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ARTN_Deploy.prototxt
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821 lines (770 loc) · 12.2 KB
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name: "Temporal Network"
input: "data"
input_dim: 512
input_dim: 3
input_dim: 64
input_dim: 64
layer{
name: "Slicer"
type: "Slice"
bottom: "data"
top: "data1"
top: "data2"
top: "data3"
slice_param {
axis: 1
slice_point: 1
slice_point: 2
}
}
layer
{
name: "conv1_1"
type: "Convolution"
bottom: "data1"
top: "conv1_1"
param { lr_mult: 1 }
param { lr_mult: 2 }
convolution_param
{
num_output: 32
kernel_size: 7
stride: 1
pad: 3
weight_filler{ type: "xavier" }
bias_filler{ type: "constant" }
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer
{
name: "conv1_2"
type: "Convolution"
bottom: "data2"
top: "conv1_2"
param { lr_mult: 1 }
param { lr_mult: 2 }
convolution_param
{
num_output: 64
kernel_size: 7
stride: 1
pad: 3
weight_filler{ type: "xavier" }
bias_filler{ type: "constant" }
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer
{
name: "conv1_3"
type: "Convolution"
bottom: "data3"
top: "conv1_3"
param { lr_mult: 1 }
param { lr_mult: 2 }
convolution_param
{
num_output: 32
kernel_size: 7
stride: 1
pad: 3
weight_filler{ type: "xavier" }
bias_filler{ type: "constant" }
}
}
layer {
name: "relu1_3"
type: "ReLU"
bottom: "conv1_3"
top: "conv1_3"
}
layer {
name: "conv2_1/3x3_reduce"
type: "Convolution"
bottom: "conv1_1"
top: "conv2_1/3x3_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2_1/relu_3x3_reduce"
type: "ReLU"
bottom: "conv2_1/3x3_reduce"
top: "conv2_1/3x3_reduce"
}
layer {
name: "conv2_2/3x3_reduce"
type: "Convolution"
bottom: "conv1_2"
top: "conv2_2/3x3_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2_2/relu_3x3_reduce"
type: "ReLU"
bottom: "conv2_2/3x3_reduce"
top: "conv2_2/3x3_reduce"
}
layer {
name: "conv2_3/3x3_reduce"
type: "Convolution"
bottom: "conv1_3"
top: "conv2_3/3x3_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2_3/relu_3x3_reduce"
type: "ReLU"
bottom: "conv2_3/3x3_reduce"
top: "conv2_3/3x3_reduce"
}
layer {
name: "conv2_1/3x3"
type: "Convolution"
bottom: "conv2_1/3x3_reduce"
top: "conv2_1/3x3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2_1/relu_3x3"
type: "ReLU"
bottom: "conv2_1/3x3"
top: "conv2_1/3x3"
}
layer {
name: "conv2_2/3x3"
type: "Convolution"
bottom: "conv2_2/3x3_reduce"
top: "conv2_2/3x3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2_2/relu_3x3"
type: "ReLU"
bottom: "conv2_2/3x3"
top: "conv2_2/3x3"
}
layer {
name: "conv2_3/3x3"
type: "Convolution"
bottom: "conv2_3/3x3_reduce"
top: "conv2_3/3x3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2_3/relu_3x3"
type: "ReLU"
bottom: "conv2_3/3x3"
top: "conv2_3/3x3"
}
layer {
name: "3_Temporal_Concat"
type: "Concat"
bottom: "conv2_1/3x3"
bottom: "conv2_2/3x3"
bottom: "conv2_3/3x3"
top: "conv2/3x3"
}
layer {
name: "inception_3a/1x1"
type: "Convolution"
bottom: "conv2/3x3"
top: "inception_3a/1x1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_1x1"
type: "ReLU"
bottom: "inception_3a/1x1"
top: "inception_3a/1x1"
}
layer {
name: "inception_3a/3x3_reduce"
type: "Convolution"
bottom: "conv2/3x3"
top: "inception_3a/3x3_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_3a/3x3_reduce"
top: "inception_3a/3x3_reduce"
}
layer {
name: "inception_3a/3x3"
type: "Convolution"
bottom: "inception_3a/3x3_reduce"
top: "inception_3a/3x3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_3x3"
type: "ReLU"
bottom: "inception_3a/3x3"
top: "inception_3a/3x3"
}
layer {
name: "inception_3a/5x5_reduce"
type: "Convolution"
bottom: "conv2/3x3"
top: "inception_3a/5x5_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_3a/5x5_reduce"
top: "inception_3a/5x5_reduce"
}
layer {
name: "inception_3a/5x5"
type: "Convolution"
bottom: "inception_3a/5x5_reduce"
top: "inception_3a/5x5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_5x5"
type: "ReLU"
bottom: "inception_3a/5x5"
top: "inception_3a/5x5"
}
layer {
name: "inception_3a/7x7_reduce"
type: "Convolution"
bottom: "conv2/3x3"
top: "inception_3a/7x7_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_7x7_reduce"
type: "ReLU"
bottom: "inception_3a/7x7_reduce"
top: "inception_3a/7x7_reduce"
}
layer {
name: "inception_3a/7x7"
type: "Convolution"
bottom: "inception_3a/7x7_reduce"
top: "inception_3a/7x7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
pad: 3
kernel_size: 7
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/relu_7x7"
type: "ReLU"
bottom: "inception_3a/7x7"
top: "inception_3a/7x7"
}
layer {
name: "inception_3a/output"
type: "Concat"
bottom: "inception_3a/1x1"
bottom: "inception_3a/3x3"
bottom: "inception_3a/5x5"
bottom: "inception_3a/7x7"
top: "inception_3a/output"
}
layer {
name: "inception_3b/1x1"
type: "Convolution"
bottom: "inception_3a/output"
top: "inception_3b/1x1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_1x1"
type: "ReLU"
bottom: "inception_3b/1x1"
top: "inception_3b/1x1"
}
layer {
name: "inception_3b/3x3_reduce"
type: "Convolution"
bottom: "inception_3a/output"
top: "inception_3b/3x3_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_3x3_reduce"
type: "ReLU"
bottom: "inception_3b/3x3_reduce"
top: "inception_3b/3x3_reduce"
}
layer {
name: "inception_3b/3x3"
type: "Convolution"
bottom: "inception_3b/3x3_reduce"
top: "inception_3b/3x3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_3x3"
type: "ReLU"
bottom: "inception_3b/3x3"
top: "inception_3b/3x3"
}
layer {
name: "inception_3b/5x5_reduce"
type: "Convolution"
bottom: "inception_3a/output"
top: "inception_3b/5x5_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_5x5_reduce"
type: "ReLU"
bottom: "inception_3b/5x5_reduce"
top: "inception_3b/5x5_reduce"
}
layer {
name: "inception_3b/5x5"
type: "Convolution"
bottom: "inception_3b/5x5_reduce"
top: "inception_3b/5x5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_5x5"
type: "ReLU"
bottom: "inception_3b/5x5"
top: "inception_3b/5x5"
}
layer {
name: "inception_3b/7x7_reduce"
type: "Convolution"
bottom: "inception_3a/output"
top: "inception_3b/7x7_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_7x7_reduce"
type: "ReLU"
bottom: "inception_3b/7x7_reduce"
top: "inception_3b/7x7_reduce"
}
layer {
name: "inception_3b/7x7"
type: "Convolution"
bottom: "inception_3b/7x7_reduce"
top: "inception_3b/7x7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
pad: 3
kernel_size: 7
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3b/relu_7x7"
type: "ReLU"
bottom: "inception_3b/7x7"
top: "inception_3b/7x7"
}
layer {
name: "inception_3b/output"
type: "Concat"
bottom: "inception_3b/1x1"
bottom: "inception_3b/3x3"
bottom: "inception_3b/5x5"
bottom: "inception_3b/7x7"
top: "inception_3b/output"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "inception_3b/output"
top: "conv5"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 1
kernel_size: 5
stride: 1
pad: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}