Skip to content

Instantly share code, notes, and snippets.

@jS5t3r

jS5t3r/WideResNet_28-10.txt

Created February 24, 2022 08:59
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save jS5t3r/fde796a3154c39f961f0d3686b88b722 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save jS5t3r/fde796a3154c39f961f0d3686b88b722 to your computer and use it in GitHub Desktop.
Download ZIP
Orignal Relu
Raw
WideResNet_28-10.txt
WideResNet(
(init_conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Sequential(
(0): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(16, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential(
(0): Conv2d(16, 160, kernel_size=(1, 1), stride=(1, 1))
)
)
(1): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
(2): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
(3): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
)
(conv3): Sequential(
(0): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(160, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential(
(0): Conv2d(160, 320, kernel_size=(1, 1), stride=(2, 2))
)
)
(1): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
(2): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
(3): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
)
(conv4): Sequential(
(0): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(320, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(3): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential(
(0): Conv2d(320, 640, kernel_size=(1, 1), stride=(2, 2))
)
)
(1): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
(2): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
(3): WideBasic(
(residual): Sequential(
(0): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(1): ReLU(inplace=True)
(2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(shortcut): Sequential()
)
)
(bn): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(avg_pool): AdaptiveAvgPool2d(output_size=(1, 1))
(linear): Linear(in_features=640, out_features=10, bias=True)
)
extract_characteristics.py
###################
model.conv2[0].residual[1].register_forward_hook( get_activation('conv2_0_relu_1') )
model.conv2[0].residual[4].register_forward_hook( get_activation('conv2_0_relu_4') )
model.conv2[1].residual[1].register_forward_hook( get_activation('conv2_1_relu_1') )
model.conv2[1].residual[4].register_forward_hook( get_activation('conv2_1_relu_4') )
model.conv2[2].residual[1].register_forward_hook( get_activation('conv2_2_relu_1') )
model.conv2[2].residual[4].register_forward_hook( get_activation('conv2_2_relu_4') )
model.conv2[3].residual[1].register_forward_hook( get_activation('conv2_3_relu_1') )
model.conv2[3].residual[4].register_forward_hook( get_activation('conv2_3_relu_4') )
######################
model.conv3[0].residual[1].register_forward_hook(get_activation('conv3_0_relu_1'))
model.conv3[0].residual[4].register_forward_hook(get_activation('conv3_0_relu_4'))
model.conv3[1].residual[1].register_forward_hook(get_activation('conv3_1_relu_1'))
model.conv3[1].residual[4].register_forward_hook(get_activation('conv3_1_relu_4'))
model.conv3[2].residual[1].register_forward_hook(get_activation('conv3_2_relu_1'))
model.conv3[2].residual[4].register_forward_hook(get_activation('conv3_2_relu_4'))
model.conv3[3].residual[1].register_forward_hook(get_activation('conv3_3_relu_1'))
model.conv3[3].residual[4].register_forward_hook(get_activation('conv3_3_relu_4'))
model.conv4[0].residual[1].register_forward_hook(get_activation('conv4_0_relu_1'))
model.conv4[0].residual[4].register_forward_hook(get_activation('conv4_0_relu_4'))
model.conv4[1].residual[1].register_forward_hook(get_activation('conv4_1_relu_1'))
model.conv4[1].residual[4].register_forward_hook(get_activation('conv4_1_relu_4'))
model.conv4[2].residual[1].register_forward_hook(get_activation('conv4_2_relu_1'))
model.conv4[2].residual[4].register_forward_hook(get_activation('conv4_2_relu_4'))
model.conv4[3].residual[1].register_forward_hook(get_activation('conv4_3_relu_1'))
model.conv4[3].residual[4].register_forward_hook(get_activation('conv4_3_relu_4'))
model.relu.register_forward_hook(get_activation('relu'))
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment