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#
include
<
vector
>
#
include
"
gtest/gtest.h
"
#
include
"
caffe/blob.hpp
"
#
include
"
caffe/common.hpp
"
#
include
"
caffe/filler.hpp
"
#
include
"
caffe/layers/conv_layer.hpp
"
#
ifdef
USE_CUDNN
#
include
"
caffe/layers/cudnn_conv_layer.hpp
"
#
endif
#
include
"
caffe/test/test_caffe_main.hpp
"
#
include
"
caffe/test/test_gradient_check_util.hpp
"
namespace
caffe
{
template
<
typename
Dtype>
static
void
dump_blob
(
const
Blob<Dtype> * blob,
const
char
* outfile)
{
std::ofstream os;
os.
open
(outfile);
for
(
int
i=
0
;i<blob->
LegacyShape
(
0
);i++)
{
os<<
"
batch:
"
<<i<<std::endl;
for
(
int
j=
0
;j<blob->
LegacyShape
(
1
);j++)
{
os<<
"
channel:
"
<<j<<std::endl;
for
(
int
k=
0
;k<blob->
LegacyShape
(
2
);k++)
{
for
(
int
l=
0
;l<blob->
LegacyShape
(
3
);l++)
{
Dtype data=blob->
data_at
(i,j,k,l);
os<<
"
"
<<data;
}
os<<std::endl;
}
os<<std::endl;
}
}
os.
close
();
}
template
<
typename
Dtype>
static
void
fill_blob_data
(Blob<Dtype >* bottom,
int
fixed,
float
val)
{
for
(
int
i=
0
;i<bottom->
num
();i++)
for
(
int
j=
0
;j<bottom->
channels
();j++)
for
(
int
l=
0
;l<bottom->
height
();l++)
for
(
int
k=
0
;k<bottom->
width
();k++)
{
int
offset;
Dtype * ptr;
offset=i*bottom->
channels
()*bottom->
height
()*bottom->
width
()+
j*bottom->
height
()*bottom->
width
()+
l*bottom->
width
()+k;
ptr=bottom->
mutable_cpu_data
();
if
(fixed)
ptr[offset]=val;
else
ptr[offset]=offset;
}
}
//
Reference convolution for checking results:
//
accumulate through explicit loops over input, output, and filters.
template
<
typename
Dtype>
void
caffe_conv
(
const
Blob<Dtype>* in, ConvolutionParameter* conv_param,
const
vector<shared_ptr<Blob<Dtype> > >& weights,
Blob<Dtype>* out) {
const
bool
has_depth = (out->
num_axes
() ==
5
);
if
(!has_depth) {
CHECK_EQ
(
4
, out->
num_axes
()); }
//
Kernel size, stride, and pad
int
kernel_h, kernel_w;
if
(conv_param->
has_kernel_h
() || conv_param->
has_kernel_w
()) {
kernel_h = conv_param->
kernel_h
();
kernel_w = conv_param->
kernel_w
();
}
else
{
kernel_h = kernel_w = conv_param->
kernel_size
(
0
);
}
int
pad_h, pad_w;
if
(conv_param->
has_pad_h
() || conv_param->
has_pad_w
()) {
pad_h = conv_param->
pad_h
();
pad_w = conv_param->
pad_w
();
}
else
{
pad_h = pad_w = conv_param->
pad_size
() ? conv_param->
pad
(
0
) :
0
;
}
int
stride_h, stride_w;
if
(conv_param->
has_stride_h
() || conv_param->
has_stride_w
()) {
stride_h = conv_param->
stride_h
();
stride_w = conv_param->
stride_w
();
}
else
{
stride_h = stride_w = conv_param->
stride_size
() ? conv_param->
stride
(
0
) :
1
;
}
int
dilation_h, dilation_w;
dilation_h = dilation_w = conv_param->
dilation_size
() ?
conv_param->
dilation
(
0
) :
1
;
int
kernel_d, pad_d, stride_d, dilation_d;
if
(has_depth) {
kernel_d = kernel_h;
stride_d = stride_h;
pad_d = pad_h;
dilation_d = dilation_h;
}
else
{
kernel_d = stride_d = dilation_d =
1
;
pad_d =
0
;
}
//
Groups
int
groups = conv_param->
group
();
int
o_g = out->
shape
(
1
) / groups;
int
k_g = in->
shape
(
1
) / groups;
int
o_head, k_head;
//
Convolution
vector<
int
>
weight_offset
(
4
+ has_depth);
vector<
int
>
in_offset
(
4
+ has_depth);
vector<
int
>
out_offset
(
4
+ has_depth);
Dtype* out_data = out->
mutable_cpu_data
();
for
(
int
n =
0
; n < out->
shape
(
0
); n++) {
for
(
int
g =
0
; g < groups; g++) {
o_head = o_g * g;
k_head = k_g * g;
for
(
int
o =
0
; o < o_g; o++) {
for
(
int
k =
0
; k < k_g; k++) {
for
(
int
z =
0
; z < (has_depth ? out->
shape
(
2
) :
1
); z++) {
for
(
int
y =
0
; y < out->
shape
(
2
+ has_depth); y++) {
for
(
int
x =
0
; x < out->
shape
(
3
+ has_depth); x++) {
for
(
int
r =
0
; r < kernel_d; r++) {
for
(
int
p =
0
; p < kernel_h; p++) {
for
(
int
q =
0
; q < kernel_w; q++) {
int
in_z = z * stride_d - pad_d + r * dilation_d;
int
in_y = y * stride_h - pad_h + p * dilation_h;
int
in_x = x * stride_w - pad_w + q * dilation_w;
if
(in_z >=
0
&& in_z < (has_depth ? in->
shape
(
2
) :
1
)
&& in_y >=
0
&& in_y < in->
shape
(
2
+ has_depth)
&& in_x >=
0
&& in_x < in->
shape
(
3
+ has_depth)) {
weight_offset[
0
] = o + o_head;
weight_offset[
1
] = k;
if
(has_depth) { weight_offset[
2
] = r; }
weight_offset[
2
+ has_depth] = p;
weight_offset[
3
+ has_depth] = q;
in_offset[
0
] = n;
in_offset[
1
] = k + k_head;
if
(has_depth) { in_offset[
2
] = in_z; }
in_offset[
2
+ has_depth] = in_y;
in_offset[
3
+ has_depth] = in_x;
out_offset[
0
] = n;
out_offset[
1
] = o + o_head;
if
(has_depth) { out_offset[
2
] = z; }
out_offset[
2
+ has_depth] = y;
out_offset[
3
+ has_depth] = x;
out_data[out->
offset
(out_offset)] +=
in->
data_at
(in_offset)
* weights[
0
]->
data_at
(weight_offset);
}
}
}
}
}
}
}
}
}
}
}
//
Bias
if
(conv_param->
bias_term
()) {
const
Dtype* bias_data = weights[
1
]->
cpu_data
();
for
(
int
n =
0
; n < out->
shape
(
0
); n++) {
for
(
int
o =
0
; o < out->
shape
(
1
); o++) {
for
(
int
z =
0
; z < (has_depth ? out->
shape
(
2
) :
1
); z++) {
for
(
int
y =
0
; y < out->
shape
(
2
+ has_depth); y++) {
for
(
int
x =
0
; x < out->
shape
(
3
+ has_depth); x++) {
out_offset[
0
] = n;
out_offset[
1
] = o;
if
(has_depth) { out_offset[
2
] = z; }
out_offset[
2
+ has_depth] = y;
out_offset[
3
+ has_depth] = x;
out_data[out->
offset
(out_offset)] += bias_data[o];
}
}
}
}
}
}
}
template
void
caffe_conv
(
const
Blob<
float
>* in,
ConvolutionParameter* conv_param,
const
vector<shared_ptr<Blob<
float
> > >& weights,
Blob<
float
>* out);
template
void
caffe_conv
(
const
Blob<
double
>* in,
ConvolutionParameter* conv_param,
const
vector<shared_ptr<Blob<
double
> > >& weights,
Blob<
double
>* out);
template
<
typename
TypeParam>
class
ConvolutionLayerTest
:
public
MultiDeviceTest
<TypeParam> {
typedef
typename
TypeParam::Dtype Dtype;
protected:
ConvolutionLayerTest
()
: blob_bottom_(
new
Blob<Dtype>(
2
,
3
,
6
,
4
)),
blob_bottom_2_
(
new
Blob<Dtype>(
2
,
3
,
6
,
4
)),
blob_top_(
new
Blob<Dtype>()),
blob_top_2_(
new
Blob<Dtype>()) {}
virtual
void
SetUp
() {
//
fill the values
FillerParameter filler_param;
filler_param.
set_value
(
1
.);
GaussianFiller<Dtype>
filler
(filler_param);
filler.
Fill
(
this
->
blob_bottom_
);
filler.
Fill
(
this
->
blob_bottom_2_
);
blob_bottom_vec_.
push_back
(blob_bottom_);
blob_top_vec_.
push_back
(blob_top_);
}
virtual
~ConvolutionLayerTest
() {
delete
blob_bottom_;
delete
blob_bottom_2_;
delete
blob_top_;
delete
blob_top_2_;
}
virtual
Blob<Dtype>*
MakeReferenceTop
(Blob<Dtype>* top) {
this
->
ref_blob_top_
.
reset
(
new
Blob<Dtype>());
this
->
ref_blob_top_
->
ReshapeLike
(*top);
return
this
->
ref_blob_top_
.
get
();
}
Blob<Dtype>*
const
blob_bottom_;
Blob<Dtype>*
const
blob_bottom_2_;
Blob<Dtype>*
const
blob_top_;
Blob<Dtype>*
const
blob_top_2_;
shared_ptr<Blob<Dtype> > ref_blob_top_;
vector<Blob<Dtype>*> blob_bottom_vec_;
vector<Blob<Dtype>*> blob_top_vec_;
};
typedef
::testing::Types<CPUDevice<
float
> > float_only;
#
define
TestDtypesAndDevices
float_only
TYPED_TEST_CASE
(ConvolutionLayerTest, TestDtypesAndDevices);
TYPED_TEST
(ConvolutionLayerTest, TestSetup) {
typedef
typename
TypeParam::Dtype Dtype;
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.
mutable_convolution_param
();
convolution_param->
add_kernel_size
(
3
);
convolution_param->
add_stride
(
2
);
convolution_param->
set_num_output
(
4
);
this
->
blob_bottom_vec_
.
push_back
(
this
->
blob_bottom_2_
);
this
->
blob_top_vec_
.
push_back
(
this
->
blob_top_2_
);
layer_param.
set_type
(
"
Convolution
"
);
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::
CreateLayer
(layer_param);
layer->
SetUp
(
this
->
blob_bottom_vec_
,
this
->
blob_top_vec_
);
EXPECT_EQ
(
this
->
blob_top_
->
num
(),
2
);
EXPECT_EQ
(
this
->
blob_top_
->
channels
(),
4
);
EXPECT_EQ
(
this
->
blob_top_
->
height
(),
2
);
EXPECT_EQ
(
this
->
blob_top_
->
width
(),
1
);
EXPECT_EQ
(
this
->
blob_top_2_
->
num
(),
2
);
EXPECT_EQ
(
this
->
blob_top_2_
->
channels
(),
4
);
EXPECT_EQ
(
this
->
blob_top_2_
->
height
(),
2
);
EXPECT_EQ
(
this
->
blob_top_2_
->
width
(),
1
);
//
setting group should not change the shape
convolution_param->
set_num_output
(
3
);
convolution_param->
set_group
(
3
);
layer.
reset
(
new
ConvolutionLayer<Dtype>(layer_param));
layer->
SetUp
(
this
->
blob_bottom_vec_
,
this
->
blob_top_vec_
);
EXPECT_EQ
(
this
->
blob_top_
->
num
(),
2
);
EXPECT_EQ
(
this
->
blob_top_
->
channels
(),
3
);
EXPECT_EQ
(
this
->
blob_top_
->
height
(),
2
);
EXPECT_EQ
(
this
->
blob_top_
->
width
(),
1
);
EXPECT_EQ
(
this
->
blob_top_2_
->
num
(),
2
);
EXPECT_EQ
(
this
->
blob_top_2_
->
channels
(),
3
);
EXPECT_EQ
(
this
->
blob_top_2_
->
height
(),
2
);
EXPECT_EQ
(
this
->
blob_top_2_
->
width
(),
1
);
}
TYPED_TEST
(ConvolutionLayerTest, TestSimpleConvolution) {
typedef
typename
TypeParam::Dtype Dtype;
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.
mutable_convolution_param
();
convolution_param->
add_kernel_size
(
3
);
convolution_param->
add_stride
(
2
);
convolution_param->
set_num_output
(
3
);
convolution_param->
mutable_weight_filler
()->
set_type
(
"
gaussian
"
);
convolution_param->
mutable_bias_filler
()->
set_type
(
"
constant
"
);
convolution_param->
mutable_bias_filler
()->
set_value
(
0.1
);
vector<
int
> bottom_shape;
bottom_shape.
push_back
(
2
);
bottom_shape.
push_back
(
3
);
bottom_shape.
push_back
(
5
);
bottom_shape.
push_back
(
5
);
this
->
blob_bottom_
->
Reshape
(bottom_shape);
this
->
blob_bottom_2_
->
Reshape
(bottom_shape);
fill_blob_data
(
this
->
blob_bottom_
,
0
,
1
);
fill_blob_data
(
this
->
blob_bottom_2_
,
1
,
1
);
layer_param.
set_type
(
"
Convolution
"
);
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::
CreateLayer
(layer_param);
this
->
blob_bottom_vec_
.
push_back
(
this
->
blob_bottom_2_
);
this
->
blob_top_vec_
.
push_back
(
this
->
blob_top_2_
);
layer->
SetUp
(
this
->
blob_bottom_vec_
,
this
->
blob_top_vec_
);
//
fill_blob_data(layer->blobs()[0].get(),1,1);
layer->
Forward
(
this
->
blob_bottom_vec_
,
this
->
blob_top_vec_
);
#
ifdef
LAYER_PERF_STAT
perf_stat * p_time_stat;
p_time_stat=layer->
get_time_stat
();
std::cout<<
"
start:
"
<<p_time_stat->
start
;
std::cout<<
"
end:
"
<<p_time_stat->
end
;
std::cout<<
"
used:
"
<<p_time_stat->
used
;
std::cout<<
"
total:
"
<<p_time_stat->
total
;
std::cout<<
"
count:
"
<<p_time_stat->
count
<<std::endl;
#
endif
//
Check against reference convolution.
const
Dtype* top_data;
const
Dtype* ref_top_data;
caffe_conv
(
this
->
blob_bottom_
, convolution_param, layer->
blobs
(),
this
->
MakeReferenceTop
(
this
->
blob_top_
));
top_data =
this
->
blob_top_
->
cpu_data
();
ref_top_data =
this
->
ref_blob_top_
->
cpu_data
();
for
(
int
i =
0
; i <
this
->
blob_top_
->
count
(); ++i) {
EXPECT_NEAR
(top_data[i], ref_top_data[i],
1e-4
);
}
//
dump_blob(this->blob_bottom_,"bottom.data");
//
dump_blob(this->blob_top_,"top.data");
//
dump_blob(this->ref_blob_top_.get(),"reftop.data");
//
dump_blob(layer->blobs()[0].get(),"weight.data");
//
dump_blob(layer->blobs()[1].get(),"bias.data");
#
if
1
caffe_conv
(
this
->
blob_bottom_2_
, convolution_param, layer->
blobs
(),
this
->
MakeReferenceTop
(
this
->
blob_top_2_
));
top_data =
this
->
blob_top_2_
->
cpu_data
();
ref_top_data =
this
->
ref_blob_top_
->
cpu_data
();
for
(
int
i =
0
; i <
this
->
blob_top_
->
count
(); ++i) {
EXPECT_NEAR
(top_data[i], ref_top_data[i],
1e-4
);
}
#
endif
}
#
if
0
TYPED_TEST(ConvolutionLayerTest, TestDilatedConvolution) {
typedef typename TypeParam::Dtype Dtype;
vector<int> bottom_shape;
bottom_shape.push_back(2);
bottom_shape.push_back(3);
bottom_shape.push_back(8);
bottom_shape.push_back(7);
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
this->blob_bottom_vec_[i]->Reshape(bottom_shape);
}
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
convolution_param->add_kernel_size(3);
convolution_param->add_dilation(2);
convolution_param->set_num_output(4);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("constant");
convolution_param->mutable_bias_filler()->set_value(0.1);
layer_param.set_type("Convolution");
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::CreateLayer(layer_param);
layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Check against reference convolution.
const Dtype* top_data;
const Dtype* ref_top_data;
caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
this->MakeReferenceTop(this->blob_top_));
top_data = this->blob_top_->cpu_data();
ref_top_data = this->ref_blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
}
caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
this->MakeReferenceTop(this->blob_top_2_));
top_data = this->blob_top_2_->cpu_data();
ref_top_data = this->ref_blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
}
}
TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
const int kNumOutput = 3;
convolution_param->set_num_output(kNumOutput);
convolution_param->set_axis(3);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("gaussian");
layer_param.set_type("Convolution");
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::CreateLayer(layer_param);
vector<int> top_shape = this->blob_bottom_->shape();
top_shape[3] = kNumOutput;
layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
EXPECT_EQ(top_shape, this->blob_top_->shape());
layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Check against reference convolution.
vector<int> weight_offset(2);
const Blob<Dtype>* weight = layer->blobs()[0].get();
const Blob<Dtype>* bias = layer->blobs()[1].get();
const int num = this->blob_top_->count(3);
const int dim = this->blob_top_->shape(3);
const int bottom_dim = this->blob_bottom_->shape(3);
for (int n = 0; n < num; ++n) {
for (int d = 0; d < dim; ++d) {
weight_offset[0] = d;
Dtype value = bias->cpu_data()[d];
for (int bottom_d = 0; bottom_d < bottom_dim; ++bottom_d) {
weight_offset[1] = bottom_d;
value += weight->data_at(weight_offset) *
this->blob_bottom_->cpu_data()[n * bottom_dim + bottom_d];
}
EXPECT_NEAR(value, this->blob_top_->cpu_data()[n * dim + d], 1e-4);
}
}
}
TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) {
typedef typename TypeParam::Dtype Dtype;
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
vector<int> bottom_shape(5);
bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
bottom_shape[2] = 5;
bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
this->blob_bottom_vec_[i]->Reshape(bottom_shape);
filler.Fill(this->blob_bottom_vec_[i]);
}
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
convolution_param->add_kernel_size(3);
convolution_param->add_stride(2);
convolution_param->set_num_output(4);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("gaussian");
layer_param.set_type("Convolution");
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::CreateLayer(layer_param);
layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Check against reference convolution.
const Dtype* top_data;
const Dtype* ref_top_data;
caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
this->MakeReferenceTop(this->blob_top_));
top_data = this->blob_top_->cpu_data();
ref_top_data = this->ref_blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
}
caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
this->MakeReferenceTop(this->blob_top_2_));
top_data = this->blob_top_2_->cpu_data();
ref_top_data = this->ref_blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
}
}
TYPED_TEST(ConvolutionLayerTest, TestDilated3DConvolution) {
typedef typename TypeParam::Dtype Dtype;
this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
this->blob_top_vec_.push_back(this->blob_top_2_);
vector<int> bottom_shape(5);
bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
bottom_shape[2] = 6;
bottom_shape[3] = 7;
bottom_shape[4] = 8;
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
this->blob_bottom_vec_[i]->Reshape(bottom_shape);
filler.Fill(this->blob_bottom_vec_[i]);
}
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
convolution_param->add_kernel_size(3);
convolution_param->add_dilation(2);
convolution_param->set_num_output(4);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("gaussian");
layer_param.set_type("Convolution");
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::CreateLayer(layer_param);
layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Check against reference convolution.
const Dtype* top_data;
const Dtype* ref_top_data;
caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
this->MakeReferenceTop(this->blob_top_));
top_data = this->blob_top_->cpu_data();
ref_top_data = this->ref_blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
}
caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
this->MakeReferenceTop(this->blob_top_2_));
top_data = this->blob_top_2_->cpu_data();
ref_top_data = this->ref_blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
}
}
#
endif
TYPED_TEST
(ConvolutionLayerTest, Test1x1Convolution) {
typedef
typename
TypeParam::Dtype Dtype;
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.
mutable_convolution_param
();
#
if
0
convolution_param->add_kernel_size(1);
convolution_param->set_num_output(2);
vector<int> bottom_shape;
bottom_shape.push_back(1);
bottom_shape.push_back(32);
bottom_shape.push_back(133);
bottom_shape.push_back(98);
this->blob_bottom_vec_[0]->Reshape(bottom_shape);
#
else
convolution_param->
add_kernel_size
(
1
);
convolution_param->
add_stride
(
1
);
convolution_param->
set_num_output
(
4
);
#
endif
convolution_param->
mutable_weight_filler
()->
set_type
(
"
gaussian
"
);
convolution_param->
mutable_bias_filler
()->
set_type
(
"
constant
"
);
convolution_param->
mutable_bias_filler
()->
set_value
(
1
);
layer_param.
set_type
(
"
Convolution
"
);
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::
CreateLayer
(layer_param);
layer->
SetUp
(
this
->
blob_bottom_vec_
,
this
->
blob_top_vec_
);
fill_blob_data
(
this
->
blob_bottom_
,
1
,
1
);
fill_blob_data
(layer->
blobs
()[
0
].
get
(),
1
,
1
);
layer->
Forward
(
this
->
blob_bottom_vec_
,
this
->
blob_top_vec_
);
fill_blob_data
(
this
->
blob_bottom_
,
1
,
3
);
layer->
Forward
(
this
->
blob_bottom_vec_
,
this
->
blob_top_vec_
);
//
Check against reference convolution.
const
Dtype* top_data;
const
Dtype* ref_top_data;
caffe_conv
(
this
->
blob_bottom_
, convolution_param, layer->
blobs
(),
this
->
MakeReferenceTop
(
this
->
blob_top_
));
top_data =
this
->
blob_top_
->
cpu_data
();
ref_top_data =
this
->
ref_blob_top_
->
cpu_data
();
for
(
int
i =
0
; i <
this
->
blob_top_
->
count
(); ++i) {
EXPECT_NEAR
(top_data[i], ref_top_data[i],
1e-4
);
//
std::cout<<i<<": "<< top_data[i]<<" "<<ref_top_data[i]<<std::endl;
}
}
#
if
0
TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
typedef typename TypeParam::Dtype Dtype;
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
convolution_param->add_kernel_size(3);
convolution_param->add_stride(2);
convolution_param->set_num_output(3);
convolution_param->set_group(3);
convolution_param->mutable_weight_filler()->set_type("gaussian");
convolution_param->mutable_bias_filler()->set_type("constant");
convolution_param->mutable_bias_filler()->set_value(0.1);
layer_param.set_type("Convolution");
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::CreateLayer(layer_param);
layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Check against reference convolution.
const Dtype* top_data;
const Dtype* ref_top_data;
caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
this->MakeReferenceTop(this->blob_top_));
top_data = this->blob_top_->cpu_data();
ref_top_data = this->ref_blob_top_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
}
}
TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
// Test separable convolution by computing the Sobel operator
// as a single filter then comparing the result
// as the convolution of two rectangular filters.
typedef typename TypeParam::Dtype Dtype;
// Fill bottoms with identical Gaussian noise.
shared_ptr<GaussianFiller<Dtype> > filler;
FillerParameter filler_param;
filler_param.set_value(1.);
filler.reset(new GaussianFiller<Dtype>(filler_param));
filler->Fill(this->blob_bottom_);
this->blob_bottom_2_->CopyFrom(*this->blob_bottom_);
// Compute Sobel G_x operator as 3 x 3 convolution.
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
convolution_param->add_kernel_size(3);
convolution_param->add_stride(2);
convolution_param->set_num_output(1);
convolution_param->set_bias_term(false);
layer_param.set_type("Convolution");
shared_ptr<Layer<Dtype> > layer=
LayerRegistry<Dtype>::CreateLayer(layer_param);
layer->blobs().resize(1);
layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 3));
Dtype* weights = layer->blobs()[0]->mutable_cpu_data();
for (int c = 0; c < 3; ++c) {
int i = c * 9; // 3 x 3 filter
weights[i + 0] = -1;
weights[i + 1] = 0;
weights[i + 2] = 1;
weights[i + 3] = -2;
weights[i + 4] = 0;
weights[i + 5] = 2;
weights[i + 6] = -1;
weights[i + 7] = 0;
weights[i + 8] = 1;
}
layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
// Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
// (1) the [1 2 1] column filter
vector<Blob<Dtype>*> sep_blob_bottom_vec;
vector<Blob<Dtype>*> sep_blob_top_vec;
shared_ptr<Blob<Dtype> > blob_sep(new Blob<Dtype>());
sep_blob_bottom_vec.push_back(this->blob_bottom_2_);
sep_blob_top_vec.push_back(this->blob_top_2_);
convolution_param->clear_kernel_size();
convolution_param->clear_stride();
convolution_param->set_kernel_h(3);
convolution_param->set_kernel_w(1);
convolution_param->set_stride_h(2);
convolution_param->set_stride_w(1);
convolution_param->set_num_output(1);
convolution_param->set_bias_term(false);
layer.reset(new ConvolutionLayer<Dtype>(layer_param));
layer->blobs().resize(1);
layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 1));
Dtype* weights_1 = layer->blobs()[0]->mutable_cpu_data();
for (int c = 0; c < 3; ++c) {
int i = c * 3; // 3 x 1 filter
weights_1[i + 0] = 1;
weights_1[i + 1] = 2;
weights_1[i + 2] = 1;
}
layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
// (2) the [-1 0 1] row filter
blob_sep->CopyFrom(*this->blob_top_2_, false, true);
sep_blob_bottom_vec.clear();
sep_blob_bottom_vec.push_back(blob_sep.get());
convolution_param->set_kernel_h(1);
convolution_param->set_kernel_w(3);
convolution_param->set_stride_h(1);
convolution_param->set_stride_w(2);
convolution_param->set_num_output(1);
convolution_param->set_bias_term(false);
layer.reset(new ConvolutionLayer<Dtype>(layer_param));
layer->blobs().resize(1);
layer->blobs()[0].reset(new Blob<Dtype>(1, 1, 1, 3));
Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
weights_2[0] = -1;
weights_2[1] = 0;
weights_2[2] = 1;
layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
// Test equivalence of full and separable filters.
const Dtype* top_data = this->blob_top_->cpu_data();
const Dtype* sep_top_data = this->blob_top_2_->cpu_data();
for (int i = 0; i < this->blob_top_->count(); ++i) {
EXPECT_NEAR(top_data[i], sep_top_data[i], 1e-4);
}
}
TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) {
typedef typename TypeParam::Dtype Dtype;
const int kernel_h = 11;
const int kernel_w = 13;
vector<int> bottom_shape(4);
bottom_shape[0] = 15;
bottom_shape[1] = 18;
bottom_shape[2] = kernel_h * 2;
bottom_shape[3] = kernel_w * 2;
FillerParameter filler_param;
GaussianFiller<Dtype> filler(filler_param);
for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
this->blob_bottom_vec_[i]->Reshape(bottom_shape);
filler.Fill(this->blob_bottom_vec_[i]);
}
LayerParameter layer_param;
ConvolutionParameter* convolution_param =
layer_param.mutable_convolution_param();
convolution_param->set_num_output(12);
convolution_param->set_bias_term(false);
convolution_param->set_group(6);
convolution_param->set_kernel_h(kernel_h);
convolution_param->set_kernel_w(kernel_w);
convolution_param->mutable_weight_filler()->set_type("gaussian");
Blob<Dtype> weights;
Blob<Dtype> top_diff;
// Shape and fill weights and top_diff.
bool copy_diff;
bool reshape;
{
ConvolutionLayer<Dtype> layer(layer_param);
layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
top_diff.ReshapeLike(*this->blob_top_);
filler.Fill(&top_diff);
ASSERT_EQ(1, layer.blobs().size());
copy_diff = false; reshape = true;
weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
}
vector<bool> propagate_down(1, true);
Blob<Dtype> result_2d;
Blob<Dtype> backward_result_2d;
Blob<Dtype> backward_weight_result_2d;
// Test with 2D im2col
{
caffe_set(this->blob_top_->count(), Dtype(0),
this->blob_top_->mutable_cpu_data());
caffe_set(this->blob_bottom_->count(), Dtype(0),
this->blob_bottom_->mutable_cpu_diff());
caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
// Do SetUp and Forward; save Forward result in result_2d.
convolution_param->set_force_nd_im2col(false);
layer_param.set_type("Convolution");
Layer<Dtype> & layer_2d=*LayerRegistry<Dtype>::CreateLayer(layer_param);
layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
ASSERT_EQ(1, layer_2d.blobs().size());
copy_diff = false; reshape = false;
layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
copy_diff = false; reshape = true;
result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
// Copy pre-generated top diff into actual top diff;
// do Backward and save result in backward_result_2d.
ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
caffe_copy(top_diff.count(), top_diff.cpu_data(),
this->blob_top_->mutable_cpu_diff());
layer_2d.Backward(this->blob_top_vec_, propagate_down,
this->blob_bottom_vec_);
copy_diff = true; reshape = true;
backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
}
Blob<Dtype> result_nd;
Blob<Dtype> backward_result_nd;
Blob<Dtype> backward_weight_result_nd;
// Test with ND im2col
{
caffe_set(this->blob_top_->count(), Dtype(0),
this->blob_top_->mutable_cpu_data());
caffe_set(this->blob_bottom_->count(), Dtype(0),
this->blob_bottom_->mutable_cpu_diff());
caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
// Do SetUp and Forward; save Forward result in result_nd.
convolution_param->set_force_nd_im2col(true);
layer_param.set_type("Convolution");
Layer<Dtype>& layer_nd=*LayerRegistry<Dtype>::CreateLayer(layer_param);
layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
ASSERT_EQ(1, layer_nd.blobs().size());
copy_diff = false; reshape = false;
layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
copy_diff = false; reshape = true;
result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
// Copy pre-generated top diff into actual top diff;
// do Backward and save result in backward_result_nd.
ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
caffe_copy(top_diff.count(), top_diff.cpu_data(),
this->blob_top_->mutable_cpu_diff());
layer_nd.Backward(this->blob_top_vec_, propagate_down,
this->blob_bottom_vec_);
copy_diff = true; reshape = true;
backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
}
ASSERT_EQ(result_nd.count(), result_2d.count());
for (int i = 0; i < result_2d.count(); ++i) {
EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
}
ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
for (int i = 0; i < backward_result_2d.count(); ++i) {
EXPECT_EQ(backward_result_2d.cpu_diff()[i],
backward_result_nd.cpu_diff()[i]);
}
ASSERT_EQ(backward_weight_result_nd.count(),
backward_weight_result_2d.count());
for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
backward_weight_result_nd.cpu_diff()[i]);
}
}
#
endif
}
//
namespace caffe
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