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Functions87 in github.com/BayesWatch/pytorch-prunes

↓ 20 callersMethodupdate
(self, val, n=1)
funcs.py:192
↓ 9 callersMethod__init__
(self)
models/wideresnet.py:16
↓ 7 callersMethod__init__
(self)
models/densenet.py:28
↓ 7 callersFunctionget_layer_param
(model)
funcs.py:250
↓ 5 callersMethodconcat
(input)
funcs.py:147
↓ 4 callersFunctionget_error
Computes the error@k for the specified values of k
funcs.py:164
↓ 4 callersMethodprune
(self, model, feat_index)
funcs.py:79
↓ 3 callersMethod_make_dense
(self, nChannels, growthRate, nDenseBlocks, bottleneck, mask=False, width=1)
models/densenet.py:245
↓ 2 callersMethod_get_fisher
(self, model)
funcs.py:107
↓ 2 callersMethodcost
(self)
models/densenet.py:108
↓ 2 callersFunctionis_leaf
(model)
funcs.py:240
↓ 2 callersFunctionsave_checkpoint
(state, filename='checkpoint.pth.tar')
funcs.py:160
↓ 2 callersFunctionvalidate
()
train.py:187
↓ 1 callersMethod_create_mask
This takes an activation to generate the exact mask required. It also records input and output shapes for posterity.
models/densenet.py:86
↓ 1 callersMethod_create_mask
(self, x, out)
models/wideresnet.py:153
↓ 1 callersMethod_get_masks
(self, model)
funcs.py:124
↓ 1 callersMethod_make_layer
(self, block, in_channels, out_channels, nb_layers, stride, dropRate)
models/wideresnet.py:231
↓ 1 callersMethod_make_layer
(self, block, in_channels, out_channels, mid_channels, nb_layers, stride, dropRate)
models/wideresnet.py:246
↓ 1 callersMethod_update_cost
(self, model)
funcs.py:133
↓ 1 callersMethodcompress
(self, model)
funcs.py:102
↓ 1 callersMethodcompress_weights
(self)
models/densenet.py:123
↓ 1 callersFunctionfinetune
()
prune.py:114
↓ 1 callersMethodfisher_prune
(self, model, prune_every)
funcs.py:24
↓ 1 callersMethodfixed_prune
(self, model, ID)
funcs.py:33
↓ 1 callersMethodforward
(self, x)
models/densenet.py:31
↓ 1 callersMethodget_cost
(self, model)
funcs.py:138
↓ 1 callersFunctionget_inf_params
(net, verbose=True, sd=False)
funcs.py:199
↓ 1 callersFunctionget_layer_info
(layer)
funcs.py:244
↓ 1 callersFunctionget_num_gen
(gen)
funcs.py:228
↓ 1 callersFunctionis_pruned
(layer)
funcs.py:232
↓ 1 callersMethodl1_prune
(self, model, prune_every)
funcs.py:52
↓ 1 callersFunctionmeasure_layer
(layer, x)
funcs.py:255
↓ 1 callersFunctionmodify_forward
(model)
funcs.py:334
↓ 1 callersFunctionnew_forward
(m)
funcs.py:337
↓ 1 callersFunctionprune
()
prune.py:175
↓ 1 callersMethodrandom_prune
(self, model)
funcs.py:37
↓ 1 callersMethodreset
(self)
funcs.py:186
↓ 1 callersMethodreset_fisher
(self)
models/densenet.py:101
↓ 1 callersFunctionrestore_forward
(model)
funcs.py:349
↓ 1 callersFunctionshould_measure
(x)
funcs.py:331
↓ 1 callersFunctiontrain
()
train.py:134
↓ 1 callersFunctionvalidate
()
prune.py:189
Method__init__
(self, module_name='MaskBlock')
funcs.py:18
Method__init__
(self)
funcs.py:183
Method__init__
(self)
models/densenet.py:20
Method__init__
(self, channels, spatial)
models/densenet.py:36
Method__init__
(self, nChannels, growthRate)
models/densenet.py:47
Method__init__
(self, nChannels, growthRate, width=1)
models/densenet.py:156
Method__init__
(self, nChannels, growthRate)
models/densenet.py:174
Method__init__
(self, nChannels, nOutChannels)
models/densenet.py:187
Method__init__
(self, growthRate, depth, reduction, nClasses, bottleneck, mask=False, width=1.)
models/densenet.py:200
Method__init__
(self)
models/wideresnet.py:8
Method__init__
(self, channels, spatial)
models/wideresnet.py:24
Method__init__
(self, in_channels, out_channels, stride, dropRate=0.0)
models/wideresnet.py:35
Method__init__
(self, in_channels, out_channels, mid_channels, stride, dropRate=0.0)
models/wideresnet.py:64
Method__init__
(self, in_channels, out_channels, stride=1, dropRate=0.0)
models/wideresnet.py:95
Method__init__
(self, nb_layers, in_channels, out_channels, block, stride, dropRate=0.0)
models/wideresnet.py:227
Method__init__
(self, nb_layers, in_channels, out_channels, mid_channels, block, stride, dropRate=0.0)
models/wideresnet.py:242
Method__init__
(self, depth, widen_factor, num_classes=10, dropRate=0.0, mask=False)
models/wideresnet.py:259
Method__init__
(self, depth, widen_factor, num_classes=10, dropRate=0.0, bottle_mult=0.5)
models/wideresnet.py:314
Method_fisher
(self, _, __, grad_output)
models/densenet.py:93
Method_fisher
(self, notused1, notused2, grad_output)
models/wideresnet.py:159
Methodcompress_weights
(self)
models/wideresnet.py:192
Functionconcat
(input)
funcs.py:156
Methodcost
(self)
models/wideresnet.py:170
Functionfind
(input)
funcs.py:151
Methodforward
(self, x)
models/densenet.py:23
Methodforward
(self, x)
models/densenet.py:41
Methodforward
(self, x)
models/densenet.py:72
Methodforward
(self, x)
models/densenet.py:166
Methodforward
(self, x)
models/densenet.py:180
Methodforward
(self, x)
models/densenet.py:193
Methodforward
(self, x)
models/densenet.py:257
Methodforward
(self, x)
models/wideresnet.py:11
Methodforward
(self, x)
models/wideresnet.py:19
Methodforward
(self, x)
models/wideresnet.py:29
Methodforward
(self, x)
models/wideresnet.py:50
Methodforward
(self, x)
models/wideresnet.py:79
Methodforward
(self, x)
models/wideresnet.py:126
Methodforward
(self, x)
models/wideresnet.py:237
Methodforward
(self, x)
models/wideresnet.py:254
Methodforward
(self, x)
models/wideresnet.py:302
Methodforward
(self, x)
models/wideresnet.py:352
Functionlambda_forward
(x)
funcs.py:338
Functionmeasure_model
(model, H, W)
funcs.py:325
Methodreset_fisher
(self)
models/wideresnet.py:167
Methodupdate
(self, previous_mask)
models/densenet.py:104