CaffeOnACL/unit_tests/test_fail.cpp at master · rootkit/CaffeOnACL · GitHub

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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"

#include <iomanip>

namespace caffe {

template <typename Dtype>

void dump_blob(const Blob<Dtype> * blob, const char * outfile)

{

std::ofstream os;

os.open(outfile);

os<<setiosflags(ios::fixed);

for(int i=0;i<blob->LegacyShape(0);i++)

{

for(int j=0;j<blob->LegacyShape(1);j++)

{

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<<std::setprecision(12)<<data<<", ";

}

os<<std::endl;

}

os<<std::endl;

}

os<<std::endl;

}

os.close();

}

template <typename Dtype>

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+100;

}

template <typename Dtype>

void load_blob_data(Blob<Dtype >* bottom, Dtype * p_data)

{

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();

ptr[offset]=p_data[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);

}

float fail3_weight[]={

-0.850632905960, -1.578843951225, -0.890021681786,

0.971448659897, -0.538104891777, 0.233876436949,

-1.242745161057, 2.211859703064, 0.525026142597,

-1.726792931557, -1.194667577744, 1.119420289993,

-1.539444208145, 1.725312829018, -1.573384165764,

0.519557833672, 0.376551657915, -0.615215837955,

0.758795797825, -0.498177528381, 0.254181325436,

-0.071698464453, -1.192728281021, 0.776199519634,

1.837580919266, -0.478745609522, -0.804457962513,

-2.220808744431, -0.892578184605, -1.422935843468,

-1.707052111626, -1.837757468224, -1.312300324440,

-1.251585721970, -1.591378808022, -0.577652215958,

1.727164268494, 0.176050186157, -1.804216146469,

0.547152698040, -0.024264926091, -2.040683984756,

-2.159983396530, 1.692966818810, -1.558626413345,

-1.242013096809, 0.122898645699, -0.146973758936,

-0.405744194984, -1.716119289398, 1.215066313744,

1.061164021492, -0.705341339111, -0.245370775461,

0.781007647514, -0.104610890150, 2.421228170395,

0.348720043898, 0.289468020201, 1.841132760048,

-0.835199236870, -0.242239400744, 1.169079542160,

0.165550187230, -0.418082803488, 0.479667782784,

-0.241552516818, 0.767971694469, -0.760977804661,

-2.419095993042, 0.774254024029, 0.541432976723,

0.855292022228, -0.144438281655, 0.251998007298,

-0.242634430528, -0.044748753309, -0.321820944548,

-0.487676948309, -0.761075556278, -0.646164357662

};

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(1);

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(1);

bottom_shape.push_back(3);

bottom_shape.push_back(5);

this->blob_bottom_->Reshape(bottom_shape);

fill_blob_data(this->blob_bottom_,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_);

//fill_blob_data(layer->blobs()[0].get(),1,1);

load_blob_data(layer->blobs()[0].get(),fail3_weight);

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);

}

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");

}

CaffeOnACL/unit_tests/test_fail.cpp at master · rootkit/CaffeOnACL · GitHub

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