CaffeOnACL/src/caffe/blob.cpp at master · 2php/CaffeOnACL · GitHub

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#include <climits>

#include <vector>

#include "caffe/blob.hpp"

#include "caffe/common.hpp"

#include "caffe/syncedmem.hpp"

#include "caffe/util/math_functions.hpp"

namespace caffe {

template <typename Dtype>

void Blob<Dtype>::Reshape(const int num, const int channels, const int height,

const int width) {

vector<int> shape(4);

shape[0] = num;

shape[1] = channels;

shape[2] = height;

shape[3] = width;

Reshape(shape);

}

template <typename Dtype>

void Blob<Dtype>::Reshape(const vector<int>& shape) {

CHECK_LE(shape.size(), kMaxBlobAxes);

count_ = 1;

shape_.resize(shape.size());

if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {

shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));

}

int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());

for (int i = 0; i < shape.size(); ++i) {

CHECK_GE(shape[i], 0);

if (count_ != 0) {

CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";

}

count_ *= shape[i];

shape_[i] = shape[i];

shape_data[i] = shape[i];

}

if (count_ > capacity_) {

capacity_ = count_;

data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));

diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));

}

template <typename Dtype>

void Blob<Dtype>::Reshape(const BlobShape& shape) {

CHECK_LE(shape.dim_size(), kMaxBlobAxes);

vector<int> shape_vec(shape.dim_size());

for (int i = 0; i < shape.dim_size(); ++i) {

shape_vec[i] = shape.dim(i);

}

Reshape(shape_vec);

}

template <typename Dtype>

void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {

Reshape(other.shape());

}

template <typename Dtype>

Blob<Dtype>::Blob(const int num, const int channels, const int height,

const int width)

// capacity_ must be initialized before calling Reshape

: capacity_(0) {

Reshape(num, channels, height, width);

}

template <typename Dtype>

Blob<Dtype>::Blob(const vector<int>& shape)

// capacity_ must be initialized before calling Reshape

: capacity_(0) {

Reshape(shape);

}

template <typename Dtype>

const int* Blob<Dtype>::gpu_shape() const {

CHECK(shape_data_);

return (const int*)shape_data_->gpu_data();

}

template <typename Dtype>

const Dtype* Blob<Dtype>::cpu_data() const {

CHECK(data_);

return (const Dtype*)data_->cpu_data();

}

template <typename Dtype>

void Blob<Dtype>::set_cpu_data(Dtype* data) {

CHECK(data);

// Make sure CPU and GPU sizes remain equal

size_t size = count_ * sizeof(Dtype);

if (data_->size() != size) {

data_.reset(new SyncedMemory(size));

diff_.reset(new SyncedMemory(size));

}

data_->set_cpu_data(data);

}

template <typename Dtype>

const Dtype* Blob<Dtype>::gpu_data() const {

CHECK(data_);

return (const Dtype*)data_->gpu_data();

}

template <typename Dtype>

void Blob<Dtype>::set_gpu_data(Dtype* data) {

CHECK(data);

// Make sure CPU and GPU sizes remain equal

size_t size = count_ * sizeof(Dtype);

if (data_->size() != size) {

data_.reset(new SyncedMemory(size));

diff_.reset(new SyncedMemory(size));

}

data_->set_gpu_data(data);

}

template <typename Dtype>

const Dtype* Blob<Dtype>::cpu_diff() const {

CHECK(diff_);

return (const Dtype*)diff_->cpu_data();

}

template <typename Dtype>

const Dtype* Blob<Dtype>::gpu_diff() const {

CHECK(diff_);

return (const Dtype*)diff_->gpu_data();

}

template <typename Dtype>

Dtype* Blob<Dtype>::mutable_cpu_data() {

CHECK(data_);

return static_cast<Dtype*>(data_->mutable_cpu_data());

}

template <typename Dtype>

Dtype* Blob<Dtype>::mutable_gpu_data() {

CHECK(data_);

return static_cast<Dtype*>(data_->mutable_gpu_data());

}

template <typename Dtype>

Dtype* Blob<Dtype>::mutable_cpu_diff() {

CHECK(diff_);

return static_cast<Dtype*>(diff_->mutable_cpu_data());

}

template <typename Dtype>

Dtype* Blob<Dtype>::mutable_gpu_diff() {

CHECK(diff_);

return static_cast<Dtype*>(diff_->mutable_gpu_data());

}

template <typename Dtype>

void Blob<Dtype>::ShareData(const Blob& other) {

CHECK_EQ(count_, other.count());

data_ = other.data();

}

template <typename Dtype>

void Blob<Dtype>::ShareDiff(const Blob& other) {

CHECK_EQ(count_, other.count());

diff_ = other.diff();

}

// The "update" method is used for parameter blobs in a Net, which are stored

// as Blob<float> or Blob<double> -- hence we do not define it for

// Blob<int> or Blob<unsigned int>.

template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }

template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }

template <typename Dtype>

void Blob<Dtype>::Update() {

// We will perform update based on where the data is located.

switch (data_->head()) {

case SyncedMemory::HEAD_AT_CPU:

// perform computation on CPU

caffe_axpy<Dtype>(count_, Dtype(-1),

static_cast<const Dtype*>(diff_->cpu_data()),

static_cast<Dtype*>(data_->mutable_cpu_data()));

break;

case SyncedMemory::HEAD_AT_GPU:

case SyncedMemory::SYNCED:

#ifndef CPU_ONLY

// perform computation on GPU

caffe_gpu_axpy<Dtype>(count_, Dtype(-1),

static_cast<const Dtype*>(diff_->gpu_data()),

static_cast<Dtype*>(data_->mutable_gpu_data()));

#else

NO_GPU;

#endif

break;

default:

LOG(FATAL) << "Syncedmem not initialized.";

}

template <> unsigned int Blob<unsigned int>::asum_data() const {

NOT_IMPLEMENTED;

return 0;

}

template <> int Blob<int>::asum_data() const {

NOT_IMPLEMENTED;

return 0;

}

template <typename Dtype>

Dtype Blob<Dtype>::asum_data() const {

if (!data_) { return 0; }

switch (data_->head()) {

case SyncedMemory::HEAD_AT_CPU:

return caffe_cpu_asum(count_, cpu_data());

case SyncedMemory::HEAD_AT_GPU:

case SyncedMemory::SYNCED:

#ifndef CPU_ONLY

{

Dtype asum;

caffe_gpu_asum(count_, gpu_data(), &asum);

return asum;

}

#else

NO_GPU;

#endif

case SyncedMemory::UNINITIALIZED:

return 0;

default:

LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();

}

return 0;

}

template <> unsigned int Blob<unsigned int>::asum_diff() const {

NOT_IMPLEMENTED;

return 0;

}

template <> int Blob<int>::asum_diff() const {

NOT_IMPLEMENTED;

return 0;

}

template <typename Dtype>

Dtype Blob<Dtype>::asum_diff() const {

if (!diff_) { return 0; }

switch (diff_->head()) {

case SyncedMemory::HEAD_AT_CPU:

return caffe_cpu_asum(count_, cpu_diff());

case SyncedMemory::HEAD_AT_GPU:

case SyncedMemory::SYNCED:

#ifndef CPU_ONLY

{

Dtype asum;

caffe_gpu_asum(count_, gpu_diff(), &asum);

return asum;

}

#else

NO_GPU;

#endif

case SyncedMemory::UNINITIALIZED:

return 0;

default:

LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();

}

return 0;

}

template <> unsigned int Blob<unsigned int>::sumsq_data() const {

NOT_IMPLEMENTED;

return 0;

}

template <> int Blob<int>::sumsq_data() const {

NOT_IMPLEMENTED;

return 0;

}

template <typename Dtype>

Dtype Blob<Dtype>::sumsq_data() const {

Dtype sumsq;

const Dtype* data;

if (!data_) { return 0; }

switch (data_->head()) {

case SyncedMemory::HEAD_AT_CPU:

data = cpu_data();

sumsq = caffe_cpu_dot(count_, data, data);

break;

case SyncedMemory::HEAD_AT_GPU:

case SyncedMemory::SYNCED:

#ifndef CPU_ONLY

data = gpu_data();

caffe_gpu_dot(count_, data, data, &sumsq);

#else

NO_GPU;

#endif

break;

case SyncedMemory::UNINITIALIZED:

return 0;

default:

LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();

}

return sumsq;

}

template <> unsigned int Blob<unsigned int>::sumsq_diff() const {

NOT_IMPLEMENTED;

return 0;

}

template <> int Blob<int>::sumsq_diff() const {

NOT_IMPLEMENTED;

return 0;

}

template <typename Dtype>

Dtype Blob<Dtype>::sumsq_diff() const {

Dtype sumsq;

const Dtype* diff;

if (!diff_) { return 0; }

switch (diff_->head()) {

case SyncedMemory::HEAD_AT_CPU:

diff = cpu_diff();

sumsq = caffe_cpu_dot(count_, diff, diff);

break;

case SyncedMemory::HEAD_AT_GPU:

case SyncedMemory::SYNCED:

#ifndef CPU_ONLY

diff = gpu_diff();

caffe_gpu_dot(count_, diff, diff, &sumsq);

break;

#else

NO_GPU;

#endif

case SyncedMemory::UNINITIALIZED:

return 0;

default:

LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();

}

return sumsq;

}

template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {

NOT_IMPLEMENTED;

}

template <> void Blob<int>::scale_data(int scale_factor) {

NOT_IMPLEMENTED;

}

template <typename Dtype>

void Blob<Dtype>::scale_data(Dtype scale_factor) {

Dtype* data;

if (!data_) { return; }

switch (data_->head()) {

case SyncedMemory::HEAD_AT_CPU:

data = mutable_cpu_data();

caffe_scal(count_, scale_factor, data);

return;

case SyncedMemory::HEAD_AT_GPU:

case SyncedMemory::SYNCED:

#ifndef CPU_ONLY

data = mutable_gpu_data();

caffe_gpu_scal(count_, scale_factor, data);

return;

#else

NO_GPU;

#endif

case SyncedMemory::UNINITIALIZED:

return;

default:

LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();

}

template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {

NOT_IMPLEMENTED;

}

template <> void Blob<int>::scale_diff(int scale_factor) {

NOT_IMPLEMENTED;

}

template <typename Dtype>

void Blob<Dtype>::scale_diff(Dtype scale_factor) {

Dtype* diff;

if (!diff_) { return; }

switch (diff_->head()) {

case SyncedMemory::HEAD_AT_CPU:

diff = mutable_cpu_diff();

caffe_scal(count_, scale_factor, diff);

return;

case SyncedMemory::HEAD_AT_GPU:

case SyncedMemory::SYNCED:

#ifndef CPU_ONLY

diff = mutable_gpu_diff();

caffe_gpu_scal(count_, scale_factor, diff);

return;

#else

NO_GPU;

#endif

case SyncedMemory::UNINITIALIZED:

return;

default:

LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();

}

template <typename Dtype>

bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {

if (other.has_num() || other.has_channels() ||

other.has_height() || other.has_width()) {

// Using deprecated 4D Blob dimensions --

// shape is (num, channels, height, width).

// Note: we do not use the normal Blob::num(), Blob::channels(), etc.

// methods as these index from the beginning of the blob shape, where legacy

// parameter blobs were indexed from the end of the blob shape (e.g., bias

// Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).

return shape_.size() <= 4 &&

LegacyShape(-4) == other.num() &&

LegacyShape(-3) == other.channels() &&

LegacyShape(-2) == other.height() &&

LegacyShape(-1) == other.width();

}

vector<int> other_shape(other.shape().dim_size());

for (int i = 0; i < other.shape().dim_size(); ++i) {

other_shape[i] = other.shape().dim(i);

}

return shape_ == other_shape;

}

template <typename Dtype>

void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {

if (source.count() != count_ || source.shape() != shape_) {

if (reshape) {

ReshapeLike(source);

} else {

LOG(FATAL) << "Trying to copy blobs of different sizes.";

}

switch (Caffe::mode()) {

case Caffe::GPU:

if (copy_diff) {

caffe_copy(count_, source.gpu_diff(),

static_cast<Dtype*>(diff_->mutable_gpu_data()));

} else {

caffe_copy(count_, source.gpu_data(),

static_cast<Dtype*>(data_->mutable_gpu_data()));

}

break;

case Caffe::CPU:

if (copy_diff) {

caffe_copy(count_, source.cpu_diff(),

static_cast<Dtype*>(diff_->mutable_cpu_data()));

} else {

caffe_copy(count_, source.cpu_data(),

static_cast<Dtype*>(data_->mutable_cpu_data()));

}

break;

default:

LOG(FATAL) << "Unknown caffe mode.";

}

template <typename Dtype>

void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {

if (reshape) {

vector<int> shape;

if (proto.has_num() || proto.has_channels() ||

proto.has_height() || proto.has_width()) {

// Using deprecated 4D Blob dimensions --

// shape is (num, channels, height, width).

shape.resize(4);

shape[0] = proto.num();

shape[1] = proto.channels();

shape[2] = proto.height();

shape[3] = proto.width();

} else {

shape.resize(proto.shape().dim_size());

for (int i = 0; i < proto.shape().dim_size(); ++i) {

shape[i] = proto.shape().dim(i);

}

Reshape(shape);

} else {

CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";

}

// copy data

Dtype* data_vec = mutable_cpu_data();

if (proto.double_data_size() > 0) {

CHECK_EQ(count_, proto.double_data_size());

for (int i = 0; i < count_; ++i) {

data_vec[i] = proto.double_data(i);

}

} else {

CHECK_EQ(count_, proto.data_size());

for (int i = 0; i < count_; ++i) {

data_vec[i] = proto.data(i);

}

if (proto.double_diff_size() > 0) {

CHECK_EQ(count_, proto.double_diff_size());

Dtype* diff_vec = mutable_cpu_diff();

for (int i = 0; i < count_; ++i) {

diff_vec[i] = proto.double_diff(i);

}

} else if (proto.diff_size() > 0) {

CHECK_EQ(count_, proto.diff_size());

Dtype* diff_vec = mutable_cpu_diff();

for (int i = 0; i < count_; ++i) {

diff_vec[i] = proto.diff(i);

}

template <>

void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {

proto->clear_shape();

for (int i = 0; i < shape_.size(); ++i) {

proto->mutable_shape()->add_dim(shape_[i]);

}

proto->clear_double_data();

proto->clear_double_diff();

const double* data_vec = cpu_data();

for (int i = 0; i < count_; ++i) {

proto->add_double_data(data_vec[i]);

}

if (write_diff) {

const double* diff_vec = cpu_diff();

for (int i = 0; i < count_; ++i) {

proto->add_double_diff(diff_vec[i]);

}

template <>

void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {

proto->clear_shape();

for (int i = 0; i < shape_.size(); ++i) {

proto->mutable_shape()->add_dim(shape_[i]);

}

proto->clear_data();

proto->clear_diff();

const float* data_vec = cpu_data();

for (int i = 0; i < count_; ++i) {

proto->add_data(data_vec[i]);

}

if (write_diff) {

const float* diff_vec = cpu_diff();

for (int i = 0; i < count_; ++i) {

proto->add_diff(diff_vec[i]);

}

INSTANTIATE_CLASS(Blob);

template class Blob<int>;

template class Blob<unsigned int>;

} // namespace caffe

CaffeOnACL/src/caffe/blob.cpp at master · 2php/CaffeOnACL · GitHub

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