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✨ Add Monte-Carlo Dropout #49

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merged 22 commits into from
Oct 18, 2023
Merged

✨ Add Monte-Carlo Dropout #49

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a7ca500
add mc dropout
badrmarani Oct 7, 2023
91e72c4
add more tests
badrmarani Oct 7, 2023
ef06150
add reference to the paper
badrmarani Oct 7, 2023
575e211
minor fix
badrmarani Oct 7, 2023
eec4b8e
improve code readability and add `enable_last_layer_dropout` argument
badrmarani Oct 8, 2023
66143f7
add a tutorial for mc dropout
badrmarani Oct 8, 2023
0f24704
update mc dropout tutorial
badrmarani Oct 10, 2023
6073975
minor fix
badrmarani Oct 12, 2023
bb40a48
minor fix
badrmarani Oct 12, 2023
df636a3
move `enable_dropout` to utils
badrmarani Oct 12, 2023
73f81f3
extend mc-dropout to vgg and wideresnet
badrmarani Oct 13, 2023
bb05696
Merge branch 'dev' of https://github.com/ENSTA-U2IS/torch-uncertainty…
o-laurent Oct 15, 2023
8a3f838
:hammer: Rename enable_ll_dropout & add some doc.
o-laurent Oct 18, 2023
88b04e6
:hammer: Rename enable_ra & add some types
o-laurent Oct 18, 2023
8906524
:ok_hand: Keep 0.3 as default dropout rate for wideresnet
o-laurent Oct 18, 2023
62fb58f
Merge branch 'dev' of https://github.com/ENSTA-U2IS/torch-uncertainty…
o-laurent Oct 18, 2023
5d4954f
:bug: Remove kwargs in MIMO
o-laurent Oct 18, 2023
7c541a5
:shirt: Reorder an import
o-laurent Oct 18, 2023
5c17358
:sparkles: Improve get_procedure for VGGs
o-laurent Oct 18, 2023
2670c2a
:bug: Fix MC Dropout Baseline & rename dropout arg.
o-laurent Oct 18, 2023
b9e9958
:sparkles: Add dropout to lenet
o-laurent Oct 18, 2023
459b362
:books: Use lenet in the MC dropout tuto. & improvements
o-laurent Oct 18, 2023
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Merge branch 'dev' of https://github.com/ENSTA-U2IS/torch-uncertainty … 
…into mc-dropout
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@o-laurent
o-laurent committed Oct 15, 2023
commit bb056969760a9ff305b48fec266fd6f5c9206e9b
2 changes: 1 addition & 1 deletion torch_uncertainty/models/wideresnet/std.py
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Expand Up @@ -183,7 +183,7 @@ def forward(self, x):
enable_dropout(self, self.enable_last_layer_dropout)
x = x.repeat(self.num_estimators, 1, 1, 1)

out = self.conv1(x)
out = F.relu(self.bn1(self.conv1(x)))
out = self.optional_pool(out)
out = self.layer1(out)
out = self.layer2(out)
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