fix code format

Adaptive_data_structure_for_SwitchOrderLayer
wanghaoshuang 8 years ago
commit ec236f4624

@ -290,8 +290,22 @@ function(go_library TARGET_NAME)
set(${TARGET_NAME}_LIB_NAME "${CMAKE_STATIC_LIBRARY_PREFIX}${TARGET_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE STRING "output library name for target ${TARGET_NAME}")
endif()
# Add dummy code to support `make target_name` under Terminal Command
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/${TARGET_NAME}_dummy.c)
# This custom command will always run since it depends on a not
# existing file.
add_custom_command(
OUTPUT dummy_rebulid_${TARGET_NAME}
COMMAND cmake -E touch ${dummyfile}
)
# Create a custom target that depends on the custom command output
# file, so the custom command can be referenced as a dependency by
# `add_dependencies`.
add_custom_target(rebuild_${TARGET_NAME}
DEPENDS dummy_rebulid_${TARGET_NAME}
)
# Add dummy code to support `make target_name` under Terminal Command
file(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
if (go_library_SHARED OR go_library_shared)
add_library(${TARGET_NAME} SHARED ${dummyfile})
@ -302,6 +316,12 @@ function(go_library TARGET_NAME)
add_dependencies(${TARGET_NAME} ${go_library_DEPS})
endif(go_library_DEPS)
# The "source file" of the library is `${dummyfile}` which never
# change, so the target will never rebuild. Make the target depends
# on the custom command that touches the library "source file", so
# rebuild will always happen.
add_dependencies(${TARGET_NAME} rebuild_${TARGET_NAME})
set(${TARGET_NAME}_LIB_PATH "${CMAKE_CURRENT_BINARY_DIR}/${${TARGET_NAME}_LIB_NAME}" CACHE STRING "output library path for target ${TARGET_NAME}")
file(GLOB GO_SOURCE RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.go")

@ -1,23 +1,25 @@
# ddim lib
cc_library(enforce SRCS enforce.cc DEPS glog)
cc_test(enforce_test SRCS enforce_test.cc DEPS enforce)
cc_library(ddim SRCS ddim.cc DEPS eigen3)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_library(tensor SRCS tensor.cc DEPS ddim place enforce paddle_memory)
cc_library(tensor SRCS tensor.cc DEPS ddim place paddle_memory)
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_test(variable_test SRCS variable_test.cc)
cc_test(scope_test SRCS scope_test.cc)
proto_library(attr_type SRCS attr_type.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attr_type)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
proto_library(op_desc SRCS op_desc.proto DEPS attr_type)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto op_desc enforce)
cc_library(op_registry SRCS op_registry.cc DEPS op_proto op_desc)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry operator)
py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.proto)

@ -4,8 +4,9 @@
#include <functional>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/framework/enforce.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {
@ -41,6 +42,35 @@ class DefaultValueSetter {
T default_value_;
};
template <typename T>
class EnumInContainer {
public:
explicit EnumInContainer(const std::unordered_set<T>& c) : container_(c) {}
void operator()(T& val) const {
PADDLE_ENFORCE(container_.find(val) != container_.end(),
"Value %s is not in enum container %s", val,
ContainerDebugString());
}
private:
std::string ContainerDebugString() const {
std::ostringstream sout;
sout << "[";
size_t cnt = 0;
for (auto& v : container_) {
sout << v;
++cnt;
if (cnt != container_.size()) {
sout << " ,";
}
}
sout << "]";
return sout.str();
}
std::unordered_set<T> container_;
};
// check whether a certain attribute fit its limits
// an attribute can have more than one limits
template <typename T>
@ -50,6 +80,11 @@ class TypedAttrChecker {
public:
TypedAttrChecker(const std::string& attr_name) : attr_name_(attr_name) {}
TypedAttrChecker& InEnum(const std::unordered_set<T>& range) {
value_checkers_.push_back(EnumInContainer<T>(range));
return *this;
}
TypedAttrChecker& LargerThan(const T& lower_bound) {
value_checkers_.push_back(LargerThanChecker<T>(lower_bound));
return *this;

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/ddim.h"
#include "paddle/framework/enforce.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {

@ -19,7 +19,7 @@ limitations under the License. */
#include <stdexcept>
#include <vector>
#include "paddle/framework/dim.h"
#include "paddle/framework/enforce.h"
#include "paddle/platform/enforce.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
@ -119,17 +119,6 @@ int arity(const DDim& ddim);
std::ostream& operator<<(std::ostream&, const DDim&);
template <int NDIMS>
Eigen::DSizes<Eigen::DenseIndex, NDIMS> ToEigenDSizes(const DDim& dims) {
int rank = arity(dims);
PADDLE_ENFORCE(rank == NDIMS, "DDim and NDIMS must be same");
Eigen::DSizes<Eigen::DenseIndex, NDIMS> dsizes;
for (int d = 0; d < rank; d++) {
dsizes[d] = dims[d];
}
return dsizes;
}
} // namespace framework
} // namespace paddle

@ -0,0 +1,84 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/tensor.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace framework {
// EigenDim converts paddle::platform::DDim into Eigen::DSizes.
template <int D>
struct EigenDim {
using Type = Eigen::DSizes<Eigen::DenseIndex, D>;
static Type From(const DDim& dims) {
PADDLE_ENFORCE(arity(dims) == D, "D must match arity(DDim)");
Type ret;
for (int d = 0; d < arity(dims); d++) {
ret[d] = dims[d];
}
return ret;
}
};
// Interpret paddle::platform::Tensor as EigenTensor and EigenConstTensor.
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenTensor {
// TODO(qijun) Now, default type in unaligned, and we will make a benchmark on
// the speed of aligned and unaligned version in future.
using Type = Eigen::TensorMap<Eigen::Tensor<T, D, MajorType, IndexType>>;
using ConstType =
Eigen::TensorMap<Eigen::Tensor<const T, D, MajorType, IndexType>>;
static Type From(Tensor& tensor, DDim dims) {
return Type(tensor.data<T>(), EigenDim<D>::From(dims));
}
static Type From(Tensor& tensor) { return From(tensor, tensor.dims_); }
static ConstType From(const Tensor& tensor, DDim dims) {
return ConstType(tensor.data<T>(), EigenDim<D>::From(dims));
}
static ConstType From(const Tensor& tensor) {
return From(tensor, tensor.dims_);
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
// Flatten is to reshape a Tensor into a one dimension EigenVector
static typename EigenTensor<T, 1>::Type Flatten(Tensor& tensor) {
return EigenTensor<T, 1>::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
static typename EigenTensor<T, 1>::ConstType Flatten(const Tensor& tensor) {
return EigenTensor<T, 1>::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = EigenTensor<T, 2, MajorType, IndexType>;
} // namespace framework
} // namespace paddle

@ -0,0 +1,101 @@
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "paddle/framework/eigen.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
TEST(EigenDim, From) {
EigenDim<3>::Type ed = EigenDim<3>::From(make_ddim({1, 2, 3}));
ASSERT_EQ(1, ed[0]);
ASSERT_EQ(2, ed[1]);
ASSERT_EQ(3, ed[2]);
}
TEST(Eigen, Tensor) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenTensor<float, 3>::Type et = EigenTensor<float, 3>::From(t);
ASSERT_EQ(1, et.dimension(0));
ASSERT_EQ(2, et.dimension(1));
ASSERT_EQ(3, et.dimension(2));
for (int i = 0; i < 1; i++) {
for (int j = 0; j < 2; j++) {
for (int k = 0; k < 3; k++) {
ASSERT_NEAR((i * 2 + j) * 3 + k, et(i, j, k), 1e-6f);
}
}
}
}
TEST(Eigen, VectorFrom) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({6}), platform::CPUPlace());
for (int i = 0; i < 6; i++) {
p[i] = static_cast<float>(i);
}
EigenVector<float>::Type ev = EigenVector<float>::From(t);
ASSERT_EQ(6, ev.dimension(0));
for (int i = 0; i < 6; i++) {
ASSERT_NEAR(i, ev(i), 1e-6f);
}
}
TEST(Eigen, VectorFlatten) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({1, 2, 3}), platform::CPUPlace());
for (int i = 0; i < 1 * 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenVector<float>::Type ev = EigenVector<float>::Flatten(t);
ASSERT_EQ(1 * 2 * 3, ev.dimension(0));
for (int i = 0; i < 1 * 2 * 3; i++) {
ASSERT_NEAR(i, ev(i), 1e-6f);
}
}
TEST(Eigen, Matrix) {
Tensor t;
float* p = t.mutable_data<float>(make_ddim({2, 3}), platform::CPUPlace());
for (int i = 0; i < 2 * 3; i++) {
p[i] = static_cast<float>(i);
}
EigenMatrix<float>::Type em = EigenMatrix<float>::From(t);
ASSERT_EQ(2, em.dimension(0));
ASSERT_EQ(3, em.dimension(1));
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 3; j++) {
ASSERT_NEAR(i * 3 + j, em(i, j), 1e-6f);
}
}
}
} // namespace framework
} // namespace paddle

@ -1,15 +0,0 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/enforce.h"

@ -1,75 +0,0 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <glog/logging.h>
#include <paddle/string/printf.h>
#include <exception>
#include <sstream>
namespace paddle {
namespace framework {
/**
* @brief Enforce exception. Inherits std::exception
*
* All enforce condition not met, will throw an EnforceNotMet exception.
*/
class EnforceNotMet : public std::exception {
public:
EnforceNotMet(const std::string& msg, const char* file, int fileline) {
std::ostringstream sout;
sout << msg << " at [" << file << ":" << fileline << "];";
all_msg_ = sout.str();
}
const char* what() const noexcept override { return all_msg_.c_str(); }
private:
std::string all_msg_;
};
// From https://stackoverflow.com/questions/30130930/
// __buildin_expect is in C++ 11 standard. Since the condition which enforced
// should be true in most situation, it will make the compiler generate faster
// code by adding `UNLIKELY` macro.
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
/**
* @brief Throw a EnforceNotMet exception, automatically filled __FILE__ &
* __LINE__
*
* This macro take __VA_ARGS__, user can pass any type if that type can
* serialize to std::ostream
*/
#define PADDLE_THROW(...) \
do { \
throw ::paddle::framework::EnforceNotMet( \
::paddle::string::Sprintf(__VA_ARGS__), __FILE__, __LINE__); \
} while (0)
/**
* @brief Enforce a condition, otherwise throw an EnforceNotMet
*/
#ifdef NDEBUG
#define PADDLE_ENFORCE(condition, ...) \
do { \
if (UNLIKELY(!(condition))) { \
PADDLE_THROW(__VA_ARGS__); \
} \
} while (0)
#else
#define PADDLE_ENFORCE(condition, ...) \
CHECK(condition) << ::paddle::string::Sprintf(__VA_ARGS__);
#endif
} // namespace framework
} // namespace paddle

@ -19,7 +19,10 @@
namespace paddle {
namespace framework {
void PlainNet::CompleteAddOp() {
void PlainNet::CompleteAddOp(bool calc) {
add_op_done_ = true;
if (!calc) return;
std::unordered_set<std::string> input_set;
std::unordered_set<std::string> output_set;
std::unordered_set<std::string> temp_output;
@ -52,7 +55,6 @@ void PlainNet::CompleteAddOp() {
}
attrs_["temporary_index"] = tmp_index;
add_op_done_ = true;
}
std::string PlainNet::DebugString() const {

@ -16,7 +16,6 @@ limitations under the License. */
#include <paddle/framework/op_desc.pb.h>
#include <paddle/framework/operator.h>
#include "paddle/framework/net_proto.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/scope.h"
@ -41,7 +40,7 @@ namespace framework {
class Net : public OperatorBase {
public:
virtual void AddOp(const OperatorPtr& op) = 0;
virtual void CompleteAddOp() = 0;
virtual void CompleteAddOp(bool calc) = 0;
};
using NetPtr = std::shared_ptr<Net>;
@ -86,7 +85,7 @@ class PlainNet : public Net {
ops_.push_back(op);
}
void CompleteAddOp() override;
void CompleteAddOp(bool calculate = true) override;
std::string DebugString() const override;

@ -63,5 +63,5 @@ TEST(OpKernel, all) {
ASSERT_EQ(2, infer_shape_cnt);
ASSERT_EQ(2, run_cnt);
ASSERT_THROW(net->AddOp(op2), paddle::framework::EnforceNotMet);
ASSERT_THROW(net->AddOp(op2), std::runtime_error);
}

@ -91,7 +91,7 @@ TEST(OpRegistry, IllegalAttr) {
try {
paddle::framework::OperatorPtr op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg = "larger_than check fail";
const char* err_msg = err.what();
@ -138,7 +138,7 @@ TEST(OpRegistry, CustomChecker) {
try {
paddle::framework::OperatorPtr op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg = "Attribute 'test_attr' is required!";
const char* err_msg = err.what();
@ -157,7 +157,7 @@ TEST(OpRegistry, CustomChecker) {
try {
paddle::framework::OperatorPtr op __attribute__((unused)) =
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg = "'test_attr' must be even!";
const char* err_msg = err.what();
@ -196,7 +196,7 @@ TEST(ProtoMaker, DuplicatedAttr) {
pd::OpProto op_proto;
pd::OpAttrChecker op_checker;
auto proto_maker = TestAttrProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::framework::EnforceNotMet);
ASSERT_THROW(proto_maker.Validate(), std::runtime_error);
}
class TestInOutProtoMaker : public pd::OpProtoAndCheckerMaker {
@ -212,5 +212,5 @@ TEST(ProtoMaker, DuplicatedInOut) {
pd::OpProto op_proto;
pd::OpAttrChecker op_checker;
auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker.Validate(), paddle::framework::EnforceNotMet);
ASSERT_THROW(proto_maker.Validate(), std::runtime_error);
}

@ -19,9 +19,8 @@ limitations under the License. */
#include <memory>
#include <typeindex>
#include "paddle/framework/ddim.h"
#include "paddle/framework/enforce.h"
#include "paddle/framework/tensor_types.h"
#include "paddle/memory/memory.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "unsupported/Eigen/CXX11/Tensor"
@ -35,6 +34,15 @@ struct CastToPyBufferImpl;
namespace framework {
class Tensor {
template <bool less, size_t i, typename... args>
friend struct paddle::pybind::details::CastToPyBufferImpl;
template <typename T, size_t D, int MajorType, typename IndexType>
friend struct EigenTensor;
template <typename T, int MajorType, typename IndexType>
friend struct EigenVector;
public:
Tensor() : offset_(0) {}
@ -46,7 +54,7 @@ class Tensor {
}
template <typename T>
T* raw_data() const {
T* data() {
CheckDims<T>();
return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
@ -86,66 +94,6 @@ class Tensor {
offset_);
}
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::Tensor shaped(DDim new_dims) {
Eigen::array<Eigen::DenseIndex, NDIMS> dims =
paddle::framework::ToEigenDSizes<NDIMS>(new_dims);
return typename TTypes<T, NDIMS>::Tensor(raw_data<T>(), dims);
}
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::Tensor tensor() {
return typename TTypes<T, NDIMS>::Tensor(
raw_data<T>(), paddle::framework::ToEigenDSizes<NDIMS>(dims_));
}
// flat to rank = 1
template <typename T>
typename TTypes<T>::Flat flat() {
return shaped<T, 1>(make_ddim({static_cast<int>(product(dims_))}));
}
// to TensorType Vec
template <typename T>
typename TTypes<T>::Vec vec() {
return tensor<T, 1>();
}
// to TensorType Matrix
template <typename T>
typename TTypes<T>::Matrix matrix() {
return tensor<T, 2>();
}
// const versions of all the methods above.
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::Tensor shaped(DDim new_dims) const {
Eigen::array<Eigen::DenseIndex, NDIMS> dims =
paddle::framework::ToEigenDSizes<NDIMS>(new_dims);
return typename TTypes<T, NDIMS>::Tensor(data<T>(), dims);
}
template <typename T, size_t NDIMS>
typename TTypes<T, NDIMS>::ConstantTensor tensor() const {
return typename TTypes<T, NDIMS>::Tensor(
data<T>(), paddle::framework::ToEigenDSizes<NDIMS>(dims_));
}
template <typename T>
typename TTypes<T>::ConstFlat flat() const {
return shaped<T, 1>(make_ddim({static_cast<int>(product(dims_))}));
}
template <typename T>
typename TTypes<T>::ConstVec vec() const {
return tensor<T, 1>();
}
template <typename T>
typename TTypes<T>::ConstMatrix matrix() const {
return tensor<T, 2>();
}
template <typename T>
void ShareDataFrom(const Tensor& src) {
src.CheckDims<T>();
@ -251,8 +199,6 @@ class Tensor {
std::shared_ptr<Placeholder> holder_; // holds the memory block if allocated.
DDim dims_;
size_t offset_; // marks the begin of tensor data area.
template <bool less, size_t i, typename... args>
friend struct paddle::pybind::details::CastToPyBufferImpl;
};
} // namespace framework

@ -33,7 +33,7 @@ TEST(Tensor, DataAssert) {
bool caught = false;
try {
src_tensor.data<double>();
} catch (paddle::framework::EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg =
"Tenosr holds no memory. Call Tensor::mutable_data first.";
@ -107,7 +107,7 @@ TEST(Tensor, ShareDataFrom) {
bool caught = false;
try {
dst_tensor.ShareDataFrom<float>(src_tensor);
} catch (EnforceNotMet err) {
} catch (std::runtime_error& err) {
caught = true;
std::string msg =
"Tenosr holds no memory. Call Tensor::mutable_data first.";

@ -1,67 +0,0 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace framework {
// Helper to define Tensor types given that the scalar is of type T.
template <typename T, int NDIMS = 1, typename IndexType = Eigen::DenseIndex>
struct TTypes {
// Rank-<NDIMS> tensor of scalar type T.
typedef Eigen::TensorMap<Eigen::Tensor<T, NDIMS, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Tensor;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, NDIMS, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstTensor;
// Scalar tensor (implemented as a rank-0 tensor) of scalar type T.
typedef Eigen::TensorMap<
Eigen::TensorFixedSize<T, Eigen::Sizes<>, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Scalar;
typedef Eigen::TensorMap<Eigen::TensorFixedSize<const T, Eigen::Sizes<>,
Eigen::RowMajor, IndexType>,
Eigen::Aligned>
ConstScalar;
// Rank-1 tensor (vector) of scalar type T.
typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Flat;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, 1, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstFlat;
typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Vec;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, 1, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstVec;
// Rank-2 tensor (matrix) of scalar type T.
typedef Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, IndexType>,
Eigen::Aligned>
Matrix;
typedef Eigen::TensorMap<
Eigen::Tensor<const T, 2, Eigen::RowMajor, IndexType>, Eigen::Aligned>
ConstMatrix;
};
} // namespace framework
} // namespace paddle

@ -37,6 +37,7 @@ if(WITH_GPU)
add_simple_unittest(CosSimOpTest)
add_simple_unittest(RowConvOpTest)
add_simple_unittest(SwitchOpTest)
add_simple_unittest(CropOpTest)
endif()
add_simple_unittest(ConvOpTest)

@ -0,0 +1,177 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "CropOp.h"
#include "paddle/function/TensorShape.h"
#include "paddle/math/Vector.h"
namespace paddle {
template <>
void Crop<DEVICE_TYPE_CPU>(real* outputs,
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner =
conf.get<std::vector<uint32_t>>("crop_corner");
int cCrop = crop_corner[1];
int hCrop = crop_corner[2];
int wCrop = crop_corner[3];
int num = inShape[0];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
for (int n = 0; n < num; n++) {
for (int c = 0; c < outC; c++) {
for (int h = 0; h < outH; h++) {
int outoff = ((n * outC + c) * outH + h) * outW;
int inoff = ((n * inC + c + cCrop) * inH + h + hCrop) * inW + wCrop;
memcpy(outputs + outoff, inputs + inoff, outW * sizeof(real));
}
}
}
}
template <>
void CropGrad<DEVICE_TYPE_CPU>(const real* inGrad,
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner =
conf.get<std::vector<uint32_t>>("crop_corner");
int cCrop = crop_corner[1];
int hCrop = crop_corner[2];
int wCrop = crop_corner[3];
int num = outShape[0];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
for (int n = 0; n < num; n++) {
for (int c = 0; c < inC; c++) {
for (int h = 0; h < inH; h++) {
int outoff = ((n * outC + c + cCrop) * outH + h + hCrop) * outW + wCrop;
int inoff = ((n * inC + c) * inH + h) * inW;
CpuVector inG = CpuVector(inW, const_cast<real*>(inGrad + inoff));
CpuVector outG = CpuVector(inW, outGrad + outoff);
outG += inG;
}
}
}
}
/**
* \brief Crop input according to the specify corner and shape.
* The input and output is a 4D tensor. In CropFunc, we only
* crop the 2nd to 4th dimension.
*
* Argument in this Function:
* \param pad_ A struct object contains the cropping corner and shape.
* \param inputs A 4D tensor, only one input.
* \param outputs A 4D tensor, the output value after cropping.
*
* For example,
* Input(2,2,2,3) = [
* [ [[1,2,3], [3,4,5]],
* [[2,3,5], [1,6,7]] ],
* [ [[4,3,1], [1,8,7]],
* [[3,8,9], [2,3,5]] ]
* ] # the input shape is (2,2,2,3)
*
* pad_: if corner = (0,1,1) and crop_shape = (2,1,2)
* Output(2,2,1,2) = [
* [ [[4,5]],
* [[6,7]] ],
* [ [[8,7]],
* [[3,5]] ]
* ] # the input shape is (2,2,2,3)
*/
template <DeviceType Device>
class CropFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override { conf_ = config; }
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
TensorShape inShape = inputs[0].shape();
TensorShape outShape = outputs[0].shape();
Crop<Device>(outputs[0].data<real>(),
inputs[0].data<real>(),
inShape,
outShape,
conf_);
}
private:
FuncConfig conf_;
};
/**
* \brief The backward propagation of cropping Function.
*
* Argument in this Function:
* \param crop_ The same meaning as it in CropFunc.
* \param inputs The gradient with respect to the output value of CropFunc.
* \param outputs The gradient with respect to the input value of CropFunc.
*/
template <DeviceType Device>
class CropGradFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override { conf_ = config; }
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
TensorShape outShape = outputs[0].shape();
TensorShape inShape = inputs[0].shape();
CropGrad<Device>(inputs[0].data<real>(),
outputs[0].data<real>(),
inShape,
outShape,
conf_);
}
private:
FuncConfig conf_;
};
REGISTER_TYPED_FUNC(Crop, CPU, CropFunc);
REGISTER_TYPED_FUNC(CropGrad, CPU, CropGradFunc);
#ifndef PADDLE_ONLY_CPU
REGISTER_TYPED_FUNC(Crop, GPU, CropFunc);
REGISTER_TYPED_FUNC(CropGrad, GPU, CropGradFunc);
#endif
} // namespace paddle

@ -0,0 +1,51 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Function.h"
namespace paddle {
/**
* \brief This funtion crops inputs according to the specify start point and
*shape.
*
* \param[out] outputs save results.
* \param[in] inputs input data.
* \param[in] inShape the shape of input tensor.
* \param[in] conf the cropping config
*/
template <DeviceType Device>
void Crop(real* outputs,
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf);
/**
* \brief Cropping operation backward.
*
* \param[out] inGrad gradients of previous layer
* \param[in] outGrad output gradient
* \param[in] inShape the shape of input tensor.
* \param[in] conf the cropping config
*/
template <DeviceType Device>
void CropGrad(const real* inGrad,
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf);
} // namespace paddle

@ -0,0 +1,113 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "CropOp.h"
namespace paddle {
__global__ void KeCrop(real* outputs, const real* inputs,
int inC, int inH, int inW,
int cropC, int cropH, int cropW,
int outC, int outH, int outW, int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % outW;
const int h = (idx / outW) % outH;
const int c = (idx / outW / outH) % outC;
const int n = idx / outW / outH / outC;
const int off = ((n * inC + c + cropC) * inH + h + cropH) * inW + cropW + w;
outputs[idx] = inputs[off];
}
}
template <>
void Crop<DEVICE_TYPE_GPU>(real* outputs,
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner = conf.get<std::vector<uint32_t>>("crop_corner");
int cropC = crop_corner[1];
int cropH = crop_corner[2];
int cropW = crop_corner[3];
int num = inShape[0];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
size_t nth = num * outC * outH * outW;
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeCrop<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(outputs, inputs, inC, inH, inW, cropC, cropH, cropW,
outC, outH, outW, nth);
CHECK_SYNC("Crop");
}
__global__ void KeCropDiff(const real* inGrad, real* outGrad,
int inC, int inH, int inW,
int cropC, int cropH, int cropW,
int outC, int outH, int outW, int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
const int h = (idx / inW) % inH;
const int c = (idx / inW / inH) % inC;
const int n = idx / inW / inH / inC;
const int off = ((n * outC + c + cropC) * outH + h + cropH) * outW + cropW + w;
outGrad[off] += inGrad[idx];
}
}
template <>
void CropGrad<DEVICE_TYPE_GPU>(const real* inGrad,
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner = conf.get<std::vector<uint32_t>>("crop_corner");
int cropC = crop_corner[1];
int cropH = crop_corner[2];
int cropW = crop_corner[3];
int num = outShape[0];
int outC = outShape[1];
int outH = outShape[2];
int outW = outShape[3];
int inC = inShape[1];
int inH = inShape[2];
int inW = inShape[3];
size_t nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeCropDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(inGrad, outGrad, inC, inH, inW, cropC, cropH, cropW,
outC, outH, outW, nth);
CHECK_SYNC("CropGrad");
}
} // namespace paddle

@ -0,0 +1,49 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "FunctionTest.h"
namespace paddle {
TEST(Crop, real) {
for (size_t numSamples : {5, 32}) {
for (size_t channels : {5, 5, 32}) {
for (size_t imgSizeH : {5, 33, 100}) {
for (size_t imgSizeW : {5, 32, 96}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
for (bool test_grad : {false, true}) {
CpuGpuFuncCompare compare(
test_grad ? "CropGrad" : "Crop",
FuncConfig()
.set<std::vector<uint32_t>>("crop_corner", {0, 1, 1, 1})
.set<std::vector<uint32_t>>("crop_shape", {0, 2, 3, 3}));
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{numSamples, 2, 3, 3};
compare.addInputs(
BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims));
compare.addOutputs(BufferArg(VALUE_TYPE_FLOAT,
test_grad ? inDims : outDims,
test_grad ? ADD_TO : ASSIGN_TO),
test_grad ? ADD_TO : ASSIGN_TO);
compare.run();
}
}
}
}
}
}
} // namespace paddle

@ -0,0 +1,146 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "CropLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(crop, CropLayer);
bool CropLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
CHECK_LE(static_cast<int>(inputLayers_.size()), 2);
CHECK_GE(static_cast<int>(inputLayers_.size()), 1);
crop_axis_ = config_.axis();
for (int i = 0; i < config_.offset_size(); i++) {
crop_offsets_.push_back(config_.offset(i));
}
// 1. get input_0 shape
auto& input0_img_conf = config_.inputs(0).image_conf();
inDims_ = TensorShape({0,
input0_img_conf.channels(),
input0_img_conf.has_img_size_y()
? input0_img_conf.img_size_y()
: input0_img_conf.img_size(),
input0_img_conf.img_size()});
// 2. get target dims from config
if (config_.inputs_size() == 1) {
targetDims_ = TensorShape({config_.shape(0),
config_.shape(1),
config_.shape(2),
config_.shape(3)});
} else {
// 2. get input_1 shape
auto& input1_img_conf = config_.inputs(1).image_conf();
targetDims_ = TensorShape({0,
input1_img_conf.channels(),
input1_img_conf.has_img_size_y()
? input1_img_conf.img_size_y()
: input1_img_conf.img_size(),
input1_img_conf.img_size()});
}
// 3. get final crop corner
int dimSize = 4;
crop_corner_ = {0, 0, 0, 0};
for (int i = 0; i < dimSize; i++) {
if (i >= crop_axis_) {
if (crop_offsets_.size() > 1) {
crop_corner_[i] = crop_offsets_[i - crop_axis_];
} else {
crop_corner_[i] = crop_offsets_[0];
}
}
}
outDims_ = TensorShape(4);
createFunction(
forward_, "Crop", FuncConfig().set("crop_corner", crop_corner_));
createFunction(
backward_, "CropGrad", FuncConfig().set("crop_corner", crop_corner_));
return true;
}
void CropLayer::setOutDims() {
MatrixPtr input = inputLayers_[1]->getOutputValue();
size_t batchSize = input->getHeight();
// get target dims from input_1
if (config_.inputs_size() == 2) {
targetDims_.setDim(0, batchSize);
int ch = config_.inputs(0).image_conf().channels();
if (ch != 0) targetDims_.setDim(1, ch);
int h = inputLayers_[1]->getOutput().getFrameHeight();
if (h != 0) targetDims_.setDim(2, h);
int w = inputLayers_[1]->getOutput().getFrameWidth();
if (w != 0) targetDims_.setDim(3, w);
}
// get final crop shape from target dims and crop axis
std::vector<uint32_t> crop_shape;
int dimSize = 4;
for (int i = 0; i < dimSize; i++) {
if (i >= crop_axis_) {
crop_shape.push_back(targetDims_[i]);
} else {
crop_shape.push_back(inDims_[i]);
}
}
outDims_.reshape(
{crop_shape[0], crop_shape[1], crop_shape[2], crop_shape[3]});
output_.setFrameHeight(crop_shape[2]);
output_.setFrameWidth(crop_shape[3]);
}
void CropLayer::setInDims() {
MatrixPtr input = inputLayers_[0]->getOutputValue();
size_t batchSize = input->getHeight();
inDims_.setDim(0, batchSize);
int h = inputLayers_[0]->getOutput().getFrameHeight();
if (h != 0) inDims_.setDim(2, h);
int w = inputLayers_[0]->getOutput().getFrameWidth();
if (w != 0) inDims_.setDim(3, w);
}
void CropLayer::forward(PassType passType) {
Layer::forward(passType);
setInDims();
setOutDims();
int size = outDims_[1] * outDims_[2] * outDims_[3];
resetOutput(outDims_[0], size);
MatrixPtr outV = getOutputValue();
REGISTER_TIMER_INFO("CropForward", getName().c_str());
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(*getInputValue(0), inDims_);
outputs.addArg(*getOutputValue(), outDims_, ASSIGN_TO);
forward_[0]->calc(inputs, outputs);
}
void CropLayer::backward(const UpdateCallback& callback) {
(void)callback;
REGISTER_TIMER_INFO("CropBackward", getName().c_str());
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(*getOutputGrad(), outDims_);
outputs.addArg(*getInputGrad(0), inDims_, ADD_TO);
backward_[0]->calc(inputs, outputs);
}
} // namespace paddle

@ -0,0 +1,52 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Layer.h"
namespace paddle {
/**
* \brief This layer crop input according to the specify conf.
* input_0: input to be cropped
* input_1: optional reference input
* axis: start dimension to be croped
* offset: offset of cropping in each dimension
* shape: if reference input layer was not setted,
* crop input as this shape conf
*/
class CropLayer : public Layer {
public:
explicit CropLayer(const LayerConfig& config) : Layer(config) {}
~CropLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override;
protected:
void setOutDims();
void setInDims();
int32_t crop_axis_;
std::vector<uint32_t> crop_offsets_;
std::vector<uint32_t> crop_corner_;
TensorShape inDims_;
TensorShape targetDims_;
TensorShape outDims_;
};
} // namespace paddle

@ -56,7 +56,7 @@ add_test(NAME test_DetectionOutput
add_unittest_without_exec(test_ConvUnify
test_ConvUnify.cpp
LayerGradUtil.cpp)
add_test(NAME test_ConvUnify
COMMAND test_ConvUnify)
################# test_BatchNorm #######################

@ -1802,6 +1802,34 @@ TEST(Layer, RowConvLayer) {
}
}
TEST(Layer, CropLayer) {
TestConfig config;
// config input_0
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
ImageConfig* img = input->mutable_image_conf();
img->set_channels(4);
img->set_img_size(16);
config.layerConfig.set_axis(2);
config.layerConfig.add_offset(0);
config.layerConfig.add_offset(0);
// config input_1
config.inputDefs.push_back({INPUT_DATA, "layer_1", 128, 0});
input = config.layerConfig.add_inputs();
img = input->mutable_image_conf();
img->set_channels(2);
img->set_img_size(8);
// config crop layer
config.layerConfig.set_type("crop");
config.layerConfig.set_name("cropLayer");
for (auto useGpu : {false, true}) {
testLayerGrad(config, "crop", 100, false, useGpu, false);
}
}
TEST(Layer, SwitchOrderLayer) {
TestConfig config;
// config input_0

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