Functions — github.com/AberHu/Knowledge-Distillation-Zoo

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Functions124 in github.com/AberHu/Knowledge-Distillation-Zoo

⨍ Functions 124◇ Types & classes 33

↓ 74 callersMethodupdate

(self, val, n=1)

utils.py:20

↓ 16 callersMethodnormalize

(self, factor)

kd_losses/ft.py:22

↓ 15 callersFunctionaccuracy

Computes the precision@k for the specified values of k

utils.py:61

↓ 13 callersFunctioncount_parameters_in_MB

(model)

utils.py:27

↓ 11 callersFunctiondefine_tsnet

(name, num_class, cuda=True)

network.py:8

↓ 10 callersMethodget_channel_num

(self)

network.py:113

↓ 10 callersFunctionload_pretrained_model

(model, pretrained_dict)

utils.py:37

↓ 6 callersFunctioncreate_exp_dir

(path)

utils.py:31

↓ 6 callersFunctionsave_checkpoint

(state, is_best, save_root)

utils.py:53

↓ 4 callersMethod__init__

(self, in_channels, out_channels, return_before_act)

network.py:25

↓ 4 callersMethodeuclidean_dist_fm

Calculating the IRG edge of feature map.

kd_losses/irg.py:76

↓ 3 callersMethod__init__

(self, s_dim, t_dim, feat_dim, nce_n, nce_t, nce_mom, n_data)

kd_losses/crd.py:31

↓ 3 callersFunctionconv1x1

(in_channels, out_channels)

kd_losses/vid.py:10

↓ 3 callersMethodmake_layer

(self, block, num, in_channels, out_channels)

network.py:90

↓ 3 callersMethodmake_layer

(self, block, num, in_channels, out_channels)

network.py:150

↓ 3 callersMethodpoly_kernel

(self, fm1, fm2)

kd_losses/nst.py:59

↓ 2 callersMethodattention_map

(self, fm, eps=1e-6)

kd_losses/at.py:27

↓ 2 callersMethodeuclidean_dist_feat

Calculating the IRG edge of feat.

kd_losses/irg.py:94

↓ 2 callersMethodeuclidean_dist_fms

Calculating the IRG Transformation, where fm1 precedes fm2 in the network.

kd_losses/irg.py:56

↓ 2 callersMethodfsp_matrix

(self, fm1, fm2)

kd_losses/fsp.py:22

↓ 2 callersMethodget_correlation_matrix

(self, feat)

kd_losses/cc.py:32

↓ 2 callersMethodpdist

(self, feat, squared=False, eps=1e-12)

kd_losses/rkd.py:56

↓ 1 callersMethod__init__

(self, root, train=True, transform=None, target_transform=None, download=False, n=4096, mode='exact

dataset.py:14

↓ 1 callersFunctionadjust_lr

(optimizer, epoch)

train_crd.py:296

↓ 1 callersFunctionadjust_lr

(optimizers, epoch)

train_dml.py:332

↓ 1 callersFunctionadjust_lr

(optimizer, epoch)

train_bss.py:319

↓ 1 callersFunctionadjust_lr

(optimizer, epoch)

train_base.py:241

↓ 1 callersFunctionadjust_lr

(optimizer, epoch)

train_ft.py:361

↓ 1 callersFunctionadjust_lr

(optimizer, epoch)

train_kd.py:543

↓ 1 callersFunctionadjust_lr_init

(optimizer, epoch)

train_kd.py:531

↓ 1 callersMethodattack

(self, model, img, target, attack_class)

kd_losses/bss.py:46

↓ 1 callersFunctiondefine_paraphraser

(in_channels_t, k, use_bn, cuda=True)

network.py:185

↓ 1 callersFunctiondefine_translator

(in_channels_s, in_channels_t, k, use_bn=True, cuda=True)

network.py:237

↓ 1 callersMethodget_margin

(self, fm, eps=1e-6)

kd_losses/ofd.py:45

↓ 1 callersFunctionl2_norm

(x, keepdim=True)

kd_losses/bss.py:18

↓ 1 callersFunctionmain

()

train_crd.py:75

↓ 1 callersFunctionmain

()

train_dml.py:67

↓ 1 callersFunctionmain

()

train_bss.py:69

↓ 1 callersFunctionmain

()

train_base.py:60

↓ 1 callersFunctionmain

()

train_ft.py:69

↓ 1 callersFunctionmain

()

train_kd.py:84

↓ 1 callersFunctionreduce_sum

(x, keepdim=True)

kd_losses/bss.py:12

↓ 1 callersMethodreset

(self)

utils.py:14

↓ 1 callersMethodrkd_angle

(self, feat_s, feat_t)

kd_losses/rkd.py:42

↓ 1 callersMethodrkd_dist

(self, feat_s, feat_t)

kd_losses/rkd.py:29

↓ 1 callersFunctiontest

(test_loader, nets, criterions, epoch)

train_crd.py:263

↓ 1 callersFunctiontest

(test_loader, nets, criterions)

train_dml.py:275

↓ 1 callersFunctiontest

(test_loader, nets, criterions, epoch)

train_bss.py:286

↓ 1 callersFunctiontest

(test_loader, net, criterion)

train_base.py:213

↓ 1 callersFunctiontest

(test_loader, nets, criterions)

train_ft.py:317

↓ 1 callersFunctiontest

(test_loader, nets, criterions, epoch)

train_kd.py:447

↓ 1 callersFunctiontrain

(train_loader, nets, optimizer, criterions, epoch)

train_crd.py:202

↓ 1 callersFunctiontrain

(train_loader, nets, optimizers, criterions, epoch)

train_dml.py:188

↓ 1 callersFunctiontrain

(train_loader, nets, optimizer, criterions, attacker, epoch)

train_bss.py:188

↓ 1 callersFunctiontrain

(train_loader, net, optimizer, criterion, epoch)

train_base.py:169

↓ 1 callersFunctiontrain

(train_loader, nets, optimizer, criterions, epoch)

train_ft.py:255

↓ 1 callersFunctiontrain

(train_loader, nets, optimizer, criterions, epoch)

train_kd.py:347

↓ 1 callersFunctiontrain_init

(train_loader, nets, optimizer, criterions, total_epoch)

train_kd.py:274

↓ 1 callersFunctiontrain_para

(train_loader, nets, optimizer_para, criterionPara, total_epoch)

(self, in_channels_t, k, use_bn=True)

network.py:196

Method__init__

(self, in_channels_s, in_channels_t, k, use_bn=True)

(self, root, train=True, transform=None, target_transform=None, download=False, n=4096, mode='exact

dataset.py:72

Method__init__

(self)

kd_losses/fitnet.py:14

Method__init__

(self, in_dim, out_dim)

kd_losses/crd.py:51

Method__init__

(self, n_data, eps=1e-7)

kd_losses/crd.py:67

Method__init__

(self, feat_dim, n_data, nce_n, nce_t, nce_mom)

kd_losses/crd.py:91

Method__init__

(self, in_channels, att_f)

kd_losses/afd.py:22

Method__init__

(self, in_channels, mid_channels, out_channels, init_var, eps=1e-6)

kd_losses/vid.py:23

Method__init__

(self)

kd_losses/sobolev.py:18

(self, w_dist, w_angle)

(self, w_irg_vert, w_irg_edge, w_irg_tran)

(self, in_channels, out_channels)

(self, step_alpha, num_steps, eps=1e-4)

kd_losses/logits.py:14

Method__init__

(self, gamma, P_order)

kd_losses/fitnet.py:17

Methodforward

(self, feat_s, feat_t, idx, sample_idx)

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