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PaddleDetection
提交 8ea2288e 编写于 作者: C chengduoZH
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remove conflict

上级 84d9c690 057efd17
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Showing with 685 addition and 207 deletion
cmake/inference_lib.cmake
浏览文件 @ 8ea2288e
......@@ -16,12 +16,10 @@ function(copy TARGET)
foreach(index RANGE ${len})
list(GET copy_lib_SRCS ${index} src)
list(GET copy_lib_DSTS ${index} dst)
add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND mkdir -p "${dst}")
if(IS_DIRECTORY ${src})
add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp -r "${src}" "${dst}")
else()
add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp "${src}" "${dst}")
endif()
add_custom_command(TARGET ${TARGET} PRE_BUILD
COMMAND mkdir -p "${dst}"
COMMAND cp -r "${src}" "${dst}"
COMMENT "copying ${src} -> ${dst}")
endforeach()
endfunction()
......@@ -53,11 +51,11 @@ IF(NOT PROTOBUF_FOUND)
ENDIF(NOT PROTOBUF_FOUND)
# paddle fluid module
set(src_dir "${PADDLE_SOURCE_DIR}/paddle")
set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle")
set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle/fluid")
set(module "framework")
copy(framework_lib DEPS framework_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/framework/framework.pb.h
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
......@@ -69,7 +67,7 @@ copy(memory_lib
set(module "inference")
copy(inference_lib DEPENDS paddle_fluid_shared
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/inference/libpaddle_fluid.so
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.so
DSTS ${dst_dir}/${module} ${dst_dir}/${module}
)
......
paddle/fluid/framework/op_registry_test.cc
浏览文件 @ 8ea2288e
......@@ -25,7 +25,10 @@ namespace framework {
class CosineOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void Run(const Scope& scope, const platform::Place& place) const override {}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......@@ -44,7 +47,10 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
class MyTestOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void Run(const Scope& scope, const platform::Place& place) const override {}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......
paddle/fluid/framework/operator.cc
浏览文件 @ 8ea2288e
......@@ -64,6 +64,18 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
}
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place);
#else
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
#endif
}
RunImpl(scope, place);
}
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
......@@ -479,7 +491,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
const Scope& scope_;
};
void OperatorWithKernel::Run(const Scope& scope,
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
......
paddle/fluid/framework/operator.h
浏览文件 @ 8ea2288e
......@@ -89,8 +89,9 @@ class OperatorBase {
std::string DebugString() const { return DebugStringEx(nullptr); }
/// Net will call this function to Run an op.
virtual void Run(const Scope& scope, const platform::Place& place) const = 0;
/// Net will call this interface function to Run an op.
// The implementation should be written at RunImpl
void Run(const Scope& scope, const platform::Place& place);
// FIXME(typhoonzero): this is only used for recv_op to stop event_loop.
virtual void Stop() {}
......@@ -144,6 +145,8 @@ class OperatorBase {
private:
void GenerateTemporaryNames();
void CheckAllInputOutputSet() const;
virtual void RunImpl(const Scope& scope,
const platform::Place& place) const = 0;
};
// Macro for define a clone method.
......@@ -168,10 +171,13 @@ class OperatorBase {
class NOP : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void Run(const Scope& scope, const platform::Place& place) const override {}
std::unique_ptr<OperatorBase> Clone() const override {
return std::unique_ptr<OperatorBase>(new NOP(*this));
}
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {}
};
class ExecutionContext {
......@@ -363,8 +369,6 @@ class OperatorWithKernel : public OperatorBase {
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const Scope& scope, const platform::Place& place) const final;
static std::unordered_map<std::string /* op_type */, OpKernelMap>&
AllOpKernels() {
static std::unordered_map<std::string, OpKernelMap> g_all_op_kernels;
......@@ -393,6 +397,7 @@ class OperatorWithKernel : public OperatorBase {
// indicate kernel DataType by input data. Defaultly all input data must be
// same.
proto::DataType IndicateDataType(const ExecutionContext& ctx) const;
void RunImpl(const Scope& scope, const platform::Place& place) const final;
};
extern bool OpSupportGPU(const std::string& op_type);
......
paddle/fluid/framework/operator_test.cc
浏览文件 @ 8ea2288e
......@@ -28,7 +28,10 @@ class OpWithoutKernelTest : public OperatorBase {
OpWithoutKernelTest(const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs), x(1) {}
void Run(const Scope& scope, const platform::Place& place) const override {
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {
++op_run_num;
ASSERT_EQ(static_cast<int>(inputs_.size()), 1);
ASSERT_EQ(static_cast<int>(outputs_.size()), 1);
......@@ -259,7 +262,9 @@ class OperatorClone : public paddle::framework::OperatorBase {
const paddle::framework::VariableNameMap& outputs,
const paddle::framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const paddle::framework::Scope& scope,
private:
void RunImpl(const paddle::framework::Scope& scope,
const paddle::platform::Place& place) const override {}
};
......
paddle/fluid/operators/array_to_lod_tensor_op.cc
浏览文件 @ 8ea2288e
......@@ -31,7 +31,9 @@ class ArrayToLoDTensorOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
auto &rank_table =
......
paddle/fluid/operators/assign_op.cc
浏览文件 @ 8ea2288e
......@@ -71,7 +71,9 @@ class AssignOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x = scope.FindVar(Input("X"));
if (x == nullptr) {
......
paddle/fluid/operators/beam_search_decode_op.cc
浏览文件 @ 8ea2288e
......@@ -55,7 +55,9 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(dev_place);
......
paddle/fluid/operators/beam_search_op.h
浏览文件 @ 8ea2288e
......@@ -204,7 +204,8 @@ class BeamSearchOp : public framework::OperatorBase {
PADDLE_THROW("Not Implemented");
}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
auto ids_var = scope.FindVar(Input("ids"));
auto scores_var = scope.FindVar(Input("scores"));
......
paddle/fluid/operators/concat_op.h
浏览文件 @ 8ea2288e
......@@ -38,7 +38,7 @@ class ConcatKernel : public framework::OpKernel<T> {
auto in_stride = framework::stride_numel(in->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis,
out->data<T>() + output_offset, out_stride,
in->data<T>(), in_stride);
in->data<T>(), in_stride, in_stride[axis]);
output_offset += in_stride[axis];
}
}
......@@ -59,7 +59,7 @@ class ConcatGradKernel : public framework::OpKernel<T> {
auto out_stride = framework::stride_numel(out->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis, out->data<T>(),
out_stride, in->data<T>() + input_offset,
in_stride);
in_stride, out_stride[axis]);
input_offset += out_stride[axis];
}
}
......
paddle/fluid/operators/cond_op.cc
浏览文件 @ 8ea2288e
......@@ -193,7 +193,7 @@ void CondOp::MergeDataFromSubnet(const framework::Scope& scope,
}
}
void CondOp::Run(const Scope& scope, const platform::Place& place) const {
void CondOp::RunImpl(const Scope& scope, const platform::Place& place) const {
// get device context from pool
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(place);
......
paddle/fluid/operators/cond_op.h
浏览文件 @ 8ea2288e
......@@ -77,7 +77,8 @@ class CondOp : public framework::OperatorBase {
sub_net_op_[FALSE_BRANCH] = std::move(net);
}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override;
private:
......
paddle/fluid/operators/conditional_block_op.cc
浏览文件 @ 8ea2288e
......@@ -65,7 +65,9 @@ class ConditionalBlockOp : public ConditionalOp {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ConditionalOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto xs = InputTensors(scope);
......@@ -128,7 +130,9 @@ class ConditionalBlockGradOp : public ConditionalOp {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ConditionalOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto xs = this->InputTensors(scope);
......
paddle/fluid/operators/create_reader_op.cc
浏览文件 @ 8ea2288e
......@@ -106,7 +106,9 @@ template <typename T>
class CreateRandomDataGeneratorOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& shape_concat = Attr<std::vector<int>>("shape_concat");
const auto& ranks = Attr<std::vector<int>>("ranks");
......@@ -155,7 +157,9 @@ class CreateRandomDataGeneratorOpMaker
class CreateShuffleReaderOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
......@@ -187,7 +191,9 @@ class CreateShuffleReaderOpMaker : public framework::OpProtoAndCheckerMaker {
class CreateBatchReaderOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
......
paddle/fluid/operators/feed_op.cc
浏览文件 @ 8ea2288e
......@@ -24,7 +24,9 @@ class FeedOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto feed_var_name = Input("X");
auto *feed_var = scope.FindVar(feed_var_name);
......
paddle/fluid/operators/fetch_op.cc
浏览文件 @ 8ea2288e
......@@ -26,7 +26,8 @@ class FetchOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto fetch_var_name = Input("X");
auto *fetch_var = scope.FindVar(fetch_var_name);
......
paddle/fluid/operators/fill_constant_op.cc
浏览文件 @ 8ea2288e
......@@ -33,7 +33,9 @@ class FillConstantInferShape : public framework::InferShapeBase {
class FillConstantOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto data_type =
static_cast<framework::proto::DataType>(Attr<int>("dtype"));
......
paddle/fluid/operators/fill_op.cc
浏览文件 @ 8ea2288e
......@@ -42,7 +42,9 @@ class FillOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &out =
detail::Ref(detail::Ref(scope.FindVar(Output("Out")),
......
paddle/fluid/operators/get_places_op.cc
浏览文件 @ 8ea2288e
......@@ -37,7 +37,9 @@ class GetPlacesOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
bool is_gpu;
if (Attr<std::string>("device_type") == "AUTO") {
......
paddle/fluid/operators/increment_op.cc
浏览文件 @ 8ea2288e
......@@ -51,7 +51,8 @@ class IncrementOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
auto &out =
......
paddle/fluid/operators/is_empty_op.cc
浏览文件 @ 8ea2288e
......@@ -28,7 +28,8 @@ class IsEmptyOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// get input
auto *var = scope.FindVar(Input(kInput));
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ 8ea2288e
......@@ -26,7 +26,9 @@ class LoadCombineOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
......
paddle/fluid/operators/load_op.cc
浏览文件 @ 8ea2288e
......@@ -25,7 +25,9 @@ class LoadOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
std::ifstream fin(filename);
......
paddle/fluid/operators/lod_array_length_op.cc
浏览文件 @ 8ea2288e
......@@ -25,7 +25,9 @@ class LoDArrayLengthOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensorArray>();
auto &out =
......
paddle/fluid/operators/lod_rank_table_op.cc
浏览文件 @ 8ea2288e
......@@ -23,7 +23,9 @@ class LoDRankTableOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
auto *out =
......
paddle/fluid/operators/lod_tensor_to_array_op.cc
浏览文件 @ 8ea2288e
......@@ -32,7 +32,9 @@ class LoDTensorToArrayOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x = detail::Ref(scope.FindVar(Input("X")), "Cannot find input %s",
Input("X"))
......
paddle/fluid/operators/max_sequence_len_op.cc
浏览文件 @ 8ea2288e
......@@ -27,7 +27,8 @@ class MaxSeqenceLenOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &rank_table =
scope.FindVar(Input("RankTable"))->Get<framework::LoDRankTable>();
......
paddle/fluid/operators/merge_lod_tensor_op.cc
浏览文件 @ 8ea2288e
......@@ -27,7 +27,9 @@ class MergeLoDTensorOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
......
paddle/fluid/operators/mine_hard_examples_op.cc
浏览文件 @ 8ea2288e
......@@ -237,6 +237,8 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
}
ctx->SetOutputDim("UpdatedMatchIndices", idx_dims);
// The first dimension of NegIndices will be set correcttly in Compute.
ctx->SetOutputDim("NegIndices", {-1, 1});
}
protected:
......
paddle/fluid/operators/nccl_op.cc
浏览文件 @ 8ea2288e
......@@ -26,7 +26,8 @@ class NCCLInitOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
const auto &name = Output("Communicator");
PADDLE_ENFORCE_NOT_NULL(scope.FindVar(name),
......
paddle/fluid/operators/net_op.h
浏览文件 @ 8ea2288e
......@@ -57,20 +57,6 @@ class NetOp : public framework::OperatorBase {
this->CompleteAddOp();
}
/**
* @brief Run the network.
*
* Run all the operators with the `scope`, if no scope is provided, default
* scope will be used instead. If no OpContext is provicded, default context
* will be used.
*/
void Run(const framework::Scope& scope,
const platform::Place& place) const override {
for (auto& op : ops_) {
op->Run(scope, place);
}
}
bool SupportGPU() const override {
for (auto& op : ops_) {
if (!op->SupportGPU()) {
......@@ -117,6 +103,20 @@ class NetOp : public framework::OperatorBase {
std::vector<std::unique_ptr<framework::OperatorBase>> ops_;
private:
/**
* @brief Run the network.
*
* Run all the operators with the `scope`, if no scope is provided, default
* scope will be used instead. If no OpContext is provicded, default context
* will be used.
*/
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
for (auto& op : ops_) {
op->Run(scope, place);
}
}
bool add_op_done_{false};
std::set<std::string> intermediate_outputs_;
......
paddle/fluid/operators/net_op_test.cc
浏览文件 @ 8ea2288e
......@@ -26,7 +26,10 @@ class TestOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
DEFINE_OP_CLONE_METHOD(TestOp);
void Run(const Scope& scope, const platform::Place& place) const override {
private:
void RunImpl(const Scope& scope,
const platform::Place& place) const override {
++run_cnt;
}
};
......
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @ 8ea2288e
......@@ -118,7 +118,8 @@ class ParallelDoOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
......@@ -207,7 +208,8 @@ class ParallelDoGradOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
auto *program = block->Program();
......
paddle/fluid/operators/print_op.cc
浏览文件 @ 8ea2288e
......@@ -130,7 +130,8 @@ class TensorPrintOp : public framework::OperatorBase {
PADDLE_THROW("Not implemented.");
}
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
const framework::Variable* in_var_ptr = nullptr;
std::string phase = kForward;
......
paddle/fluid/operators/read_op.cc
浏览文件 @ 8ea2288e
......@@ -54,7 +54,9 @@ class ReadInferVarType : public framework::VarTypeInference {
class ReadOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope& scope,
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
framework::ReaderHolder* reader =
scope.FindVar(Input("Reader"))->GetMutable<framework::ReaderHolder>();
......
paddle/fluid/operators/recurrent_op.cc
浏览文件 @ 8ea2288e
......@@ -226,7 +226,8 @@ class RecurrentOp : public RecurrentBase {
const framework::AttributeMap &attrs)
: RecurrentBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto seq_len = static_cast<size_t>(this->GetSequenceLength(scope));
VLOG(3) << "Static RNN input sequence length = " << seq_len;
......@@ -315,7 +316,8 @@ class RecurrentGradOp : public RecurrentBase {
const framework::AttributeMap &attrs)
: RecurrentBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto seq_len = static_cast<size_t>(GetSequenceLength(scope));
StepScopes scopes = CreateStepScopes(scope, seq_len);
......
paddle/fluid/operators/reorder_lod_tensor_by_rank_op.cc
浏览文件 @ 8ea2288e
......@@ -75,7 +75,9 @@ class ReorderLoDTensorByRankTableBase : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &x =
detail::Ref(scope.FindVar(Input("X")),
......
paddle/fluid/operators/rnn_memory_helper_op.cc
浏览文件 @ 8ea2288e
......@@ -24,7 +24,9 @@ class RNNMemoryHelperOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto mem_var_name = Input("X");
auto *mem_var = scope.FindVar(mem_var_name);
......@@ -76,7 +78,9 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto out_grad_var_name = Input(framework::GradVarName("Out"));
auto *out_grad_var = scope.FindVar(out_grad_var_name);
......
paddle/fluid/operators/save_combine_op.cc
浏览文件 @ 8ea2288e
......@@ -63,7 +63,9 @@ class SaveCombineOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
......
paddle/fluid/operators/save_op.cc
浏览文件 @ 8ea2288e
......@@ -62,7 +62,9 @@ class SaveOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
......
paddle/fluid/operators/shrink_rnn_memory_op.cc
浏览文件 @ 8ea2288e
......@@ -27,7 +27,8 @@ class ShrinkRNNMemoryOp : public ArrayOp {
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x_var = scope.FindVar(Input("X"));
PADDLE_ENFORCE(x_var != nullptr, "Input X must be set");
......@@ -108,7 +109,8 @@ class ShrinkRNNMemoryGradOp : public ArrayOp {
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *dout_var = scope.FindVar(Input(framework::GradVarName("Out")));
auto *dx_var = scope.FindVar(Output(framework::GradVarName("X")));
......
paddle/fluid/operators/split_lod_tensor_op.cc
浏览文件 @ 8ea2288e
......@@ -33,7 +33,9 @@ class SplitLoDTensorOp : public framework::OperatorBase {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &x = scope.FindVar(Input("X"))->Get<framework::LoDTensor>();
auto &mask = scope.FindVar(Input("Mask"))->Get<framework::LoDTensor>();
......
paddle/fluid/operators/split_op.h
浏览文件 @ 8ea2288e
......@@ -38,7 +38,7 @@ class SplitOpKernel : public framework::OpKernel<T> {
auto out_stride = framework::stride_numel(out->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis, out->data<T>(),
out_stride, in->data<T>() + input_offset,
in_stride);
in_stride, out_stride[axis]);
input_offset += out_stride[axis];
}
}
......
paddle/fluid/operators/strided_memcpy.h
浏览文件 @ 8ea2288e
......@@ -54,7 +54,8 @@ inline void StridedNumelCopyWithAxis(const platform::DeviceContext& ctx,
int64_t axis, T* dst,
const framework::DDim& dst_stride_numel,
const T* src,
const framework::DDim& src_stride_numel) {
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];
......@@ -82,15 +83,14 @@ inline void StridedNumelCopyWithAxis(const platform::DeviceContext& ctx,
if (platform::is_cpu_place(place)) {
auto& cpu_place = boost::get<platform::CPUPlace>(place);
memory::Copy(cpu_place, dst + i * dst_after, cpu_place,
src + i * src_after, sizeof(T) * src_after);
src + i * src_after, sizeof(T) * size);
} else {
#ifdef PADDLE_WITH_CUDA
auto& gpu_place = boost::get<platform::CUDAPlace>(place);
auto& cuda_ctx =
reinterpret_cast<const platform::CUDADeviceContext&>(ctx);
memory::Copy(gpu_place, dst + i * dst_after, gpu_place,
src + i * src_after, sizeof(T) * src_after,
cuda_ctx.stream());
src + i * src_after, sizeof(T) * size, cuda_ctx.stream());
#else
PADDLE_THROW("Paddle is not compiled with GPU");
#endif
......
paddle/fluid/operators/tensor_array_read_write_op.cc
浏览文件 @ 8ea2288e
......@@ -24,7 +24,8 @@ class WriteToArrayOp : public ArrayOp {
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x = scope.FindVar(Input("X"));
if (x == nullptr) return;
......@@ -122,7 +123,9 @@ class ReadFromArrayOp : public ArrayOp {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *x = scope.FindVar(Input("X"));
PADDLE_ENFORCE(x != nullptr, "X must be set");
......
paddle/fluid/operators/while_op.cc
浏览文件 @ 8ea2288e
......@@ -39,7 +39,8 @@ class WhileOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
PADDLE_ENFORCE_NOT_NULL(scope.FindVar(Input(kCondition)));
auto &cond = scope.FindVar(Input(kCondition))->Get<LoDTensor>();
......@@ -99,7 +100,8 @@ class WhileGradOp : public framework::OperatorBase {
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
......
paddle/scripts/docker/build.sh
浏览文件 @ 8ea2288e
......@@ -204,6 +204,17 @@ function gen_capi_package() {
fi
}
function gen_fluid_inference_lib() {
if [ ${WITH_C_API:-OFF} == "OFF" ] ; then
cat <<EOF
========================================
Building fluid inference library ...
========================================
EOF
make inference_lib_dist
fi
}
set -xe
cmake_gen ${PYTHON_ABI:-""}
......@@ -212,6 +223,7 @@ run_test
gen_docs
gen_dockerfile
gen_capi_package
gen_fluid_inference_lib
if [[ ${WITH_C_API:-OFF} == "ON" ]]; then
printf "PaddlePaddle C-API libraries was generated on build/paddle.tgz\n"
......
python/paddle/v2/fluid/distribute_transpiler.py
浏览文件 @ 8ea2288e
......@@ -121,6 +121,7 @@ def split_dense_variable(var_list,
block_size += dim1 - remains
# update split_count after aligning
split_count = int(math.ceil(var_numel / float(block_size)))
print("###split var ", var.name, var.shape, block_size, split_count)
for block_id in xrange(split_count):
curr_block_size = min(block_size, var_numel - (
(block_id) * block_size))
......@@ -191,7 +192,6 @@ class DistributeTranspiler:
for b in param_blocks:
varname, block_id, _ = b.split(":")
send_outputs.append(param_var_mapping[varname][int(block_id)])
# let send_op know which endpoint to send which var to, eplist has the same
# order as send_inputs.
eplist = split_method(send_inputs, pserver_endpoints)
......@@ -230,21 +230,6 @@ class DistributeTranspiler:
outputs={"Out": [orig_param]},
attrs={"axis": 0})
self.lr_param_mapping = self._create_lr_param_mapping()
def _create_lr_param_mapping(self):
lr_mapping = dict()
for _, opt_op in enumerate(self.optimize_ops):
if not opt_op.inputs or not opt_op.inputs.has_key("LearningRate") \
or not opt_op.inputs.has_key("Param"):
continue
lr = opt_op.inputs["LearningRate"].name
param = opt_op.inputs["Param"].name
if not lr_mapping.has_key(lr):
lr_mapping.update({lr: list()})
lr_mapping[lr].append(param)
return lr_mapping
def _create_vars_from_blocklist(self, program, block_list):
# Create respective variables using the block_list
block_map = dict()
......@@ -271,6 +256,7 @@ class DistributeTranspiler:
splited_shape = [rows]
if len(orig_shape) >= 2:
splited_shape.extend(orig_shape[1:])
print("###splited: ", size, rows, splited_shape)
var = program.global_block().create_var(
name="%s.block%d" % (varname, i),
psersistable=False,
......@@ -278,6 +264,7 @@ class DistributeTranspiler:
type=orig_var.type,
shape=splited_shape) # flattend splited var
var_mapping[varname].append(var)
print("###created split var ", var)
return var_mapping
def _clone_var(self, block, var):
......@@ -369,18 +356,9 @@ class DistributeTranspiler:
pass
return orig_shape
def _fetch_var_names(self, param_dict):
res = []
if not param_dict:
return res
for _, values in param_dict.iteritems():
if not isinstance(values, list):
values = [values]
res += [v.name for v in values]
return res
def _append_pserver_ops(self, optimize_block, opt_op, endpoint):
program = optimize_block.program
pserver_block = program.global_block()
new_inputs = dict()
# update param/grad shape first, then other inputs like
# moment can use the updated shape
......@@ -395,11 +373,11 @@ class DistributeTranspiler:
# do not append this op if current endpoint
# is not dealing with this grad block
return
merged_var = program.global_block().vars[grad_block.name]
merged_var = pserver_block.vars[grad_block.name]
# append merging ops if trainers > 1
if self.trainers > 1:
vars2merge = self._create_var_for_trainers(
program.global_block(), grad_block, self.trainers)
pserver_block, grad_block, self.trainers)
optimize_block.append_op(
type="sum",
inputs={"X": vars2merge},
......@@ -419,29 +397,27 @@ class DistributeTranspiler:
break
if not param_block:
return
tmpvar = program.global_block().create_var(
tmpvar = pserver_block.create_var(
name=param_block.name,
persistable=True,
dtype=param_block.dtype,
shape=param_block.shape)
new_inputs[key] = tmpvar
elif key == "LearningRate":
# leraning rate variable has already be created by non-optimize op,
# don't create it once again.
new_inputs[key] = program.global_block().vars[opt_op.input(key)[
0]]
new_inputs[key] = pserver_block.vars[opt_op.input(key)[0]]
for key in opt_op.input_names:
new_shape = None
if key in ["Param", "Grad", "LearningRate"]:
continue
var = program.global_block().vars[opt_op.input(key)[0]]
var = self.program.global_block().vars[opt_op.input(key)[0]]
# update accumulator variable shape
param_shape = new_inputs["Param"].shape
new_shape = self._get_optimizer_input_shape(opt_op.type, key,
var.shape, param_shape)
tmpvar = program.global_block().create_var(
tmpvar = pserver_block.create_var(
name=var.name,
persistable=var.persistable,
dtype=var.dtype,
......@@ -449,11 +425,14 @@ class DistributeTranspiler:
new_inputs[key] = tmpvar
# change output's ParamOut variable
opt_op.outputs["ParamOut"] = new_inputs["Param"]
outputs = self._get_output_map_from_op(self.program.global_block().vars,
opt_op)
outputs["ParamOut"] = new_inputs["Param"]
optimize_block.append_op(
type=opt_op.type,
inputs=new_inputs,
outputs=opt_op.outputs,
outputs=outputs,
attrs=opt_op.attrs)
def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
......@@ -497,11 +476,12 @@ class DistributeTranspiler:
# If one op's input is another op's output or
# one op's output is another op's input, we say
# the two operator is connected.
op1_input_names = self._fetch_var_names(op1.inputs)
op1_output_names = self._fetch_var_names(op1.outputs)
op1_input_names = op1.desc.input_arg_names()
op1_output_names = op1.desc.output_arg_names()
op2_input_names = op2.desc.input_arg_names()
op2_output_names = op2.desc.output_arg_names()
op2_input_names = self._fetch_var_names(op2.inputs)
op2_output_names = self._fetch_var_names(op2.outputs)
if set(op1_output_names) & set(op2_input_names) or \
set(op1_input_names) & set(op2_output_names):
return True
......@@ -521,8 +501,8 @@ class DistributeTranspiler:
def _is_opt_op(self, op):
# NOTE: It's a HACK implement.
# optimize op: SGDOptimize, MomentumOptimizer, AdamOptimizer and etc...
if op.inputs and op.inputs.has_key("Param") \
and op.inputs.has_key("LearningRate"):
if "Param" in op.input_names and \
"LearningRate" in op.input_names:
return True
return False
......@@ -530,12 +510,12 @@ class DistributeTranspiler:
param_names = [
p.name for p in self.param_grad_ep_mapping[endpoint]["params"]
]
if op.inputs["Param"].name in param_names:
if op.input("Param") in param_names:
return True
else:
for n in param_names:
param = op.inputs["Param"].name
if same_or_split_var(n, param) and n != op.inputs["Param"].name:
param = op.input("Param")[0]
if same_or_split_var(n, param) and n != param:
return True
return False
return False
......@@ -551,6 +531,8 @@ class DistributeTranspiler:
"""
# step5
pserver_program = Program()
print("param mapping on pserver: #### ",
self.param_grad_ep_mapping[endpoint]["params"])
for v in self.param_grad_ep_mapping[endpoint]["params"]:
self._clone_var(pserver_program.global_block(), v)
for v in self.param_grad_ep_mapping[endpoint]["grads"]:
......@@ -564,7 +546,6 @@ class DistributeTranspiler:
persistable=True,
dtype=v.dtype,
shape=v.shape)
# step6
optimize_block = pserver_program.create_block(0)
# step 6.1
......
python/paddle/v2/fluid/framework.py
浏览文件 @ 8ea2288e
......@@ -400,9 +400,6 @@ class Operator(object):
"""
self.block = block
self.desc = desc
# for clone a new operator
self.inputs = inputs
self.outputs = outputs
self.attrs = attrs
if len(self.desc.type()) != 0:
return
......
python/paddle/v2/fluid/layers/__init__.py
浏览文件 @ 8ea2288e
......@@ -16,8 +16,6 @@ import ops
from ops import *
import nn
from nn import *
import detection
from detection import *
import io
from io import *
import tensor
......@@ -33,7 +31,6 @@ from detection import *
__all__ = []
__all__ += math_op_patch.__all__
__all__ += detection.__all__
__all__ += nn.__all__
__all__ += io.__all__
__all__ += tensor.__all__
......
python/paddle/v2/fluid/layers/detection.py
浏览文件 @ 8ea2288e
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2018 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.
......@@ -15,20 +15,32 @@
All layers just related to the detection neural network.
"""
from layer_function_generator import generate_layer_fn
from ..layer_helper import LayerHelper
from ..param_attr import ParamAttr
from ..framework import Variable
import tensor
import ops
import nn
import math
__all__ = [
'detection_output',
'prior_box',
'multi_box_head',
'bipartite_match',
'target_assign',
'detection_output',
'ssd_loss',
]
__auto__ = [
'iou_similarity',
'box_coder',
]
__all__ += __auto__
for _OP in set(__auto__):
globals()[_OP] = generate_layer_fn(_OP)
def detection_output(scores,
loc,
......@@ -98,18 +110,13 @@ def detection_output(scores,
"""
helper = LayerHelper("detection_output", **locals())
decoded_box = helper.create_tmp_variable(dtype=loc.dtype)
helper.append_op(
type="box_coder",
inputs={
'PriorBox': prior_box,
'PriorBoxVar': prior_box_var,
'TargetBox': loc
},
outputs={'OutputBox': decoded_box},
attrs={'code_type': 'decode_center_size'})
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
decoded_box = box_coder(
prior_box=prior_box,
prior_box_var=prior_box_var,
target_box=loc,
code_type='decode_center_size')
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
helper.append_op(
type="multiclass_nms",
inputs={'Scores': scores,
......@@ -280,22 +287,22 @@ def prior_box(inputs,
if aspect_ratios:
_is_list_or_tuple_and_equal(
aspect_ratios, num_layer,
'aspect_ratios should be list and the length of inputs '
'aspect_ratios should be list or tuple, and the length of inputs '
'and aspect_ratios should be the same.')
if step_h:
_is_list_or_tuple_and_equal(
step_h, num_layer,
'step_h should be list and the length of inputs and '
'step_h should be list or tuple, and the length of inputs and '
'step_h should be the same.')
if step_w:
_is_list_or_tuple_and_equal(
step_w, num_layer,
'step_w should be list and the length of inputs and '
'step_w should be list or tuple, and the length of inputs and '
'step_w should be the same.')
if steps:
_is_list_or_tuple_and_equal(
steps, num_layer,
'steps should be list and the length of inputs and '
'steps should be list or tuple, and the length of inputs and '
'step_w should be the same.')
step_w = steps
step_h = steps
......@@ -339,6 +346,331 @@ def prior_box(inputs,
return box, var
def bipartite_match(dist_matrix, name=None):
"""
**Bipartite matchint operator**
This operator is a greedy bipartite matching algorithm, which is used to
obtain the matching with the maximum distance based on the input
distance matrix. For input 2D matrix, the bipartite matching algorithm can
find the matched column for each row, also can find the matched row for
each column. And this operator only calculate matched indices from column
to row. For each instance, the number of matched indices is the number of
of columns of the input ditance matrix.
There are two outputs to save matched indices and distance.
A simple description, this algothrim matched the best (maximum distance)
row entity to the column entity and the matched indices are not duplicated
in each row of ColToRowMatchIndices. If the column entity is not matched
any row entity, set -1 in ColToRowMatchIndices.
Please note that the input DistMat can be LoDTensor (with LoD) or Tensor.
If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size.
If Tensor, the height of ColToRowMatchIndices is 1.
Args:
dist_matrix(Variable): This input is a 2-D LoDTensor with shape
[K, M]. It is pair-wise distance matrix between the entities
represented by each row and each column. For example, assumed one
entity is A with shape [K], another entity is B with shape [M]. The
dist_matirx[i][j] is the distance between A[i] and B[j]. The bigger
the distance is, the better macthing the pairs are. Please note,
This tensor can contain LoD information to represent a batch of
inputs. One instance of this batch can contain different numbers of
entities.
Returns:
match_indices(Variable): A 2-D Tensor with shape [N, M] in int type.
N is the batch size. If match_indices[i][j] is -1, it
means B[j] does not match any entity in i-th instance.
Otherwise, it means B[j] is matched to row
match_indices[i][j] in i-th instance. The row number of
i-th instance is saved in match_indices[i][j].
match_distance(Variable): A 2-D Tensor with shape [N, M] in float type.
N is batch size. If match_indices[i][j] is -1,
match_distance[i][j] is also -1.0. Otherwise, assumed
match_distance[i][j] = d, and the row offsets of each instance
are called LoD. Then match_distance[i][j] = dist_matrix[d+LoD[i]][j].
"""
helper = LayerHelper('bipartite_match', **locals())
match_indices = helper.create_tmp_variable(dtype='int32')
match_distance = helper.create_tmp_variable(dtype=dist_matrix.dtype)
helper.append_op(
type='bipartite_match',
inputs={'DistMat': dist_matrix},
outputs={
'ColToRowMatchIndices': match_indices,
'ColToRowMatchDist': match_distance
})
return match_indices, match_distance
def target_assign(input,
matched_indices,
negative_indices=None,
mismatch_value=None,
name=None):
"""
**Target assigner operator**
This operator can be, for given the target bounding boxes or labels,
to assign classification and regression targets to each prediction as well as
weights to prediction. The weights is used to specify which prediction would
not contribute to training loss.
For each instance, the output `out` and`out_weight` are assigned based on
`match_indices` and `negative_indices`.
Assumed that the row offset for each instance in `input` is called lod,
this operator assigns classification/regression targets by performing the
following steps:
1. Assigning all outpts based on `match_indices`:
If id = match_indices[i][j] > 0,
out[i][j][0 : K] = X[lod[i] + id][j % P][0 : K]
out_weight[i][j] = 1.
Otherwise,
out[j][j][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][j] = 0.
2. Assigning out_weight based on `neg_indices` if `neg_indices` is provided:
Assumed that the row offset for each instance in `neg_indices` is called neg_lod,
for i-th instance and each `id` of neg_indices in this instance:
out[i][id][0 : K] = {mismatch_value, mismatch_value, ...}
out_weight[i][id] = 1.0
Args:
inputs (Variable): This input is a 3D LoDTensor with shape [M, P, K].
matched_indices (Variable): Tensor<int>), The input matched indices
is 2D Tenosr<int32> with shape [N, P], If MatchIndices[i][j] is -1,
the j-th entity of column is not matched to any entity of row in
i-th instance.
negative_indices (Variable): The input negative example indices are
an optional input with shape [Neg, 1] and int32 type, where Neg is
the total number of negative example indices.
mismatch_value (float32): Fill this value to the mismatched location.
Returns:
out (Variable): The output is a 3D Tensor with shape [N, P, K],
N and P is the same as they are in `neg_indices`, K is the
same as it in input of X. If `match_indices[i][j]`.
out_weight (Variable): The weight for output with the shape of [N, P, 1].
"""
helper = LayerHelper('target_assign', **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
out_weight = helper.create_tmp_variable(dtype='float32')
helper.append_op(
type='target_assign',
inputs={
'X': input,
'MatchIndices': matched_indices,
'NegIndices': negative_indices
},
outputs={'Out': out,
'OutWeight': out_weight},
attrs={'mismatch_value': mismatch_value})
return out, out_weight
def ssd_loss(location,
confidence,
gt_box,
gt_label,
prior_box,
prior_box_var=None,
background_label=0,
overlap_threshold=0.5,
neg_pos_ratio=3.0,
neg_overlap=0.5,
loc_loss_weight=1.0,
conf_loss_weight=1.0,
match_type='per_prediction',
mining_type='max_negative',
sample_size=None):
"""
**Multi-box loss layer for object dection algorithm of SSD**
This layer is to compute dection loss for SSD given the location offset
predictions, confidence predictions, prior boxes and ground-truth boudding
boxes and labels, and the type of hard example mining. The returned loss
is a weighted sum of the localization loss (or regression loss) and
confidence loss (or classification loss) by performing the following steps:
1. Find matched boundding box by bipartite matching algorithm.
1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
1.2 Compute matched boundding box by bipartite matching algorithm.
2. Compute confidence for mining hard examples
2.1. Get the target label based on matched indices.
2.2. Compute confidence loss.
3. Apply hard example mining to get the negative example indices and update
the matched indices.
4. Assign classification and regression targets
4.1. Encoded bbox according to the prior boxes.
4.2. Assign regression targets.
4.3. Assign classification targets.
5. Compute the overall objective loss.
5.1 Compute confidence loss.
5.1 Compute localization loss.
5.3 Compute the overall weighted loss.
Args:
location (Variable): The location predictions are a 3D Tensor with
shape [N, Np, 4], N is the batch size, Np is total number of
predictions for each instance. 4 is the number of coordinate values,
the layout is [xmin, ymin, xmax, ymax].
confidence (Variable): The confidence predictions are a 3D Tensor
with shape [N, Np, C], N and Np are the same as they are in
`location`, C is the class number.
gt_box (Variable): The ground-truth boudding boxes (bboxes) are a 2D
LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth
bboxes of mini-batch input.
gt_label (Variable): The ground-truth labels are a 2D LoDTensor
with shape [Ng, 1].
prior_box (Variable): The prior boxes are a 2D Tensor with shape [Np, 4].
prior_box_var (Variable): The variance of prior boxes are a 2D Tensor
with shape [Np, 4].
background_label (int): The index of background label, 0 by default.
overlap_threshold (float): If match_type is 'per_prediction', use
`overlap_threshold` to determine the extra matching bboxes when
finding matched boxes. 0.5 by default.
neg_pos_ratio (float): The ratio of the negative boxes to the positive
boxes, used only when mining_type is max_negative, 3.0 by defalut.
neg_overlap (float): The negative overlap upper bound for the unmatched
predictions. Use only when mining_type is max_negative,
0.5 by default.
sample_size (int): The max sample size of negative box, used only when
mining_type is hard_example.
loc_loss_weight (float): Weight for localization loss, 1.0 by default.
conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
match_type (str): The type of matching method during training, should
be 'bipartite' or 'per_prediction'.
mining_type (str): The hard example mining type, should be 'hard_example'
or 'max_negative', now only support `max_negative`.
Returns:
Variable: The weighted sum of the localization loss and confidence loss,
with shape [N * Np, 1], N and Np are the same as they are
in `location`.
Raises:
ValueError: If mining_type is 'hard_example', now only support
mining type of `max_negative`.
Examples:
.. code-block:: python
pb = layers.data(
name='prior_box',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
pbv = layers.data(
name='prior_box_var',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
gt_box = layers.data(
name='gt_box', shape=[4], lod_level=1, dtype='float32')
gt_label = layers.data(
name='gt_label', shape=[1], lod_level=1, dtype='float32')
loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
"""
helper = LayerHelper('ssd_loss', **locals())
if mining_type != 'max_negative':
raise ValueError("Only support mining_type == max_negative now.")
num, num_prior, num_class = confidence.shape
def __reshape_to_2d(var):
return ops.reshape(x=var, shape=[-1, var.shape[-1]])
# 1. Find matched boundding box by prior box.
# 1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
iou = iou_similarity(x=gt_box, y=prior_box)
# 1.2 Compute matched boundding box by bipartite matching algorithm.
matched_indices, matched_dist = bipartite_match(iou)
# 2. Compute confidence for mining hard examples
# 2.1. Get the target label based on matched indices
gt_label = ops.reshape(x=gt_label, shape=gt_label.shape + (1, ))
target_label, _ = target_assign(
gt_label, matched_indices, mismatch_value=background_label)
# 2.2. Compute confidence loss.
# Reshape confidence to 2D tensor.
confidence = __reshape_to_2d(confidence)
target_label = tensor.cast(x=target_label, dtype='int64')
target_label = __reshape_to_2d(target_label)
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
conf_loss = ops.reshape(x=conf_loss, shape=(num, num_prior))
neg_indices = helper.create_tmp_variable(dtype='int32')
dtype = matched_indices.dtype
updated_matched_indices = helper.create_tmp_variable(dtype=dtype)
helper.append_op(
type='mine_hard_examples',
inputs={
'ClsLoss': conf_loss,
'LocLoss': None,
'MatchIndices': matched_indices,
'MatchDist': matched_dist,
},
outputs={
'NegIndices': neg_indices,
'UpdatedMatchIndices': updated_matched_indices
},
attrs={
'neg_pos_ratio': neg_pos_ratio,
'neg_dist_threshold': neg_pos_ratio,
'mining_type': mining_type,
'sample_size': sample_size,
})
# 4. Assign classification and regression targets
# 4.1. Encoded bbox according to the prior boxes.
encoded_bbox = box_coder(
prior_box=prior_box,
prior_box_var=prior_box_var,
target_box=gt_box,
code_type='encode_center_size')
# 4.2. Assign regression targets
target_bbox, target_loc_weight = target_assign(
encoded_bbox, updated_matched_indices, mismatch_value=background_label)
# 4.3. Assign classification targets
target_label, target_conf_weight = target_assign(
gt_label,
updated_matched_indices,
negative_indices=neg_indices,
mismatch_value=background_label)
# 5. Compute loss.
# 5.1 Compute confidence loss.
target_label = __reshape_to_2d(target_label)
target_label = tensor.cast(x=target_label, dtype='int64')
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
target_conf_weight = __reshape_to_2d(target_conf_weight)
conf_loss = conf_loss * target_conf_weight
# 5.2 Compute regression loss.
location = __reshape_to_2d(location)
target_bbox = __reshape_to_2d(target_bbox)
loc_loss = nn.smooth_l1(location, target_bbox)
target_loc_weight = __reshape_to_2d(target_loc_weight)
loc_loss = loc_loss * target_loc_weight
# 5.3 Compute overall weighted loss.
loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss
return loss
def multi_box_head(inputs,
num_classes,
min_sizes=None,
......
python/paddle/v2/fluid/tests/test_detection.py
浏览文件 @ 8ea2288e
......@@ -15,12 +15,11 @@
from __future__ import print_function
import paddle.v2.fluid as fluid
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.layers.detection as detection
from paddle.v2.fluid.framework import Program, program_guard
import unittest
class TestBook(unittest.TestCase):
class TestDetection(unittest.TestCase):
def test_detection_output(self):
program = Program()
with program_guard(program):
......@@ -47,7 +46,67 @@ class TestBook(unittest.TestCase):
out = layers.detection_output(
scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
self.assertIsNotNone(out)
# print(str(program))
self.assertEqual(out.shape[-1], 6)
print(str(program))
def test_detection_api(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[4], dtype='float32')
y = layers.data(name='y', shape=[4], dtype='float32')
z = layers.data(name='z', shape=[4], dtype='float32', lod_level=1)
iou = layers.iou_similarity(x=x, y=y)
bcoder = layers.box_coder(
prior_box=x,
prior_box_var=y,
target_box=z,
code_type='encode_center_size')
self.assertIsNotNone(iou)
self.assertIsNotNone(bcoder)
matched_indices, matched_dist = layers.bipartite_match(iou)
self.assertIsNotNone(matched_indices)
self.assertIsNotNone(matched_dist)
gt = layers.data(
name='gt', shape=[1, 1], dtype='int32', lod_level=1)
trg, trg_weight = layers.target_assign(
gt, matched_indices, mismatch_value=0)
self.assertIsNotNone(trg)
self.assertIsNotNone(trg_weight)
gt2 = layers.data(
name='gt2', shape=[10, 4], dtype='float32', lod_level=1)
trg, trg_weight = layers.target_assign(
gt2, matched_indices, mismatch_value=0)
self.assertIsNotNone(trg)
self.assertIsNotNone(trg_weight)
print(str(program))
def test_ssd_loss(self):
program = Program()
with program_guard(program):
pb = layers.data(
name='prior_box',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
pbv = layers.data(
name='prior_box_var',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
gt_box = layers.data(
name='gt_box', shape=[4], lod_level=1, dtype='float32')
gt_label = layers.data(
name='gt_label', shape=[1], lod_level=1, dtype='int32')
loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
self.assertIsNotNone(loss)
self.assertEqual(loss.shape[-1], 1)
print(str(program))
class TestPriorBox(unittest.TestCase):
......@@ -68,7 +127,7 @@ class TestPriorBox(unittest.TestCase):
conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
box, var = detection.prior_box(
box, var = layers.prior_box(
inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
image=images,
min_ratio=20,
......
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