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caffe/src/caffe/layers/lrn_layer.cpp at master · BVLC/caffe · GitHub
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#
include
<
vector
>
#
include
"
caffe/layers/lrn_layer.hpp
"
#
include
"
caffe/util/math_functions.hpp
"
namespace
caffe
{
template
<
typename
Dtype>
void
LRNLayer<Dtype>::LayerSetUp(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
size_ =
this
->
layer_param_
.
lrn_param
().
local_size
();
CHECK_EQ
(size_ %
2
,
1
) <<
"
LRN only supports odd values for local_size
"
;
pre_pad_ = (size_ -
1
) /
2
;
alpha_ =
this
->
layer_param_
.
lrn_param
().
alpha
();
beta_ =
this
->
layer_param_
.
lrn_param
().
beta
();
k_ =
this
->
layer_param_
.
lrn_param
().
k
();
if
(
this
->
layer_param_
.
lrn_param
().
norm_region
() ==
LRNParameter_NormRegion_WITHIN_CHANNEL) {
//
Set up split_layer_ to use inputs in the numerator and denominator.
split_top_vec_.
clear
();
split_top_vec_.
push_back
(&product_input_);
split_top_vec_.
push_back
(&square_input_);
LayerParameter split_param;
split_layer_.
reset
(
new
SplitLayer<Dtype>(split_param));
split_layer_->
SetUp
(bottom, split_top_vec_);
//
Set up square_layer_ to square the inputs.
square_bottom_vec_.
clear
();
square_top_vec_.
clear
();
square_bottom_vec_.
push_back
(&square_input_);
square_top_vec_.
push_back
(&square_output_);
LayerParameter square_param;
square_param.
mutable_power_param
()->
set_power
(
Dtype
(
2
));
square_layer_.
reset
(
new
PowerLayer<Dtype>(square_param));
square_layer_->
SetUp
(square_bottom_vec_, square_top_vec_);
//
Set up pool_layer_ to sum over square neighborhoods of the input.
pool_top_vec_.
clear
();
pool_top_vec_.
push_back
(&pool_output_);
LayerParameter pool_param;
pool_param.
mutable_pooling_param
()->
set_pool
(
PoolingParameter_PoolMethod_AVE);
pool_param.
mutable_pooling_param
()->
set_pad
(pre_pad_);
pool_param.
mutable_pooling_param
()->
set_kernel_size
(size_);
pool_layer_.
reset
(
new
PoolingLayer<Dtype>(pool_param));
pool_layer_->
SetUp
(square_top_vec_, pool_top_vec_);
//
Set up power_layer_ to compute (1 + alpha_/N^2 s)^-beta_, where s is
//
the sum of a squared neighborhood (the output of pool_layer_).
power_top_vec_.
clear
();
power_top_vec_.
push_back
(&power_output_);
LayerParameter power_param;
power_param.
mutable_power_param
()->
set_power
(-beta_);
power_param.
mutable_power_param
()->
set_scale
(alpha_);
power_param.
mutable_power_param
()->
set_shift
(
Dtype
(
1
));
power_layer_.
reset
(
new
PowerLayer<Dtype>(power_param));
power_layer_->
SetUp
(pool_top_vec_, power_top_vec_);
//
Set up a product_layer_ to compute outputs by multiplying inputs by the
//
inverse demoninator computed by the power layer.
product_bottom_vec_.
clear
();
product_bottom_vec_.
push_back
(&product_input_);
product_bottom_vec_.
push_back
(&power_output_);
LayerParameter product_param;
EltwiseParameter* eltwise_param = product_param.
mutable_eltwise_param
();
eltwise_param->
set_operation
(EltwiseParameter_EltwiseOp_PROD);
product_layer_.
reset
(
new
EltwiseLayer<Dtype>(product_param));
product_layer_->
SetUp
(product_bottom_vec_, top);
}
}
template
<
typename
Dtype>
void
LRNLayer<Dtype>::Reshape(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
CHECK_EQ
(
4
, bottom[
0
]->
num_axes
()) <<
"
Input must have 4 axes,
"
<<
"
corresponding to (num, channels, height, width)
"
;
num_ = bottom[
0
]->
num
();
channels_ = bottom[
0
]->
channels
();
height_ = bottom[
0
]->
height
();
width_ = bottom[
0
]->
width
();
switch
(
this
->
layer_param_
.
lrn_param
().
norm_region
()) {
case
LRNParameter_NormRegion_ACROSS_CHANNELS:
top[
0
]->
Reshape
(num_, channels_, height_, width_);
scale_.
Reshape
(num_, channels_, height_, width_);
break
;
case
LRNParameter_NormRegion_WITHIN_CHANNEL:
split_layer_->
Reshape
(bottom, split_top_vec_);
square_layer_->
Reshape
(square_bottom_vec_, square_top_vec_);
pool_layer_->
Reshape
(square_top_vec_, pool_top_vec_);
power_layer_->
Reshape
(pool_top_vec_, power_top_vec_);
product_layer_->
Reshape
(product_bottom_vec_, top);
break
;
}
}
template
<
typename
Dtype>
void
LRNLayer<Dtype>::Forward_cpu(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
switch
(
this
->
layer_param_
.
lrn_param
().
norm_region
()) {
case
LRNParameter_NormRegion_ACROSS_CHANNELS:
CrossChannelForward_cpu
(bottom, top);
break
;
case
LRNParameter_NormRegion_WITHIN_CHANNEL:
WithinChannelForward
(bottom, top);
break
;
default
:
LOG
(
FATAL
) <<
"
Unknown normalization region.
"
;
}
}
template
<
typename
Dtype>
void
LRNLayer<Dtype>::CrossChannelForward_cpu(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
const
Dtype* bottom_data = bottom[
0
]->
cpu_data
();
Dtype* top_data = top[
0
]->
mutable_cpu_data
();
Dtype* scale_data = scale_.
mutable_cpu_data
();
//
start with the constant value
for
(
int
i =
0
; i < scale_.
count
(); ++i) {
scale_data[i] = k_;
}
Blob<Dtype>
padded_square
(
1
, channels_ + size_ -
1
, height_, width_);
Dtype* padded_square_data = padded_square.
mutable_cpu_data
();
caffe_set
(padded_square.
count
(),
Dtype
(
0
), padded_square_data);
Dtype alpha_over_size = alpha_ / size_;
//
go through the images
for
(
int
n =
0
; n < num_; ++n) {
//
compute the padded square
caffe_sqr
(channels_ * height_ * width_,
bottom_data + bottom[
0
]->
offset
(n),
padded_square_data + padded_square.
offset
(
0
, pre_pad_));
//
Create the first channel scale
for
(
int
c =
0
; c < size_; ++c) {
caffe_axpy<Dtype>(height_ * width_, alpha_over_size,
padded_square_data + padded_square.
offset
(
0
, c),
scale_data + scale_.
offset
(n,
0
));
}
for
(
int
c =
1
; c < channels_; ++c) {
//
copy previous scale
caffe_copy<Dtype>(height_ * width_,
scale_data + scale_.
offset
(n, c -
1
),
scale_data + scale_.
offset
(n, c));
//
add head
caffe_axpy<Dtype>(height_ * width_, alpha_over_size,
padded_square_data + padded_square.
offset
(
0
, c + size_ -
1
),
scale_data + scale_.
offset
(n, c));
//
subtract tail
caffe_axpy<Dtype>(height_ * width_, -alpha_over_size,
padded_square_data + padded_square.
offset
(
0
, c -
1
),
scale_data + scale_.
offset
(n, c));
}
}
//
In the end, compute output
caffe_powx<Dtype>(scale_.
count
(), scale_data, -beta_, top_data);
caffe_mul<Dtype>(scale_.
count
(), top_data, bottom_data, top_data);
}
template
<
typename
Dtype>
void
LRNLayer<Dtype>::WithinChannelForward(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
split_layer_->
Forward
(bottom, split_top_vec_);
square_layer_->
Forward
(square_bottom_vec_, square_top_vec_);
pool_layer_->
Forward
(square_top_vec_, pool_top_vec_);
power_layer_->
Forward
(pool_top_vec_, power_top_vec_);
product_layer_->
Forward
(product_bottom_vec_, top);
}
template
<
typename
Dtype>
void
LRNLayer<Dtype>::Backward_cpu(
const
vector<Blob<Dtype>*>& top,
const
vector<
bool
>& propagate_down,
const
vector<Blob<Dtype>*>& bottom) {
switch
(
this
->
layer_param_
.
lrn_param
().
norm_region
()) {
case
LRNParameter_NormRegion_ACROSS_CHANNELS:
CrossChannelBackward_cpu
(top, propagate_down, bottom);
break
;
case
LRNParameter_NormRegion_WITHIN_CHANNEL:
WithinChannelBackward
(top, propagate_down, bottom);
break
;
default
:
LOG
(
FATAL
) <<
"
Unknown normalization region.
"
;
}
}
template
<
typename
Dtype>
void
LRNLayer<Dtype>::CrossChannelBackward_cpu(
const
vector<Blob<Dtype>*>& top,
const
vector<
bool
>& propagate_down,
const
vector<Blob<Dtype>*>& bottom) {
const
Dtype* top_diff = top[
0
]->
cpu_diff
();
const
Dtype* top_data = top[
0
]->
cpu_data
();
const
Dtype* bottom_data = bottom[
0
]->
cpu_data
();
const
Dtype* scale_data = scale_.
cpu_data
();
Dtype* bottom_diff = bottom[
0
]->
mutable_cpu_diff
();
Blob<Dtype>
padded_ratio
(
1
, channels_ + size_ -
1
, height_, width_);
Blob<Dtype>
accum_ratio
(
1
,
1
, height_, width_);
Dtype* padded_ratio_data = padded_ratio.
mutable_cpu_data
();
Dtype* accum_ratio_data = accum_ratio.
mutable_cpu_data
();
//
We hack a little bit by using the diff() to store an additional result
Dtype* accum_ratio_times_bottom = accum_ratio.
mutable_cpu_diff
();
caffe_set
(padded_ratio.
count
(),
Dtype
(
0
), padded_ratio_data);
Dtype cache_ratio_value =
2
. * alpha_ * beta_ / size_;
caffe_powx<Dtype>(scale_.
count
(), scale_data, -beta_, bottom_diff);
caffe_mul<Dtype>(scale_.
count
(), top_diff, bottom_diff, bottom_diff);
//
go through individual data
int
inverse_pre_pad = size_ - (size_ +
1
) /
2
;
for
(
int
n =
0
; n < num_; ++n) {
int
block_offset = scale_.
offset
(n);
//
first, compute diff_i * y_i / s_i
caffe_mul<Dtype>(channels_ * height_ * width_,
top_diff + block_offset, top_data + block_offset,
padded_ratio_data + padded_ratio.
offset
(
0
, inverse_pre_pad));
caffe_div<Dtype>(channels_ * height_ * width_,
padded_ratio_data + padded_ratio.
offset
(
0
, inverse_pre_pad),
scale_data + block_offset,
padded_ratio_data + padded_ratio.
offset
(
0
, inverse_pre_pad));
//
Now, compute the accumulated ratios and the bottom diff
caffe_set
(accum_ratio.
count
(),
Dtype
(
0
), accum_ratio_data);
for
(
int
c =
0
; c < size_ -
1
; ++c) {
caffe_axpy<Dtype>(height_ * width_,
1
.,
padded_ratio_data + padded_ratio.
offset
(
0
, c), accum_ratio_data);
}
for
(
int
c =
0
; c < channels_; ++c) {
caffe_axpy<Dtype>(height_ * width_,
1
.,
padded_ratio_data + padded_ratio.
offset
(
0
, c + size_ -
1
),
accum_ratio_data);
//
compute bottom diff
caffe_mul<Dtype>(height_ * width_,
bottom_data + top[
0
]->
offset
(n, c),
accum_ratio_data, accum_ratio_times_bottom);
caffe_axpy<Dtype>(height_ * width_, -cache_ratio_value,
accum_ratio_times_bottom, bottom_diff + top[
0
]->
offset
(n, c));
caffe_axpy<Dtype>(height_ * width_, -
1
.,
padded_ratio_data + padded_ratio.
offset
(
0
, c), accum_ratio_data);
}
}
}
template
<
typename
Dtype>
void
LRNLayer<Dtype>::WithinChannelBackward(
const
vector<Blob<Dtype>*>& top,
const
vector<
bool
>& propagate_down,
const
vector<Blob<Dtype>*>& bottom) {
if
(propagate_down[
0
]) {
vector<
bool
>
product_propagate_down
(
2
,
true
);
product_layer_->
Backward
(top, product_propagate_down, product_bottom_vec_);
power_layer_->
Backward
(power_top_vec_, propagate_down, pool_top_vec_);
pool_layer_->
Backward
(pool_top_vec_, propagate_down, square_top_vec_);
square_layer_->
Backward
(square_top_vec_, propagate_down,
square_bottom_vec_);
split_layer_->
Backward
(split_top_vec_, propagate_down, bottom);
}
}
#
ifdef
CPU_ONLY
STUB_GPU
(LRNLayer);
STUB_GPU_FORWARD
(LRNLayer, CrossChannelForward);
STUB_GPU_BACKWARD
(LRNLayer, CrossChannelBackward);
#
endif
INSTANTIATE_CLASS
(LRNLayer);
}
//
namespace caffe
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