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jackersson/resnet10.txt

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NN architectures
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resnet10.txt
name: "ResNet-10"
input: "data"
input_shape {
dim: 1
dim: 3
dim: 272
dim: 480
}
layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 32
kernel_size: 3
pad: 1
stride: 2
bias_term: false
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "conv1_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "conv1_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "conv1_relu"
type: "ReLU"
}
layer {
bottom: "conv1"
top: "conv2"
name: "conv2"
type: "Convolution"
convolution_param {
num_output: 32
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "conv2"
top: "conv2"
name: "conv2_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "conv2"
top: "conv2"
name: "conv2_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "conv2"
top: "conv2"
name: "conv2_relu"
type: "ReLU"
}
layer {
bottom: "conv2"
top: "conv3"
name: "conv3"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "conv3"
top: "conv3"
name: "conv3_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "conv3"
top: "conv3"
name: "conv3_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "conv3"
top: "conv3"
name: "conv3_relu"
type: "ReLU"
}
layer {
bottom: "conv3"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
kernel_size: 3
stride: 2
pad:1
pool: MAX
ceil_mode: false
}
}
layer {
bottom: "pool1"
top: "res1_conv1"
name: "res1_conv1"
type: "Convolution"
convolution_param {
num_output: 96
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res1_conv1"
top: "res1_conv1"
name: "res1_conv1_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res1_conv1"
top: "res1_conv1"
name: "res1_conv1_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res1_conv1"
top: "res1_conv1"
name: "res1_conv1_relu"
type: "ReLU"
}
layer {
bottom: "res1_conv1"
top: "res1_conv2"
name: "res1_conv2"
type: "Convolution"
convolution_param {
num_output: 96
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res1_conv2"
top: "res1_conv2"
name: "res1_conv2_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res1_conv2"
top: "res1_conv2"
name: "res1_conv2_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "pool1"
top: "res1_match"
name: "res1_match"
type: "Convolution"
convolution_param {
num_output: 96
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
}
layer {
bottom: "res1_match"
top: "res1_match"
name: "res1_match_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res1_match"
top: "res1_match"
name: "res1_match_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res1_match"
bottom: "res1_conv2"
top: "res1"
name: "res1"
type: "Eltwise"
}
layer {
bottom: "res1"
top: "res1"
name: "res1_relu"
type: "ReLU"
}
layer {
bottom: "res1"
top: "res2_conv1"
name: "res2_conv1"
type: "Convolution"
convolution_param {
num_output: 192
kernel_size: 3
pad: 1
stride: 2
bias_term: false
}
}
layer {
bottom: "res2_conv1"
top: "res2_conv1"
name: "res2_conv1_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2_conv1"
top: "res2_conv1"
name: "res2_conv1_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2_conv1"
top: "res2_conv1"
name: "res2_conv1_relu"
type: "ReLU"
}
layer {
bottom: "res2_conv1"
top: "res2_conv2"
name: "res2_conv2"
type: "Convolution"
convolution_param {
num_output: 192
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res2_conv2"
top: "res2_conv2"
name: "res2_conv2_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2_conv2"
top: "res2_conv2"
name: "res2_conv2_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res1"
top: "res2_match"
name: "res2_match"
type: "Convolution"
convolution_param {
num_output: 192
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
}
layer {
bottom: "res2_match"
top: "res2_match"
name: "res2_match_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res2_match"
top: "res2_match"
name: "res2_match_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2_match"
bottom: "res2_conv2"
top: "res2"
name: "res2"
type: "Eltwise"
}
layer {
bottom: "res2"
top: "res2"
name: "res2_relu"
type: "ReLU"
}
layer {
bottom: "res2"
top: "res3_conv1"
name: "res3_conv1"
type: "Convolution"
convolution_param {
num_output: 384
kernel_size: 3
pad: 1
stride: 2
bias_term: false
}
}
layer {
bottom: "res3_conv1"
top: "res3_conv1"
name: "res3_conv1_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res3_conv1"
top: "res3_conv1"
name: "res3_conv1_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3_conv1"
top: "res3_conv1"
name: "res3_conv1_relu"
type: "ReLU"
}
layer {
bottom: "res3_conv1"
top: "res3_conv2"
name: "res3_conv2"
type: "Convolution"
convolution_param {
num_output: 384
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res3_conv2"
top: "res3_conv2"
name: "res3_conv2_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res3_conv2"
top: "res3_conv2"
name: "res3_conv2_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res2"
top: "res3_match"
name: "res3_match"
type: "Convolution"
convolution_param {
num_output: 384
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
}
layer {
bottom: "res3_match"
top: "res3_match"
name: "res3_match_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res3_match"
top: "res3_match"
name: "res3_match_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3_match"
bottom: "res3_conv2"
top: "res3"
name: "res3"
type: "Eltwise"
}
layer {
bottom: "res3"
top: "res3"
name: "res3_relu"
type: "ReLU"
}
layer {
bottom: "res3"
top: "res4_conv1"
name: "res4_conv1"
type: "Convolution"
convolution_param {
num_output: 768
kernel_size: 3
pad: 1
stride: 2
bias_term: false
}
}
layer {
bottom: "res4_conv1"
top: "res4_conv1"
name: "res4_conv1_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res4_conv1"
top: "res4_conv1"
name: "res4_conv1_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res4_conv1"
top: "res4_conv1"
name: "res4_conv1_relu"
type: "ReLU"
}
layer {
bottom: "res4_conv1"
top: "res4_conv2"
name: "res4_conv2"
type: "Convolution"
convolution_param {
num_output: 768
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
}
layer {
bottom: "res4_conv2"
top: "res4_conv2"
name: "res4_conv2_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res4_conv2"
top: "res4_conv2"
name: "res4_conv2_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res3"
top: "res4_match"
name: "res4_match"
type: "Convolution"
convolution_param {
num_output: 768
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
}
layer {
bottom: "res4_match"
top: "res4_match"
name: "res4_match_bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "res4_match"
top: "res4_match"
name: "res4_match_scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "res4_match"
bottom: "res4_conv2"
top: "res4"
name: "res4"
type: "Eltwise"
}
layer {
bottom: "res4"
top: "res4"
name: "res4_relu"
type: "ReLU"
}
layer {
bottom: "res4"
top: "pool_avg"
name: "pool_avg"
type: "Pooling"
pooling_param {
global_pooling: true
pool: AVE
}
}
layer {
bottom: "pool_avg"
top: "classifier"
name: "classifier"
type: "InnerProduct"
inner_product_param {
num_output: 1000
}
}
layer {
bottom: "classifier"
top: "prob"
name: "prob"
type: "Softmax"
}
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