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train_MnasNet.prototxt
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name:"MnasNet"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
transform_param {
scale: 0.00390625
mirror: True
crop_size: 224
}
data_param {
source: "/home/lijianfei/datasets/kaggle_cat_dog/train_lmdb"
batch_size: 8
backend: LMDB
}
image_data_param {
shuffle: True
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 32
bias_term: False
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv1/bn"
type: "BatchNorm"
bottom: "conv1"
top: "conv1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv1/scale"
type: "Scale"
bottom: "conv1"
top: "conv1"
scale_param {
bias_term: true
}
}
layer {
name: "conv1/ReLU"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "conv2_1/dw"
type: "ConvolutionDepthwise"
bottom: "conv1"
top: "conv2_1/dw"
convolution_param {
num_output: 32
bias_term: False
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/dw/bn"
type: "BatchNorm"
bottom: "conv2_1/dw"
top: "conv2_1/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv2_1/dw/scale"
type: "Scale"
bottom: "conv2_1/dw"
top: "conv2_1/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_1/dw/ReLU"
type: "ReLU"
bottom: "conv2_1/dw"
top: "conv2_1/dw"
}
layer {
name: "conv2_1/sep"
type: "Convolution"
bottom: "conv2_1/dw"
top: "conv2_1/sep"
convolution_param {
num_output: 16
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/sep/bn"
type: "BatchNorm"
bottom: "conv2_1/sep"
top: "conv2_1/sep"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv2_1/sep/scale"
type: "Scale"
bottom: "conv2_1/sep"
top: "conv2_1/sep"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_1/sep/ReLU"
type: "ReLU"
bottom: "conv2_1/sep"
top: "conv2_1/sep"
}
layer {
name: "conv3_1/sep1"
type: "Convolution"
bottom: "conv2_1/sep"
top: "conv3_1/sep1"
convolution_param {
num_output: 48
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/sep1/bn"
type: "BatchNorm"
bottom: "conv3_1/sep1"
top: "conv3_1/sep1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_1/sep1/scale"
type: "Scale"
bottom: "conv3_1/sep1"
top: "conv3_1/sep1"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1/sep1/ReLU"
type: "ReLU"
bottom: "conv3_1/sep1"
top: "conv3_1/sep1"
}
layer {
name: "conv3_1/dw"
type: "ConvolutionDepthwise"
bottom: "conv3_1/sep1"
top: "conv3_1/dw"
convolution_param {
num_output: 48
bias_term: False
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/dw/bn"
type: "BatchNorm"
bottom: "conv3_1/dw"
top: "conv3_1/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_1/dw/scale"
type: "Scale"
bottom: "conv3_1/dw"
top: "conv3_1/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1/dw/ReLU"
type: "ReLU"
bottom: "conv3_1/dw"
top: "conv3_1/dw"
}
layer {
name: "conv3_1/sep2"
type: "Convolution"
bottom: "conv3_1/dw"
top: "conv3_1/sep2"
convolution_param {
num_output: 24
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/sep2/bn"
type: "BatchNorm"
bottom: "conv3_1/sep2"
top: "conv3_1/sep2"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_1/sep2/scale"
type: "Scale"
bottom: "conv3_1/sep2"
top: "conv3_1/sep2"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1/sep2/ReLU"
type: "ReLU"
bottom: "conv3_1/sep2"
top: "conv3_1/sep2"
}
layer {
name: "conv3_2/sep1"
type: "Convolution"
bottom: "conv3_1/sep2"
top: "conv3_2/sep1"
convolution_param {
num_output: 72
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/sep1/bn"
type: "BatchNorm"
bottom: "conv3_2/sep1"
top: "conv3_2/sep1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_2/sep1/scale"
type: "Scale"
bottom: "conv3_2/sep1"
top: "conv3_2/sep1"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_2/sep1/ReLU"
type: "ReLU"
bottom: "conv3_2/sep1"
top: "conv3_2/sep1"
}
layer {
name: "conv3_2/dw"
type: "ConvolutionDepthwise"
bottom: "conv3_2/sep1"
top: "conv3_2/dw"
convolution_param {
num_output: 72
bias_term: False
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/dw/bn"
type: "BatchNorm"
bottom: "conv3_2/dw"
top: "conv3_2/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_2/dw/scale"
type: "Scale"
bottom: "conv3_2/dw"
top: "conv3_2/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_2/dw/ReLU"
type: "ReLU"
bottom: "conv3_2/dw"
top: "conv3_2/dw"
}
layer {
name: "conv3_2/sep2"
type: "Convolution"
bottom: "conv3_2/dw"
top: "conv3_2/sep2"
convolution_param {
num_output: 24
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/sep2/bn"
type: "BatchNorm"
bottom: "conv3_2/sep2"
top: "conv3_2/sep2"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_2/sep2/scale"
type: "Scale"
bottom: "conv3_2/sep2"
top: "conv3_2/sep2"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_2/Eltwise1"
type: "Eltwise"
bottom: "conv3_1/sep2"
bottom: "conv3_2/sep2"
top: "conv3_2/Eltwise1"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv3_2/Eltwise/ReLU"
type: "ReLU"
bottom: "conv3_2/Eltwise1"
top: "conv3_2/Eltwise1"
}
layer {
name: "conv3_3/sep1"
type: "Convolution"
bottom: "conv3_2/Eltwise1"
top: "conv3_3/sep1"
convolution_param {
num_output: 72
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_3/sep1/bn"
type: "BatchNorm"
bottom: "conv3_3/sep1"
top: "conv3_3/sep1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_3/sep1/scale"
type: "Scale"
bottom: "conv3_3/sep1"
top: "conv3_3/sep1"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_3/sep1/ReLU"
type: "ReLU"
bottom: "conv3_3/sep1"
top: "conv3_3/sep1"
}
layer {
name: "conv3_3/dw"
type: "ConvolutionDepthwise"
bottom: "conv3_3/sep1"
top: "conv3_3/dw"
convolution_param {
num_output: 72
bias_term: False
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_3/dw/bn"
type: "BatchNorm"
bottom: "conv3_3/dw"
top: "conv3_3/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_3/dw/scale"
type: "Scale"
bottom: "conv3_3/dw"
top: "conv3_3/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_3/dw/ReLU"
type: "ReLU"
bottom: "conv3_3/dw"
top: "conv3_3/dw"
}
layer {
name: "conv3_3/sep2"
type: "Convolution"
bottom: "conv3_3/dw"
top: "conv3_3/sep2"
convolution_param {
num_output: 24
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_3/sep2/bn"
type: "BatchNorm"
bottom: "conv3_3/sep2"
top: "conv3_3/sep2"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv3_3/sep2/scale"
type: "Scale"
bottom: "conv3_3/sep2"
top: "conv3_3/sep2"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_3/Eltwise1"
type: "Eltwise"
bottom: "conv3_2/Eltwise1"
bottom: "conv3_3/sep2"
top: "conv3_3/Eltwise1"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv3_3/Eltwise/ReLU"
type: "ReLU"
bottom: "conv3_3/Eltwise1"
top: "conv3_3/Eltwise1"
}
layer {
name: "conv4_1/sep1"
type: "Convolution"
bottom: "conv3_3/Eltwise1"
top: "conv4_1/sep1"
convolution_param {
num_output: 72
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/sep1/bn"
type: "BatchNorm"
bottom: "conv4_1/sep1"
top: "conv4_1/sep1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_1/sep1/scale"
type: "Scale"
bottom: "conv4_1/sep1"
top: "conv4_1/sep1"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1/sep1/ReLU"
type: "ReLU"
bottom: "conv4_1/sep1"
top: "conv4_1/sep1"
}
layer {
name: "conv4_1/dw"
type: "ConvolutionDepthwise"
bottom: "conv4_1/sep1"
top: "conv4_1/dw"
convolution_param {
num_output: 72
bias_term: False
pad: 2
kernel_size: 5
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/dw/bn"
type: "BatchNorm"
bottom: "conv4_1/dw"
top: "conv4_1/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_1/dw/scale"
type: "Scale"
bottom: "conv4_1/dw"
top: "conv4_1/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1/dw/ReLU"
type: "ReLU"
bottom: "conv4_1/dw"
top: "conv4_1/dw"
}
layer {
name: "conv4_1/sep2"
type: "Convolution"
bottom: "conv4_1/dw"
top: "conv4_1/sep2"
convolution_param {
num_output: 40
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/sep2/bn"
type: "BatchNorm"
bottom: "conv4_1/sep2"
top: "conv4_1/sep2"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_1/sep2/scale"
type: "Scale"
bottom: "conv4_1/sep2"
top: "conv4_1/sep2"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1/sep2/ReLU"
type: "ReLU"
bottom: "conv4_1/sep2"
top: "conv4_1/sep2"
}
layer {
name: "conv4_2/sep1"
type: "Convolution"
bottom: "conv4_1/sep2"
top: "conv4_2/sep1"
convolution_param {
num_output: 120
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/sep1/bn"
type: "BatchNorm"
bottom: "conv4_2/sep1"
top: "conv4_2/sep1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_2/sep1/scale"
type: "Scale"
bottom: "conv4_2/sep1"
top: "conv4_2/sep1"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_2/sep1/ReLU"
type: "ReLU"
bottom: "conv4_2/sep1"
top: "conv4_2/sep1"
}
layer {
name: "conv4_2/dw"
type: "ConvolutionDepthwise"
bottom: "conv4_2/sep1"
top: "conv4_2/dw"
convolution_param {
num_output: 120
bias_term: False
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/dw/bn"
type: "BatchNorm"
bottom: "conv4_2/dw"
top: "conv4_2/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_2/dw/scale"
type: "Scale"
bottom: "conv4_2/dw"
top: "conv4_2/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_2/dw/ReLU"
type: "ReLU"
bottom: "conv4_2/dw"
top: "conv4_2/dw"
}
layer {
name: "conv4_2/sep2"
type: "Convolution"
bottom: "conv4_2/dw"
top: "conv4_2/sep2"
convolution_param {
num_output: 40
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/sep2/bn"
type: "BatchNorm"
bottom: "conv4_2/sep2"
top: "conv4_2/sep2"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_2/sep2/scale"
type: "Scale"
bottom: "conv4_2/sep2"
top: "conv4_2/sep2"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_2/Eltwise1"
type: "Eltwise"
bottom: "conv4_1/sep2"
bottom: "conv4_2/sep2"
top: "conv4_2/Eltwise1"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv4_2/Eltwise/ReLU"
type: "ReLU"
bottom: "conv4_2/Eltwise1"
top: "conv4_2/Eltwise1"
}
layer {
name: "conv4_3/sep1"
type: "Convolution"
bottom: "conv4_2/Eltwise1"
top: "conv4_3/sep1"
convolution_param {
num_output: 120
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_3/sep1/bn"
type: "BatchNorm"
bottom: "conv4_3/sep1"
top: "conv4_3/sep1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_3/sep1/scale"
type: "Scale"
bottom: "conv4_3/sep1"
top: "conv4_3/sep1"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_3/sep1/ReLU"
type: "ReLU"
bottom: "conv4_3/sep1"
top: "conv4_3/sep1"
}
layer {
name: "conv4_3/dw"
type: "ConvolutionDepthwise"
bottom: "conv4_3/sep1"
top: "conv4_3/dw"
convolution_param {
num_output: 120
bias_term: False
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_3/dw/bn"
type: "BatchNorm"
bottom: "conv4_3/dw"
top: "conv4_3/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_3/dw/scale"
type: "Scale"
bottom: "conv4_3/dw"
top: "conv4_3/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_3/dw/ReLU"
type: "ReLU"
bottom: "conv4_3/dw"
top: "conv4_3/dw"
}
layer {
name: "conv4_3/sep2"
type: "Convolution"
bottom: "conv4_3/dw"
top: "conv4_3/sep2"
convolution_param {
num_output: 40
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_3/sep2/bn"
type: "BatchNorm"
bottom: "conv4_3/sep2"
top: "conv4_3/sep2"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv4_3/sep2/scale"
type: "Scale"
bottom: "conv4_3/sep2"
top: "conv4_3/sep2"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_3/Eltwise1"
type: "Eltwise"
bottom: "conv4_2/Eltwise1"
bottom: "conv4_3/sep2"
top: "conv4_3/Eltwise1"
eltwise_param {
operation: SUM
}
}
layer {
name: "conv4_3/Eltwise/ReLU"
type: "ReLU"
bottom: "conv4_3/Eltwise1"
top: "conv4_3/Eltwise1"
}
layer {
name: "conv5_1/sep1"
type: "Convolution"
bottom: "conv4_3/Eltwise1"
top: "conv5_1/sep1"
convolution_param {
num_output: 240
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/sep1/bn"
type: "BatchNorm"
bottom: "conv5_1/sep1"
top: "conv5_1/sep1"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv5_1/sep1/scale"
type: "Scale"
bottom: "conv5_1/sep1"
top: "conv5_1/sep1"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_1/sep1/ReLU"
type: "ReLU"
bottom: "conv5_1/sep1"
top: "conv5_1/sep1"
}
layer {
name: "conv5_1/dw"
type: "ConvolutionDepthwise"
bottom: "conv5_1/sep1"
top: "conv5_1/dw"
convolution_param {
num_output: 240
bias_term: False
pad: 2
kernel_size: 5
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/dw/bn"
type: "BatchNorm"
bottom: "conv5_1/dw"
top: "conv5_1/dw"
batch_norm_param {
use_global_stats: False
}
}
layer {
name: "conv5_1/dw/scale"
type: "Scale"
bottom: "conv5_1/dw"
top: "conv5_1/dw"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_1/dw/ReLU"
type: "ReLU"
bottom: "conv5_1/dw"
top: "conv5_1/dw"
}
layer {
name: "conv5_1/sep2"
type: "Convolution"
bottom: "conv5_1/dw"
top: "conv5_1/sep2"
convolution_param {
num_output: 80
bias_term: False
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/sep2/bn"
type: "BatchNorm"
bottom: "conv5_1/sep2"