Simshang
12/21/2016 - 3:03 AM
COWC_Detection_ResCeption
# ================================================================================================
#
# Cars Overhead With Context
#
# http://gdo-datasci.ucllnl.org/cowc/
#
# T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla, Kofi Boakye
#
# Lawrence Livermore National Laboratory
# Global Security Directorate
#
# February 2016
#
# ================================================================================================
#
# Copyright (C) 2016 Lawrence Livermore National Security
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# ================================================================================================
#
# This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore
# National Laboratory under Contract DE-AC52-07NA27344.
#
# LLNL-MI-699521
#
# ================================================================================================
name: "COWC_Detection_ResCeption"
layer {
top: "data"
top: "label"
name: "data"
type: "ImageData"
image_data_param {
source: "train_list.txt"
batch_size: 64
shuffle: true
}
transform_param {
mirror: true
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
include {
phase: TRAIN
}
}
layer {
top: "data"
top: "label"
name: "data"
type: "ImageData"
image_data_param {
source: "test_list.txt"
batch_size: 10
}
transform_param {
mirror: false
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
include {
phase: TEST
}
}
# ================================================================================================
# ======== Layer1_7x7
# ================================================================================================
layer {
name: "Layer1_7x7/Convolution_Stride_2"
type: "Convolution"
bottom: "data"
top: "Layer1_7x7/Convolution_Stride_2"
convolution_param {
num_output: 64
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer1_7x7/Convolution_Stride_2_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer1_7x7/Convolution_Stride_2_b"
}
}
layer {
name: "Layer1_7x7/BatchNorm_Stride_2"
type: "BatchNorm"
bottom: "Layer1_7x7/Convolution_Stride_2"
top: "Layer1_7x7/BatchNorm_Stride_2"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer1_7x7/ReLU_Stride_2"
type: "ReLU"
bottom: "Layer1_7x7/BatchNorm_Stride_2"
top: "Layer1_7x7/ReLU_Stride_2"
}
layer {
name: "Layer1_Pooling/Pooling_Stride_2"
type: "Pooling"
bottom: "Layer1_7x7/ReLU_Stride_2"
top: "Layer1_Pooling/Pooling_Stride_2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 0
}
}
# ================================================================================================
# ======== Layer2_3x3
# ================================================================================================
layer {
name: "Layer2_3x3/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer1_Pooling/Pooling_Stride_2"
top: "Layer2_3x3/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer2_3x3/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer2_3x3/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer2_3x3/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer2_3x3/Convolution_Inception3x3_reduce"
top: "Layer2_3x3/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer2_3x3/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer2_3x3/BatchNorm_Inception3x3_reduce"
top: "Layer2_3x3/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer2_3x3/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer2_3x3/ReLU_Inception3x3_reduce"
top: "Layer2_3x3/Convolution_Inception3x3"
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer2_3x3/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer2_3x3/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer2_3x3/BatchNorm_Inception3x3"
type: "BatchNorm"
bottom: "Layer2_3x3/Convolution_Inception3x3"
top: "Layer2_3x3/BatchNorm_Inception3x3"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer2_3x3/ReLU_Inception3x3"
type: "ReLU"
bottom: "Layer2_3x3/BatchNorm_Inception3x3"
top: "Layer2_3x3/ReLU_Inception3x3"
}
layer {
name: "Layer2_Pooling/Pooling_Stride_2"
type: "Pooling"
bottom: "Layer2_3x3/ReLU_Inception3x3"
top: "Layer2_Pooling/Pooling_Stride_2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 0
}
}
# ================================================================================================
# ======== Layer3a
# ================================================================================================
layer {
name: "Layer3a/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer2_Pooling/Pooling_Stride_2"
top: "Layer3a/Convolution_ResCeption1x1"
convolution_param {
num_output: 192
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3a/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3a/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer3a/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer2_Pooling/Pooling_Stride_2"
top: "Layer3a/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 96
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3a/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3a/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer3a/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer3a/Convolution_Inception3x3_reduce"
top: "Layer3a/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer3a/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer3a/BatchNorm_Inception3x3_reduce"
top: "Layer3a/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer3a/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer3a/ReLU_Inception3x3_reduce"
top: "Layer3a/Convolution_Inception3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3a/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3a/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer3a/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer2_Pooling/Pooling_Stride_2"
top: "Layer3a/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 16
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3a/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3a/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer3a/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer3a/Convolution_Inception5x5_reduce"
top: "Layer3a/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer3a/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer3a/BatchNorm_Inception5x5_reduce"
top: "Layer3a/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer3a/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer3a/ReLU_Inception5x5_reduce"
top: "Layer3a/Convolution_Inception5x5"
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3a/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3a/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer3a/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer2_Pooling/Pooling_Stride_2"
top: "Layer3a/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer3a/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer3a/Pooling_Inception_Pool"
top: "Layer3a/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 32
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3a/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3a/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer3a/Concat_Output"
type: "Concat"
bottom: "Layer3a/Convolution_Inception3x3"
bottom: "Layer3a/Convolution_Inception5x5"
bottom: "Layer3a/Convolution_Inception_Pool_1x1"
top: "Layer3a/Concat_Output"
}
layer {
name: "Layer3a/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer3a/Concat_Output"
bottom: "Layer3a/Convolution_ResCeption1x1"
top: "Layer3a/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer3a/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer3a/Eltwise_EltWise"
top: "Layer3a/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer3a/ReLU_EltWise"
type: "ReLU"
bottom: "Layer3a/BatchNorm_EltWise"
top: "Layer3a/ReLU_EltWise"
}
# ================================================================================================
# ======== Layer3b
# ================================================================================================
layer {
name: "Layer3b/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer3a/ReLU_EltWise"
top: "Layer3b/Convolution_ResCeption1x1"
convolution_param {
num_output: 352
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3b/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3b/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer3b/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer3a/ReLU_EltWise"
top: "Layer3b/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3b/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3b/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer3b/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer3b/Convolution_Inception3x3_reduce"
top: "Layer3b/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer3b/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer3b/BatchNorm_Inception3x3_reduce"
top: "Layer3b/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer3b/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer3b/ReLU_Inception3x3_reduce"
top: "Layer3b/Convolution_Inception3x3"
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3b/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3b/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer3b/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer3a/ReLU_EltWise"
top: "Layer3b/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 32
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3b/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3b/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer3b/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer3b/Convolution_Inception5x5_reduce"
top: "Layer3b/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer3b/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer3b/BatchNorm_Inception5x5_reduce"
top: "Layer3b/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer3b/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer3b/ReLU_Inception5x5_reduce"
top: "Layer3b/Convolution_Inception5x5"
convolution_param {
num_output: 96
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3b/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3b/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer3b/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer3a/ReLU_EltWise"
top: "Layer3b/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer3b/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer3b/Pooling_Inception_Pool"
top: "Layer3b/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer3b/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer3b/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer3b/Concat_Output"
type: "Concat"
bottom: "Layer3b/Convolution_Inception3x3"
bottom: "Layer3b/Convolution_Inception5x5"
bottom: "Layer3b/Convolution_Inception_Pool_1x1"
top: "Layer3b/Concat_Output"
}
layer {
name: "Layer3b/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer3b/Concat_Output"
bottom: "Layer3b/Convolution_ResCeption1x1"
top: "Layer3b/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer3b/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer3b/Eltwise_EltWise"
top: "Layer3b/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer3b/ReLU_EltWise"
type: "ReLU"
bottom: "Layer3b/BatchNorm_EltWise"
top: "Layer3b/ReLU_EltWise"
}
layer {
name: "Layer3_Pooling/Pooling_Stride_2"
type: "Pooling"
bottom: "Layer3b/ReLU_EltWise"
top: "Layer3_Pooling/Pooling_Stride_2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 0
}
}
# ================================================================================================
# ======== Layer4a
# ================================================================================================
layer {
name: "Layer4a/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer3_Pooling/Pooling_Stride_2"
top: "Layer4a/Convolution_ResCeption1x1"
convolution_param {
num_output: 320
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4a/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4a/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer4a/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer3_Pooling/Pooling_Stride_2"
top: "Layer4a/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 96
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4a/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4a/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer4a/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer4a/Convolution_Inception3x3_reduce"
top: "Layer4a/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4a/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer4a/BatchNorm_Inception3x3_reduce"
top: "Layer4a/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer4a/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer4a/ReLU_Inception3x3_reduce"
top: "Layer4a/Convolution_Inception3x3"
convolution_param {
num_output: 208
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4a/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4a/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer4a/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer3_Pooling/Pooling_Stride_2"
top: "Layer4a/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 16
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4a/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4a/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer4a/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer4a/Convolution_Inception5x5_reduce"
top: "Layer4a/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4a/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer4a/BatchNorm_Inception5x5_reduce"
top: "Layer4a/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer4a/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer4a/ReLU_Inception5x5_reduce"
top: "Layer4a/Convolution_Inception5x5"
convolution_param {
num_output: 48
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4a/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4a/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer4a/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer3_Pooling/Pooling_Stride_2"
top: "Layer4a/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer4a/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer4a/Pooling_Inception_Pool"
top: "Layer4a/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4a/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4a/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer4a/Concat_Output"
type: "Concat"
bottom: "Layer4a/Convolution_Inception3x3"
bottom: "Layer4a/Convolution_Inception5x5"
bottom: "Layer4a/Convolution_Inception_Pool_1x1"
top: "Layer4a/Concat_Output"
}
layer {
name: "Layer4a/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer4a/Concat_Output"
bottom: "Layer4a/Convolution_ResCeption1x1"
top: "Layer4a/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer4a/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer4a/Eltwise_EltWise"
top: "Layer4a/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4a/ReLU_EltWise"
type: "ReLU"
bottom: "Layer4a/BatchNorm_EltWise"
top: "Layer4a/ReLU_EltWise"
}
# ================================================================================================
# ======== Loss1
# ================================================================================================
layer {
name: "Loss1/Pooling_Loss"
type: "Pooling"
bottom: "Layer4a/ReLU_EltWise"
top: "Loss1/Pooling_Loss"
pooling_param {
pool: AVE
kernel_size: 5
stride: 3
pad: 0
}
}
layer {
name: "Loss1/Convolution_Loss"
type: "Convolution"
bottom: "Loss1/Pooling_Loss"
top: "Loss1/Convolution_Loss"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Loss1/Convolution_Loss_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Loss1/Convolution_Loss_b"
}
}
layer {
name: "Loss1/BatchNorm_Loss"
type: "BatchNorm"
bottom: "Loss1/Convolution_Loss"
top: "Loss1/BatchNorm_Loss"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Loss1/ReLU_Loss"
type: "ReLU"
bottom: "Loss1/BatchNorm_Loss"
top: "Loss1/ReLU_Loss"
}
layer {
name: "Loss1/InnerProduct_Loss_fc1"
type: "InnerProduct"
bottom: "Loss1/ReLU_Loss"
top: "Loss1/InnerProduct_Loss_fc1"
inner_product_param {
num_output: 1024
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Loss1/InnerProduct_Loss_fc1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Loss1/InnerProduct_Loss_fc1_b"
}
}
layer {
name: "Loss1/BatchNorm_Loss_fc1"
type: "BatchNorm"
bottom: "Loss1/InnerProduct_Loss_fc1"
top: "Loss1/BatchNorm_Loss_fc1"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Loss1/ReLU_Loss_fc1"
type: "ReLU"
bottom: "Loss1/BatchNorm_Loss_fc1"
top: "Loss1/ReLU_Loss_fc1"
}
layer {
name: "Loss1/InnerProduct_Loss_fc2"
type: "InnerProduct"
bottom: "Loss1/ReLU_Loss_fc1"
top: "Loss1/InnerProduct_Loss_fc2"
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Loss1/InnerProduct_Loss_fc2_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Loss1/InnerProduct_Loss_fc2_b"
}
}
layer {
name: "Loss1/SoftmaxWithLoss_Loss"
type: "SoftmaxWithLoss"
bottom: "Loss1/InnerProduct_Loss_fc2"
bottom: "label"
top: "Loss1/SoftmaxWithLoss_Loss"
loss_weight: 0.3
}
layer {
name: "Loss1/Accuracy_Accuracy"
type: "Accuracy"
bottom: "Loss1/InnerProduct_Loss_fc2"
bottom: "label"
top: "Loss1/Accuracy_Accuracy"
include {
phase: TEST
}
}
# ================================================================================================
# ======== Layer4b
# ================================================================================================
layer {
name: "Layer4b/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer4a/ReLU_EltWise"
top: "Layer4b/Convolution_ResCeption1x1"
convolution_param {
num_output: 352
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4b/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4b/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer4b/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer4a/ReLU_EltWise"
top: "Layer4b/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 112
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4b/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4b/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer4b/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer4b/Convolution_Inception3x3_reduce"
top: "Layer4b/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4b/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer4b/BatchNorm_Inception3x3_reduce"
top: "Layer4b/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer4b/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer4b/ReLU_Inception3x3_reduce"
top: "Layer4b/Convolution_Inception3x3"
convolution_param {
num_output: 224
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4b/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4b/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer4b/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer4a/ReLU_EltWise"
top: "Layer4b/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 24
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4b/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4b/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer4b/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer4b/Convolution_Inception5x5_reduce"
top: "Layer4b/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4b/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer4b/BatchNorm_Inception5x5_reduce"
top: "Layer4b/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer4b/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer4b/ReLU_Inception5x5_reduce"
top: "Layer4b/Convolution_Inception5x5"
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4b/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4b/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer4b/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer4a/ReLU_EltWise"
top: "Layer4b/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer4b/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer4b/Pooling_Inception_Pool"
top: "Layer4b/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4b/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4b/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer4b/Concat_Output"
type: "Concat"
bottom: "Layer4b/Convolution_Inception3x3"
bottom: "Layer4b/Convolution_Inception5x5"
bottom: "Layer4b/Convolution_Inception_Pool_1x1"
top: "Layer4b/Concat_Output"
}
layer {
name: "Layer4b/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer4b/Concat_Output"
bottom: "Layer4b/Convolution_ResCeption1x1"
top: "Layer4b/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer4b/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer4b/Eltwise_EltWise"
top: "Layer4b/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4b/ReLU_EltWise"
type: "ReLU"
bottom: "Layer4b/BatchNorm_EltWise"
top: "Layer4b/ReLU_EltWise"
}
# ================================================================================================
# ======== Layer4c
# ================================================================================================
layer {
name: "Layer4c/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer4b/ReLU_EltWise"
top: "Layer4c/Convolution_ResCeption1x1"
convolution_param {
num_output: 384
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4c/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4c/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer4c/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer4b/ReLU_EltWise"
top: "Layer4c/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4c/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4c/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer4c/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer4c/Convolution_Inception3x3_reduce"
top: "Layer4c/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4c/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer4c/BatchNorm_Inception3x3_reduce"
top: "Layer4c/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer4c/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer4c/ReLU_Inception3x3_reduce"
top: "Layer4c/Convolution_Inception3x3"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4c/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4c/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer4c/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer4b/ReLU_EltWise"
top: "Layer4c/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 24
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4c/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4c/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer4c/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer4c/Convolution_Inception5x5_reduce"
top: "Layer4c/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4c/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer4c/BatchNorm_Inception5x5_reduce"
top: "Layer4c/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer4c/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer4c/ReLU_Inception5x5_reduce"
top: "Layer4c/Convolution_Inception5x5"
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4c/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4c/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer4c/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer4b/ReLU_EltWise"
top: "Layer4c/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer4c/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer4c/Pooling_Inception_Pool"
top: "Layer4c/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4c/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4c/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer4c/Concat_Output"
type: "Concat"
bottom: "Layer4c/Convolution_Inception3x3"
bottom: "Layer4c/Convolution_Inception5x5"
bottom: "Layer4c/Convolution_Inception_Pool_1x1"
top: "Layer4c/Concat_Output"
}
layer {
name: "Layer4c/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer4c/Concat_Output"
bottom: "Layer4c/Convolution_ResCeption1x1"
top: "Layer4c/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer4c/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer4c/Eltwise_EltWise"
top: "Layer4c/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4c/ReLU_EltWise"
type: "ReLU"
bottom: "Layer4c/BatchNorm_EltWise"
top: "Layer4c/ReLU_EltWise"
}
# ================================================================================================
# ======== Layer4d
# ================================================================================================
layer {
name: "Layer4d/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer4c/ReLU_EltWise"
top: "Layer4d/Convolution_ResCeption1x1"
convolution_param {
num_output: 416
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4d/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4d/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer4d/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer4c/ReLU_EltWise"
top: "Layer4d/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 144
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4d/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4d/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer4d/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer4d/Convolution_Inception3x3_reduce"
top: "Layer4d/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4d/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer4d/BatchNorm_Inception3x3_reduce"
top: "Layer4d/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer4d/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer4d/ReLU_Inception3x3_reduce"
top: "Layer4d/Convolution_Inception3x3"
convolution_param {
num_output: 288
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4d/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4d/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer4d/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer4c/ReLU_EltWise"
top: "Layer4d/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 32
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4d/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4d/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer4d/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer4d/Convolution_Inception5x5_reduce"
top: "Layer4d/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4d/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer4d/BatchNorm_Inception5x5_reduce"
top: "Layer4d/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer4d/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer4d/ReLU_Inception5x5_reduce"
top: "Layer4d/Convolution_Inception5x5"
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4d/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4d/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer4d/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer4c/ReLU_EltWise"
top: "Layer4d/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer4d/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer4d/Pooling_Inception_Pool"
top: "Layer4d/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4d/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4d/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer4d/Concat_Output"
type: "Concat"
bottom: "Layer4d/Convolution_Inception3x3"
bottom: "Layer4d/Convolution_Inception5x5"
bottom: "Layer4d/Convolution_Inception_Pool_1x1"
top: "Layer4d/Concat_Output"
}
layer {
name: "Layer4d/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer4d/Concat_Output"
bottom: "Layer4d/Convolution_ResCeption1x1"
top: "Layer4d/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer4d/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer4d/Eltwise_EltWise"
top: "Layer4d/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4d/ReLU_EltWise"
type: "ReLU"
bottom: "Layer4d/BatchNorm_EltWise"
top: "Layer4d/ReLU_EltWise"
}
# ================================================================================================
# ======== Loss2
# ================================================================================================
layer {
name: "Loss2/Pooling_Loss"
type: "Pooling"
bottom: "Layer4d/ReLU_EltWise"
top: "Loss2/Pooling_Loss"
pooling_param {
pool: AVE
kernel_size: 5
stride: 3
pad: 0
}
}
layer {
name: "Loss2/Convolution_Loss"
type: "Convolution"
bottom: "Loss2/Pooling_Loss"
top: "Loss2/Convolution_Loss"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Loss2/Convolution_Loss_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Loss2/Convolution_Loss_b"
}
}
layer {
name: "Loss2/BatchNorm_Loss"
type: "BatchNorm"
bottom: "Loss2/Convolution_Loss"
top: "Loss2/BatchNorm_Loss"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Loss2/ReLU_Loss"
type: "ReLU"
bottom: "Loss2/BatchNorm_Loss"
top: "Loss2/ReLU_Loss"
}
layer {
name: "Loss2/InnerProduct_Loss_fc1"
type: "InnerProduct"
bottom: "Loss2/ReLU_Loss"
top: "Loss2/InnerProduct_Loss_fc1"
inner_product_param {
num_output: 1024
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Loss2/InnerProduct_Loss_fc1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Loss2/InnerProduct_Loss_fc1_b"
}
}
layer {
name: "Loss2/BatchNorm_Loss_fc1"
type: "BatchNorm"
bottom: "Loss2/InnerProduct_Loss_fc1"
top: "Loss2/BatchNorm_Loss_fc1"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Loss2/ReLU_Loss_fc1"
type: "ReLU"
bottom: "Loss2/BatchNorm_Loss_fc1"
top: "Loss2/ReLU_Loss_fc1"
}
layer {
name: "Loss2/InnerProduct_Loss_fc2"
type: "InnerProduct"
bottom: "Loss2/ReLU_Loss_fc1"
top: "Loss2/InnerProduct_Loss_fc2"
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Loss2/InnerProduct_Loss_fc2_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Loss2/InnerProduct_Loss_fc2_b"
}
}
layer {
name: "Loss2/SoftmaxWithLoss_Loss"
type: "SoftmaxWithLoss"
bottom: "Loss2/InnerProduct_Loss_fc2"
bottom: "label"
top: "Loss2/SoftmaxWithLoss_Loss"
loss_weight: 0.3
}
layer {
name: "Loss2/Accuracy_Accuracy"
type: "Accuracy"
bottom: "Loss2/InnerProduct_Loss_fc2"
bottom: "label"
top: "Loss2/Accuracy_Accuracy"
include {
phase: TEST
}
}
# ================================================================================================
# ======== Layer4e
# ================================================================================================
layer {
name: "Layer4e/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer4d/ReLU_EltWise"
top: "Layer4e/Convolution_ResCeption1x1"
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4e/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4e/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer4e/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer4d/ReLU_EltWise"
top: "Layer4e/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 160
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4e/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4e/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer4e/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer4e/Convolution_Inception3x3_reduce"
top: "Layer4e/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4e/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer4e/BatchNorm_Inception3x3_reduce"
top: "Layer4e/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer4e/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer4e/ReLU_Inception3x3_reduce"
top: "Layer4e/Convolution_Inception3x3"
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4e/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4e/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer4e/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer4d/ReLU_EltWise"
top: "Layer4e/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 32
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4e/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4e/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer4e/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer4e/Convolution_Inception5x5_reduce"
top: "Layer4e/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4e/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer4e/BatchNorm_Inception5x5_reduce"
top: "Layer4e/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer4e/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer4e/ReLU_Inception5x5_reduce"
top: "Layer4e/Convolution_Inception5x5"
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4e/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4e/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer4e/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer4d/ReLU_EltWise"
top: "Layer4e/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer4e/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer4e/Pooling_Inception_Pool"
top: "Layer4e/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer4e/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer4e/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer4e/Concat_Output"
type: "Concat"
bottom: "Layer4e/Convolution_Inception3x3"
bottom: "Layer4e/Convolution_Inception5x5"
bottom: "Layer4e/Convolution_Inception_Pool_1x1"
top: "Layer4e/Concat_Output"
}
layer {
name: "Layer4e/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer4e/Concat_Output"
bottom: "Layer4e/Convolution_ResCeption1x1"
top: "Layer4e/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer4e/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer4e/Eltwise_EltWise"
top: "Layer4e/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer4e/ReLU_EltWise"
type: "ReLU"
bottom: "Layer4e/BatchNorm_EltWise"
top: "Layer4e/ReLU_EltWise"
}
layer {
name: "Layer4_Pooling/Pooling_Stride_2"
type: "Pooling"
bottom: "Layer4e/ReLU_EltWise"
top: "Layer4_Pooling/Pooling_Stride_2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 0
}
}
# ================================================================================================
# ======== Layer5a
# ================================================================================================
layer {
name: "Layer5a/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer4_Pooling/Pooling_Stride_2"
top: "Layer5a/Convolution_ResCeption1x1"
convolution_param {
num_output: 576
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5a/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5a/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer5a/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer4_Pooling/Pooling_Stride_2"
top: "Layer5a/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 160
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5a/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5a/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer5a/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer5a/Convolution_Inception3x3_reduce"
top: "Layer5a/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer5a/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer5a/BatchNorm_Inception3x3_reduce"
top: "Layer5a/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer5a/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer5a/ReLU_Inception3x3_reduce"
top: "Layer5a/Convolution_Inception3x3"
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5a/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5a/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer5a/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer4_Pooling/Pooling_Stride_2"
top: "Layer5a/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 32
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5a/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5a/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer5a/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer5a/Convolution_Inception5x5_reduce"
top: "Layer5a/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer5a/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer5a/BatchNorm_Inception5x5_reduce"
top: "Layer5a/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer5a/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer5a/ReLU_Inception5x5_reduce"
top: "Layer5a/Convolution_Inception5x5"
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5a/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5a/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer5a/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer4_Pooling/Pooling_Stride_2"
top: "Layer5a/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer5a/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer5a/Pooling_Inception_Pool"
top: "Layer5a/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5a/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5a/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer5a/Concat_Output"
type: "Concat"
bottom: "Layer5a/Convolution_Inception3x3"
bottom: "Layer5a/Convolution_Inception5x5"
bottom: "Layer5a/Convolution_Inception_Pool_1x1"
top: "Layer5a/Concat_Output"
}
layer {
name: "Layer5a/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer5a/Concat_Output"
bottom: "Layer5a/Convolution_ResCeption1x1"
top: "Layer5a/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer5a/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer5a/Eltwise_EltWise"
top: "Layer5a/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer5a/ReLU_EltWise"
type: "ReLU"
bottom: "Layer5a/BatchNorm_EltWise"
top: "Layer5a/ReLU_EltWise"
}
# ================================================================================================
# ======== Layer5b
# ================================================================================================
layer {
name: "Layer5b/Convolution_ResCeption1x1"
type: "Convolution"
bottom: "Layer5a/ReLU_EltWise"
top: "Layer5b/Convolution_ResCeption1x1"
convolution_param {
num_output: 640
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5b/Convolution_ResCeption1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5b/Convolution_ResCeption1x1_b"
}
}
layer {
name: "Layer5b/Convolution_Inception3x3_reduce"
type: "Convolution"
bottom: "Layer5a/ReLU_EltWise"
top: "Layer5b/Convolution_Inception3x3_reduce"
convolution_param {
num_output: 192
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5b/Convolution_Inception3x3_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5b/Convolution_Inception3x3_reduce_b"
}
}
layer {
name: "Layer5b/BatchNorm_Inception3x3_reduce"
type: "BatchNorm"
bottom: "Layer5b/Convolution_Inception3x3_reduce"
top: "Layer5b/BatchNorm_Inception3x3_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer5b/ReLU_Inception3x3_reduce"
type: "ReLU"
bottom: "Layer5b/BatchNorm_Inception3x3_reduce"
top: "Layer5b/ReLU_Inception3x3_reduce"
}
layer {
name: "Layer5b/Convolution_Inception3x3"
type: "Convolution"
bottom: "Layer5b/ReLU_Inception3x3_reduce"
top: "Layer5b/Convolution_Inception3x3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5b/Convolution_Inception3x3_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5b/Convolution_Inception3x3_b"
}
}
layer {
name: "Layer5b/Convolution_Inception5x5_reduce"
type: "Convolution"
bottom: "Layer5a/ReLU_EltWise"
top: "Layer5b/Convolution_Inception5x5_reduce"
convolution_param {
num_output: 48
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5b/Convolution_Inception5x5_reduce_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5b/Convolution_Inception5x5_reduce_b"
}
}
layer {
name: "Layer5b/BatchNorm_Inception5x5_reduce"
type: "BatchNorm"
bottom: "Layer5b/Convolution_Inception5x5_reduce"
top: "Layer5b/BatchNorm_Inception5x5_reduce"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer5b/ReLU_Inception5x5_reduce"
type: "ReLU"
bottom: "Layer5b/BatchNorm_Inception5x5_reduce"
top: "Layer5b/ReLU_Inception5x5_reduce"
}
layer {
name: "Layer5b/Convolution_Inception5x5"
type: "Convolution"
bottom: "Layer5b/ReLU_Inception5x5_reduce"
top: "Layer5b/Convolution_Inception5x5"
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5b/Convolution_Inception5x5_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5b/Convolution_Inception5x5_b"
}
}
layer {
name: "Layer5b/Pooling_Inception_Pool"
type: "Pooling"
bottom: "Layer5a/ReLU_EltWise"
top: "Layer5b/Pooling_Inception_Pool"
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: "Layer5b/Convolution_Inception_Pool_1x1"
type: "Convolution"
bottom: "Layer5b/Pooling_Inception_Pool"
top: "Layer5b/Convolution_Inception_Pool_1x1"
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Layer5b/Convolution_Inception_Pool_1x1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Layer5b/Convolution_Inception_Pool_1x1_b"
}
}
layer {
name: "Layer5b/Concat_Output"
type: "Concat"
bottom: "Layer5b/Convolution_Inception3x3"
bottom: "Layer5b/Convolution_Inception5x5"
bottom: "Layer5b/Convolution_Inception_Pool_1x1"
top: "Layer5b/Concat_Output"
}
layer {
name: "Layer5b/Eltwise_EltWise"
type: "Eltwise"
bottom: "Layer5b/Concat_Output"
bottom: "Layer5b/Convolution_ResCeption1x1"
top: "Layer5b/Eltwise_EltWise"
eltwise_param {
operation: SUM
}
}
layer {
name: "Layer5b/BatchNorm_EltWise"
type: "BatchNorm"
bottom: "Layer5b/Eltwise_EltWise"
top: "Layer5b/BatchNorm_EltWise"
param {
lr_mult: 0
}
param {
lr_mult: 0
}
param {
lr_mult: 0
}
}
layer {
name: "Layer5b/ReLU_EltWise"
type: "ReLU"
bottom: "Layer5b/BatchNorm_EltWise"
top: "Layer5b/ReLU_EltWise"
}
# ================================================================================================
# ======== Loss3
# ================================================================================================
layer {
name: "Loss3/Pooling_Loss"
type: "Pooling"
bottom: "Layer5b/ReLU_EltWise"
top: "Loss3/Pooling_Loss"
pooling_param {
pool: AVE
kernel_size: 7
stride: 1
pad: 0
}
}
layer {
name: "Loss3/InnerProduct_Loss_fc1"
type: "InnerProduct"
bottom: "Loss3/Pooling_Loss"
top: "Loss3/InnerProduct_Loss_fc1"
inner_product_param {
num_output: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
param {
lr_mult: 1
decay_mult: 1
name: "Loss3/InnerProduct_Loss_fc1_w"
}
param {
lr_mult: 2
decay_mult: 0
name: "Loss3/InnerProduct_Loss_fc1_b"
}
}
layer {
name: "Loss3/SoftmaxWithLoss_Loss"
type: "SoftmaxWithLoss"
bottom: "Loss3/InnerProduct_Loss_fc1"
bottom: "label"
top: "Loss3/SoftmaxWithLoss_Loss"
loss_weight: 1.0
}
layer {
name: "Loss3/Accuracy_Accuracy"
type: "Accuracy"
bottom: "Loss3/InnerProduct_Loss_fc1"
bottom: "label"
top: "Loss3/Accuracy_Accuracy"
include {
phase: TEST
}
}
layer {
name: "loss3/Softmax_plain"
type: "Softmax"
bottom: "Loss3/InnerProduct_Loss_fc1"
top: "loss3/Softmax_plain"
}