FCN源码解读之net.py
转载自 https://blog.csdn.net/qq_21368481/article/details/80196819
开篇先从FCN的net.py开始，net.py用于构建网络，即运行net.py可以直接生成所需要的train.prototxt和val.prototxt。
即当需要修改网络时，不需要自己逐个修改train.prototxt和val.prototxt，只需要修改net.py中的相应内容，运行后即可重新全盘生成所修改后的网络。
以下先拿fcn.berkeleyvision.org-master下的voc-fcn32s中的net.py文件进行分析，其源码如下：
-
import caffe
-
from caffe
import layers
as L, params
as P
-
from caffe.coord_map
import crop
-
-
-
def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
-
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
-
num_output=nout, pad=pad,
-
param=[dict(lr_mult=
1, decay_mult=
1), dict(lr_mult=
2, decay_mult=
0)])
-
return conv, L.ReLU(conv, in_place=
True)
-
-
-
def max_pool(bottom, ks=2, stride=2):
-
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
-
-
-
def fcn(split):
-
n = caffe.NetSpec()
-
pydata_params = dict(split=split, mean=(
104.00699,
116.66877,
122.67892),
-
seed=
1337)
-
if split ==
'train':
-
pydata_params[
'sbdd_dir'] =
'../data/sbdd/dataset'
-
pylayer =
'SBDDSegDataLayer'
-
else:
-
pydata_params[
'voc_dir'] =
'../data/pascal/VOC2011'
-
pylayer =
'VOCSegDataLayer'
-
n.data, n.label = L.Python(module=
'voc_layers', layer=pylayer,
-
ntop=
2, param_str=str(pydata_params))
-
-
-
# the base net
-
n.conv1_1, n.relu1_1 = conv_relu(n.data,
64, pad=
100)
-
n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1,
64)
-
n.pool1 = max_pool(n.relu1_2)
-
-
-
n.conv2_1, n.relu2_1 = conv_relu(n.pool1,
128)
-
n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1,
128)
-
n.pool2 = max_pool(n.relu2_2)
-
-
-
n.conv3_1, n.relu3_1 = conv_relu(n.pool2,
256)
-
n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1,
256)
-
n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2,
256)
-
n.pool3 = max_pool(n.relu3_3)
-
-
-
n.conv4_1, n.relu4_1 = conv_relu(n.pool3,
512)
-
n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1,
512)
-
n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2,
512)
-
n.pool4 = max_pool(n.relu4_3)
-
-
-
n.conv5_1, n.relu5_1 = conv_relu(n.pool4,
512)
-
n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1,
512)
-
n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2,
512)
-
n.pool5 = max_pool(n.relu5_3)
-
-
-
# fully conv
-
n.fc6, n.relu6 = conv_relu(n.pool5,
4096, ks=
7, pad=
0)
-
n.drop6 = L.Dropout(n.relu6, dropout_ratio=
0.5, in_place=
True)
-
n.
fc7, n.relu7 = conv_relu(n.drop6,
4096, ks=
1, pad=
0)
-
n.drop7 = L.Dropout(n.relu7, dropout_ratio=
0.5, in_place=
True)
-
n.score_fr = L.Convolution(n.drop7, num_output=
21, kernel_size=
1, pad=
0,
-
param=[dict(lr_mult=
1, decay_mult=
1), dict(lr_mult=
2, decay_mult=
0)])
-
n.upscore = L.Deconvolution(n.score_fr,
-
convolution_param=dict(num_output=
21, kernel_size=
64, stride=
32,
-
bias_term=
False),
-
param=[dict(lr_mult=
0)])
-
n.score = crop(n.upscore, n.data)
-
n.loss = L.SoftmaxWithLoss(n.score, n.label,
-
loss_param=dict(normalize=
False, ignore_label=
255))
-
-
-
return n.to_proto()
-
-
-
def make_net():
-
with open(
'train.prototxt',
'w')
as f:
-
f.write(str(fcn(
'train')))
-
-
-
with open(
'val.prototxt',
'w')
as f:
-
f.write(str(fcn(
'seg11valid')))
-
-
-
if __name__ ==
'__main__':
-
make_net()
1.函数conv_relu()
调用caffe中用于定义卷积层和ReLU激活函数层的Convolution和ReLU函数
-
#用于定义卷积层以及该层的激活函数层（均采用ReLU激活函数）
-
'''
-
参数bottum：即该层的上一层的输出
-
参数nout：该层输出的数目（对应于卷积层，即输出的特征图数目）
-
参数ks：卷积核的大小
-
参数stride：步长
-
参数pad：填充数
-
'''
-
def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
-
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
-
num_output=nout, pad=pad,
-
param=[dict(lr_mult=
1, decay_mult=
1), dict(lr_mult=
2, decay_mult=
0)])
-
#前者为权重W的学习速率和衰减系数设置;后者为偏置b的学习速率（偏置不设置衰减系数）设置
-
#lr_mult为学习速率倍增参数（即最终的学习速率需乘上solver.prototxt中的base_lr)
-
#decay_mult为权重衰减系数倍增参数（即最终的权重衰减系数需乘上solver.prototxt中的weight_decay）
-
#返回该层及其激活层（激活层的输入和输出都为该卷积层的输出）
-
return conv, L.ReLU(conv, in_place=
True)
2.函数max_pool()
调用caffe中用于定义最大池化层的Pooling函数（Pooling.MAX即表示最大池化）
-
#用于定义池化层（均采用最大池化）
-
def max_pool(bottom, ks=2, stride=2):
-
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
3.函数fcn()
-
#用于生成train.prototxt和val.prototxt的函数
-
#输入参数split为'train'或'val'（前者为训练，后者为测试）
-
def fcn(split):
-
n = caffe.NetSpec()
#使用pycaffe定义Net
-
pydata_params = dict(split=split, mean=(
104.00699,
116.66877,
122.67892),
-
seed=
1337)
-
if split ==
'train':
-
pydata_params[
'sbdd_dir'] =
'../data/sbdd/dataset'
#对应训练集的路径
-
pylayer =
'SBDDSegDataLayer'
#类的名称，详见voc_layers.py
-
else:
-
pydata_params[
'voc_dir'] =
'../data/pascal/VOC2011'
#对应测试或验证集的路径
-
pylayer =
'VOCSegDataLayer'
-
'''
-
输入参数
-
参数module:即模型名称，一般对应自己所写的一个.py文件（用于实现自己想要的该层的功能，这里对应voc_layers.py文件）
-
参数layer:即所定义的类的名称
-
参数ntop：即该层的输出数目（一般而言第一层输出的是data和label）
-
参数param_str：即该层所需要的各项参数
-
输出参数
-
输出n.data和n.label，即对应ntop=2
-
'''
-
n.data, n.label = L.Python(module=
'voc_layers', layer=pylayer,
-
ntop=
2, param_str=str(pydata_params))
-
-
-
# the base net
-
n.conv1_1, n.relu1_1 = conv_relu(n.data,
64, pad=
100)
#conv1_1，1_1卷积层（激活函数为ReLU）
-
n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1,
64)
#conv1_2,1_2卷积层（激活函数为ReLU）
-
n.pool1 = max_pool(n.relu1_2)
#pool1,第一个最大池化层
此处对应.prototxt中的内容如下：
-
layer {
-
name:
"conv1_1"
-
type:
"Convolution"
-
bottom:
"data"
-
top:
"conv1_1"
-
param {
-
lr_mult:
1.0
-
decay_mult:
1.0
-
}
-
param {
-
lr_mult:
2.0
-
decay_mult:
0.0
-
}
-
convolution_param {
-
num_output:
64
-
pad:
100
-
kernel_size:
3
-
stride:
1
-
}
-
}
-
layer {
-
name:
"relu1_1"
-
type:
"ReLU"
-
bottom:
"conv1_1"
-
top:
"conv1_1"
-
}
-
layer {
-
name:
"conv1_2"
-
type:
"Convolution"
-
bottom:
"conv1_1"
-
top:
"conv1_2"
-
param {
-
lr_mult:
1.0
-
decay_mult:
1.0
-
}
-
param {
-
lr_mult:
2.0
-
decay_mult:
0.0
-
}
-
convolution_param {
-
num_output:
64
-
pad:
1
-
kernel_size:
3
-
stride:
1
-
}
-
}
-
layer {
-
name:
"relu1_2"
-
type:
"ReLU"
-
bottom:
"conv1_2"
-
top:
"conv1_2"
-
}
-
layer {
-
name:
"pool1"
-
type:
"Pooling"
-
bottom:
"conv1_2"
-
top:
"pool1"
-
pooling_param {
-
pool: MAX
-
kernel_size:
2
-
stride:
2
-
}
-
}
这里第一个卷积层conv1_1采用pad=100进行填充是为了防止输入图片过小，也是FCN可以输入任意大小图片进行训练和测试的原因，具体分析如下：
从程序中可以看出conv1到conv5各卷积层的卷积核大小均为k=3，填充均为p=1（除了conv1_1）,步长均为s=1，即卷积层的输出大小和输入大小是一样的，即（此处拿height这一维举例子，且假设conv1_1的填充也为1，同时假设输出的大小可以为小数，即去掉向下取整操作）
hout=hin−k+2×ps+1=hin−3+2×11+1=hinhout=hin−k+2×ps+1=hin−3+2×11+1=hin
其中的40就是输出所多余的部分，和conv1_1层的填充数pad有关，而score层的目的就是要裁剪掉这多余部分，怎么裁剪，即对称裁剪（取中间部分，原因和填充是对称填充有关），故offset为19，即从每行每列的第20个像素点开始裁剪，取原图像大小为裁剪步长，得到和原图一样大小的输出（为何是19，不是20，这个也是因为caffe是用C++写的，数组的索引号从0开始），如下示意图：
要是没看懂coord_map.py文件的原理，可以自己改一下conv1_1的pad，然后在终端输入一下语句运行修改过的net.py文件，看看score这一层的offset的变化，可以间接来理解conv1_1的pad以及这个offset的作用。
python net.py
-
#损失函数采用softmax
-
n.loss = L.SoftmaxWithLoss(n.score, n.label,
-
loss_param=dict(normalize=
False, ignore_label=
255))
-
#忽略255这个像素值
-
-
return n.to_proto()
#返回生成的整个网络
4.主函数及其入口
-
#创建train.prototxt和val.prototxt并写入相应的网络
-
def make_net():
-
with open(
'train.prototxt',
'w')
as f:
-
f.write(str(fcn(
'train')))
-
-
with open(
'val.prototxt',
'w')
as f:
-
f.write(str(fcn(
'seg11valid')))
-
#主函数入口
-
if __name__ ==
'__main__':
-
make_net()
以下为部分训练日志：
大家可以对照每一层的核的大小、步长和填充数来计算每一层的输出大小，然后可以和caffe的输出结果对照一下，需要说明的一点是caffe中的卷积层计算输出大小时采用向下取整的方式，但其池化层计算输出大小采用向上取整，具体原因暂未知，还没深入看代码，但个人感觉是互补吧，将卷积层丢失的在池化层补回来。故在pool2层caffe的输出是144*175，而不是144*174（174是概念里都是向下取整，但caffe并不是这么做的）。
具体可参见caffe/src/caffe/layers下的conv_layer.cpp和pooling_layer.cpp文件，在此截取部分代码，方便查看：
（1）conv_layer.cpp（采用除号/向下取整）
-
template <
typename Dtype>
-
void ConvolutionLayer
::compute_output_shape() {
-
const
int* kernel_shape_data =
this->kernel_shape_.cpu_data();
-
const
int* stride_data =
this->stride_.cpu_data();
-
const
int* pad_data =
this->pad_.cpu_data();
-
const
int* dilation_data =
this->dilation_.cpu_data();
-
this->output_shape_.clear();
-
for (
int i =
0; i <
this->num_spatial_axes_; ++i) {
-
// i + 1 to skip channel axis
-
const
int input_dim =
this->input_shape(i +
1);
-
const
int kernel_extent = dilation_data[i] * (kernel_shape_data[i] -
1) +
1;
-
const
int output_dim = (input_dim +
2 * pad_data[i] - kernel_extent)
-
/ stride_data[i] +
1;
-
this->output_shape_.push_back(output_dim);
-
}
-
}
（2）pooling_layer.cpp（采用ceil函数向上取整）
-
template <
typename Dtype>
-
void PoolingLayer
::Reshape(
const
vector*>& bottom,
-
const
vector
*>& top) {
-
CHECK_EQ(
4, bottom[
0]->num_axes()) <<
"Input must have 4 axes, "
-
<<
"corresponding to (num, channels, height, width)";
-
channels_ = bottom[
0]->channels();
-
height_ = bottom[
0]->height();
-
width_ = bottom[
0]->width();
-
if (global_pooling_) {
-
kernel_h_ = bottom[
0]->height();
-
kernel_w_ = bottom[
0]->width();
-
}
-
pooled_height_ =
static_cast<
int>(
ceil(
static_cast<
float>(
-
height_ +
2 * pad_h_ - kernel_h_) / stride_h_)) +
1;
-
pooled_width_ =
static_cast<
int>(
ceil(
static_cast<
float>(
-
width_ +
2 * pad_w_ - kernel_w_) / stride_w_)) +
1;
-
if (pad_h_ || pad_w_) {
-
// If we have padding, ensure that the last pooling starts strictly
-
// inside the image (instead of at the padding); otherwise clip the last.
-
if ((pooled_height_ -
1) * stride_h_ >= height_ + pad_h_) {
-
--pooled_height_;
-
}
-
if ((pooled_width_ -
1) * stride_w_ >= width_ + pad_w_) {
-
--pooled_width_;
-
}
-
CHECK_LT((pooled_height_ -
1) * stride_h_, height_ + pad_h_);
-
CHECK_LT((pooled_width_ -
1) * stride_w_, width_ + pad_w_);
-
}
-
top[
0]->Reshape(bottom[
0]->num(), channels_, pooled_height_,
-
pooled_width_);
-
if (top.size() >
1) {
-
top[
1]->ReshapeLike(*top[
0]);
-
}
-
// If max pooling, we will initialize the vector index part.
-
if (
this->layer_param_.pooling_param().pool() ==
-
PoolingParameter_PoolMethod_MAX && top.size() ==
1) {
-
max_idx_.Reshape(bottom[
0]->num(), channels_, pooled_height_,
-
pooled_width_);
-
}
-
// If stochastic pooling, we will initialize the random index part.
-
if (
this->layer_param_.pooling_param().pool() ==
-
PoolingParameter_PoolMethod_STOCHASTIC) {
-
rand_idx_.Reshape(bottom[
0]->num(), channels_, pooled_height_,
-
pooled_width_);
-
}
-
}
具体的输出日志：
-
I0505
15:
40:
16.159765
5317 layer_factory.hpp:
77] Creating layer data
-
I0505
15:
40:
16.174499
5317 net.cpp:
84] Creating Layer data
-
I0505
15:
40:
16.174676
5317 net.cpp:
380] data -> data
-
I0505
15:
40:
16.174806
5317 net.cpp:
380] data -> label
-
I0505
15:
40:
16.330996
5317 net.cpp:
122] Setting up data
-
I0505
15:
40:
16.331058
5317 net.cpp:
129] Top shape:
1
3
375
500 (
562500)
-
I0505
15:
40:
16.331099
5317 net.cpp:
129] Top shape:
1
1
375
500 (
187500)
-
I0505
15:
40:
16.331130
5317 net.cpp:
137] Memory required
for data:
3000000
-
I0505
15:
40:
16.331173
5317 layer_factory.hpp:
77] Creating layer data_data_0_split
-
I0505
15:
40:
16.331220
5317 net.cpp:
84] Creating Layer data_data_0_split
-
I0505
15:
40:
16.331256
5317 net.cpp:
406] data_data_0_split <- data
-
I0505
15:
40:
16.331293
5317 net.cpp:
380] data_data_0_split -> data_data_0_split_0
-
I0505
15:
40:
16.331348
5317 net.cpp:
380] data_data_0_split -> data_data_0_split_1
-
I0505
15:
40:
16.331512
5317 net.cpp:
122] Setting up data_data_0_split
-
I0505
15:
40:
16.331542
5317 net.cpp:
129] Top shape:
1
3
375
500 (
562500)
-
I0505
15:
40:
16.331569
5317 net.cpp:
129] Top shape:
1
3
375
500 (
562500)
-
I0505
15:
40:
16.331596
5317 net.cpp:
137] Memory required
for data:
7500000
-
I0505
15:
40:
16.331619
5317 layer_factory.hpp:
77] Creating layer conv1_1
-
I0505
15:
40:
16.331663
5317 net.cpp:
84] Creating Layer conv1_1
-
I0505
15:
40:
16.331687
5317 net.cpp:
406] conv1_1 <- data_data_0_split_0
-
I0505
15:
40:
16.331720
5317 net.cpp:
380] conv1_1 -> conv1_1
-
I0505
15:
40:
17.840531
5317 net.cpp:
122] Setting up conv1_1
-
I0505
15:
40:
17.840585
5317 net.cpp:
129] Top shape:
1
64
573
698 (
25597056)
-
I0505
15:
40:
17.840610
5317 net.cpp:
137] Memory required
for data:
109888224
-
I0505
15:
40:
17.840651
5317 layer_factory.hpp:
77] Creating layer relu1_1
-
I0505
15:
40:
17.840685
5317 net.cpp:
84] Creating Layer relu1_1
-
I0505
15:
40:
17.840703
5317 net.cpp:
406] relu1_1 <- conv1_1
-
I0505
15:
40:
17.840723
5317 net.cpp:
367] relu1_1 -> conv1_1 (
in-place)
-
I0505
15:
40:
17.842114
5317 net.cpp:
122] Setting up relu1_1
-
I0505
15:
40:
17.842149
5317 net.cpp:
129] Top shape:
1
64
573
698 (
25597056)
-
I0505
15:
40:
17.842166
5317 net.cpp:
137] Memory required
for data:
212276448
-
I0505
15:
40:
17.842182
5317 layer_factory.hpp:
77] Creating layer conv1_2
-
I0505
15:
40:
17.842221
5317 net.cpp:
84] Creating Layer conv1_2
-
I0505
15:
40:
17.842236
5317 net.cpp:
406] conv1_2 <- conv1_1
-
I0505
15:
40:
17.842260
5317 net.cpp:
380] conv1_2 -> conv1_2
-
I0505
15:
40:
17.847681
5317 net.cpp:
122] Setting up conv1_2
-
I0505
15:
40:
17.847723
5317 net.cpp:
129] Top shape:
1
64
573
698 (
25597056)
-
I0505
15:
40:
17.847745
5317 net.cpp:
137] Memory required
for data:
314664672
-
I0505
15:
40:
17.847779
5317 layer_factory.hpp:
77] Creating layer relu1_2
-
I0505
15:
40:
17.847810
5317 net.cpp:
84] Creating Layer relu1_2
-
I0505
15:
40:
17.847826
5317 net.cpp:
406] relu1_2 <- conv1_2
-
I0505
15:
40:
17.847846
5317 net.cpp:
367] relu1_2 -> conv1_2 (
in-place)
-
I0505
15:
40:
17.849138
5317 net.cpp:
122] Setting up relu1_2
-
I0505
15:
40:
17.849169
5317 net.cpp:
129] Top shape:
1
64
573
698 (
25597056)
-
I0505
15:
40:
17.849186
5317 net.cpp:
137] Memory required
for data:
417052896
-
I0505
15:
40:
17.849202
5317 layer_factory.hpp:
77] Creating layer pool1
-
I0505
15:
40:
17.849226
5317 net.cpp:
84] Creating Layer pool1
-
I0505
15:
40:
17.849241
5317 net.cpp:
406] pool1 <- conv1_2
-
I0505
15:
40:
17.849261
5317 net.cpp:
380] pool1 -> pool1
-
I0505
15:
40:
17.849390
5317 net.cpp:
122] Setting up pool1
-
I0505
15:
40:
17.849406
5317 net.cpp:
129] Top shape:
1
64
287
349 (
6410432)
-
I0505
15:
40:
17.849421
5317 net.cpp:
137] Memory required
for data:
442694624
-
I0505
15:
40:
17.849434
5317 layer_factory.hpp:
77] Creating layer conv2_1
-
I0505
15:
40:
17.849462
5317 net.cpp:
84] Creating Layer conv2_1
-
I0505
15:
40:
17.849475
5317 net.cpp:
406] conv2_1 <- pool1
-
I0505
15:
40:
17.849496
5317 net.cpp:
380] conv2_1 -> conv2_1
-
I0505
15:
40:
17.853972
5317 net.cpp:
122] Setting up conv2_1
-
I0505
15:
40:
17.854018
5317 net.cpp:
129] Top shape:
1
128
287
349 (
12820864)
-
I0505
15:
40:
17.854041
5317 net.cpp:
137] Memory required
for data:
493978080
-
I0505
15:
40:
17.854084
5317 layer_factory.hpp:
77] Creating layer relu2_1
-
I0505
15:
40:
17.854116
5317 net.cpp:
84] Creating Layer relu2_1
-
I0505
15:
40:
17.854133
5317 net.cpp:
406] relu2_1 <- conv2_1
-
I0505
15:
40:
17.854153
5317 net.cpp:
367] relu2_1 -> conv2_1 (
in-place)
-
I0505
15:
40:
17.855494
5317 net.cpp:
122] Setting up relu2_1
-
I0505
15:
40:
17.855537
5317 net.cpp:
129] Top shape:
1
128
287
349 (
12820864)
-
I0505
15:
40:
17.855561
5317 net.cpp:
137] Memory required
for data:
545261536
-
I0505
15:
40:
17.855579
5317 layer_factory.hpp:
77] Creating layer conv2_2
-
I0505
15:
40:
17.855623
5317 net.cpp:
84] Creating Layer conv2_2
-
I0505
15:
40:
17.855640
5317 net.cpp:
406] conv2_2 <- conv2_1
-
I0505
15:
40:
17.855669
5317 net.cpp:
380] conv2_2 -> conv2_2
-
I0505
15:
40:
17.861979
5317 net.cpp:
122] Setting up conv2_2
-
I0505
15:
40:
17.862028
5317 net.cpp:
129] Top shape:
1
128
287
349 (
12820864)
-
I0505
15:
40:
17.862051
5317 net.cpp:
137] Memory required
for data:
596544992
-
I0505
15:
40:
17.862080
5317 layer_factory.hpp:
77] Creating layer relu2_2
-
I0505
15:
40:
17.862112
5317 net.cpp:
84] Creating Layer relu2_2
-
I0505
15:
40:
17.862129
5317 net.cpp:
406] relu2_2 <- conv2_2
-
I0505
15:
40:
17.862150
5317 net.cpp:
367] relu2_2 -> conv2_2 (
in-place)
-
I0505
15:
40:
17.863425
5317 net.cpp:
122] Setting up relu2_2
-
I0505
15:
40:
17.863457
5317 net.cpp:
129] Top shape:
1
128
287
349 (
12820864)
-
I0505
15:
40:
17.863476
5317 net.cpp:
137] Memory required
for data:
647828448
-
I0505
15:
40:
17.863492
5317 layer_factory.hpp:
77] Creating layer pool2
-
I0505
15:
40:
17.863514
5317 net.cpp:
84] Creating Layer pool2
-
I0505
15:
40:
17.863529
5317 net.cpp:
406] pool2 <- conv2_2
-
I0505
15:
40:
17.863554
5317 net.cpp:
380] pool2 -> pool2
-
I0505
15:
40:
17.863689
5317 net.cpp:
122] Setting up pool2
-
I0505
15:
40:
17.863708
5317 net.cpp:
129] Top shape:
1
128
144
175 (
3225600)
-
I0505
15:
40:
17.863723
5317 net.cpp:
137] Memory required
for data:
660730848
-
I0505
15:
40:
17.863735
5317 layer_factory.hpp:
77] Creating layer conv3_1
-
I0505
15:
40:
17.863765
5317 net.cpp:
84] Creating Layer conv3_1
-
I0505
15:
40:
17.863780
5317 net.cpp:
406] conv3_1 <- pool2
-
I0505
15:
40:
17.863798
5317 net.cpp:
380] conv3_1 -> conv3_1
-
I0505
15:
40:
17.869124
5317 net.cpp:
122] Setting up conv3_1
-
I0505
15:
40:
17.869174
5317 net.cpp:
129] Top shape:
1
256
144
175 (
6451200)
-
I0505
15:
40:
17.869195
5317 net.cpp:
137] Memory required
for data:
686535648
-
I0505
15:
40:
17.869246
5317 layer_factory.hpp:
77] Creating layer relu3_1
-
I0505
15:
40:
17.869279
5317 net.cpp:
84] Creating Layer relu3_1
-
I0505
15:
40:
17.869299
5317 net.cpp:
406] relu3_1 <- conv3_1
-
I0505
15:
40:
17.869324
5317 net.cpp:
367] relu3_1 -> conv3_1 (
in-place)
-
I0505
15:
40:
17.870867
5317 net.cpp:
122] Setting up relu3_1
-
I0505
15:
40:
17.870905
5317 net.cpp:
129] Top shape:
1
256
144
175 (
6451200)
-
I0505
15:
40:
17.870934
5317 net.cpp:
137] Memory required
for data:
712340448
-
I0505
15:
40:
17.870950
5317 layer_factory.hpp:
77] Creating layer conv3_2
-
I0505
15:
40:
17.870985
5317 net.cpp:
84] Creating Layer conv3_2
-
I0505
15:
40:
17.871001
5317 net.cpp:
406] conv3_2 <- conv3_1
-
I0505
15:
40:
17.871023
5317 net.cpp:
380] conv3_2 -> conv3_2
-
I0505
15:
40:
17.877616
5317 net.cpp:
122] Setting up conv3_2
-
I0505
15:
40:
17.877665
5317 net.cpp:
129] Top shape:
1
256
144
175 (
6451200)
-
I0505
15:
40:
17.877687
5317 net.cpp:
137] Memory required
for data:
738145248
-
I0505
15:
40:
17.877713
5317 layer_factory.hpp:
77] Creating layer relu3_2
-
I0505
15:
40:
17.877748
5317 net.cpp:
84] Creating Layer relu3_2
-
I0505
15:
40:
17.877765
5317 net.cpp:
406] relu3_2 <- conv3_2
-
I0505
15:
40:
17.877785
5317 net.cpp:
367] relu3_2 -> conv3_2 (
in-place)
-
I0505
15:
40:
17.879271
5317 net.cpp:
122] Setting up relu3_2
-
I0505
15:
40:
17.879315
5317 net.cpp:
129] Top shape:
1
256
144
175 (
6451200)
-
I0505
15:
40:
17.879339
5317 net.cpp:
137] Memory required
for data:
763950048
-
I0505
15:
40:
17.879357
5317 layer_factory.hpp:
77] Creating layer conv3_3
-
I0505
15:
40:
17.879406
5317 net.cpp:
84] Creating Layer conv3_3
-
I0505
15:
40:
17.879428
5317 net.cpp:
406] conv3_3 <- conv3_2
-
I0505
15:
40:
17.879454
5317 net.cpp:
380] conv3_3 -> conv3_3
-
I0505
15:
40:
17.887154
5317 net.cpp:
122] Setting up conv3_3
-
I0505
15:
40:
17.887202
5317 net.cpp:
129] Top shape:
1
256
144
175 (
6451200)
-
I0505
15:
40:
17.887228
5317 net.cpp:
137] Memory required
for data:
789754848
-
I0505
15:
40:
17.887259
5317 layer_factory.hpp:
77] Creating layer relu3_3
-
I0505
15:
40:
17.887291
5317 net.cpp:
84] Creating Layer relu3_3
-
I0505
15:
40:
17.887310
5317 net.cpp:
406] relu3_3 <- conv3_3
-
I0505
15:
40:
17.887331
5317 net.cpp:
367] relu3_3 -> conv3_3 (
in-place)
-
I0505
15:
40:
17.888519
5317 net.cpp:
122] Setting up relu3_3
-
I0505
15:
40:
17.888551
5317 net.cpp:
129] Top shape:
1
256
144
175 (
6451200)
-
I0505
15:
40:
17.888572
5317 net.cpp:
137] Memory required
for data:
815559648
-
I0505
15:
40:
17.888586
5317 layer_factory.hpp:
77] Creating layer pool3
-
I0505
15:
40:
17.888613
5317 net.cpp:
84] Creating Layer pool3
-
I0505
15:
40:
17.888628
5317 net.cpp:
406] pool3 <- conv3_3
-
I0505
15:
40:
17.888649
5317 net.cpp:
380] pool3 -> pool3
-
I0505
15:
40:
17.888797
5317 net.cpp:
122] Setting up pool3
-
I0505
15:
40:
17.888816
5317 net.cpp:
129] Top shape:
1
256
72
88 (
1622016)
-
I0505
15:
40:
17.888833
5317 net.cpp:
137] Memory required
for data:
822047712
-
I0505
15:
40:
17.888845
5317 layer_factory.hpp:
77] Creating layer conv4_1
-
I0505
15:
40:
17.888873
5317 net.cpp:
84] Creating Layer conv4_1
-
I0505
15:
40:
17.888887
5317 net.cpp:
406] conv4_1 <- pool3
-
I0505
15:
40:
17.888906
5317 net.cpp:
380] conv4_1 -> conv4_1
-
I0505
15:
40:
17.897431
5317 net.cpp:
122] Setting up conv4_1
-
I0505
15:
40:
17.897486
5317 net.cpp:
129] Top shape:
1
512
72
88 (
3244032)
-
I0505
15:
40:
17.897514
5317 net.cpp:
137] Memory required
for data:
835023840
-
I0505
15:
40:
17.897552
5317 layer_factory.hpp:
77] Creating layer relu4_1
-
I0505
15:
40:
17.897588
5317 net.cpp:
84] Creating Layer relu4_1
-
I0505
15:
40:
17.897605
5317 net.cpp:
406] relu4_1 <- conv4_1
-
I0505
15:
40:
17.897629
5317 net.cpp:
367] relu4_1 -> conv4_1 (
in-place)
-
I0505
15:
40:
17.899286
5317 net.cpp:
122] Setting up relu4_1
-
I0505
15:
40:
17.899335
5317 net.cpp:
129] Top shape:
1
512
72
88 (
3244032)
-
I0505
15:
40:
17.899360
5317 net.cpp:
137] Memory required
for data:
847999968
-
I0505
15:
40:
17.899381
5317 layer_factory.hpp:
77] Creating layer conv4_2
-
I0505
15:
40:
17.899421
5317 net.cpp:
84] Creating Layer conv4_2
-
I0505
15:
40:
17.899440
5317 net.cpp:
406] conv4_2 <- conv4_1
-
I0505
15:
40:
17.899468
5317 net.cpp:
380] conv4_2 -> conv4_2
-
I0505
15:
40:
17.911787
5317 net.cpp:
122] Setting up conv4_2
-
I0505
15:
40:
17.911835
5317 net.cpp:
129] Top shape:
1
512
72
88 (
3244032)
-
I0505
15:
40:
17.911859
5317 net.cpp:
137] Memory required
for data:
860976096
-
I0505
15:
40:
17.911897
5317 layer_factory.hpp:
77] Creating layer relu4_2
-
I0505
15:
40:
17.911931
5317 net.cpp:
84] Creating Layer relu4_2
-
I0505
15:
40:
17.911952
5317 net.cpp:
406] relu4_2 <- conv4_2
-
I0505
15:
40:
17.911975
5317 net.cpp:
367] relu4_2 -> conv4_2 (
in-place)
-
I0505
15:
40:
17.913126
5317 net.cpp:
122] Setting up relu4_2
-
I0505
15:
40:
17.913168
5317 net.cpp:
129] Top shape:
1
512
72
88 (
3244032)
-
I0505
15:
40:
17.913190
5317 net.cpp:
137] Memory required
for data:
873952224
-
I0505
15:
40:
17.913206
5317 layer_factory.hpp:
77] Creating layer conv4_3
-
I0505
15:
40:
17.913239
5317 net.cpp:
84] Creating Layer conv4_3
-
I0505
15:
40:
17.913257
5317 net.cpp:
406] conv4_3 <- conv4_2
-
I0505
15:
40:
17.913282
5317 net.cpp:
380] conv4_3 -> conv4_3
-
I0505
15:
40:
17.925925
5317 net.cpp:
122] Setting up conv4_3
-
I0505
15:
40:
17.925977
5317 net.cpp:
129] Top shape:
1
512
72
88 (
3244032)
-
I0505
15:
40:
17.926003
5317 net.cpp:
137] Memory required
for data:
886928352
-
I0505
15:
40:
17.926033
5317 layer_factory.hpp: