netrans/examples/multi_input/multi_input.prototxt

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name: "test"
layer{
name:"target"
type:"Input"
top:"target"
input_param{
shape:{dim:1 dim:3 dim:112 dim:112 }
}
}
layer{
name:"image"
type:"Input"
top:"image"
input_param{
shape:{dim:1 dim:3 dim:112 dim:112 }
}
}
layer {
name: "conv1_1"
type: "Convolution"
bottom: "target"
top: "conv1_1"
param {
name: "conv1_1_w"
lr_mult: 1
decay_mult: 1
}
param {
name: "conv1_1_b"
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
########################################
layer {
name: "conv1_1_p"
type: "Convolution"
bottom: "image"
top: "conv1_1_p"
param {
name: "conv1_1_w"
lr_mult: 1
decay_mult: 1
}
param {
name: "conv1_1_b"
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1_1_p"
type: "ReLU"
bottom: "conv1_1_p"
top: "conv1_1_p"
}
#########################
layer {
name: "pool_concat"
type: "Concat"
bottom: "conv1_1"
bottom: "conv1_1_p"
top: "pool_concat"
concat_param {
axis: 1
}
}