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import os
import numpy as np
from .glove_simi import generate_glove, AD_DIRECTORY
class AD_Distribution():
def __init__(self, simi_name, alpha, beta):
print('using ........ ', simi_name, ' .... knowledge')
path_simi_name = os.path.join(AD_DIRECTORY, 'imagenet_cos_similarity')
file_simi_name = path_simi_name +'_'+ simi_name + '.npy'
if os.path.exists(file_simi_name):
self.cos_similarity = np.load(file_simi_name)
print(simi_name+" cos_similarity loaded")
else:
self.cos_similarity = self.generate_similarity(simi_name)
self.alpha = alpha
self.beta = beta
def generate_similarity(self,simi_name):
if simi_name == 'glove':
similarity = generate_glove()
else:
print(simi_name + 'not implemented yet')
return similarity
def generate_distribution(self, gt_label, target):
distribution=[]
for i in range(len(target)):
distri = self.single_distribution_build(i, target[i], gt_label[i])
distribution.append(distri)
distribution = np.array(distribution)
return distribution
def single_distribution_build(self,index, target_id, gt_id):
if target_id.shape == ():
target_id = np.array([target_id])
simil_logits = np.zeros(self.cos_similarity[target_id[0],:].shape)
for i in range(len(target_id)):
simil_logits += self.cos_similarity[target_id[i],:]
simil_logits = (simil_logits)/ len(target_id)
logit_value = self.alpha
for i in range(len(target_id)):
simil_logits[target_id[i]] = logit_value
logit_value = logit_value - self.beta
if not self.check_oreder_target_no_groundtruth(simil_logits, target_id):
print('fail to generate distribution for index: ', index)
logits = self.softmax(simil_logits)
return logits
def check_oreder_target_no_groundtruth(self, probs, target_id):
sort_labels = np.argsort(probs)
cnt = 0
for i in range(len(target_id)):
if target_id[-(i+1)] == sort_labels[-(len(target_id)-i)]:
cnt +=1
if (cnt == len(target_id)):
return True
else:
return False
def softmax(self,logits):
prob=np.exp(logits) / np.sum(np.exp(logits))
return prob
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