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Rewrite the deinplace tool in C++.

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More consistency for a better build process.
This commit is contained in:
Bert Peters
2018-03-23 16:49:37 +01:00
parent effa90c178
commit f8be25adb9
6 changed files with 202 additions and 103 deletions
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64
data/models/alexnet/model-dedup.prototxt
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@@ -18,12 +18,12 @@ layer {
bottom: "data"
top: "conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
@@ -65,12 +65,12 @@ layer {
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
@@ -113,12 +113,12 @@ layer {
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
@@ -138,12 +138,12 @@ layer {
bottom: "relu3"
top: "conv4"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
@@ -164,12 +164,12 @@ layer {
bottom: "relu4"
top: "conv5"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
@@ -201,12 +201,12 @@ layer {
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
@@ -233,12 +233,12 @@ layer {
bottom: "drop6"
top: "fc7"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
@@ -265,12 +265,12 @@ layer {
bottom: "drop7"
top: "fc8"
param {
lr_mult: 1.0
decay_mult: 1.0
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2.0
decay_mult: 0.0
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1000