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# @article{ThiNet_ICCV17, | |
# Author = {Jian-Hao Luo, Jianxin Wu, and Weiyao Lin}, | |
# Title = {ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression}, | |
# Journal = {arXiv:1707.06342}, | |
# Year = {2017} | |
# } | |
# fixed size: center 224x224 crop from resized image with 256x256 | |
# The accuracy should be: Top-1: 0.6734, Top-5: 0.8792 | |
# If you have any problem, please feel free to contact Jian-Hao Luo (luojh@lamda.nju.edu.cn). | |
# 2017-07-29 | |
layer { | |
name: "data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
include { | |
phase: TRAIN | |
} | |
transform_param { | |
mirror: true | |
crop_size: 224 | |
mean_value: 104 | |
mean_value: 117 | |
mean_value: 123 | |
} | |
data_param { | |
source: "/opt/luojh/Dataset/ImageNet/lmdb/ilsvrc12_train_lmdb" | |
batch_size: 32 | |
backend: LMDB | |
} | |
} | |
layer { | |
name: "data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
include { | |
phase: TEST | |
} | |
transform_param { | |
mirror: false | |
crop_size: 224 | |
mean_value: 104 | |
mean_value: 117 | |
mean_value: 123 | |
} | |
data_param { | |
source: "/opt/luojh/Dataset/ImageNet/lmdb/ilsvrc12_val_lmdb" | |
batch_size: 50 | |
backend: LMDB | |
} | |
} | |
layer { | |
name: "conv1_1" | |
type: "Convolution" | |
bottom: "data" | |
top: "conv1_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 32 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu1_1" | |
type: "ReLU" | |
bottom: "conv1_1" | |
top: "relu1_1" | |
} | |
layer { | |
name: "conv1_2" | |
type: "Convolution" | |
bottom: "relu1_1" | |
top: "conv1_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 32 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu1_2" | |
type: "ReLU" | |
bottom: "conv1_2" | |
top: "relu1_2" | |
} | |
layer { | |
name: "pool1" | |
type: "Pooling" | |
bottom: "relu1_2" | |
top: "pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv2_1" | |
type: "Convolution" | |
bottom: "pool1" | |
top: "conv2_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu2_1" | |
type: "ReLU" | |
bottom: "conv2_1" | |
top: "relu2_1" | |
} | |
layer { | |
name: "conv2_2" | |
type: "Convolution" | |
bottom: "relu2_1" | |
top: "conv2_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu2_2" | |
type: "ReLU" | |
bottom: "conv2_2" | |
top: "relu2_2" | |
} | |
layer { | |
name: "pool2" | |
type: "Pooling" | |
bottom: "relu2_2" | |
top: "pool2" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv3_1" | |
type: "Convolution" | |
bottom: "pool2" | |
top: "conv3_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_1" | |
type: "ReLU" | |
bottom: "conv3_1" | |
top: "relu3_1" | |
} | |
layer { | |
name: "conv3_2" | |
type: "Convolution" | |
bottom: "relu3_1" | |
top: "conv3_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_2" | |
type: "ReLU" | |
bottom: "conv3_2" | |
top: "relu3_2" | |
} | |
layer { | |
name: "conv3_3" | |
type: "Convolution" | |
bottom: "relu3_2" | |
top: "conv3_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_3" | |
type: "ReLU" | |
bottom: "conv3_3" | |
top: "relu3_3" | |
} | |
layer { | |
name: "pool3" | |
type: "Pooling" | |
bottom: "relu3_3" | |
top: "pool3" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv4_1" | |
type: "Convolution" | |
bottom: "pool3" | |
top: "conv4_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_1" | |
type: "ReLU" | |
bottom: "conv4_1" | |
top: "relu4_1" | |
} | |
layer { | |
name: "conv4_2" | |
type: "Convolution" | |
bottom: "relu4_1" | |
top: "conv4_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_2" | |
type: "ReLU" | |
bottom: "conv4_2" | |
top: "relu4_2" | |
} | |
layer { | |
name: "conv4_3" | |
type: "Convolution" | |
bottom: "relu4_2" | |
top: "conv4_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_3" | |
type: "ReLU" | |
bottom: "conv4_3" | |
top: "relu4_3" | |
} | |
layer { | |
name: "pool4" | |
type: "Pooling" | |
bottom: "relu4_3" | |
top: "pool4" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv5_1" | |
type: "Convolution" | |
bottom: "pool4" | |
top: "conv5_1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu5_1" | |
type: "ReLU" | |
bottom: "conv5_1" | |
top: "relu5_1" | |
} | |
layer { | |
name: "conv5_2" | |
type: "Convolution" | |
bottom: "relu5_1" | |
top: "conv5_2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu5_2" | |
type: "ReLU" | |
bottom: "conv5_2" | |
top: "relu5_2" | |
} | |
layer { | |
name: "conv5_3" | |
type: "Convolution" | |
bottom: "relu5_2" | |
top: "conv5_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu5_3" | |
type: "ReLU" | |
bottom: "conv5_3" | |
top: "relu5_3" | |
} | |
layer { | |
name: "pool5" | |
type: "Pooling" | |
bottom: "relu5_3" | |
top: "pool5" | |
pooling_param { | |
pool: AVE | |
kernel_size: 14 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "softmax" | |
type: "InnerProduct" | |
bottom: "pool5" | |
top: "softmax" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 1000 | |
} | |
} | |
layer { | |
name: "loss" | |
type: "SoftmaxWithLoss" | |
bottom: "softmax" | |
bottom: "label" | |
top: "loss" | |
} | |
layer { | |
name: "acc_top_1" | |
type: "Accuracy" | |
bottom: "softmax" | |
bottom: "label" | |
top: "acc_top_1" | |
accuracy_param { | |
top_k: 1 | |
} | |
} | |
layer { | |
name: "acc_top_5" | |
type: "Accuracy" | |
bottom: "softmax" | |
bottom: "label" | |
top: "acc_top_5" | |
accuracy_param { | |
top_k: 5 | |
} | |
} |
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