'true', 'predicted', 'success']])348349 print('Saving to', args.save)350 with open(args.save, 'wb') as file:351 pickle.dump(results, file)
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statistics.py
Source:statistics.py
1import pickle2import matplotlib.pyplot as plt3from mpl_toolkits.mplot3d import Axes3D4from matplotlib import cm5from matplotlib.colors import LightSource6import numpy as np7def read_st(fn, n):8# fn: file name9# n: number of samples10# read the data from the file fn and return the data11 # get the number sample from pickle file12 _f = open(fn.replace('txt','pkl'), 'rb')13 _d = pickle.load(_f)14 sn = len(_d)15 _f.close()16 Data = [] # for all samples17 f = open(fn,"r")18 # skip the two lines:19 # Evaluating model_120 # Starting attack21 line = f.readline()22 line = f.readline()23 line = f.readline().split()24 for i in range(sn):25 data = [] # for one sample26 data.append( float(line[5] ) )27 data.append( float( f.readline().split()[3] ))28 line = f.readline().split()29 while(len(line) == 5):30 data.append( float(line[4]) )31 line = f.readline().split()32 # skip the line 33 # The minimal number of successful attacking pixels is 34 if len(line) != 8: line = f.readline().split()35 Data.append(data)36 # get the time37 line = f.readline()38 while(line.rfind('user') == -1): 39 line = f.readline()40 user = line.split()[1]41 sys = f.readline().split()[1]42 m1, s1 = user.split('m')[0], user.split('m')[1].split('s')[0]43 m2, s2 = sys.split('m')[0], user.split('m')[1].split('s')[0]44 Time = str(int(m1)+int(m2)) + 'm' + format(float(s1)+float(s2),'.2f') + 's'45 #line.split()[1]46 f.close()47 return Data, Time48def draw_1000s(post_fn='-0-1-0.01-500-1000-10-1'):49 # get the Data50 types = ['DE', 'SHA', 'EBL']51 path='./results/dr/'52 #post_fn = '-0-1-0.01-500-1000-10-1'53 DATA = []54 TIME = []55 for i in range(3):56 _data,_time = read_st(path + types[i] + post_fn + '.txt',10)57 DATA.append(_data)58 TIME.append(_time)59 # print the original confidence60 for i in range(len(DATA[0])):61 print('ID=',i, ' Confidence=', DATA[0][i][1]) 62 # draw DE, SHA, EBL63 for i in range(3):64 data = np.array(DATA[i]) 65 fig, ax = plt.subplots() 66 cdiff,wdiff = 0.0, 0.067 for j in range(len(data)):68 plt.plot(data[j][2:], label=str(j)+': '+format(data[j][1],'.4f')) 69 cdiff += (data[j][1]-data[j][len(data[j])-1])70 wdiff += data[j][1]*(data[j][1]-data[j][len(data[j])-1])71 fig.subplots_adjust(right=0.7, bottom=0.2)72 plt.legend(bbox_to_anchor=(1.01,1), loc=2, borderaxespad=0) #loc='lower right',mode='expand')73 plt.xlabel('iterations' + '\ncdiff='+format(cdiff/len(data),'.4f') + ' wdiff=' + format(wdiff/len(data),'.4f'))74 plt.ylabel('confidence')75 plt.title(types[i] + post_fn + '-' + TIME[i])76 plt.savefig(path+types[i]+post_fn+'.png')77 plt.cla()78 plt.clf()79 plt.close('all')80 81 # draw the confidence for each sample with DE, SHA and EBL82 for i in range(len(DATA[0])):83 plt.figure(clear=True)84 X = np.linspace(1,1000,1000,endpoint=True)85 plt.title('ID=' + str(i) +' Confidence='+ format(DATA[0][i][1],'.5f') )86 for j in range(3):87 plt.plot(X, DATA[j][i][2:], label=types[j])88 plt.legend()89 plt.savefig(path + 'id-' + str(i) + post_fn + '.png')90# draw the scater of positive and negative samples91def draw_1000_scater(path='./results/dr/', post_fn='',le=0):92 # get the Data93 types = ['EBL'] 94 DATA = []95 TIME = []96 label = ['negative', 'positive']97 for i in range(2):98 _data,_time = read_st(path + types[0] + post_fn + str(i)+'.txt',1000)99 DATA.append(_data)100 TIME.append(_time)101 102 cdiff,wdiff = 0.0, 0.0103 for j in range(2):104 nsucess = 0 105 fig, ax = plt.subplots()106 data = DATA[j]107 dlen = len(data)108 x, y = np.zeros((2,dlen)), np.zeros((2,dlen))109 for k in range(dlen):110 elen = len(data[k])111 x[j][k] = data[k][1]112 y[j][k] = data[k][elen-1]113 if y[j][k] < 0.5: nsucess +=1 # the number of sucessful attack114 plt.scatter(x[j], y[j], label=label[j], alpha=0.5)115 cbar = plt.colorbar()116 cdiff = np.sum(x[j] - y[j])117 wdiff = np.sum(x[j]*(x[j] - y[j]))118 fig.subplots_adjust(bottom=0.2) 119 plt.xlabel('Confidence before attack' + '\ncdiff='+format(cdiff/len(data),'.4f') + ' wdiff=' + format(wdiff/len(data),'.4f') + ' sucess=' + format(nsucess/len(data), '.4f'))120 plt.ylabel('Confidence after attack')121 stitle = post_fn.split('-')122 stitle[6]= str(dlen)123 st = '-'.join(stitle) 124 plt.title(types[0] + st + str(j) + '-' + TIME[j])125 plt.savefig(path+types[0]+ st + str(j)+'.png')126 plt.cla()127 plt.clf()128 plt.close('all')129def draw_LP(path='./results/dr/', dtype=1, DE='EBL',post_fn='', sn=0):130 #path='./results/dr/'131 f = open(path + DE + post_fn + str(dtype) + '.pkl', 'rb')132 data = pickle.load(f)133 f.close()134 sn = len(data)135 LP_data = np.zeros((3,sn))136 nsucess, npixels = 0, 0137 for i in range(sn):138 LP_data[0,i] = data[i][1] # the number of pixels of the attack139 LP_data[1,i] = data[i][8][dtype] # the confidence after the attack140 LP_data[2,i] = data[i][7][dtype] # the confidence before the attack141 if data[i][8][dtype] < 0.5: 142 nsucess += 1143 npixels += data[i][1]144 145 cdiff, wdiff = np.sum(LP_data[2] - LP_data[1])/sn, np.sum((LP_data[2]-LP_data[1])/LP_data[0])/sn146 # 147 ylabels = ['The least number of pixels', 'The confiderence after attack']148 for i in range(2):149 fig, ax = plt.subplots()150 plt.scatter(LP_data[2], LP_data[i], alpha=0.5)151 plt.colorbar()152 plt.xlabel('Confidence before attack' + '\ncdiff='+format(cdiff,'.4f') + ' wdiff=' + format(wdiff,'.4f') + ' success=' + format(nsucess/len(data), '.4f') )153 plt.ylabel(ylabels[i])154 fig.subplots_adjust(bottom=0.2)155 plt.title(DE + post_fn + str(dtype) + '-LP-' + format(npixels/nsucess,'.2f'))156 plt.savefig(path + DE + post_fn + str(dtype) + '-LP-' + str(i) + '.png')157 plt.cla()158 plt.clf()159 plt.close('all')160 ## draw 3d scatter plots161 #ax = plt.subplot(projection='3d') 162 # for xx, yy, zz in zip(LP_data[2], LP_data[1], LP_data[0]):163 # color = np.random.random(3) # éæºé¢è²å
ç¥164 # ax.bar3d(165 # xx, # æ¯ä¸ªæ±çxåæ 166 # yy, # æ¯ä¸ªæ±çyåæ 167 # 0, # æ¯ä¸ªæ±çèµ·å§åæ 168 # dx=1, # xæ¹åç宽度169 # dy=1, # yæ¹åçå度170 # dz=zz)#, # zæ¹åçé«åº¦171 # #color=color) #æ¯ä¸ªæ±çé¢è²172 fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) 173 cmap = cm.viridis174 ax.scatter(LP_data[2], LP_data[1], LP_data[0], c='b', cmap=cmap, alpha=0.4, linewidth=0) 175 #fig.colorbar()176 ax.set_zlabel('Number of attacking pixels') # åæ è½´177 ax.set_ylabel('Confidence after attack')178 ax.set_xlabel('Confidence before attack') 179 plt.title('cdiff='+format(cdiff,'.4f') + ' wdiff=' + format(wdiff,'.4f') + ' LP=' + format(npixels/nsucess,'.2f') + ' success=' + format(nsucess/len(data), '.4f'))180 plt.savefig(path + DE + post_fn + str(dtype) + '-LP-3D.png')181 plt.close()182def draw_RA(source='dr',type=0, RAn=10, RA=True):183 for i in range(10): #min(len(_ids), args.RAn)):184 _path = './results/' + source + '/'185 _file_name = _path + 'RA-' +str(i) + '-' + str( int(_ids[i][0]) ) + '-' + str(type) + '.png'186 # the original image187 fix,ax = plt.subplots(1)188 ax.imshow(_test[i], origin='lower')189 plt.title(' Prior conf=' + format(RA_results[i][7][type],'.4f'))190 plt.xlabel('ID='+str(int(_ids[i][0])))191 plt.savefig(_path + 'RA-ori-' + str(i) + '-' + str( int(_ids[i][0]) ) + '-' + str(type) + '.png')192 plt.close('all')193 # the attacked image194 fig,ax = plt.subplots(1)195 ax.set_aspect('equal')196 _cdiff = 0.0197 198 # get the attacking pixels 199 ## draw circles of repeated attacks 200 for j in range(RAn):201 _px, _py = int(RA_results[j * RAn + i][9][0]), int(RA_results[j * RAn + i][9][1])202 _cdiff += RA_results[j * RAn + i][6] 203 plt.scatter(_px, _py, color='', edgecolor='b', marker='o', alpha=.5, s=150)204 _test[i][_py,_px] = RA_results[j * RAn + i][9][2], RA_results[j * RAn + i][9][3], RA_results[j * RAn + i][9][4] 205 _title = ' cdiff=' + format(_cdiff/RAn,'.4f')206 plt.title(_title)207 plt.xlabel('ID='+str(int(_ids[i][0])))208 ax.imshow(_test[i], origin='lower')209 plt.savefig(_file_name)210 plt.close('all') 211if __name__ == '__main__': 212 draw_1000s('-0-1-0.01-1000-1000-10-1')...
diff.py
Source:diff.py
...10from ..output.formatting import to_hex_and_str11__all__ = ['cdiff', 'cdiff_objs', 'unified_byte_diff', 'unified_obj_diff']12def cdiff(path1, path2, n: int = 3):13 with open(path1, 'r', encoding='utf-8') as f1, open(path2, 'r', encoding='utf-8') as f2:14 _cdiff(f1.read().splitlines(), f2.read().splitlines(), path1, path2, n=n)15def cdiff_objs(obj1, obj2, fmt: Optional[str] = 'yaml', n: int = 3):16 """17 Print a comparison of the given objects when serialized with the given output format18 :param obj1: A serializable object19 :param obj2: A serializable object20 :param fmt: An output format (one of: json, yaml, str)21 :param n: Number of lines of context to include22 """23 str1, str2 = _objs_to_strs(obj1, obj2, fmt)24 _cdiff(str1.splitlines(), str2.splitlines(), 'obj1', 'obj2', n=n)25def _objs_to_strs(obj1, obj2, fmt: Optional[str] = 'yaml') -> tuple[str, str]:26 if fmt == 'json':27 str1 = json.dumps(obj1, sort_keys=True, indent=4, cls=PermissiveJSONEncoder)28 str2 = json.dumps(obj2, sort_keys=True, indent=4, cls=PermissiveJSONEncoder)29 elif fmt in ('yaml', 'yml'):30 str1, str2 = yaml_dump(obj1), yaml_dump(obj2)31 elif fmt in (None, 'str'):32 str1, str2 = str(obj1), str(obj2)33 else:34 raise ValueError(f'Invalid cdiff format: {fmt!r}')35 return str1, str236def _cdiff(a, b, name_a: str = '', name_b: str = '', n: int = 3):37 for i, line in enumerate(unified_diff(a, b, name_a, name_b, n=n, lineterm='')):38 if line.startswith('+') and i > 1:39 print(colored(line, 2))40 elif line.startswith('-') and i > 1:41 print(colored(line, 1))42 elif line.startswith('@@ '):43 print(colored(line, 6), end='\n\n')44 else:45 print(line)46def unified_obj_diff(47 obj1, obj2, fmt: Optional[str] = 'yaml', n: int = 3, lineterm: str = '', color: bool = True,48):49 str1, str2 = _objs_to_strs(obj1, obj2, fmt)50 a = str1.splitlines()...
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