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caffe_extract_features.py
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2322 lines (2269 loc) · 60.3 KB
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#!/usr/bin/python
# Code to easily extract deep features from pre-trained networks using Caffe
# Author: Keiller Nogueira
# Date: June 28, 2018
import os
import shutil
import sys
import caffe
import numpy as np
import plyvel # use 0.9
from caffe.proto import caffe_pb2
def create_AlexNet_prototxt(output_file, caffe_path, tmp_path):
f = open(output_file, 'w')
f.write("name: \"AlexNet\" \n\
layer \n\
{ \n\
name: \"data\" \n\
type: \"ImageData\" \n\
top: \"data\" \n\
top: \"label\" \n\
transform_param { \n\
mirror: false \n\
crop_size: 227 \n\
mean_file: \"" + caffe_path + "/data/ilsvrc12/imagenet_mean.binaryproto\" \n\
} \n\
image_data_param { \n\
source: \"" + str(os.path.join(tmp_path, 'file_list.txt')) + "\" \n\
batch_size: 1 \n\
new_height: 256 \n\
new_width: 256 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"conv1\" \n\
type: \"Convolution\" \n\
bottom: \"data\" \n\
top: \"conv1\" \n\
convolution_param { \n\
num_output: 96 \n\
kernel_size: 11 \n\
stride: 4 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"relu1\" \n\
type: \"ReLU\" \n\
bottom: \"conv1\" \n\
top: \"conv1\" \n\
} \n\
layer \n\
{ \n\
name: \"norm1\" \n\
type: \"LRN\" \n\
bottom: \"conv1\" \n\
top: \"norm1\" \n\
lrn_param { \n\
local_size: 5 \n\
alpha: 0.0001 \n\
beta: 0.75 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"pool1\" \n\
type: \"Pooling\" \n\
bottom: \"norm1\" \n\
top: \"pool1\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"conv2\" \n\
type: \"Convolution\" \n\
bottom: \"pool1\" \n\
top: \"conv2\" \n\
convolution_param { \n\
num_output: 256 \n\
pad: 2 \n\
kernel_size: 5 \n\
group: 2 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"relu2\" \n\
type: \"ReLU\" \n\
bottom: \"conv2\" \n\
top: \"conv2\" \n\
} \n\
layer \n\
{ \n\
name: \"norm2\" \n\
type: \"LRN\" \n\
bottom: \"conv2\" \n\
top: \"norm2\" \n\
lrn_param { \n\
local_size: 5 \n\
alpha: 0.0001 \n\
beta: 0.75 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"pool2\" \n\
type: \"Pooling\" \n\
bottom: \"norm2\" \n\
top: \"pool2\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"conv3\" \n\
type: \"Convolution\" \n\
bottom: \"pool2\" \n\
top: \"conv3\" \n\
convolution_param { \n\
num_output: 384 \n\
pad: 1 \n\
kernel_size: 3 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"relu3\" \n\
type: \"ReLU\" \n\
bottom: \"conv3\" \n\
top: \"conv3\" \n\
} \n\
layer \n\
{ \n\
name: \"conv4\" \n\
type: \"Convolution\" \n\
bottom: \"conv3\" \n\
top: \"conv4\" \n\
convolution_param { \n\
num_output: 384 \n\
pad: 1 \n\
kernel_size: 3 \n\
group: 2 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"relu4\" \n\
type: \"ReLU\" \n\
bottom: \"conv4\" \n\
top: \"conv4\" \n\
} \n\
layer \n\
{ \n\
name: \"conv5\" \n\
type: \"Convolution\" \n\
bottom: \"conv4\" \n\
top: \"conv5\" \n\
convolution_param { \n\
num_output: 256 \n\
pad: 1 \n\
kernel_size: 3 \n\
group: 2 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"relu5\" \n\
type: \"ReLU\" \n\
bottom: \"conv5\" \n\
top: \"conv5\" \n\
} \n\
layer \n\
{ \n\
name: \"pool5\" \n\
type: \"Pooling\" \n\
bottom: \"conv5\" \n\
top: \"pool5\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"fc6\" \n\
type: \"InnerProduct\" \n\
bottom: \"pool5\" \n\
top: \"fc6\" \n\
inner_product_param { \n\
num_output: 4096 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"relu6\" \n\
type: \"ReLU\" \n\
bottom: \"fc6\" \n\
top: \"fc6\" \n\
} \n\
layer \n\
{ \n\
name: \"drop6\" \n\
type: \"Dropout\" \n\
bottom: \"fc6\" \n\
top: \"fc6\" \n\
dropout_param { \n\
dropout_ratio: 0.5 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"fc7\" \n\
type: \"InnerProduct\" \n\
bottom: \"fc6\" \n\
top: \"fc7\" \n\
inner_product_param { \n\
num_output: 4096 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"relu7\" \n\
type: \"ReLU\" \n\
bottom: \"fc7\" \n\
top: \"fc7\" \n\
} \n\
layer \n\
{ \n\
name: \"drop7\" \n\
type: \"Dropout\" \n\
bottom: \"fc7\" \n\
top: \"fc7\" \n\
dropout_param { \n\
dropout_ratio: 0.5 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"fc8\" \n\
type: \"InnerProduct\" \n\
bottom: \"fc7\" \n\
top: \"fc8\" \n\
inner_product_param { \n\
num_output: 1000 \n\
} \n\
} \n\
layer \n\
{ \n\
name: \"prob\" \n\
type: \"Softmax\" \n\
bottom: \"fc8\" \n\
top: \"prob\" \n\
} \n\
layer \n\
{ \n\
name: \"accuracy\" \n\
type: \"Accuracy\" \n\
bottom: \"prob\" \n\
bottom: \"label\" \n\
top: \"accuracy\" \n\
} \n\
layer \n\
{ \n\
name: \"loss\" \n\
type: \"SoftmaxWithLoss\" \n\
bottom: \"fc8\" \n\
bottom: \"label\" \n\
top: \"loss\" \n\
} \n ")
f.close()
def create_CaffeNet_prototxt(output_file, caffe_path, tmp_path):
f = open(output_file, 'w')
f.write("name: \"CaffeNet\" \n\
layer { \n\
name: \"data\" \n\
type: \"ImageData\" \n\
top: \"data\" \n\
top: \"label\" \n\
transform_param { \n\
mirror: false \n\
crop_size: 227 \n\
mean_file: \"" + caffe_path + "/data/ilsvrc12/imagenet_mean.binaryproto\" \n\
} \n\
image_data_param { \n\
source: \"" + str(os.path.join(tmp_path, 'file_list.txt')) + "\" \n\
batch_size: 1 \n\
new_height: 256 \n\
new_width: 256 \n\
} \n\
} \n\
layer { \n\
name: \"conv1\" \n\
type: \"Convolution\" \n\
bottom: \"data\" \n\
top: \"conv1\" \n\
convolution_param { \n\
num_output: 96 \n\
kernel_size: 11 \n\
stride: 4 \n\
} \n\
} \n\
layer { \n\
name: \"relu1\" \n\
type: \"ReLU\" \n\
bottom: \"conv1\" \n\
top: \"conv1\" \n\
} \n\
layer { \n\
name: \"pool1\" \n\
type: \"Pooling\" \n\
bottom: \"conv1\" \n\
top: \"pool1\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer { \n\
name: \"norm1\" \n\
type: \"LRN\" \n\
bottom: \"pool1\" \n\
top: \"norm1\" \n\
lrn_param { \n\
local_size: 5 \n\
alpha: 0.0001 \n\
beta: 0.75 \n\
} \n\
} \n\
layer { \n\
name: \"conv2\" \n\
type: \"Convolution\" \n\
bottom: \"norm1\" \n\
top: \"conv2\" \n\
convolution_param { \n\
num_output: 256 \n\
pad: 2 \n\
kernel_size: 5 \n\
group: 2 \n\
} \n\
} \n\
layer { \n\
name: \"relu2\" \n\
type: \"ReLU\" \n\
bottom: \"conv2\" \n\
top: \"conv2\" \n\
} \n\
layer { \n\
name: \"pool2\" \n\
type: \"Pooling\" \n\
bottom: \"conv2\" \n\
top: \"pool2\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer { \n\
name: \"norm2\" \n\
type: \"LRN\" \n\
bottom: \"pool2\" \n\
top: \"norm2\" \n\
lrn_param { \n\
local_size: 5 \n\
alpha: 0.0001 \n\
beta: 0.75 \n\
} \n\
} \n\
layer { \n\
name: \"conv3\" \n\
type: \"Convolution\" \n\
bottom: \"norm2\" \n\
top: \"conv3\" \n\
convolution_param { \n\
num_output: 384 \n\
pad: 1 \n\
kernel_size: 3 \n\
} \n\
} \n\
layer { \n\
name: \"relu3\" \n\
type: \"ReLU\" \n\
bottom: \"conv3\" \n\
top: \"conv3\" \n\
} \n\
layer { \n\
name: \"conv4\" \n\
type: \"Convolution\" \n\
bottom: \"conv3\" \n\
top: \"conv4\" \n\
convolution_param { \n\
num_output: 384 \n\
pad: 1 \n\
kernel_size: 3 \n\
group: 2 \n\
} \n\
} \n\
layer { \n\
name: \"relu4\" \n\
type: \"ReLU\" \n\
bottom: \"conv4\" \n\
top: \"conv4\" \n\
} \n\
layer { \n\
name: \"conv5\" \n\
type: \"Convolution\" \n\
bottom: \"conv4\" \n\
top: \"conv5\" \n\
convolution_param { \n\
num_output: 256 \n\
pad: 1 \n\
kernel_size: 3 \n\
group: 2 \n\
} \n\
} \n\
layer { \n\
name: \"relu5\" \n\
type: \"ReLU\" \n\
bottom: \"conv5\" \n\
top: \"conv5\" \n\
} \n\
layer { \n\
name: \"pool5\" \n\
type: \"Pooling\" \n\
bottom: \"conv5\" \n\
top: \"pool5\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer { \n\
name: \"fc6\" \n\
type: \"InnerProduct\" \n\
bottom: \"pool5\" \n\
top: \"fc6\" \n\
inner_product_param { \n\
num_output: 4096 \n\
} \n\
} \n\
layer { \n\
name: \"relu6\" \n\
type: \"ReLU\" \n\
bottom: \"fc6\" \n\
top: \"fc6\" \n\
} \n\
layer { \n\
name: \"drop6\" \n\
type: \"Dropout\" \n\
bottom: \"fc6\" \n\
top: \"fc6\" \n\
dropout_param { \n\
dropout_ratio: 0.5 \n\
} \n\
} \n\
layer { \n\
name: \"fc7\" \n\
type: \"InnerProduct\" \n\
bottom: \"fc6\" \n\
top: \"fc7\" \n\
inner_product_param { \n\
num_output: 4096 \n\
} \n\
} \n\
layer { \n\
name: \"relu7\" \n\
type: \"ReLU\" \n\
bottom: \"fc7\" \n\
top: \"fc7\" \n\
} \n\
layer { \n\
name: \"drop7\" \n\
type: \"Dropout\" \n\
bottom: \"fc7\" \n\
top: \"fc7\" \n\
dropout_param { \n\
dropout_ratio: 0.5 \n\
} \n\
} \n\
layer { \n\
name: \"fc8\" \n\
type: \"InnerProduct\" \n\
bottom: \"fc7\" \n\
top: \"fc8\" \n\
inner_product_param { \n\
num_output: 1000 \n\
} \n\
} \n\
layer { \n\
name: \"prob\" \n\
type: \"Softmax\" \n\
bottom: \"fc8\" \n\
top: \"prob\" \n\
} \n\
layer { \n\
name: \"accuracy\" \n\
type: \"Accuracy\" \n\
bottom: \"prob\" \n\
bottom: \"label\" \n\
top: \"accuracy\" \n\
} \n\
layer { \n\
name: \"loss\" \n\
type: \"SoftmaxWithLoss\" \n\
bottom: \"fc8\" \n\
bottom: \"label\" \n\
top: \"loss\" \n\
} \n ")
f.close()
def create_GoogLeNet_prototxt(output_file, caffe_path, tmp_path):
f = open(output_file, 'w')
f.write("name: \"GoogleNet\" \n\
layer { \n\
name: \"data\" \n\
type: \"ImageData\" \n\
top: \"data\" \n\
top: \"label\" \n\
transform_param { \n\
mirror: false \n\
crop_size: 224 \n\
mean_file: \"" + caffe_path + "/data/ilsvrc12/imagenet_mean.binaryproto\" \n\
} \n\
image_data_param { \n\
source: \"" + str(os.path.join(tmp_path, 'file_list.txt')) + "\" \n\
batch_size: 1 \n\
new_height: 256 \n\
new_width: 256 \n\
} \n\
} \n\
layer { \n\
name: \"conv1/7x7_s2\" \n\
type: \"Convolution\" \n\
bottom: \"data\" \n\
top: \"conv1/7x7_s2\" \n\
convolution_param { \n\
num_output: 64 \n\
pad: 3 \n\
kernel_size: 7 \n\
stride: 2 \n\
} \n\
} \n\
layer { \n\
name: \"conv1/relu_7x7\" \n\
type: \"ReLU\" \n\
bottom: \"conv1/7x7_s2\" \n\
top: \"conv1/7x7_s2\" \n\
} \n\
layer { \n\
name: \"pool1/3x3_s2\" \n\
type: \"Pooling\" \n\
bottom: \"conv1/7x7_s2\" \n\
top: \"pool1/3x3_s2\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer { \n\
name: \"pool1/norm1\" \n\
type: \"LRN\" \n\
bottom: \"pool1/3x3_s2\" \n\
top: \"pool1/norm1\" \n\
lrn_param { \n\
local_size: 5 \n\
alpha: 0.0001 \n\
beta: 0.75 \n\
} \n\
} \n\
layer { \n\
name: \"conv2/3x3_reduce\" \n\
type: \"Convolution\" \n\
bottom: \"pool1/norm1\" \n\
top: \"conv2/3x3_reduce\" \n\
convolution_param { \n\
num_output: 64 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"conv2/relu_3x3_reduce\" \n\
type: \"ReLU\" \n\
bottom: \"conv2/3x3_reduce\" \n\
top: \"conv2/3x3_reduce\" \n\
} \n\
layer { \n\
name: \"conv2/3x3\" \n\
type: \"Convolution\" \n\
bottom: \"conv2/3x3_reduce\" \n\
top: \"conv2/3x3\" \n\
convolution_param { \n\
num_output: 192 \n\
pad: 1 \n\
kernel_size: 3 \n\
} \n\
} \n\
layer { \n\
name: \"conv2/relu_3x3\" \n\
type: \"ReLU\" \n\
bottom: \"conv2/3x3\" \n\
top: \"conv2/3x3\" \n\
} \n\
layer { \n\
name: \"conv2/norm2\" \n\
type: \"LRN\" \n\
bottom: \"conv2/3x3\" \n\
top: \"conv2/norm2\" \n\
lrn_param { \n\
local_size: 5 \n\
alpha: 0.0001 \n\
beta: 0.75 \n\
} \n\
} \n\
layer { \n\
name: \"pool2/3x3_s2\" \n\
type: \"Pooling\" \n\
bottom: \"conv2/norm2\" \n\
top: \"pool2/3x3_s2\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/1x1\" \n\
type: \"Convolution\" \n\
bottom: \"pool2/3x3_s2\" \n\
top: \"inception_3a/1x1\" \n\
convolution_param { \n\
num_output: 64 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/relu_1x1\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3a/1x1\" \n\
top: \"inception_3a/1x1\" \n\
} \n\
layer { \n\
name: \"inception_3a/3x3_reduce\" \n\
type: \"Convolution\" \n\
bottom: \"pool2/3x3_s2\" \n\
top: \"inception_3a/3x3_reduce\" \n\
convolution_param { \n\
num_output: 96 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/relu_3x3_reduce\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3a/3x3_reduce\" \n\
top: \"inception_3a/3x3_reduce\" \n\
} \n\
layer { \n\
name: \"inception_3a/3x3\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3a/3x3_reduce\" \n\
top: \"inception_3a/3x3\" \n\
convolution_param { \n\
num_output: 128 \n\
pad: 1 \n\
kernel_size: 3 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/relu_3x3\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3a/3x3\" \n\
top: \"inception_3a/3x3\" \n\
} \n\
layer { \n\
name: \"inception_3a/5x5_reduce\" \n\
type: \"Convolution\" \n\
bottom: \"pool2/3x3_s2\" \n\
top: \"inception_3a/5x5_reduce\" \n\
convolution_param { \n\
num_output: 16 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/relu_5x5_reduce\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3a/5x5_reduce\" \n\
top: \"inception_3a/5x5_reduce\" \n\
} \n\
layer { \n\
name: \"inception_3a/5x5\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3a/5x5_reduce\" \n\
top: \"inception_3a/5x5\" \n\
convolution_param { \n\
num_output: 32 \n\
pad: 2 \n\
kernel_size: 5 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/relu_5x5\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3a/5x5\" \n\
top: \"inception_3a/5x5\" \n\
} \n\
layer { \n\
name: \"inception_3a/pool\" \n\
type: \"Pooling\" \n\
bottom: \"pool2/3x3_s2\" \n\
top: \"inception_3a/pool\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 1 \n\
pad: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/pool_proj\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3a/pool\" \n\
top: \"inception_3a/pool_proj\" \n\
convolution_param { \n\
num_output: 32 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3a/relu_pool_proj\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3a/pool_proj\" \n\
top: \"inception_3a/pool_proj\" \n\
} \n\
layer { \n\
name: \"inception_3a/output\" \n\
type: \"Concat\" \n\
bottom: \"inception_3a/1x1\" \n\
bottom: \"inception_3a/3x3\" \n\
bottom: \"inception_3a/5x5\" \n\
bottom: \"inception_3a/pool_proj\" \n\
top: \"inception_3a/output\" \n\
} \n\
layer { \n\
name: \"inception_3b/1x1\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3a/output\" \n\
top: \"inception_3b/1x1\" \n\
convolution_param { \n\
num_output: 128 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3b/relu_1x1\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3b/1x1\" \n\
top: \"inception_3b/1x1\" \n\
} \n\
layer { \n\
name: \"inception_3b/3x3_reduce\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3a/output\" \n\
top: \"inception_3b/3x3_reduce\" \n\
convolution_param { \n\
num_output: 128 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3b/relu_3x3_reduce\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3b/3x3_reduce\" \n\
top: \"inception_3b/3x3_reduce\" \n\
} \n\
layer { \n\
name: \"inception_3b/3x3\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3b/3x3_reduce\" \n\
top: \"inception_3b/3x3\" \n\
convolution_param { \n\
num_output: 192 \n\
pad: 1 \n\
kernel_size: 3 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3b/relu_3x3\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3b/3x3\" \n\
top: \"inception_3b/3x3\" \n\
} \n\
layer { \n\
name: \"inception_3b/5x5_reduce\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3a/output\" \n\
top: \"inception_3b/5x5_reduce\" \n\
convolution_param { \n\
num_output: 32 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3b/relu_5x5_reduce\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3b/5x5_reduce\" \n\
top: \"inception_3b/5x5_reduce\" \n\
} \n\
layer { \n\
name: \"inception_3b/5x5\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3b/5x5_reduce\" \n\
top: \"inception_3b/5x5\" \n\
convolution_param { \n\
num_output: 96 \n\
pad: 2 \n\
kernel_size: 5 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3b/relu_5x5\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3b/5x5\" \n\
top: \"inception_3b/5x5\" \n\
} \n\
layer { \n\
name: \"inception_3b/pool\" \n\
type: \"Pooling\" \n\
bottom: \"inception_3a/output\" \n\
top: \"inception_3b/pool\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 1 \n\
pad: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3b/pool_proj\" \n\
type: \"Convolution\" \n\
bottom: \"inception_3b/pool\" \n\
top: \"inception_3b/pool_proj\" \n\
convolution_param { \n\
num_output: 64 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_3b/relu_pool_proj\" \n\
type: \"ReLU\" \n\
bottom: \"inception_3b/pool_proj\" \n\
top: \"inception_3b/pool_proj\" \n\
} \n\
layer { \n\
name: \"inception_3b/output\" \n\
type: \"Concat\" \n\
bottom: \"inception_3b/1x1\" \n\
bottom: \"inception_3b/3x3\" \n\
bottom: \"inception_3b/5x5\" \n\
bottom: \"inception_3b/pool_proj\" \n\
top: \"inception_3b/output\" \n\
} \n\
layer { \n\
name: \"pool3/3x3_s2\" \n\
type: \"Pooling\" \n\
bottom: \"inception_3b/output\" \n\
top: \"pool3/3x3_s2\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 2 \n\
} \n\
} \n\
layer { \n\
name: \"inception_4a/1x1\" \n\
type: \"Convolution\" \n\
bottom: \"pool3/3x3_s2\" \n\
top: \"inception_4a/1x1\" \n\
convolution_param { \n\
num_output: 192 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_4a/relu_1x1\" \n\
type: \"ReLU\" \n\
bottom: \"inception_4a/1x1\" \n\
top: \"inception_4a/1x1\" \n\
} \n\
layer { \n\
name: \"inception_4a/3x3_reduce\" \n\
type: \"Convolution\" \n\
bottom: \"pool3/3x3_s2\" \n\
top: \"inception_4a/3x3_reduce\" \n\
convolution_param { \n\
num_output: 96 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_4a/relu_3x3_reduce\" \n\
type: \"ReLU\" \n\
bottom: \"inception_4a/3x3_reduce\" \n\
top: \"inception_4a/3x3_reduce\" \n\
} \n\
layer { \n\
name: \"inception_4a/3x3\" \n\
type: \"Convolution\" \n\
bottom: \"inception_4a/3x3_reduce\" \n\
top: \"inception_4a/3x3\" \n\
convolution_param { \n\
num_output: 208 \n\
pad: 1 \n\
kernel_size: 3 \n\
} \n\
} \n\
layer { \n\
name: \"inception_4a/relu_3x3\" \n\
type: \"ReLU\" \n\
bottom: \"inception_4a/3x3\" \n\
top: \"inception_4a/3x3\" \n\
} \n\
layer { \n\
name: \"inception_4a/5x5_reduce\" \n\
type: \"Convolution\" \n\
bottom: \"pool3/3x3_s2\" \n\
top: \"inception_4a/5x5_reduce\" \n\
convolution_param { \n\
num_output: 16 \n\
kernel_size: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_4a/relu_5x5_reduce\" \n\
type: \"ReLU\" \n\
bottom: \"inception_4a/5x5_reduce\" \n\
top: \"inception_4a/5x5_reduce\" \n\
} \n\
layer { \n\
name: \"inception_4a/5x5\" \n\
type: \"Convolution\" \n\
bottom: \"inception_4a/5x5_reduce\" \n\
top: \"inception_4a/5x5\" \n\
convolution_param { \n\
num_output: 48 \n\
pad: 2 \n\
kernel_size: 5 \n\
} \n\
} \n\
layer { \n\
name: \"inception_4a/relu_5x5\" \n\
type: \"ReLU\" \n\
bottom: \"inception_4a/5x5\" \n\
top: \"inception_4a/5x5\" \n\
} \n\
layer { \n\
name: \"inception_4a/pool\" \n\
type: \"Pooling\" \n\
bottom: \"pool3/3x3_s2\" \n\
top: \"inception_4a/pool\" \n\
pooling_param { \n\
pool: MAX \n\
kernel_size: 3 \n\
stride: 1 \n\
pad: 1 \n\
} \n\
} \n\
layer { \n\
name: \"inception_4a/pool_proj\" \n\
type: \"Convolution\" \n\
bottom: \"inception_4a/pool\" \n\
top: \"inception_4a/pool_proj\" \n\
convolution_param { \n\
num_output: 64 \n\
kernel_size: 1 \n\
} \n\
} \n\