MCPcopy Create free account

hub / github.com/cure-lab/deep-active-learning / functions

Functions479 in github.com/cure-lab/deep-active-learning

⨍ Functions 479◇ Types & classes 111
↓ 1 callersFunctiondist_ext
Compute squared euclidean distance between two 2D arrays representing n-dimensional points using extended arrays based approach. For more
query_strategies/cpu_dist.py:131
↓ 1 callersFunctiondistance
(point_1,point_2)
query_strategies/batch_active_learning_at_scale.py:39
↓ 1 callersMethodevaluate_each_class
Input: @X: data @Y: label Return: @Acc: the accuracy of all classes
query_strategies/mcadl.py:65
↓ 1 callersFunctionext_arrs
Create extended version of arrays for matrix-multiplication based squared euclidean distance between two 2D arrays representing n-dimensional
query_strategies/cpu_dist.py:4
↓ 1 callersMethodflatten_X
(self)
query_strategies/coreGCN.py:64
↓ 1 callersMethodfurthest_first
(self, X, X_set, n)
query_strategies/core_set.py:14
↓ 1 callersMethodgetWeightM
Input: @last_acc: the accuracy of last round @now_acc: the accuracy of current round Return: @q: the wei
query_strategies/mcadl.py:129
↓ 1 callersFunctionget_CIFAR10
(path)
dataset.py:109
↓ 1 callersFunctionget_CIFAR100
(path)
dataset.py:119
↓ 1 callersFunctionget_FashionMNIST
(path)
dataset.py:91
↓ 1 callersFunctionget_GTSRB
(path)
dataset.py:128
↓ 1 callersFunctionget_ImageNet
(path)
dataset.py:28
↓ 1 callersFunctionget_MNIST
(path)
dataset.py:82
↓ 1 callersFunctionget_SVHN
(path)
dataset.py:100
↓ 1 callersFunctionget_dataset
(name, path)
dataset.py:12
↓ 1 callersFunctionget_handler
(name)
dataset.py:141
↓ 1 callersFunctionget_tinyImageNet
(path)
dataset.py:52
↓ 1 callersFunctionget_tsa_thresh
(schedule, global_step, num_train_steps, start, end)
query_strategies/aug_uda.py:25
↓ 1 callersMethodget_uncertainty
(self,models, unlabeled_loader)
query_strategies/learning_loss_for_al.py:249
↓ 1 callersFunctionget_wa_handler
(name)
dataset.py:224
↓ 1 callersFunctiongram_aug
(L_Y, L_Y_inv, b_u, c_u)
query_strategies/baseline_sampling.py:58
↓ 1 callersFunctiongram_red
(L, L_inv, u_loc)
query_strategies/baseline_sampling.py:44
↓ 1 callersFunctioninit_centers
(X, K)
query_strategies/badge_sampling.py:46
↓ 1 callersFunctioninterleave_offsets
(batch, nu)
query_strategies/util.py:138
↓ 1 callersMethodinverse
(self, cluster_dict)
query_strategies/batch_active_learning_at_scale.py:18
↓ 1 callersFunctionkaiming_init
(m)
models/wa_model.py:170
↓ 1 callersMethodkaiming_init
(self, m)
query_strategies/vaal.py:92
↓ 1 callersMethodkaiming_init
(self, m)
query_strategies/vaal.py:213
↓ 1 callersFunctionlinear_rampup
(current, rampup_length=200)
query_strategies/util.py:131
↓ 1 callersMethodll_train
(self, epoch, loader_tr, optimizers,criterion)
query_strategies/learning_loss_for_al.py:132
↓ 1 callersMethodload_model
(self)
query_strategies/strategy.py:309
↓ 1 callersFunctionmain
()
main.py:240
↓ 1 callersMethodmargin_data
(self, n)
query_strategies/batch_active_learning_at_scale.py:112
↓ 1 callersMethodmargin_data
(self,k)
query_strategies/ssl_diff2augdirect.py:38
↓ 1 callersMethodmargin_data
(self)
query_strategies/ssl_diff2augkmeans.py:53
↓ 1 callersMethodpred_dis_score
prediction discrimnator score :param X: :param Y: FOR numerical simplification, NEVER USE Y information for prediction
query_strategies/wasserstein_adversarial.py:306
↓ 1 callersMethodpredict
(self, X, Y)
query_strategies/selection_via_proxy.py:136
↓ 1 callersMethodpredict
(self, X, Y)
query_strategies/semi_strategy.py:264
↓ 1 callersMethodpredict_consistency_aug
(self, X, Y)
query_strategies/ssl_consistency.py:32
↓ 1 callersMethodpredict_prob
prediction output score probability :param X: :param Y: NEVER USE the Y information for direct prediction :return:
query_strategies/wasserstein_adversarial.py:278
↓ 1 callersMethodpredict_prob
(self, X, Y)
query_strategies/selection_via_proxy.py:160
↓ 1 callersMethodpredict_prob_aug
(self, X, Y)
query_strategies/ssl_diff2augdirect.py:22
↓ 1 callersMethodpredict_prob_aug
(self, X, Y)
query_strategies/ssl_diff2augkmeans.py:37
↓ 1 callersMethodpredict_prob_dropout_split
(self, X, Y, n_drop)
query_strategies/strategy.py:232
↓ 1 callersMethodprepare_emb
(self)
query_strategies/batch_active_learning_at_scale.py:98
↓ 1 callersMethodprepare_emb
(self)
query_strategies/ssl_diff2augkmeans.py:23
↓ 1 callersMethodquery
adversarial query strategy :param n: :return:
query_strategies/wasserstein_adversarial.py:372
↓ 1 callersMethodreparameterize
(self, mu, logvar)
query_strategies/vaal.py:167
↓ 1 callersMethodreset
(self)
utils.py:29
↓ 1 callersMethodreset
(self, total_epoch)
utils.py:49
↓ 1 callersMethodround_robin
(self, unlabeled_index, hac_list, k)
query_strategies/batch_active_learning_at_scale.py:140
↓ 1 callersMethodsample
(self, vae, discriminator, data)
query_strategies/vaal.py:248
↓ 1 callersFunctionsample_k_imp
(Phi, k, max_iter, rng=np.random)
query_strategies/baseline_sampling.py:67
↓ 1 callersMethodsampling
(self, mu, log_var)
query_strategies/vaal.py:55
↓ 1 callersMethodsave_model
(self)
query_strategies/semi_strategy.py:424
↓ 1 callersMethodselect_batch_
(self)
query_strategies/coreGCN.py:73
↓ 1 callersMethodselect_batch_
Diversity promoting active learning method that greedily forms a batch to minimize the maximum distance to a cluster center among all
query_strategies/coreGCN.py:125
↓ 1 callersMethodset_wd
(self,lr)
query_strategies/util.py:177
↓ 1 callersMethodtrain
(self, alpha=0.1, n_epoch=10)
query_strategies/aug_uda.py:250
↓ 1 callersMethodtrain
(self, alpha=0.1, n_epoch=10)
query_strategies/semi_flexmatch.py:274
↓ 1 callersMethodtrain
(self, alpha=0.1, n_epoch=10)
query_strategies/semi_pseudolabel.py:96
↓ 1 callersMethodtrain
(self, alpha=0.1, n_epoch=10)
query_strategies/selection_via_proxy.py:67
↓ 1 callersMethodtrain
(self, alpha=0.1, n_epoch=10, batch_ratio=0.6)
query_strategies/semi_fixmatch.py:257
↓ 1 callersMethodtrain_lal_model
(self, all_data_for_lal, all_labels_for_lal)
query_strategies/lal.py:156
↓ 1 callersMethoduncertainty
Input: @proba: probability for all samples, n_sample x nb_class @flag: a mark for samples, n_sample x 1, 0 represents unse
query_strategies/mcadl.py:36
↓ 1 callersMethodupdate_id
(self, cluster_id)
query_strategies/batch_active_learning_at_scale.py:23
↓ 1 callersMethodvaal_train
(self, epoch, loader_tr, optimizer, labeled_data, unlabeled_data, optim_vae, optim_discriminator)
query_strategies/vaal.py:314
↓ 1 callersMethodweight_init
(self)
query_strategies/vaal.py:87
↓ 1 callersMethodweight_init
(self)
query_strategies/vaal.py:147
↓ 1 callersMethodweight_init
(self)
query_strategies/vaal.py:208
MethodAutoContrast
(self, img, **kwarg)
query_strategies/semi_flexmatch.py:35
MethodAutoContrast
(self, img, **kwarg)
query_strategies/semi_fixmatch.py:36
MethodBrightness
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:38
MethodColor
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:42
MethodContrast
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:46
MethodCutout
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:50
MethodCutout
(self, img, v, max_v, bias=0)
query_strategies/semi_fixmatch.py:51
MethodEqualize
(self, img, **kwarg)
query_strategies/semi_flexmatch.py:72
MethodEqualize
(self, img, **kwarg)
query_strategies/semi_fixmatch.py:73
FunctionHAC
(points_set)
query_strategies/batch_active_learning_at_scale.py:49
MethodIdentity
(self, img, **kwarg)
query_strategies/semi_flexmatch.py:75
MethodIdentity
(self, img, **kwarg)
query_strategies/semi_fixmatch.py:76
MethodInvert
(self, img, **kwarg)
query_strategies/semi_flexmatch.py:78
MethodInvert
(self, img, **kwarg)
query_strategies/semi_fixmatch.py:79
MethodPosterize
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:81
MethodPosterize
(self, img, v, max_v, bias=0)
query_strategies/semi_fixmatch.py:82
FunctionResNet101
(n_class, bayesian=False)
models/resnet.py:186
FunctionResNet101_64
(n_class, bayesian=False)
models/resnet64.py:185
FunctionResNet152
(n_class, bayesian=False)
models/resnet.py:189
FunctionResNet152_64
(n_class, bayesian=False)
models/resnet64.py:188
FunctionResNet18_64
(n_class, bayesian=False)
models/resnet64.py:176
FunctionResNet34
(n_class, bayesian=False)
models/resnet.py:180
FunctionResNet34_64
(n_class, bayesian=False)
models/resnet64.py:179
FunctionResNet50
(n_class, bayesian=False)
models/resnet.py:183
FunctionResNet50_64
(n_class, bayesian=False)
models/resnet64.py:182
MethodRotate
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:85
MethodRotate
(self, img, v, max_v, bias=0)
query_strategies/semi_fixmatch.py:86
MethodSharpness
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:91
MethodShearX
(self, img, v, max_v, bias=0)
query_strategies/semi_flexmatch.py:95
MethodShearX
(self, img, v, max_v, bias=0)
query_strategies/semi_fixmatch.py:96
← previousnext →101–200 of 479, ranked by callers