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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 | |