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PaddleDetection

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PaddleDetection
提交 201d4f2a 编写于 作者: P phlrain
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_dropout_att_new

上级 a6e6bc45 e906c8e5
隐藏空白更改
内联 并排
Showing with 193 addition and 286 deletion
paddle/fluid/framework/framework.proto
浏览文件 @ 201d4f2a
......@@ -80,7 +80,6 @@ message OpProto {
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool dispensable = 5 [ default = false ];
optional string reuse = 6;
}
// AttrProto describes the C++ type Attribute.
......
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ 201d4f2a
......@@ -21,7 +21,6 @@ namespace framework {
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
CheckReuseVars();
}
OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
......@@ -40,40 +39,6 @@ OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
return OpProtoAndCheckerMaker::VariableBuilder{output};
}
void OpProtoAndCheckerMaker::Reuse(const std::string& name,
const std::string& reused_name) {
bool found = false;
proto::OpProto::Var* var;
for (auto& var : proto_->inputs()) {
if (var.name() == reused_name) {
found = true;
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
found = false;
for (int i = 0; i < proto_->outputs().size(); ++i) {
var = proto_->mutable_outputs()->Mutable(i);
if (var->name() == name) {
PADDLE_ENFORCE(!var->has_reuse(),
"Output(%s) has been set reused var of %s", name,
var->reuse());
found = true;
var->set_reuse(reused_name);
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
}
void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
auto checker = [&](const std::string& name) {
......@@ -91,24 +56,6 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
}
}
void OpProtoAndCheckerMaker::CheckReuseVars() {
std::unordered_set<std::string> names;
for (auto& input : proto_->inputs()) {
names.insert(input.name());
}
auto checker = [&](const std::string& name, const std::string& reused) {
PADDLE_ENFORCE(
names.count(reused),
"Output [%s] reuse Input [%s], but the input is not registered.", name,
reused);
};
for (auto& output : proto_->outputs()) {
if (output.has_reuse()) {
checker(output.name(), output.reuse());
}
}
}
void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
OpAttrChecker* attr_checker) {
proto_ = proto;
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ 201d4f2a
......@@ -14,8 +14,6 @@ limitations under the License. */
#pragma once
#include <string>
#include <unordered_set>
#include "glog/logging.h"
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
......@@ -73,11 +71,6 @@ class OpProtoAndCheckerMaker {
var_->set_dispensable(true);
return *this;
}
VariableBuilder &Reuse(const std::string &name) {
var_->set_reuse(name);
return *this;
}
};
VariableBuilder AddInput(const std::string &name, const std::string &comment);
......@@ -85,8 +78,6 @@ class OpProtoAndCheckerMaker {
VariableBuilder AddOutput(const std::string &name,
const std::string &comment);
void Reuse(const std::string &name, const std::string &reused_name);
template <typename T>
TypedAttrChecker<T> &AddAttr(const std::string &name,
const std::string &comment,
......@@ -105,8 +96,6 @@ class OpProtoAndCheckerMaker {
void CheckNoDuplicatedInOutAttrs();
void Validate();
void CheckReuseVars();
proto::OpProto *proto_;
OpAttrChecker *op_checker_;
bool validated_{false};
......
paddle/fluid/framework/op_proto_maker_test.cc
浏览文件 @ 201d4f2a
......@@ -47,120 +47,3 @@ TEST(ProtoMaker, DuplicatedInOut) {
ASSERT_THROW(proto_maker(&op_proto, &op_checker),
paddle::platform::EnforceNotMet);
}
class TestInplaceProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
}
};
class TestInplaceProtoMaker2
: public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
AddOutput("NoOut", "output of test op").Reuse("NotExists");
}
};
TEST(ProtoMaker, InplaceOutput) {
paddle::framework::proto::OpProto op_proto, op_proto2;
paddle::framework::OpAttrChecker op_checker;
TestInplaceProtoMaker proto_maker;
TestInplaceProtoMaker2 proto_maker2;
proto_maker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
}
// normal reuse
class TestReuseProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddInput("Y", "input of test op");
AddOutput("Out", "output of test op");
AddOutput("XOut", "output of test op");
// avoid destructor exception.
// Validate();
TestReuse();
}
virtual void TestReuse() {}
};
// test duplicate reuse error
class TestReuseProtoMaker2 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "X");
Reuse("Out", "Y");
}
};
// NotExists Input
class TestReuseProtoMaker3 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "NotExists");
Reuse("XOut", "X");
}
};
// NotExists Output
class TestReuseProtoMaker4 : public TestReuseProtoMaker {
public:
void TestReuse() { Reuse("NotExists", "X"); }
};
TEST(ProtoMaker, Reuse) {
paddle::framework::proto::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker proto_maker;
proto_maker(&op_proto, &op_checker);
}
// NOTE(dzhwinter):
// There is a Fatal CHECK on base class destructor, which will call abort inside
// instead of
// throw an exception. If we throw an exception in Make(), we will trigger the
// CHECK and terminate the tests.
//
// I had tried to replace the default CHECK with a exception, however, it's
// still not supported by glog.
// the details:
// https://github.com/google/glog/issues/249
// https://github.com/facebookresearch/TensorComprehensions/issues/351
/*
TEST(ProtoMaker, ReuseWithException) {
paddle::framework::proto::OpProto op_proto2, op_proto3, op_proto4;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker2 proto_maker2;
TestReuseProtoMaker3 proto_maker3;
TestReuseProtoMaker4 proto_maker4;
EXPECT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker3(&op_proto3, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker4(&op_proto4, &op_checker),
paddle::platform::EnforceNotMet);
}
void FailureFunction() {
throw std::runtime_error("Check failed in destructor.");
// return 0;
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
google::InstallFailureFunction(&FailureFunction);
return RUN_ALL_TESTS();
}
*/
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 201d4f2a
......@@ -156,12 +156,10 @@ ParallelExecutor::ParallelExecutor(
params, member_->local_scopes_, member_->use_cuda_);
#endif
if (VLOG_IS_ON(5)) {
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
......
paddle/fluid/inference/api/demo_ci/run.sh
浏览文件 @ 201d4f2a
......@@ -21,7 +21,7 @@ else
fi
USE_TENSORRT=OFF
if [ [-d"$TENSORRT_INCLUDE_DIR"] -a [-d"$TENSORRT_LIB_DIR"] ]; then
if [ -d "$TENSORRT_INCLUDE_DIR" -a -d "$TENSORRT_LIB_DIR" ]; then
USE_TENSORRT=ON
fi
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 201d4f2a
......@@ -28,7 +28,7 @@ using paddle::framework::Tensor;
public: \
void Make() override { \
AddInput("X", "Input of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X"); \
AddOutput("Out", "Output of " #OP_NAME " operator"); \
AddAttr<bool>("use_mkldnn", \
"(bool, default false) Only used in mkldnn kernel") \
.SetDefault(false); \
......
paddle/fluid/operators/adam_op.cc
浏览文件 @ 201d4f2a
......@@ -92,9 +92,9 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
AddOutput("ParamOut", "(Tensor) Output parameter").Reuse("Param");
AddOutput("Moment1Out", "(Tensor) Output first moment").Reuse("Moment1");
AddOutput("Moment2Out", "(Tensor) Output second moment").Reuse("Moment2");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddOutput("Moment1Out", "(Tensor) Output first moment");
AddOutput("Moment2Out", "(Tensor) Output second moment");
AddAttr<float>("beta1",
"(float, default 0.9) "
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 201d4f2a
......@@ -135,15 +135,13 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Variance",
"The global variance (for training) "
"or estimated Variance (for testing)");
AddOutput("Y", "result after normalization").Reuse("X");
AddOutput("Y", "result after normalization");
AddOutput("MeanOut",
"Share memory with Mean. "
"Store the global mean when training")
.Reuse("Mean");
"Store the global mean when training");
AddOutput("VarianceOut",
"Share memory with Variance. "
"Store the global Variance when training")
.Reuse("Variance");
"Store the global Variance when training");
AddOutput("SavedMean",
"Mean of the current mini batch, "
"will apply to output when training")
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 201d4f2a
......@@ -130,8 +130,7 @@ void Conv2DOpMaker::Make() {
.AsDispensable();
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.")
.Reuse("Input");
"The format of output tensor is also NCHW.");
AddInput("ResidualData",
"(Tensor) Tensor with residual data "
"to which convolution output will be added."
......@@ -238,8 +237,7 @@ void Conv3DOpMaker::Make() {
"input image channels divided by the groups.");
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.")
.Reuse("Input");
"The format of output tensor is also NCDHW.");
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ 201d4f2a
......@@ -52,6 +52,9 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(
ctx->HasOutput("TargetBBox"),
"Output(TargetBBox) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BBoxInsideWeight"),
"Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
auto anchor_dims = ctx->GetInputDim("Anchor");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
......@@ -68,6 +71,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ScoreIndex", {-1});
ctx->SetOutputDim("TargetLabel", {-1, 1});
ctx->SetOutputDim("TargetBBox", {-1, 4});
ctx->SetOutputDim("BBoxInsideWeight", {-1, 4});
}
protected:
......@@ -169,6 +173,7 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
const float rpn_positive_overlap,
const float rpn_negative_overlap, std::vector<int>* fg_inds,
std::vector<int>* bg_inds, std::vector<int>* tgt_lbl,
std::vector<int>* fg_fake, std::vector<T>* bbox_inside_weight,
std::minstd_rand engine, bool use_random) {
float epsilon = 0.00001;
int anchor_num = anchor_to_gt_max.dims()[0];
......@@ -201,12 +206,12 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
// Reservoir Sampling
int fg_num = static_cast<int>(rpn_fg_fraction * rpn_batch_size_per_im);
ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random);
fg_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_num; ++i) {
int fg_fake_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_fake_num; ++i) {
target_label[fg_inds_fake[i]] = 1;
}
int bg_num = rpn_batch_size_per_im - fg_num;
int bg_num = rpn_batch_size_per_im - fg_fake_num;
for (int64_t i = 0; i < anchor_num; ++i) {
if (anchor_to_gt_max_data[i] < rpn_negative_overlap) {
bg_inds_fake.push_back(i);
......@@ -214,12 +219,28 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
}
ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random);
bg_num = static_cast<int>(bg_inds_fake.size());
int fake_num = 0;
for (int64_t i = 0; i < bg_num; ++i) {
// fg fake found
if (target_label[bg_inds_fake[i]] == 1) {
fake_num++;
fg_fake->emplace_back(fg_inds_fake[0]);
for (int j = 0; j < 4; ++j) {
bbox_inside_weight->emplace_back(T(0.));
}
}
target_label[bg_inds_fake[i]] = 0;
}
for (int64_t i = 0; i < (fg_fake_num - fake_num) * 4; ++i) {
bbox_inside_weight->emplace_back(T(1.));
}
for (int64_t i = 0; i < anchor_num; ++i) {
if (target_label[i] == 1) fg_inds->emplace_back(i);
if (target_label[i] == 1) {
fg_inds->emplace_back(i);
fg_fake->emplace_back(i);
}
if (target_label[i] == 0) bg_inds->emplace_back(i);
}
fg_num = fg_inds->size();
......@@ -248,7 +269,8 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
std::vector<int> bg_inds;
std::vector<int> gt_inds;
std::vector<int> tgt_lbl;
std::vector<int> fg_fake;
std::vector<T> bbox_inside_weight;
// Calculate the max IoU between anchors and gt boxes
// Map from anchor to gt box that has highest overlap
auto place = ctx.GetPlace();
......@@ -275,32 +297,37 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
// Follow the Faster RCNN's implementation
ScoreAssign(anchor_by_gt_overlap_data, anchor_to_gt_max, gt_to_anchor_max,
rpn_batch_size_per_im, rpn_fg_fraction, rpn_positive_overlap,
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, engine,
use_random);
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, &fg_fake,
&bbox_inside_weight, engine, use_random);
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
gt_inds.reserve(fg_num);
for (int i = 0; i < fg_num; ++i) {
gt_inds.emplace_back(argmax[fg_inds[i]]);
int fg_fake_num = fg_fake.size();
gt_inds.reserve(fg_fake_num);
for (int i = 0; i < fg_fake_num; ++i) {
gt_inds.emplace_back(argmax[fg_fake[i]]);
}
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_num}, place);
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t, bbox_inside_weight_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_fake_num}, place);
int* score_index_data =
score_index_t.mutable_data<int>({fg_num + bg_num}, place);
int* tgt_lbl_data = tgt_lbl_t.mutable_data<int>({fg_num + bg_num}, place);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_num}, place);
std::copy(fg_inds.begin(), fg_inds.end(), loc_index_data);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_fake_num}, place);
T* bbox_inside_weight_data =
bbox_inside_weight_t.mutable_data<T>({fg_fake_num, 4}, place);
std::copy(fg_fake.begin(), fg_fake.end(), loc_index_data);
std::copy(fg_inds.begin(), fg_inds.end(), score_index_data);
std::copy(bg_inds.begin(), bg_inds.end(), score_index_data + fg_num);
std::copy(tgt_lbl.begin(), tgt_lbl.end(), tgt_lbl_data);
std::copy(gt_inds.begin(), gt_inds.end(), gt_inds_data);
std::copy(bbox_inside_weight.begin(), bbox_inside_weight.end(),
bbox_inside_weight_data);
std::vector<Tensor> loc_score_tgtlbl_gt;
loc_score_tgtlbl_gt.emplace_back(loc_index_t);
loc_score_tgtlbl_gt.emplace_back(score_index_t);
loc_score_tgtlbl_gt.emplace_back(tgt_lbl_t);
loc_score_tgtlbl_gt.emplace_back(gt_inds_t);
loc_score_tgtlbl_gt.emplace_back(bbox_inside_weight_t);
return loc_score_tgtlbl_gt;
}
......@@ -318,6 +345,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
auto* score_index = context.Output<LoDTensor>("ScoreIndex");
auto* tgt_bbox = context.Output<LoDTensor>("TargetBBox");
auto* tgt_lbl = context.Output<LoDTensor>("TargetLabel");
auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
"RpnTargetAssignOp gt_boxes needs 1 level of LoD");
......@@ -340,7 +368,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->mutable_data<int>({max_num}, place);
tgt_bbox->mutable_data<T>({max_num, 4}, place);
tgt_lbl->mutable_data<int>({max_num, 1}, place);
bbox_inside_weight->mutable_data<T>({max_num, 4}, place);
auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
std::random_device rnd;
......@@ -394,6 +422,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
Tensor sampled_score_index = loc_score_tgtlbl_gt[1];
Tensor sampled_tgtlbl = loc_score_tgtlbl_gt[2];
Tensor sampled_gt_index = loc_score_tgtlbl_gt[3];
Tensor sampled_bbox_inside_weight = loc_score_tgtlbl_gt[4];
int loc_num = sampled_loc_index.dims()[0];
int score_num = sampled_score_index.dims()[0];
......@@ -432,6 +461,8 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
AppendRpns<int>(score_index, total_score_num, &sampled_score_index_unmap);
AppendRpns<T>(tgt_bbox, total_loc_num * 4, &sampled_tgt_bbox);
AppendRpns<int>(tgt_lbl, total_score_num, &sampled_tgtlbl);
AppendRpns<T>(bbox_inside_weight, total_loc_num * 4,
&sampled_bbox_inside_weight);
total_loc_num += loc_num;
total_score_num += score_num;
......@@ -448,10 +479,12 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->set_lod(loc_score);
tgt_bbox->set_lod(lod_loc);
tgt_lbl->set_lod(loc_score);
bbox_inside_weight->set_lod(lod_loc);
loc_index->Resize({total_loc_num});
score_index->Resize({total_score_num});
tgt_bbox->Resize({total_loc_num, 4});
tgt_lbl->Resize({total_score_num, 1});
bbox_inside_weight->Resize({total_loc_num, 4});
}
};
......@@ -514,6 +547,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
"TargetLabel",
"(Tensor<int>), The target labels of each anchor with shape "
"[F + B, 1], F and B are sampled foreground and backgroud number.");
AddOutput("BBoxInsideWeight",
"(Tensor), The bbox inside weight with shape "
"[F, 4], F is the sampled foreground number.");
AddComment(R"DOC(
This operator can be, for a given set of ground truth bboxes and the
anchors, to assign classification and regression targets to each prediction.
......
paddle/fluid/operators/elementwise_op.h
浏览文件 @ 201d4f2a
......@@ -80,8 +80,6 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() final {
AddInput("X", "(Tensor), The first input tensor of elementwise op.");
AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
// AddOutput("SavedShape", "(Tensor), save X, Y shape for grad to save
// memory.").AsIntermediate();
AddOutput("Out", "The output of elementwise op.");
AddAttr<int>("axis",
"(int, default -1). The start dimension index "
......@@ -129,13 +127,11 @@ But the output only shares the LoD information with the input $X$.
)DOC",
GetName(), GetEquation()));
SetReuse();
}
protected:
virtual std::string GetName() const = 0;
virtual std::string GetEquation() const = 0;
virtual void SetReuse() {}
};
class ElementwiseOpGrad : public framework::OperatorWithKernel {
......@@ -269,7 +265,6 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
protected: \
virtual std::string GetName() const { return op_name; } \
virtual std::string GetEquation() const { return equation; } \
virtual void SetReuse() { Reuse(__VA_ARGS__); } \
}; \
REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \
__ElemwiseOp##op_type##Maker__, \
......
paddle/fluid/operators/mean_op.cc
浏览文件 @ 201d4f2a
......@@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of mean op");
AddOutput("Out", "(Tensor) The output of mean op").Reuse("X");
AddOutput("Out", "(Tensor) The output of mean op");
AddComment(R"DOC(
Mean Operator calculates the mean of all elements in X.
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ 201d4f2a
......@@ -151,8 +151,7 @@ void Pool2dOpMaker::Make() {
"The format of output tensor is also NCHW, "
"where N is batch size, C is the number of channels, "
"H is the height of the feature, "
"and W is the width of the feature.")
.Reuse("X");
"and W is the width of the feature.");
AddAttr<std::string>("pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
......@@ -252,8 +251,7 @@ void Pool3dOpMaker::Make() {
"The format of output tensor is also NCDHW, "
"where N is batch size, C is "
"the number of channels, and D, H and W is the depth, height and "
"width of the feature, respectively.")
.Reuse("X");
"width of the feature, respectively.");
AddAttr<std::string>("pooling_type",
"(string) Pooling type, can be \"max\" for max-pooling "
......
paddle/fluid/operators/sgd_op.cc
浏览文件 @ 201d4f2a
......@@ -77,8 +77,7 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
AddOutput("ParamOut",
"(Tensor or SelectedRows, same with Param) "
"Output parameter, should share the same memory with Param")
.Reuse("Param");
"Output parameter, should share the same memory with Param");
AddComment(R"DOC(
SGD operator
......
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 201d4f2a
......@@ -80,8 +80,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"The input tensor of softmax, "
"whose last dimension is the input_feature_dimensions.");
AddOutput("Out", "The normalized values with the same shape as X.")
.Reuse("X");
AddOutput("Out", "The normalized values with the same shape as X.");
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
......
paddle/fluid/operators/sum_op.cc
浏览文件 @ 201d4f2a
......@@ -132,7 +132,7 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
.AsDuplicable();
AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
AddOutput("Out", "(Tensor) The output tensor of sum operator.");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 201d4f2a
......@@ -50,7 +50,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of Topk op");
AddOutput("Out", "(Tensor) The output tensor of Topk op").Reuse("X");
AddOutput("Out", "(Tensor) The output tensor of Topk op");
AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
AddComment(R"DOC(
Top K operator
......
paddle/fluid/operators/top_k_op.cu
浏览文件 @ 201d4f2a
......@@ -262,31 +262,31 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
const T* src, int lds, int dim, int k,
int grid_dim, int num) {
__shared__ Pair<T> sh_topk[BlockSize];
__shared__ int maxid[BlockSize / 2];
const int tid = threadIdx.x;
const int warp = threadIdx.x / 32;
const int bid = blockIdx.x;
for (int i = bid; i < num; i += grid_dim) {
output += i * output_stride;
indices += i * k;
int top_num = k;
__shared__ int maxid[BlockSize / 2];
T* out = output + i * output_stride;
int64_t* inds = indices + i * k;
Pair<T> topk[MaxLength];
int beam = MaxLength;
Pair<T> max;
bool is_empty = false;
bool firststep = true;
for (int k = 0; k < MaxLength; k++) {
topk[k].set(-INFINITY, -1);
for (int j = 0; j < MaxLength; j++) {
topk[j].set(-INFINITY, -1);
}
while (k) {
while (top_num) {
ThreadGetTopK<T, MaxLength, BlockSize>(
topk, &beam, k, src + i * lds, &firststep, &is_empty, &max, dim, tid);
sh_topk[tid] = topk[0];
BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
&indices, &beam, &k, tid, warp);
BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &out, &inds,
&beam, &top_num, tid, warp);
}
}
}
......@@ -327,13 +327,15 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
size_t k = static_cast<int>(ctx.Attr<int>("k"));
const T* input_data = input->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
// FIXME(typhoonzero): data is always converted to type T?
int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
size_t input_height = input->dims()[0];
size_t input_width = input->dims()[1];
framework::DDim inputdims = input->dims();
const size_t input_height = framework::product(
framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
const size_t input_width = inputdims[inputdims.size() - 1];
if (k > input_width) k = input_width;
// NOTE: pass lds and dim same to input width.
......@@ -342,14 +344,12 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
const int kMaxHeight = 2048;
int gridx = input_height < kMaxHeight ? input_height : kMaxHeight;
auto& dev_ctx = ctx.cuda_device_context();
switch (GetDesiredBlockDim(input_width)) {
FIXED_BLOCK_DIM(
KeMatrixTopK<T, 5,
kBlockDim><<<gridx, kBlockDim, 0, dev_ctx.stream()>>>(
output_data, output->dims()[1], indices_data, input_data,
input_width, input_width, static_cast<int>(k), gridx,
input_height));
output_data, k, indices_data, input_data, input_width,
input_width, static_cast<int>(k), gridx, input_height));
default:
PADDLE_THROW("Error");
}
......
paddle/fluid/operators/top_k_op.h
浏览文件 @ 201d4f2a
......@@ -34,7 +34,6 @@ class TopkKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
// Get the top k elements of each row of input tensor
// FIXME: only deal with matrix(2d tensor).
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
auto* indices = ctx.Output<Tensor>("Indices");
......@@ -44,8 +43,6 @@ class TopkKernel : public framework::OpKernel<T> {
T* output_data = output->mutable_data<T>(ctx.GetPlace());
int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
auto eg_input = EigenMatrix<T>::From(*input);
// reshape input to a flattern matrix(like flat_inner_dims)
framework::DDim inputdims = input->dims();
const size_t row = framework::product(
......@@ -53,7 +50,7 @@ class TopkKernel : public framework::OpKernel<T> {
const size_t col = inputdims[inputdims.size() - 1];
Eigen::DSizes<int, 2> flat2dims(row, col);
// NOTE: eigen shape doesn't affect paddle tensor.
eg_input.reshape(flat2dims);
auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 201d4f2a
......@@ -116,8 +116,8 @@ def rpn_target_assign(bbox_pred,
Returns:
tuple:
A tuple(predicted_scores, predicted_location, target_label,
target_bbox) is returned. The predicted_scores and
predicted_location is the predicted result of the RPN.
target_bbox, bbox_inside_weight) is returned. The predicted_scores
and predicted_location is the predicted result of the RPN.
The target_label and target_bbox is the ground truth,
respectively. The predicted_location is a 2D Tensor with shape
[F, 4], and the shape of target_bbox is same as the shape of
......@@ -126,6 +126,8 @@ def rpn_target_assign(bbox_pred,
[F + B, 1], and the shape of target_label is same as the shape
of the predicted_scores, B is the number of the background
anchors, the F and B is depends on the input of this operator.
Bbox_inside_weight represents whether the predicted loc is fake_fg
or not and the shape is [F, 4].
Examples:
.. code-block:: python
......@@ -138,7 +140,7 @@ def rpn_target_assign(bbox_pred,
append_batch_size=False, dtype='float32')
gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target =
loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
......@@ -152,6 +154,8 @@ def rpn_target_assign(bbox_pred,
target_label = helper.create_variable_for_type_inference(dtype='int32')
target_bbox = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
bbox_inside_weight = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
helper.append_op(
type="rpn_target_assign",
inputs={
......@@ -164,7 +168,8 @@ def rpn_target_assign(bbox_pred,
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': target_label,
'TargetBBox': target_bbox
'TargetBBox': target_bbox,
'BBoxInsideWeight': bbox_inside_weight
},
attrs={
'rpn_batch_size_per_im': rpn_batch_size_per_im,
......@@ -179,13 +184,14 @@ def rpn_target_assign(bbox_pred,
score_index.stop_gradient = True
target_label.stop_gradient = True
target_bbox.stop_gradient = True
bbox_inside_weight.stop_gradient = True
cls_logits = nn.reshape(x=cls_logits, shape=(-1, 1))
bbox_pred = nn.reshape(x=bbox_pred, shape=(-1, 4))
predicted_cls_logits = nn.gather(cls_logits, score_index)
predicted_bbox_pred = nn.gather(bbox_pred, loc_index)
return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox
return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight
def detection_output(loc,
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 201d4f2a
......@@ -301,7 +301,7 @@ class TestRpnTargetAssign(unittest.TestCase):
dtype='float32',
lod_level=1,
append_batch_size=False)
pred_scores, pred_loc, tgt_lbl, tgt_bbox = layers.rpn_target_assign(
pred_scores, pred_loc, tgt_lbl, tgt_bbox, bbox_inside_weight = layers.rpn_target_assign(
bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
......@@ -313,15 +313,18 @@ class TestRpnTargetAssign(unittest.TestCase):
rpn_straddle_thresh=0.0,
rpn_fg_fraction=0.5,
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3)
rpn_negative_overlap=0.3,
use_random=False)
self.assertIsNotNone(pred_scores)
self.assertIsNotNone(pred_loc)
self.assertIsNotNone(tgt_lbl)
self.assertIsNotNone(tgt_bbox)
self.assertIsNotNone(bbox_inside_weight)
assert pred_scores.shape[1] == 1
assert pred_loc.shape[1] == 4
assert pred_loc.shape[1] == tgt_bbox.shape[1]
print(str(program))
class TestGenerateProposals(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
浏览文件 @ 201d4f2a
......@@ -50,8 +50,10 @@ def rpn_target_assign(anchor_by_gt_overlap,
fg_inds, size=(len(fg_inds) - num_fg), replace=False)
else:
disable_inds = fg_inds[num_fg:]
labels[disable_inds] = -1
fg_inds = np.where(labels == 1)[0]
bbox_inside_weight = np.zeros((len(fg_inds), 4), dtype=np.float32)
num_bg = rpn_batch_size_per_im - np.sum(labels == 1)
bg_inds = np.where(anchor_to_gt_max < rpn_negative_overlap)[0]
......@@ -59,18 +61,27 @@ def rpn_target_assign(anchor_by_gt_overlap,
enable_inds = bg_inds[np.random.randint(len(bg_inds), size=num_bg)]
else:
enable_inds = bg_inds[:num_bg]
fg_fake_inds = np.array([], np.int32)
fg_value = np.array([fg_inds[0]], np.int32)
fake_num = 0
for bg_id in enable_inds:
if bg_id in fg_inds:
fake_num += 1
fg_fake_inds = np.hstack([fg_fake_inds, fg_value])
labels[enable_inds] = 0
bbox_inside_weight[fake_num:, :] = 1
fg_inds = np.where(labels == 1)[0]
bg_inds = np.where(labels == 0)[0]
loc_index = fg_inds
score_index = np.hstack((fg_inds, bg_inds))
loc_index = np.hstack([fg_fake_inds, fg_inds])
score_index = np.hstack([fg_inds, bg_inds])
labels = labels[score_index]
assert not np.any(labels == -1), "Wrong labels with -1"
gt_inds = anchor_to_gt_argmax[fg_inds]
gt_inds = anchor_to_gt_argmax[loc_index]
return loc_index, score_index, labels, gt_inds
return loc_index, score_index, labels, gt_inds, bbox_inside_weight
def get_anchor(n, c, h, w):
......@@ -123,9 +134,12 @@ def rpn_target_assign_in_python(all_anchors,
gt_boxes_slice = gt_boxes_slice[not_crowd_inds]
iou = _bbox_overlaps(inside_anchors, gt_boxes_slice)
loc_inds, score_inds, labels, gt_inds = rpn_target_assign(
iou, rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap, rpn_fg_fraction, use_random)
loc_inds, score_inds, labels, gt_inds, bbox_inside_weight = \
rpn_target_assign(iou, rpn_batch_size_per_im,
rpn_positive_overlap,
rpn_negative_overlap,
rpn_fg_fraction,
use_random)
# unmap to all anchor
loc_inds = inds_inside[loc_inds]
score_inds = inds_inside[score_inds]
......@@ -139,6 +153,7 @@ def rpn_target_assign_in_python(all_anchors,
score_indexes = score_inds
tgt_labels = labels
tgt_bboxes = box_deltas
bbox_inside_weights = bbox_inside_weight
else:
loc_indexes = np.concatenate(
[loc_indexes, loc_inds + i * anchor_num])
......@@ -146,8 +161,10 @@ def rpn_target_assign_in_python(all_anchors,
[score_indexes, score_inds + i * anchor_num])
tgt_labels = np.concatenate([tgt_labels, labels])
tgt_bboxes = np.vstack([tgt_bboxes, box_deltas])
bbox_inside_weights = np.vstack([bbox_inside_weights, \
bbox_inside_weight])
return loc_indexes, score_indexes, tgt_bboxes, tgt_labels
return loc_indexes, score_indexes, tgt_bboxes, tgt_labels, bbox_inside_weights
class TestRpnTargetAssignOp(OpTest):
......@@ -182,10 +199,12 @@ class TestRpnTargetAssignOp(OpTest):
rpn_fg_fraction = 0.5
use_random = False
loc_index, score_index, tgt_bbox, labels = rpn_target_assign_in_python(
all_anchors, gt_boxes, is_crowd, im_info, lod, rpn_straddle_thresh,
rpn_batch_size_per_im, rpn_positive_overlap, rpn_negative_overlap,
rpn_fg_fraction, use_random)
loc_index, score_index, tgt_bbox, labels, bbox_inside_weights = \
rpn_target_assign_in_python(all_anchors, gt_boxes, is_crowd,
im_info, lod, rpn_straddle_thresh,
rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap,
rpn_fg_fraction, use_random)
labels = labels[:, np.newaxis]
self.op_type = "rpn_target_assign"
......@@ -207,7 +226,8 @@ class TestRpnTargetAssignOp(OpTest):
'LocationIndex': loc_index.astype('int32'),
'ScoreIndex': score_index.astype('int32'),
'TargetBBox': tgt_bbox.astype('float32'),
'TargetLabel': labels.astype('int32')
'TargetLabel': labels.astype('int32'),
'BBoxInsideWeight': bbox_inside_weights.astype('float32')
}
def test_check_output(self):
......
python/paddle/fluid/tests/unittests/test_top_k_op.py
浏览文件 @ 201d4f2a
......@@ -21,22 +21,27 @@ from op_test import OpTest
class TestTopkOp(OpTest):
def setUp(self):
self.set_args()
self.op_type = "top_k"
k = 1
input = np.random.random((32, 84)).astype("float32")
output = np.ndarray((32, k))
indices = np.ndarray((32, k)).astype("int64")
k = self.top_k
input = np.random.random((self.row, k)).astype("float32")
output = np.ndarray((self.row, k))
indices = np.ndarray((self.row, k)).astype("int64")
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(32):
for rowid in range(self.row):
row = input[rowid]
output[rowid] = np.sort(row)[-k:]
indices[rowid] = row.argsort()[-k:]
output[rowid] = np.sort(row)[::-1][:k]
indices[rowid] = row.argsort()[::-1][:k]
self.outputs = {'Out': output, 'Indices': indices}
def set_args(self):
self.row = 32
self.top_k = 1
def test_check_output(self):
self.check_output()
......@@ -50,14 +55,39 @@ class TestTopkOp3d(OpTest):
output = np.ndarray((64, k))
indices = np.ndarray((64, k)).astype("int64")
# FIXME: should use 'X': input for a 3d input
self.inputs = {'X': input_flat_2d}
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(64):
row = input_flat_2d[rowid]
output[rowid] = np.sort(row)[-k:]
indices[rowid] = row.argsort()[-k:]
output[rowid] = np.sort(row)[::-1][:k]
indices[rowid] = row.argsort()[::-1][:k]
self.outputs = {
'Out': output.reshape((32, 2, k)),
'Indices': indices.reshape((32, 2, k))
}
def test_check_output(self):
self.check_output()
class TestTopkOp2(OpTest):
def setUp(self):
self.op_type = "top_k"
k = 1
m = 2056
input = np.random.random((m, 84)).astype("float32")
output = np.ndarray((m, k))
indices = np.ndarray((m, k)).astype("int64")
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(m):
row = input[rowid]
output[rowid] = -np.sort(-row)[:k]
indices[rowid] = (-row).argsort()[:k]
self.outputs = {'Out': output, 'Indices': indices}
......@@ -65,5 +95,17 @@ class TestTopkOp3d(OpTest):
self.check_output()
class TestTopkOp3(TestTopkOp):
def set_args(self):
self.row = 2056
self.top_k = 3
class TestTopkOp4(TestTopkOp):
def set_args(self):
self.row = 40000
self.top_k = 1
if __name__ == "__main__":
unittest.main()
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