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提交 ddf6b722 编写于 作者: 朔-望's avatar 朔-望
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modify to 2 spaces indent & format code & rm build folder

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......@@ -2,5 +2,4 @@
Language: Cpp
BasedOnStyle: LLVM
Standard: Cpp11
IndentWidth: 4
...
cmake-build-release/compile_commands.json 已删除 100644 → 0
浏览文件 @ e35ef6fe
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]
\ No newline at end of file
src/common/log.h
浏览文件 @ ddf6b722
......@@ -28,15 +28,15 @@ SOFTWARE.
namespace paddle_mobile {
enum LogLevel {
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
};
// log level
......@@ -49,119 +49,117 @@ struct ToLog;
struct Print;
struct Print {
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {
buffer_ << value;
return *this;
}
private:
void print(LogLevel level) {
buffer_ << std::endl;
if (level == kLOG_ERROR) {
std::cerr << buffer_.str();
} else {
std::cout << buffer_.str();
}
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {
buffer_ << value;
return *this;
}
private:
void print(LogLevel level) {
buffer_ << std::endl;
if (level == kLOG_ERROR) {
std::cerr << buffer_.str();
} else {
std::cout << buffer_.str();
}
std::ostringstream buffer_;
}
std::ostringstream buffer_;
};
struct ToLog {
ToLog(LogLevel level = kLOG_DEBUG, const std::string &info = "")
: level_(level) {
unsigned blanks =
(unsigned)(level > kLOG_DEBUG ? (level - kLOG_DEBUG) * 4 : 1);
printer_ << logs[level] << " " << info << ":"
<< std::string(blanks, ' ');
}
template <typename T> ToLog &operator<<(T const &value) {
printer_ << value;
return *this;
}
~ToLog() { printer_.print(level_); }
private:
LogLevel level_;
Print printer_;
ToLog(LogLevel level = kLOG_DEBUG, const std::string &info = "")
: level_(level) {
unsigned blanks =
(unsigned)(level > kLOG_DEBUG ? (level - kLOG_DEBUG) * 4 : 1);
printer_ << logs[level] << " " << info << ":" << std::string(blanks, ' ');
}
template <typename T> ToLog &operator<<(T const &value) {
printer_ << value;
return *this;
}
~ToLog() { printer_.print(level_); }
private:
LogLevel level_;
Print printer_;
};
#define LOG(level) \
if (level > paddle_mobile::log_level) { \
} else \
paddle_mobile::ToLog( \
level, (std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) \
: __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
if (level > paddle_mobile::log_level) { \
} else \
paddle_mobile::ToLog( \
level, \
(std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) : __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
#define DLOG \
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
paddle_mobile::ToLog( \
paddle_mobile::kLOG_DEBUG, \
(std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) \
: __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
paddle_mobile::ToLog( \
paddle_mobile::kLOG_DEBUG, \
(std::stringstream() \
<< "[file: " \
<< (strrchr(__FILE__, '/') ? (strrchr(__FILE__, '/') + 1) : __FILE__) \
<< "] [line: " << __LINE__ << "] ") \
.str())
} // namespace paddle_mobile
#define LOGF(level, format, ...) \
if (level > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
if (level > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
#define DLOGF(format, ...) \
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
if (paddle_mobile::kLOG_DEBUG > paddle_mobile::log_level) { \
} else \
printf(format, ##__VA_ARGS__)
#else
namespace paddle_mobile {
enum LogLevel {
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
kNO_LOG,
kLOG_ERROR,
kLOG_WARNING,
kLOG_INFO,
kLOG_DEBUG,
kLOG_DEBUG1,
kLOG_DEBUG2,
kLOG_DEBUG3,
kLOG_DEBUG4
};
struct ToLog;
struct Print {
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {}
friend struct ToLog;
template <typename T> Print &operator<<(T const &value) {}
private:
private:
};
struct ToLog {
ToLog(LogLevel level) {}
ToLog(LogLevel level) {}
template <typename T> ToLog &operator<<(T const &value) { return *this; }
template <typename T> ToLog &operator<<(T const &value) { return *this; }
};
#define LOG(level) \
if (true) { \
} else \
paddle_mobile::ToLog(level)
if (true) { \
} else \
paddle_mobile::ToLog(level)
#define DLOG \
if (true) { \
} else \
paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG)
if (true) { \
} else \
paddle_mobile::ToLog(paddle_mobile::kLOG_DEBUG)
#define LOGF(level, format, ...)
......
src/common/types.h
浏览文件 @ ddf6b722
......@@ -32,32 +32,32 @@ typedef DeviceType<kGPU_MALI> GPU_MALI;
//! data type
enum DataType {
PM_INVALID = -1,
PM_HALF = 0,
PM_FLOAT = 1,
PM_DOUBLE = 2,
PM_INT8 = 3,
PM_INT16 = 4,
PM_INT32 = 5,
PM_INT64 = 6,
PM_UINT8 = 7,
PM_UINT16 = 8,
PM_UINT32 = 9,
PM_STRING = 10,
PM_BOOL = 11,
PM_SHAPE = 12,
PM_TENSOR = 13
PM_INVALID = -1,
PM_HALF = 0,
PM_FLOAT = 1,
PM_DOUBLE = 2,
PM_INT8 = 3,
PM_INT16 = 4,
PM_INT32 = 5,
PM_INT64 = 6,
PM_UINT8 = 7,
PM_UINT16 = 8,
PM_UINT32 = 9,
PM_STRING = 10,
PM_BOOL = 11,
PM_SHAPE = 12,
PM_TENSOR = 13
};
//!
enum PMStatus {
PMSuccess = 0xFF, /*!< No errors */
PMNotInitialized = 0x01, /*!< Data not initialized. */
PMInvalidValue = 0x02, /*!< Incorrect variable value. */
PMMemAllocFailed = 0x03, /*!< Memory allocation error. */
PMUnKownError = 0x04, /*!< Unknown error. */
PMOutOfAuthority = 0x05, /*!< Try to modified data not your own*/
PMOutOfMem = 0x06, /*!< OOM error*/
PMUnImplError = 0x07, /*!< Unimplement error. */
PMWrongDevice = 0x08 /*!< un-correct device. */
PMSuccess = 0xFF, /*!< No errors */
PMNotInitialized = 0x01, /*!< Data not initialized. */
PMInvalidValue = 0x02, /*!< Incorrect variable value. */
PMMemAllocFailed = 0x03, /*!< Memory allocation error. */
PMUnKownError = 0x04, /*!< Unknown error. */
PMOutOfAuthority = 0x05, /*!< Try to modified data not your own*/
PMOutOfMem = 0x06, /*!< OOM error*/
PMUnImplError = 0x07, /*!< Unimplement error. */
PMWrongDevice = 0x08 /*!< un-correct device. */
};
} // namespace paddle_mobile
src/common/variant.h
浏览文件 @ ddf6b722
......@@ -24,74 +24,74 @@ namespace paddle_mobile {
template <int ID, typename Type> struct IDToType { typedef Type type_t; };
template <typename F, typename... Ts> struct VariantHelper {
static const size_t size = sizeof(F) > VariantHelper<Ts...>::size
? sizeof(F)
: VariantHelper<Ts...>::size;
static const size_t size = sizeof(F) > VariantHelper<Ts...>::size
? sizeof(F)
: VariantHelper<Ts...>::size;
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
reinterpret_cast<F *>(data)->~F();
} else {
VariantHelper<Ts...>::Destroy(id, data);
}
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
reinterpret_cast<F *>(data)->~F();
} else {
VariantHelper<Ts...>::Destroy(id, data);
}
}
};
template <typename F> struct VariantHelper<F> {
static const size_t size = sizeof(F);
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
// reinterpret_cast<F*>(data)->~F();
} else {
// std::cout << "未匹配到 " << std::endl;
}
static const size_t size = sizeof(F);
inline static void Destroy(size_t id, void *data) {
if (id == typeid(F).hash_code()) {
// reinterpret_cast<F*>(data)->~F();
} else {
// std::cout << "未匹配到 " << std::endl;
}
}
};
template <size_t size> class RawData {
public:
char data[size];
RawData() {}
RawData(const RawData &raw_data) { strcpy(data, raw_data.data); }
// void operator=(const RawData &raw_data){
// strcpy(data, raw_data.data);
// }
public:
char data[size];
RawData() {}
RawData(const RawData &raw_data) { strcpy(data, raw_data.data); }
// void operator=(const RawData &raw_data){
// strcpy(data, raw_data.data);
// }
};
template <typename... Ts> struct Variant {
Variant(const Variant &variant) {
// std::cout << " 赋值构造函数 " << std::endl;
type_id = variant.type_id;
data = variant.data;
}
Variant(const Variant &variant) {
// std::cout << " 赋值构造函数 " << std::endl;
type_id = variant.type_id;
data = variant.data;
}
Variant() : type_id(invalid_type()) {}
~Variant() {
// helper::Destroy(type_id, &data);
}
Variant() : type_id(invalid_type()) {}
~Variant() {
// helper::Destroy(type_id, &data);
}
template <typename T, typename... Args> void Set(Args &&... args) {
helper::Destroy(type_id, &data);
new (&data) T(std::forward<Args>(args)...);
type_id = typeid(T).hash_code();
}
template <typename T, typename... Args> void Set(Args &&... args) {
helper::Destroy(type_id, &data);
new (&data) T(std::forward<Args>(args)...);
type_id = typeid(T).hash_code();
}
template <typename T> T &Get() const {
if (type_id == typeid(T).hash_code()) {
return *const_cast<T *>(reinterpret_cast<const T *>(&data));
} else {
// std::cout << " bad cast in variant " << std::endl;
throw std::bad_cast();
}
template <typename T> T &Get() const {
if (type_id == typeid(T).hash_code()) {
return *const_cast<T *>(reinterpret_cast<const T *>(&data));
} else {
// std::cout << " bad cast in variant " << std::endl;
throw std::bad_cast();
}
}
size_t TypeId() const { return type_id; }
size_t TypeId() const { return type_id; }
private:
static inline size_t invalid_type() { return typeid(void).hash_code(); }
typedef VariantHelper<Ts...> helper;
size_t type_id;
RawData<helper::size> data;
private:
static inline size_t invalid_type() { return typeid(void).hash_code(); }
typedef VariantHelper<Ts...> helper;
size_t type_id;
RawData<helper::size> data;
};
template <typename T> struct Vistor { typedef T type_t; };
......
src/framework/attribute.h
浏览文件 @ ddf6b722
......@@ -27,102 +27,102 @@ namespace framework {
class BlockDesc;
class Attribute {
public:
static Attribute GetAttrValue(const proto::OpDesc::Attr &attr_desc) {
// std::cout << "begin get attr value" << std::endl;
Attribute attr;
switch (attr_desc.type()) {
case proto::AttrType::BOOLEAN: {
attr.Set<bool>(attr_desc.b());
break;
}
case proto::AttrType::INT: {
attr.Set<int>(attr_desc.i());
break;
}
case proto::AttrType::FLOAT: {
attr.Set<float>(attr_desc.f());
break;
}
case proto::AttrType::STRING: {
attr.Set<std::string>(attr_desc.s());
break;
}
case proto::AttrType::BOOLEANS: {
std::vector<bool> val(attr_desc.bools_size());
for (int i = 0; i < attr_desc.bools_size(); ++i) {
val[i] = attr_desc.bools(i);
}
attr.Set<std::vector<bool>>(val);
break;
}
case proto::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
attr.Set<std::vector<int>>(val);
break;
}
case proto::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
attr.Set<std::vector<float>>(val);
break;
}
case proto::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
attr.Set<std::vector<std::string>>(val);
break;
}
case proto::AttrType::LONG: {
attr.Set<int64_t>(attr_desc.l());
break;
}
default:
// std::cout << " not support " << std::endl;
break;
}
// std::cout << "end get attr value" << std::endl;
return attr;
public:
static Attribute GetAttrValue(const proto::OpDesc::Attr &attr_desc) {
// std::cout << "begin get attr value" << std::endl;
Attribute attr;
switch (attr_desc.type()) {
case proto::AttrType::BOOLEAN: {
attr.Set<bool>(attr_desc.b());
break;
}
Attribute() {}
template <typename T, typename... Args> Attribute &Set(Args &&... args) {
variant_.Set<T>(args...);
return *this;
case proto::AttrType::INT: {
attr.Set<int>(attr_desc.i());
break;
}
case proto::AttrType::FLOAT: {
attr.Set<float>(attr_desc.f());
break;
}
case proto::AttrType::STRING: {
attr.Set<std::string>(attr_desc.s());
break;
}
case proto::AttrType::BOOLEANS: {
std::vector<bool> val(attr_desc.bools_size());
for (int i = 0; i < attr_desc.bools_size(); ++i) {
val[i] = attr_desc.bools(i);
}
attr.Set<std::vector<bool>>(val);
break;
}
case proto::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
attr.Set<std::vector<int>>(val);
break;
}
case proto::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
attr.Set<std::vector<float>>(val);
break;
}
case proto::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
attr.Set<std::vector<std::string>>(val);
break;
}
case proto::AttrType::LONG: {
attr.Set<int64_t>(attr_desc.l());
break;
}
default:
// std::cout << " not support " << std::endl;
break;
}
// std::cout << "end get attr value" << std::endl;
return attr;
}
template <typename T> T &Get() const { return variant_.Get<T>(); }
Attribute() {}
template <typename T, typename... Args> Attribute &Set(Args &&... args) {
variant_.Set<T>(args...);
return *this;
}
private:
Variant<int, float, std::string, std::vector<int>, std::vector<float>,
std::vector<std::string>, bool, std::vector<bool>, BlockDesc *,
int64_t>
variant_;
template <typename T> T &Get() const { return variant_.Get<T>(); }
private:
Variant<int, float, std::string, std::vector<int>, std::vector<float>,
std::vector<std::string>, bool, std::vector<bool>, BlockDesc *,
int64_t>
variant_;
};
using AttributeMap = std::unordered_map<std::string, Attribute>;
class AttrReader {
public:
explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
public:
explicit AttrReader(const AttributeMap &attrs) : attrs_(attrs) {}
template <typename T> inline T Get(const std::string &name) const {
// PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should
// be in
// AttributeMap",
// name);
return ((Attribute)attrs_.at(name)).Get<T>();
}
template <typename T> inline T Get(const std::string &name) const {
// PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should
// be in
// AttributeMap",
// name);
return ((Attribute)attrs_.at(name)).Get<T>();
}
private:
const AttributeMap &attrs_;
private:
const AttributeMap &attrs_;
};
} // namespace framework
......
src/framework/block_desc.cpp
浏览文件 @ ddf6b722
......@@ -22,28 +22,28 @@ namespace paddle_mobile {
namespace framework {
std::vector<std::shared_ptr<VarDesc>> BlockDesc::Vars() const {
std::vector<std::shared_ptr<VarDesc>> res;
for (const auto &p : vars_) {
res.push_back(p.second);
}
return res;
std::vector<std::shared_ptr<VarDesc>> res;
for (const auto &p : vars_) {
res.push_back(p.second);
}
return res;
}
std::vector<std::shared_ptr<OpDesc>> BlockDesc::Ops() const {
std::vector<std::shared_ptr<OpDesc>> res;
for (const auto &op : ops_) {
res.push_back(op);
}
return res;
std::vector<std::shared_ptr<OpDesc>> res;
for (const auto &op : ops_) {
res.push_back(op);
}
return res;
}
BlockDesc::BlockDesc(const proto::BlockDesc &desc) : desc_(desc) {
for (const proto::VarDesc &var_desc : desc_.vars()) {
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const proto::OpDesc &op_desc : desc_.ops()) {
ops_.emplace_back(new framework::OpDesc(op_desc));
}
for (const proto::VarDesc &var_desc : desc_.vars()) {
vars_[var_desc.name()].reset(new VarDesc(var_desc));
}
for (const proto::OpDesc &op_desc : desc_.ops()) {
ops_.emplace_back(new framework::OpDesc(op_desc));
}
}
} // namespace framework
......
src/framework/block_desc.h
浏览文件 @ ddf6b722
......@@ -27,29 +27,28 @@ namespace paddle_mobile {
namespace framework {
class BlockDesc : PaddleMobileObject {
public:
BlockDesc(const proto::BlockDesc &desc);
public:
BlockDesc(const proto::BlockDesc &desc);
const int &ID() const { return desc_.idx(); }
const int &ID() const { return desc_.idx(); }
const int &Parent() const { return desc_.parent_idx(); }
const int &Parent() const { return desc_.parent_idx(); }
bool operator==(const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() == in_block.ID() &&
this->Parent() == in_block.Parent();
}
bool operator==(const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() == in_block.ID() && this->Parent() == in_block.Parent();
}
bool operator<(const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() < in_block.ID() && this->Parent() < in_block.Parent();
}
bool operator<(const paddle_mobile::framework::BlockDesc &in_block) const {
return this->ID() < in_block.ID() && this->Parent() < in_block.Parent();
}
std::vector<std::shared_ptr<VarDesc>> Vars() const;
std::vector<std::shared_ptr<OpDesc>> Ops() const;
std::vector<std::shared_ptr<VarDesc>> Vars() const;
std::vector<std::shared_ptr<OpDesc>> Ops() const;
private:
proto::BlockDesc desc_;
std::vector<std::shared_ptr<OpDesc>> ops_;
std::unordered_map<std::string, std::shared_ptr<VarDesc>> vars_;
private:
proto::BlockDesc desc_;
std::vector<std::shared_ptr<OpDesc>> ops_;
std::unordered_map<std::string, std::shared_ptr<VarDesc>> vars_;
};
} // namespace framework
......@@ -58,13 +57,13 @@ class BlockDesc : PaddleMobileObject {
namespace std {
template <> struct hash<paddle_mobile::framework::BlockDesc> {
typedef paddle_mobile::framework::BlockDesc argument_type;
typedef std::size_t result_type;
result_type operator()(argument_type const &s) const noexcept {
result_type const h1(std::hash<int>{}(s.ID()));
result_type const h2(std::hash<int>{}(s.ID()));
return h1 ^ (h2 << 1);
}
typedef paddle_mobile::framework::BlockDesc argument_type;
typedef std::size_t result_type;
result_type operator()(argument_type const &s) const noexcept {
result_type const h1(std::hash<int>{}(s.ID()));
result_type const h2(std::hash<int>{}(s.ID()));
return h1 ^ (h2 << 1);
}
};
} // namespace std
src/framework/data_layout.h
浏览文件 @ ddf6b722
......@@ -22,45 +22,45 @@ namespace paddle_mobile {
namespace framework {
enum class DataLayout {
kNHWC = 0,
kNCHW = 1,
kAnyLayout = 2,
kNHWC = 0,
kNCHW = 1,
kAnyLayout = 2,
};
inline DataLayout StringToDataLayout(const std::string &str) {
std::string s(str);
for (size_t i = 0; i < s.size(); ++i) {
s[i] = toupper(s[i]);
}
std::string s(str);
for (size_t i = 0; i < s.size(); ++i) {
s[i] = toupper(s[i]);
}
if (s == "NHWC") {
return DataLayout::kNHWC;
} else if (s == "NCHW") {
return DataLayout::kNCHW;
} else if (s == "ANYLAYOUT") {
return DataLayout::kAnyLayout;
} else {
// std::cout << "Unknown storage order string: %s", s;
}
if (s == "NHWC") {
return DataLayout::kNHWC;
} else if (s == "NCHW") {
return DataLayout::kNCHW;
} else if (s == "ANYLAYOUT") {
return DataLayout::kAnyLayout;
} else {
// std::cout << "Unknown storage order string: %s", s;
}
}
inline std::string DataLayoutToString(const DataLayout &data_layout) {
switch (data_layout) {
case DataLayout::kNHWC:
return "NHWC";
case DataLayout::kNCHW:
return "NCHW";
case DataLayout::kAnyLayout:
return "ANY_LAYOUT";
default:
break;
// std::cout << "unknown DataLayou %d", data_layout;
}
switch (data_layout) {
case DataLayout::kNHWC:
return "NHWC";
case DataLayout::kNCHW:
return "NCHW";
case DataLayout::kAnyLayout:
return "ANY_LAYOUT";
default:
break;
// std::cout << "unknown DataLayou %d", data_layout;
}
}
inline std::ostream &operator<<(std::ostream &out, const DataLayout &l) {
out << DataLayoutToString(l);
return out;
out << DataLayoutToString(l);
return out;
}
} // namespace framework
......
src/framework/data_transform.cpp
浏览文件 @ ddf6b722
......@@ -24,68 +24,68 @@ namespace paddle_mobile {
namespace framework {
static void PassTensorData(Tensor *from, Tensor *to) {
to->ShareDataWith(*from);
*from = Tensor();
to->ShareDataWith(*from);
*from = Tensor();
}
void DataTransform(const OpKernelType &expected_kernel_type,
const OpKernelType &kernel_type_for_var,
const Tensor &input_tensor, Tensor *output_tensor) {
bool transformed = false;
Tensor in;
in.ShareDataWith(input_tensor);
Tensor out;
bool transformed = false;
Tensor in;
in.ShareDataWith(input_tensor);
Tensor out;
// // do layout transform
// if (NeedTransformLayout(expected_kernel_type.data_layout_,
// kernel_type_for_var.data_layout_)) {
// TransDataLayout(kernel_type_for_var, expected_kernel_type, in,
// &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do data type transform
// if (expected_kernel_type.data_type_ !=
// kernel_type_for_var.data_type_) {
// TransDataType(kernel_type_for_var, expected_kernel_type, in,
// &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do device transform
// if (!platform::is_same_place(kernel_type_for_var.place_,
// expected_kernel_type.place_)) {
// TransDataDevice(in, expected_kernel_type.place_, &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// PADDLE_ENFORCE(transformed, "No transform is applied, please
// check!");
// get output data
output_tensor->ShareDataWith(in);
// // do layout transform
// if (NeedTransformLayout(expected_kernel_type.data_layout_,
// kernel_type_for_var.data_layout_)) {
// TransDataLayout(kernel_type_for_var, expected_kernel_type, in,
// &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do data type transform
// if (expected_kernel_type.data_type_ !=
// kernel_type_for_var.data_type_) {
// TransDataType(kernel_type_for_var, expected_kernel_type, in,
// &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// // do device transform
// if (!platform::is_same_place(kernel_type_for_var.place_,
// expected_kernel_type.place_)) {
// TransDataDevice(in, expected_kernel_type.place_, &out);
// transformed = true;
// PassTensorData(&out, &in);
// }
//
// PADDLE_ENFORCE(transformed, "No transform is applied, please
// check!");
// get output data
output_tensor->ShareDataWith(in);
}
void CopyVariableWithTensor(const Variable &in_var, const Tensor &tensor,
Variable &out_var) {
// if (in_var.IsType<LoDTensor>()) {
// auto& in_lod_tensor = in_var.Get<LoDTensor>();
// auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>();
// tran_lod_tensor->set_lod(in_lod_tensor.lod());
// tran_lod_tensor->set_layout(in_lod_tensor.layout());
// tran_lod_tensor->ShareDataWith(tensor);
// } else if (in_var.IsType<SelectedRows>()) {
// auto& in_selected_rows = in_var.Get<SelectedRows>();
// auto* trans_selected_rows =
// out_var.GetMutable<SelectedRows>();
// trans_selected_rows->set_height(in_selected_rows.height());
// trans_selected_rows->set_rows(in_selected_rows.rows());
// trans_selected_rows->mutable_value()->ShareDataWith(tensor);
// } else {
// PADDLE_THROW("unknown var type");
// }
// if (in_var.IsType<LoDTensor>()) {
// auto& in_lod_tensor = in_var.Get<LoDTensor>();
// auto* tran_lod_tensor = out_var.GetMutable<LoDTensor>();
// tran_lod_tensor->set_lod(in_lod_tensor.lod());
// tran_lod_tensor->set_layout(in_lod_tensor.layout());
// tran_lod_tensor->ShareDataWith(tensor);
// } else if (in_var.IsType<SelectedRows>()) {
// auto& in_selected_rows = in_var.Get<SelectedRows>();
// auto* trans_selected_rows =
// out_var.GetMutable<SelectedRows>();
// trans_selected_rows->set_height(in_selected_rows.height());
// trans_selected_rows->set_rows(in_selected_rows.rows());
// trans_selected_rows->mutable_value()->ShareDataWith(tensor);
// } else {
// PADDLE_THROW("unknown var type");
// }
}
} // namespace framework
......
src/framework/ddim.cc
浏览文件 @ ddf6b722
......@@ -20,158 +20,156 @@ namespace framework {
/// @cond HIDDEN
template <int i> Dim<i> make_dim(const int64_t *d) {
return Dim<i>(*d, make_dim<i - 1>(d + 1));
return Dim<i>(*d, make_dim<i - 1>(d + 1));
}
template <> Dim<0> make_dim<0>(const int64_t *d) { return Dim<0>(*d); }
void make_ddim(DDim &ddim, const int64_t *dims, int n) {
switch (n) {
case 0:
ddim = make_dim<0>(dims);
break;
case 1:
ddim = make_dim<1>(dims);
break;
case 2:
ddim = make_dim<2>(dims);
break;
case 3:
ddim = make_dim<3>(dims);
break;
case 4:
ddim = make_dim<4>(dims);
break;
case 5:
ddim = make_dim<5>(dims);
break;
case 6:
ddim = make_dim<6>(dims);
break;
case 7:
ddim = make_dim<7>(dims);
break;
case 8:
ddim = make_dim<8>(dims);
break;
case 9:
ddim = make_dim<9>(dims);
break;
default:
// std::cout << "Dynamic dimensions must have between [1,
// 9]
// dimensions.";
break;
}
switch (n) {
case 0:
ddim = make_dim<0>(dims);
break;
case 1:
ddim = make_dim<1>(dims);
break;
case 2:
ddim = make_dim<2>(dims);
break;
case 3:
ddim = make_dim<3>(dims);
break;
case 4:
ddim = make_dim<4>(dims);
break;
case 5:
ddim = make_dim<5>(dims);
break;
case 6:
ddim = make_dim<6>(dims);
break;
case 7:
ddim = make_dim<7>(dims);
break;
case 8:
ddim = make_dim<8>(dims);
break;
case 9:
ddim = make_dim<9>(dims);
break;
default:
// std::cout << "Dynamic dimensions must have between [1,
// 9]
// dimensions.";
break;
}
}
/// @endcond
DDim make_ddim(std::initializer_list<int64_t> dims) {
DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size());
return result;
DDim result(make_dim(0));
make_ddim(result, dims.begin(), dims.size());
return result;
}
DDim make_ddim(const std::vector<int64_t> &dims) {
DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size());
return result;
DDim result(make_dim(0));
make_ddim(result, &dims[0], dims.size());
return result;
}
DDim make_ddim(const std::vector<int> &dims) {
std::vector<int64_t> res(dims.size());
std::transform(dims.begin(), dims.end(), res.begin(),
[](int d) { return static_cast<int64_t>(d); });
return make_ddim(res);
std::vector<int64_t> res(dims.size());
std::transform(dims.begin(), dims.end(), res.begin(),
[](int d) { return static_cast<int64_t>(d); });
return make_ddim(res);
}
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes
// errors
struct DynamicMutableIndexer : Vistor<int64_t &> {
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
public:
explicit DynamicMutableIndexer(int idx) : idx_(idx) {}
template <int D> int64_t &operator()(Dim<D> &dim) const {
return dim[idx_];
}
template <int D> int64_t &operator()(Dim<D> &dim) const { return dim[idx_]; }
private:
int idx_;
private:
int idx_;
};
struct DynamicConstIndexer : public Vistor<int64_t> {
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
public:
explicit DynamicConstIndexer(int idx) : idx_(idx) {}
template <int D> int64_t operator()(const Dim<D> &dim) const {
return dim[idx_];
}
template <int D> int64_t operator()(const Dim<D> &dim) const {
return dim[idx_];
}
private:
int idx_;
private:
int idx_;
};
/// @endcond
int64_t &DDim::operator[](int idx) {
return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
return DDim::ApplyVistor(DynamicMutableIndexer(idx), *this);
}
int64_t DDim::operator[](int idx) const {
return DDim::ApplyVistor(DynamicConstIndexer(idx), *this);
return DDim::ApplyVistor(DynamicConstIndexer(idx), *this);
}
int DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const {
// if (var.which() != d.getVar().which()) {
// return false;
// } else {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) {
return false;
}
// if (var.which() != d.getVar().which()) {
// return false;
// } else {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
for (unsigned int i = 0; i < v1.size(); i++) {
if (v1[i] != v2[i]) {
return false;
}
}
return true;
// }
return true;
// }
}
bool DDim::operator!=(DDim d) const { return !(*this == d); }
DDim DDim::operator+(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] + v2[i]);
}
return make_ddim(v3);
return make_ddim(v3);
}
DDim DDim::operator*(DDim d) const {
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v1 = vectorize(*this);
std::vector<int64_t> v2 = vectorize(d);
std::vector<int64_t> v3;
std::vector<int64_t> v3;
assert(v1.size() == v2.size());
assert(v1.size() == v2.size());
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
for (unsigned int i = 0; i < v1.size(); i++) {
v3.push_back(v1[i] * v2[i]);
}
return make_ddim(v3);
return make_ddim(v3);
}
int64_t get(const DDim &ddim, int idx) { return ddim[idx]; }
......@@ -180,152 +178,152 @@ void set(DDim &ddim, int idx, int value) { ddim[idx] = value; }
/// @cond HIDDEN
struct VectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
std::vector<int64_t> &vector;
explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {}
explicit VectorizeVisitor(std::vector<int64_t> &v) : vector(v) {}
template <typename T> void operator()(const T &t) {
vector.push_back(t.head);
this->operator()(t.tail);
}
template <typename T> void operator()(const T &t) {
vector.push_back(t.head);
this->operator()(t.tail);
}
void operator()(const Dim<0> &t) {}
void operator()(const Dim<0> &t) {}
};
/// @endcond
std::vector<int64_t> vectorize(const DDim &ddim) {
std::vector<int64_t> result;
VectorizeVisitor visitor(result);
DDim::ApplyVistor(visitor, ddim);
return result;
std::vector<int64_t> result;
VectorizeVisitor visitor(result);
DDim::ApplyVistor(visitor, ddim);
return result;
}
// NOTE: framework::vectorize converts to type int64_t
// which does not fit cudnn inputs.
std::vector<int> vectorize2int(const DDim &ddim) {
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
return result;
std::vector<int64_t> temp = vectorize(ddim);
std::vector<int> result(temp.begin(), temp.end());
return result;
}
struct ProductVisitor : Vistor<int64_t> {
template <int D> int64_t operator()(const Dim<D> &dim) {
return product(dim);
}
template <int D> int64_t operator()(const Dim<D> &dim) {
return product(dim);
}
};
int64_t product(const DDim &ddim) {
ProductVisitor visitor;
return DDim::ApplyVistor(visitor, ddim);
ProductVisitor visitor;
return DDim::ApplyVistor(visitor, ddim);
}
struct SliceVectorizeVisitor : Vistor<void> {
std::vector<int64_t> &vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int64_t> &v, int b, int e)
: vector(v), begin(b), end(e) {
// PADDLE_ENFORCE(begin < end,
// "Begin index must be less than end index in
// ddim
// slice.");
// PADDLE_ENFORCE(begin >= 0,
// "Begin index can't be less than zero in
// ddim slice.");
std::vector<int64_t> &vector;
int begin;
int end;
SliceVectorizeVisitor(std::vector<int64_t> &v, int b, int e)
: vector(v), begin(b), end(e) {
// PADDLE_ENFORCE(begin < end,
// "Begin index must be less than end index in
// ddim
// slice.");
// PADDLE_ENFORCE(begin >= 0,
// "Begin index can't be less than zero in
// ddim slice.");
}
template <int S> void operator()(const Dim<S> &dim) {
if (begin == 0) {
vector.push_back(dim.head);
} else {
--begin;
}
template <int S> void operator()(const Dim<S> &dim) {
if (begin == 0) {
vector.push_back(dim.head);
} else {
--begin;
}
--end;
if (end > 0) {
this->operator()(dim.tail);
}
--end;
if (end > 0) {
this->operator()(dim.tail);
}
}
void operator()(const Dim<0> &dim) {
// PADDLE_ENFORCE(end == 0, "End index in ddim slice is out
// of bound.");
}
void operator()(const Dim<0> &dim) {
// PADDLE_ENFORCE(end == 0, "End index in ddim slice is out
// of bound.");
}
};
DDim slice_ddim(const DDim &ddim, int begin, int end) {
std::vector<int64_t> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
// boost::apply_visitor(visitor, dim);
DDim::ApplyVistor(visitor, ddim);
// visitor(ddim.var.Get<Dim<4>>());
return make_ddim(vec);
std::vector<int64_t> vec;
vec.reserve(end - begin);
SliceVectorizeVisitor visitor(vec, begin, end);
// boost::apply_visitor(visitor, dim);
DDim::ApplyVistor(visitor, ddim);
// visitor(ddim.var.Get<Dim<4>>());
return make_ddim(vec);
}
/// \cond HIDDEN
struct ArityVisitor : Vistor<int> {
template <int D> int operator()(Dim<D>) const { return D; }
template <int D> int operator()(Dim<D>) const { return D; }
};
/// \endcond
int arity(const DDim &d) {
ArityVisitor arityVisitor = ArityVisitor();
return DDim::ApplyVistor(arityVisitor, d);
// return arityVisitor(d.var.Get<Dim<4>>());
// return boost::apply_visitor(ArityVisitor(), d); }
ArityVisitor arityVisitor = ArityVisitor();
return DDim::ApplyVistor(arityVisitor, d);
// return arityVisitor(d.var.Get<Dim<4>>());
// return boost::apply_visitor(ArityVisitor(), d); }
}
/// \cond HIDDEN
/// \endcond
struct OSVistor : Vistor<std::ostream &> {
OSVistor(std::ostream &os) : os_(os) {}
OSVistor(std::ostream &os) : os_(os) {}
template <int D> std::ostream &operator()(Dim<D> dim) const {
return os_ << dim;
}
template <int D> std::ostream &operator()(Dim<D> dim) const {
return os_ << dim;
}
private:
std::ostream &os_;
private:
std::ostream &os_;
};
std::ostream &operator<<(std::ostream &os, const DDim &ddim) {
auto vistor = OSVistor(os);
DDim::ApplyVistor(vistor, ddim);
return os;
auto vistor = OSVistor(os);
DDim::ApplyVistor(vistor, ddim);
return os;
}
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
*this = make_ddim(init_list);
}
DDim flatten_to_2d(const DDim &src, int num_col_dims) {
int rank = src.size();
return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, rank))});
int rank = src.size();
return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, rank))});
}
DDim flatten_to_1d(const DDim &src) { return make_ddim({product(src)}); }
DDim stride(const DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = 1;
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i + 1];
}
return framework::make_ddim(strides);
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = 1;
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i + 1];
}
return framework::make_ddim(strides);
}
DDim stride_numel(const framework::DDim &ddim) {
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = ddim[ddim.size() - 1];
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i];
}
return framework::make_ddim(strides);
std::vector<int64_t> strides(ddim.size());
strides[ddim.size() - 1] = ddim[ddim.size() - 1];
for (int i = ddim.size() - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * ddim[i];
}
return framework::make_ddim(strides);
}
} // namespace framework
......
src/framework/ddim.h
浏览文件 @ ddf6b722
......@@ -30,77 +30,77 @@ namespace framework {
* The number of dimensions must be between [1, 9].
*/
struct DDim {
typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>,
Dim<7>, Dim<8>, Dim<9>>
DDimVar;
DDimVar var;
template <typename Vistor>
static typename Vistor::type_t ApplyVistor(Vistor vistor, const DDim &d) {
if (d.var.TypeId() == typeid(Dim<0>).hash_code()) {
return vistor(d.var.Get<Dim<0>>());
} else if (d.var.TypeId() == typeid(Dim<1>).hash_code()) {
return vistor(d.var.Get<Dim<1>>());
} else if (d.var.TypeId() == typeid(Dim<2>).hash_code()) {
return vistor(d.var.Get<Dim<2>>());
} else if (d.var.TypeId() == typeid(Dim<3>).hash_code()) {
return vistor(d.var.Get<Dim<3>>());
} else if (d.var.TypeId() == typeid(Dim<4>).hash_code()) {
return vistor(d.var.Get<Dim<4>>());
} else if (d.var.TypeId() == typeid(Dim<5>).hash_code()) {
return vistor(d.var.Get<Dim<5>>());
} else if (d.var.TypeId() == typeid(Dim<6>).hash_code()) {
return vistor(d.var.Get<Dim<6>>());
} else if (d.var.TypeId() == typeid(Dim<7>).hash_code()) {
return vistor(d.var.Get<Dim<7>>());
} else if (d.var.TypeId() == typeid(Dim<8>).hash_code()) {
return vistor(d.var.Get<Dim<8>>());
} else if (d.var.TypeId() == typeid(Dim<9>).hash_code()) {
return vistor(d.var.Get<Dim<9>>());
} else {
printf(" dim not support \n");
throw std::bad_exception();
// return typename Vistor::type_t();
}
typedef Variant<Dim<0>, Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>,
Dim<7>, Dim<8>, Dim<9>>
DDimVar;
DDimVar var;
template <typename Vistor>
static typename Vistor::type_t ApplyVistor(Vistor vistor, const DDim &d) {
if (d.var.TypeId() == typeid(Dim<0>).hash_code()) {
return vistor(d.var.Get<Dim<0>>());
} else if (d.var.TypeId() == typeid(Dim<1>).hash_code()) {
return vistor(d.var.Get<Dim<1>>());
} else if (d.var.TypeId() == typeid(Dim<2>).hash_code()) {
return vistor(d.var.Get<Dim<2>>());
} else if (d.var.TypeId() == typeid(Dim<3>).hash_code()) {
return vistor(d.var.Get<Dim<3>>());
} else if (d.var.TypeId() == typeid(Dim<4>).hash_code()) {
return vistor(d.var.Get<Dim<4>>());
} else if (d.var.TypeId() == typeid(Dim<5>).hash_code()) {
return vistor(d.var.Get<Dim<5>>());
} else if (d.var.TypeId() == typeid(Dim<6>).hash_code()) {
return vistor(d.var.Get<Dim<6>>());
} else if (d.var.TypeId() == typeid(Dim<7>).hash_code()) {
return vistor(d.var.Get<Dim<7>>());
} else if (d.var.TypeId() == typeid(Dim<8>).hash_code()) {
return vistor(d.var.Get<Dim<8>>());
} else if (d.var.TypeId() == typeid(Dim<9>).hash_code()) {
return vistor(d.var.Get<Dim<9>>());
} else {
printf(" dim not support \n");
throw std::bad_exception();
// return typename Vistor::type_t();
}
}
DDim() { var.Set<Dim<1>>(Dim<1>()); }
DDim() { var.Set<Dim<1>>(Dim<1>()); }
template <int D> explicit DDim(const Dim<D> &in) { var.Set<Dim<D>>(in); }
template <int D> explicit DDim(const Dim<D> &in) { var.Set<Dim<D>>(in); }
/*implicit*/ DDim(std::initializer_list<int64_t> init_list);
/*implicit*/ DDim(std::initializer_list<int64_t> init_list);
template <int D> DDim &operator=(const Dim<D> &in) {
var.Set<Dim<D>>(in);
return *this;
}
template <int D> DDim &operator=(const Dim<D> &in) {
var.Set<Dim<D>>(in);
return *this;
}
int64_t &operator[](int idx);
int64_t &operator[](int idx);
int64_t operator[](int idx) const;
int64_t operator[](int idx) const;
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor) {
// return var.apply_visitor(visitor);
// }
//
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor)
// const {
// return var.apply_visitor(visitor);
// }
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor) {
// return var.apply_visitor(visitor);
// }
//
// template <typename Visitor>
// typename Visitor::result_type apply_visitor(Visitor& visitor)
// const {
// return var.apply_visitor(visitor);
// }
DDimVar getVar() { return var; }
DDimVar getVar() { return var; }
bool operator==(DDim d) const;
bool operator==(DDim d) const;
bool operator!=(DDim d) const;
bool operator!=(DDim d) const;
DDim operator+(DDim d) const;
DDim operator+(DDim d) const;
DDim operator*(DDim d) const;
DDim operator*(DDim d) const;
int size() const;
int size() const;
};
/**
......
src/framework/dim.h
浏览文件 @ ddf6b722
......@@ -25,199 +25,197 @@ namespace framework {
// Statically sized, statically indexed dimension
template <int i> struct Dim {
static constexpr int dimensions = i;
static constexpr int dimensions = i;
template <typename... Args>
HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
static_assert(sizeof...(_tail) == i - 1,
"Dim initialized with the wrong number of parameters");
}
template <typename... Args>
HOSTDEVICE Dim(int64_t _head, Args... _tail) : head(_head), tail(_tail...) {
static_assert(sizeof...(_tail) == i - 1,
"Dim initialized with the wrong number of parameters");
}
HOSTDEVICE
Dim(int64_t _head, const Dim<i - 1> &_tail) : head(_head), tail(_tail) {}
HOSTDEVICE
Dim(int64_t _head, const Dim<i - 1> &_tail) : head(_head), tail(_tail) {}
HOSTDEVICE
Dim() : head(0), tail() {}
HOSTDEVICE
Dim() : head(0), tail() {}
/** Construct a Dim from a linear index and size. Uses Fortran
* order
* indexing. */
HOSTDEVICE
Dim(int64_t idx, const Dim<i> &size)
: head(idx % size.head), tail(idx / size.head, size.tail) {}
/** Construct a Dim from a linear index and size. Uses Fortran
* order
* indexing. */
HOSTDEVICE
Dim(int64_t idx, const Dim<i> &size)
: head(idx % size.head), tail(idx / size.head, size.tail) {}
/** Construct a Dim with each dimension set to the given index */
HOSTDEVICE
Dim(int64_t idx) : head(idx), tail(idx) {}
/** Construct a Dim with each dimension set to the given index */
HOSTDEVICE
Dim(int64_t idx) : head(idx), tail(idx) {}
HOSTDEVICE
bool operator==(const Dim<i> &o) const {
return (head == o.head) && (tail == o.tail);
}
HOSTDEVICE
bool operator==(const Dim<i> &o) const {
return (head == o.head) && (tail == o.tail);
}
HOSTDEVICE
bool operator!=(const Dim<i> &o) const { return !(*this == o); }
HOSTDEVICE
bool operator!=(const Dim<i> &o) const { return !(*this == o); }
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
HOST std::string to_string() const;
HOST std::string to_string() const;
int64_t head;
Dim<i - 1> tail;
int64_t head;
Dim<i - 1> tail;
};
// Base case specialization
template <> struct Dim<0> {
static constexpr int dimensions = 0;
static constexpr int dimensions = 0;
HOSTDEVICE
Dim(int64_t _head) {}
HOSTDEVICE
Dim(int64_t _head) {}
HOSTDEVICE
Dim() {}
HOSTDEVICE
Dim() {}
HOSTDEVICE
Dim(int idx, const Dim<0> &size) {
HOSTDEVICE
Dim(int idx, const Dim<0> &size) {
#ifndef __CUDA_ARCH__
if (idx > 0) {
throw std::invalid_argument("Index out of range.");
}
if (idx > 0) {
throw std::invalid_argument("Index out of range.");
}
#else
PADDLE_ASSERT(idx == 0);
PADDLE_ASSERT(idx == 0);
#endif
}
}
HOSTDEVICE
bool operator==(const Dim<0> &o) const { return true; }
HOSTDEVICE
bool operator==(const Dim<0> &o) const { return true; }
HOSTDEVICE
bool operator!=(const Dim<0> &o) const { return false; }
HOSTDEVICE
bool operator!=(const Dim<0> &o) const { return false; }
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
HOSTDEVICE
int64_t &operator[](int idx);
HOSTDEVICE
int64_t operator[](int idx) const;
};
namespace {
// Helper for accessing Dim classes
template <int i> struct DimGetter {
// Return a copy if Dim is const
template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a copy if Dim is const
template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
return DimGetter<i - 1>::impl(d.tail);
}
};
// Eureka! We found the element!
template <> struct DimGetter<0> {
// Return a copy if Dim is const
template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
return d.head;
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) {
return d.head;
}
// Return a copy if Dim is const
template <typename D> HOSTDEVICE static int64_t impl(const D &d) {
return d.head;
}
// Return a reference if Dim is mutable
template <typename D> HOSTDEVICE static int64_t &impl(D &d) { return d.head; }
};
template <int D> HOSTDEVICE int64_t &indexer(Dim<D> &dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
#else
PADDLE_ASSERT(idx >= 0);
PADDLE_ASSERT(idx >= 0);
#endif
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
template <> HOSTDEVICE int64_t &indexer<0>(Dim<0> &dim, int idx) {
#ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index");
throw std::invalid_argument("Invalid index");
#else
PADDLE_ASSERT(false);
PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
#else
static int64_t head = 0;
static int64_t head = 0;
#endif
return head;
return head;
#endif
}
template <int D> HOSTDEVICE int64_t indexer(const Dim<D> &dim, int idx) {
#ifndef __CUDA_ARCH__
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
if (idx < 0) {
throw std::invalid_argument("Tried to access a negative dimension");
}
#else
PADDLE_ASSERT(idx >= 0);
PADDLE_ASSERT(idx >= 0);
#endif
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
if (idx == 0) {
return dim.head;
}
return indexer(dim.tail, idx - 1);
}
template <> HOSTDEVICE int64_t indexer<0>(const Dim<0> &dim, int idx) {
#ifndef __CUDA_ARCH__
throw std::invalid_argument("Invalid index");
throw std::invalid_argument("Invalid index");
#else
PADDLE_ASSERT(false);
PADDLE_ASSERT(false);
#if CUDA_VERSION < 8000
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
// On CUDA versions previous to 8.0, only __shared__ variables
// could be declared as static in the device code.
int64_t head = 0;
#else
static int64_t head = 0;
static int64_t head = 0;
#endif
return head;
return head;
#endif
}
} // namespace
// Static access to constant Dim
template <int i, int l> HOSTDEVICE int64_t get(const Dim<l> &d) {
return DimGetter<i>::impl(d);
return DimGetter<i>::impl(d);
}
// Static access to mutable Dim
template <int i, int l> HOSTDEVICE int64_t &get(Dim<l> &d) {
return DimGetter<i>::impl(d);
return DimGetter<i>::impl(d);
}
// Dynamic access to constant Dim
template <int l> HOSTDEVICE int64_t Dim<l>::operator[](int i) const {
// std::cout << "l: " << l << std::endl;
return indexer(*this, i);
// std::cout << "l: " << l << std::endl;
return indexer(*this, i);
}
// Dynamic access to mutable Dim
template <int l> HOSTDEVICE int64_t &Dim<l>::operator[](int i) {
return indexer(*this, i);
return indexer(*this, i);
}
// Dynamic access to constant Dim
inline HOSTDEVICE int64_t Dim<0>::operator[](int i) const {
return indexer(*this, i);
return indexer(*this, i);
}
// Dynamic access to mutable Dim
inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
return indexer(*this, i);
return indexer(*this, i);
}
// Dynamic access to constant Dim
......@@ -225,52 +223,52 @@ inline HOSTDEVICE int64_t &Dim<0>::operator[](int i) {
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t>::type get(const Dim<l> &d,
int i) {
return d[i];
return d[i];
}
// Dynamic access to mutable Dim
template <int l>
HOSTDEVICE typename std::enable_if<(l > 0), int64_t &>::type get(Dim<l> &d,
int i) {
return d[i];
return d[i];
}
// Dot product of two dims
template <int i>
HOSTDEVICE int64_t linearize(const Dim<i> &a, const Dim<i> &b) {
return a.head * b.head + linearize(a.tail, b.tail);
return a.head * b.head + linearize(a.tail, b.tail);
}
// Base case dot product of two Dims
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline int64_t linearize(const Dim<0> &a, const Dim<0> &b) {
return 0;
return 0;
}
// Product of a Dim
template <int i> HOSTDEVICE int64_t product(const Dim<i> &a, int prod = 1) {
return prod * a.head * product(a.tail);
return prod * a.head * product(a.tail);
}
// Base case product of a Dim
// Notice it is inline because it is no longer a template
template <> HOSTDEVICE inline int64_t product(const Dim<0> &a, int prod) {
return prod;
return prod;
}
// Is 0 <= idx_i < size_i for all i?
template <int i>
HOSTDEVICE bool contained(const Dim<i> &idx, const Dim<i> &size) {
return ((0 <= idx.head) && (idx.head < size.head) &&
contained(idx.tail, size.tail));
return ((0 <= idx.head) && (idx.head < size.head) &&
contained(idx.tail, size.tail));
}
// Base case of is 0 <= idx_i < size_i ?
// Notice it is inline because it is no longer a template
template <>
HOSTDEVICE inline bool contained(const Dim<0> &idx, const Dim<0> &size) {
return true;
return true;
}
/**
......@@ -278,14 +276,14 @@ HOSTDEVICE inline bool contained(const Dim<0> &idx, const Dim<0> &size) {
*/
template <int i>
HOSTDEVICE Dim<i> ex_prefix_mul(const Dim<i> &src, int mul = 1) {
return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head));
return Dim<i>(mul, ex_prefix_mul(src.tail, mul * src.head));
}
///\cond HIDDEN
// Base case of ex_prefix_mul
// Notice it is inline because it is no longer a template
template <> HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
return Dim<0>();
return Dim<0>();
}
///\endcond
......@@ -293,36 +291,36 @@ template <> HOSTDEVICE inline Dim<0> ex_prefix_mul(const Dim<0> &src, int mul) {
* Add two dimensions together
*/
template <int i> HOSTDEVICE Dim<i> dim_plus(const Dim<i> &a, const Dim<i> &b) {
return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail));
return Dim<i>(a.head + b.head, dim_plus(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_plus(const Dim<0> &a, const Dim<0> &b) {
return Dim<0>();
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator+(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_plus(lhs, rhs);
return dim_plus(lhs, rhs);
}
/**
* Multiply two dimensions together
*/
template <int i> HOSTDEVICE Dim<i> dim_mult(const Dim<i> &a, const Dim<i> &b) {
return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail));
return Dim<i>(a.head * b.head, dim_mult(a.tail, b.tail));
}
// Base case
template <>
HOSTDEVICE inline Dim<0> dim_mult(const Dim<0> &a, const Dim<0> &b) {
return Dim<0>();
return Dim<0>();
}
template <int i>
HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
return dim_mult(lhs, rhs);
return dim_mult(lhs, rhs);
}
/**
......@@ -337,8 +335,8 @@ HOSTDEVICE Dim<i> operator*(const Dim<i> &lhs, const Dim<i> &rhs) {
template <int i>
HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size, const Dim<i> &stride) {
int norm_stride = size.head == 1 ? 0 : stride.head;
return Dim<i>(norm_stride, normalize_strides(size.tail, stride.tail));
int norm_stride = size.head == 1 ? 0 : stride.head;
return Dim<i>(norm_stride, normalize_strides(size.tail, stride.tail));
}
///\cond HIDDEN
......@@ -346,7 +344,7 @@ HOSTDEVICE Dim<i> normalize_strides(const Dim<i> &size, const Dim<i> &stride) {
template <>
HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
const Dim<0> &stride) {
return Dim<0>();
return Dim<0>();
}
///\endcond
......@@ -361,7 +359,7 @@ HOSTDEVICE inline Dim<0> normalize_strides(const Dim<0> &size,
template <typename... Args>
HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
return Dim<sizeof...(Args)>(idxes...);
return Dim<sizeof...(Args)>(idxes...);
}
// Allows us to output a Dim
......@@ -369,8 +367,8 @@ HOSTDEVICE Dim<sizeof...(Args)> make_dim(Args... idxes) {
template <int i>
typename std::enable_if<(i > 1), std::ostream &>::type
operator<<(std::ostream &os, const Dim<i> &d) {
os << d.head << ", " << d.tail;
return os;
os << d.head << ", " << d.tail;
return os;
}
// Base case that allows us to output a Dim
......@@ -378,34 +376,34 @@ operator<<(std::ostream &os, const Dim<i> &d) {
template <int i>
typename std::enable_if<(i == 1), std::ostream &>::type
operator<<(std::ostream &os, const Dim<i> &d) {
os << d.head;
return os;
os << d.head;
return os;
}
inline std::ostream &operator<<(std::ostream &os, const Dim<0> &d) {
return os;
return os;
}
template <int i> HOST std::string Dim<i>::to_string() const {
std::stringstream stream;
std::stringstream stream;
stream << *this;
stream << *this;
return stream.str();
return stream.str();
}
template <int D>
HOSTDEVICE Dim<D> linear_to_dimension(int linear_index, Dim<D> extents) {
Dim<D> result;
Dim<D> result;
for (int i = 0; i < D - 1; ++i) {
result[i] = linear_index % extents[i];
linear_index /= extents[i];
}
for (int i = 0; i < D - 1; ++i) {
result[i] = linear_index % extents[i];
linear_index /= extents[i];
}
result[D - 1] = linear_index;
result[D - 1] = linear_index;
return result;
return result;
}
} // namespace framework
......
src/framework/executor.cpp
浏览文件 @ ddf6b722
......@@ -26,66 +26,66 @@ namespace framework {
template <typename Dtype>
Executor<Dtype>::Executor(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
// const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// for (int i = 0; i < blocks.size(); ++i) {
// std::shared_ptr<BlockDesc> block_desc = blocks[i];
// std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// for (int j = 0; j < ops.size(); ++j) {
// std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() == "conv2d" && op->Input("Input")[0] ==
// "pixel") {
// Attribute strides_attr = op->GetAttrMap().at("strides");
// std::vector<int> stride =
// strides_attr.Get<std::vector<int>>(); for (int k = 0; k <
// stride.size(); ++k) {
// }
// std::shared_ptr<operators::ConvOp<Dtype, float>> conv =
// std::make_shared<operators::ConvOp<Dtype, float>>(
// op->Type(), op->GetInputs(), op->GetOutputs(),
// op->GetAttrMap(), program_.scope);
// ops_of_block_[*block_desc.get()].push_back(conv);
// }
// }
// }
// const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// for (int i = 0; i < blocks.size(); ++i) {
// std::shared_ptr<BlockDesc> block_desc = blocks[i];
// std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// for (int j = 0; j < ops.size(); ++j) {
// std::shared_ptr<OpDesc> op = ops[j];
// if (op->Type() == "conv2d" && op->Input("Input")[0] ==
// "pixel") {
// Attribute strides_attr = op->GetAttrMap().at("strides");
// std::vector<int> stride =
// strides_attr.Get<std::vector<int>>(); for (int k = 0; k <
// stride.size(); ++k) {
// }
// std::shared_ptr<operators::ConvOp<Dtype, float>> conv =
// std::make_shared<operators::ConvOp<Dtype, float>>(
// op->Type(), op->GetInputs(), op->GetOutputs(),
// op->GetAttrMap(), program_.scope);
// ops_of_block_[*block_desc.get()].push_back(conv);
// }
// }
// }
}
template <typename Dtype>
std::shared_ptr<Tensor> Executor<Dtype>::predict(Tensor &t) {
// feed
auto scope = program_.scope;
Variable *g_feed_value = scope->Var("pixel");
auto tensor = g_feed_value->GetMutable<Tensor>();
tensor->ShareDataWith(t);
// feed
auto scope = program_.scope;
Variable *g_feed_value = scope->Var("pixel");
auto tensor = g_feed_value->GetMutable<Tensor>();
tensor->ShareDataWith(t);
Variable *con_output = scope->Var("conv2d_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 16, 32, 32});
// std::cout << typeid(output_tensor).name() << std::endl;
// std::cout << "output_tensor dims: " << output_tensor->dims() <<
// std::endl;
Variable *con_output = scope->Var("conv2d_0.tmp_0");
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 16, 32, 32});
// std::cout << typeid(output_tensor).name() << std::endl;
// std::cout << "output_tensor dims: " << output_tensor->dims() <<
// std::endl;
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict(t, 0);
return out_tensor;
predict(t, 0);
return out_tensor;
}
template <typename Dtype>
void Executor<Dtype>::predict(const Tensor &t, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
op->Run();
}
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
op->Run();
}
}
template class Executor<CPU>;
......
src/framework/executor.h
浏览文件 @ ddf6b722
......@@ -35,23 +35,23 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> class Executor {
public:
Executor();
public:
Executor();
Executor(const Program<Dtype> p);
Executor(const Program<Dtype> p);
std::shared_ptr<Tensor> predict(Tensor &t);
std::shared_ptr<Tensor> predict(Tensor &t);
public:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
public:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
void predict(const Tensor &t, int block_id);
void predict(const Tensor &t, int block_id);
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
};
} // namespace framework
......
src/framework/framework.pb.cpp
浏览文件 @ ddf6b722
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
src/framework/framework.pb.h
浏览文件 @ ddf6b722
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
src/framework/lod_tensor.cc
浏览文件 @ ddf6b722
......@@ -22,291 +22,289 @@ namespace paddle_mobile {
namespace framework {
std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os << "{";
for (auto &v : lod) {
os << "{";
for (auto &v : lod) {
os << "{";
bool is_first = true;
for (auto &i : v) {
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
bool is_first = true;
for (auto &i : v) {
if (is_first) {
os << i;
is_first = false;
} else {
os << ", " << i;
}
}
os << "}";
}
os << "}";
return os;
return os;
}
std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
// PADDLE_ENFORCE(t.type().hash_code() ==
// typeid(float).hash_code());
// if (!platform::is_cpu_place(t.place())) {
// LoDTensor tt;
// framework::TensorCopy(t, platform::CPUPlace(), &tt);
// platform::DeviceContextPool &pool =
// platform::DeviceContextPool::Instance(); auto &dev_ctx =
// *pool.Get(t.place()); dev_ctx.Wait();
//
// os << tt;
// return os;
// }
os << "dim: " << t.dims() << "\n";
os << "lod: " << t.lod() << "\n";
// only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " ";
}
return os;
// PADDLE_ENFORCE(t.type().hash_code() ==
// typeid(float).hash_code());
// if (!platform::is_cpu_place(t.place())) {
// LoDTensor tt;
// framework::TensorCopy(t, platform::CPUPlace(), &tt);
// platform::DeviceContextPool &pool =
// platform::DeviceContextPool::Instance(); auto &dev_ctx =
// *pool.Get(t.place()); dev_ctx.Wait();
//
// os << tt;
// return os;
// }
os << "dim: " << t.dims() << "\n";
os << "lod: " << t.lod() << "\n";
// only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " ";
}
return os;
}
std::string LoDToString(const LoD &lod) {
std::ostringstream stream;
stream << lod;
return stream.str();
std::ostringstream stream;
stream << lod;
return stream.str();
}
LoD SliceInLevel(const LoD &in, size_t level, size_t elem_begin,
size_t elem_end) {
// PADDLE_ENFORCE_LT(level, in.size());
// PADDLE_ENFORCE_LT(elem_end, in[level].size());
LoD res;
res.resize(in.size() - level);
// copy the first level
res[0].assign(in[level].begin() + elem_begin,
in[level].begin() + elem_end + 1);
for (size_t lvl = 1; lvl < res.size(); lvl++) {
const auto &in_level = in[level + lvl];
const auto &above_level = res[lvl - 1];
auto &out_level = res[lvl];
out_level.assign(in_level.begin() + above_level.front(),
in_level.begin() + above_level.back() + 1);
}
for (size_t lvl = 0; lvl < res.size(); lvl++) {
// to make the first offset equals 0, all the elements minus the
// first
// element
size_t front = res[lvl].front();
for (auto &ele : res[lvl]) {
ele -= front;
}
// PADDLE_ENFORCE_LT(level, in.size());
// PADDLE_ENFORCE_LT(elem_end, in[level].size());
LoD res;
res.resize(in.size() - level);
// copy the first level
res[0].assign(in[level].begin() + elem_begin,
in[level].begin() + elem_end + 1);
for (size_t lvl = 1; lvl < res.size(); lvl++) {
const auto &in_level = in[level + lvl];
const auto &above_level = res[lvl - 1];
auto &out_level = res[lvl];
out_level.assign(in_level.begin() + above_level.front(),
in_level.begin() + above_level.back() + 1);
}
for (size_t lvl = 0; lvl < res.size(); lvl++) {
// to make the first offset equals 0, all the elements minus the
// first
// element
size_t front = res[lvl].front();
for (auto &ele : res[lvl]) {
ele -= front;
}
return res;
}
return res;
}
LoD ToAbsOffset(const LoD &in) {
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1)
return in;
LoD result = in;
for (auto level = static_cast<int>(in.size() - 2); level >= 0; level--) {
for (size_t i = 0; i < in[level].size(); ++i) {
size_t index = in[level][i];
result[level][i] = result[level + 1][index];
}
// the lowest level stores relative offsets
if (in.empty() || in.size() == 1)
return in;
LoD result = in;
for (auto level = static_cast<int>(in.size() - 2); level >= 0; level--) {
for (size_t i = 0; i < in[level].size(); ++i) {
size_t index = in[level][i];
result[level][i] = result[level + 1][index];
}
return result;
}
return result;
}
bool operator==(const LoD &a, const LoD &b) {
if (a.size() != b.size()) {
return false;
if (a.size() != b.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
const auto &a_level = a[i];
const auto &b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
const auto &a_level = a[i];
const auto &b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
return false;
}
}
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
return false;
}
}
return true;
}
return true;
}
bool CheckLoD(const LoD &in, int tensor_height) {
if (in.empty())
return true;
for (const auto &level : in) {
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset(the begin offset) of each level
// should be 0.
if (level.front() != 0)
return false;
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(),
[](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
std::cout << "ascending error";
return false;
}
}
// check: the lowest level's last offset should equals
// `tensor_height` if
// tensor_height>0.
if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
return false;
// check: the higher level's last offset should equals the lower
// level's
// size-1.
// NOTE LoD store the levels from top to bottom, so the higher level
// goes
// first.
for (size_t level = 0; level < in.size() - 1; level++) {
if (in[level].back() != in[level + 1].size() - 1)
return false;
}
if (in.empty())
return true;
for (const auto &level : in) {
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset(the begin offset) of each level
// should be 0.
if (level.front() != 0)
return false;
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
std::cout << "ascending error";
return false;
}
}
// check: the lowest level's last offset should equals
// `tensor_height` if
// tensor_height>0.
if (tensor_height > 0 && (size_t)tensor_height != in.back().back())
return false;
// check: the higher level's last offset should equals the lower
// level's
// size-1.
// NOTE LoD store the levels from top to bottom, so the higher level
// goes
// first.
for (size_t level = 0; level < in.size() - 1; level++) {
if (in[level].back() != in[level + 1].size() - 1)
return false;
}
return true;
}
bool CheckAbsLoD(const LoD &in, int tensor_height) {
if (in.empty())
return true;
for (const auto &level : in) {
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(),
[](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
return false;
}
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset of each level should be 0, and the
// last should be
// the same(the height of underlying tensor).
if (level.front() != 0)
return false;
if (tensor_height < 0) {
tensor_height = level.back();
} else if ((size_t)tensor_height != level.back()) {
return false;
}
}
if (in.empty())
return true;
for (const auto &level : in) {
// check: all the offsets in a level should be ascending(no same
// items
// allows).
if (!std::is_sorted(level.begin(), level.begin(), [](size_t a, size_t b) {
if (a < b)
return true;
return false;
})) {
return false;
}
// check: there should be more than 2 offsets existing in each
// level.
if (level.size() < 2)
return false;
// check: the first offset of each level should be 0, and the
// last should be
// the same(the height of underlying tensor).
if (level.front() != 0)
return false;
if (tensor_height < 0) {
tensor_height = level.back();
} else if ((size_t)tensor_height != level.back()) {
return false;
}
}
return true;
}
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD &lod, size_t start_idx,
size_t end_idx, size_t start_level) {
LoD sub_lod;
for (size_t level_idx = start_level; level_idx < lod.size(); ++level_idx) {
// PADDLE_ENFORCE_LE(start_idx, end_idx);
// PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
std::vector<size_t> level_lens;
for (size_t i = start_idx; i < end_idx; ++i) {
level_lens.push_back(lod[level_idx][i + 1] - lod[level_idx][i]);
}
sub_lod.emplace_back(level_lens);
start_idx = lod[level_idx][start_idx];
end_idx = lod[level_idx][end_idx];
LoD sub_lod;
for (size_t level_idx = start_level; level_idx < lod.size(); ++level_idx) {
// PADDLE_ENFORCE_LE(start_idx, end_idx);
// PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
std::vector<size_t> level_lens;
for (size_t i = start_idx; i < end_idx; ++i) {
level_lens.push_back(lod[level_idx][i + 1] - lod[level_idx][i]);
}
sub_lod.emplace_back(level_lens);
start_idx = lod[level_idx][start_idx];
end_idx = lod[level_idx][end_idx];
}
return LoDAndOffset{sub_lod, {start_idx, end_idx}};
return LoDAndOffset{sub_lod, {start_idx, end_idx}};
}
void AppendLoD(LoD *lod, const LoD &lod_length) {
// PADDLE_ENFORCE(
// lod->empty() || lod->size() == lod_length.size(),
// "The lod_length should has the same size with the appended
// lod.");
if (lod->empty()) {
for (size_t i = 0; i < lod_length.size(); ++i) {
lod->emplace_back(1, 0); // size = 1, value = 0;
}
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
// PADDLE_ENFORCE(
// lod->empty() || lod->size() == lod_length.size(),
// "The lod_length should has the same size with the appended
// lod.");
if (lod->empty()) {
for (size_t i = 0; i < lod_length.size(); ++i) {
lod->emplace_back(1, 0); // size = 1, value = 0;
}
for (size_t i = 0; i < lod->size(); ++i) {
auto &level = (*lod)[i];
for (size_t len : lod_length[i]) {
level.push_back(level.back() + len);
}
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
}
for (size_t i = 0; i < lod->size(); ++i) {
auto &level = (*lod)[i];
for (size_t len : lod_length[i]) {
level.push_back(level.back() + len);
}
}
}
void SerializeToStream(std::ostream &os, const LoDTensor &tensor) {
{ // the 1st field, uint32_t version for LoDTensor
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
{ // the 1st field, uint32_t version for LoDTensor
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
}
{
// the 2st field, LoD information
// uint64_t lod_level
// uint64_t lod_level_1 size in byte.
// int* lod_level_1 data
// ...
auto lod = tensor.lod();
uint64_t size = lod.size();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
for (auto &each : lod) {
size = each.size() * sizeof(framework::LoD::value_type::value_type);
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
os.write(reinterpret_cast<const char *>(each.data()),
static_cast<std::streamsize>(size));
}
{
// the 2st field, LoD information
// uint64_t lod_level
// uint64_t lod_level_1 size in byte.
// int* lod_level_1 data
// ...
auto lod = tensor.lod();
uint64_t size = lod.size();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
for (auto &each : lod) {
size = each.size() * sizeof(framework::LoD::value_type::value_type);
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
os.write(reinterpret_cast<const char *>(each.data()),
static_cast<std::streamsize>(size));
}
}
// the 3st field, Tensor
TensorToStream(os, static_cast<Tensor>(tensor));
}
// the 3st field, Tensor
TensorToStream(os, static_cast<Tensor>(tensor));
}
void DeserializeFromStream(std::istream &is, LoDTensor *tensor) {
{
// the 1st field, unit32_t version for LoDTensor
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is
// supported");
}
{
// the 2st field, LoD information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
lod[i] = tmp;
}
{
// the 1st field, unit32_t version for LoDTensor
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is
// supported");
}
{
// the 2st field, LoD information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
lod[i] = tmp;
}
// the 3st filed, Tensor
TensorFromStream(is, static_cast<Tensor *>(tensor));
}
// the 3st filed, Tensor
TensorFromStream(is, static_cast<Tensor *>(tensor));
}
} // namespace framework
......
src/framework/lod_tensor.h
浏览文件 @ ddf6b722
......@@ -102,45 +102,45 @@ bool CheckAbsLoD(const LoD &in, int tensor_height = -1);
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
class LoDTensor : public Tensor {
public:
LoDTensor() : Tensor() {}
explicit LoDTensor(const LoD &lod) : lod_(lod) {}
void set_lod(const LoD &lod) { lod_ = lod; }
const LoD &lod() const { return lod_; }
LoD *mutable_lod() { return &lod_; }
/*
* Get the start offset and end offset of an element from LoD.
*/
std::pair<size_t, size_t> lod_element(size_t level, size_t elem) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// PADDLE_ENFORCE_LT(elem, NumElements(level));
return std::make_pair((lod_)[level][elem], (lod_)[level][elem + 1]);
}
/*
* Number of LoDTensor's levels, each level has units of data, for
* example,
* in the sentence's view, article, paragraph, sentence are 3
* levels.
*/
size_t NumLevels() const { return lod_.size(); }
/*
* Number of elements in a level.
*/
size_t NumElements(size_t level = 0) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// the last offset is the end of last element
return (lod_)[level].size() - 1;
}
private:
LoD lod_;
public:
LoDTensor() : Tensor() {}
explicit LoDTensor(const LoD &lod) : lod_(lod) {}
void set_lod(const LoD &lod) { lod_ = lod; }
const LoD &lod() const { return lod_; }
LoD *mutable_lod() { return &lod_; }
/*
* Get the start offset and end offset of an element from LoD.
*/
std::pair<size_t, size_t> lod_element(size_t level, size_t elem) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// PADDLE_ENFORCE_LT(elem, NumElements(level));
return std::make_pair((lod_)[level][elem], (lod_)[level][elem + 1]);
}
/*
* Number of LoDTensor's levels, each level has units of data, for
* example,
* in the sentence's view, article, paragraph, sentence are 3
* levels.
*/
size_t NumLevels() const { return lod_.size(); }
/*
* Number of elements in a level.
*/
size_t NumElements(size_t level = 0) const {
// PADDLE_ENFORCE_LT(level, NumLevels());
// the last offset is the end of last element
return (lod_)[level].size() - 1;
}
private:
LoD lod_;
};
/*
......@@ -155,26 +155,26 @@ class LoDTensor : public Tensor {
*/
template <typename T>
LoDTensor LodExpand(const LoDTensor &source, const LoD &lod, size_t level) {
LoD abs_lod = ToAbsOffset(lod);
const auto &lod_level = lod[level];
size_t num_instances = source.dims()[0];
// new tensor
LoDTensor tensor;
tensor.set_lod(lod);
auto dims = source.dims();
dims[0] = lod_level.back();
tensor.Resize(dims);
tensor.mutable_data<T>();
// PADDLE_ENFORCE_EQ(num_instances, lod_level.size() - 1);
for (size_t ins = 0; ins < num_instances; ins++) {
for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1]; elem++) {
auto slice = tensor.Slice(elem, elem + 1);
TensorCopy(source.Slice(ins, ins + 1), &slice);
}
LoD abs_lod = ToAbsOffset(lod);
const auto &lod_level = lod[level];
size_t num_instances = source.dims()[0];
// new tensor
LoDTensor tensor;
tensor.set_lod(lod);
auto dims = source.dims();
dims[0] = lod_level.back();
tensor.Resize(dims);
tensor.mutable_data<T>();
// PADDLE_ENFORCE_EQ(num_instances, lod_level.size() - 1);
for (size_t ins = 0; ins < num_instances; ins++) {
for (size_t elem = lod_level[ins]; elem < lod_level[ins + 1]; elem++) {
auto slice = tensor.Slice(elem, elem + 1);
TensorCopy(source.Slice(ins, ins + 1), &slice);
}
return tensor;
}
return tensor;
}
// Get the absolute offset of a lod[start_level][start_idx:end_idx] and
......
src/framework/op_desc.cpp
浏览文件 @ ddf6b722
......@@ -8,51 +8,51 @@ namespace paddle_mobile {
namespace framework {
OpDesc::OpDesc(const proto::OpDesc &desc) : desc_(desc) {
for (int i = 0; i < desc_.inputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.inputs(i);
std::vector<std::string> &args = inputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
for (int i = 0; i < desc_.inputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.inputs(i);
std::vector<std::string> &args = inputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
for (int i = 0; i < desc_.outputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
}
for (int i = 0; i < desc_.outputs_size(); ++i) {
const proto::OpDesc::Var &var = desc_.outputs(i);
std::vector<std::string> &args = outputs_[var.parameter()];
int arg_size = var.arguments_size();
for (int j = 0; j < arg_size; ++j) {
args.push_back(var.arguments(j));
}
for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = Attribute::GetAttrValue(attr);
// if (attr.type() == proto::AttrType::INT){
// std::cout << " attrName " << attr_name << " " <<
// attrs_[attr_name].Get<int>() << std::endl;
// }
}
}
for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
if (attr.type() != proto::AttrType::BLOCK) {
attrs_[attr_name] = Attribute::GetAttrValue(attr);
// if (attr.type() == proto::AttrType::INT){
// std::cout << " attrName " << attr_name << " " <<
// attrs_[attr_name].Get<int>() << std::endl;
// }
}
}
}
const std::vector<std::string> &OpDesc::Input(const std::string &name) const {
return inputs_.find(name)->second;
return inputs_.find(name)->second;
}
const std::vector<std::string> &OpDesc::Output(const std::string &name) const {
return outputs_.find(name)->second;
return outputs_.find(name)->second;
}
Attribute OpDesc::GetAttr(const std::string &name) const {
auto it = attrs_.find(name);
return it->second;
auto it = attrs_.find(name);
return it->second;
}
const std::unordered_map<std::string, Attribute> &OpDesc::GetAttrMap() const {
return attrs_;
return attrs_;
}
} // namespace framework
......
src/framework/op_desc.h
浏览文件 @ ddf6b722
......@@ -26,25 +26,25 @@ namespace paddle_mobile {
namespace framework {
class OpDesc : PaddleMobileObject {
public:
OpDesc(const proto::OpDesc &desc);
const std::vector<std::string> &Input(const std::string &name) const;
const std::vector<std::string> &Output(const std::string &name) const;
Attribute GetAttr(const std::string &name) const;
public:
OpDesc(const proto::OpDesc &desc);
const std::vector<std::string> &Input(const std::string &name) const;
const std::vector<std::string> &Output(const std::string &name) const;
Attribute GetAttr(const std::string &name) const;
const VariableNameMap &GetInputs() { return inputs_; }
const VariableNameMap &GetInputs() { return inputs_; }
const VariableNameMap &GetOutputs() { return outputs_; }
const VariableNameMap &GetOutputs() { return outputs_; }
const AttributeMap &GetAttrMap() const;
const AttributeMap &GetAttrMap() const;
const std::string &Type() { return desc_.type(); };
const std::string &Type() { return desc_.type(); };
private:
proto::OpDesc desc_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
private:
proto::OpDesc desc_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
};
} // namespace framework
......
src/framework/op_info.h
浏览文件 @ ddf6b722
......@@ -25,13 +25,13 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> struct OpInfo {
OpCreator<Dtype> creator_;
const OpCreator<Dtype> &Creator() const {
// PADDLE_ENFORCE_NOT_NULL(creator_,
// "Operator Creator has not been
// registered");
return creator_;
}
OpCreator<Dtype> creator_;
const OpCreator<Dtype> &Creator() const {
// PADDLE_ENFORCE_NOT_NULL(creator_,
// "Operator Creator has not been
// registered");
return creator_;
}
};
template <typename Dtype> class OpInfoMap;
......@@ -39,55 +39,55 @@ template <typename Dtype> class OpInfoMap;
template <typename Dtype> static OpInfoMap<Dtype> *g_op_info_map = nullptr;
template <typename Dtype> class OpInfoMap {
public:
static OpInfoMap &Instance() {
if (g_op_info_map<Dtype> == nullptr) {
g_op_info_map<Dtype> = new OpInfoMap();
}
return *g_op_info_map<Dtype>;
};
bool Has(const std::string &op_type) const {
return map_.find(op_type) != map_.end();
}
void Insert(const std::string &type, const OpInfo<Dtype> &info) {
// PADDLE_ENFORCE(!Has(type), "Operator %s has been
// registered", type);
map_.insert({type, info});
}
const OpInfo<Dtype> &Get(const std::string &type) const {
auto op_info_ptr = GetNullable(type);
// PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not
// been
// registered",
// type);
return *op_info_ptr;
public:
static OpInfoMap &Instance() {
if (g_op_info_map<Dtype> == nullptr) {
g_op_info_map<Dtype> = new OpInfoMap();
}
const OpInfo<Dtype> *GetNullable(const std::string &type) const {
auto it = map_.find(type);
if (it == map_.end()) {
return nullptr;
} else {
return &it->second;
}
return *g_op_info_map<Dtype>;
};
bool Has(const std::string &op_type) const {
return map_.find(op_type) != map_.end();
}
void Insert(const std::string &type, const OpInfo<Dtype> &info) {
// PADDLE_ENFORCE(!Has(type), "Operator %s has been
// registered", type);
map_.insert({type, info});
}
const OpInfo<Dtype> &Get(const std::string &type) const {
auto op_info_ptr = GetNullable(type);
// PADDLE_ENFORCE_NOT_NULL(op_info_ptr, "Operator %s has not
// been
// registered",
// type);
return *op_info_ptr;
}
const OpInfo<Dtype> *GetNullable(const std::string &type) const {
auto it = map_.find(type);
if (it == map_.end()) {
return nullptr;
} else {
return &it->second;
}
}
const std::unordered_map<std::string, OpInfo<Dtype>> &map() const {
return map_;
}
const std::unordered_map<std::string, OpInfo<Dtype>> &map() const {
return map_;
}
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() {
return &map_;
}
std::unordered_map<std::string, OpInfo<Dtype>> *mutable_map() {
return &map_;
}
private:
OpInfoMap() = default;
std::unordered_map<std::string, OpInfo<Dtype>> map_;
private:
OpInfoMap() = default;
std::unordered_map<std::string, OpInfo<Dtype>> map_;
// DISABLE_COPY_AND_ASSIGN(OpInfoMap);
// DISABLE_COPY_AND_ASSIGN(OpInfoMap);
};
} // namespace framework
......
src/framework/op_kernel_type.h
浏览文件 @ ddf6b722
......@@ -24,41 +24,40 @@ SOFTWARE.
namespace paddle_mobile {
namespace framework {
struct OpKernelType {
struct Hash {
size_t operator()(const OpKernelType &key) const {
int data_type = static_cast<int>(key.data_type_) << LEFT_SHIFT;
int data_layout = static_cast<int>(key.data_layout_)
<< (LEFT_SHIFT * 2);
struct Hash {
size_t operator()(const OpKernelType &key) const {
int data_type = static_cast<int>(key.data_type_) << LEFT_SHIFT;
int data_layout = static_cast<int>(key.data_layout_) << (LEFT_SHIFT * 2);
std::hash<int> hasher;
return hasher(data_type + data_layout);
}
};
std::hash<int> hasher;
return hasher(data_type + data_layout);
}
};
// place, data_type, library_type kinds less than 2^8
constexpr static int LEFT_SHIFT = 8;
// place, data_type, library_type kinds less than 2^8
constexpr static int LEFT_SHIFT = 8;
proto::VarType::Type data_type_;
DataLayout data_layout_;
proto::VarType::Type data_type_;
DataLayout data_layout_;
OpKernelType(proto::VarType::Type data_type,
DataLayout data_layout = DataLayout::kAnyLayout)
: data_type_(data_type), data_layout_(data_layout) {}
OpKernelType(proto::VarType::Type data_type,
DataLayout data_layout = DataLayout::kAnyLayout)
: data_type_(data_type), data_layout_(data_layout) {}
bool operator==(const OpKernelType &o) const {
return data_type_ == o.data_type_ && data_layout_ == o.data_layout_;
}
bool operator==(const OpKernelType &o) const {
return data_type_ == o.data_type_ && data_layout_ == o.data_layout_;
}
bool operator!=(const OpKernelType &o) const { return !(*this == o); }
bool operator!=(const OpKernelType &o) const { return !(*this == o); }
};
inline bool NeedTransformLayout(const DataLayout &l, const DataLayout &r) {
return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && l != r;
return l != DataLayout::kAnyLayout && r != DataLayout::kAnyLayout && l != r;
}
inline bool TransFromNeeded(const OpKernelType &l, const OpKernelType &r) {
return (l.data_type_ != r.data_type_) ||
NeedTransformLayout(l.data_layout_, r.data_layout_);
return (l.data_type_ != r.data_type_) ||
NeedTransformLayout(l.data_layout_, r.data_layout_);
}
} // namespace framework
......
src/framework/operator.cpp
浏览文件 @ ddf6b722
......@@ -30,7 +30,7 @@ OperatorBase<Dtype>::OperatorBase(const std::string &type,
std::shared_ptr<Scope> scope)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs),
scope_(scope) {
CheckAllInputOutputSet();
CheckAllInputOutputSet();
}
template <typename Dtype>
void OperatorBase<Dtype>::CheckAllInputOutputSet() const {}
......
src/framework/operator.h
浏览文件 @ ddf6b722
......@@ -49,50 +49,50 @@ static std::unordered_map<
{"fetch", {{"X"}, {"Out"}}}};
template <typename Dtype> class OperatorBase : PaddleMobileObject {
public:
OperatorBase(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs,
std::shared_ptr<Scope> scope);
virtual ~OperatorBase() {}
virtual void Run() const = 0;
public:
OperatorBase(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs,
std::shared_ptr<Scope> scope);
virtual ~OperatorBase() {}
virtual void Run() const = 0;
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
const std::string &Type() const { return type_; }
const AttributeMap &Attrs() const { return attrs_; }
void ClearVariables(const std::vector<std::string> &var_names) const {
if (this->scope_) {
this->scope_->EraseVars(var_names);
}
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
const std::string &Type() const { return type_; }
const AttributeMap &Attrs() const { return attrs_; }
void ClearVariables(const std::vector<std::string> &var_names) const {
if (this->scope_) {
this->scope_->EraseVars(var_names);
}
}
protected:
std::shared_ptr<Scope> scope_;
std::string type_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
protected:
std::shared_ptr<Scope> scope_;
std::string type_;
VariableNameMap inputs_;
VariableNameMap outputs_;
AttributeMap attrs_;
private:
void CheckAllInputOutputSet() const;
private:
void CheckAllInputOutputSet() const;
};
template <typename Dtype>
class OperatorWithKernel : public OperatorBase<Dtype> {
public:
OperatorWithKernel(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const AttributeMap &attrs, std::shared_ptr<Scope> scope)
: OperatorBase<Dtype>(type, inputs, outputs, attrs, scope) {}
virtual void InferShape() const = 0;
virtual void Run() const = 0;
public:
OperatorWithKernel(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const AttributeMap &attrs,
std::shared_ptr<Scope> scope)
: OperatorBase<Dtype>(type, inputs, outputs, attrs, scope) {}
virtual void InferShape() const = 0;
virtual void Run() const = 0;
};
template <typename Dtype, typename P> class OpKernelBase : PaddleMobileObject {
public:
virtual void Compute(const P &para) const = 0;
public:
virtual void Compute(const P &para) const = 0;
virtual ~OpKernelBase() = default;
virtual ~OpKernelBase() = default;
};
} // namespace framework
......
src/framework/paddle_mobile_object.h
浏览文件 @ ddf6b722
......@@ -24,13 +24,13 @@ SOFTWARE.
namespace paddle_mobile {
class PaddleMobileObject {
public:
virtual std::string ToString() {
char address[128] = {0};
sprintf(address, "%p", this);
return std::string(address);
}
public:
virtual std::string ToString() {
char address[128] = {0};
sprintf(address, "%p", this);
return std::string(address);
}
private:
private:
};
} // namespace paddle_mobile
src/framework/program-optimize/node.cpp
浏览文件 @ ddf6b722
......@@ -25,71 +25,71 @@ namespace paddle_mobile {
namespace framework {
Node &Node::operator>(std::shared_ptr<Node> node) {
outputs_.push_back(node);
std::shared_ptr<Node> this_node;
node->inputs_.push_back(this);
return *node;
outputs_.push_back(node);
std::shared_ptr<Node> this_node;
node->inputs_.push_back(this);
return *node;
}
bool Node::operator==(const Node &in) {
if (in.type_ == this->type_) {
if (this->outputs_.size() == in.outputs_.size()) {
for (int i = 0; i < outputs_.size(); ++i) {
if (!(*outputs_[i] == *in.outputs_[i])) {
return false;
}
}
} else {
return false;
if (in.type_ == this->type_) {
if (this->outputs_.size() == in.outputs_.size()) {
for (int i = 0; i < outputs_.size(); ++i) {
if (!(*outputs_[i] == *in.outputs_[i])) {
return false;
}
}
} else {
return false;
return false;
}
return true;
} else {
return false;
}
return true;
}
std::string Node::ToString(std::string blank, const Node *node) const {
std::stringstream ss;
ss << type_ << "-> \n";
std::stringstream ss;
ss << type_ << "-> \n";
if (inputs_.size() > 1 && node != inputs_.back()) {
return ss.str();
} else if (inputs_.size() > 1 && node == inputs_.back()) {
ss << "\n" << blank << type_ << "\n";
}
for (int i = 0; i < outputs_.size(); ++i) {
ss << blank << outputs_[i]->ToString(blank + " ", this) << "";
}
if (inputs_.size() > 1 && node != inputs_.back()) {
return ss.str();
} else if (inputs_.size() > 1 && node == inputs_.back()) {
ss << "\n" << blank << type_ << "\n";
}
for (int i = 0; i < outputs_.size(); ++i) {
ss << blank << outputs_[i]->ToString(blank + " ", this) << "";
}
return ss.str();
}
std::string Node::ToString() const { return this->ToString(" ", this); }
Node &Node::To(int index) {
if (index == 0) {
this->outputs_.clear();
}
if (index == 0) {
this->outputs_.clear();
}
for (int j = 0; j < this->outputs_.size(); ++j) {
outputs_[j]->To(index - 1);
}
return *this;
for (int j = 0; j < this->outputs_.size(); ++j) {
outputs_[j]->To(index - 1);
}
return *this;
}
uint Node::depth(uint begin) {
uint depth = 0;
begin++;
for (int i = 0; i < outputs_.size(); ++i) {
uint output_depth = outputs_[i]->depth(begin);
depth = output_depth > depth ? output_depth : depth;
}
return begin > depth ? begin : depth;
uint depth = 0;
begin++;
for (int i = 0; i < outputs_.size(); ++i) {
uint output_depth = outputs_[i]->depth(begin);
depth = output_depth > depth ? output_depth : depth;
}
return begin > depth ? begin : depth;
}
Print &operator<<(Print &printer, const Node &node) {
printer << node.ToString();
return printer;
printer << node.ToString();
return printer;
}
} // namespace framework
......
src/framework/program-optimize/node.h
浏览文件 @ ddf6b722
......@@ -29,22 +29,22 @@ namespace paddle_mobile {
namespace framework {
class Node : PaddleMobileObject {
public:
Node(const std::string &type) : type_(type) {}
Node(std::shared_ptr<OpDesc> op_desc)
: op_desc_(op_desc), type_(op_desc->Type()){};
Node &operator>(std::shared_ptr<Node> node);
bool operator==(const Node &in);
std::string ToString() const;
Node &To(int index);
uint depth(uint begin = 0);
public:
Node(const std::string &type) : type_(type) {}
Node(std::shared_ptr<OpDesc> op_desc)
: op_desc_(op_desc), type_(op_desc->Type()){};
Node &operator>(std::shared_ptr<Node> node);
bool operator==(const Node &in);
std::string ToString() const;
Node &To(int index);
uint depth(uint begin = 0);
private:
std::shared_ptr<OpDesc> op_desc_;
std::string ToString(std::string blank, const Node *node) const;
std::vector<std::shared_ptr<Node>> outputs_;
std::vector<Node *> inputs_;
std::string type_;
private:
std::shared_ptr<OpDesc> op_desc_;
std::string ToString(std::string blank, const Node *node) const;
std::vector<std::shared_ptr<Node>> outputs_;
std::vector<Node *> inputs_;
std::string type_;
};
Print &operator<<(Print &printer, const Node &node);
......
src/framework/program-optimize/program_optimize.cpp
浏览文件 @ ddf6b722
......@@ -26,49 +26,48 @@ std::shared_ptr<ProgramDesc> ProgramOptimize::Optimize() {}
std::shared_ptr<ProgramDesc>
ProgramOptimize::FushionOptimize(std::shared_ptr<ProgramDesc> ori_des) {
for (int i = 0; i < ori_des->Blocks().size(); ++i) {
std::unordered_map<std::string, std::shared_ptr<Node>> output_nodes;
std::shared_ptr<Node> begin_node;
auto block = ori_des->Block(i);
// DLOG << " ops size: " << block->Ops().size();
for (int j = 0; j < block->Ops().size(); ++j) {
auto op = block->Ops()[j];
auto op_type = op->Type();
// DLOG << "op type: " << op_type << " index: " << j;
if (op_input_output_key.find(op->Type()) ==
op_input_output_key.end()) {
return NULL;
}
for (int i = 0; i < ori_des->Blocks().size(); ++i) {
std::unordered_map<std::string, std::shared_ptr<Node>> output_nodes;
std::shared_ptr<Node> begin_node;
auto block = ori_des->Block(i);
// DLOG << " ops size: " << block->Ops().size();
for (int j = 0; j < block->Ops().size(); ++j) {
auto op = block->Ops()[j];
auto op_type = op->Type();
// DLOG << "op type: " << op_type << " index: " << j;
if (op_input_output_key.find(op->Type()) == op_input_output_key.end()) {
return NULL;
}
std::shared_ptr<Node> node = std::make_shared<Node>(op);
if (j == 0) {
begin_node = node;
}
std::shared_ptr<Node> node = std::make_shared<Node>(op);
if (j == 0) {
begin_node = node;
}
auto input_keys = op_input_output_key.at(op->Type()).first;
for (auto input_key : input_keys) {
auto op_inputs = op->Input(input_key);
for (int l = 0; l < op_inputs.size(); ++l) {
std::string input_key = op_inputs[l];
if (output_nodes.find(input_key) != output_nodes.end()) {
auto input_node = output_nodes[input_key];
*input_node > node;
}
}
}
auto output_keys = op_input_output_key.at(op_type).second;
for (auto output_key : output_keys) {
auto op_outputs = op->Output(output_key);
for (int k = 0; k < op_outputs.size(); ++k) {
output_nodes[op_outputs[k]] = node;
}
}
auto input_keys = op_input_output_key.at(op->Type()).first;
for (auto input_key : input_keys) {
auto op_inputs = op->Input(input_key);
for (int l = 0; l < op_inputs.size(); ++l) {
std::string input_key = op_inputs[l];
if (output_nodes.find(input_key) != output_nodes.end()) {
auto input_node = output_nodes[input_key];
*input_node > node;
}
}
}
DLOG << "node: \n" << *begin_node;
auto output_keys = op_input_output_key.at(op_type).second;
for (auto output_key : output_keys) {
auto op_outputs = op->Output(output_key);
for (int k = 0; k < op_outputs.size(); ++k) {
output_nodes[op_outputs[k]] = node;
}
}
}
return ori_des;
DLOG << "node: \n" << *begin_node;
}
return ori_des;
}
} // namespace framework
} // namespace paddle_mobile
src/framework/program-optimize/program_optimize.h
浏览文件 @ ddf6b722
......@@ -26,16 +26,16 @@ namespace paddle_mobile {
namespace framework {
class ProgramOptimize {
public:
ProgramOptimize() {}
std::shared_ptr<ProgramDesc> Optimize();
std::shared_ptr<ProgramDesc>
FushionOptimize(std::shared_ptr<ProgramDesc> ori_des);
public:
ProgramOptimize() {}
std::shared_ptr<ProgramDesc> Optimize();
std::shared_ptr<ProgramDesc>
FushionOptimize(std::shared_ptr<ProgramDesc> ori_des);
private:
// std::shared_ptr<ProgramDesc> ori_desc_;
std::vector<std::unordered_map<std::string, std::shared_ptr<Node>>>
outputs_nodes_;
private:
// std::shared_ptr<ProgramDesc> ori_desc_;
std::vector<std::unordered_map<std::string, std::shared_ptr<Node>>>
outputs_nodes_;
};
} // namespace framework
} // namespace paddle_mobile
src/framework/program.h
浏览文件 @ ddf6b722
......@@ -28,12 +28,12 @@ namespace framework {
template <typename Dtype, Precision P = Precision::FP32>
class Program : PaddleMobileObject {
public:
std::shared_ptr<ProgramDesc> originProgram;
std::shared_ptr<ProgramDesc> optimizeProgram;
std::shared_ptr<Scope> scope;
public:
std::shared_ptr<ProgramDesc> originProgram;
std::shared_ptr<ProgramDesc> optimizeProgram;
std::shared_ptr<Scope> scope;
private:
private:
};
} // namespace framework
......
src/framework/program_desc.cpp
浏览文件 @ ddf6b722
......@@ -8,14 +8,14 @@ namespace paddle_mobile {
namespace framework {
ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) : desc_(desc) {
for (auto &block_desc : *desc_.mutable_blocks()) {
// new framework::BlockDesc(block_desc)
blocks_.emplace_back(std::make_shared<BlockDesc>(block_desc));
}
for (auto &block_desc : *desc_.mutable_blocks()) {
// new framework::BlockDesc(block_desc)
blocks_.emplace_back(std::make_shared<BlockDesc>(block_desc));
}
}
std::shared_ptr<BlockDesc> ProgramDesc::Block(size_t idx) {
return blocks_[idx];
return blocks_[idx];
}
} // namespace framework
......
src/framework/program_desc.h
浏览文件 @ ddf6b722
......@@ -28,14 +28,14 @@ namespace paddle_mobile {
namespace framework {
class ProgramDesc : PaddleMobileObject {
public:
ProgramDesc(const proto::ProgramDesc &desc);
std::shared_ptr<BlockDesc> Block(size_t idx);
const std::vector<std::shared_ptr<BlockDesc>> &Blocks() { return blocks_; };
public:
ProgramDesc(const proto::ProgramDesc &desc);
std::shared_ptr<BlockDesc> Block(size_t idx);
const std::vector<std::shared_ptr<BlockDesc>> &Blocks() { return blocks_; };
private:
std::vector<std::shared_ptr<BlockDesc>> blocks_;
proto::ProgramDesc desc_;
private:
std::vector<std::shared_ptr<BlockDesc>> blocks_;
proto::ProgramDesc desc_;
};
} // namespace framework
......
src/framework/scope.cc
浏览文件 @ ddf6b722
......@@ -7,20 +7,20 @@ namespace paddle_mobile {
namespace framework {
Scope &Scope::NewScope() const {
std::unique_lock<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this));
return *kids_.back();
std::unique_lock<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this));
return *kids_.back();
}
Variable *Scope::Var(const std::string &name) {
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
};
pvar = new Variable;
vars_[name] = pvar;
pvar->name_ = &(vars_.find(name)->first);
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
};
pvar = new Variable;
vars_[name] = pvar;
pvar->name_ = &(vars_.find(name)->first);
return pvar;
}
// Variable* Scope::Var(std::string* name) {
......@@ -33,70 +33,70 @@ Variable *Scope::Var(const std::string &name) {
// }
Variable *Scope::FindVar(const std::string &name) const {
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
}
return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
auto *pvar = FindVarLocally(name);
if (pvar != nullptr) {
return pvar;
}
return (parent_ == nullptr) ? nullptr : parent_->FindVar(name);
}
const Scope *Scope::FindScope(const Variable *var) const {
for (auto &name_var : vars_) {
if (name_var.second == var) {
return this;
}
for (auto &name_var : vars_) {
if (name_var.second == var) {
return this;
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
return (parent_ == nullptr) ? nullptr : parent_->FindScope(var);
}
void Scope::DropKids() {
for (Scope *s : kids_) {
delete s;
}
kids_.clear();
for (Scope *s : kids_) {
delete s;
}
kids_.clear();
}
std::vector<std::string> Scope::LocalVarNames() const {
std::vector<std::string> known_vars;
known_vars.reserve(vars_.size());
for (auto &name_var : vars_) {
known_vars.emplace_back(name_var.first);
}
return known_vars;
std::vector<std::string> known_vars;
known_vars.reserve(vars_.size());
for (auto &name_var : vars_) {
known_vars.emplace_back(name_var.first);
}
return known_vars;
}
void Scope::DeleteScope(Scope *scope) const {
std::unique_lock<std::mutex> lock(mutex_);
auto it = std::find(kids_.begin(), kids_.end(), scope);
kids_.erase(it);
delete scope;
// deferent
std::unique_lock<std::mutex> lock(mutex_);
auto it = std::find(kids_.begin(), kids_.end(), scope);
kids_.erase(it);
delete scope;
// deferent
}
void Scope::EraseVars(const std::vector<std::string> &var_names) {
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
delete it->second;
it = vars_.erase(it);
} else {
++it;
}
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
delete it->second;
it = vars_.erase(it);
} else {
++it;
}
}
}
void Scope::Rename(const std::string &origin_name,
const std::string &new_name) const {
auto origin_it = vars_.find(origin_name);
if (origin_it == vars_.end()) {
return;
}
auto new_it = vars_.find(new_name);
if (new_it != vars_.end()) {
return;
}
vars_[new_name] = origin_it->second;
vars_.erase(origin_it);
auto origin_it = vars_.find(origin_name);
if (origin_it == vars_.end()) {
return;
}
auto new_it = vars_.find(new_name);
if (new_it != vars_.end()) {
return;
}
vars_[new_name] = origin_it->second;
vars_.erase(origin_it);
}
//
// std::string Scope::Rename(const std::string& origin_name)
......@@ -108,11 +108,11 @@ void Scope::Rename(const std::string &origin_name,
// }
Variable *Scope::FindVarLocally(const std::string &name) const {
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second;
}
return nullptr;
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second;
}
return nullptr;
}
} // namespace framework
......
src/framework/scope.h
浏览文件 @ ddf6b722
......@@ -26,56 +26,56 @@ SOFTWARE.
namespace paddle_mobile {
namespace framework {
class Scope {
public:
Scope() {}
~Scope() {}
public:
Scope() {}
~Scope() {}
Scope &NewScope() const;
Scope &NewScope() const;
/// Create a variable with given name if it doesn't exist.
Variable *Var(const std::string &name);
/// Create a variable with given name if it doesn't exist.
Variable *Var(const std::string &name);
/// Create a variable with a scope-unique name.
Variable *Var(std::string *name = nullptr);
/// Create a variable with a scope-unique name.
Variable *Var(std::string *name = nullptr);
void EraseVars(const std::vector<std::string> &var_names);
void EraseVars(const std::vector<std::string> &var_names);
/// Find a variable in the scope or any of its ancestors. Returns
/// nullptr if cannot find.
Variable *FindVar(const std::string &name) const;
/// Find a variable in the scope or any of its ancestors. Returns
/// nullptr if cannot find.
Variable *FindVar(const std::string &name) const;
const Scope *parent() const { return parent_; }
const Scope *parent() const { return parent_; }
/// Find the scope or an ancestor scope that contains the given
/// variable.
const Scope *FindScope(const Variable *var) const;
/// Find the scope or an ancestor scope that contains the given
/// variable.
const Scope *FindScope(const Variable *var) const;
void DeleteScope(Scope *scope) const;
void DeleteScope(Scope *scope) const;
/// Drop all kids scopes belonged to this scope.
void DropKids();
/// Drop all kids scopes belonged to this scope.
void DropKids();
// enumerate all the variables current contains.
std::vector<std::string> LocalVarNames() const;
// enumerate all the variables current contains.
std::vector<std::string> LocalVarNames() const;
// Rename variable to a new name
void Rename(const std::string &origin_name,
const std::string &new_name) const;
// Rename variable to a new name
void Rename(const std::string &origin_name,
const std::string &new_name) const;
// Rename variable to a new name and return the new name
std::string Rename(const std::string &origin_name) const;
// Rename variable to a new name and return the new name
std::string Rename(const std::string &origin_name) const;
Variable *FindVarLocally(const std::string &name) const;
Variable *FindVarLocally(const std::string &name) const;
private:
// Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const *parent) : parent_(parent) {}
private:
// Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const *parent) : parent_(parent) {}
mutable std::unordered_map<std::string, Variable *> vars_;
mutable std::list<Scope *> kids_;
Scope const *parent_{nullptr};
mutable std::unordered_map<std::string, Variable *> vars_;
mutable std::list<Scope *> kids_;
Scope const *parent_{nullptr};
mutable std::mutex mutex_;
mutable std::mutex mutex_;
};
} // namespace framework
} // namespace paddle_mobile
src/framework/selected_rows.h
浏览文件 @ ddf6b722
......@@ -27,54 +27,54 @@ namespace paddle_mobile {
namespace framework {
class SelectedRows {
public:
SelectedRows(const std::vector<int64_t> &rows, const int64_t &height)
: rows_(rows), height_(height) {
value_.reset(new Tensor());
}
SelectedRows() {
height_ = 0;
value_.reset(new Tensor());
}
const Tensor &value() const { return *value_; }
Tensor *mutable_value() { return value_.get(); }
int64_t height() const { return height_; }
void set_height(int64_t height) { height_ = height; }
const std::vector<int64_t> &rows() const { return rows_; }
std::vector<int64_t> *mutable_rows() { return &rows_; }
void set_rows(const std::vector<int64_t> &rows) { rows_ = rows; }
/**
* get the index of id in rows
*/
int64_t index(int64_t id) const {
auto it = std::find(rows_.begin(), rows_.end(), id);
// PADDLE_ENFORCE(it != rows_.end(), "id should be in rows");
return static_cast<int64_t>(std::distance(rows_.begin(), it));
}
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_;
return make_ddim(dims);
}
private:
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9}
// here.
// SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
std::vector<int64_t> rows_;
std::unique_ptr<Tensor> value_{nullptr};
int64_t height_;
public:
SelectedRows(const std::vector<int64_t> &rows, const int64_t &height)
: rows_(rows), height_(height) {
value_.reset(new Tensor());
}
SelectedRows() {
height_ = 0;
value_.reset(new Tensor());
}
const Tensor &value() const { return *value_; }
Tensor *mutable_value() { return value_.get(); }
int64_t height() const { return height_; }
void set_height(int64_t height) { height_ = height; }
const std::vector<int64_t> &rows() const { return rows_; }
std::vector<int64_t> *mutable_rows() { return &rows_; }
void set_rows(const std::vector<int64_t> &rows) { rows_ = rows; }
/**
* get the index of id in rows
*/
int64_t index(int64_t id) const {
auto it = std::find(rows_.begin(), rows_.end(), id);
// PADDLE_ENFORCE(it != rows_.end(), "id should be in rows");
return static_cast<int64_t>(std::distance(rows_.begin(), it));
}
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_;
return make_ddim(dims);
}
private:
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9}
// here.
// SelectedRows are simply concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
std::vector<int64_t> rows_;
std::unique_ptr<Tensor> value_{nullptr};
int64_t height_;
};
} // namespace framework
......
src/framework/tensor.h
浏览文件 @ ddf6b722
......@@ -29,305 +29,304 @@ namespace framework {
template <typename... T> struct SizeOfTypeFunctor;
template <typename T> struct SizeOfTypeFunctor<T> {
size_t operator()(std::type_index type) const {
if (typeid(T).hash_code() == type.hash_code()) {
return sizeof(T);
} else {
return 0UL;
}
size_t operator()(std::type_index type) const {
if (typeid(T).hash_code() == type.hash_code()) {
return sizeof(T);
} else {
return 0UL;
}
}
};
template <> struct SizeOfTypeFunctor<> {
size_t operator()(std::type_index type) const { return 0UL; }
size_t operator()(std::type_index type) const { return 0UL; }
};
template <typename HEAD, typename... TAIL>
struct SizeOfTypeFunctor<HEAD, TAIL...> {
size_t operator()(std::type_index type) const {
SizeOfTypeFunctor<HEAD> head;
size_t head_size = head(type);
if (head_size != 0) {
return head_size;
}
SizeOfTypeFunctor<TAIL...> tail;
return tail(type);
size_t operator()(std::type_index type) const {
SizeOfTypeFunctor<HEAD> head;
size_t head_size = head(type);
if (head_size != 0) {
return head_size;
}
SizeOfTypeFunctor<TAIL...> tail;
return tail(type);
}
};
static inline size_t SizeOfType(std::type_index type) {
SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool, size_t>
functor;
size_t size = functor(type);
// PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s",
// type.name());
return size;
SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool, size_t> functor;
size_t size = functor(type);
// PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s",
// type.name());
return size;
}
class LoDTensor;
class Tensor {
public:
Tensor() : offset_(0) {}
/*! Return a pointer to mutable memory block. */
template <typename T> inline T *data() {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
public:
Tensor() : offset_(0) {}
/*! Return a pointer to mutable memory block. */
template <typename T> inline T *data() {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<T *>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
/*! Return a pointer to constant memory block. */
template <typename T> inline const T *data() const {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<const T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
inline bool IsInitialized() const { return holder_ != nullptr; }
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data() {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T *>(mutable_data(typeid(T)));
}
inline void *mutable_data(std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
/*! Return a pointer to constant memory block. */
template <typename T> inline const T *data() const {
check_memory_size();
// PADDLE_ENFORCE(std::is_same<T, void>::value ||
// holder_->type().hash_code() ==
// typeid(T).hash_code(),
// "Tensor holds the wrong type, it holds %s",
// this->holder_->type().name());
return reinterpret_cast<const T *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
// PADDLE_ENFORCE_GE(numel(), 0,
// "When calling this method, the Tensor's
// numel must be
// " "equal or larger than zero. " "Please
// check
// Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || holder_->size() < size + offset_) {
holder_.reset(new PlaceholderImpl(size, type));
offset_ = 0;
}
inline bool IsInitialized() const { return holder_ != nullptr; }
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data() {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T *>(mutable_data(typeid(T)));
return reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
inline void *mutable_data() {
// PADDLE_ENFORCE(this->holder_ != nullptr,
// "Cannot invoke mutable data if current hold
// nothing.");
return mutable_data(holder_->type());
}
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
*
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data(DDim dims) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>();
}
/*! Return the dimensions of the memory block. */
inline const DDim &dims() const { return dims_; }
/*! Return the numel of the memory block. */
inline int64_t numel() const { return product(dims_); }
/*! Resize the dimensions of the memory block. */
inline Tensor &Resize(const DDim &dims) {
dims_ = dims;
return *this;
}
/*! The internal of two tensors share the same memory block. */
inline Tensor &ShareDataWith(const Tensor &src) {
src.check_memory_size();
*this = src;
return *this;
}
/**
* @brief Return a sub-tensor of the given tensor.
*
* @param[in] begin_idx The index of the start row(inclusive) to
* slice.
* The index number begins from 0.
* @param[in] end_idx The index of the end row(exclusive) to
* slice.
* The index number begins from 0.
*/
inline Tensor Slice(int begin_idx, int end_idx) const {
check_memory_size();
// PADDLE_ENFORCE_GE(begin_idx, 0,
// "The start row index must be greater than
// 0.");
// PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is
// out of
// bound."); PADDLE_ENFORCE_LT(
// begin_idx, end_idx,
// "The start row index must be lesser than the end row
// index.");
if (dims_[0] == 1) {
return *this;
} else {
size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
dst.set_layout(layout_);
DDim dst_dims = dims_;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
return dst;
}
inline void *mutable_data(std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
// PADDLE_ENFORCE_GE(numel(), 0,
// "When calling this method, the Tensor's
// numel must be
// " "equal or larger than zero. " "Please
// check
// Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || holder_->size() < size + offset_) {
holder_.reset(new PlaceholderImpl(size, type));
offset_ = 0;
}
return reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
}
std::type_index type() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor not initialized yet
// when
// Tensor::type() is called.");
return holder_->type();
}
// memory size returns the holding memory size in byte.
size_t memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
inline void check_memory_size() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor holds no memory. Call
// Tensor::mutable_data
// first.");
// PADDLE_ENFORCE_LE(
// numel() * SizeOfType(type()), memory_size(),
// "Tensor's dims_ is out of bound. Call
// Tensor::mutable_data "
// "first to re-allocate memory.\n"
// "or maybe the required data-type mismatches the data
// already
// stored.");
}
inline DataLayout layout() const { return layout_; }
inline void set_layout(const DataLayout layout) { layout_ = layout; }
private:
/**
* @note Placeholder hides type T, so it doesn't appear as a
* template
* parameter of Variable.
*/
struct Placeholder {
virtual ~Placeholder() = default;
virtual void *ptr() const = 0;
virtual size_t size() const = 0;
virtual std::type_index type() const = 0;
virtual void set_type(std::type_index type) = 0;
};
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(size_t size, std::type_index type)
: ptr_(static_cast<uint8_t *>(memory::Alloc(size)),
memory::PODDeleter<uint8_t>()),
size_(size), type_(type) {
// PADDLE_ENFORCE_NOT_NULL(ptr_,
// "Insufficient %s
// memory to allocation.",
// (is_cpu_place(place_)
// ?
// "CPU" :
// "GPU"));
}
inline void *mutable_data() {
// PADDLE_ENFORCE(this->holder_ != nullptr,
// "Cannot invoke mutable data if current hold
// nothing.");
return mutable_data(holder_->type());
}
virtual size_t size() const { return size_; }
/**
* @brief Return a pointer to mutable memory block.
*
* @param[in] dims The dimensions of the memory block.
* @param[in] place The place of the memory block.
*
* @note If not exist, then allocation.
*/
template <typename T> inline T *mutable_data(DDim dims) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>();
}
virtual void *ptr() const { return static_cast<void *>(ptr_.get()); }
/*! Return the dimensions of the memory block. */
inline const DDim &dims() const { return dims_; }
virtual std::type_index type() const { return type_; }
/*! Return the numel of the memory block. */
inline int64_t numel() const { return product(dims_); }
virtual void set_type(std::type_index type) { type_ = type; }
/*! Resize the dimensions of the memory block. */
inline Tensor &Resize(const DDim &dims) {
dims_ = dims;
return *this;
}
/*! the pointer of memory block. */
std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t>> ptr_;
/*! The internal of two tensors share the same memory block. */
inline Tensor &ShareDataWith(const Tensor &src) {
src.check_memory_size();
*this = src;
return *this;
}
/*! the size of memory block. */
size_t size_;
/**
* @brief Return a sub-tensor of the given tensor.
*
* @param[in] begin_idx The index of the start row(inclusive) to
* slice.
* The index number begins from 0.
* @param[in] end_idx The index of the end row(exclusive) to
* slice.
* The index number begins from 0.
*/
inline Tensor Slice(int begin_idx, int end_idx) const {
check_memory_size();
// PADDLE_ENFORCE_GE(begin_idx, 0,
// "The start row index must be greater than
// 0.");
// PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is
// out of
// bound."); PADDLE_ENFORCE_LT(
// begin_idx, end_idx,
// "The start row index must be lesser than the end row
// index.");
if (dims_[0] == 1) {
return *this;
} else {
size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
dst.set_layout(layout_);
DDim dst_dims = dims_;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
return dst;
}
}
/* the current type of memory */
std::type_index type_;
};
std::type_index type() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor not initialized yet
// when
// Tensor::type() is called.");
return holder_->type();
}
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
// memory size returns the holding memory size in byte.
size_t memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
/**
* @brief points to elements dimensions.
*
* @note dims_ do not indicate the memory block size.
*/
inline void check_memory_size() const {
// PADDLE_ENFORCE_NOT_NULL(
// holder_, "Tensor holds no memory. Call
// Tensor::mutable_data
// first.");
// PADDLE_ENFORCE_LE(
// numel() * SizeOfType(type()), memory_size(),
// "Tensor's dims_ is out of bound. Call
// Tensor::mutable_data "
// "first to re-allocate memory.\n"
// "or maybe the required data-type mismatches the data
// already
// stored.");
}
DDim dims_;
/**
* @brief the layout of memory block, default is NHWC.
*
* @note the memory allocation order, describe how weight/data is
* stored
* For example, in 4-D Tensor(rank=4), there are three
* commonly
* used layout. They are
* NCHW, NHWC, CHWN.
* N,C,H,W for respectively the batch size, the number of
* feature maps, the height, the width.
*/
DataLayout layout_ = DataLayout::kNHWC;
inline DataLayout layout() const { return layout_; }
inline void set_layout(const DataLayout layout) { layout_ = layout; }
private:
/**
* @note Placeholder hides type T, so it doesn't appear as a
* template
* parameter of Variable.
*/
struct Placeholder {
virtual ~Placeholder() = default;
virtual void *ptr() const = 0;
virtual size_t size() const = 0;
virtual std::type_index type() const = 0;
virtual void set_type(std::type_index type) = 0;
};
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(size_t size, std::type_index type)
: ptr_(static_cast<uint8_t *>(memory::Alloc(size)),
memory::PODDeleter<uint8_t>()),
size_(size), type_(type) {
// PADDLE_ENFORCE_NOT_NULL(ptr_,
// "Insufficient %s
// memory to allocation.",
// (is_cpu_place(place_)
// ?
// "CPU" :
// "GPU"));
}
virtual size_t size() const { return size_; }
virtual void *ptr() const { return static_cast<void *>(ptr_.get()); }
virtual std::type_index type() const { return type_; }
virtual void set_type(std::type_index type) { type_ = type; }
/*! the pointer of memory block. */
std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t>> ptr_;
/*! the size of memory block. */
size_t size_;
/* the current type of memory */
std::type_index type_;
};
/*! holds the memory block if allocated. */
std::shared_ptr<Placeholder> holder_;
/**
* @brief points to elements dimensions.
*
* @note dims_ do not indicate the memory block size.
*/
DDim dims_;
/**
* @brief the layout of memory block, default is NHWC.
*
* @note the memory allocation order, describe how weight/data is
* stored
* For example, in 4-D Tensor(rank=4), there are three
* commonly
* used layout. They are
* NCHW, NHWC, CHWN.
* N,C,H,W for respectively the batch size, the number of
* feature maps, the height, the width.
*/
DataLayout layout_ = DataLayout::kNHWC;
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
* @note Some of them may be slices of the others. So the offset_
* is introduced here to indicate the byte offset between
* PlaceHolder::ptr_ and where the tensor data really
* begins.
*/
size_t offset_;
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
* @note Some of them may be slices of the others. So the offset_
* is introduced here to indicate the byte offset between
* PlaceHolder::ptr_ and where the tensor data really
* begins.
*/
size_t offset_;
};
inline Tensor ReshapeToMatrix(const Tensor &src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
Tensor res;
res.ShareDataWith(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
}
} // namespace framework
......
src/framework/tensor_util.cc
浏览文件 @ ddf6b722
......@@ -21,183 +21,182 @@ namespace paddle_mobile {
namespace framework {
void TensorCopy(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopy " << src.dims() << " from " <<
// src.place() << " to
// "
// << dst_place;
src.check_memory_size();
// VLOG(3) << "TensorCopy " << src.dims() << " from " <<
// src.place() << " to
// "
// << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type());
auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type());
auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size);
memory::Copy(dst_ptr, src_ptr, size);
}
void TensorCopySync(const Tensor &src, Tensor *dst) {
// VLOG(3) << "TensorCopySync " << src.dims() << " from " <<
// src.place()
// << " to " << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size);
// VLOG(3) << "TensorCopySync " << src.dims() << " from " <<
// src.place()
// << " to " << dst_place;
src.check_memory_size();
dst->Resize(src.dims());
dst->set_layout(src.layout());
auto src_ptr = src.data<void>();
auto dst_ptr = dst->mutable_data(src.type());
auto size = src.numel() * SizeOfType(src.type());
memory::Copy(dst_ptr, src_ptr, size);
}
template <typename Predicate> struct AnyDTypeVisitor {
Predicate predicate_;
const Tensor &tensor_;
Tensor *out_;
AnyDTypeVisitor(Predicate predicate, const Tensor &tensor, Tensor *out)
: predicate_(predicate), tensor_(tensor), out_(out) {}
template <typename T> void operator()() const {
// auto t = EigenVector<T>::Flatten(tensor_);
// auto o = EigenScalar<bool>::From(*out_);
// return any of predicate_(t) is true.
// o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
Predicate predicate_;
const Tensor &tensor_;
Tensor *out_;
AnyDTypeVisitor(Predicate predicate, const Tensor &tensor, Tensor *out)
: predicate_(predicate), tensor_(tensor), out_(out) {}
template <typename T> void operator()() const {
// auto t = EigenVector<T>::Flatten(tensor_);
// auto o = EigenScalar<bool>::From(*out_);
// return any of predicate_(t) is true.
// o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
};
template <typename Predicate>
inline void AnyImpl(Predicate predicate, const Tensor &tensor,
framework::Tensor *out) {
VisitDataType(ToDataType(tensor.type()),
AnyDTypeVisitor<Predicate>(predicate, tensor, out));
VisitDataType(ToDataType(tensor.type()),
AnyDTypeVisitor<Predicate>(predicate, tensor, out));
}
template <typename Predicate> struct AnyVisitor {
const framework::Tensor &tensor_;
Predicate predicate_;
AnyVisitor(const framework::Tensor &tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
bool operator()(void) const {
framework::Tensor out;
out.Resize({1});
out.mutable_data<bool>();
AnyImpl(predicate_, tensor_, &out);
return this->GetResult(out);
}
bool GetResult(const framework::Tensor &out) const {
return *out.data<bool>();
}
const framework::Tensor &tensor_;
Predicate predicate_;
AnyVisitor(const framework::Tensor &tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
bool operator()(void) const {
framework::Tensor out;
out.Resize({1});
out.mutable_data<bool>();
AnyImpl(predicate_, tensor_, &out);
return this->GetResult(out);
}
bool GetResult(const framework::Tensor &out) const {
return *out.data<bool>();
}
};
template <typename Predicate>
inline bool Any(const framework::Tensor &tensor, Predicate predicate) {
AnyVisitor<Predicate> visitor(tensor, predicate);
// return platform::VisitPlace(visitor);
return visitor();
AnyVisitor<Predicate> visitor(tensor, predicate);
// return platform::VisitPlace(visitor);
return visitor();
}
struct ContainsNANPredicate {
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isnan();
}
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isnan();
}
};
bool TensorContainsNAN(const framework::Tensor &tensor) {
ContainsNANPredicate predicate;
return Any(tensor, predicate);
ContainsNANPredicate predicate;
return Any(tensor, predicate);
}
struct ContainsInfPredicate {
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isinf();
}
template <typename T>
auto operator()(const T &eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isinf();
}
};
bool TensorContainsInf(const framework::Tensor &tensor) {
ContainsInfPredicate predicate;
return Any(tensor, predicate);
ContainsInfPredicate predicate;
return Any(tensor, predicate);
}
void TensorToStream(std::ostream &os, const Tensor &tensor) {
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto::VarType::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto *pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.memory_size();
auto *data_ptr = tensor.data<void>();
// PADDLE_ENFORCE(size <
// std::numeric_limits<std::streamsize>::max(),
// "Index overflow when writing tensor");
os.write(static_cast<const char *>(data_ptr),
static_cast<std::streamsize>(size));
}
{ // the 1st field, uint32_t version
constexpr uint32_t version = 0;
os.write(reinterpret_cast<const char *>(&version), sizeof(version));
}
{ // the 2nd field, tensor description
// int32_t size
// void* protobuf message
proto::VarType::TensorDesc desc;
desc.set_data_type(framework::ToDataType(tensor.type()));
auto dims = framework::vectorize(tensor.dims());
auto *pb_dims = desc.mutable_dims();
pb_dims->Resize(static_cast<int>(dims.size()), 0);
std::copy(dims.begin(), dims.end(), pb_dims->begin());
int32_t size = desc.ByteSize();
os.write(reinterpret_cast<const char *>(&size), sizeof(size));
auto out = desc.SerializeAsString();
os.write(out.data(), size);
}
{ // the 3rd field, tensor data
uint64_t size = tensor.memory_size();
auto *data_ptr = tensor.data<void>();
// PADDLE_ENFORCE(size <
// std::numeric_limits<std::streamsize>::max(),
// "Index overflow when writing tensor");
os.write(static_cast<const char *>(data_ptr),
static_cast<std::streamsize>(size));
}
}
struct DeserializedDataFunctor {
DeserializedDataFunctor(void **buf, Tensor *tensor)
: buf_(buf), tensor_(tensor) {}
DeserializedDataFunctor(void **buf, Tensor *tensor)
: buf_(buf), tensor_(tensor) {}
template <typename T> void operator()() {
*buf_ = tensor_->mutable_data<T>();
}
template <typename T> void operator()() {
*buf_ = tensor_->mutable_data<T>();
}
void **buf_;
Tensor *tensor_;
void **buf_;
Tensor *tensor_;
};
void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
proto::VarType::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
// PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
// "Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(),
std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *buf;
framework::VisitDataType(desc.data_type(),
DeserializedDataFunctor(&buf, tensor));
is.read(static_cast<char *>(buf), tensor->memory_size());
}
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
proto::VarType::TensorDesc desc;
{ // int32_t size
// proto buffer
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
// PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
// "Cannot parse tensor desc");
}
{ // read tensor
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *buf;
framework::VisitDataType(desc.data_type(),
DeserializedDataFunctor(&buf, tensor));
is.read(static_cast<char *>(buf), tensor->memory_size());
}
}
} // namespace framework
......
src/framework/tensor_util.h
浏览文件 @ ddf6b722
......@@ -43,23 +43,23 @@ void TensorFromStream(std::istream &is, Tensor *tensor);
template <typename T>
void TensorFromVector(const std::vector<T> &src, Tensor *dst) {
auto src_ptr = static_cast<const void *>(src.data());
dst->Resize({static_cast<int64_t>(src.size())});
auto dst_ptr = static_cast<void *>(dst->mutable_data<T>());
auto size = src.size() * sizeof(T);
auto src_ptr = static_cast<const void *>(src.data());
dst->Resize({static_cast<int64_t>(src.size())});
auto dst_ptr = static_cast<void *>(dst->mutable_data<T>());
auto size = src.size() * sizeof(T);
memory::Copy(dst_ptr, src_ptr, size);
memory::Copy(dst_ptr, src_ptr, size);
}
template <typename T>
void TensorToVector(const Tensor &src, std::vector<T> *dst) {
auto src_ptr = static_cast<const void *>(src.data<T>());
auto size = src.numel() * sizeof(T);
auto src_ptr = static_cast<const void *>(src.data<T>());
auto size = src.numel() * sizeof(T);
dst->resize(src.numel());
auto dst_ptr = static_cast<void *>(dst->data());
dst->resize(src.numel());
auto dst_ptr = static_cast<void *>(dst->data());
memory::Copy(dst_ptr, src_ptr, size);
memory::Copy(dst_ptr, src_ptr, size);
}
} // namespace framework
......
src/framework/var_desc.h
浏览文件 @ ddf6b722
......@@ -25,63 +25,63 @@ namespace paddle_mobile {
namespace framework {
class VarDesc {
public:
VarDesc(const proto::VarDesc &desc);
public:
VarDesc(const proto::VarDesc &desc);
std::string Name() const { return desc_.name(); }
std::string Name() const { return desc_.name(); }
proto::VarType::Type GetType() const { return desc_.type().type(); }
proto::VarType::Type GetType() const { return desc_.type().type(); }
bool Persistable() const { return desc_.persistable(); }
bool Persistable() const { return desc_.persistable(); }
const proto::VarType::ChannelDesc &channel_desc() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return desc_.type().channel();
default:
break;
}
const proto::VarType::ChannelDesc &channel_desc() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return desc_.type().channel();
default:
break;
}
}
const proto::VarType::TensorDesc &tensor_desc() const {
switch (desc_.type().type()) {
case proto::VarType::SELECTED_ROWS:
return desc_.type().selected_rows();
case proto::VarType::LOD_TENSOR:
return desc_.type().lod_tensor().tensor();
case proto::VarType::LOD_TENSOR_ARRAY:
return desc_.type().tensor_array().tensor();
default:
break;
}
const proto::VarType::TensorDesc &tensor_desc() const {
switch (desc_.type().type()) {
case proto::VarType::SELECTED_ROWS:
return desc_.type().selected_rows();
case proto::VarType::LOD_TENSOR:
return desc_.type().lod_tensor().tensor();
case proto::VarType::LOD_TENSOR_ARRAY:
return desc_.type().tensor_array().tensor();
default:
break;
}
}
proto::VarType::Type GetDataType() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return channel_desc().data_type();
break;
default:
return tensor_desc().data_type();
}
proto::VarType::Type GetDataType() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return channel_desc().data_type();
break;
default:
return tensor_desc().data_type();
}
}
template <typename T>
std::vector<T> RepeatedToVector(
const google::protobuf::RepeatedField<T> &repeated_field) const {
std::vector<T> ret;
ret.reserve(repeated_field.size());
std::copy(repeated_field.begin(), repeated_field.end(),
std::back_inserter(ret));
return ret;
}
template <typename T>
std::vector<T> RepeatedToVector(
const google::protobuf::RepeatedField<T> &repeated_field) const {
std::vector<T> ret;
ret.reserve(repeated_field.size());
std::copy(repeated_field.begin(), repeated_field.end(),
std::back_inserter(ret));
return ret;
}
std::vector<int64_t> GetShape() const {
return this->RepeatedToVector(tensor_desc().dims());
}
std::vector<int64_t> GetShape() const {
return this->RepeatedToVector(tensor_desc().dims());
}
private:
proto::VarDesc desc_;
private:
proto::VarDesc desc_;
};
} // namespace framework
......
src/framework/var_type.h
浏览文件 @ ddf6b722
......@@ -25,14 +25,14 @@ SOFTWARE.
namespace paddle_mobile {
namespace framework {
inline proto::VarType::Type ToVarType(std::type_index type) {
if (type.hash_code() == typeid(LoDTensor).hash_code()) {
return proto::VarType_Type_LOD_TENSOR;
} else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
return proto::VarType_Type_SELECTED_ROWS;
} else {
// PADDLE_THROW("ToVarType:Unsupported type %s",
// type.name());
}
if (type.hash_code() == typeid(LoDTensor).hash_code()) {
return proto::VarType_Type_LOD_TENSOR;
} else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
return proto::VarType_Type_SELECTED_ROWS;
} else {
// PADDLE_THROW("ToVarType:Unsupported type %s",
// type.name());
}
}
} // namespace framework
......
src/framework/variable.h
浏览文件 @ ddf6b722
......@@ -28,69 +28,69 @@ SOFTWARE.
namespace paddle_mobile {
namespace framework {
class Variable : public PaddleMobileObject {
public:
template <typename T> const T *Get() const {
return static_cast<const T *>(holder_->Ptr());
public:
template <typename T> const T *Get() const {
return static_cast<const T *>(holder_->Ptr());
}
bool IsInitialized() const { return holder_ != nullptr; }
const std::string *Name() { return name_; }
template <typename T> T *GetMutable() {
if (!IsType<T>()) {
if (*Name() == "pixel") {
// std::cout << " reset " << *Name() <<
// std::endl;
}
holder_.reset(new PlaceholderImp<T>(new T()));
}
bool IsInitialized() const { return holder_ != nullptr; }
const std::string *Name() { return name_; }
template <typename T> T *GetMutable() {
if (!IsType<T>()) {
if (*Name() == "pixel") {
// std::cout << " reset " << *Name() <<
// std::endl;
}
holder_.reset(new PlaceholderImp<T>(new T()));
}
return static_cast<T *>(holder_->Ptr());
return static_cast<T *>(holder_->Ptr());
}
template <typename T> bool IsType() const {
if (holder_) {
// printf("not null \n");
printf(" holder type : %s, this type %s \n", holder_->Type().name(),
typeid(T).name());
}
template <typename T> bool IsType() const {
if (holder_) {
// printf("not null \n");
printf(" holder type : %s, this type %s \n", holder_->Type().name(),
typeid(T).name());
}
// std::cout << " " << holder_->Type() << " " <<
// typeid(T) <<
// std::endl;
return holder_ != nullptr && holder_->Type() == typeid(T);
}
// std::cout << " " << holder_->Type() << " " <<
// typeid(T) <<
// std::endl;
return holder_ != nullptr && holder_->Type() == typeid(T);
}
void Clear() { holder_.reset(); }
void Clear() { holder_.reset(); }
std::type_index Type() const { return holder_->Type(); }
std::type_index Type() const { return holder_->Type(); }
void SetName(const std::string *name) { name_ = name; }
void SetName(const std::string *name) { name_ = name; }
private:
struct Placeholder {
Placeholder() = default;
virtual ~Placeholder() = default;
private:
struct Placeholder {
Placeholder() = default;
virtual ~Placeholder() = default;
virtual const std::type_info &Type() const = 0;
virtual void *Ptr() const = 0;
};
virtual const std::type_info &Type() const = 0;
virtual void *Ptr() const = 0;
};
template <typename T> struct PlaceholderImp : public Placeholder {
explicit PlaceholderImp(T *ptr) : ptr_(ptr), type_(typeid(T)) {}
template <typename T> struct PlaceholderImp : public Placeholder {
explicit PlaceholderImp(T *ptr) : ptr_(ptr), type_(typeid(T)) {}
virtual const std::type_info &Type() const { return type_; }
virtual void *Ptr() const override {
return static_cast<void *>(ptr_.get());
}
virtual const std::type_info &Type() const { return type_; }
virtual void *Ptr() const override {
return static_cast<void *>(ptr_.get());
}
std::unique_ptr<T> ptr_;
const std::type_info &type_;
};
std::unique_ptr<T> ptr_;
const std::type_info &type_;
};
std::unique_ptr<Placeholder> holder_;
friend class Scope;
const std::string *name_;
std::unique_ptr<Placeholder> holder_;
friend class Scope;
const std::string *name_;
};
} // namespace framework
} // namespace paddle_mobile
src/io.cpp
浏览文件 @ ddf6b722
......@@ -29,361 +29,357 @@ SOFTWARE.
namespace paddle_mobile {
void ReadBinaryFile(const std::string &filename, std::string *contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
std::ifstream fin(filename, std::ios::in | std::ios::binary);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
}
template <typename Dtype, Precision P>
void Loader<Dtype, P>::LoadVar(framework::LoDTensor *tensor,
const std::string &file_path) {
// LOG(kLOG_DEBUG) << " to load " << file_path;
// Log(kLOG_DEBUG) << "123";
std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end);
// LOG(kLOG_DEBUG) << " file length = " << is.tellg();
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// LOG(kLOG_INFO) << " version: " << version;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
// LOG(kLOG_DEBUG) << " load level: " << lod_level;
// LOG(kLOG_DEBUG) << " lod info: ";
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
LOG(kLOG_DEBUG1) << " lod - " << tmp[j];
}
lod[i] = tmp;
}
// 3. tensor version
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));
// std::cout << " tensor_version: " << tensor_version << std::endl;
// 4. tensor desc
int32_t size;
// LOG(kLOG_DEBUG) << " to load " << file_path;
// Log(kLOG_DEBUG) << "123";
std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end);
// LOG(kLOG_DEBUG) << " file length = " << is.tellg();
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// LOG(kLOG_INFO) << " version: " << version;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
// LOG(kLOG_DEBUG) << " load level: " << lod_level;
// LOG(kLOG_DEBUG) << " lod info: ";
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size << std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " << desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: " << desc.dims()[l]
// <<
// std::endl;
memory_size *= desc.dims()[l];
}
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *memory;
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
memory = tensor->mutable_data<float>();
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" << std::endl;
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
LOG(kLOG_DEBUG1) << " lod - " << tmp[j];
}
// std::cout << " malloc size: " << memory_size * type_size <<
// std::endl;
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory << std::endl;
is.close();
lod[i] = tmp;
}
// 3. tensor version
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&tensor_version), sizeof(tensor_version));
// std::cout << " tensor_version: " << tensor_version << std::endl;
// 4. tensor desc
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size << std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " << desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: " << desc.dims()[l]
// <<
// std::endl;
memory_size *= desc.dims()[l];
}
std::vector<int64_t> dims;
dims.reserve(static_cast<size_t>(desc.dims().size()));
std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
tensor->Resize(framework::make_ddim(dims));
void *memory;
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
memory = tensor->mutable_data<float>();
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" << std::endl;
}
// std::cout << " malloc size: " << memory_size * type_size <<
// std::endl;
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory << std::endl;
is.close();
};
template <typename Dtype, Precision P>
const framework::Program<Dtype, P>
Loader<Dtype, P>::Load(const std::string &dirname) {
std::string model_filename = dirname + "/__model__";
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
framework::proto::ProgramDesc program_desc_proto;
program_desc_proto.ParseFromString(program_desc_str);
std::shared_ptr<framework::ProgramDesc> originProgramDesc =
std::make_shared<framework::ProgramDesc>(program_desc_proto);
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
std::shared_ptr<framework::Scope> scope =
std::make_shared<framework::Scope>();
program.scope = scope;
auto block = originProgramDesc->Block(0);
for (auto block : originProgramDesc->Blocks()) {
// std::cout << "for block" << std::endl;
for (int i = 0; i < block->Vars().size(); ++i) {
std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
auto var = scope->Var(var_desc->Name());
if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
if (var_desc->Persistable() &&
var_desc->GetType() !=
framework::proto::VarType::FEED_MINIBATCH &&
var_desc->GetType() !=
framework::proto::VarType::FETCH_LIST) {
framework::LoDTensor *tensor =
var->GetMutable<framework::LoDTensor>();
// to load
LoadVar(tensor, dirname + "/" + var_desc->Name());
}
} else {
// std::cout << "非 lod" << std::endl;
}
std::string model_filename = dirname + "/__model__";
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
framework::proto::ProgramDesc program_desc_proto;
program_desc_proto.ParseFromString(program_desc_str);
std::shared_ptr<framework::ProgramDesc> originProgramDesc =
std::make_shared<framework::ProgramDesc>(program_desc_proto);
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
std::shared_ptr<framework::Scope> scope =
std::make_shared<framework::Scope>();
program.scope = scope;
auto block = originProgramDesc->Block(0);
for (auto block : originProgramDesc->Blocks()) {
// std::cout << "for block" << std::endl;
for (int i = 0; i < block->Vars().size(); ++i) {
std::shared_ptr<framework::VarDesc> var_desc = block->Vars()[i];
auto var = scope->Var(var_desc->Name());
if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
if (var_desc->Persistable() &&
var_desc->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
var_desc->GetType() != framework::proto::VarType::FETCH_LIST) {
framework::LoDTensor *tensor =
var->GetMutable<framework::LoDTensor>();
// to load
LoadVar(tensor, dirname + "/" + var_desc->Name());
}
} else {
// std::cout << "非 lod" << std::endl;
}
}
}
#ifdef PADDLE_MOBILE_DEBUG
for (int i = 0; i < program_desc_proto.blocks().size(); ++i) {
framework::proto::BlockDesc block = program_desc_proto.blocks()[i];
LOG(kLOG_DEBUG) << "block: " << block.idx();
for (int j = 0; j < block.ops().size(); ++j) {
if (j == 2) {
break;
}
framework::proto::OpDesc op = block.ops()[j];
LOG(kLOG_DEBUG1) << "op: " << op.type();
for (int m = 0; m < op.inputs_size(); ++m) {
const framework::proto::OpDesc::Var &var = op.inputs(m);
LOG(kLOG_DEBUG2) << "input parameter: " << var.parameter();
for (int n = 0; n < var.arguments().size(); ++n) {
LOG(kLOG_DEBUG3) << "argument - " << var.arguments()[n];
}
}
for (int y = 0; y < op.outputs_size(); ++y) {
const framework::proto::OpDesc::Var &var = op.outputs(y);
LOG(kLOG_DEBUG2) << "out parameter: " << var.parameter();
for (int z = 0; z < var.arguments().size(); ++z) {
LOG(kLOG_DEBUG3) << "argument - " << var.arguments()[z];
}
}
for (int x = 0; x < op.attrs().size(); ++x) {
const framework::proto::OpDesc_Attr attr = op.attrs()[x];
LOG(kLOG_DEBUG2) << "attr name: " << attr.name();
switch (attr.type()) {
case framework::proto::AttrType::BOOLEAN:
LOG(kLOG_DEBUG3) << "boolen: " << attr.b();
break;
case framework::proto::AttrType::INT:
LOG(kLOG_DEBUG3) << "int: " << attr.i();
break;
case framework::proto::AttrType::FLOAT:
LOG(kLOG_DEBUG3) << "float: " << attr.f();
case framework::proto::AttrType::STRING:
LOG(kLOG_DEBUG3) << "string: " << attr.s();
case framework::proto::AttrType::BOOLEANS:
for (int y = 0; y < attr.bools_size(); ++y) {
LOG(kLOG_DEBUG3) << "bools: " << attr.bools(y);
}
case framework::proto::AttrType::LONG:
LOG(kLOG_DEBUG3) << "long: " << attr.l();
case framework::proto::AttrType::FLOATS:
for (int y = 0; y < attr.floats_size(); ++y) {
LOG(kLOG_DEBUG3) << "floats: " << attr.floats(y);
}
case framework::proto::AttrType::INTS:
for (int y = 0; y < attr.ints_size(); ++y) {
LOG(kLOG_DEBUG3) << "ints: " << attr.ints(y);
}
case framework::proto::AttrType::STRINGS:
for (int y = 0; y < attr.strings_size(); ++y) {
LOG(kLOG_DEBUG3) << "strings: " << attr.strings(y);
}
}
}
for (int i = 0; i < program_desc_proto.blocks().size(); ++i) {
framework::proto::BlockDesc block = program_desc_proto.blocks()[i];
LOG(kLOG_DEBUG) << "block: " << block.idx();
for (int j = 0; j < block.ops().size(); ++j) {
if (j == 2) {
break;
}
framework::proto::OpDesc op = block.ops()[j];
LOG(kLOG_DEBUG1) << "op: " << op.type();
for (int m = 0; m < op.inputs_size(); ++m) {
const framework::proto::OpDesc::Var &var = op.inputs(m);
LOG(kLOG_DEBUG2) << "input parameter: " << var.parameter();
for (int n = 0; n < var.arguments().size(); ++n) {
LOG(kLOG_DEBUG3) << "argument - " << var.arguments()[n];
}
}
for (int y = 0; y < op.outputs_size(); ++y) {
const framework::proto::OpDesc::Var &var = op.outputs(y);
LOG(kLOG_DEBUG2) << "out parameter: " << var.parameter();
for (int z = 0; z < var.arguments().size(); ++z) {
LOG(kLOG_DEBUG3) << "argument - " << var.arguments()[z];
}
}
for (int x = 0; x < op.attrs().size(); ++x) {
const framework::proto::OpDesc_Attr attr = op.attrs()[x];
LOG(kLOG_DEBUG2) << "attr name: " << attr.name();
switch (attr.type()) {
case framework::proto::AttrType::BOOLEAN:
LOG(kLOG_DEBUG3) << "boolen: " << attr.b();
break;
case framework::proto::AttrType::INT:
LOG(kLOG_DEBUG3) << "int: " << attr.i();
break;
case framework::proto::AttrType::FLOAT:
LOG(kLOG_DEBUG3) << "float: " << attr.f();
case framework::proto::AttrType::STRING:
LOG(kLOG_DEBUG3) << "string: " << attr.s();
case framework::proto::AttrType::BOOLEANS:
for (int y = 0; y < attr.bools_size(); ++y) {
LOG(kLOG_DEBUG3) << "bools: " << attr.bools(y);
}
case framework::proto::AttrType::LONG:
LOG(kLOG_DEBUG3) << "long: " << attr.l();
case framework::proto::AttrType::FLOATS:
for (int y = 0; y < attr.floats_size(); ++y) {
LOG(kLOG_DEBUG3) << "floats: " << attr.floats(y);
}
case framework::proto::AttrType::INTS:
for (int y = 0; y < attr.ints_size(); ++y) {
LOG(kLOG_DEBUG3) << "ints: " << attr.ints(y);
}
case framework::proto::AttrType::STRINGS:
for (int y = 0; y < attr.strings_size(); ++y) {
LOG(kLOG_DEBUG3) << "strings: " << attr.strings(y);
}
}
}
}
for (int k = 0; k < block.vars().size(); ++k) {
framework::proto::VarDesc var = block.vars()[k];
if (var.type().type() == framework::proto::VarType::LOD_TENSOR) {
LOG(kLOG_DEBUG1) << "var name: " << var.name();
const framework::proto::VarType::TensorDesc &tensor_desc =
var.type().lod_tensor().tensor();
LOG(kLOG_DEBUG2) << "in var tensor desc dims size: "
<< tensor_desc.dims().size();
int memory_size = 1;
for (int l = 0; l < tensor_desc.dims().size(); ++l) {
LOG(kLOG_DEBUG3) << "var tensor desc dim " << l
<< " value: " << tensor_desc.dims()[l];
}
}
if (var.persistable() &&
var.type().type() != framework::proto::VarType::FEED_MINIBATCH &&
var.type().type() != framework::proto::VarType::FETCH_LIST) {
// std::cout << " to load " << var.name() <<
// std::endl;
std::string file_path = dirname + "/" + var.name();
std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end);
// std::cout << " file length = " << is.tellg() <<
// std::endl;
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// std::cout << " version: " << version <<
// std::endl;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
// std::cout << " load level: " << lod_level <<
// std::endl;
// std::cout << " lod info: " << std::endl;
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
// std::cout << " lod - " << tmp[j] <<
// std::endl;
}
}
for (int k = 0; k < block.vars().size(); ++k) {
framework::proto::VarDesc var = block.vars()[k];
if (var.type().type() == framework::proto::VarType::LOD_TENSOR) {
LOG(kLOG_DEBUG1) << "var name: " << var.name();
const framework::proto::VarType::TensorDesc &tensor_desc =
var.type().lod_tensor().tensor();
LOG(kLOG_DEBUG2) << "in var tensor desc dims size: "
<< tensor_desc.dims().size();
int memory_size = 1;
for (int l = 0; l < tensor_desc.dims().size(); ++l) {
LOG(kLOG_DEBUG3) << "var tensor desc dim " << l
<< " value: " << tensor_desc.dims()[l];
}
}
if (var.persistable() &&
var.type().type() !=
framework::proto::VarType::FEED_MINIBATCH &&
var.type().type() != framework::proto::VarType::FETCH_LIST) {
// std::cout << " to load " << var.name() <<
// std::endl;
std::string file_path = dirname + "/" + var.name();
std::ifstream is(file_path);
std::streampos pos = is.tellg(); // save current position
is.seekg(0, std::ios::end);
// std::cout << " file length = " << is.tellg() <<
// std::endl;
is.seekg(pos); // restore saved position
// 1. version
uint32_t version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// std::cout << " version: " << version <<
// std::endl;
// 2 Lod information
uint64_t lod_level;
is.read(reinterpret_cast<char *>(&lod_level),
sizeof(lod_level));
// std::cout << " load level: " << lod_level <<
// std::endl;
// std::cout << " lod info: " << std::endl;
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
std::vector<size_t> tmp(size / sizeof(size_t));
is.read(reinterpret_cast<char *>(tmp.data()),
static_cast<std::streamsize>(size));
for (int j = 0; j < tmp.size(); ++j) {
// std::cout << " lod - " << tmp[j] <<
// std::endl;
}
}
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// std::cout << " tensor_version: " <<
// tensor_version <<
// std::endl;
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size <<
// std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " <<
// desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: "
// <<
// desc.dims()[l]
// << std::endl;
memory_size *= desc.dims()[l];
}
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" <<
// std::endl;
}
// std::cout << " malloc size: " << memory_size *
// type_size
// << std::endl;
void *memory = malloc(memory_size * type_size);
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory <<
// std::endl;
is.close();
} else {
// std::cout << " *not load "
// << " var : " << var.name() << std::endl;
}
uint32_t tensor_version;
is.read(reinterpret_cast<char *>(&version), sizeof(version));
// std::cout << " tensor_version: " <<
// tensor_version <<
// std::endl;
int32_t size;
is.read(reinterpret_cast<char *>(&size), sizeof(size));
// std::cout << " tensor desc size: " << size <<
// std::endl;
std::unique_ptr<char[]> buf(new char[size]);
is.read(reinterpret_cast<char *>(buf.get()), size);
framework::proto::VarType::TensorDesc desc;
desc.ParseFromArray(buf.get(), size);
// std::cout << " desc dims size " <<
// desc.dims().size() <<
// std::endl;
int memory_size = 1;
for (int l = 0; l < desc.dims().size(); ++l) {
// std::cout << " dim " << l << " value: "
// <<
// desc.dims()[l]
// << std::endl;
memory_size *= desc.dims()[l];
}
int type_size = 0;
// std::cout << " desc pre type: ";
switch (desc.data_type()) {
case framework::proto::VarType::FP16:
// std::cout << "FP16" << std::endl;
type_size = 2;
break;
case framework::proto::VarType::FP32:
type_size = 4;
// std::cout << "FP32" << std::endl;
break;
case framework::proto::VarType::FP64:
type_size = 8;
// std::cout << "FP64" << std::endl;
break;
case framework::proto::VarType::INT32:
type_size = 4;
// std::cout << "INT32" << std::endl;
break;
case framework::proto::VarType::INT64:
type_size = 8;
// std::cout << "INT64" << std::endl;
break;
case framework::proto::VarType::BOOL:
type_size = 1;
// std::cout << "BOOL" << std::endl;
break;
default:
break;
// std::cout << " not support" <<
// std::endl;
}
// std::cout << " malloc size: " << memory_size *
// type_size
// << std::endl;
void *memory = malloc(memory_size * type_size);
is.read(static_cast<char *>(memory), memory_size * type_size);
// std::cout << " memory: " << memory <<
// std::endl;
is.close();
} else {
// std::cout << " *not load "
// << " var : " << var.name() << std::endl;
}
}
}
#endif
return program;
return program;
}
template class Loader<CPU, Precision::FP32>;
......
src/io.h
浏览文件 @ ddf6b722
......@@ -29,11 +29,11 @@ namespace paddle_mobile {
template <typename Dtype, Precision P = Precision::FP32>
class Loader : PaddleMobileObject {
public:
const framework::Program<Dtype, P> Load(const std::string &dirname);
public:
const framework::Program<Dtype, P> Load(const std::string &dirname);
private:
void LoadVar(framework::LoDTensor *tensor, const std::string &file_path);
private:
void LoadVar(framework::LoDTensor *tensor, const std::string &file_path);
};
} // namespace paddle_mobile
src/memory/t_malloc.cc
浏览文件 @ ddf6b722
......@@ -26,25 +26,25 @@ namespace memory {
const int MALLOC_ALIGN = 16;
void Copy(void *dst, const void *src, size_t num) {
std::memcpy(dst, src, num);
std::memcpy(dst, src, num);
};
void *Alloc(size_t size) {
size_t offset = sizeof(void *) + MALLOC_ALIGN - 1;
char *p = static_cast<char *>(malloc(offset + size));
if (!p) {
return nullptr;
}
void *r = reinterpret_cast<void *>(reinterpret_cast<size_t>(p + offset) &
(~(MALLOC_ALIGN - 1)));
static_cast<void **>(r)[-1] = p;
return r;
size_t offset = sizeof(void *) + MALLOC_ALIGN - 1;
char *p = static_cast<char *>(malloc(offset + size));
if (!p) {
return nullptr;
}
void *r = reinterpret_cast<void *>(reinterpret_cast<size_t>(p + offset) &
(~(MALLOC_ALIGN - 1)));
static_cast<void **>(r)[-1] = p;
return r;
}
void Free(void *ptr) {
if (ptr) {
free(static_cast<void **>(ptr)[-1]);
}
if (ptr) {
free(static_cast<void **>(ptr)[-1]);
}
}
} // namespace memory
......
src/memory/t_malloc.h
浏览文件 @ ddf6b722
......@@ -38,12 +38,12 @@ void Free(void *ptr);
* static_cast
*/
template <typename T> class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
static_assert(std::is_pod<T>::value, "T must be POD");
public:
explicit PODDeleter(){};
public:
explicit PODDeleter(){};
void operator()(T *ptr) { Free(static_cast<void *>(ptr)); }
void operator()(T *ptr) { Free(static_cast<void *>(ptr)); }
};
/**
......@@ -55,10 +55,10 @@ template <typename T> class PODDeleter {
* reinterpret_cast
*/
template <typename T> class PlainDeleter {
public:
explicit PlainDeleter(){};
public:
explicit PlainDeleter(){};
void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
void operator()(T *ptr) { Free(reinterpret_cast<void *>(ptr)); }
};
} // namespace memory
} // namespace paddle_mobile
src/operators/batchnorm_op.cpp
浏览文件 @ ddf6b722
......@@ -23,8 +23,8 @@ namespace operators {
template <typename Dtype, typename T>
void BatchNormOp<Dtype, T>::InferShape() const {
auto x_dims = param_.InputX()->dims();
param_.OutputY()->Resize(x_dims);
auto x_dims = param_.InputX()->dims();
param_.OutputY()->Resize(x_dims);
}
template class BatchNormOp<CPU, float>;
} // namespace operators
......
src/operators/batchnorm_op.h
浏览文件 @ ddf6b722
......@@ -27,25 +27,25 @@ using namespace framework;
template <typename DeviceType, typename T>
class BatchNormOp : public framework::OperatorWithKernel<DeviceType> {
public:
BatchNormOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::BatchNormKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
BatchNormParam param_;
public:
BatchNormOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::BatchNormKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
BatchNormParam param_;
};
} // namespace operators
......
src/operators/concat_op.cpp
浏览文件 @ ddf6b722
......@@ -23,40 +23,40 @@ namespace operators {
template <typename Dtype, typename T>
void ConcatOp<Dtype, T>::InferShape() const {
auto inputs = param_.Inputs();
const size_t n = inputs.size();
auto inputs = param_.Inputs();
const size_t n = inputs.size();
std::vector<DDim> inputs_dims;
inputs_dims.reserve(n);
for (int i = 0; i < n; i++) {
inputs_dims.push_back(inputs[i]->dims());
}
std::vector<DDim> inputs_dims;
inputs_dims.reserve(n);
for (int i = 0; i < n; i++) {
inputs_dims.push_back(inputs[i]->dims());
}
auto axis = static_cast<size_t>(param_.Axis());
auto axis = static_cast<size_t>(param_.Axis());
if (n == 1) {
DLOG << "Warning: concat op have only one input, "
"may waste memory";
}
if (n == 1) {
DLOG << "Warning: concat op have only one input, "
"may waste memory";
}
/// add all dim[axis] and check other dims if equal.
auto out_dims = inputs_dims[0];
int in_zero_dims_size = out_dims.size();
for (size_t i = 1; i < n; i++) {
for (size_t j = 0; j < in_zero_dims_size; j++) {
if (j == axis) {
out_dims[axis] += inputs_dims[i][j];
} else {
assert(out_dims[j] == inputs_dims[i][j]);
}
}
/// add all dim[axis] and check other dims if equal.
auto out_dims = inputs_dims[0];
int in_zero_dims_size = out_dims.size();
for (size_t i = 1; i < n; i++) {
for (size_t j = 0; j < in_zero_dims_size; j++) {
if (j == axis) {
out_dims[axis] += inputs_dims[i][j];
} else {
assert(out_dims[j] == inputs_dims[i][j]);
}
}
}
if (out_dims[axis] < 0) {
out_dims[axis] = -1;
}
if (out_dims[axis] < 0) {
out_dims[axis] = -1;
}
param_.Out()->Resize(out_dims);
param_.Out()->Resize(out_dims);
}
template class ConcatOp<CPU, float>;
......
src/operators/concat_op.h
浏览文件 @ ddf6b722
......@@ -26,25 +26,24 @@ using namespace framework;
template <typename DeviceType, typename T>
class ConcatOp : public framework::OperatorWithKernel<DeviceType> {
public:
ConcatOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
public:
ConcatOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::ConcatKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
void Run() const {
operators::ConcatKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
ConcatParam param_;
protected:
ConcatParam param_;
};
} // namespace operators
......
src/operators/conv_op.cpp
浏览文件 @ ddf6b722
......@@ -25,48 +25,48 @@ namespace operators {
int ConvOutputSize(int input_size, int filter_size, int dilation, int padding,
int stride) {
const int dkernel = dilation * (filter_size - 1) + 1;
int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
return output_size;
const int dkernel = dilation * (filter_size - 1) + 1;
int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
return output_size;
}
template <typename Dtype, typename T>
void ConvOp<Dtype, T>::InferShape() const {
// std::cout << " begin get dims: " << std::endl;
// std::cout << " begin get dims: " << std::endl;
auto in_dims = param_.Input()->dims();
auto in_dims = param_.Input()->dims();
// std::cout << " end get in dims: " << std::endl;
// std::cout << " end get in dims: " << std::endl;
// std::cout << " in_dims: " << in_dims << std::endl;
// std::cout << " in_dims: " << in_dims << std::endl;
// std::cout << " begin get Filter " << std::endl;
// std::cout << " begin get Filter " << std::endl;
auto filter_dims = param_.Filter()->dims();
auto filter_dims = param_.Filter()->dims();
// std::cout << " end get Filter " << std::endl;
// std::cout << " end get Filter " << std::endl;
// std::cout << " begin get Attrs " << std::endl;
// std::cout << " begin get Attrs " << std::endl;
const std::vector<int> &strides = param_.Strides();
const std::vector<int> &strides = param_.Strides();
// std::cout << " end get Attrs " << strides[0] << std::endl;
// std::cout << " end get Attrs " << strides[0] << std::endl;
std::vector<int> paddings = param_.Paddings();
std::vector<int> paddings = param_.Paddings();
int groups = param_.Groups();
int groups = param_.Groups();
std::vector<int> dilations = param_.Dilations();
std::vector<int> dilations = param_.Dilations();
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back(ConvOutputSize(in_dims[i + 2],
filter_dims[i + 2], dilations[i],
paddings[i], strides[i]));
}
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
dilations[i], paddings[i],
strides[i]));
}
framework::DDim ddim = framework::make_ddim(output_shape);
param_.Output()->Resize(ddim);
framework::DDim ddim = framework::make_ddim(output_shape);
param_.Output()->Resize(ddim);
}
template class ConvOp<CPU, float>;
......
src/operators/conv_op.h
浏览文件 @ ddf6b722
......@@ -28,25 +28,25 @@ using namespace framework;
template <typename DeviceType, typename T>
class ConvOp : public framework::OperatorWithKernel<DeviceType> {
public:
ConvOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
void Run() const {
operators::ConvKernel<DeviceType, T> kernel;
kernel.Compute(param_);
this->ClearVariables({"Filter", "Input"});
}
private:
ConvParam param_;
public:
ConvOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
void Run() const {
operators::ConvKernel<DeviceType, T> kernel;
kernel.Compute(param_);
this->ClearVariables({"Filter", "Input"});
}
private:
ConvParam param_;
};
} // namespace operators
......
src/operators/elementwise_add_op.cpp
浏览文件 @ ddf6b722
......@@ -23,8 +23,8 @@ namespace operators {
template <typename Dtype, typename T>
void ElementwiseAddOp<Dtype, T>::InferShape() const {
auto x_dim = param_.InputX()->dims();
param_.Out()->Resize(x_dim);
auto x_dim = param_.InputX()->dims();
param_.Out()->Resize(x_dim);
}
template class ElementwiseAddOp<CPU, float>;
} // namespace operators
......
src/operators/elementwise_add_op.h
浏览文件 @ ddf6b722
......@@ -27,25 +27,25 @@ using namespace framework;
template <typename DeviceType, typename T>
class ElementwiseAddOp : public framework::OperatorWithKernel<DeviceType> {
public:
ElementwiseAddOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
public:
ElementwiseAddOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::ElementwiseAddKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
void Run() const {
operators::ElementwiseAddKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
ElementwiseAddParam param_;
protected:
ElementwiseAddParam param_;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/arm/batchnorm_kernel.cpp
浏览文件 @ ddf6b722
......@@ -21,73 +21,72 @@ namespace operators {
template <>
void BatchNormKernel<CPU, float>::Compute(const BatchNormParam &param) const {
/// todo: test.
const Tensor *input_x = param.InputX();
auto input_x_ptr = input_x->data<float>();
const auto &x_dims = input_x->dims();
const int N = x_dims[0];
const int C = x_dims[1];
const int H = x_dims[2];
const int W = x_dims[3];
const int stride0 = C * H * W;
const int stride1 = H * W;
const int stride2 = W;
Tensor *out = param.OutputY();
auto out_ptr = out->mutable_data<float>();
const float epsilon = param.Epsilon();
const Tensor *mean = param.InputMean();
const Tensor *variance = param.InputVariance();
const Tensor *scale = param.InputScale();
const Tensor *bias = param.InputBias();
auto mean_ptr = mean->data<float>();
auto variance_ptr = variance->data<float>();
auto scale_ptr = scale->data<float>();
auto bias_ptr = bias->data<float>();
/// todo: test.
const Tensor *input_x = param.InputX();
auto input_x_ptr = input_x->data<float>();
const auto &x_dims = input_x->dims();
const int N = x_dims[0];
const int C = x_dims[1];
const int H = x_dims[2];
const int W = x_dims[3];
const int stride0 = C * H * W;
const int stride1 = H * W;
const int stride2 = W;
Tensor *out = param.OutputY();
auto out_ptr = out->mutable_data<float>();
const float epsilon = param.Epsilon();
const Tensor *mean = param.InputMean();
const Tensor *variance = param.InputVariance();
const Tensor *scale = param.InputScale();
const Tensor *bias = param.InputBias();
auto mean_ptr = mean->data<float>();
auto variance_ptr = variance->data<float>();
auto scale_ptr = scale->data<float>();
auto bias_ptr = bias->data<float>();
Tensor inv_std;
auto inv_std_ptr = inv_std.mutable_data<float>(make_ddim({C}));
if (C != variance->numel()) {
std::cout << "C must equal to variance.numel()" << std::endl;
}
assert(C == variance->numel());
Tensor inv_std;
auto inv_std_ptr = inv_std.mutable_data<float>(make_ddim({C}));
if (C != variance->numel()) {
std::cout << "C must equal to variance.numel()" << std::endl;
}
assert(C == variance->numel());
/// std = (var + epsilon).sqrt();
/// inv_std = 1 / std;
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
/// std = (var + epsilon).sqrt();
/// inv_std = 1 / std;
for (int i = 0; i < C; i++) {
inv_std_ptr[i] =
1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
}
Tensor new_scale;
auto new_scale_ptr = new_scale.mutable_data<float>(make_ddim({C}));
Tensor new_bias;
auto new_bias_ptr = new_bias.mutable_data<float>(make_ddim({C}));
Tensor new_scale;
auto new_scale_ptr = new_scale.mutable_data<float>(make_ddim({C}));
Tensor new_bias;
auto new_bias_ptr = new_bias.mutable_data<float>(make_ddim({C}));
/// ((x - est_mean) * (inv_var) * scale + bias equal to
/// (x * inv_var * scale) + (bias - est_mean * inv_var * scale)
for (int i = 0; i < C; i++) {
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
new_bias_ptr[i] =
bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
{
for (int n = 0; n < N; n++) {
for (int h = 0; h < H; h++) {
for (int w = 0; w < W; w++) {
int index = n * stride0 + i * stride1 + h * stride2 + w;
out_ptr[index] = input_x_ptr[index] * new_scale_ptr[i] +
new_bias_ptr[i];
}
}
}
/// ((x - est_mean) * (inv_var) * scale + bias equal to
/// (x * inv_var * scale) + (bias - est_mean * inv_var * scale)
for (int i = 0; i < C; i++) {
new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
{
for (int n = 0; n < N; n++) {
for (int h = 0; h < H; h++) {
for (int w = 0; w < W; w++) {
int index = n * stride0 + i * stride1 + h * stride2 + w;
out_ptr[index] =
input_x_ptr[index] * new_scale_ptr[i] + new_bias_ptr[i];
}
}
}
}
DLOG << "input[2,5,1,0](input[102]) ,channel 5 :";
DLOG << "input_x_ptr : " << input_x_ptr[102];
DLOG << "variance : " << variance_ptr[5];
DLOG << "inv_std_ptr : " << inv_std_ptr[5];
DLOG << "new_scale_ptr : " << new_scale_ptr[5];
DLOG << "new_bias_ptr : " << new_bias_ptr[5];
DLOG << "out_ptr : " << out_ptr[102];
}
DLOG << "input[2,5,1,0](input[102]) ,channel 5 :";
DLOG << "input_x_ptr : " << input_x_ptr[102];
DLOG << "variance : " << variance_ptr[5];
DLOG << "inv_std_ptr : " << inv_std_ptr[5];
DLOG << "new_scale_ptr : " << new_scale_ptr[5];
DLOG << "new_bias_ptr : " << new_bias_ptr[5];
DLOG << "out_ptr : " << out_ptr[102];
}
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/arm/concat_kernel.cpp
浏览文件 @ ddf6b722
......@@ -19,36 +19,36 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
template <typename T> class ConcatFunctor {
public:
void operator()(const std::vector<framework::Tensor> &input, const int axis,
framework::Tensor *output) {
size_t num = input.size();
int rows = 1;
auto dim_0 = input[0].dims();
for (int i = 0; i < axis; ++i) {
rows *= dim_0[i];
}
int out_rows = rows, out_cols = 0;
public:
void operator()(const std::vector<framework::Tensor> &input, const int axis,
framework::Tensor *output) {
size_t num = input.size();
int rows = 1;
auto dim_0 = input[0].dims();
for (int i = 0; i < axis; ++i) {
rows *= dim_0[i];
}
int out_rows = rows, out_cols = 0;
std::vector<int64_t> input_cols(input.size());
for (int i = 0; i < num; ++i) {
int t_cols = input[i].numel() / rows;
out_cols += t_cols;
input_cols[i] = t_cols;
}
std::vector<int64_t> input_cols(input.size());
for (int i = 0; i < num; ++i) {
int t_cols = input[i].numel() / rows;
out_cols += t_cols;
input_cols[i] = t_cols;
}
// computation
for (int k = 0; k < out_rows; ++k) {
T *dst_ptr = output->data<T>() + k * out_cols;
int col_idx = 0;
for (int j = 0; j < num; ++j) {
int col_len = input_cols[j];
const T *src_prt = input[j].data<T>() + k * col_len;
memory::Copy(dst_ptr + col_idx, src_prt, sizeof(T) * col_len);
col_idx += col_len;
}
}
// computation
for (int k = 0; k < out_rows; ++k) {
T *dst_ptr = output->data<T>() + k * out_cols;
int col_idx = 0;
for (int j = 0; j < num; ++j) {
int col_len = input_cols[j];
const T *src_prt = input[j].data<T>() + k * col_len;
memory::Copy(dst_ptr + col_idx, src_prt, sizeof(T) * col_len);
col_idx += col_len;
}
}
}
};
template <typename T>
void StridedNumelCopyWithAxis(int64_t axis, T *dst,
......@@ -56,61 +56,60 @@ void StridedNumelCopyWithAxis(int64_t axis, T *dst,
const T *src,
const framework::DDim &src_stride_numel,
int64_t size) {
int64_t before = dst_stride_numel[0] / dst_stride_numel[axis];
int64_t src_after = src_stride_numel[axis];
int64_t dst_after = dst_stride_numel[axis];
int64_t before = dst_stride_numel[0] / dst_stride_numel[axis];
int64_t src_after = src_stride_numel[axis];
int64_t dst_after = dst_stride_numel[axis];
///"src and dst tensor should have the same dims size."
assert(src_stride_numel.size() == dst_stride_numel.size());
///"src and dst tensor should have the same dims size."
assert(src_stride_numel.size() == dst_stride_numel.size());
for (int64_t i = 0; i < axis; ++i) {
if (i < axis) {
/// src and dst should have the same elements
/// except the specified axis.
assert(src_stride_numel[i] / src_stride_numel[axis] ==
dst_stride_numel[i] / dst_stride_numel[axis]);
for (int64_t i = 0; i < axis; ++i) {
if (i < axis) {
/// src and dst should have the same elements
/// except the specified axis.
assert(src_stride_numel[i] / src_stride_numel[axis] ==
dst_stride_numel[i] / dst_stride_numel[axis]);
} else if (i == axis) {
continue;
} else {
/// "src and dst should have the same elements "
/// "except the specified axis."
assert(src_stride_numel[i] == dst_stride_numel[i]);
}
} else if (i == axis) {
continue;
} else {
/// "src and dst should have the same elements "
/// "except the specified axis."
assert(src_stride_numel[i] == dst_stride_numel[i]);
}
}
for (int64_t i = 0; i < before; ++i) {
memory::Copy(dst + i * dst_after, src + i * src_after,
sizeof(T) * size);
}
for (int64_t i = 0; i < before; ++i) {
memory::Copy(dst + i * dst_after, src + i * src_after, sizeof(T) * size);
}
}
template <>
void ConcatKernel<CPU, float>::Compute(const ConcatParam &param) const {
auto inputs = param.Inputs();
auto *out = param.Out();
int64_t axis = param.Axis();
out->mutable_data<float>();
auto inputs = param.Inputs();
auto *out = param.Out();
int64_t axis = param.Axis();
out->mutable_data<float>();
/// Sometimes direct copies will be faster, this maybe need deeply analysis.
if (axis == 0 && inputs.size() < 10) {
size_t output_offset = 0;
for (auto *in : inputs) {
auto in_stride = framework::stride_numel(in->dims());
auto out_stride = framework::stride_numel(out->dims());
StridedNumelCopyWithAxis<float>(
axis, out->data<float>() + output_offset, out_stride,
in->data<float>(), in_stride, in_stride[axis]);
output_offset += in_stride[axis];
}
} else {
std::vector<framework::Tensor> inputs_concat(inputs.size());
for (int j = 0; j < inputs.size(); ++j) {
inputs_concat[j] = *inputs[j];
}
ConcatFunctor<float> concat_functor;
concat_functor(inputs_concat, static_cast<int>(axis), out);
/// Sometimes direct copies will be faster, this maybe need deeply analysis.
if (axis == 0 && inputs.size() < 10) {
size_t output_offset = 0;
for (auto *in : inputs) {
auto in_stride = framework::stride_numel(in->dims());
auto out_stride = framework::stride_numel(out->dims());
StridedNumelCopyWithAxis<float>(axis, out->data<float>() + output_offset,
out_stride, in->data<float>(), in_stride,
in_stride[axis]);
output_offset += in_stride[axis];
}
} else {
std::vector<framework::Tensor> inputs_concat(inputs.size());
for (int j = 0; j < inputs.size(); ++j) {
inputs_concat[j] = *inputs[j];
}
ConcatFunctor<float> concat_functor;
concat_functor(inputs_concat, static_cast<int>(axis), out);
}
}
} // namespace operators
......
src/operators/kernel/arm/conv_kernel.cpp
浏览文件 @ ddf6b722
......@@ -24,128 +24,126 @@ namespace operators {
bool IsExpand(const std::vector<int64_t> &filter_dim,
const std::vector<int> &strides, const std::vector<int> &paddings,
const std::vector<int> &dilations) {
bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
for (size_t j = 0; j < strides.size(); ++j) {
filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
strides_1 = strides_1 && (strides[j] == 1);
padding_0 = padding_0 && (paddings[j] == 0);
dilation_1 = dilation_1 && (dilations[j] == 1);
}
return !(filter_1 && strides_1 && padding_0 && dilation_1);
bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
for (size_t j = 0; j < strides.size(); ++j) {
filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
strides_1 = strides_1 && (strides[j] == 1);
padding_0 = padding_0 && (paddings[j] == 0);
dilation_1 = dilation_1 && (dilations[j] == 1);
}
return !(filter_1 && strides_1 && padding_0 && dilation_1);
}
template <> void ConvKernel<CPU, float>::Compute(const ConvParam &param) const {
LOG(kLOG_DEBUG) << param;
const Tensor *input = param.Input();
// The filter will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *param.Filter();
Tensor *output = param.Output();
// output->mutable_data<T>(context.GetPlace());
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
DLOG << " compute end get Attrs " << strides[0];
const int batch_size = static_cast<int>(input->dims()[0]);
// filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
// k_w}
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
// output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
// o_w}
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
// use col_shape in the im2col calculation
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
// k_w, o_d,
// o_h, o_w}
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
// o_d *
// o_h * o_w)
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
LOG(kLOG_DEBUG) << param;
const Tensor *input = param.Input();
// The filter will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *param.Filter();
Tensor *output = param.Output();
// output->mutable_data<T>(context.GetPlace());
int groups = param.Groups();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
std::vector<int> dilations = param.Dilations();
DLOG << " compute end get Attrs " << strides[0];
const int batch_size = static_cast<int>(input->dims()[0]);
// filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
// k_w}
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
// output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
// o_w}
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
// use col_shape in the im2col calculation
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
// k_w, o_d,
// o_h, o_w}
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
col_shape_vec[0] = input->dims()[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
}
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
// o_d *
// o_h * o_w)
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, data_dim + 1);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<float>(col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
// auto& dev_ctx = context.template
// device_context<DeviceContext>();
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false, float(1.0),
&out_slice, float(0.0));
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<CPU, float> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;
// auto& dev_ctx = context.template
// device_context<DeviceContext>();
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (data_dim == 2U) {
// im2col
im2col(in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (data_dim == 3U) {
// vol2col
vol2col(in_slice, dilations, strides, paddings, &col);
}
// gemm
Tensor out_slice =
out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice =
filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<float>(filter_slice, false, col_matrix, false,
float(1.0), &out_slice, float(0.0));
}
}
}
}
template class ConvKernel<CPU, float>;
......
src/operators/kernel/arm/elementwise_add_kernel.cpp
浏览文件 @ ddf6b722
......@@ -20,19 +20,19 @@ namespace paddle_mobile {
namespace operators {
template <typename T> struct AddFunctor {
inline T operator()(T a, T b) const { return a + b; }
inline T operator()(T a, T b) const { return a + b; }
};
template <>
void ElementwiseAddKernel<CPU, float>::Compute(
const ElementwiseAddParam &param) const {
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *Out = param.Out();
Out->mutable_data<float>();
const int axis = param.Axis();
ElementwiseComputeEx<AddFunctor<float>, float>(input_x, input_y, axis,
AddFunctor<float>(), Out);
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *Out = param.Out();
Out->mutable_data<float>();
const int axis = param.Axis();
ElementwiseComputeEx<AddFunctor<float>, float>(input_x, input_y, axis,
AddFunctor<float>(), Out);
}
template class ElementwiseAddKernel<CPU, float>;
......
src/operators/kernel/arm/lrn_kernel.cpp
浏览文件 @ ddf6b722
......@@ -24,21 +24,21 @@ namespace paddle_mobile {
namespace operators {
template <> void LrnKernel<CPU, float>::Compute(const LrnParam &param) const {
const Tensor *input_x = param.InputX();
auto x_dims = input_x->dims();
/// data_format = NCHW
const int N = x_dims[0];
const int C = x_dims[1];
const int H = x_dims[2];
const int W = x_dims[3];
Tensor *out = param.Out();
out->mutable_data<float>();
const int n = param.N();
const float alpha = param.Alpha();
const float beta = param.Beta();
const float k = param.K();
LRNFunctor<float> lrnFunctor;
lrnFunctor(*input_x, out, N, C, H, W, n, k, alpha, beta);
const Tensor *input_x = param.InputX();
auto x_dims = input_x->dims();
/// data_format = NCHW
const int N = x_dims[0];
const int C = x_dims[1];
const int H = x_dims[2];
const int W = x_dims[3];
Tensor *out = param.Out();
out->mutable_data<float>();
const int n = param.N();
const float alpha = param.Alpha();
const float beta = param.Beta();
const float k = param.K();
LRNFunctor<float> lrnFunctor;
lrnFunctor(*input_x, out, N, C, H, W, n, k, alpha, beta);
}
template class LrnKernel<CPU, float>;
......
src/operators/kernel/arm/mul_kernel.cpp
浏览文件 @ ddf6b722
......@@ -24,27 +24,27 @@ namespace paddle_mobile {
namespace operators {
template <> void MulKernel<CPU, float>::Compute(const MulParam &param) const {
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *out = param.Out();
out->mutable_data<float>();
const Tensor x_matrix =
input_x->dims().size() > 2
? framework::ReshapeToMatrix(*input_x, param.XNumColDims())
: *input_x;
const Tensor y_matrix =
input_y->dims().size() > 2
? framework::ReshapeToMatrix(*input_y, param.YNumColDims())
: *input_y;
auto out_dim = out->dims();
if (out_dim.size() != 2) {
out->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
}
math::matmul<float>(x_matrix, false, y_matrix, false, static_cast<float>(1),
out, static_cast<float>(0));
if (out_dim.size() != 2) {
out->Resize(out_dim);
}
const Tensor *input_x = param.InputX();
const Tensor *input_y = param.InputY();
Tensor *out = param.Out();
out->mutable_data<float>();
const Tensor x_matrix =
input_x->dims().size() > 2
? framework::ReshapeToMatrix(*input_x, param.XNumColDims())
: *input_x;
const Tensor y_matrix =
input_y->dims().size() > 2
? framework::ReshapeToMatrix(*input_y, param.YNumColDims())
: *input_y;
auto out_dim = out->dims();
if (out_dim.size() != 2) {
out->Resize({x_matrix.dims()[0], y_matrix.dims()[1]});
}
math::matmul<float>(x_matrix, false, y_matrix, false, static_cast<float>(1),
out, static_cast<float>(0));
if (out_dim.size() != 2) {
out->Resize(out_dim);
}
}
template class MulKernel<CPU, float>;
......
src/operators/kernel/arm/pool_kernel.cpp
浏览文件 @ ddf6b722
......@@ -24,54 +24,54 @@ namespace operators {
inline void PoolBasic(std::string pooling_type, std::vector<int> ksize,
std::vector<int> strides, std::vector<int> paddings,
const Tensor *in_x, Tensor *out) {
if (pooling_type == "max") {
math::PoolFunctor<CPU, math::MaxPool<float>, float> pool2d_forward;
math::MaxPool<float> pool_process;
pool2d_forward(*in_x, ksize, strides, paddings, pool_process, out);
if (pooling_type == "max") {
math::PoolFunctor<CPU, math::MaxPool<float>, float> pool2d_forward;
math::MaxPool<float> pool_process;
pool2d_forward(*in_x, ksize, strides, paddings, pool_process, out);
} else if (pooling_type == "avg") {
math::PoolFunctor<CPU, math::AvgPool<float>, float> pool2d_forward;
math::AvgPool<float> pool_process;
pool2d_forward(*in_x, ksize, strides, paddings, pool_process, out);
}
} else if (pooling_type == "avg") {
math::PoolFunctor<CPU, math::AvgPool<float>, float> pool2d_forward;
math::AvgPool<float> pool_process;
pool2d_forward(*in_x, ksize, strides, paddings, pool_process, out);
}
}
template <> void PoolKernel<CPU, float>::Compute(const PoolParam &param) const {
const Tensor *in_x = param.Input();
Tensor *out = param.Output();
std::string pooling_type = param.PoolingType();
const Tensor *in_x = param.Input();
Tensor *out = param.Output();
std::string pooling_type = param.PoolingType();
std::vector<int> ksize = param.Ksize();
std::vector<int> ksize = param.Ksize();
std::vector<int> strides = param.Strides();
std::vector<int> strides = param.Strides();
std::vector<int> paddings = param.Paddings();
if (ksize.size() != 2) {
LOG(paddle_mobile::LogLevel::kLOG_ERROR)
<< "Pool op only supports 2D and 3D input.";
}
std::vector<int> paddings = param.Paddings();
if (ksize.size() != 2) {
LOG(paddle_mobile::LogLevel::kLOG_ERROR)
<< "Pool op only supports 2D and 3D input.";
}
if (param.isGlobalPooling()) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
if (param.isGlobalPooling()) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
}
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
// if (param.isGlobalPooling() || ksize[0] != ksize[1] ||
// strides[0] != strides[1] || strides[1] != 2 ||
// paddings[0] != paddings[1] || paddings[1] > 1) {
// PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
//
// } else if (ksize[0] == 2) {
//
// } else if (ksize[0] == 3) {
//
// } else {
// PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
// }
// if (param.isGlobalPooling() || ksize[0] != ksize[1] ||
// strides[0] != strides[1] || strides[1] != 2 ||
// paddings[0] != paddings[1] || paddings[1] > 1) {
// PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
//
// } else if (ksize[0] == 2) {
//
// } else if (ksize[0] == 3) {
//
// } else {
// PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
// }
}
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/batchnorm_kernel.h
浏览文件 @ ddf6b722
......@@ -28,8 +28,8 @@ using namespace framework;
template <typename DeviceType, typename T>
class BatchNormKernel
: public framework::OpKernelBase<DeviceType, BatchNormParam> {
public:
void Compute(const BatchNormParam &param) const;
public:
void Compute(const BatchNormParam &param) const;
};
} // namespace operators
......
src/operators/kernel/concat_kernel.h
浏览文件 @ ddf6b722
......@@ -26,8 +26,8 @@ using namespace framework;
template <typename DeviceType, typename T>
class ConcatKernel : public framework::OpKernelBase<DeviceType, ConcatParam> {
public:
void Compute(const ConcatParam &param) const;
public:
void Compute(const ConcatParam &param) const;
};
} // namespace operators
......
src/operators/kernel/conv_kernel.h
浏览文件 @ ddf6b722
......@@ -31,8 +31,8 @@ using namespace framework;
template <typename DeviceType, typename T>
class ConvKernel : public framework::OpKernelBase<DeviceType, ConvParam> {
public:
void Compute(const ConvParam &param) const;
public:
void Compute(const ConvParam &param) const;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/elementwise_add_kernel.h
浏览文件 @ ddf6b722
......@@ -29,8 +29,8 @@ using namespace framework;
template <typename DeviceType, typename T>
class ElementwiseAddKernel
: public framework::OpKernelBase<DeviceType, ElementwiseAddParam> {
public:
void Compute(const ElementwiseAddParam &param) const;
public:
void Compute(const ElementwiseAddParam &param) const;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/lrn_kernel.h
浏览文件 @ ddf6b722
......@@ -26,52 +26,49 @@ namespace operators {
using namespace framework;
template <typename T> struct LRNFunctor {
void operator()(const framework::Tensor &input, framework::Tensor *out,
int N, int C, int H, int W, int n, T k, T alpha, T beta) {
auto input_ptr = input.data<T>();
const int start = -(n - 1) / 2;
const int end = start + n;
void operator()(const framework::Tensor &input, framework::Tensor *out, int N,
int C, int H, int W, int n, T k, T alpha, T beta) {
auto input_ptr = input.data<T>();
const int start = -(n - 1) / 2;
const int end = start + n;
const int stride0 = C * H * W;
const int stride1 = H * W;
const int stride2 = W;
const int stride3 = 1;
const int stride0 = C * H * W;
const int stride1 = H * W;
const int stride2 = W;
const int stride3 = 1;
framework::Tensor sqr_buffer;
auto sqr_buffer_ptr = sqr_buffer.mutable_data<T>(input.dims());
std::fill(sqr_buffer_ptr, sqr_buffer_ptr + sqr_buffer.numel(), k);
for (int a = 0; a < N; a++) {
for (int b = 0; b < C; b++) {
for (int index = start; index < end; index++) {
int channel = b + index;
if (channel >= 0 && channel < C) {
for (int c = 0; c < H; c++) {
for (int d = 0; d < W; d++) {
int u =
a * stride0 + b * stride1 + c * stride2 + d;
framework::Tensor sqr_buffer;
auto sqr_buffer_ptr = sqr_buffer.mutable_data<T>(input.dims());
std::fill(sqr_buffer_ptr, sqr_buffer_ptr + sqr_buffer.numel(), k);
for (int a = 0; a < N; a++) {
for (int b = 0; b < C; b++) {
for (int index = start; index < end; index++) {
int channel = b + index;
if (channel >= 0 && channel < C) {
for (int c = 0; c < H; c++) {
for (int d = 0; d < W; d++) {
int u = a * stride0 + b * stride1 + c * stride2 + d;
int i = a * stride0 + channel * stride1 +
c * stride2 + d;
int i = a * stride0 + channel * stride1 + c * stride2 + d;
sqr_buffer_ptr[u] +=
alpha * input_ptr[i] * input_ptr[i];
}
}
}
}
sqr_buffer_ptr[u] += alpha * input_ptr[i] * input_ptr[i];
}
}
}
}
auto out_ptr = out->data<T>();
for (int i = 0; i < input.numel(); i++) {
out_ptr[i] = input_ptr[i] / pow(sqr_buffer_ptr[i], beta);
}
}
}
auto out_ptr = out->data<T>();
for (int i = 0; i < input.numel(); i++) {
out_ptr[i] = input_ptr[i] / pow(sqr_buffer_ptr[i], beta);
}
}
};
template <typename DeviceType, typename T>
class LrnKernel : public framework::OpKernelBase<DeviceType, LrnParam> {
public:
void Compute(const LrnParam &param) const;
public:
void Compute(const LrnParam &param) const;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/mul_kernel.h
浏览文件 @ ddf6b722
......@@ -28,8 +28,8 @@ using namespace framework;
template <typename DeviceType, typename T>
class MulKernel : public framework::OpKernelBase<DeviceType, MulParam> {
public:
void Compute(const MulParam &param) const;
public:
void Compute(const MulParam &param) const;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/kernel/pool_kernel.h
浏览文件 @ ddf6b722
......@@ -28,8 +28,8 @@ using namespace framework;
template <typename DeviceType, typename T>
class PoolKernel : public framework::OpKernelBase<DeviceType, PoolParam> {
public:
void Compute(const PoolParam &param) const;
public:
void Compute(const PoolParam &param) const;
};
} // namespace operators
} // namespace paddle_mobile
src/operators/lrn_op.cpp
浏览文件 @ ddf6b722
......@@ -22,8 +22,8 @@ namespace paddle_mobile {
namespace operators {
template <typename Dtype, typename T> void LrnOp<Dtype, T>::InferShape() const {
auto x_dims = param_.InputX()->dims();
param_.Out()->Resize(x_dims);
auto x_dims = param_.InputX()->dims();
param_.Out()->Resize(x_dims);
}
template class LrnOp<CPU, float>;
} // namespace operators
......
src/operators/lrn_op.h
浏览文件 @ ddf6b722
......@@ -27,24 +27,24 @@ using namespace framework;
template <typename DeviceType, typename T>
class LrnOp : public framework::OperatorWithKernel<DeviceType> {
public:
LrnOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::LrnKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
LrnParam param_;
public:
LrnOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::LrnKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
LrnParam param_;
};
} // namespace operators
......
src/operators/math/elementwise_op_function.h
浏览文件 @ ddf6b722
......@@ -34,66 +34,66 @@ namespace operators {
inline void get_mid_dims(const framework::DDim &x_dims,
const framework::DDim &y_dims, const int axis,
int *pre, int *n, int *post) {
*pre = 1;
*n = 1;
*post = 1;
// compute pre
for (int i = 0; i < axis; ++i) {
(*pre) *= x_dims[i];
}
for (int i = 0; i < y_dims.size(); ++i) {
assert(x_dims[i + axis] == y_dims[i]);
/// "Broadcast dimension mismatch.");
(*n) *= y_dims[i];
}
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
(*post) *= x_dims[i];
}
*pre = 1;
*n = 1;
*post = 1;
// compute pre
for (int i = 0; i < axis; ++i) {
(*pre) *= x_dims[i];
}
for (int i = 0; i < y_dims.size(); ++i) {
assert(x_dims[i + axis] == y_dims[i]);
/// "Broadcast dimension mismatch.");
(*n) *= y_dims[i];
}
for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
(*post) *= x_dims[i];
}
}
/// remove dims tail 1. (4,20,1,1) -> (4,20)
inline void trim_trailing_singular_dims(framework::DDim *dims) {
// Remove trailing dimensions of size 1 for y
auto actual_dims_size = dims->size();
for (; actual_dims_size != 0; --actual_dims_size) {
if ((*dims)[actual_dims_size - 1] != 1)
break;
}
if (actual_dims_size != dims->size()) {
auto actual_dims = framework::vectorize(*dims);
actual_dims.resize(actual_dims_size);
*dims = framework::make_ddim(actual_dims);
}
// Remove trailing dimensions of size 1 for y
auto actual_dims_size = dims->size();
for (; actual_dims_size != 0; --actual_dims_size) {
if ((*dims)[actual_dims_size - 1] != 1)
break;
}
if (actual_dims_size != dims->size()) {
auto actual_dims = framework::vectorize(*dims);
actual_dims.resize(actual_dims_size);
*dims = framework::make_ddim(actual_dims);
}
}
template <typename T> class RowwiseTransformIterator {
public:
RowwiseTransformIterator(const T *ptr, int n) : ptr_(ptr), i_(0), n_(n) {}
RowwiseTransformIterator<T> &operator++() {
++i_;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
return *this;
}
public:
RowwiseTransformIterator(const T *ptr, int n) : ptr_(ptr), i_(0), n_(n) {}
bool operator==(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
RowwiseTransformIterator<T> &operator++() {
++i_;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
return *this;
}
bool operator!=(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
bool operator==(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
}
const T &operator*() { return ptr_[i_]; }
bool operator!=(const RowwiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
private:
const T *ptr_;
int i_;
int64_t n_;
const T &operator*() { return ptr_[i_]; }
private:
const T *ptr_;
int i_;
int64_t n_;
};
/// (4,20,2)+(20,): (20,) just as (20,1), when move 2 strides in last
......@@ -101,106 +101,105 @@ template <typename T> class RowwiseTransformIterator {
/// in (4,20,2) is 2 ,
/// (20,1) move 1 stride , to fill(add) 2 element with the same number.
template <typename T> class MidWiseTransformIterator {
public:
MidWiseTransformIterator(const T *ptr, int n, int post)
: ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}
MidWiseTransformIterator<T> &operator++() {
++j_;
if (UNLIKELY(j_ == post_)) {
++i_;
j_ = 0;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
}
return *this;
}
bool operator==(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator!=(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
const T &operator*() { return ptr_[i_]; }
private:
const T *ptr_;
int64_t i_;
int64_t j_;
int64_t n_;
int64_t post_;
public:
MidWiseTransformIterator(const T *ptr, int n, int post)
: ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}
MidWiseTransformIterator<T> &operator++() {
++j_;
if (UNLIKELY(j_ == post_)) {
++i_;
j_ = 0;
if (UNLIKELY(i_ == n_)) {
i_ = 0;
}
}
return *this;
}
bool operator==(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) == &(*rhs);
}
bool operator!=(const MidWiseTransformIterator<T> &rhs) const {
return (ptr_ + i_) != &(*rhs);
}
const T &operator*() { return ptr_[i_]; }
private:
const T *ptr_;
int64_t i_;
int64_t j_;
int64_t n_;
int64_t post_;
};
template <typename Functor, typename T, typename OutType = T>
class TransformFunctor {
public:
TransformFunctor(const framework::Tensor *x, const framework::Tensor *y,
framework::Tensor *z, Functor func)
: x_(x->data<T>()), y_(y->data<T>()), z_(z->mutable_data<OutType>()),
nx_(x->numel()), func_(func) {}
inline void Run() const {
math::Transform trans;
// 同时执行func(x_, y_)传入z_。
trans(x_, x_ + nx_, y_, z_, func_);
}
inline void RunRowWise(int n, int pre) const {
math::Transform trans;
trans(x_, x_ + nx_, RowwiseTransformIterator<T>(y_, n), z_, func_);
}
inline void RunMidWise(int n, int pre, int post) const {
math::Transform trans;
trans(x_, x_ + nx_, MidWiseTransformIterator<T>(y_, n, post), z_,
func_);
}
private:
const T *x_;
const T *y_;
OutType *z_;
int64_t nx_;
Functor func_;
public:
TransformFunctor(const framework::Tensor *x, const framework::Tensor *y,
framework::Tensor *z, Functor func)
: x_(x->data<T>()), y_(y->data<T>()), z_(z->mutable_data<OutType>()),
nx_(x->numel()), func_(func) {}
inline void Run() const {
math::Transform trans;
// 同时执行func(x_, y_)传入z_。
trans(x_, x_ + nx_, y_, z_, func_);
}
inline void RunRowWise(int n, int pre) const {
math::Transform trans;
trans(x_, x_ + nx_, RowwiseTransformIterator<T>(y_, n), z_, func_);
}
inline void RunMidWise(int n, int pre, int post) const {
math::Transform trans;
trans(x_, x_ + nx_, MidWiseTransformIterator<T>(y_, n, post), z_, func_);
}
private:
const T *x_;
const T *y_;
OutType *z_;
int64_t nx_;
Functor func_;
};
template <typename Functor, typename T, typename OutType = T>
void ElementwiseComputeEx(const framework::Tensor *x,
const framework::Tensor *y, int axis, Functor func,
framework::Tensor *z) {
TransformFunctor<Functor, T, OutType> functor(x, y, z, func);
auto x_dims = x->dims();
auto y_dims = y->dims();
// PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
// "Rank of first input must >= rank of second
// input.");
if (x_dims == y_dims) {
functor.Run();
return;
}
/// axis = -1 represent the last dimension.
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
// PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
// "Axis should be in range [0, x_dims)");
trim_trailing_singular_dims(&y_dims);
axis = (y_dims.size() == 0) ? x_dims.size() : axis;
int pre, n, post;
get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
if (post == 1) {
functor.RunRowWise(n, pre);
return;
} else {
functor.RunMidWise(n, pre, post);
return;
}
TransformFunctor<Functor, T, OutType> functor(x, y, z, func);
auto x_dims = x->dims();
auto y_dims = y->dims();
// PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
// "Rank of first input must >= rank of second
// input.");
if (x_dims == y_dims) {
functor.Run();
return;
}
/// axis = -1 represent the last dimension.
axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
// PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
// "Axis should be in range [0, x_dims)");
trim_trailing_singular_dims(&y_dims);
axis = (y_dims.size() == 0) ? x_dims.size() : axis;
int pre, n, post;
get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post);
if (post == 1) {
functor.RunRowWise(n, pre);
return;
} else {
functor.RunMidWise(n, pre, post);
return;
}
}
} // namespace operators
......
src/operators/math/im2col.cc
浏览文件 @ ddf6b722
......@@ -26,70 +26,65 @@ namespace math {
* output_width]
*/
template <class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
public:
void operator()(const framework::Tensor &im, const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
int col_width = col->dims()[4];
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
int col_width = col->dims()[4];
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
int channels_col = im_channels * filter_height * filter_width;
int channels_col = im_channels * filter_height * filter_width;
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx =
h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx =
w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * col_height + h) * col_width + w;
int im_idx =
(im_row_idx + c_im * im_height) * im_width + im_col_idx;
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * col_height + h) * col_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] =
(im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
}
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
}
}
}
};
/*
......@@ -99,67 +94,64 @@ template <class T> class Im2ColFunctor<ColFormat::kCFO, CPU, T> {
* output_width]
*/
template <class T> class Col2ImFunctor<ColFormat::kCFO, CPU, T> {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int col_height = col.dims()[3];
int col_width = col.dims()[4];
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int col_height = col.dims()[3];
int col_width = col.dims()[4];
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2]
// -
// ((dilation[0] * (filter_height - 1)
// + 1))) /
// stride[0] +
// 1,
// col_height,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3]
// -
// ((dilation[1] * (filter_width - 1)
// + 1))) /
// stride[1] +
// 1,
// col_width,
// "Output_height and
// padding(padding_up, padding_down)
// are " "inconsistent.");
int channels_col = im_channels * filter_height * filter_width;
int channels_col = im_channels * filter_height * filter_width;
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx =
h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx =
w * stride[1] - padding[1] + w_offset * dilation[1];
if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
(im_col_idx) >= 0 && (im_col_idx) < im_width) {
im_data[(im_row_idx + c_im * im_height) * im_width +
im_col_idx] +=
col_data[(c * col_height + h) * col_width + w];
}
}
}
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < col_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < col_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
(im_col_idx) >= 0 && (im_col_idx) < im_width) {
im_data[(im_row_idx + c_im * im_height) * im_width + im_col_idx] +=
col_data[(c * col_height + h) * col_width + w];
}
}
}
}
}
};
template class Im2ColFunctor<ColFormat::kCFO, CPU, float>;
......@@ -174,74 +166,69 @@ template class Col2ImFunctor<ColFormat::kCFO, CPU, double>;
* filter_width]
*/
template <class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[3];
int filter_width = col->dims()[4];
int col_height = col->dims()[0];
int col_width = col->dims()[1];
public:
void operator()(const framework::Tensor &im, const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col) {
// PADDLE_ENFORCE(im.dims().size() == 3);
// PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[3];
int filter_width = col->dims()[4];
int col_height = col->dims()[0];
int col_width = col->dims()[1];
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
const T *im_data = im.data<T>();
T *col_data = col->data<T>();
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset = col_row_idx * stride[0] +
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width; ++filter_col_idx) {
int im_col_offset = col_col_idx * stride[1] +
filter_col_idx - padding[1];
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset =
col_row_idx * stride[0] + filter_row_idx - padding[0];
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_col_offset =
col_col_idx * stride[1] + filter_col_idx - padding[1];
int col_offset =
((((col_row_idx)*col_width + col_col_idx) *
im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
int col_offset =
((((col_row_idx)*col_width + col_col_idx) * im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
int im_offset =
(channel * im_height + im_row_offset) *
im_width +
im_col_offset;
col_data[col_offset] =
(im_row_offset < 0 ||
im_row_offset >= im_height ||
im_col_offset < 0 || im_col_offset >= im_width)
? static_cast<T>(0)
: im_data[im_offset];
}
}
}
int im_offset = (channel * im_height + im_row_offset) * im_width +
im_col_offset;
col_data[col_offset] =
(im_row_offset < 0 || im_row_offset >= im_height ||
im_col_offset < 0 || im_col_offset >= im_width)
? static_cast<T>(0)
: im_data[im_offset];
}
}
}
}
}
}
};
/*
......@@ -251,74 +238,70 @@ template <class T> class Im2ColFunctor<ColFormat::kOCF, CPU, T> {
* filter_width]
*/
template <class T> class Col2ImFunctor<ColFormat::kOCF, CPU, T> {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int col_height = col.dims()[0];
int col_width = col.dims()[1];
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im) {
// PADDLE_ENFORCE(im->dims().size() == 3);
// PADDLE_ENFORCE(col.dims().size() == 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
int filter_height = col.dims()[3];
int filter_width = col.dims()[4];
int col_height = col.dims()[0];
int col_width = col.dims()[1];
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_height + padding[0] + padding[2] -
// filter_height) / stride[0]
// + 1, col_height, "Output_height and
// padding(padding_up,
// padding_down) are " "inconsistent.");
// PADDLE_ENFORCE_EQ(
// (im_width + padding[1] + padding[3] -
// filter_width) / stride[1] +
// 1, col_width, "col_width and padding(padding_left,
// padding_right)
// are " "inconsistent.");
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
T *im_data = im->data<T>();
const T *col_data = col.data<T>();
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset = col_row_idx * stride[0] +
filter_row_idx - padding[0];
for (int filter_col_idx = 0;
filter_col_idx < filter_width; ++filter_col_idx) {
int im_col_offset = col_col_idx * stride[1] +
filter_col_idx - padding[1];
for (int col_row_idx = 0; col_row_idx < col_height; ++col_row_idx) {
for (int col_col_idx = 0; col_col_idx < col_width; ++col_col_idx) {
for (int channel = 0; channel < im_channels; ++channel) {
for (int filter_row_idx = 0; filter_row_idx < filter_height;
++filter_row_idx) {
int im_row_offset =
col_row_idx * stride[0] + filter_row_idx - padding[0];
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_col_offset =
col_col_idx * stride[1] + filter_col_idx - padding[1];
int col_offset =
(((col_row_idx * col_width + col_col_idx) *
im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
int col_offset =
(((col_row_idx * col_width + col_col_idx) * im_channels +
channel) *
filter_height +
filter_row_idx) *
filter_width +
filter_col_idx;
if (im_row_offset >= 0 &&
im_row_offset < im_height &&
im_col_offset >= 0 &&
im_col_offset < im_width) {
int im_offset =
(channel * im_height + im_row_offset) *
im_width +
im_col_offset;
im_data[im_offset] += col_data[col_offset];
}
}
}
}
if (im_row_offset >= 0 && im_row_offset < im_height &&
im_col_offset >= 0 && im_col_offset < im_width) {
int im_offset =
(channel * im_height + im_row_offset) * im_width +
im_col_offset;
im_data[im_offset] += col_data[col_offset];
}
}
}
}
}
}
}
};
template class Im2ColFunctor<ColFormat::kOCF, CPU, float>;
......
src/operators/math/im2col.h
浏览文件 @ ddf6b722
......@@ -89,20 +89,19 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
*/
template <ColFormat Format, typename DeviceType, typename T>
class Im2ColFunctor {
public:
void operator()(const framework::Tensor &im,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col);
public:
void operator()(const framework::Tensor &im, const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *col);
};
template <ColFormat Format, typename DeviceType, typename T>
class Col2ImFunctor {
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im);
public:
void operator()(const framework::Tensor &col,
const std::vector<int> &dilation,
const std::vector<int> &stride,
const std::vector<int> &padding, framework::Tensor *im);
};
} // namespace math
......
src/operators/math/math_function.cc
浏览文件 @ ddf6b722
......@@ -22,11 +22,11 @@ template <>
void gemm<float>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const int M, const int N, const int K, const float alpha,
const float *A, const float *B, const float beta, float *C) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <>
......@@ -34,11 +34,11 @@ void gemm<double>(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const int M, const int N, const int K, const double alpha,
const double *A, const double *B, const double beta,
double *C) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <>
......@@ -46,9 +46,9 @@ void gemm<float>(const bool transA, const bool transB, const int M, const int N,
const int K, const float alpha, const float *A, const int lda,
const float *B, const int ldb, const float beta, float *C,
const int ldc) {
cblas_sgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
cblas_sgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
template <>
......@@ -56,67 +56,67 @@ void gemm<double>(const bool transA, const bool transB, const int M,
const int N, const int K, const double alpha, const double *A,
const int lda, const double *B, const int ldb,
const double beta, double *C, const int ldc) {
cblas_dgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
cblas_dgemm(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
template <>
void matmul<float>(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b, float alpha,
framework::Tensor *matrix_out, float beta) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<float>(transA, transB, M, N, K, alpha, matrix_a.data<float>(),
matrix_b.data<float>(), beta, matrix_out->data<float>());
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<float>(transA, transB, M, N, K, alpha, matrix_a.data<float>(),
matrix_b.data<float>(), beta, matrix_out->data<float>());
}
template <>
void matmul<double>(const framework::Tensor &matrix_a, bool trans_a,
const framework::Tensor &matrix_b, bool trans_b,
double alpha, framework::Tensor *matrix_out, double beta) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<double>(transA, transB, M, N, K, alpha, matrix_a.data<double>(),
matrix_b.data<double>(), beta, matrix_out->data<double>());
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
// PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 &&
// dim_out.size() ==
// 2,
// "The input and output of matmul be matrix");
//
// PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
// platform::is_cpu_place(matrix_b.place())
// &&
// platform::is_cpu_place(matrix_out->place()),
// "Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<double>(transA, transB, M, N, K, alpha, matrix_a.data<double>(),
matrix_b.data<double>(), beta, matrix_out->data<double>());
}
} // namespace math
......
src/operators/math/pool3x3.h
浏览文件 @ ddf6b722
......@@ -22,9 +22,9 @@ SOFTWARE.
#endif // __ARM_NEON
static void Pool3x3Max() {
// todo impl with neon
// todo impl with neon
}
static void Pool3x3Avg() {
// todo impl with neon
// todo impl with neon
}
src/operators/math/pool_2x2.h
浏览文件 @ ddf6b722
......@@ -22,9 +22,9 @@ SOFTWARE.
#endif // __ARM_NEON
static void Pool2x2Max() {
// todo impl with neon
// todo impl with neon
}
static void Pool2x2Avg() {
// todo impl with neon
// todo impl with neon
}
src/operators/math/pooling.cpp
浏览文件 @ ddf6b722
......@@ -30,67 +30,65 @@ namespace math {
*/
template <typename PoolProcess, typename T>
class PoolFunctor<CPU, PoolProcess, T> {
public:
void operator()(const framework::Tensor &input,
const std::vector<int> &ksize,
const std::vector<int> &strides,
const std::vector<int> &paddings, PoolProcess pool_process,
framework::Tensor *output) {
public:
void operator()(const framework::Tensor &input, const std::vector<int> &ksize,
const std::vector<int> &strides,
const std::vector<int> &paddings, PoolProcess pool_process,
framework::Tensor *output) {
const int batch_size = input.dims()[0];
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
if (output == nullptr) {
DLOG << "output tensor is null";
}
const int output_channels = output->dims()[1];
const int input_width = input.dims()[3];
if (output == nullptr) {
DLOG << "output tensor is null";
}
const int output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = ksize[0];
const int ksize_width = ksize[1];
const int stride_height = strides[0];
const int stride_width = strides[1];
const int padding_height = paddings[0];
const int padding_width = paddings[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = ksize[0];
const int ksize_width = ksize[1];
const int stride_height = strides[0];
const int stride_width = strides[1];
const int padding_height = paddings[0];
const int padding_width = paddings[1];
const int input_stride = input_height * input_width;
const int output_stride = output_height * output_width;
const int input_stride = input_height * input_width;
const int output_stride = output_height * output_width;
const T *input_data = input.data<T>();
T *output_data = output->mutable_data<T>();
const T *input_data = input.data<T>();
T *output_data = output->mutable_data<T>();
for (int i = 0; i < batch_size; i++) {
for (int i = 0; i < batch_size; i++) {
#pragma omp parallel for
for (int c = 0; c < output_channels; ++c) {
for (int ph = 0; ph < output_height; ++ph) {
int hstart = ph * stride_height - padding_height;
int hend = std::min(hstart + ksize_height, input_height);
hstart = std::max(hstart, 0);
for (int pw = 0; pw < output_width; ++pw) {
int wstart = pw * stride_width - padding_width;
int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0);
for (int c = 0; c < output_channels; ++c) {
for (int ph = 0; ph < output_height; ++ph) {
int hstart = ph * stride_height - padding_height;
int hend = std::min(hstart + ksize_height, input_height);
hstart = std::max(hstart, 0);
for (int pw = 0; pw < output_width; ++pw) {
int wstart = pw * stride_width - padding_width;
int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0);
T ele = pool_process.initial();
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
pool_process.compute(
input_data[h * input_width + w], &ele);
}
}
int pool_size = (hend - hstart) * (wend - wstart);
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[ph * output_width + pw] = ele;
}
}
input_data += input_stride;
output_data += output_stride;
T ele = pool_process.initial();
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
pool_process.compute(input_data[h * input_width + w], &ele);
}
}
int pool_size = (hend - hstart) * (wend - wstart);
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[ph * output_width + pw] = ele;
}
}
input_data += input_stride;
output_data += output_stride;
}
}
}
};
template class PoolFunctor<CPU, math::AvgPool<float>, float>;
......
src/operators/math/pooling.h
浏览文件 @ ddf6b722
......@@ -38,31 +38,30 @@ namespace math {
* MaxPoolGrad and AvgPoolGrad are gradient operations respectively.
*/
template <class T> class MaxPool {
public:
inline T initial() { return static_cast<T>(-FLT_MAX); }
public:
inline T initial() { return static_cast<T>(-FLT_MAX); }
inline void compute(const T &x, T *y) { *y = *y > x ? *y : x; }
inline void compute(const T &x, T *y) { *y = *y > x ? *y : x; }
inline void finalize(const T &pool_field, T *y) {}
inline void finalize(const T &pool_field, T *y) {}
};
template <class T> class AvgPool {
public:
inline T initial() { return static_cast<T>(0); }
public:
inline T initial() { return static_cast<T>(0); }
inline void compute(const T &x, T *y) { *y += x; }
inline void compute(const T &x, T *y) { *y += x; }
inline void finalize(const T &pool_field, T *y) { *y /= pool_field; }
inline void finalize(const T &pool_field, T *y) { *y /= pool_field; }
};
template <typename DeviceType, typename PoolProcess, typename T>
class PoolFunctor {
public:
void operator()(const framework::Tensor &input,
const std::vector<int> &ksize,
const std::vector<int> &strides,
const std::vector<int> &paddings, PoolProcess pool_compute,
framework::Tensor *output);
public:
void operator()(const framework::Tensor &input, const std::vector<int> &ksize,
const std::vector<int> &strides,
const std::vector<int> &paddings, PoolProcess pool_compute,
framework::Tensor *output);
};
}
} // namespace operators
......
src/operators/math/transform.h
浏览文件 @ ddf6b722
......@@ -37,18 +37,18 @@ namespace math {
// class, paddle::fluid::operators::RowwiseTRansformIterator.
struct Transform {
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void operator()(InputIter first, InputIter last, OutputIter result,
UnaryOperation op) {
std::transform(first, last, result, op);
}
template <typename InputIter1, typename InputIter2, typename OutputIter,
typename BinaryOperation>
void operator()(InputIter1 first1, InputIter1 last1, InputIter2 first2,
OutputIter result, BinaryOperation op) {
std::transform(first1, last1, first2, result, op);
}
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void operator()(InputIter first, InputIter last, OutputIter result,
UnaryOperation op) {
std::transform(first, last, result, op);
}
template <typename InputIter1, typename InputIter2, typename OutputIter,
typename BinaryOperation>
void operator()(InputIter1 first1, InputIter1 last1, InputIter2 first2,
OutputIter result, BinaryOperation op) {
std::transform(first1, last1, first2, result, op);
}
};
} // namespace math
} // namespace operators
......
src/operators/math/vol2col.cc
浏览文件 @ ddf6b722
......@@ -26,90 +26,83 @@ using Tensor = paddle_mobile::framework::Tensor;
* output_depth, output_height, output_width]
*/
template <typename T> class Vol2ColFunctor<CPU, T> {
public:
void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *col) const {
// PADDLE_ENFORCE(vol.dims().size() == 4);
// PADDLE_ENFORCE(col->dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
int output_depth = col->dims()[4];
int output_height = col->dims()[5];
int output_width = col->dims()[6];
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
const T *vol_data = vol.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset = (c / filter_width / filter_height) % filter_depth;
int c_in = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad =
d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad =
h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] +
w_offset * dilations[2];
int col_idx =
((c * output_depth + d) * output_height + h) *
output_width +
w;
int vol_idx =
((c_in * input_depth + d_pad) * input_height +
h_pad) *
input_width +
w_pad;
col_data[col_idx] =
(h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
w_pad >= input_width || d_pad < 0 ||
d_pad >= input_depth)
? static_cast<T>(0)
: vol_data[vol_idx];
}
}
}
public:
void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *col) const {
// PADDLE_ENFORCE(vol.dims().size() == 4);
// PADDLE_ENFORCE(col->dims().size() == 7);
int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1];
int input_height = vol.dims()[2];
int input_width = vol.dims()[3];
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
int output_depth = col->dims()[4];
int output_height = col->dims()[5];
int output_width = col->dims()[6];
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
const T *vol_data = vol.data<T>();
T *col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset = (c / filter_width / filter_height) % filter_depth;
int c_in = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2];
int col_idx =
((c * output_depth + d) * output_height + h) * output_width + w;
int vol_idx =
((c_in * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
col_data[col_idx] =
(h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
w_pad >= input_width || d_pad < 0 || d_pad >= input_depth)
? static_cast<T>(0)
: vol_data[vol_idx];
}
}
}
}
}
};
/*
......@@ -119,89 +112,83 @@ template <typename T> class Vol2ColFunctor<CPU, T> {
* output_depth, output_height, output_width]
*/
template <typename T> class Col2VolFunctor<CPU, T> {
public:
void operator()(const Tensor &col, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *vol) const {
// PADDLE_ENFORCE(vol->dims().size() == 4);
// PADDLE_ENFORCE(col.dims().size() == 7);
int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1];
int input_height = vol->dims()[2];
int input_width = vol->dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
int output_depth = col.dims()[4];
int output_height = col.dims()[5];
int output_width = col.dims()[6];
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
T *vol_data = vol->data<T>();
const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset = (c / filter_width / filter_height) % filter_depth;
int cIm = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad =
d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad =
h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] +
w_offset * dilations[2];
if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
w_pad < input_width && d_pad >= 0 &&
d_pad < input_depth) {
int vol_idx =
((cIm * input_depth + d_pad) * input_height +
h_pad) *
input_width +
w_pad;
int col_idx =
((c * output_depth + d) * output_height + h) *
output_width +
w;
vol_data[vol_idx] += col_data[col_idx];
}
}
}
public:
void operator()(const Tensor &col, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *vol) const {
// PADDLE_ENFORCE(vol->dims().size() == 4);
// PADDLE_ENFORCE(col.dims().size() == 7);
int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1];
int input_height = vol->dims()[2];
int input_width = vol->dims()[3];
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
int output_depth = col.dims()[4];
int output_height = col.dims()[5];
int output_width = col.dims()[6];
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
// PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] -
// ((dilations[0] * (filter_depth - 1)
// + 1))) /
// strides[0] +
// 1,
// output_depth,
// "input_depth and output_depth are "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
// ((dilations[1] * (filter_height -
// 1) + 1))) /
// strides[1] +
// 1,
// output_height,
// "input_height and output_height are
// "
// "mismatching.");
// PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
// ((dilations[2] * (filter_width - 1)
// + 1))) /
// strides[2] +
// 1,
// output_width,
// "input_width and output_width are "
// "mismatching.");
T *vol_data = vol->data<T>();
const T *col_data = col.data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int d_offset = (c / filter_width / filter_height) % filter_depth;
int cIm = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) {
int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) {
int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2];
if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
w_pad < input_width && d_pad >= 0 && d_pad < input_depth) {
int vol_idx =
((cIm * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
int col_idx =
((c * output_depth + d) * output_height + h) * output_width +
w;
vol_data[vol_idx] += col_data[col_idx];
}
}
}
}
}
}
};
template class Vol2ColFunctor<CPU, float>;
......
src/operators/math/vol2col.h
浏览文件 @ ddf6b722
......@@ -73,17 +73,17 @@ namespace math {
using Tensor = paddle_mobile::framework::Tensor;
template <typename DeviceType, typename T> class Vol2ColFunctor {
public:
void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *col) const;
public:
void operator()(const Tensor &vol, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *col) const;
};
template <typename DeviceType, typename T> class Col2VolFunctor {
public:
void operator()(const Tensor &col, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *vol) const;
public:
void operator()(const Tensor &col, const std::vector<int> &dilations,
const std::vector<int> &strides,
const std::vector<int> &paddings, Tensor *vol) const;
};
} // namespace math
......
src/operators/mul_op.cpp
浏览文件 @ ddf6b722
......@@ -22,34 +22,34 @@ namespace paddle_mobile {
namespace operators {
template <typename Dtype, typename T> void MulOp<Dtype, T>::InferShape() const {
auto x_dims = param_.InputX()->dims();
auto y_dims = param_.InputY()->dims();
int x_num_col_dims = param_.XNumColDims();
int y_num_col_dims = param_.YNumColDims();
auto x_dims = param_.InputX()->dims();
auto y_dims = param_.InputY()->dims();
int x_num_col_dims = param_.XNumColDims();
int y_num_col_dims = param_.YNumColDims();
assert(x_dims.size() > x_num_col_dims);
assert(y_dims.size() > y_num_col_dims);
assert(x_dims.size() > x_num_col_dims);
assert(y_dims.size() > y_num_col_dims);
/// (1,2,3,4) , x_num_col_dims = 2 -> (2,12)
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
/// (1,2,3,4) , x_num_col_dims = 2 -> (2,12)
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
assert(x_mat_dims[1] == y_mat_dims[0]);
assert(x_mat_dims[1] == y_mat_dims[0]);
std::vector<int64_t> output_dims;
output_dims.reserve(
static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));
std::vector<int64_t> output_dims;
output_dims.reserve(
static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));
for (int i = 0; i < x_num_col_dims; ++i) {
output_dims.push_back(x_dims[i]);
}
for (int i = 0; i < x_num_col_dims; ++i) {
output_dims.push_back(x_dims[i]);
}
for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
output_dims.push_back(y_dims[i]);
}
for (int i = y_num_col_dims; i < y_dims.size(); ++i) {
output_dims.push_back(y_dims[i]);
}
framework::DDim ddim = framework::make_ddim(output_dims);
param_.Out()->Resize(ddim);
framework::DDim ddim = framework::make_ddim(output_dims);
param_.Out()->Resize(ddim);
}
template class MulOp<CPU, float>;
} // namespace operators
......
src/operators/mul_op.h
浏览文件 @ ddf6b722
......@@ -27,24 +27,24 @@ using namespace framework;
template <typename DeviceType, typename T>
class MulOp : public framework::OperatorWithKernel<DeviceType> {
public:
MulOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::MulKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
MulParam param_;
public:
MulOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
void Run() const {
operators::MulKernel<DeviceType, T> kernel;
kernel.Compute(param_);
}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
protected:
MulParam param_;
};
} // namespace operators
......
src/operators/op_param.cpp
浏览文件 @ ddf6b722
......@@ -21,25 +21,24 @@ SOFTWARE.
namespace paddle_mobile {
namespace operators {
Print &operator<<(Print &printer, const ConvParam &conv_param) {
printer << "parameter of conv: "
<< "\n";
printer << " stride: "
<< " (" << conv_param.Strides()[0] << conv_param.Strides()[1]
<< ") "
<< "\n";
printer << " paddings: "
<< " (" << conv_param.Paddings()[0] << conv_param.Paddings()[1]
<< ") "
<< "\n";
printer << " dilations: "
<< " (" << conv_param.Dilations()[0] << conv_param.Dilations()[1]
<< ") "
<< "\n";
printer << " groups: " << conv_param.Groups() << "\n";
printer << " input dims: " << conv_param.Input()->dims() << "\n";
printer << " filter dims: " << conv_param.Filter()->dims() << "\n";
printer << " output dims: " << conv_param.Output()->dims();
return printer;
printer << "parameter of conv: "
<< "\n";
printer << " stride: "
<< " (" << conv_param.Strides()[0] << conv_param.Strides()[1] << ") "
<< "\n";
printer << " paddings: "
<< " (" << conv_param.Paddings()[0] << conv_param.Paddings()[1]
<< ") "
<< "\n";
printer << " dilations: "
<< " (" << conv_param.Dilations()[0] << conv_param.Dilations()[1]
<< ") "
<< "\n";
printer << " groups: " << conv_param.Groups() << "\n";
printer << " input dims: " << conv_param.Input()->dims() << "\n";
printer << " filter dims: " << conv_param.Filter()->dims() << "\n";
printer << " output dims: " << conv_param.Output()->dims();
return printer;
}
} // namespace operators
} // namespace paddle_mobile
src/operators/op_param.h
浏览文件 @ ddf6b722
......@@ -31,357 +31,355 @@ namespace operators {
using namespace framework;
class OpParam : PaddleMobileObject {
public:
protected:
template <typename T>
static T *InputFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Input", inputs, scope);
public:
protected:
template <typename T>
static T *InputFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Input", inputs, scope);
}
template <typename T>
static T *InputXFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("X", inputs, scope);
}
template <typename T>
static T *InputYFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Y", inputs, scope);
}
template <typename T>
static T *InputBiasFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Bias", inputs, scope);
}
template <typename T>
static T *InputVarianceFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("Variance", inputs, scope);
}
template <typename T>
static T *InputMeanFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Mean", inputs, scope);
}
template <typename T>
static T *InputScaleFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Scale", inputs, scope);
}
template <typename T>
static std::vector<T *> InputMultiFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetMultiVarValue<T>("X", inputs, scope);
}
template <typename T>
static T *OutputFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Output", outputs, scope);
}
template <typename T>
static T *OutFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Out", outputs, scope);
}
template <typename T>
static T *OutputYFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Y", outputs, scope);
}
template <typename T>
static T *MidOutFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("MidOut", outputs, scope);
}
template <typename T>
static T *FilterFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Filter", inputs, scope);
}
template <typename T>
static const T GetAttr(const std::string &key, const AttributeMap &map) {
return ((Attribute)map.at(key)).Get<T>();
}
template <typename T>
static T *GetVarValue(const std::string &key, const VariableNameMap &var_map,
const Scope &scope) {
auto var_vec = var_map.at(key);
if (!var_vec.empty()) {
// std::cout << " get var value -- " << var_vec[0] <<
// std::endl;
auto var = scope.FindVar(var_vec[0]);
return var->GetMutable<T>();
} else {
return nullptr;
}
}
template <typename T>
static T *InputXFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("X", inputs, scope);
}
template <typename T>
static T *InputYFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Y", inputs, scope);
}
template <typename T>
static T *InputBiasFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Bias", inputs, scope);
}
template <typename T>
static T *InputVarianceFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("Variance", inputs, scope);
}
template <typename T>
static T *InputMeanFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Mean", inputs, scope);
}
template <typename T>
static T *InputScaleFrom(const VariableNameMap &inputs,
const Scope &scope) {
return GetVarValue<T>("Scale", inputs, scope);
}
template <typename T>
static std::vector<T *> InputMultiFrom(const VariableNameMap &inputs,
template <typename T>
static std::vector<T *> GetMultiVarValue(const std::string &key,
const VariableNameMap &var_map,
const Scope &scope) {
return GetMultiVarValue<T>("X", inputs, scope);
}
template <typename T>
static T *OutputFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Output", outputs, scope);
}
template <typename T>
static T *OutFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Out", outputs, scope);
}
template <typename T>
static T *OutputYFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("Y", outputs, scope);
}
template <typename T>
static T *MidOutFrom(const VariableNameMap &outputs, const Scope &scope) {
return GetVarValue<T>("MidOut", outputs, scope);
}
template <typename T>
static T *FilterFrom(const VariableNameMap &inputs, const Scope &scope) {
return GetVarValue<T>("Filter", inputs, scope);
}
template <typename T>
static const T GetAttr(const std::string &key, const AttributeMap &map) {
return ((Attribute)map.at(key)).Get<T>();
}
template <typename T>
static T *GetVarValue(const std::string &key,
const VariableNameMap &var_map, const Scope &scope) {
auto var_vec = var_map.at(key);
if (!var_vec.empty()) {
// std::cout << " get var value -- " << var_vec[0] <<
// std::endl;
auto var = scope.FindVar(var_vec[0]);
return var->GetMutable<T>();
} else {
return nullptr;
}
}
template <typename T>
static std::vector<T *> GetMultiVarValue(const std::string &key,
const VariableNameMap &var_map,
const Scope &scope) {
auto var_vecs = var_map.at(key);
assert(var_vecs.size() > 1);
std::vector<T *> var_res;
for (auto &var_vec : var_vecs) {
auto var = scope.FindVar(var_vec);
var_res.push_back(var->GetMutable<T>());
}
return var_res;
auto var_vecs = var_map.at(key);
assert(var_vecs.size() > 1);
std::vector<T *> var_res;
for (auto &var_vec : var_vecs) {
auto var = scope.FindVar(var_vec);
var_res.push_back(var->GetMutable<T>());
}
return var_res;
}
};
class ConvParam : OpParam {
public:
ConvParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
input_ = InputFrom<framework::Tensor>(inputs, scope);
output_ = OutputFrom<framework::Tensor>(outputs, scope);
strides_ = GetAttr<std::vector<int>>("strides", attrs);
paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
dilations_ = GetAttr<std::vector<int>>("dilations", attrs);
groups = GetAttr<int>("groups", attrs);
}
public:
ConvParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
filter_ = FilterFrom<framework::LoDTensor>(inputs, scope);
input_ = InputFrom<framework::Tensor>(inputs, scope);
output_ = OutputFrom<framework::Tensor>(outputs, scope);
strides_ = GetAttr<std::vector<int>>("strides", attrs);
paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
dilations_ = GetAttr<std::vector<int>>("dilations", attrs);
groups = GetAttr<int>("groups", attrs);
}
const Tensor *Input() const { return input_; }
const Tensor *Input() const { return input_; }
const LoDTensor *Filter() const { return filter_; }
const LoDTensor *Filter() const { return filter_; }
Tensor *Output() const { return output_; }
Tensor *Output() const { return output_; }
const std::vector<int> &Strides() const { return strides_; }
const std::vector<int> &Strides() const { return strides_; }
const std::vector<int> &Paddings() const { return paddings_; }
const std::vector<int> &Paddings() const { return paddings_; }
const std::vector<int> &Dilations() const { return dilations_; }
const std::vector<int> &Dilations() const { return dilations_; }
const int &Groups() const { return groups; }
const int &Groups() const { return groups; }
private:
Tensor *input_;
Tensor *output_;
LoDTensor *filter_;
std::vector<int> strides_;
std::vector<int> paddings_;
std::vector<int> dilations_;
int groups;
private:
Tensor *input_;
Tensor *output_;
LoDTensor *filter_;
std::vector<int> strides_;
std::vector<int> paddings_;
std::vector<int> dilations_;
int groups;
};
Print &operator<<(Print &printer, const ConvParam &conv_param);
class ElementwiseAddParam : OpParam {
public:
ElementwiseAddParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int axis_;
public:
ElementwiseAddParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int axis_;
};
class MulParam : OpParam {
public:
MulParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
}
public:
MulParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
input_y_ = InputYFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
x_num_col_dims_ = GetAttr<int>("x_num_col_dims", attrs);
y_num_col_dims_ = GetAttr<int>("y_num_col_dims", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputX() const { return input_x_; }
const Tensor *InputY() const { return input_y_; }
const Tensor *InputY() const { return input_y_; }
Tensor *Out() const { return out_; }
Tensor *Out() const { return out_; }
const int &XNumColDims() const { return x_num_col_dims_; }
const int &XNumColDims() const { return x_num_col_dims_; }
const int &YNumColDims() const { return y_num_col_dims_; }
const int &YNumColDims() const { return y_num_col_dims_; }
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int x_num_col_dims_;
int y_num_col_dims_;
private:
Tensor *input_x_;
Tensor *input_y_;
Tensor *out_;
int x_num_col_dims_;
int y_num_col_dims_;
};
class ConcatParam : public OpParam {
public:
ConcatParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
public:
ConcatParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
inputs_ = InputMultiFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
axis_ = GetAttr<int>("axis", attrs);
}
std::vector<Tensor *> Inputs() const { return inputs_; }
std::vector<Tensor *> Inputs() const { return inputs_; }
Tensor *Out() const { return out_; }
Tensor *Out() const { return out_; }
const int &Axis() const { return axis_; }
const int &Axis() const { return axis_; }
private:
std::vector<Tensor *> inputs_;
Tensor *out_;
int axis_;
private:
std::vector<Tensor *> inputs_;
Tensor *out_;
int axis_;
};
class LrnParam : public OpParam {
public:
LrnParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
mid_out_ = MidOutFrom<framework::Tensor>(outputs, scope);
n_ = GetAttr<int>("n", attrs);
alpha_ = GetAttr<float>("alpha", attrs);
beta_ = GetAttr<float>("beta", attrs);
k_ = GetAttr<float>("k", attrs);
data_format_ = GetAttr<std::string>("data_format", attrs);
}
public:
LrnParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
out_ = OutFrom<framework::Tensor>(outputs, scope);
mid_out_ = MidOutFrom<framework::Tensor>(outputs, scope);
n_ = GetAttr<int>("n", attrs);
alpha_ = GetAttr<float>("alpha", attrs);
beta_ = GetAttr<float>("beta", attrs);
k_ = GetAttr<float>("k", attrs);
data_format_ = GetAttr<std::string>("data_format", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputX() const { return input_x_; }
Tensor *Out() const { return out_; }
Tensor *Out() const { return out_; }
Tensor *MidOut() const { return mid_out_; }
Tensor *MidOut() const { return mid_out_; }
const int &N() const { return n_; }
const int &N() const { return n_; }
const float &Alpha() const { return alpha_; }
const float &Alpha() const { return alpha_; }
const float &Beta() const { return beta_; }
const float &Beta() const { return beta_; }
const float &K() const { return k_; }
const float &K() const { return k_; }
const std::string &DataFormat() const { return data_format_; }
const std::string &DataFormat() const { return data_format_; }
private:
Tensor *input_x_;
Tensor *out_;
Tensor *mid_out_;
int n_;
float alpha_;
float beta_;
float k_;
std::string data_format_;
private:
Tensor *input_x_;
Tensor *out_;
Tensor *mid_out_;
int n_;
float alpha_;
float beta_;
float k_;
std::string data_format_;
};
class BatchNormParam : OpParam {
public:
BatchNormParam(const VariableNameMap &inputs,
const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
output_y_ = OutputYFrom<framework::Tensor>(outputs, scope);
input_bias_ = InputBiasFrom<framework::Tensor>(inputs, scope);
input_mean_ = InputMeanFrom<framework::Tensor>(inputs, scope);
input_scale_ = InputScaleFrom<framework::Tensor>(inputs, scope);
input_variance_ = InputVarianceFrom<framework::Tensor>(inputs, scope);
epsilon_ = GetAttr<float>("epsilon", attrs);
momentum_ = GetAttr<float>("momentum", attrs);
is_test_ = GetAttr<bool>("is_test", attrs);
}
public:
BatchNormParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
output_y_ = OutputYFrom<framework::Tensor>(outputs, scope);
input_bias_ = InputBiasFrom<framework::Tensor>(inputs, scope);
input_mean_ = InputMeanFrom<framework::Tensor>(inputs, scope);
input_scale_ = InputScaleFrom<framework::Tensor>(inputs, scope);
input_variance_ = InputVarianceFrom<framework::Tensor>(inputs, scope);
epsilon_ = GetAttr<float>("epsilon", attrs);
momentum_ = GetAttr<float>("momentum", attrs);
is_test_ = GetAttr<bool>("is_test", attrs);
}
const Tensor *InputX() const { return input_x_; }
const Tensor *InputX() const { return input_x_; }
Tensor *OutputY() const { return output_y_; }
Tensor *OutputY() const { return output_y_; }
const Tensor *InputBias() const { return input_bias_; }
const Tensor *InputBias() const { return input_bias_; }
const Tensor *InputMean() const { return input_mean_; }
const Tensor *InputMean() const { return input_mean_; }
const Tensor *InputScale() const { return input_scale_; }
const Tensor *InputScale() const { return input_scale_; }
const Tensor *InputVariance() const { return input_variance_; }
const Tensor *InputVariance() const { return input_variance_; }
const float &Epsilon() const { return epsilon_; }
const float &Epsilon() const { return epsilon_; }
const float &Momentum() const { return momentum_; }
const float &Momentum() const { return momentum_; }
const bool &IsTest() const { return is_test_; }
const bool &IsTest() const { return is_test_; }
const std::string &DataFormat() const { return data_format_; }
const std::string &DataFormat() const { return data_format_; }
private:
Tensor *input_x_;
Tensor *output_y_;
Tensor *input_bias_;
Tensor *input_mean_;
Tensor *input_scale_;
Tensor *input_variance_;
float epsilon_;
float momentum_;
bool is_test_;
std::string data_format_;
private:
Tensor *input_x_;
Tensor *output_y_;
Tensor *input_bias_;
Tensor *input_mean_;
Tensor *input_scale_;
Tensor *input_variance_;
float epsilon_;
float momentum_;
bool is_test_;
std::string data_format_;
};
class PoolParam : public OpParam {
public:
PoolParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_ = InputXFrom<framework::Tensor>(inputs, scope);
output_ = OutFrom<framework::Tensor>(outputs, scope);
pooling_type_ = GetAttr<std::string>("pooling_type", attrs);
ksize_ = GetAttr<std::vector<int>>("ksize", attrs);
strides_ = GetAttr<std::vector<int>>("strides", attrs);
paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
ceil_mode_ = GetAttr<bool>("ceil_mode", attrs);
gloabal_pooling_ = GetAttr<bool>("global_pooling", attrs);
}
public:
PoolParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const framework::AttributeMap &attrs,
const framework::Scope &scope) {
input_ = InputXFrom<framework::Tensor>(inputs, scope);
output_ = OutFrom<framework::Tensor>(outputs, scope);
pooling_type_ = GetAttr<std::string>("pooling_type", attrs);
ksize_ = GetAttr<std::vector<int>>("ksize", attrs);
strides_ = GetAttr<std::vector<int>>("strides", attrs);
paddings_ = GetAttr<std::vector<int>>("paddings", attrs);
ceil_mode_ = GetAttr<bool>("ceil_mode", attrs);
gloabal_pooling_ = GetAttr<bool>("global_pooling", attrs);
}
const Tensor *Input() const { return input_; }
const Tensor *Input() const { return input_; }
Tensor *Output() const { return output_; }
Tensor *Output() const { return output_; }
const std::string &PoolingType() const { return pooling_type_; }
const std::string &PoolingType() const { return pooling_type_; }
const std::vector<int> &Ksize() const { return ksize_; }
const std::vector<int> &Ksize() const { return ksize_; }
const std::vector<int> &Strides() const { return strides_; }
const std::vector<int> &Strides() const { return strides_; }
const std::vector<int> &Paddings() const { return paddings_; }
const std::vector<int> &Paddings() const { return paddings_; }
bool isCeilMode() const { return ceil_mode_; }
bool isCeilMode() const { return ceil_mode_; }
bool isGlobalPooling() const { return gloabal_pooling_; }
bool isGlobalPooling() const { return gloabal_pooling_; }
private:
Tensor *input_;
Tensor *output_;
std::string pooling_type_;
std::vector<int> ksize_;
std::vector<int> strides_;
std::vector<int> paddings_;
bool ceil_mode_;
bool gloabal_pooling_ = false;
private:
Tensor *input_;
Tensor *output_;
std::string pooling_type_;
std::vector<int> ksize_;
std::vector<int> strides_;
std::vector<int> paddings_;
bool ceil_mode_;
bool gloabal_pooling_ = false;
};
} // namespace operators
......
src/operators/pool_op.cpp
浏览文件 @ ddf6b722
......@@ -23,37 +23,37 @@ namespace operators {
int PoolOutputSize(int input_size, int filter_size, int padding, int stride,
bool ceil_mode) {
int output_size;
if (!ceil_mode) {
output_size = (input_size - filter_size + 2 * padding) / stride + 1;
} else {
output_size =
(input_size - filter_size + 2 * padding + stride - 1) / stride + 1;
}
return output_size;
int output_size;
if (!ceil_mode) {
output_size = (input_size - filter_size + 2 * padding) / stride + 1;
} else {
output_size =
(input_size - filter_size + 2 * padding + stride - 1) / stride + 1;
}
return output_size;
}
template <typename DeviceType, typename T>
void PoolOp<DeviceType, T>::InferShape() const {
auto in_x_dims = param_.Input()->dims();
std::vector<int> ksize = param_.Ksize();
std::vector<int> paddings = param_.Paddings();
std::vector<int> strides = param_.Strides();
bool ceil_mode = param_.isCeilMode();
auto in_x_dims = param_.Input()->dims();
std::vector<int> ksize = param_.Ksize();
std::vector<int> paddings = param_.Paddings();
std::vector<int> strides = param_.Strides();
bool ceil_mode = param_.isCeilMode();
if (param_.isGlobalPooling()) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x_dims[i + 2]);
}
}
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
if (param_.isGlobalPooling()) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i) {
output_shape.push_back(PoolOutputSize(
in_x_dims[i + 2], ksize[i], paddings[i], strides[i], ceil_mode));
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x_dims[i + 2]);
}
param_.Output()->Resize(framework::make_ddim(output_shape));
DLOG << "infer shape out size =" << param_.Output()->numel();
}
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) {
output_shape.push_back(PoolOutputSize(in_x_dims[i + 2], ksize[i],
paddings[i], strides[i], ceil_mode));
}
param_.Output()->Resize(framework::make_ddim(output_shape));
DLOG << "infer shape out size =" << param_.Output()->numel();
}
template class PoolOp<CPU, float>;
} // namespace operators
......
src/operators/pool_op.h
浏览文件 @ ddf6b722
......@@ -28,25 +28,25 @@ using namespace framework;
template <typename DeviceType, typename T>
class PoolOp : public framework::OperatorWithKernel<DeviceType> {
public:
PoolOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
attrs, scope),
param_(inputs, outputs, attrs, *scope) {}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
public:
PoolOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, const framework::AttributeMap &attrs,
std::shared_ptr<framework::Scope> scope)
: framework::OperatorWithKernel<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
void InferShape() const override;
void Run() const {
// InferShape();
operators::PoolKernel<DeviceType, T> kernel;
kernel.Compute(param_);
this->ClearVariables({"X"});
}
void Run() const {
// InferShape();
operators::PoolKernel<DeviceType, T> kernel;
kernel.Compute(param_);
this->ClearVariables({"X"});
}
private:
PoolParam param_;
private:
PoolParam param_;
};
} // namespace operators
} // namespace paddle_mobile
src/platform/data_type.h
浏览文件 @ ddf6b722
......@@ -22,103 +22,103 @@ namespace paddle_mobile {
namespace framework {
inline proto::VarType::Type ToDataType(std::type_index type) {
/*if (typeid(platform::float16).hash_code() == type.hash_code()) {
return proto::VarType::FP16;
} else */
if (typeid(const float).hash_code() == type.hash_code()) {
// CPPLint complains Using C-style cast. Use
// static_cast<float>() instead
// One fix to this is to replace float with const float because
// typeid(T) == typeid(const T)
// http://en.cppreference.com/w/cpp/language/typeid
return proto::VarType::FP32;
} else if (typeid(const double).hash_code() == type.hash_code()) {
return proto::VarType::FP64;
} else if (typeid(const int).hash_code() == type.hash_code()) {
return proto::VarType::INT32;
} else if (typeid(const int64_t).hash_code() == type.hash_code()) {
return proto::VarType::INT64;
} else if (typeid(const bool).hash_code() == type.hash_code()) {
return proto::VarType::BOOL;
} else {
// PADDLE_THROW("Not supported");
// std::cout << "Not supported";
}
/*if (typeid(platform::float16).hash_code() == type.hash_code()) {
return proto::VarType::FP16;
} else */
if (typeid(const float).hash_code() == type.hash_code()) {
// CPPLint complains Using C-style cast. Use
// static_cast<float>() instead
// One fix to this is to replace float with const float because
// typeid(T) == typeid(const T)
// http://en.cppreference.com/w/cpp/language/typeid
return proto::VarType::FP32;
} else if (typeid(const double).hash_code() == type.hash_code()) {
return proto::VarType::FP64;
} else if (typeid(const int).hash_code() == type.hash_code()) {
return proto::VarType::INT32;
} else if (typeid(const int64_t).hash_code() == type.hash_code()) {
return proto::VarType::INT64;
} else if (typeid(const bool).hash_code() == type.hash_code()) {
return proto::VarType::BOOL;
} else {
// PADDLE_THROW("Not supported");
// std::cout << "Not supported";
}
}
inline std::type_index ToTypeIndex(proto::VarType::Type type) {
switch (type) {
// case proto::VarType::FP16:
// return typeid(platform::float16);
case proto::VarType::FP32:
return typeid(float);
case proto::VarType::FP64:
return typeid(double);
case proto::VarType::INT32:
return typeid(int);
case proto::VarType::INT64:
return typeid(int64_t);
case proto::VarType::BOOL:
return typeid(bool);
default:
// PADDLE_THROW("Not support type %d", type);
printf("Not support type %d", type);
}
switch (type) {
// case proto::VarType::FP16:
// return typeid(platform::float16);
case proto::VarType::FP32:
return typeid(float);
case proto::VarType::FP64:
return typeid(double);
case proto::VarType::INT32:
return typeid(int);
case proto::VarType::INT64:
return typeid(int64_t);
case proto::VarType::BOOL:
return typeid(bool);
default:
// PADDLE_THROW("Not support type %d", type);
printf("Not support type %d", type);
}
}
template <typename Visitor>
inline void VisitDataType(proto::VarType::Type type, Visitor visitor) {
switch (type) {
// case proto::VarType::FP16:
// visitor.template operator()<platform::float16>();
// break;
case proto::VarType::FP32:
visitor.template operator()<float>();
break;
case proto::VarType::FP64:
visitor.template operator()<double>();
break;
case proto::VarType::INT32:
visitor.template operator()<int>();
break;
case proto::VarType::INT64:
visitor.template operator()<int64_t>();
break;
case proto::VarType::BOOL:
visitor.template operator()<bool>();
break;
default:
// PADDLE_THROW("Not supported");
printf("Not supported");
}
switch (type) {
// case proto::VarType::FP16:
// visitor.template operator()<platform::float16>();
// break;
case proto::VarType::FP32:
visitor.template operator()<float>();
break;
case proto::VarType::FP64:
visitor.template operator()<double>();
break;
case proto::VarType::INT32:
visitor.template operator()<int>();
break;
case proto::VarType::INT64:
visitor.template operator()<int64_t>();
break;
case proto::VarType::BOOL:
visitor.template operator()<bool>();
break;
default:
// PADDLE_THROW("Not supported");
printf("Not supported");
}
}
inline std::string DataTypeToString(const proto::VarType::Type type) {
switch (type) {
case proto::VarType::FP16:
return "float16";
case proto::VarType::FP32:
return "float32";
case proto::VarType::FP64:
return "float64";
case proto::VarType::INT16:
return "int16";
case proto::VarType::INT32:
return "int32";
case proto::VarType::INT64:
return "int64";
case proto::VarType::BOOL:
return "bool";
default:
// PADDLE_THROW("Not support type %d", type);
printf("Not support type %d", type);
}
switch (type) {
case proto::VarType::FP16:
return "float16";
case proto::VarType::FP32:
return "float32";
case proto::VarType::FP64:
return "float64";
case proto::VarType::INT16:
return "int16";
case proto::VarType::INT32:
return "int32";
case proto::VarType::INT64:
return "int64";
case proto::VarType::BOOL:
return "bool";
default:
// PADDLE_THROW("Not support type %d", type);
printf("Not support type %d", type);
}
}
inline std::ostream &operator<<(std::ostream &out,
const proto::VarType::Type &type) {
out << DataTypeToString(type);
return out;
out << DataTypeToString(type);
return out;
}
} // namespace framework
......
src/platform/macros.h
浏览文件 @ ddf6b722
......@@ -17,9 +17,9 @@ limitations under the License. */
// Disable the copy and assignment operator for a class.
#ifndef DISABLE_COPY_AND_ASSIGN
#define DISABLE_COPY_AND_ASSIGN(classname) \
private: \
classname(const classname &) = delete; \
classname(classname &&) = delete; \
classname &operator=(const classname &) = delete; \
classname &operator=(classname &&) = delete
private: \
classname(const classname &) = delete; \
classname(classname &&) = delete; \
classname &operator=(const classname &) = delete; \
classname &operator=(classname &&) = delete
#endif
test/common/test_log.cpp
浏览文件 @ ddf6b722
......@@ -20,20 +20,20 @@ SOFTWARE.
int main() {
DLOGF("DASJFDAFJ%d -- %f", 12345, 344.234);
DLOGF("DASJFDAFJ%d -- %f", 12345, 344.234);
LOGF(paddle_mobile::kLOG_DEBUG, "DASJFDAFJ%d -- %f", 12345, 344.234);
LOGF(paddle_mobile::kLOG_DEBUG, "DASJFDAFJ%d -- %f", 12345, 344.234);
LOG(paddle_mobile::kLOG_DEBUG) << "test debug"
<< " next log";
LOG(paddle_mobile::kLOG_DEBUG) << "test debug"
<< " next log";
LOG(paddle_mobile::kLOG_DEBUG1) << "test debug1"
<< " next log";
LOG(paddle_mobile::kLOG_DEBUG2) << "test debug2"
<< " next log";
DLOG << "test DLOG";
LOG(paddle_mobile::kLOG_DEBUG1) << "test debug1"
<< " next log";
LOG(paddle_mobile::kLOG_DEBUG2) << "test debug2"
<< " next log";
DLOG << "test DLOG";
LOG(paddle_mobile::kLOG_ERROR) << " error occur !";
LOG(paddle_mobile::kLOG_ERROR) << " error occur !";
return 0;
return 0;
}
test/framework/executor_for_test.cpp
浏览文件 @ ddf6b722
......@@ -23,29 +23,29 @@ Executor4Test<DeviceType, OpType>::Executor4Test(const Program<DeviceType> p,
std::string op_type)
: Executor<DeviceType>(p) {
if (this->program_.originProgram == nullptr) {
LOG(paddle_mobile::LogLevel::kLOG_ERROR)
<< "to_predict_program_ == nullptr";
}
if (this->program_.originProgram == nullptr) {
LOG(paddle_mobile::LogLevel::kLOG_ERROR)
<< "to_predict_program_ == nullptr";
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
this->to_predict_program_->Blocks();
const std::vector<std::shared_ptr<BlockDesc>> blocks =
this->to_predict_program_->Blocks();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == op_type) {
std::shared_ptr<OpType> op_ptr = std::make_shared<OpType>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), this->program_.scope);
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == op_type) {
std::shared_ptr<OpType> op_ptr = std::make_shared<OpType>(
op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
this->program_.scope);
this->ops_of_block_[*block_desc.get()].push_back(op_ptr);
break;
}
}
this->ops_of_block_[*block_desc.get()].push_back(op_ptr);
break;
}
}
}
}
template <typename DeviceType, typename OpType>
......@@ -53,19 +53,19 @@ std::shared_ptr<Tensor>
Executor4Test<DeviceType, OpType>::predict(Tensor &t, std::string input,
std::string output, DDim dDim) {
auto scope = this->program_.scope;
Variable *g_feed_value = scope->Var(input);
auto tensor = g_feed_value->GetMutable<Tensor>();
tensor->ShareDataWith(t);
auto scope = this->program_.scope;
Variable *g_feed_value = scope->Var(input);
auto tensor = g_feed_value->GetMutable<Tensor>();
tensor->ShareDataWith(t);
Variable *con_output = scope->Var(output);
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>(dDim);
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
Variable *con_output = scope->Var(output);
Tensor *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>(dDim);
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
Executor<DeviceType>::predict(t, 0);
return out_tensor;
Executor<DeviceType>::predict(t, 0);
return out_tensor;
}
template class Executor4Test<
......
test/framework/executor_for_test.h
浏览文件 @ ddf6b722
......@@ -27,9 +27,9 @@ using namespace paddle_mobile::framework;
template <typename DeviceType, typename OpType>
class Executor4Test : public Executor<DeviceType> {
public:
Executor4Test(const Program<DeviceType> p, std::string op_type);
public:
Executor4Test(const Program<DeviceType> p, std::string op_type);
std::shared_ptr<Tensor> predict(Tensor &t, std::string input,
std::string output, DDim dDim);
std::shared_ptr<Tensor> predict(Tensor &t, std::string input,
std::string output, DDim dDim);
};
test/framework/test_load.cpp
浏览文件 @ ddf6b722
......@@ -19,10 +19,10 @@ SOFTWARE.
#include "io.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::Loader<paddle_mobile::CPU> loader;
//../../../test/models/googlenet
//../../../test/models/mobilenet
auto program = loader.Load(std::string("../models/googlenet"));
return 0;
//../../../test/models/googlenet
//../../../test/models/mobilenet
auto program = loader.Load(std::string("../models/googlenet"));
return 0;
}
\ No newline at end of file
test/framework/test_optimize.cpp
浏览文件 @ ddf6b722
......@@ -25,16 +25,16 @@ using namespace paddle_mobile::framework;
int main() {
Loader<paddle_mobile::CPU> loader;
// "../../../test/models/googlenet"
auto program = loader.Load("../models/googlenet");
Loader<paddle_mobile::CPU> loader;
// "../../../test/models/googlenet"
auto program = loader.Load("../models/googlenet");
ProgramOptimize optimize;
ProgramOptimize optimize;
auto optimize_program = optimize.FushionOptimize(program.originProgram);
if (optimize_program) {
auto optimize_program = optimize.FushionOptimize(program.originProgram);
if (optimize_program) {
} else {
DLOG << "optimize_program is null";
}
} else {
DLOG << "optimize_program is null";
}
}
test/operators/test_batchnorm_op.cpp
浏览文件 @ ddf6b722
......@@ -24,102 +24,100 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> class TestBatchNormOp {
public:
explicit TestBatchNormOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "batch_norm" &&
op->Input("X")[0] == "conv2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Mean is : " << op->Input("Mean")[0];
DLOG << " Input Variance is : " << op->Input("Variance")[0];
DLOG << " Input Scale is : " << op->Input("Scale")[0];
DLOG << " Input Bias is : " << op->Input("Bias")[0];
DLOG << " Output Y is : " << op->Output("Y")[0];
DLOG << " epsilon : "
<< op->GetAttrMap().at("epsilon").Get<float>();
std::shared_ptr<operators::BatchNormOp<Dtype, float>> lrn =
std::make_shared<operators::BatchNormOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(lrn);
}
}
}
public:
explicit TestBatchNormOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
std::shared_ptr<Tensor> predict_bn(Tensor &t1, Tensor &t2, Tensor &t3,
Tensor &t4, Tensor &t5) {
// feed
auto scope = program_.scope;
Variable *x1_feed_value = scope->Var("conv2d_0.tmp_0");
auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
tensor_x1->ShareDataWith(t1);
Variable *mean_feed_value = scope->Var("batch_norm_0.w_1");
auto tensor_mean = mean_feed_value->GetMutable<Tensor>();
tensor_mean->ShareDataWith(t2);
Variable *scale_feed_value = scope->Var("batch_norm_0.w_0");
auto tensor_scale = scale_feed_value->GetMutable<Tensor>();
tensor_scale->ShareDataWith(t3);
Variable *variance_feed_value = scope->Var("batch_norm_0.w_2");
auto tensor_variance = variance_feed_value->GetMutable<Tensor>();
tensor_variance->ShareDataWith(t4);
Variable *bias_feed_value = scope->Var("batch_norm_0.b_0");
auto tensor_bias = bias_feed_value->GetMutable<Tensor>();
tensor_bias->ShareDataWith(t5);
Variable *output = scope->Var("batch_norm_0.tmp_2");
auto *output_tensor = output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({4, 10, 2, 2});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_bn(t1, t2, t3, t4, t5, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_bn(const Tensor &t1, const Tensor &t2, const Tensor &t3,
const Tensor &t4, const Tensor &t5, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "batch_norm" &&
op->Input("X")[0] == "conv2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Mean is : " << op->Input("Mean")[0];
DLOG << " Input Variance is : " << op->Input("Variance")[0];
DLOG << " Input Scale is : " << op->Input("Scale")[0];
DLOG << " Input Bias is : " << op->Input("Bias")[0];
DLOG << " Output Y is : " << op->Output("Y")[0];
DLOG << " epsilon : " << op->GetAttrMap().at("epsilon").Get<float>();
std::shared_ptr<operators::BatchNormOp<Dtype, float>> lrn =
std::make_shared<operators::BatchNormOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(lrn);
}
}
}
}
std::shared_ptr<Tensor> predict_bn(Tensor &t1, Tensor &t2, Tensor &t3,
Tensor &t4, Tensor &t5) {
// feed
auto scope = program_.scope;
Variable *x1_feed_value = scope->Var("conv2d_0.tmp_0");
auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
tensor_x1->ShareDataWith(t1);
Variable *mean_feed_value = scope->Var("batch_norm_0.w_1");
auto tensor_mean = mean_feed_value->GetMutable<Tensor>();
tensor_mean->ShareDataWith(t2);
Variable *scale_feed_value = scope->Var("batch_norm_0.w_0");
auto tensor_scale = scale_feed_value->GetMutable<Tensor>();
tensor_scale->ShareDataWith(t3);
Variable *variance_feed_value = scope->Var("batch_norm_0.w_2");
auto tensor_variance = variance_feed_value->GetMutable<Tensor>();
tensor_variance->ShareDataWith(t4);
Variable *bias_feed_value = scope->Var("batch_norm_0.b_0");
auto tensor_bias = bias_feed_value->GetMutable<Tensor>();
tensor_bias->ShareDataWith(t5);
Variable *output = scope->Var("batch_norm_0.tmp_2");
auto *output_tensor = output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({4, 10, 2, 2});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_bn(t1, t2, t3, t4, t5, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_bn(const Tensor &t1, const Tensor &t2, const Tensor &t3,
const Tensor &t4, const Tensor &t5, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestBatchNormOp<CPU>;
......@@ -127,50 +125,48 @@ template class TestBatchNormOp<CPU>;
} // namespace paddle_mobile
int main() {
DLOG << "----------**********----------";
DLOG << "begin to run BatchNormOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string(
"../../test/models/image_classification_resnet.inference.model"));
/// input x (4,10,2,2)
paddle_mobile::framework::Tensor inputx1;
SetupTensor<float>(&inputx1, {4, 10, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx1_ptr = inputx1.data<float>();
paddle_mobile::framework::Tensor mean;
SetupTensor<float>(&mean, {10}, static_cast<float>(0),
static_cast<float>(1));
auto *mean_ptr = mean.data<float>();
paddle_mobile::framework::Tensor scale;
SetupTensor<float>(&scale, {10}, static_cast<float>(0),
static_cast<float>(1));
auto *scale_ptr = scale.data<float>();
paddle_mobile::framework::Tensor variance;
SetupTensor<float>(&variance, {10}, static_cast<float>(0),
static_cast<float>(1));
auto *variance_ptr = variance.data<float>();
paddle_mobile::framework::Tensor bias;
SetupTensor<float>(&bias, {10}, static_cast<float>(0),
static_cast<float>(1));
auto *bias_ptr = bias.data<float>();
paddle_mobile::framework::TestBatchNormOp<paddle_mobile::CPU>
testBatchNormOp(program);
auto output_bn =
testBatchNormOp.predict_bn(inputx1, mean, scale, variance, bias);
auto *output_bn_ptr = output_bn->data<float>();
/// [2, 5, 1, 0]
DLOG << " (" << inputx1_ptr[102] << " - " << mean_ptr[5] << ")/(("
<< variance_ptr[5] << " + 0.00001"
<< ")^0.5)* " << scale_ptr[5] << " + " << bias_ptr[5] << " = ";
DLOG << output_bn_ptr[102];
return 0;
DLOG << "----------**********----------";
DLOG << "begin to run BatchNormOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string(
"../../test/models/image_classification_resnet.inference.model"));
/// input x (4,10,2,2)
paddle_mobile::framework::Tensor inputx1;
SetupTensor<float>(&inputx1, {4, 10, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx1_ptr = inputx1.data<float>();
paddle_mobile::framework::Tensor mean;
SetupTensor<float>(&mean, {10}, static_cast<float>(0), static_cast<float>(1));
auto *mean_ptr = mean.data<float>();
paddle_mobile::framework::Tensor scale;
SetupTensor<float>(&scale, {10}, static_cast<float>(0),
static_cast<float>(1));
auto *scale_ptr = scale.data<float>();
paddle_mobile::framework::Tensor variance;
SetupTensor<float>(&variance, {10}, static_cast<float>(0),
static_cast<float>(1));
auto *variance_ptr = variance.data<float>();
paddle_mobile::framework::Tensor bias;
SetupTensor<float>(&bias, {10}, static_cast<float>(0), static_cast<float>(1));
auto *bias_ptr = bias.data<float>();
paddle_mobile::framework::TestBatchNormOp<paddle_mobile::CPU> testBatchNormOp(
program);
auto output_bn =
testBatchNormOp.predict_bn(inputx1, mean, scale, variance, bias);
auto *output_bn_ptr = output_bn->data<float>();
/// [2, 5, 1, 0]
DLOG << " (" << inputx1_ptr[102] << " - " << mean_ptr[5] << ")/(("
<< variance_ptr[5] << " + 0.00001"
<< ")^0.5)* " << scale_ptr[5] << " + " << bias_ptr[5] << " = ";
DLOG << output_bn_ptr[102];
return 0;
}
test/operators/test_concat_op.cpp
浏览文件 @ ddf6b722
......@@ -24,95 +24,92 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> class TestConcatOp {
public:
explicit TestConcatOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "concat" &&
op->Input("X")[0] == "conv2d_3.tmp_1") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << " axis : "
<< op->GetAttrMap().at("axis").Get<int>();
std::shared_ptr<operators::ConcatOp<Dtype, float>> concat =
std::make_shared<operators::ConcatOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(concat);
}
}
}
public:
explicit TestConcatOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
std::shared_ptr<Tensor> predict_concat(Tensor &t1, Tensor &t2, Tensor &t3,
Tensor &t4) {
// feed
auto scope = program_.scope;
Variable *x1_feed_value = scope->Var("conv2d_3.tmp_1");
auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
tensor_x1->ShareDataWith(t1);
Variable *x2_feed_value = scope->Var("conv2d_5.tmp_1");
auto tensor_x2 = x2_feed_value->GetMutable<Tensor>();
tensor_x2->ShareDataWith(t2);
Variable *x3_feed_value = scope->Var("conv2d_7.tmp_1");
auto tensor_x3 = x3_feed_value->GetMutable<Tensor>();
tensor_x3->ShareDataWith(t3);
Variable *x4_feed_value = scope->Var("conv2d_8.tmp_1");
auto tensor_x4 = x4_feed_value->GetMutable<Tensor>();
tensor_x4->ShareDataWith(t4);
Variable *con_output = scope->Var("concat_0.tmp_0");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({4, 100, 2, 2});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_concat(t1, t2, t3, t4, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_concat(const Tensor &t1, const Tensor &t2, const Tensor &t3,
const Tensor &t4, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "concat" && op->Input("X")[0] == "conv2d_3.tmp_1") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << " axis : " << op->GetAttrMap().at("axis").Get<int>();
std::shared_ptr<operators::ConcatOp<Dtype, float>> concat =
std::make_shared<operators::ConcatOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(concat);
}
}
}
}
std::shared_ptr<Tensor> predict_concat(Tensor &t1, Tensor &t2, Tensor &t3,
Tensor &t4) {
// feed
auto scope = program_.scope;
Variable *x1_feed_value = scope->Var("conv2d_3.tmp_1");
auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
tensor_x1->ShareDataWith(t1);
Variable *x2_feed_value = scope->Var("conv2d_5.tmp_1");
auto tensor_x2 = x2_feed_value->GetMutable<Tensor>();
tensor_x2->ShareDataWith(t2);
Variable *x3_feed_value = scope->Var("conv2d_7.tmp_1");
auto tensor_x3 = x3_feed_value->GetMutable<Tensor>();
tensor_x3->ShareDataWith(t3);
Variable *x4_feed_value = scope->Var("conv2d_8.tmp_1");
auto tensor_x4 = x4_feed_value->GetMutable<Tensor>();
tensor_x4->ShareDataWith(t4);
Variable *con_output = scope->Var("concat_0.tmp_0");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({4, 100, 2, 2});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_concat(t1, t2, t3, t4, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_concat(const Tensor &t1, const Tensor &t2, const Tensor &t3,
const Tensor &t4, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestConcatOp<CPU>;
......@@ -120,60 +117,59 @@ template class TestConcatOp<CPU>;
} // namespace paddle_mobile
int main() {
DLOG << "----------**********----------";
DLOG << "begin to run ConcatOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../../test/models/googlenet"));
/// input x (4,10,2,2)
paddle_mobile::framework::Tensor inputx1;
SetupTensor<float>(&inputx1, {4, 10, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx1_ptr = inputx1.data<float>();
/// input x (4,20,2,2)
paddle_mobile::framework::Tensor inputx2;
SetupTensor<float>(&inputx2, {4, 20, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx2_ptr = inputx2.data<float>();
/// input x (4,30,2,2)
paddle_mobile::framework::Tensor inputx3;
SetupTensor<float>(&inputx3, {4, 30, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx3_ptr = inputx3.data<float>();
/// input x (4,40,2,2)
paddle_mobile::framework::Tensor inputx4;
SetupTensor<float>(&inputx4, {4, 40, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx4_ptr = inputx4.data<float>();
paddle_mobile::framework::TestConcatOp<paddle_mobile::CPU> testConcatOp(
program);
auto output_concat =
testConcatOp.predict_concat(inputx1, inputx2, inputx3, inputx4);
auto *output_concat_ptr = output_concat->data<float>();
int input_n = 1;
int input_c = 2;
int input_h = 0;
int input_w = 1;
int stride0 = inputx3.numel() / inputx3.dims()[0];
int stride1 = inputx3.numel() / inputx3.dims()[0] / inputx3.dims()[1];
int stride2 = inputx3.dims()[3];
/// inputx1 (4,10,2,2),
/// inputx2 (4,20,2,2),
/// inputx3 (4,30,2,2),
/// inputx4 (4,40,2,2),
/// axis = 1
/// output (4,100,2,2)
int input_index =
input_n * stride0 + input_c * stride1 + input_h * stride2 + input_w;
int output_index =
input_n * 100 * 2 * 2 +
(input_c + inputx1.dims()[1] + inputx2.dims()[1]) * 2 * 2 +
input_h * 2 + input_w;
DLOG << " inputx3[1,2,0,1] = " << inputx3_ptr[input_index];
DLOG << " output[1,12,0,1] = " << output_concat_ptr[output_index];
return 0;
DLOG << "----------**********----------";
DLOG << "begin to run ConcatOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../../test/models/googlenet"));
/// input x (4,10,2,2)
paddle_mobile::framework::Tensor inputx1;
SetupTensor<float>(&inputx1, {4, 10, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx1_ptr = inputx1.data<float>();
/// input x (4,20,2,2)
paddle_mobile::framework::Tensor inputx2;
SetupTensor<float>(&inputx2, {4, 20, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx2_ptr = inputx2.data<float>();
/// input x (4,30,2,2)
paddle_mobile::framework::Tensor inputx3;
SetupTensor<float>(&inputx3, {4, 30, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx3_ptr = inputx3.data<float>();
/// input x (4,40,2,2)
paddle_mobile::framework::Tensor inputx4;
SetupTensor<float>(&inputx4, {4, 40, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx4_ptr = inputx4.data<float>();
paddle_mobile::framework::TestConcatOp<paddle_mobile::CPU> testConcatOp(
program);
auto output_concat =
testConcatOp.predict_concat(inputx1, inputx2, inputx3, inputx4);
auto *output_concat_ptr = output_concat->data<float>();
int input_n = 1;
int input_c = 2;
int input_h = 0;
int input_w = 1;
int stride0 = inputx3.numel() / inputx3.dims()[0];
int stride1 = inputx3.numel() / inputx3.dims()[0] / inputx3.dims()[1];
int stride2 = inputx3.dims()[3];
/// inputx1 (4,10,2,2),
/// inputx2 (4,20,2,2),
/// inputx3 (4,30,2,2),
/// inputx4 (4,40,2,2),
/// axis = 1
/// output (4,100,2,2)
int input_index =
input_n * stride0 + input_c * stride1 + input_h * stride2 + input_w;
int output_index = input_n * 100 * 2 * 2 +
(input_c + inputx1.dims()[1] + inputx2.dims()[1]) * 2 * 2 +
input_h * 2 + input_w;
DLOG << " inputx3[1,2,0,1] = " << inputx3_ptr[input_index];
DLOG << " output[1,12,0,1] = " << output_concat_ptr[output_index];
return 0;
}
test/operators/test_cov_op.cpp
浏览文件 @ ddf6b722
......@@ -21,26 +21,26 @@ SOFTWARE.
#include "io.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../models/googlenet"));
if (program.originProgram == nullptr) {
DLOG << "program file read fail";
}
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../models/googlenet"));
if (program.originProgram == nullptr) {
DLOG << "program file read fail";
}
Executor4Test<paddle_mobile::CPU,
paddle_mobile::operators::ConvOp<paddle_mobile::CPU, float>>
executor(program, "conv2d");
Executor4Test<paddle_mobile::CPU,
paddle_mobile::operators::ConvOp<paddle_mobile::CPU, float>>
executor(program, "conv2d");
paddle_mobile::framework::Tensor input;
SetupTensor<float>(&input, {1, 3, 32, 32}, static_cast<float>(0),
static_cast<float>(1));
paddle_mobile::framework::Tensor input;
SetupTensor<float>(&input, {1, 3, 32, 32}, static_cast<float>(0),
static_cast<float>(1));
auto output =
executor.predict(input, "data", "conv2d_0.tmp_0", {1, 64, 56, 56});
auto output =
executor.predict(input, "data", "conv2d_0.tmp_0", {1, 64, 56, 56});
float *output_ptr = output->data<float>();
for (int j = 0; j < output->numel(); ++j) {
DLOG << " value of output: " << output_ptr[j];
}
return 0;
float *output_ptr = output->data<float>();
for (int j = 0; j < output->numel(); ++j) {
DLOG << " value of output: " << output_ptr[j];
}
return 0;
}
test/operators/test_elementwise_add_op.cpp
浏览文件 @ ddf6b722
......@@ -24,127 +24,124 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> class TestElementwiseAddOp {
public:
explicit TestElementwiseAddOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "elementwise_add" &&
op->Input("X")[0] == "batch_norm_2.tmp_2") {
DLOG << " elementwise_add attr size: "
<< op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
Attribute axis_attr = op->GetAttrMap().at("axis");
int axis = axis_attr.Get<int>();
DLOG << " Attr axis is : " << axis;
std::shared_ptr<operators::ElementwiseAddOp<Dtype, float>>
add = std::make_shared<
operators::ElementwiseAddOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
}
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 3, 224, 224});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
public:
explicit TestElementwiseAddOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "elementwise_add" &&
op->Input("X")[0] == "batch_norm_2.tmp_2") {
DLOG << " elementwise_add attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
Attribute axis_attr = op->GetAttrMap().at("axis");
int axis = axis_attr.Get<int>();
DLOG << " Attr axis is : " << axis;
std::shared_ptr<operators::ElementwiseAddOp<Dtype, float>> add =
std::make_shared<operators::ElementwiseAddOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(add);
}
}
}
}
std::shared_ptr<Tensor> predict_add(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("batch_norm_2.tmp_2");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("batch_norm_0.tmp_3");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("elementwise_add_0.tmp_0");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({1, 3, 224, 224});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_add(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_add(const Tensor &t1, const Tensor &t2, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestElementwiseAddOp<CPU>;
} // namespace framework
} // namespace paddle_mobile
int main() {
DLOG << "----------**********----------";
DLOG << "begin to run ElementAddOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (1,3,224,224)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx_ptr = inputx.data<float>();
/// input y (224,)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
static_cast<float>(1));
auto *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
testElementwiseAddOp(program);
auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
auto *output_add_ptr = output_add->data<float>();
// for (int j = 0; j < output_add->numel(); ++j) {
// DLOG << "value of output: " << output_add_ptr[j];
// }
/// output (1,3,224,224)
DLOG << "output memory size : " << output_add->memory_size();
DLOG << "output numel : " << output_add->numel();
DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
<< output_add_ptr[226];
return 0;
DLOG << "----------**********----------";
DLOG << "begin to run ElementAddOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (1,3,224,224)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx_ptr = inputx.data<float>();
/// input y (224,)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {224}, static_cast<float>(0),
static_cast<float>(1));
auto *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestElementwiseAddOp<paddle_mobile::CPU>
testElementwiseAddOp(program);
auto output_add = testElementwiseAddOp.predict_add(inputx, inputy);
auto *output_add_ptr = output_add->data<float>();
// for (int j = 0; j < output_add->numel(); ++j) {
// DLOG << "value of output: " << output_add_ptr[j];
// }
/// output (1,3,224,224)
DLOG << "output memory size : " << output_add->memory_size();
DLOG << "output numel : " << output_add->numel();
DLOG << inputx_ptr[226] << " + " << inputy_ptr[2] << " = "
<< output_add_ptr[226];
return 0;
}
test/operators/test_lrn_op.cpp
浏览文件 @ ddf6b722
......@@ -24,84 +24,80 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> class TestLrnOp {
public:
explicit TestLrnOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "lrn" &&
op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << " n : " << op->GetAttrMap().at("n").Get<int>();
DLOG << " alpha : "
<< op->GetAttrMap().at("alpha").Get<float>();
DLOG << " beta : "
<< op->GetAttrMap().at("beta").Get<float>();
DLOG << " k : " << op->GetAttrMap().at("k").Get<float>();
std::shared_ptr<operators::LrnOp<Dtype, float>> lrn =
std::make_shared<operators::LrnOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(lrn);
}
}
}
public:
explicit TestLrnOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
std::shared_ptr<Tensor> predict_lrn(Tensor &t1) {
// feed
auto scope = program_.scope;
Variable *x1_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
tensor_x1->ShareDataWith(t1);
Variable *con_output = scope->Var("pool1_norm1.tmp_1");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 4, 2, 2});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_lrn(t1, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_lrn(const Tensor &t1, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "lrn" && op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << " n : " << op->GetAttrMap().at("n").Get<int>();
DLOG << " alpha : " << op->GetAttrMap().at("alpha").Get<float>();
DLOG << " beta : " << op->GetAttrMap().at("beta").Get<float>();
DLOG << " k : " << op->GetAttrMap().at("k").Get<float>();
std::shared_ptr<operators::LrnOp<Dtype, float>> lrn =
std::make_shared<operators::LrnOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(lrn);
}
}
}
}
std::shared_ptr<Tensor> predict_lrn(Tensor &t1) {
// feed
auto scope = program_.scope;
Variable *x1_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
tensor_x1->ShareDataWith(t1);
Variable *con_output = scope->Var("pool1_norm1.tmp_1");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 4, 2, 2});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_lrn(t1, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_lrn(const Tensor &t1, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestLrnOp<CPU>;
......@@ -109,51 +105,50 @@ template class TestLrnOp<CPU>;
} // namespace paddle_mobile
int main() {
DLOG << "----------**********----------";
DLOG << "begin to run LrnOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../../test/models/googlenet"));
/// input x (3,4,2,2)
paddle_mobile::framework::Tensor inputx1;
SetupTensor<float>(&inputx1, {3, 4, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx1_ptr = inputx1.data<float>();
paddle_mobile::framework::TestLrnOp<paddle_mobile::CPU> testLrnOp(program);
auto output_lrn = testLrnOp.predict_lrn(inputx1);
auto *output_lrn_ptr = output_lrn->data<float>();
DLOG << " LrnOp input: ";
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
for (int c = 0; c < 2; c++) {
for (int d = 0; d < 2; d++) {
DLOGF("%f ", inputx1_ptr[i * 16 + j * 4 + c * 2 + d]);
}
DLOGF("\n");
}
DLOGF("\n");
DLOG << "----------**********----------";
DLOG << "begin to run LrnOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../../test/models/googlenet"));
/// input x (3,4,2,2)
paddle_mobile::framework::Tensor inputx1;
SetupTensor<float>(&inputx1, {3, 4, 2, 2}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx1_ptr = inputx1.data<float>();
paddle_mobile::framework::TestLrnOp<paddle_mobile::CPU> testLrnOp(program);
auto output_lrn = testLrnOp.predict_lrn(inputx1);
auto *output_lrn_ptr = output_lrn->data<float>();
DLOG << " LrnOp input: ";
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
for (int c = 0; c < 2; c++) {
for (int d = 0; d < 2; d++) {
DLOGF("%f ", inputx1_ptr[i * 16 + j * 4 + c * 2 + d]);
}
DLOGF("\n");
}
DLOGF("\n");
}
DLOG << " LrnOp output: ";
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
for (int c = 0; c < 2; c++) {
for (int d = 0; d < 2; d++) {
DLOGF("%f ", output_lrn_ptr[i * 16 + j * 4 + c * 2 + d]);
}
DLOGF("\n");
}
DLOGF("\n");
DLOGF("\n");
}
DLOG << " LrnOp output: ";
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 4; j++) {
for (int c = 0; c < 2; c++) {
for (int d = 0; d < 2; d++) {
DLOGF("%f ", output_lrn_ptr[i * 16 + j * 4 + c * 2 + d]);
}
DLOGF("\n");
}
DLOGF("\n");
}
DLOG << inputx1_ptr[0] << " / ((1 + 0.00002 * ( " << inputx1_ptr[0]
<< "^2 + " << inputx1_ptr[4] << "^2 + " << inputx1_ptr[8]
<< "^2 ))^0.75) = ";
DLOG << output_lrn_ptr[0];
return 0;
DLOGF("\n");
}
DLOG << inputx1_ptr[0] << " / ((1 + 0.00002 * ( " << inputx1_ptr[0] << "^2 + "
<< inputx1_ptr[4] << "^2 + " << inputx1_ptr[8] << "^2 ))^0.75) = ";
DLOG << output_lrn_ptr[0];
return 0;
}
test/operators/test_mul_op.cpp
浏览文件 @ ddf6b722
......@@ -24,88 +24,86 @@ namespace paddle_mobile {
namespace framework {
template <typename Dtype> class TestMulOp {
public:
explicit TestMulOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "mul" &&
op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << "x_num_col_dims : "
<< op->GetAttrMap().at("x_num_col_dims").Get<int>();
DLOG << "y_num_col_dims : "
<< op->GetAttrMap().at("y_num_col_dims").Get<int>();
std::shared_ptr<operators::MulOp<Dtype, float>> mul =
std::make_shared<operators::MulOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(mul);
}
}
}
public:
explicit TestMulOp(const Program<Dtype> p) : program_(p) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
to_predict_program_ = program_.originProgram;
}
std::shared_ptr<Tensor> predict_mul(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("fc_0.w_0");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("fc_0.tmp_0");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 3});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_mul(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_mul(const Tensor &t1, const Tensor &t2, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
const std::vector<std::shared_ptr<BlockDesc>> blocks =
to_predict_program_->Blocks();
// DLOG << " **block size " << blocks.size();
for (int i = 0; i < blocks.size(); ++i) {
std::shared_ptr<BlockDesc> block_desc = blocks[i];
std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
// DLOG << " ops " << ops.size();
for (int j = 0; j < ops.size(); ++j) {
std::shared_ptr<OpDesc> op = ops[j];
if (op->Type() == "mul" && op->Input("X")[0] == "pool2d_0.tmp_0") {
DLOG << " mul attr size: " << op->GetAttrMap().size();
DLOG << " inputs size: " << op->GetInputs().size();
DLOG << " outputs size: " << op->GetOutputs().size();
DLOG << " Input X is : " << op->Input("X")[0];
DLOG << " Input Y is : " << op->Input("Y")[0];
DLOG << " Output Out is : " << op->Output("Out")[0];
DLOG << "x_num_col_dims : "
<< op->GetAttrMap().at("x_num_col_dims").Get<int>();
DLOG << "y_num_col_dims : "
<< op->GetAttrMap().at("y_num_col_dims").Get<int>();
std::shared_ptr<operators::MulOp<Dtype, float>> mul =
std::make_shared<operators::MulOp<Dtype, float>>(
op->Type(), op->GetInputs(), op->GetOutputs(),
op->GetAttrMap(), program_.scope);
ops_of_block_[*block_desc.get()].push_back(mul);
}
}
}
}
std::shared_ptr<Tensor> predict_mul(Tensor &t1, Tensor &t2) {
// feed
auto scope = program_.scope;
Variable *x_feed_value = scope->Var("pool2d_0.tmp_0");
auto tensor_x = x_feed_value->GetMutable<Tensor>();
tensor_x->ShareDataWith(t1);
Variable *y_feed_value = scope->Var("fc_0.w_0");
auto tensor_y = y_feed_value->GetMutable<Tensor>();
tensor_y->ShareDataWith(t2);
Variable *con_output = scope->Var("fc_0.tmp_0");
auto *output_tensor = con_output->GetMutable<Tensor>();
output_tensor->mutable_data<float>({3, 3});
// DLOG << typeid(output_tensor).name();
// DLOG << "output_tensor dims: " << output_tensor->dims();
std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
out_tensor.reset(output_tensor);
predict_mul(t1, t2, 0);
return out_tensor;
}
private:
const framework::Program<Dtype> program_;
std::shared_ptr<ProgramDesc> to_predict_program_;
std::map<framework::BlockDesc,
std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
ops_of_block_;
bool use_optimize_ = false;
void predict_mul(const Tensor &t1, const Tensor &t2, int block_id) {
std::shared_ptr<BlockDesc> to_predict_block =
to_predict_program_->Block(block_id);
for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size(); ++j) {
auto op = ops_of_block_[*to_predict_block.get()][j];
DLOG << "op -> run()";
op->Run();
}
}
};
template class TestMulOp<CPU>;
......@@ -113,62 +111,62 @@ template class TestMulOp<CPU>;
} // namespace paddle_mobile
int main() {
DLOG << "----------**********----------";
DLOG << "begin to run MulOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (3,2,1,1)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {3, 2, 1, 1}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx_ptr = inputx.data<float>();
/// input y (2,3)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {2, 3}, static_cast<float>(0),
static_cast<float>(1));
auto *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestMulOp<paddle_mobile::CPU> testMulOp(program);
auto output_mul = testMulOp.predict_mul(inputx, inputy);
auto *output_mul_ptr = output_mul->data<float>();
auto dimx_1 = inputx.numel() / inputx.dims()[0];
DLOG << " inputx : ";
for (int i = 0; i < inputx.dims()[0]; ++i) {
for (int j = 0; j < dimx_1; ++j) {
DLOGF("%f ", inputx_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
DLOG << "----------**********----------";
DLOG << "begin to run MulOp Test";
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program =
loader.Load(std::string("../../test/models/"
"image_classification_resnet.inference.model"));
/// input x (3,2,1,1)
paddle_mobile::framework::Tensor inputx;
SetupTensor<float>(&inputx, {3, 2, 1, 1}, static_cast<float>(0),
static_cast<float>(1));
auto *inputx_ptr = inputx.data<float>();
/// input y (2,3)
paddle_mobile::framework::Tensor inputy;
SetupTensor<float>(&inputy, {2, 3}, static_cast<float>(0),
static_cast<float>(1));
auto *inputy_ptr = inputy.data<float>();
paddle_mobile::framework::TestMulOp<paddle_mobile::CPU> testMulOp(program);
auto output_mul = testMulOp.predict_mul(inputx, inputy);
auto *output_mul_ptr = output_mul->data<float>();
auto dimx_1 = inputx.numel() / inputx.dims()[0];
DLOG << " inputx : ";
for (int i = 0; i < inputx.dims()[0]; ++i) {
for (int j = 0; j < dimx_1; ++j) {
DLOGF("%f ", inputx_ptr[i * dimx_1 + j]);
}
auto dimy_1 = inputy.numel() / inputy.dims()[0];
DLOG << " inputy : ";
for (int i = 0; i < inputy.dims()[0]; ++i) {
for (int j = 0; j < dimy_1; ++j) {
DLOGF("%f ", inputy_ptr[i * dimx_1 + j]);
}
DLOGF("\n");
DLOGF("\n");
}
auto dimy_1 = inputy.numel() / inputy.dims()[0];
DLOG << " inputy : ";
for (int i = 0; i < inputy.dims()[0]; ++i) {
for (int j = 0; j < dimy_1; ++j) {
DLOGF("%f ", inputy_ptr[i * dimx_1 + j]);
}
auto dim_output_1 = output_mul->numel() / output_mul->dims()[0];
DLOG << " output : ";
for (int i = 0; i < output_mul->dims()[0]; ++i) {
for (int j = 0; j < dim_output_1; ++j) {
DLOGF("%f ", output_mul_ptr[i * dimy_1 + j]);
}
DLOGF("\n");
DLOGF("\n");
}
auto dim_output_1 = output_mul->numel() / output_mul->dims()[0];
DLOG << " output : ";
for (int i = 0; i < output_mul->dims()[0]; ++i) {
for (int j = 0; j < dim_output_1; ++j) {
DLOGF("%f ", output_mul_ptr[i * dimy_1 + j]);
}
DLOGF("\n");
}
/// output (3,3)
DLOG << "output memory size : " << output_mul->memory_size();
DLOG << "output numel : " << output_mul->numel();
/// output (3,3)
DLOG << "output memory size : " << output_mul->memory_size();
DLOG << "output numel : " << output_mul->numel();
DLOG << inputx_ptr[0] << " x " << inputy_ptr[0] << " + " << inputx_ptr[1]
<< " x " << inputy_ptr[0 + 3] << " = " << output_mul_ptr[0];
return 0;
DLOG << inputx_ptr[0] << " x " << inputy_ptr[0] << " + " << inputx_ptr[1]
<< " x " << inputy_ptr[0 + 3] << " = " << output_mul_ptr[0];
return 0;
}
test/operators/test_pool_op.cpp
浏览文件 @ ddf6b722
......@@ -21,26 +21,26 @@ SOFTWARE.
#include "io.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../models/googlenet"));
if (program.originProgram == nullptr) {
DLOG << "program read file";
}
paddle_mobile::Loader<paddle_mobile::CPU> loader;
auto program = loader.Load(std::string("../models/googlenet"));
if (program.originProgram == nullptr) {
DLOG << "program read file";
}
Executor4Test<paddle_mobile::CPU,
paddle_mobile::operators::PoolOp<paddle_mobile::CPU, float>>
executor(program, "pool2d");
Executor4Test<paddle_mobile::CPU,
paddle_mobile::operators::PoolOp<paddle_mobile::CPU, float>>
executor(program, "pool2d");
paddle_mobile::framework::Tensor input;
SetupTensor<float>(&input, {1, 64, 112, 112}, static_cast<float>(0),
static_cast<float>(1));
paddle_mobile::framework::Tensor input;
SetupTensor<float>(&input, {1, 64, 112, 112}, static_cast<float>(0),
static_cast<float>(1));
auto output = executor.predict(input, "conv2d_0.tmp_1", "pool2d_0.tmp_0",
{1, 64, 56, 56});
auto output = executor.predict(input, "conv2d_0.tmp_1", "pool2d_0.tmp_0",
{1, 64, 56, 56});
float *output_ptr = output->data<float>();
for (int j = 0; j < output->numel(); ++j) {
DLOG << " value of output: " << output_ptr[j];
}
return 0;
float *output_ptr = output->data<float>();
for (int j = 0; j < output->numel(); ++j) {
DLOG << " value of output: " << output_ptr[j];
}
return 0;
}
test/test_helper.h
浏览文件 @ ddf6b722
......@@ -24,13 +24,12 @@ SOFTWARE.
template <typename T>
void SetupTensor(paddle_mobile::framework::Tensor *input,
paddle_mobile::framework::DDim dims, T lower, T upper) {
static unsigned int seed = 100;
std::mt19937 rng(seed++);
std::uniform_real_distribution<double> uniform_dist(0, 1);
static unsigned int seed = 100;
std::mt19937 rng(seed++);
std::uniform_real_distribution<double> uniform_dist(0, 1);
T *input_ptr = input->mutable_data<T>(dims);
for (int i = 0; i < input->numel(); ++i) {
input_ptr[i] =
static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
T *input_ptr = input->mutable_data<T>(dims);
for (int i = 0; i < input->numel(); ++i) {
input_ptr[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
}
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