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PaddleDetection
未验证 提交 3043f51b 编写于 作者: Y Yu Yang 提交者： GitHub
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Merge pull request #13511 from reyoung/fix_ce 

Revert "Merge pull request #13431 from chengduoZH/refine_lod"
上级 cffad81c a6c8d6b9
隐藏空白更改
内联 并排
Showing with 326 addition and 381 deletion
paddle/fluid/framework/details/cow_ptr.h
浏览文件 @ 3043f51b
...@@ -20,41 +20,79 @@ namespace paddle { ...@@ -20,41 +20,79 @@ namespace paddle {
namespace framework { namespace framework {
namespace details { namespace details {
template <class T> // Change it to thread safe flags if needed.
class COWPtr { class ThreadUnsafeOwnershipFlags {
public: public:
typedef std::shared_ptr<T> RefPtr; explicit ThreadUnsafeOwnershipFlags(bool flag) : flag_(flag) {}
private: ThreadUnsafeOwnershipFlags(const ThreadUnsafeOwnershipFlags& other) = delete;
RefPtr m_sp; ThreadUnsafeOwnershipFlags& operator=(
const ThreadUnsafeOwnershipFlags& other) = delete;
ThreadUnsafeOwnershipFlags(ThreadUnsafeOwnershipFlags&& other) = default;
void detach() { void SetOwnership(bool flag) { flag_ = flag; }
T* tmp = m_sp.get();
if (!(tmp == nullptr || m_sp.unique())) { // Invoke the callback if it is not owned.
m_sp = RefPtr(new T(*tmp)); template <typename Callback>
void AcquireOwnershipOnce(Callback acquire) {
if (!flag_) {
acquire();
flag_ = true;
} }
} }
public: private:
COWPtr() : m_sp(nullptr) {} bool flag_;
explicit COWPtr(T* t) : m_sp(t) {} };
explicit COWPtr(const RefPtr& refptr) : m_sp(refptr) {}
const T& Data() const { return operator*(); } // Copy-On-Write pointer.
// It will hold a T* pointer, and only copy once when `MutableData` is invoked.
//
// The template parameter OwnershipFlags should have:
// * a constructor takes a bool. True if own.
// * SetOwnership(bool flag).
// * AcquireOwnershipOnce(Callback). It will invoke the callback if it is not
// owned.
//
// https://en.wikipedia.org/wiki/Copy-on-write
template <typename T, typename OwnershipFlags = ThreadUnsafeOwnershipFlags>
class COWPtr {
public:
// Ctor from raw pointer.
explicit COWPtr(T* ptr) : payload_(ptr), ownership_{true} {}
T* MutableData() { return operator->(); } // Move methods. Steal ownership from origin
COWPtr(COWPtr&& other)
: payload_(other.payload_), ownership_{std::move(other.ownership_)} {}
COWPtr& operator=(COWPtr&& origin) = default;
const T& operator*() const { return *m_sp; } // Copy methods. Not own payload
T& operator*() { COWPtr(const COWPtr& other) : payload_(other.payload_), ownership_{false} {}
detach(); COWPtr& operator=(const COWPtr& other) {
return *m_sp; payload_ = other.payload_;
ownership_.SetOwnership(false);
return *this;
} }
const T* operator->() const { return m_sp.operator->(); }
T* operator->() { // Access read only data.
detach(); const T& Data() const { return *payload_; }
return m_sp.operator->();
// Access mutable data. If the data is not owned, the data will be copied
// before.
T* MutableData() {
ownership_.AcquireOwnershipOnce(
[this] { payload_.reset(new T(*payload_)); });
return payload_.get();
} }
private:
// Actual data pointer.
std::shared_ptr<T> payload_;
// Ownership flag.
OwnershipFlags ownership_;
}; };
} // namespace details } // namespace details
} // namespace framework } // namespace framework
} // namespace paddle } // namespace paddle
paddle/fluid/framework/details/cow_ptr_test.cc
浏览文件 @ 3043f51b
...@@ -30,14 +30,6 @@ TEST(COWPtr, all) { ...@@ -30,14 +30,6 @@ TEST(COWPtr, all) {
ASSERT_EQ(ptr2.Data(), 10); ASSERT_EQ(ptr2.Data(), 10);
} }
TEST(COWPtr, change_old) {
COWPtr<int> ptr(new int{0});
COWPtr<int> ptr2 = ptr;
*ptr.MutableData() = 10;
ASSERT_EQ(ptr2.Data(), 0);
ASSERT_EQ(ptr.Data(), 10);
}
} // namespace details } // namespace details
} // namespace framework } // namespace framework
} // namespace paddle } // namespace paddle
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 3043f51b
...@@ -17,12 +17,10 @@ ...@@ -17,12 +17,10 @@
#include <algorithm> #include <algorithm>
#include <initializer_list> #include <initializer_list>
#include <memory> #include <memory>
#include <utility>
#include <vector> #include <vector>
#include "paddle/fluid/framework/details/cow_ptr.h"
#include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/memory/memcpy.h"
#include "glog/logging.h" #include "glog/logging.h"
...@@ -30,401 +28,206 @@ namespace paddle { ...@@ -30,401 +28,206 @@ namespace paddle {
namespace framework { namespace framework {
#if defined(PADDLE_WITH_CUDA) #if defined(PADDLE_WITH_CUDA)
namespace details {
struct CUDABuffer {
void *data_{nullptr};
size_t size_{0};
platform::CUDAPlace place_;
CUDABuffer() {}
CUDABuffer(platform::Place place, size_t size)
: size_(size), place_(boost::get<platform::CUDAPlace>(place)) {
data_ = memory::Alloc(place_, size);
}
~CUDABuffer() { ClearMemory(); }
CUDABuffer(const CUDABuffer &o) = delete;
CUDABuffer &operator=(const CUDABuffer &o) = delete;
void Resize(platform::Place place, size_t size) {
ClearMemory();
place_ = boost::get<platform::CUDAPlace>(place);
data_ = memory::Alloc(place_, size);
size_ = size;
}
void Swap(CUDABuffer &o) {
std::swap(data_, o.data_);
std::swap(place_, o.place_);
std::swap(size_, o.size_);
}
private:
void ClearMemory() const {
if (data_) {
memory::Free(place_, data_);
}
}
};
} // namespace details
// Vector<T> implements the std::vector interface, and can get Data or // Vector<T> implements the std::vector interface, and can get Data or
// MutableData from any place. The data will be synced implicitly inside. // MutableData from any place. The data will be synced implicitly inside.
template <typename T> template <typename T>
class Vector { class Vector {
public: public:
using value_type = T; using value_type = T;
using iterator = typename std::vector<T>::iterator;
using const_iterator = typename std::vector<T>::const_iterator;
private:
// The actual class to implement vector logic
class VectorData {
public:
VectorData() : flag_(kDataInCPU) {}
VectorData(size_t count, const T &value)
: cpu_(count, value), flag_(kDataInCPU) {}
VectorData(std::initializer_list<T> init) : cpu_(init), flag_(kDataInCPU) {}
template <typename U>
explicit VectorData(const std::vector<U> &dat)
: cpu_(dat), flag_(kDataInCPU) {}
VectorData(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
}
VectorData &operator=(const VectorData &o) {
o.ImmutableCPU();
cpu_ = o.cpu_;
flag_ = kDataInCPU;
details::CUDABuffer null;
gpu_.Swap(null);
return *this;
}
T &operator[](size_t i) {
MutableCPU();
return cpu_[i];
}
const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_[i];
}
size_t size() const { return cpu_.size(); }
iterator begin() {
MutableCPU();
return cpu_.begin();
}
iterator end() {
MutableCPU();
return cpu_.end();
}
T &front() {
MutableCPU();
return cpu_.front();
}
T &back() {
MutableCPU();
return cpu_.back();
}
const_iterator begin() const {
ImmutableCPU();
return cpu_.begin();
}
const_iterator end() const {
ImmutableCPU();
return cpu_.end();
}
const T &back() const {
ImmutableCPU();
return cpu_.back();
}
T *data() { return &(*this)[0]; }
const T *data() const { return &(*this)[0]; }
const T &front() const {
ImmutableCPU();
return cpu_.front();
}
// assign this from iterator.
// NOTE: the iterator must support `end-begin`
template <typename Iter>
void assign(Iter begin, Iter end) {
MutableCPU();
cpu_.assign(begin, end);
}
// push_back. If the previous capacity is not enough, the memory will
// double.
void push_back(T elem) {
MutableCPU();
cpu_.push_back(elem);
}
// extend a vector by iterator.
// NOTE: the iterator must support end-begin
template <typename It>
void Extend(It begin, It end) {
MutableCPU();
auto out_it = std::back_inserter<std::vector<T>>(this->cpu_);
std::copy(begin, end, out_it);
}
// resize the vector
void resize(size_t size) {
MutableCPU();
cpu_.resize(size);
}
// get cuda ptr. immutable
const T *CUDAData(platform::Place place) const {
PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return reinterpret_cast<T *>(gpu_.data_);
}
// get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) {
const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
}
// clear
void clear() {
cpu_.clear();
flag_ = kDirty | kDataInCPU;
}
size_t capacity() const { return cpu_.capacity(); }
// reserve data
void reserve(size_t size) { cpu_.reserve(size); }
// implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const {
ImmutableCPU();
return cpu_;
}
bool operator==(const VectorData &other) const {
ImmutableCPU();
other.ImmutableCPU();
return cpu_ == other.cpu_;
}
private:
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
void *src = gpu_.data_;
void *dst = cpu_.data();
memory::Copy(platform::CPUPlace(), dst, gpu_.place_, src, gpu_.size_,
nullptr);
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
CopyCPUDataToCUDA(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() &&
!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
CopyCUDADataToAnotherPlace(place);
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
CopyCPUDataToCUDA(place);
SetFlag(kDataInCUDA);
} else if (!(boost::get<platform::CUDAPlace>(place) == gpu_.place_)) {
CopyCUDADataToAnotherPlace(place);
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void CopyCUDADataToAnotherPlace(const platform::Place &place) const {
details::CUDABuffer tmp(place, gpu_.size_);
const void *src = gpu_.data_;
void *dst = tmp.data_;
memory::Copy(tmp.place_, dst, gpu_.place_, src, gpu_.size_, nullptr);
gpu_.Swap(tmp);
}
void CopyCPUDataToCUDA(const platform::Place &place) const {
void *src = cpu_.data();
gpu_.Resize(place, cpu_.size() * sizeof(T));
void *dst = gpu_.data_;
auto stream = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place))
->stream();
memory::Copy(gpu_.place_, dst, platform::CPUPlace(), src, gpu_.size_,
stream);
}
void ImmutableCPU() const {
if (IsDirty() && !IsInCPU()) { // If data has been changed in CUDA, or
// CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
mutable std::vector<T> cpu_;
mutable details::CUDABuffer gpu_;
mutable int flag_;
};
public:
// Default ctor. Create empty Vector // Default ctor. Create empty Vector
Vector() : m_(new VectorData()) {} Vector() { InitEmpty(); }
// Fill vector with value. The vector size is `count`. // Fill vector with value. The vector size is `count`.
explicit Vector(size_t count, const T &value = T()) explicit Vector(size_t count, const T &value = T()) {
: m_(new VectorData(count, value)) {} InitEmpty();
if (count != 0) {
resize(count);
T *ptr = begin();
for (size_t i = 0; i < count; ++i) {
ptr[i] = value;
}
}
}
// Ctor with init_list // Ctor with init_list
Vector(std::initializer_list<T> init) : m_(new VectorData(init)) {} Vector(std::initializer_list<T> init) {
if (init.size() == 0) {
InitEmpty();
} else {
InitByIter(init.size(), init.begin(), init.end());
}
}
// implicit cast from std::vector. // implicit cast from std::vector.
template <typename U> template <typename U>
Vector(const std::vector<U> &dat) : m_(new VectorData(dat)) { // NOLINT Vector(const std::vector<U> &dat) { // NOLINT
if (dat.size() == 0) {
InitEmpty();
} else {
InitByIter(dat.size(), dat.begin(), dat.end());
}
} }
// Copy ctor // Copy ctor
Vector(const Vector<T> &other) { m_ = other.m_; } Vector(const Vector<T> &other) { this->operator=(other); }
// Copy operator // Copy operator
Vector<T> &operator=(const Vector<T> &other) { Vector<T> &operator=(const Vector<T> &other) {
m_ = other.m_; if (other.size() != 0) {
this->InitByIter(other.size(), other.begin(), other.end());
} else {
InitEmpty();
}
return *this; return *this;
} }
// Move ctor // Move ctor
Vector(Vector<T> &&other) { m_ = std::move(other.m_); } Vector(Vector<T> &&other) {
this->size_ = other.size_;
this->flag_ = other.flag_;
if (other.cuda_vec_.memory_size()) {
this->cuda_vec_.ShareDataWith(other.cuda_vec_);
}
if (other.cpu_vec_.memory_size()) {
this->cpu_vec_.ShareDataWith(other.cpu_vec_);
}
}
// CPU data access method. Mutable. // CPU data access method. Mutable.
T &operator[](size_t i) { return (*m_)[i]; } T &operator[](size_t i) {
MutableCPU();
return const_cast<T *>(cpu_vec_.data<T>())[i];
}
// CPU data access method. Immutable. // CPU data access method. Immutable.
const T &operator[](size_t i) const { return (*m_)[i]; } const T &operator[](size_t i) const {
ImmutableCPU();
return cpu_vec_.data<T>()[i];
}
// std::vector iterator methods. Based on CPU data access method // std::vector iterator methods. Based on CPU data access method
size_t size() const { return m_->size(); } size_t size() const { return size_; }
iterator begin() { return m_->begin(); } T *begin() { return capacity() == 0 ? &EmptyDummy() : &this->operator[](0); }
iterator end() { return m_->end(); } T *end() {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
T &front() { return m_->front(); } T &front() { return *begin(); }
T &back() { return m_->back(); } T &back() {
auto it = end();
--it;
return *it;
}
const_iterator begin() const { return m_->begin(); } const T *begin() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](0);
}
const_iterator end() const { return m_->end(); } const T *end() const {
return capacity() == 0 ? &EmptyDummy() : &this->operator[](size());
}
const_iterator cbegin() const { return begin(); } const T *cbegin() const { return begin(); }
const_iterator cend() const { return end(); } const T *cend() const { return end(); }
const T &back() const { return m_->back(); } const T &back() const {
auto it = end();
--it;
return *it;
}
T *data() { return m_->data(); } T *data() { return begin(); }
const T *data() const { return m_->data(); } const T *data() const { return begin(); }
const T &front() const { return m_->front(); } const T &front() const { return *begin(); }
// end of std::vector iterator methods // end of std::vector iterator methods
// assign this from iterator. // assign this from iterator.
// NOTE: the iterator must support `end-begin` // NOTE: the iterator must support `end-begin`
template <typename Iter> template <typename Iter>
void assign(Iter begin, Iter end) { void assign(Iter begin, Iter end) {
m_->assign(begin, end); InitByIter(end - begin, begin, end);
} }
// push_back. If the previous capacity is not enough, the memory will // push_back. If the previous capacity is not enough, the memory will
// double. // double.
void push_back(T elem) { m_->push_back(elem); } void push_back(T elem) {
if (size_ + 1 > capacity()) {
reserve((size_ + 1) << 1);
}
*end() = elem;
++size_;
}
// extend a vector by iterator. // extend a vector by iterator.
// NOTE: the iterator must support end-begin // NOTE: the iterator must support end-begin
template <typename It> template <typename It>
void Extend(It begin, It end) { void Extend(It begin, It end) {
m_->Extend(begin, end); size_t pre_size = size_;
resize(pre_size + (end - begin));
T *ptr = this->begin() + pre_size;
for (; begin < end; ++begin, ++ptr) {
*ptr = *begin;
}
} }
// resize the vector // resize the vector
void resize(size_t size) { void resize(size_t size) {
if (m_.Data().size() != size) { if (size + 1 <= capacity()) {
m_->resize(size); size_ = size;
} else {
MutableCPU();
Tensor cpu_tensor;
platform::Place cpu = platform::CPUPlace();
T *ptr = cpu_tensor.mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
const T *old_ptr =
cpu_vec_.memory_size() == 0 ? nullptr : cpu_vec_.data<T>();
if (old_ptr != nullptr) {
std::copy(old_ptr, old_ptr + size_, ptr);
}
size_ = size;
cpu_vec_.ShareDataWith(cpu_tensor);
} }
} }
// get cuda ptr. immutable // get cuda ptr. immutable
const T *CUDAData(platform::Place place) const { const T *CUDAData(platform::Place place) const {
return m_.Data().CUDAData(place); PADDLE_ENFORCE(platform::is_gpu_place(place),
"CUDA Data must on CUDA place");
ImmutableCUDA(place);
return cuda_vec_.data<T>();
} }
// get cuda ptr. mutable // get cuda ptr. mutable
T *CUDAMutableData(platform::Place place) { T *CUDAMutableData(platform::Place place) {
return m_->CUDAMutableData(place); const T *ptr = CUDAData(place);
flag_ = kDirty | kDataInCUDA;
return const_cast<T *>(ptr);
} }
// clear // clear
void clear() { m_->clear(); } void clear() {
size_ = 0;
flag_ = kDirty | kDataInCPU;
}
size_t capacity() const { return m_->capacity(); } size_t capacity() const {
return cpu_vec_.memory_size() / SizeOfType(typeid(T));
}
// reserve data // reserve data
void reserve(size_t size) { m_->reserve(size); } void reserve(size_t size) {
size_t pre_size = size_;
resize(size);
resize(pre_size);
}
// the unify method to access CPU or CUDA data. immutable. // the unify method to access CPU or CUDA data. immutable.
const T *Data(platform::Place place) const { const T *Data(platform::Place place) const {
...@@ -445,7 +248,12 @@ class Vector { ...@@ -445,7 +248,12 @@ class Vector {
} }
// implicit cast operator. Vector can be cast to std::vector implicitly. // implicit cast operator. Vector can be cast to std::vector implicitly.
operator std::vector<T>() const { return *m_; } operator std::vector<T>() const {
std::vector<T> result;
result.resize(size());
std::copy(begin(), end(), result.begin());
return result;
}
bool operator==(const Vector<T> &other) const { bool operator==(const Vector<T> &other) const {
if (size() != other.size()) return false; if (size() != other.size()) return false;
...@@ -459,11 +267,118 @@ class Vector { ...@@ -459,11 +267,118 @@ class Vector {
return true; return true;
} }
const void *Handle() const { return &m_.Data(); }
private: private:
// Vector is an COW object. void InitEmpty() {
details::COWPtr<VectorData> m_; size_ = 0;
flag_ = kDataInCPU;
}
template <typename Iter>
void InitByIter(size_t size, Iter begin, Iter end) {
platform::Place cpu = platform::CPUPlace();
T *ptr = this->cpu_vec_.template mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(size)}), cpu);
for (size_t i = 0; i < size; ++i) {
*ptr++ = *begin++;
}
flag_ = kDataInCPU | kDirty;
size_ = size;
}
enum DataFlag {
kDataInCPU = 0x01,
kDataInCUDA = 0x02,
// kDirty means the data has been changed in one device.
kDirty = 0x10
};
void CopyToCPU() const {
// COPY GPU Data To CPU
TensorCopy(cuda_vec_, platform::CPUPlace(), &cpu_vec_);
WaitPlace(cuda_vec_.place());
}
void MutableCPU() {
if (IsInCUDA() && IsDirty()) {
CopyToCPU();
}
flag_ = kDirty | kDataInCPU;
}
void ImmutableCUDA(platform::Place place) const {
if (IsDirty()) {
if (IsInCPU()) {
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
UnsetFlag(kDirty);
SetFlag(kDataInCUDA);
} else if (IsInCUDA() && !(place == cuda_vec_.place())) {
framework::Tensor tmp;
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
cuda_vec_.ShareDataWith(tmp);
// Still dirty
} else {
// Dirty && DataInCUDA && Device is same
// Do nothing
}
} else {
if (!IsInCUDA()) {
// Even data is not dirty. However, data is not in CUDA. Copy data.
TensorCopy(cpu_vec_, boost::get<platform::CUDAPlace>(place),
&cuda_vec_);
WaitPlace(place);
SetFlag(kDataInCUDA);
} else if (!(place == cuda_vec_.place())) {
framework::Tensor tmp;
WaitPlace(cuda_vec_.place());
TensorCopy(cuda_vec_, boost::get<platform::CUDAPlace>(place), &tmp);
WaitPlace(cuda_vec_.place());
WaitPlace(place);
cuda_vec_.ShareDataWith(tmp);
} else {
// Not Dirty && DataInCUDA && Device is same
// Do nothing.
}
}
}
void ImmutableCPU() const {
if (IsDirty() &&
!IsInCPU()) { // If data has been changed in CUDA, or CPU has no data.
CopyToCPU();
UnsetFlag(kDirty);
}
SetFlag(kDataInCPU);
}
void UnsetFlag(int flag) const { flag_ &= ~flag; }
void SetFlag(int flag) const { flag_ |= flag; }
bool IsDirty() const { return flag_ & kDirty; }
bool IsInCUDA() const { return flag_ & kDataInCUDA; }
bool IsInCPU() const { return flag_ & kDataInCPU; }
static void WaitPlace(const platform::Place place) {
if (platform::is_gpu_place(place)) {
platform::DeviceContextPool::Instance()
.Get(boost::get<platform::CUDAPlace>(place))
->Wait();
}
}
static T &EmptyDummy() {
static T dummy = T();
return dummy;
}
mutable int flag_;
mutable Tensor cpu_vec_;
mutable Tensor cuda_vec_;
size_t size_;
}; };
#else // PADDLE_WITH_CUDA #else // PADDLE_WITH_CUDA
......
paddle/fluid/operators/detection_map_op.h
浏览文件 @ 3043f51b
...@@ -76,8 +76,8 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> { ...@@ -76,8 +76,8 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type")); auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
int class_num = ctx.Attr<int>("class_num"); int class_num = ctx.Attr<int>("class_num");
auto& label_lod = in_label->lod(); auto label_lod = in_label->lod();
auto& detect_lod = in_detect->lod(); auto detect_lod = in_detect->lod();
PADDLE_ENFORCE_EQ(label_lod.size(), 1UL, PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
"Only support one level sequence now."); "Only support one level sequence now.");
PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(), PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
...@@ -166,11 +166,11 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> { ...@@ -166,11 +166,11 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto labels = framework::EigenTensor<T, 2>::From(input_label); auto labels = framework::EigenTensor<T, 2>::From(input_label);
auto detect = framework::EigenTensor<T, 2>::From(input_detect); auto detect = framework::EigenTensor<T, 2>::From(input_detect);
auto& label_lod = input_label.lod(); auto label_lod = input_label.lod();
auto& detect_lod = input_detect.lod(); auto detect_lod = input_detect.lod();
int batch_size = label_lod[0].size() - 1; int batch_size = label_lod[0].size() - 1;
auto& label_index = label_lod[0]; auto label_index = label_lod[0];
for (int n = 0; n < batch_size; ++n) { for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes; std::map<int, std::vector<Box>> boxes;
...@@ -274,6 +274,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> { ...@@ -274,6 +274,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
output_true_pos->set_lod(true_pos_lod); output_true_pos->set_lod(true_pos_lod);
output_false_pos->set_lod(false_pos_lod); output_false_pos->set_lod(false_pos_lod);
return;
} }
void GetInputPos(const framework::Tensor& input_pos_count, void GetInputPos(const framework::Tensor& input_pos_count,
...@@ -291,7 +292,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> { ...@@ -291,7 +292,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
auto SetData = [](const framework::LoDTensor& pos_tensor, auto SetData = [](const framework::LoDTensor& pos_tensor,
std::map<int, std::vector<std::pair<T, int>>>& pos) { std::map<int, std::vector<std::pair<T, int>>>& pos) {
const T* pos_data = pos_tensor.data<T>(); const T* pos_data = pos_tensor.data<T>();
auto& pos_data_lod = pos_tensor.lod()[0]; auto pos_data_lod = pos_tensor.lod()[0];
for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) { for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) {
for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) { for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) {
T score = pos_data[j * 2]; T score = pos_data[j * 2];
...@@ -316,23 +317,20 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> { ...@@ -316,23 +317,20 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
std::map<int, std::vector<std::pair<T, int>>>* false_pos) const { std::map<int, std::vector<std::pair<T, int>>>* false_pos) const {
int batch_size = gt_boxes.size(); int batch_size = gt_boxes.size();
for (int n = 0; n < batch_size; ++n) { for (int n = 0; n < batch_size; ++n) {
auto& image_gt_boxes = gt_boxes[n]; auto image_gt_boxes = gt_boxes[n];
for (auto& image_gt_box : image_gt_boxes) { for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
size_t count = 0; size_t count = 0;
auto& labeled_bboxes = image_gt_box.second; auto labeled_bboxes = it->second;
if (evaluate_difficult) { if (evaluate_difficult) {
count = labeled_bboxes.size(); count = labeled_bboxes.size();
} else { } else {
for (auto& box : labeled_bboxes) { for (size_t i = 0; i < labeled_bboxes.size(); ++i)
if (!box.is_difficult) { if (!(labeled_bboxes[i].is_difficult)) ++count;
++count;
}
}
} }
if (count == 0) { if (count == 0) {
continue; continue;
} }
int label = image_gt_box.first; int label = it->first;
if (label_pos_count->find(label) == label_pos_count->end()) { if (label_pos_count->find(label) == label_pos_count->end()) {
(*label_pos_count)[label] = count; (*label_pos_count)[label] = count;
} else { } else {
......
paddle/fluid/operators/extract_rows_op.cc
浏览文件 @ 3043f51b
...@@ -50,7 +50,7 @@ class ExtractRowsOp : public framework::OperatorBase { ...@@ -50,7 +50,7 @@ class ExtractRowsOp : public framework::OperatorBase {
auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>(); auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>(); auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto &in_rows = in.rows(); auto in_rows = in.rows();
auto out_dim = framework::make_ddim( auto out_dim = framework::make_ddim(
std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1}); std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place()); auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());
......
paddle/fluid/operators/math/selected_rows_functor.cu
浏览文件 @ 3043f51b
...@@ -60,9 +60,11 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> { ...@@ -60,9 +60,11 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> {
auto out_place = context.GetPlace(); auto out_place = context.GetPlace();
PADDLE_ENFORCE(platform::is_gpu_place(out_place)); PADDLE_ENFORCE(platform::is_gpu_place(out_place));
memory::Copy(boost::get<platform::CUDAPlace>(out_place), out_data, memory::Copy(
boost::get<platform::CUDAPlace>(in1_place), in1_data, boost::get<platform::CUDAPlace>(out_place), out_data,
in1_value.numel() * sizeof(T), context.stream()); boost::get<platform::CUDAPlace>(in1_place), in1_data,
in1_value.numel() * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream());
auto* in2_data = in2_value.data<T>(); auto* in2_data = in2_value.data<T>();
memory::Copy(boost::get<platform::CUDAPlace>(out_place), memory::Copy(boost::get<platform::CUDAPlace>(out_place),
...@@ -146,7 +148,7 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> { ...@@ -146,7 +148,7 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
auto in1_height = input1.height(); auto in1_height = input1.height();
PADDLE_ENFORCE_EQ(in1_height, input2->height()); PADDLE_ENFORCE_EQ(in1_height, input2->height());
auto& in1_rows = input1.rows(); framework::Vector<int64_t> in1_rows(input1.rows());
auto& in2_rows = *(input2->mutable_rows()); auto& in2_rows = *(input2->mutable_rows());
auto& in1_value = input1.value(); auto& in1_value = input1.value();
......
paddle/fluid/operators/sum_op.h
浏览文件 @ 3043f51b
...@@ -123,6 +123,7 @@ class SumKernel : public framework::OpKernel<T> { ...@@ -123,6 +123,7 @@ class SumKernel : public framework::OpKernel<T> {
out_value->Resize(framework::make_ddim(in_dim)); out_value->Resize(framework::make_ddim(in_dim));
out_value->mutable_data<T>(context.GetPlace()); out_value->mutable_data<T>(context.GetPlace());
// if all the input sparse vars are empty, no need to // if all the input sparse vars are empty, no need to
// merge these vars. // merge these vars.
if (first_dim == 0UL) { if (first_dim == 0UL) {
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 3043f51b
...@@ -345,7 +345,7 @@ class OpTest(unittest.TestCase): ...@@ -345,7 +345,7 @@ class OpTest(unittest.TestCase):
actual_t, expect_t, atol=atol, equal_nan=equal_nan), actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) + "Output (" + out_name + ") has diff at " + str(place) +
"\nExpect " + str(expect_t) + "\n" + "But Got" + "\nExpect " + str(expect_t) + "\n" + "But Got" +
str(actual_t) + " in class " + self.__class__.__name__) str(actual_t))
if isinstance(expect, tuple): if isinstance(expect, tuple):
self.assertListEqual(actual.recursive_sequence_lengths(), self.assertListEqual(actual.recursive_sequence_lengths(),
expect[1], "Output (" + out_name + expect[1], "Output (" + out_name +
......
python/paddle/fluid/tests/unittests/test_detection_map_op.py
浏览文件 @ 3043f51b
...@@ -20,7 +20,6 @@ import six ...@@ -20,7 +20,6 @@ import six
import sys import sys
import collections import collections
import math import math
import paddle.fluid as fluid
from op_test import OpTest from op_test import OpTest
...@@ -33,7 +32,7 @@ class TestDetectionMAPOp(OpTest): ...@@ -33,7 +32,7 @@ class TestDetectionMAPOp(OpTest):
self.detect = np.array(self.detect).astype('float32') self.detect = np.array(self.detect).astype('float32')
self.mAP = np.array(self.mAP).astype('float32') self.mAP = np.array(self.mAP).astype('float32')
if len(self.class_pos_count) > 0: if (len(self.class_pos_count) > 0):
self.class_pos_count = np.array(self.class_pos_count).astype( self.class_pos_count = np.array(self.class_pos_count).astype(
'int32') 'int32')
self.true_pos = np.array(self.true_pos).astype('float32') self.true_pos = np.array(self.true_pos).astype('float32')
...@@ -274,7 +273,7 @@ class TestDetectionMAPOp11Point(TestDetectionMAPOp): ...@@ -274,7 +273,7 @@ class TestDetectionMAPOp11Point(TestDetectionMAPOp):
class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp): class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp):
def init_test_case(self): def init_test_case(self):
super(TestDetectionMAPOpMultiBatch, self).init_test_case() super(TestDetectionMAPOpMultiBatch, self).init_test_case()
self.class_pos_count = [0, 2, 1, 0] self.class_pos_count = [0, 2, 1]
self.true_pos_lod = [[0, 3, 2]] self.true_pos_lod = [[0, 3, 2]]
self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]] self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]]
self.false_pos_lod = [[0, 3, 2]] self.false_pos_lod = [[0, 3, 2]]
......
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