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提交 8c757059 编写于 作者: J JiabinYang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize_hs_op 

, test=develop
上级 b387a194 6d04a9cf
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Showing with 1840 addition and 244 deletion
cmake/external/ngraph.cmake
浏览文件 @ 8c757059
......@@ -32,6 +32,8 @@ IF(NOT ${WITH_NGRAPH})
return()
ENDIF()
INCLUDE(GNUInstallDirs)
INCLUDE(ExternalProject)
SET(NGRAPH_PROJECT "extern_ngraph")
......@@ -40,10 +42,14 @@ SET(NGRAPH_GIT_TAG "f9fd9d4cc318dc59dd4b68448e7fbb5f67a28bd0")
SET(NGRAPH_SOURCES_DIR ${THIRD_PARTY_PATH}/ngraph)
SET(NGRAPH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/ngraph)
SET(NGRAPH_INC_DIR ${NGRAPH_INSTALL_DIR}/include)
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR})
SET(NGRAPH_SHARED_LIB_NAME libngraph.so.${NGRAPH_VERSION})
SET(NGRAPH_CPU_LIB_NAME libcpu_backend.so)
SET(NGRAPH_TBB_LIB_NAME libtbb.so.2)
SET(NGRAPH_GIT_REPO "https://github.com/NervanaSystems/ngraph.git")
SET(NGRAPH_SHARED_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_SHARED_LIB_NAME})
SET(NGRAPH_CPU_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_CPU_LIB_NAME})
SET(NGRAPH_TBB_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_TBB_LIB_NAME})
ExternalProject_Add(
${NGRAPH_PROJECT}
......@@ -63,18 +69,6 @@ ExternalProject_Add(
CMAKE_ARGS -DMKLDNN_LIB_DIR=${MKLDNN_INSTALL_DIR}/lib
)
if(UNIX AND NOT APPLE)
include(GNUInstallDirs)
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR})
else()
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/lib)
endif()
MESSAGE(STATUS "nGraph lib will be installed at: ${NGRAPH_LIB_DIR}")
SET(NGRAPH_SHARED_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_SHARED_LIB_NAME})
SET(NGRAPH_CPU_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_CPU_LIB_NAME})
SET(NGRAPH_TBB_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_TBB_LIB_NAME})
# Workaround for nGraph expecting mklml to be in mkldnn install directory.
ExternalProject_Add_Step(
${NGRAPH_PROJECT}
......
cmake/inference_lib.cmake
浏览文件 @ 8c757059
......@@ -129,6 +129,15 @@ if (WITH_MKLDNN)
)
endif ()
if (WITH_NGRAPH)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/ngraph")
copy(ngraph_lib
SRCS ${NGRAPH_INC_DIR} ${NGRAPH_LIB_DIR}
DSTS ${dst_dir} ${dst_dir}
DEPS ngraph
)
endif ()
if (NOT WIN32)
if (NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
......
paddle/fluid/API.spec
浏览文件 @ 8c757059
......@@ -182,7 +182,7 @@ paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None,
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'ignore_index', 'name'], varargs=None, keywords=None, defaults=(-100, None))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -299,6 +299,7 @@ paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'i
paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 8c757059
......@@ -127,8 +127,9 @@ cc_library(version SRCS version.cc)
cc_test(version_test SRCS version_test.cc DEPS version)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto)
if(NOT WIN32)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto ngraph)
cc_library(ngraph_operator SRCS ngraph_operator.cc DEPS ngraph_bridge operator op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
endif(NOT WIN32)
......
paddle/fluid/framework/details/op_registry.h
浏览文件 @ 8c757059
......@@ -32,9 +32,7 @@ enum OpInfoFillType {
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2,
kVarTypeInference = 3,
kShapeInference = 4,
kEstimateFlops = 5,
kUnknown = -1
kShapeInference = 4
};
template <typename T>
......@@ -50,10 +48,8 @@ struct OpInfoFillTypeID {
? kVarTypeInference
: (std::is_base_of<InferShapeBase, T>::value
? kShapeInference
: (std::is_base_of<EstimateFlopsBase,
T>::value
? kEstimateFlops
: kUnknown)))));
: static_cast<OpInfoFillType>(
-1)))));
}
};
......@@ -143,16 +139,6 @@ struct OpInfoFiller<T, kShapeInference> {
}
};
template <typename T>
struct OpInfoFiller<T, kEstimateFlops> {
void operator()(const char* op_tpe, OpInfo* info) const {
info->estimate_flops_ = [](InferShapeContext* ctx) {
T estimate_flops;
return estimate_flops(ctx);
};
}
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/ir/is_test_pass.cc
浏览文件 @ 8c757059
......@@ -38,7 +38,7 @@ std::unique_ptr<ir::Graph> IsTestPass::ApplyImpl(
for (const Node* n : graph->Nodes()) {
if (n->IsOp()) {
auto* op = n->Op();
if (op->HasAttr("is_test")) {
if (n->RuntimeHasAttr("is_test")) {
op->SetAttr("is_test", true);
} else if (std::find(begin(op_list), end(op_list), op->Type()) !=
end(op_list)) {
......
paddle/fluid/framework/ir/is_test_pass_tester.cc
浏览文件 @ 8c757059
......@@ -104,9 +104,9 @@ TEST(IsTestPass, basic) {
auto* op = node->Op();
auto op_name = boost::get<std::string>(op->GetAttr("name"));
if (op_name == "conv3") {
ASSERT_FALSE(op->HasAttr("is_test"));
ASSERT_FALSE(node->RuntimeHasAttr("is_test"));
} else {
ASSERT_TRUE(op->HasAttr("is_test"));
ASSERT_TRUE(node->RuntimeHasAttr("is_test"));
EXPECT_TRUE(boost::get<bool>(op->GetAttr("is_test")));
}
}
......
paddle/fluid/framework/ir/mkldnn_placement_pass.cc
浏览文件 @ 8c757059
......@@ -22,7 +22,7 @@ std::unique_ptr<ir::Graph> MKLDNNPlacementPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Aplies MKL-DNN placement strategy.";
for (const Node* n : graph->Nodes()) {
if (n->IsOp() && n->Op()->HasAttr("use_mkldnn")) {
if (n->IsOp() && n->RuntimeHasAttr("use_mkldnn")) {
n->Op()->SetAttr("use_mkldnn", true);
}
}
......
paddle/fluid/framework/ir/node.cc
浏览文件 @ 8c757059
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_info.h"
namespace paddle {
namespace framework {
......@@ -24,10 +25,33 @@ constexpr char Node::kControlDepVarName[];
const char Node::kControlDepVarName[] = "__control_var";
#endif
std::unique_ptr<Node> CreateNodeForTest(const std::string& name,
std::unique_ptr<Node> CreateNodeForTest(const std::string &name,
Node::Type type) {
return std::unique_ptr<Node>(new Node(name, type));
}
bool Node::RuntimeHasAttr(const std::string &name) const {
if (Op()->HasAttr(name)) {
return true;
} else {
auto &op_info = OpInfoMap::Instance();
auto op_type = Op()->Type();
if (op_info.Has(op_type)) {
auto op_info_ptr = op_info.Get(op_type);
if (op_info_ptr.HasOpProtoAndChecker()) {
const proto::OpProto &proto = op_info_ptr.Proto();
for (int i = 0; i != proto.attrs_size(); ++i) {
const proto::OpProto::Attr &attr = proto.attrs(i);
if (attr.name() == name) {
return true;
}
}
}
}
}
return false;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ 8c757059
......@@ -108,6 +108,18 @@ class Node {
Name().find(ir::Node::kControlDepVarName) != std::string::npos;
}
// RuntimeHasAttr is different with HasAttr now.
// 1. For Op()->HasAttr(), it judges whether a stored program_desc_ has attr,
// thus, if stored program_desc_ are old which don't have an attr, a new
// library which adds the attr already will fail on this function.
// Details:
// https://github.com/PaddlePaddle/Paddle/pull/14608#issuecomment-442309087
// 2. For Op()->RuntimeHasAttr, it judges the attr in runtime to avoid above
// problem.
// TODO(luotao): Maybe we should enhance HasAttr later, instead of adding
// RuntimeHasAttr.
bool RuntimeHasAttr(const std::string& name) const;
std::vector<Node*> inputs;
std::vector<Node*> outputs;
......
paddle/fluid/framework/ngraph_bridge.cc
浏览文件 @ 8c757059
......@@ -15,23 +15,105 @@ limitations under the License. */
#ifdef PADDLE_WITH_NGRAPH
#include <algorithm>
#include <functional>
#include <vector>
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
#include "ngraph/ngraph.hpp"
namespace paddle {
namespace framework {
static std::shared_ptr<ngraph::Node> GetNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
const VariableNameMap& var_map,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = var_map.at(prm);
PADDLE_ENFORCE_EQ(var_names.size(), 1,
"op %s prm %s expects one associated var", op->Type(), prm);
if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
return (*ngb_node_map)[var_names[0]];
} else {
return nullptr;
}
}
static std::shared_ptr<ngraph::Node> GetInputNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Inputs(), ngb_node_map);
}
static std::shared_ptr<ngraph::Node> GetOutputNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Outputs(), ngb_node_map);
}
static void SetOutputNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
std::shared_ptr<ngraph::Node> node,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = op->Outputs().at(prm);
if (var_names.size() == 1) {
(*ngb_node_map)[var_names[0]] = node;
} else if (var_names.size() == 0) {
(*ngb_node_map)[""] = node;
} else {
PADDLE_THROW("prm %s has more than 1 var_names.", prm);
}
}
static bool HasOutput(const std::shared_ptr<OperatorBase>& op,
const std::string prm) {
auto& outputs = op->Outputs();
if (outputs.find(prm) == outputs.end()) return false;
return outputs.at(prm).size() > 0;
}
template <typename T>
static void BuildBinaryNode(
const std::shared_ptr<OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = GetInputNode(op, "X", ngb_node_map);
auto y = GetInputNode(op, "Y", ngb_node_map);
auto out = std::make_shared<T>(x, y);
SetOutputNode(op, "Out", out, ngb_node_map);
}
template <typename T>
static void BuildUnaryNode(
const std::shared_ptr<OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto input = GetInputNode(op, "X", ngb_node_map);
auto out = std::make_shared<T>(input);
SetOutputNode(op, "Out", out, ngb_node_map);
}
std::map<std::string,
std::function<void(const std::shared_ptr<OperatorBase>&,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {};
NgraphBridge::NG_NODE_MAP = {{"relu", BuildUnaryNode<ngraph::op::Relu>},
{"tanh", BuildUnaryNode<ngraph::op::Tanh>}};
void NgraphBridge::build_graph(const std::shared_ptr<OperatorBase>& op) {
void NgraphBridge::BuildNgNode(const std::shared_ptr<OperatorBase>& op) {
auto& op_type = op->Type();
NG_NODE_MAP[op_type](op, ngb_node_map);
NG_NODE_MAP[op_type](op, ngb_node_map_);
}
} // namespace framework
......
paddle/fluid/framework/ngraph_bridge.h
浏览文件 @ 8c757059
......@@ -20,16 +20,14 @@ limitations under the License. */
#include <map>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
#include "ngraph/ngraph.hpp"
#include "ngraph/node.hpp"
namespace paddle {
namespace framework {
class OperatorBase;
class NgraphBridge {
public:
static std::map<
......@@ -43,14 +41,14 @@ class NgraphBridge {
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
var_node_map)
: ngb_node_map(var_node_map) {}
: ngb_node_map_(var_node_map) {}
void build_graph(const std::shared_ptr<OperatorBase>& op);
void BuildNgNode(const std::shared_ptr<OperatorBase>& op);
private:
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map;
ngb_node_map_;
};
} // namespace framework
......
paddle/fluid/framework/ngraph_operator.cc
浏览文件 @ 8c757059
......@@ -19,14 +19,29 @@ limitations under the License. */
#include <map>
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/ngraph_operator.h"
#include "paddle/fluid/framework/shape_inference.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/framework/var_type.h"
#include "ngraph/ngraph.hpp"
namespace paddle {
namespace framework {
static ngraph::Shape Ddim2Shape(const DDim& dims) {
ngraph::Shape sp;
for (int i = 0; i < dims.size(); ++i) {
int k = dims[i];
k = k == 0 ? 1 : k;
sp.push_back(k);
}
return sp;
}
static std::map<proto::VarType::Type, ngraph::element::Type> pd2ng_type_map = {
{proto::VarType::FP32, ngraph::element::f32},
{proto::VarType::FP64, ngraph::element::f64},
......@@ -42,6 +57,7 @@ typedef enum { /* nGraph support state on ops */
PARTIAL_TEST /* Support partial list of ops for test */
} op_state;
// perform graph build through bridge and execute computation
class NgraphOperator {
public:
explicit NgraphOperator(const Scope& scope, const platform::Place& place,
......@@ -59,13 +75,23 @@ class NgraphOperator {
persistables_(persist),
fetches_(fetches),
post_op_inputs_(post_op_inputs),
ng_op_state_(ng_op_state) {}
ng_op_state_(ng_op_state) {
var_in_node_map_ = std::make_shared<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>();
var_node_map_ = std::make_shared<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>();
BuildNgIO();
GetNgFunction();
}
void Run(const Scope& scope, const platform::Place& place) const;
private:
static std::unordered_map<std::string, std::shared_ptr<ngraph::Function>>
func_cache;
func_cache_;
const Scope& scope_;
const platform::Place& place_;
std::vector<std::shared_ptr<OperatorBase>> fused_ops_;
......@@ -74,6 +100,35 @@ class NgraphOperator {
std::unordered_set<std::string> fetches_;
std::unordered_set<std::string> post_op_inputs_;
op_state ng_op_state_;
// ngraph backend eg. CPU
static std::shared_ptr<ngraph::runtime::Backend> backend_;
// ngraph function to call and execute
std::shared_ptr<ngraph::Function> ngraph_function_;
// var_name of inputs
std::vector<std::string> var_in_;
// var_name of outputs from fetch in order
std::vector<std::string> var_out_;
// map input vars to nodes
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
var_in_node_map_;
// map each var name with a ngraph node
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
var_node_map_;
// cache key to check if function is cached
std::shared_ptr<std::string> GetCacheKey();
// get ngraph input and define ngraph input parameters
void GetNgInputShape(std::shared_ptr<OperatorBase> op);
// Call ngraph bridge to map ops
void BuildNgNodes();
// get the ngraph input and output var list
void BuildNgIO();
// build ngraph function call
void BuildNgFunction();
// Check cache for ngraph function or otherwise build the function
void GetNgFunction();
};
std::vector<std::vector<std::vector<std::unique_ptr<OperatorBase>>::iterator>>
......@@ -86,7 +141,7 @@ FusedOperator::FusedOpIntervals(
}
size_t size = ops->size();
size_t left = 0;
while (left < size && ops.at(left)->Type() != kFeedOpType) {
while (left < size && ops->at(left)->Type() != kFeedOpType) {
++left;
}
if (left == size) {
......@@ -116,7 +171,7 @@ FusedOperator::FusedOpIntervals(
size_t start = pivot, end = start;
while (pivot < right &&
(paddle::framework::NgraphBridge::NG_NODE_MAP.find(
ops.at(pivot)->Type()) !=
ops->at(pivot)->Type()) !=
paddle::framework::NgraphBridge::NG_NODE_MAP.end())) {
++pivot;
++end;
......@@ -136,7 +191,9 @@ FusedOperator::FusedOperator(
std::vector<std::unique_ptr<OperatorBase>>::iterator end,
const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs), pdesc(prog), block(block_id) {
: OperatorBase(type, inputs, outputs, attrs),
pdesc_(prog),
block_(block_id) {
for (std::vector<std::unique_ptr<OperatorBase>>::iterator it = start;
it != end; ++it) {
fused_ops_.push_back(std::move(*it));
......@@ -152,7 +209,7 @@ FusedOperator::FusedOperator(
}
if ((*(start - 1))->Type() == kFeedOpType && (*end)->Type() == kFetchOpType) {
is_complete = true;
is_full_ = true;
}
Process();
......@@ -205,7 +262,7 @@ void FusedOperator::RunImpl(const Scope& scope,
}
}
if (is_full) {
if (is_full_) {
ng_op_state = ng_op_state == PARTIAL_TEST ? FULL_TEST : FULL_TRAIN;
}
......@@ -215,6 +272,280 @@ void FusedOperator::RunImpl(const Scope& scope,
ngraph_op.Run(scope, place);
}
std::unordered_map<std::string, std::shared_ptr<ngraph::Function>>
NgraphOperator::func_cache_ = {};
std::shared_ptr<ngraph::runtime::Backend> NgraphOperator::backend_ =
ngraph::runtime::Backend::create("CPU");
void NgraphOperator::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
op->RuntimeInferShape(scope_, place_);
for (auto& var_name_item : op->Inputs()) {
for (auto& var_name : var_name_item.second) {
auto* var = scope_.FindVar(var_name);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
auto sp = Ddim2Shape(tensor_pd->dims());
if (std::find(var_in_.begin(), var_in_.end(), var_name) !=
var_in_.end()) {
if (var_node_map_->find(var_name) == var_node_map_->end()) {
auto ng_type = var_type_map_.at(var_name);
auto prm =
std::make_shared<ngraph::op::Parameter>(ng_type, sp, true);
(*var_node_map_)[var_name] = prm;
(*var_in_node_map_)[var_name] = prm;
}
}
}
}
}
}
void NgraphOperator::BuildNgNodes() {
for (auto& var_name : var_out_) {
if (var_node_map_->find(var_name) == var_node_map_->end()) {
auto* var = scope_.FindVar(var_name);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
auto& ddim = tensor_pd->dims();
auto ng_shape = Ddim2Shape(ddim);
auto ng_type = var_type_map_.at(var_name);
auto prm =
std::make_shared<ngraph::op::Parameter>(ng_type, ng_shape, true);
(*var_node_map_)[var_name] = prm;
}
}
}
paddle::framework::NgraphBridge ngb(var_node_map_);
for (auto& op : fused_ops_) {
ngb.BuildNgNode(op);
}
}
void NgraphOperator::BuildNgIO() {
std::unordered_set<std::string> inputs;
std::unordered_set<std::string> outputs;
for (auto& op : fused_ops_) {
for (auto& var_name_item : op->Inputs()) {
for (auto& var_name : var_name_item.second) {
inputs.insert(var_name);
const bool is_output = outputs.find(var_name) != outputs.end();
if (!is_output &&
std::find(var_in_.begin(), var_in_.end(), var_name) ==
var_in_.end()) {
// fill var_in here to keep lhs and rhs order
var_in_.push_back(var_name);
}
}
}
if (op->Type() != "fill_constant") {
GetNgInputShape(op);
}
for (auto& var_name_item : op->Outputs()) {
PADDLE_ENFORCE_LE(var_name_item.second.size(), 1,
"op %s has more than 1 output - Not handling yet",
op->Type());
for (auto& var_name : var_name_item.second) {
outputs.insert(var_name);
}
}
}
// var_out.clear();
for (auto& op : fused_ops_) {
for (auto& var_name_item : op->Outputs()) {
PADDLE_ENFORCE_LE(var_name_item.second.size(), 1,
"op %s has more than 1 output - Not handling yet",
op->Type());
for (auto& var_name : var_name_item.second) {
switch (ng_op_state_) {
case PARTIAL_TEST:
if (post_op_inputs_.find(var_name) != post_op_inputs_.end() ||
fetches_.find(var_name) != fetches_.end()) {
var_out_.push_back(var_name);
}
break;
case FULL_TEST:
if (fetches_.find(var_name) != fetches_.end()) {
var_out_.push_back(var_name);
}
break;
case PARTIAL_TRAIN:
if (fetches_.find(var_name) != fetches_.end() ||
post_op_inputs_.find(var_name) != post_op_inputs_.end() ||
persistables_.find(var_name) != persistables_.end()) {
var_out_.push_back(var_name);
}
break;
case FULL_TRAIN:
if (fetches_.find(var_name) != fetches_.end() ||
persistables_.find(var_name) != persistables_.end()) {
var_out_.push_back(var_name);
}
break;
default:
var_out_.push_back(var_name);
}
}
}
}
}
void NgraphOperator::BuildNgFunction() {
BuildNgNodes();
ngraph_function_ = nullptr;
ngraph::NodeVector func_outputs;
ngraph::op::ParameterVector func_inputs;
for (auto& vo : var_out_) {
func_outputs.push_back(var_node_map_->at(vo));
}
for (auto& vi : var_in_) {
std::shared_ptr<ngraph::op::Parameter> prm =
std::dynamic_pointer_cast<ngraph::op::Parameter>(
var_in_node_map_->at(vi));
func_inputs.push_back(prm);
}
ngraph_function_ =
std::make_shared<ngraph::Function>(func_outputs, func_inputs);
}
std::shared_ptr<std::string> NgraphOperator::GetCacheKey() {
auto cache_key = std::make_shared<std::string>("");
*cache_key += std::to_string(fused_ops_.size());
for (auto& op : fused_ops_) {
*cache_key += op->Type();
}
for (auto& var_name : var_in_) {
auto shape = var_node_map_->at(var_name)->get_shape();
*cache_key += var_name;
*cache_key += var_type_map_.at(var_name).c_type_string();
for (size_t i = 0; i < shape.size(); ++i) {
*cache_key += std::to_string(shape.at(i));
}
}
for (auto& var_name : var_out_) {
auto* var = scope_.FindVar(var_name);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
auto& ddim = tensor_pd->dims();
for (int i = 0; i < ddim.size(); ++i) {
*cache_key += std::to_string(ddim[i]);
}
}
}
return cache_key;
}
void NgraphOperator::GetNgFunction() {
bool cache_on = true;
if (cache_on) {
std::string cache_key_val = *GetCacheKey();
if (func_cache_.find(cache_key_val) != func_cache_.end()) {
ngraph_function_ = func_cache_.at(cache_key_val);
} else {
BuildNgFunction();
func_cache_[cache_key_val] = ngraph_function_;
}
} else {
BuildNgFunction();
}
}
void NgraphOperator::Run(const Scope& scope,
const platform::Place& place) const {
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> t_in;
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> t_out;
for (size_t i = 0; i < var_in_.size(); ++i) {
auto vi = var_in_.at(i);
auto sp = var_node_map_->at(vi)->get_shape();
std::shared_ptr<ngraph::runtime::Tensor> ti;
auto* var = scope.FindVar(vi);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
PADDLE_ENFORCE(sp == Ddim2Shape(tensor_pd->dims()),
"Ensure ngraph tensor layout align with paddle tensor");
if (tensor_pd->type().hash_code() ==
typeid(float).hash_code()) { // NOLINT
const float* arr = tensor_pd->data<float>();
ti = backend_->create_tensor(ngraph::element::f32, sp,
const_cast<float*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(int).hash_code()) { // NOLINT
const int* arr = tensor_pd->data<int>();
ti = backend_->create_tensor(ngraph::element::i32, sp,
const_cast<int*>(arr));
} else if (tensor_pd->type().hash_code() == typeid(int64_t).hash_code()) {
const int64_t* arr = tensor_pd->data<int64_t>();
ti = backend_->create_tensor(ngraph::element::i64, sp,
const_cast<int64_t*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(double).hash_code()) { // NOLINT
const double* arr = tensor_pd->data<double>();
ti = backend_->create_tensor(ngraph::element::f64, sp,
const_cast<double*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(bool).hash_code()) { // NOLINT
const bool* arr = tensor_pd->data<bool>();
ti = backend_->create_tensor(ngraph::element::boolean, sp,
const_cast<bool*>(arr));
} else {
PADDLE_THROW("Data type not handling for var %s", vi);
}
} else {
PADDLE_THROW("Cannot find var or tensor with var name %s", vi);
}
bool is_test = (ng_op_state_ == PARTIAL_TEST || ng_op_state_ == FULL_TEST)
? true
: false;
bool is_persistable =
(persistables_.find(vi) != persistables_.end()) ? true : false;
if (is_test && is_persistable) {
ti->set_stale(false);
}
t_in.push_back(ti);
}
for (size_t i = 0; i < var_out_.size(); ++i) {
auto var_name = var_out_[i];
auto* var = scope.FindVar(var_name);
std::shared_ptr<ngraph::runtime::Tensor> to;
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetMutableLoDTensorOrSelectedRowsValueFromVar(var);
auto dd = tensor_pd->dims();
ngraph::Shape sp = Ddim2Shape(dd);
auto ng_type = var_type_map_.at(var_name);
if (ng_type == ngraph::element::f32) {
auto pd_arr = tensor_pd->mutable_data<float>(place);
to = backend_->create_tensor(ngraph::element::f32, sp, pd_arr);
} else if (ng_type == ngraph::element::i64) {
auto pd_arr = tensor_pd->mutable_data<int64_t>(place);
to = backend_->create_tensor(ngraph::element::i64, sp, pd_arr);
} else if (ng_type == ngraph::element::f64) {
auto pd_arr = tensor_pd->mutable_data<double>(place);
to = backend_->create_tensor(ngraph::element::f64, sp, pd_arr);
} else if (ng_type == ngraph::element::boolean) {
auto pd_arr = tensor_pd->mutable_data<bool>(place);
to = backend_->create_tensor(ngraph::element::boolean, sp, pd_arr);
} else {
PADDLE_THROW("Data type not handled in for var %s", var_name);
}
t_out.push_back(to);
} else {
PADDLE_THROW("Cannot find var or tensor with var name %s", var_name);
}
}
backend_->call(ngraph_function_, t_out, t_in);
} // NgraphOperator::RunImpl
} // namespace framework
} // namespace paddle
#endif
paddle/fluid/framework/ngraph_operator.h
浏览文件 @ 8c757059
......@@ -17,24 +17,19 @@ limitations under the License. */
#ifdef PADDLE_WITH_NGRAPH
#include <algorithm>
#include <atomic>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/variant.h"
#include "ngraph/ngraph.hpp"
#include "ngraph/type/element_type.hpp"
namespace paddle {
namespace framework {
......
paddle/fluid/framework/op_info.h
浏览文件 @ 8c757059
......@@ -31,12 +31,6 @@ class InferShapeBase {
virtual void operator()(InferShapeContext*) const = 0;
};
class EstimateFlopsBase {
public:
virtual ~EstimateFlopsBase() = default;
virtual size_t operator()(InferShapeContext*) const = 0;
};
struct OpInfo {
OpCreator creator_;
GradOpMakerFN grad_op_maker_;
......@@ -44,7 +38,6 @@ struct OpInfo {
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
EstimateFlopsFN estimate_flops_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
......
paddle/fluid/framework/operator.cc
浏览文件 @ 8c757059
......@@ -695,6 +695,12 @@ static void CheckTensorNANOrInf(const std::string& name,
"Tensor %s contains NAN", name);
}
void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
}
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
......
paddle/fluid/framework/operator.h
浏览文件 @ 8c757059
......@@ -128,6 +128,8 @@ class OperatorBase {
virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
virtual void RuntimeInferShape(const Scope& scope,
const platform::Place& place) const {}
protected:
std::string type_;
......@@ -348,6 +350,9 @@ class OperatorWithKernel : public OperatorBase {
OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
}
void RuntimeInferShape(const Scope& scope,
const platform::Place& place) const override;
protected:
virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
virtual OpKernelType GetKernelTypeForVar(
......
paddle/fluid/framework/type_defs.h
浏览文件 @ 8c757059
......@@ -54,7 +54,5 @@ using InferVarTypeFN =
using InferShapeFN = std::function<void(InferShapeContext*)>;
using EstimateFlopsFN = std::function<void(InferShapeContext*)>;
} // namespace framework
} // namespace paddle
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ 8c757059
......@@ -79,6 +79,16 @@ link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
link_directories("${PADDLE_LIB}/paddle/lib")
if (NOT WIN32)
set(NGRAPH_PATH "${PADDLE_LIB}/third_party/install/ngraph")
if(EXISTS ${NGRAPH_PATH})
include(GNUInstallDirs)
include_directories("${NGRAPH_PATH}/include")
link_directories("${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}")
set(NGRAPH_LIB ${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}/libngraph${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
endif()
add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
if(WITH_MKL)
......@@ -106,7 +116,7 @@ endif()
if (NOT WIN32)
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
${MATH_LIB} ${MKLDNN_LIB} ${NGRAPH_LIB}
glog gflags protobuf snappystream snappy z xxhash
${EXTERNAL_LIB})
else()
......
paddle/fluid/operators/sequence_ops/sequence_mask_op.h
浏览文件 @ 8c757059
......@@ -36,13 +36,11 @@ class SequenceMaskOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
auto maxlen = ctx->Attrs().Get<int>("maxlen");
if (maxlen > 0) { // We can only infershape when maxlen > 0
int maxlen = ctx->Attrs().Get<int>("maxlen");
auto dim = framework::vectorize2int(ctx->GetInputDim("X"));
dim.push_back(maxlen);
dim.push_back(maxlen > 0 ? maxlen : -1);
ctx->SetOutputDim("Y", framework::make_ddim(dim));
}
}
};
class SequenceMaskOpMaker : public framework::OpProtoAndCheckerMaker {
......
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
浏览文件 @ 8c757059
......@@ -18,6 +18,7 @@ namespace paddle {
namespace operators {
using framework::Tensor;
const int kIgnoreIndex = -100;
class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
public:
......@@ -100,6 +101,11 @@ class SigmoidCrossEntropyWithLogitsOpMaker
AddOutput("Out",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D "
" of elementwise logistic losses.");
AddAttr<int>("ignore_index",
"(int, default kIgnoreIndex), Specifies a target value that "
"is ignored and"
"does not contribute to the input gradient.")
.SetDefault(kIgnoreIndex);
AddComment(R"DOC(
SigmoidCrossEntropyWithLogits Operator.
......
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.h
浏览文件 @ 8c757059
......@@ -15,33 +15,72 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/hostdevice.h"
#include "paddle/legacy/utils/Logging.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
struct SigmoidCrossEntropyWithLogitsForward {
HOSTDEVICE SigmoidCrossEntropyWithLogitsForward(const int &ignore_index)
: ignore_index(ignore_index) {}
HOSTDEVICE T operator()(const T &x, const T &label) const {
if (static_cast<int>(label) == ignore_index) {
return static_cast<T>(0.);
}
T term1 = (x > 0) ? x : 0;
T term2 = x * label;
T term3 = std::log(static_cast<T>(1) + std::exp(-(std::abs(x))));
return term1 - term2 + term3;
}
int ignore_index;
};
template <typename T>
struct SigmoidCrossEntropyWithLogitsBackward {
HOSTDEVICE SigmoidCrossEntropyWithLogitsBackward(const int &ignore_index)
: ignore_index(ignore_index) {}
HOSTDEVICE T operator()(const T &x, const T &label) const {
if (static_cast<int>(label) == ignore_index) {
return static_cast<T>(0.);
}
T simoid_x = static_cast<T>(1) / (static_cast<T>(1) + std::exp(-x));
return simoid_x - label;
}
int ignore_index;
};
// Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
template <typename DeviceContext, typename T>
class SigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *X = context.Input<framework::Tensor>("X");
const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
framework::Tensor *Out = context.Output<framework::Tensor>("Out");
const Tensor *X = context.Input<Tensor>("X");
const Tensor *Labels = context.Input<Tensor>("Label");
Tensor *Out = context.Output<Tensor>("Out");
Out->mutable_data<T>(context.GetPlace());
int ignore_index = context.Attr<int>("ignore_index");
auto x = framework::EigenVector<T>::Flatten(*X);
auto labels = framework::EigenVector<T>::Flatten(*Labels);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto x = EigenVector<T>::Flatten(*X);
auto labels = EigenVector<T>::Flatten(*Labels);
auto out = EigenVector<T>::Flatten(*Out);
auto &place = *context.device_context<DeviceContext>().eigen_device();
// term1 = max(x, 0)
auto term1 = x.cwiseMax(static_cast<T>(0));
// term2 = x * labels
auto term2 = x * labels;
// term3 = log(1 + exp(-abs(x)))
auto term3 = (static_cast<T>(1) + (-(x.abs())).exp()).log();
out.device(place) = term1 - term2 + term3;
out.device(place) = x.binaryExpr(
labels, SigmoidCrossEntropyWithLogitsForward<T>(ignore_index));
}
};
......@@ -50,23 +89,23 @@ template <typename DeviceContext, typename T>
class SigmoidCrossEntropyWithLogitsGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *X = context.Input<framework::Tensor>("X");
const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
const framework::Tensor *dOut =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor *dX =
context.Output<framework::Tensor>(framework::GradVarName("X"));
const Tensor *X = context.Input<Tensor>("X");
const Tensor *Labels = context.Input<Tensor>("Label");
const Tensor *dOut = context.Input<Tensor>(framework::GradVarName("Out"));
Tensor *dX = context.Output<Tensor>(framework::GradVarName("X"));
dX->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(*X);
auto labels = framework::EigenVector<T>::Flatten(*Labels);
auto dout = framework::EigenVector<T>::Flatten(*dOut);
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto ignore_index = context.Attr<int>("ignore_index");
auto x = EigenVector<T>::Flatten(*X);
auto labels = EigenVector<T>::Flatten(*Labels);
auto dout = EigenVector<T>::Flatten(*dOut);
auto dx = EigenVector<T>::Flatten(*dX);
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
auto sigmoid_x = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
dx.device(place) = dout * (sigmoid_x - labels);
auto diff = x.binaryExpr(labels, SigmoidCrossEntropyWithLogitsBackward<T>(
static_cast<int>(ignore_index)));
dx.device(place) = dout * diff;
}
};
......
paddle/fluid/operators/yolov3_loss_op.cc 0 → 100644
浏览文件 @ 8c757059
/* 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.
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/fluid/operators/yolov3_loss_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class Yolov3LossOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("GTBox"),
"Input(GTBox) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("GTLabel"),
"Input(GTLabel) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Loss"),
"Output(Loss) of Yolov3LossOp should not be null.");
auto dim_x = ctx->GetInputDim("X");
auto dim_gtbox = ctx->GetInputDim("GTBox");
auto dim_gtlabel = ctx->GetInputDim("GTLabel");
auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
auto class_num = ctx->Attrs().Get<int>("class_num");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
"Input(X) dim[3] and dim[4] should be euqal.");
PADDLE_ENFORCE_EQ(dim_x[1], anchors.size() / 2 * (5 + class_num),
"Input(X) dim[1] should be equal to (anchor_number * (5 "
"+ class_num)).");
PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
"Input(GTBox) should be a 3-D tensor");
PADDLE_ENFORCE_EQ(dim_gtbox[2], 4, "Input(GTBox) dim[2] should be 5");
PADDLE_ENFORCE_EQ(dim_gtlabel.size(), 2,
"Input(GTBox) should be a 2-D tensor");
PADDLE_ENFORCE_EQ(dim_gtlabel[0], dim_gtbox[0],
"Input(GTBox) and Input(GTLabel) dim[0] should be same");
PADDLE_ENFORCE_EQ(dim_gtlabel[1], dim_gtbox[1],
"Input(GTBox) and Input(GTLabel) dim[1] should be same");
PADDLE_ENFORCE_GT(anchors.size(), 0,
"Attr(anchors) length should be greater then 0.");
PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
"Attr(anchors) length should be even integer.");
PADDLE_ENFORCE_GT(class_num, 0,
"Attr(class_num) should be an integer greater then 0.");
std::vector<int64_t> dim_out({1});
ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()),
platform::CPUPlace());
}
};
class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of YOLO v3 loss operator, "
"This is a 4-D tensor with shape of [N, C, H, W]."
"H and W should be same, and the second dimention(C) stores"
"box locations, confidence score and classification one-hot"
"key of each anchor box");
AddInput("GTBox",
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
"max_box_num is the max number of boxes in each image, "
"In the third dimention, stores x, y, w, h coordinates, "
"x, y is the center cordinate of boxes and w, h is the "
"width and height and x, y, w, h should be divided by "
"input image height to scale to [0, 1].");
AddInput("GTLabel",
"The input tensor of ground truth label, "
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element shoudl be an integer to indicate the "
"box class id.");
AddOutput("Loss",
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [1]");
AddAttr<int>("class_num", "The number of classes to predict.");
AddAttr<std::vector<int>>("anchors",
"The anchor width and height, "
"it will be parsed pair by pair.");
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.");
AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_wh", "The weight of w, h location loss.")
.SetDefault(1.0);
AddAttr<float>(
"loss_weight_conf_target",
"The weight of confidence score loss in locations with target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_conf_notarget",
"The weight of confidence score loss in locations without "
"target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_class", "The weight of classification loss.")
.SetDefault(1.0);
AddComment(R"DOC(
This operator generate yolov3 loss by given predict result and ground
truth boxes.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, specify the grid size, each grid point predict given
number boxes, this given number is specified by anchors, it should be
half anchors length, which following will be represented as S. In the
second dimention(the channel dimention), C should be S * (class_num + 5),
class_num is the box categoriy number of source dataset(such as coco),
so in the second dimention, stores 4 box location coordinates x, y, w, h
and confidence score of the box and class one-hot key of each anchor box.
While the 4 location coordinates if $$tx, ty, tw, th$$, the box predictions
correspnd to:
$$
b_x = \sigma(t_x) + c_x
b_y = \sigma(t_y) + c_y
b_w = p_w e^{t_w}
b_h = p_h e^{t_h}
$$
While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
is specified by anchors.
As for confidence score, it is the logistic regression value of IoU between
anchor boxes and ground truth boxes, the score of the anchor box which has
the max IoU should be 1, and if the anchor box has IoU bigger then ignore
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
confidence score loss, and classification loss. The MSE loss is used for
box location, and binary cross entropy loss is used for confidence score
loss and classification loss.
Final loss will be represented as follow.
$$
loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+ \loss_weight_{conf_target} * loss_{conf_target}
+ \loss_weight_{conf_notarget} * loss_{conf_notarget}
+ \loss_weight_{class} * loss_{class}
$$
)DOC");
}
};
class Yolov3LossOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
"Input(Loss@GRAD) should not be null");
auto dim_x = ctx->GetInputDim("X");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()),
platform::CPUPlace());
}
};
class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("yolov3_loss_grad");
op->SetInput("X", Input("X"));
op->SetInput("GTBox", Input("GTBox"));
op->SetInput("GTLabel", Input("GTLabel"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetAttrMap(Attrs());
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("GTBox"), {});
op->SetOutput(framework::GradVarName("GTLabel"), {});
return std::unique_ptr<framework::OpDesc>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(yolov3_loss, ops::Yolov3LossOp, ops::Yolov3LossOpMaker,
ops::Yolov3LossGradMaker);
REGISTER_OPERATOR(yolov3_loss_grad, ops::Yolov3LossOpGrad);
REGISTER_OP_CPU_KERNEL(yolov3_loss, ops::Yolov3LossKernel<float>,
ops::Yolov3LossKernel<double>);
REGISTER_OP_CPU_KERNEL(yolov3_loss_grad, ops::Yolov3LossGradKernel<float>,
ops::Yolov3LossGradKernel<double>);
paddle/fluid/operators/yolov3_loss_op.h 0 → 100644
浏览文件 @ 8c757059
此差异已折叠。
paddle/scripts/paddle_build.sh
浏览文件 @ 8c757059
......@@ -442,6 +442,8 @@ EOF
make install -j 8
if [ "$1" == "cp27-cp27m" ]; then
pip install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
set -e
python -c "import paddle.fluid"
elif [ "$1" == "cp35-cp35m" ]; then
pip3.5 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
elif [ "$1" == "cp36-cp36m" ]; then
......
python/paddle/fluid/clip.py
浏览文件 @ 8c757059
......@@ -134,12 +134,12 @@ class GradientClipByValue(BaseGradientClipAttr):
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
w_param_attrs = fluid.ParamAttr(name=None,
initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
regularizer=fluid.regularizer.L1Decay(1.0),
trainable=True,
clip=GradientClipByValue(-1.0, 1.0))
clip=fluid.clip.GradientClipByValue(-1.0, 1.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
......@@ -185,12 +185,12 @@ class GradientClipByNorm(BaseGradientClipAttr):
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
w_param_attrs = flui.ParamAttr(name=None,
initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
regularizer=fluid.regularizer.L1Decay(1.0),
trainable=True,
clip=GradientClipByNorm(clip_norm=2.0))
clip=fluid.clip.GradientClipByNorm(clip_norm=2.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
......
python/paddle/fluid/executor.py
浏览文件 @ 8c757059
......@@ -20,7 +20,7 @@ import six
from .framework import Program, default_main_program, Variable
from . import core
__all__ = ['Executor', 'global_scope', 'scope_guard', '_switch_scope']
__all__ = ['Executor', 'global_scope', 'scope_guard']
g_scope = core.Scope()
......@@ -407,16 +407,17 @@ class Executor(object):
Examples:
>>> data = layers.data(name='X', shape=[1], dtype='float32')
>>> hidden = layers.fc(input=data, size=10)
>>> layers.assign(hidden, out)
>>> loss = layers.mean(out)
>>> data = fluid.layers.data(name='X', shape=[1], dtype='float32')
>>> out = fluid.layers.create_tensor(dtype='float32')
>>> hidden = fluid.layers.fc(input=data, size=10)
>>> fluid.layers.assign(hidden,out)
>>> loss = fluid.layers.mean(out)
>>> adam = fluid.optimizer.Adam()
>>> adam.minimize(loss)
>>> cpu = core.CPUPlace()
>>> exe = Executor(cpu)
>>> exe.run(default_startup_program())
>>> exe = fluid.Executor(cpu)
>>> exe.run(fluid.default_startup_program())
>>> x = numpy.random.random(size=(10, 1)).astype('float32')
>>> outs = exe.run(
......
python/paddle/fluid/framework.py
浏览文件 @ 8c757059
......@@ -89,6 +89,7 @@ def name_scope(prefix=None):
Examples:
.. code-block:: python
with name_scope("encoder"):
...
with name_scope("decoder"):
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 8c757059
......@@ -20,6 +20,7 @@ from __future__ import print_function
from .layer_function_generator import generate_layer_fn
from .layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
from ..framework import Variable
from . import tensor
from . import nn
from . import ops
......@@ -46,6 +47,7 @@ __all__ = [
'iou_similarity',
'box_coder',
'polygon_box_transform',
'yolov3_loss',
]
......@@ -401,6 +403,113 @@ def polygon_box_transform(input, name=None):
return output
@templatedoc(op_type="yolov3_loss")
def yolov3_loss(x,
gtbox,
gtlabel,
anchors,
class_num,
ignore_thresh,
loss_weight_xy=None,
loss_weight_wh=None,
loss_weight_conf_target=None,
loss_weight_conf_notarget=None,
loss_weight_class=None,
name=None):
"""
${comment}
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
in the third dimenstion, x, y, w, h should be stored
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be ins shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
loss_weight_xy (float|None): ${loss_weight_xy_comment}
loss_weight_wh (float|None): ${loss_weight_wh_comment}
loss_weight_conf_target (float|None): ${loss_weight_conf_target_comment}
loss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget_comment}
loss_weight_class (float|None): ${loss_weight_class_comment}
name (string): the name of yolov3 loss
Returns:
Variable: A 1-D tensor with shape [1], the value of yolov3 loss
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable"
TypeError: Input gtlabel of yolov3_loss must be Variable"
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
anchors=anchors, ignore_thresh=0.5)
"""
helper = LayerHelper('yolov3_loss', **locals())
if not isinstance(x, Variable):
raise TypeError("Input x of yolov3_loss must be Variable")
if not isinstance(gtbox, Variable):
raise TypeError("Input gtbox of yolov3_loss must be Variable")
if not isinstance(gtlabel, Variable):
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
if not isinstance(class_num, int):
raise TypeError("Attr class_num of yolov3_loss must be an integer")
if not isinstance(ignore_thresh, float):
raise TypeError(
"Attr ignore_thresh of yolov3_loss must be a float number")
if name is None:
loss = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
loss = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
attrs = {
"anchors": anchors,
"class_num": class_num,
"ignore_thresh": ignore_thresh,
}
if loss_weight_xy is not None and isinstance(loss_weight_xy, float):
self.attrs['loss_weight_xy'] = loss_weight_xy
if loss_weight_wh is not None and isinstance(loss_weight_wh, float):
self.attrs['loss_weight_wh'] = loss_weight_wh
if loss_weight_conf_target is not None and isinstance(
loss_weight_conf_target, float):
self.attrs['loss_weight_conf_target'] = loss_weight_conf_target
if loss_weight_conf_notarget is not None and isinstance(
loss_weight_conf_notarget, float):
self.attrs['loss_weight_conf_notarget'] = loss_weight_conf_notarget
if loss_weight_class is not None and isinstance(loss_weight_class, float):
self.attrs['loss_weight_class'] = loss_weight_class
helper.append_op(
type='yolov3_loss',
inputs={"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel},
outputs={'Loss': loss},
attrs=attrs)
return loss
@templatedoc()
def detection_map(detect_res,
label,
......
python/paddle/fluid/layers/io.py
浏览文件 @ 8c757059
......@@ -943,7 +943,18 @@ def __create_unshared_decorated_reader__(op_type, reader, attrs, name=None):
def shuffle(reader, buffer_size):
"""
Shuffle the reader.
Creates a data reader whose data output is shuffled.
Output from the iterator that created by original reader will be
buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
is determined by argument buf_size.
Args:
param reader: the original reader whose output will be shuffled.
type reader: callable
param buf_size: shuffle buffer size.
type buf_size: int
return: the new reader whose output is shuffled.
rtype: callable
"""
return __create_unshared_decorated_reader__(
'create_shuffle_reader', reader, {'buffer_size': int(buffer_size)})
......
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ 8c757059
......@@ -308,14 +308,10 @@ def piecewise_decay(boundaries, values):
def append_LARS(params_grads, learning_rate, weight_decay):
"""Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
"""
Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
each layer.
```python
learning_rate *= local_gw_ratio * sqrt(sumsq(param))
/ (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
```
Args:
learning_rate: A learning rate Variable. This
is the global learning rate for LARS.
......@@ -323,6 +319,11 @@ def append_LARS(params_grads, learning_rate, weight_decay):
Returns:
The decayed learning rate
Examples:
.. code-block:: python
learning_rate *= local_gw_ratio * sqrt(sumsq(param))
/ (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
"""
def _balanced_weight(param_norm, grad_norm):
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 8c757059
......@@ -172,6 +172,8 @@ __all__ = [
'lstm',
]
kIgnoreIndex = -100
def fc(input,
size,
......@@ -926,7 +928,7 @@ def dynamic_gru(input,
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim = 512
x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)
"""
helper = LayerHelper('gru', **locals())
......@@ -1267,7 +1269,7 @@ def dropout(x,
return out
def cross_entropy(input, label, soft_label=False, ignore_index=-100):
def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
"""
**Cross Entropy Layer**
......@@ -1314,7 +1316,7 @@ def cross_entropy(input, label, soft_label=False, ignore_index=-100):
labels. Default: `False`.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
if soft_label is set to False. Default: kIgnoreIndex
Returns:
A 2-D tensor with shape [N x 1], the cross entropy loss.
......@@ -3584,6 +3586,7 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
Examples:
.. code-block:: python
# Suppose `ids` and `scores` are LodTensorArray variables reserving
# the selected ids and scores of all steps
finished_ids, finished_scores = layers.beam_search_decode(
......@@ -5185,7 +5188,7 @@ def multiplex(inputs, index):
def softmax_with_cross_entropy(logits,
label,
soft_label=False,
ignore_index=-100,
ignore_index=kIgnoreIndex,
numeric_stable_mode=False,
return_softmax=False):
"""
......@@ -5243,7 +5246,7 @@ def softmax_with_cross_entropy(logits,
labels as soft labels. By default, `soft_label` is set to False.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
if soft_label is set to False. Default: kIgnoreIndex
numeric_stable_mode (bool): A flag to indicate whether to use a more
numerically stable algorithm. Only valid
when soft_label is False and GPU is used.
......@@ -5868,7 +5871,6 @@ def pad_constant_like(x, y, pad_value=0., name=None):
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
......@@ -6124,6 +6126,7 @@ def image_resize(input,
Supporting resample methods:
'BILINEAR' : Bilinear interpolation
'NEAREST' : Nearest neighbor interpolation
Args:
......@@ -6779,7 +6782,7 @@ def crop(x, shape=None, offsets=None, name=None):
# or
z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
crop = fluid.layers.crop(z, shape=[2, 3])
crop = fluid.layers.crop(z, shape=[-1, 2, 3])
"""
helper = LayerHelper('crop', **locals())
......@@ -7060,6 +7063,7 @@ def pad2d(input,
than height-1. And the width dimension has the same condition.
Example:
.. code-block:: text
Given that X is a channel of image from input:
......@@ -8415,13 +8419,17 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
@templatedoc()
def sigmoid_cross_entropy_with_logits(x, label, name=None):
def sigmoid_cross_entropy_with_logits(x,
label,
ignore_index=kIgnoreIndex,
name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
label(${label_type}): ${label_comment}
ignore_index(&{ignore_index}): ${ignore_index_comment}
name(basestring|None): Name of the output.
Returns:
......@@ -8440,7 +8448,7 @@ def sigmoid_cross_entropy_with_logits(x, label, name=None):
type="sigmoid_cross_entropy_with_logits",
inputs={"X": x,
"Label": label},
attrs={},
attrs={"ignore_index": ignore_index},
outputs={"Out": out})
return out
......
python/paddle/fluid/metrics.py
浏览文件 @ 8c757059
......@@ -451,6 +451,7 @@ class EditDistance(MetricBase):
In the above example:
'distance' is the average of the edit distance in a pass.
'instance_error' is the instance error rate in a pass.
"""
......
python/paddle/fluid/param_attr.py
浏览文件 @ 8c757059
......@@ -50,8 +50,9 @@ class ParamAttr(object):
w_param_attrs = fluid.ParamAttr(name="fc_weight",
learning_rate=0.5,
regularizer=fluid.L2Decay(1.0),
regularizer=fluid.regularizer.L2Decay(1.0),
trainable=True)
x = fluid.layers.data(name='X', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
"""
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 8c757059
......@@ -388,5 +388,18 @@ class TestGenerateProposals(unittest.TestCase):
print(rpn_rois.shape)
class TestYoloDetection(unittest.TestCase):
def test_yolov3_loss(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13], 10,
0.5)
self.assertIsNotNone(loss)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 8c757059
......@@ -170,9 +170,10 @@ class TestBook(unittest.TestCase):
with program_guard(program):
dat = layers.data(name='data', shape=[10], dtype='float32')
lbl = layers.data(name='label', shape=[10], dtype='float32')
ignore_index = -1
self.assertIsNotNone(
layers.sigmoid_cross_entropy_with_logits(
x=dat, label=lbl))
x=dat, label=lbl, ignore_index=ignore_index))
print(str(program))
def test_hsigmoid(self):
......
python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
浏览文件 @ 8c757059
......@@ -56,6 +56,40 @@ class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
batch_size = 64
num_classes = 20
ignore_index = -1
self.inputs = {
'X': logit(
np.random.uniform(0, 1, (batch_size, num_classes))
.astype("float32")),
'Label': np.random.randint(-1, 2, (batch_size, num_classes))
.astype("float32")
}
self.attrs = {'ignore_index': ignore_index, }
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X = expit(self.inputs['X'])
term1 = self.inputs['Label'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
out = -term1 - term2
out[np.where(self.inputs['Label'] == ignore_index)] = 0
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSigmoidCrossEntropyWithLogitsOp3(OpTest):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
batch_size = 64
......
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py 0 → 100644
浏览文件 @ 8c757059
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import division
import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid import core
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
def mse(x, y, num):
return ((y - x)**2).sum() / num
def bce(x, y, mask):
x = x.reshape((-1))
y = y.reshape((-1))
mask = mask.reshape((-1))
error_sum = 0.0
count = 0
for i in range(x.shape[0]):
if mask[i] > 0:
error_sum += y[i] * np.log(x[i]) + (1 - y[i]) * np.log(1 - x[i])
count += 1
return error_sum / (-1.0 * count)
def box_iou(box1, box2):
b1_x1 = box1[0] - box1[2] / 2
b1_x2 = box1[0] + box1[2] / 2
b1_y1 = box1[1] - box1[3] / 2
b1_y2 = box1[1] + box1[3] / 2
b2_x1 = box2[0] - box2[2] / 2
b2_x2 = box2[0] + box2[2] / 2
b2_y1 = box2[1] - box2[3] / 2
b2_y2 = box2[1] + box2[3] / 2
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
inter_rect_x1 = max(b1_x1, b2_x1)
inter_rect_y1 = max(b1_y1, b2_y1)
inter_rect_x2 = min(b1_x2, b2_x2)
inter_rect_y2 = min(b1_y2, b2_y2)
inter_area = max(inter_rect_x2 - inter_rect_x1, 0) * max(
inter_rect_y2 - inter_rect_y1, 0)
return inter_area / (b1_area + b2_area + inter_area)
def build_target(gtboxs, gtlabel, attrs, grid_size):
n, b, _ = gtboxs.shape
ignore_thresh = attrs["ignore_thresh"]
anchors = attrs["anchors"]
class_num = attrs["class_num"]
an_num = len(anchors) // 2
obj_mask = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
noobj_mask = np.ones((n, an_num, grid_size, grid_size)).astype('float32')
tx = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
ty = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tw = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
th = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tconf = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tcls = np.zeros(
(n, an_num, grid_size, grid_size, class_num)).astype('float32')
for i in range(n):
for j in range(b):
if gtboxs[i, j, :].sum() == 0:
continue
gt_label = gtlabel[i, j]
gx = gtboxs[i, j, 0] * grid_size
gy = gtboxs[i, j, 1] * grid_size
gw = gtboxs[i, j, 2] * grid_size
gh = gtboxs[i, j, 3] * grid_size
gi = int(gx)
gj = int(gy)
gtbox = [0, 0, gw, gh]
max_iou = 0
for k in range(an_num):
anchor_box = [0, 0, anchors[2 * k], anchors[2 * k + 1]]
iou = box_iou(gtbox, anchor_box)
if iou > max_iou:
max_iou = iou
best_an_index = k
if iou > ignore_thresh:
noobj_mask[i, best_an_index, gj, gi] = 0
obj_mask[i, best_an_index, gj, gi] = 1
noobj_mask[i, best_an_index, gj, gi] = 0
tx[i, best_an_index, gj, gi] = gx - gi
ty[i, best_an_index, gj, gi] = gy - gj
tw[i, best_an_index, gj, gi] = np.log(gw / anchors[2 *
best_an_index])
th[i, best_an_index, gj, gi] = np.log(
gh / anchors[2 * best_an_index + 1])
tconf[i, best_an_index, gj, gi] = 1
tcls[i, best_an_index, gj, gi, gt_label] = 1
return (tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask)
def YoloV3Loss(x, gtbox, gtlabel, attrs):
n, c, h, w = x.shape
an_num = len(attrs['anchors']) // 2
class_num = attrs["class_num"]
x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
pred_x = sigmoid(x[:, :, :, :, 0])
pred_y = sigmoid(x[:, :, :, :, 1])
pred_w = x[:, :, :, :, 2]
pred_h = x[:, :, :, :, 3]
pred_conf = sigmoid(x[:, :, :, :, 4])
pred_cls = sigmoid(x[:, :, :, :, 5:])
tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
gtbox, gtlabel, attrs, x.shape[2])
obj_mask_expand = np.tile(
np.expand_dims(obj_mask, 4), (1, 1, 1, 1, int(attrs['class_num'])))
loss_x = mse(pred_x * obj_mask, tx * obj_mask, obj_mask.sum())
loss_y = mse(pred_y * obj_mask, ty * obj_mask, obj_mask.sum())
loss_w = mse(pred_w * obj_mask, tw * obj_mask, obj_mask.sum())
loss_h = mse(pred_h * obj_mask, th * obj_mask, obj_mask.sum())
loss_conf_target = bce(pred_conf * obj_mask, tconf * obj_mask, obj_mask)
loss_conf_notarget = bce(pred_conf * noobj_mask, tconf * noobj_mask,
noobj_mask)
loss_class = bce(pred_cls * obj_mask_expand, tcls * obj_mask_expand,
obj_mask_expand)
return attrs['loss_weight_xy'] * (loss_x + loss_y) \
+ attrs['loss_weight_wh'] * (loss_w + loss_h) \
+ attrs['loss_weight_conf_target'] * loss_conf_target \
+ attrs['loss_weight_conf_notarget'] * loss_conf_notarget \
+ attrs['loss_weight_class'] * loss_class
class TestYolov3LossOp(OpTest):
def setUp(self):
self.loss_weight_xy = 1.0
self.loss_weight_wh = 1.0
self.loss_weight_conf_target = 1.0
self.loss_weight_conf_notarget = 1.0
self.loss_weight_class = 1.0
self.initTestCase()
self.op_type = 'yolov3_loss'
x = np.random.random(size=self.x_shape).astype('float32')
gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
gtlabel = np.random.randint(0, self.class_num,
self.gtbox_shape[:2]).astype('int32')
self.attrs = {
"anchors": self.anchors,
"class_num": self.class_num,
"ignore_thresh": self.ignore_thresh,
"loss_weight_xy": self.loss_weight_xy,
"loss_weight_wh": self.loss_weight_wh,
"loss_weight_conf_target": self.loss_weight_conf_target,
"loss_weight_conf_notarget": self.loss_weight_conf_notarget,
"loss_weight_class": self.loss_weight_class,
}
self.inputs = {'X': x, 'GTBox': gtbox, 'GTLabel': gtlabel}
self.outputs = {
'Loss': np.array(
[YoloV3Loss(x, gtbox, gtlabel, self.attrs)]).astype('float32')
}
def test_check_output(self):
place = core.CPUPlace()
self.check_output_with_place(place, atol=1e-3)
def test_check_grad_ignore_gtbox(self):
place = core.CPUPlace()
self.check_grad_with_place(
place, ['X'],
'Loss',
no_grad_set=set(["GTBox", "GTLabel"]),
max_relative_error=0.06)
def initTestCase(self):
self.anchors = [10, 13, 12, 12]
self.class_num = 10
self.ignore_thresh = 0.5
self.x_shape = (5, len(self.anchors) // 2 * (5 + self.class_num), 7, 7)
self.gtbox_shape = (5, 10, 4)
self.loss_weight_xy = 2.5
self.loss_weight_wh = 0.8
self.loss_weight_conf_target = 1.5
self.loss_weight_conf_notarget = 0.5
self.loss_weight_class = 1.2
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 8c757059
......@@ -125,6 +125,7 @@ def slice_variable(var_list, slice_count, min_block_size):
class DistributeTranspilerConfig(object):
"""
Args:
slice_var_up (bool): Do Tensor slice for pservers, default is True.
split_method (PSDispatcher): RoundRobin or HashName can be used
try to choose the best method to balance loads for pservers.
......
python/setup.py.in
浏览文件 @ 8c757059
......@@ -165,9 +165,9 @@ if '${WITH_MKL}' == 'ON':
shutil.copy('${MKLML_LIB}', libs_path)
shutil.copy('${MKLML_IOMP_LIB}', libs_path)
package_data['paddle.libs']+=['libmklml_intel' + ext_name,'libiomp5' + ext_name]
if '${CMAKE_BUILD_TYPE}' == 'Release':
if '${WITH_MKLDNN}' == 'ON':
if '${CMAKE_BUILD_TYPE}' == 'Release':
# only change rpath in Release mode.
if '${WITH_MKLDNN}' == 'ON':
# TODO(typhoonzero): use install_name_tool to patch mkl libs once
# we can support mkl on mac.
#
......@@ -180,8 +180,13 @@ if '${CMAKE_BUILD_TYPE}' == 'Release':
package_data['paddle.libs']+=['libmkldnn.so.0']
shutil.copy('${MKLDNN_SHARED_LIB}', libs_path)
if '${WITH_NGRAPH}' == 'ON':
# only change rpath in Release mode,
# since in Debug mode, nGraph lib may be too large to be changed?
if '${CMAKE_BUILD_TYPE}' == 'Release':
# only change rpath in Release mode.
if os.name != 'nt':
if "@APPLE@" == "1":
command = "install_name_tool -id \"@loader_path/\" ${NGRAPH_SHARED_LIB}"
else:
command = "patchelf --set-rpath '$ORIGIN/' ${NGRAPH_SHARED_LIB}"
if os.system(command) != 0:
raise Exception("patch ${NGRAPH_SHARED_LIB_NAME} failed, command: %s" % command)
......
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