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Save from python for net surgery #455

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Jun 12, 2014
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Save from python for net surgery #455

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6a5b2c1
save from python for net surgery
shelhamer May 26, 2014
f0ee675
define fully-convolutional imagenet model
shelhamer Jun 11, 2014
c3440fa
make notebook for net surgery of fully-convolutional model
shelhamer Jun 12, 2014
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215 changes: 215 additions & 0 deletions examples/imagenet/imagenet_full_conv.prototxt
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Original file line number Diff line number Diff line change
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name: "CaffeNetConv"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 454
input_dim: 454
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm1"
type: LRN
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "norm1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm2"
type: LRN
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
bottom: "conv3"
top: "conv4"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layers {
name: "relu4"
type: RELU
bottom: "conv4"
top: "conv4"
}
layers {
name: "conv5"
type: CONVOLUTION
bottom: "conv4"
top: "conv5"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6-conv"
type: CONVOLUTION
bottom: "pool5"
top: "fc6-conv"
convolution_param {
num_output: 4096
kernel_size: 6
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6-conv"
top: "fc6-conv"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6-conv"
top: "fc6-conv"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc7-conv"
type: CONVOLUTION
bottom: "fc6-conv"
top: "fc7-conv"
convolution_param {
num_output: 4096
kernel_size: 1
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7-conv"
top: "fc7-conv"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7-conv"
top: "fc7-conv"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc8-conv"
type: CONVOLUTION
bottom: "fc7-conv"
top: "fc8-conv"
convolution_param {
num_output: 1000
kernel_size: 1
}
}
layers {
name: "prob"
type: SOFTMAX
bottom: "fc8-conv"
top: "prob"
}
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