Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 c5c7dc2e 编写于 作者: A Abhinav Arora 提交者： GitHub
浏览文件
操作

Fix CPPLint errors in multiclass_nms, nccl, nce, reduce and save_load_combine (#10032) 

* Fix CPPLint errors in multiclass_nms, nccl, nce, reduce and save_load_combine

* Fix
上级 598035f9
隐藏空白更改
内联 并排
Showing with 70 addition and 68 deletion
paddle/fluid/operators/multiclass_nms_op.cc
浏览文件 @ c5c7dc2e
......@@ -173,8 +173,8 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
void MultiClassNMS(const framework::ExecutionContext& ctx,
const Tensor& scores, const Tensor& bboxes,
std::map<int, std::vector<int>>& indices,
int& num_nmsed_out) const {
std::map<int, std::vector<int>>* indices,
int* num_nmsed_out) const {
int64_t background_label = ctx.Attr<int>("background_label");
int64_t nms_top_k = ctx.Attr<int>("nms_top_k");
int64_t keep_top_k = ctx.Attr<int>("keep_top_k");
......@@ -189,15 +189,15 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
if (c == background_label) continue;
Tensor score = scores.Slice(c, c + 1);
NMSFast(bboxes, score, score_threshold, nms_threshold, nms_eta, nms_top_k,
&(indices[c]));
num_det += indices[c].size();
&((*indices)[c]));
num_det += (*indices)[c].size();
}
num_nmsed_out = num_det;
*num_nmsed_out = num_det;
const T* scores_data = scores.data<T>();
if (keep_top_k > -1 && num_det > keep_top_k) {
std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
for (const auto& it : indices) {
for (const auto& it : *indices) {
int label = it.first;
const T* sdata = scores_data + label * predict_dim;
const std::vector<int>& label_indices = it.second;
......@@ -220,13 +220,13 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
int idx = score_index_pairs[j].second.second;
new_indices[label].push_back(idx);
}
new_indices.swap(indices);
num_nmsed_out = keep_top_k;
new_indices.swap(*indices);
*num_nmsed_out = keep_top_k;
}
}
void MultiClassOutput(const Tensor& scores, const Tensor& bboxes,
std::map<int, std::vector<int>>& selected_indices,
const std::map<int, std::vector<int>>& selected_indices,
Tensor* outs) const {
int predict_dim = scores.dims()[1];
auto* scores_data = scores.data<T>();
......@@ -273,7 +273,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
std::map<int, std::vector<int>> indices;
int num_nmsed_out = 0;
MultiClassNMS(ctx, ins_score, ins_boxes, indices, num_nmsed_out);
MultiClassNMS(ctx, ins_score, ins_boxes, &indices, &num_nmsed_out);
all_indices.push_back(indices);
batch_starts.push_back(batch_starts.back() + num_nmsed_out);
}
......
paddle/fluid/operators/nccl_op.cu.cc
浏览文件 @ c5c7dc2e
......@@ -135,8 +135,9 @@ class NCCLBcastKernel : public framework::OpKernel<T> {
auto* x = ctx.Input<LoDTensor>("X");
VLOG(3) << "gpu : " << gpu_id << " invoke Bcast. send " << x->numel();
PADDLE_ENFORCE(platform::dynload::ncclBcast(
(void*)x->data<T>(), x->numel(), NCCLTypeWrapper<T>::type, root,
comm->comms().at(idx), ctx.cuda_device_context().stream()));
reinterpret_cast<void*>(const_cast<T*>(x->data<T>())), x->numel(),
NCCLTypeWrapper<T>::type, root, comm->comms().at(idx),
ctx.cuda_device_context().stream()));
VLOG(3) << "gpu : " << gpu_id << " finished Bcast.";
} else {
auto* out = ctx.Output<LoDTensor>("Out");
......
paddle/fluid/operators/nce_op.h
浏览文件 @ c5c7dc2e
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <math.h>
#include <random>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "unsupported/Eigen/CXX11/Tensor"
......@@ -108,7 +109,7 @@ class NCEKernel : public framework::OpKernel<T> {
auto weight_mat = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
Eigen::Tensor<T, 0, Eigen::RowMajor, Eigen::DenseIndex> result =
(input_mat.chip((int)(i / sample_labels->dims()[1]), 0) *
(input_mat.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
weight_mat.chip(sample_labels_data[i], 0))
.sum();
sample_out_data[i] += result(0);
......@@ -190,7 +191,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
auto x_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Input")));
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
d_w_matrix.chip(sample_labels_data[i], 0) +=
x_matrix.chip((int)(i / sample_labels->dims()[1]), 0) *
x_matrix.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) *
sample_grad_data[i];
}
}
......@@ -202,7 +203,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
auto d_x_matrix = EigenMatrix<T>::From(*d_x);
auto w_matrix = EigenMatrix<T>::From(*(context.Input<Tensor>("Weight")));
for (int64_t i = 0; i < sample_labels->numel(); ++i) {
d_x_matrix.chip((int)(i / sample_labels->dims()[1]), 0) +=
d_x_matrix.chip(static_cast<int>(i / sample_labels->dims()[1]), 0) +=
w_matrix.chip(sample_labels_data[i], 0) * sample_grad_data[i];
}
}
......
paddle/fluid/operators/reduce_op.h
浏览文件 @ c5c7dc2e
......@@ -35,77 +35,77 @@ using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
struct SumFunctor {
template <typename DeviceContext, typename X, typename Y, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
y.device(place) = x.sum(dim);
void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
y->device(place) = x->sum(dim);
}
};
struct SumGradFunctor {
template <typename DeviceContext, typename X, typename Y, typename DX,
typename DY, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, DX& dx, DY& dy,
void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
const Dim& dim, int size) {
dx.device(place) = dy.broadcast(dim);
dx->device(place) = dy->broadcast(dim);
}
};
struct MeanFunctor {
template <typename DeviceContext, typename X, typename Y, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
y.device(place) = x.mean(dim);
void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
y->device(place) = x->mean(dim);
}
};
struct MeanGradFunctor {
template <typename DeviceContext, typename X, typename Y, typename DX,
typename DY, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, DX& dx, DY& dy,
void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
const Dim& dim, int size) {
dx.device(place) = dy.broadcast(dim) / dx.constant(size);
dx->device(place) = dy->broadcast(dim) / dx->constant(size);
}
};
struct MaxFunctor {
template <typename DeviceContext, typename X, typename Y, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
y.device(place) = x.maximum(dim);
void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
y->device(place) = x->maximum(dim);
}
};
struct MinFunctor {
template <typename DeviceContext, typename X, typename Y, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
y.device(place) = x.minimum(dim);
void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
y->device(place) = x->minimum(dim);
}
};
struct MaxOrMinGradFunctor {
template <typename DeviceContext, typename X, typename Y, typename DX,
typename DY, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, DX& dx, DY& dy,
void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
const Dim& dim, int size) {
auto equals = x == y.broadcast(dim);
auto ones = dx.constant(1);
auto zeros = dx.constant(0);
auto equals = (*x) == y->broadcast(dim);
auto ones = dx->constant(1);
auto zeros = dx->constant(0);
// If there are multiple minimum or maximum elements, the subgradient of
// each is the set [0, 1], and we pass gradient to all of them here.
dx.device(place) = dy.broadcast(dim) * equals.select(ones, zeros);
dx->device(place) = dy->broadcast(dim) * equals.select(ones, zeros);
}
};
struct ProdFunctor {
template <typename DeviceContext, typename X, typename Y, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
y.device(place) = x.prod(dim);
void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) {
y->device(place) = x->prod(dim);
}
};
struct ProdGradFunctor {
template <typename DeviceContext, typename X, typename Y, typename DX,
typename DY, typename Dim>
void operator()(const DeviceContext& place, X& x, Y& y, DX& dx, DY& dy,
void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
const Dim& dim, int size) {
dx.device(place) = dy.broadcast(dim) * y.broadcast(dim) * x.inverse();
dx->device(place) = dy->broadcast(dim) * y->broadcast(dim) * x->inverse();
}
};
......@@ -125,7 +125,7 @@ class ReduceKernel : public framework::OpKernel<T> {
*context.template device_context<DeviceContext>().eigen_device();
auto reduce_dim = Eigen::array<int, 1>({{0}});
Functor functor;
functor(place, x, out, reduce_dim);
functor(place, &x, &out, reduce_dim);
} else {
int rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
......@@ -178,10 +178,10 @@ class ReduceKernel : public framework::OpKernel<T> {
if (D == 1) {
auto out = EigenScalar<T>::From(*output);
functor(place, x, out, reduce_dim);
functor(place, &x, &out, reduce_dim);
} else {
auto out = EigenTensor<T, (D - 1)>::From(*output, dims);
functor(place, x, out, reduce_dim);
functor(place, &x, &out, reduce_dim);
}
}
};
......@@ -206,7 +206,7 @@ class ReduceGradKernel : public framework::OpKernel<T> {
auto broadcast_dim =
Eigen::array<int, 1>({{static_cast<int>(input0->numel())}});
Functor functor;
functor(place, x, x_reduce, x_grad, x_reduce_grad, broadcast_dim,
functor(place, &x, &x_reduce, &x_grad, &x_reduce_grad, broadcast_dim,
broadcast_dim[0]);
} else {
int rank = context.Input<Tensor>("X")->dims().size();
......@@ -258,7 +258,7 @@ class ReduceGradKernel : public framework::OpKernel<T> {
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
Functor functor;
functor(place, x, x_reduce, x_grad, x_reduce_grad, broadcast_dim,
functor(place, &x, &x_reduce, &x_grad, &x_reduce_grad, broadcast_dim,
broadcast_dim[dim]);
}
};
......
paddle/fluid/operators/save_load_combine_op_test.cc
浏览文件 @ c5c7dc2e
......@@ -23,17 +23,17 @@ USE_NO_KERNEL_OP(load_combine);
int* CreateForSaveCombineOp(int x, int y, const std::vector<int>& lod_info,
std::string var_name,
paddle::platform::CPUPlace& place,
paddle::framework::Scope& scope,
paddle::framework::LoD& expect_lod) {
auto var = scope.Var(var_name);
const paddle::platform::CPUPlace& place,
paddle::framework::Scope* scope,
paddle::framework::LoD* expect_lod) {
auto var = scope->Var(var_name);
auto tensor = var->GetMutable<paddle::framework::LoDTensor>();
tensor->Resize({x, y});
expect_lod.resize(1);
expect_lod->resize(1);
for (size_t i = 0; i < lod_info.size(); i++) {
expect_lod[0].push_back(lod_info[i]);
(*expect_lod)[0].push_back(lod_info[i]);
}
tensor->set_lod(expect_lod);
tensor->set_lod(*expect_lod);
int* expect = tensor->mutable_data<int>(place);
for (int64_t i = 0; i < tensor->numel(); ++i) {
expect[i] = static_cast<int>(i);
......@@ -42,17 +42,17 @@ int* CreateForSaveCombineOp(int x, int y, const std::vector<int>& lod_info,
}
paddle::framework::LoDTensor* GeneratePlaceholderBeforeLoad(
const std::string out_var_name, paddle::framework::Scope& scope) {
auto load_var = scope.Var(out_var_name);
const std::string out_var_name, paddle::framework::Scope* scope) {
auto load_var = scope->Var(out_var_name);
auto target = load_var->GetMutable<paddle::framework::LoDTensor>();
return target;
}
int* GetValuesAfterLoadCombineOp(paddle::framework::LoDTensor* target,
paddle::framework::Scope& scope,
paddle::framework::LoD& actual_lod) {
const paddle::framework::Scope& scope,
paddle::framework::LoD* actual_lod) {
int* actual = target->data<int>();
actual_lod = target->lod();
*actual_lod = target->lod();
return actual;
}
......@@ -78,26 +78,26 @@ TEST(SaveLoadCombineOp, CPU) {
std::vector<int> lod1 = {0, 1, 2, 3, 10};
int numel1 = 100;
paddle::framework::LoD expect_lod1;
int* expect1 = CreateForSaveCombineOp(10, 10, lod1, "test_var1", place, scope,
expect_lod1);
int* expect1 = CreateForSaveCombineOp(10, 10, lod1, "test_var1", place,
&scope, &expect_lod1);
std::vector<int> lod2 = {0, 2, 5, 10};
int numel2 = 200;
paddle::framework::LoD expect_lod2;
int* expect2 = CreateForSaveCombineOp(10, 20, lod2, "test_var2", place, scope,
expect_lod2);
int* expect2 = CreateForSaveCombineOp(10, 20, lod2, "test_var2", place,
&scope, &expect_lod2);
std::vector<int> lod3 = {0, 2, 3, 20};
int numel3 = 4000;
paddle::framework::LoD expect_lod3;
int* expect3 = CreateForSaveCombineOp(20, 200, lod3, "test_var3", place,
scope, expect_lod3);
&scope, &expect_lod3);
std::vector<int> lod4 = {0, 1, 20};
int numel4 = 1000;
paddle::framework::LoD expect_lod4;
int* expect4 = CreateForSaveCombineOp(20, 50, lod4, "test_var4", place, scope,
expect_lod4);
int* expect4 = CreateForSaveCombineOp(20, 50, lod4, "test_var4", place,
&scope, &expect_lod4);
// Set attributes
std::string filename = "check_tensor.ls";
......@@ -111,10 +111,10 @@ TEST(SaveLoadCombineOp, CPU) {
save_combine_op->Run(scope, place);
// Set up output vars
auto target1 = GeneratePlaceholderBeforeLoad("out_var1", scope);
auto target2 = GeneratePlaceholderBeforeLoad("out_var2", scope);
auto target3 = GeneratePlaceholderBeforeLoad("out_var3", scope);
auto target4 = GeneratePlaceholderBeforeLoad("out_var4", scope);
auto target1 = GeneratePlaceholderBeforeLoad("out_var1", &scope);
auto target2 = GeneratePlaceholderBeforeLoad("out_var2", &scope);
auto target3 = GeneratePlaceholderBeforeLoad("out_var3", &scope);
auto target4 = GeneratePlaceholderBeforeLoad("out_var4", &scope);
// Run the load_combine_op
auto load_combine_op = paddle::framework::OpRegistry::CreateOp(
......@@ -123,10 +123,10 @@ TEST(SaveLoadCombineOp, CPU) {
load_combine_op->Run(scope, place);
paddle::framework::LoD actual_lod1, actual_lod2, actual_lod3, actual_lod4;
int* actual1 = GetValuesAfterLoadCombineOp(target1, scope, actual_lod1);
int* actual2 = GetValuesAfterLoadCombineOp(target2, scope, actual_lod2);
int* actual3 = GetValuesAfterLoadCombineOp(target3, scope, actual_lod3);
int* actual4 = GetValuesAfterLoadCombineOp(target4, scope, actual_lod4);
int* actual1 = GetValuesAfterLoadCombineOp(target1, scope, &actual_lod1);
int* actual2 = GetValuesAfterLoadCombineOp(target2, scope, &actual_lod2);
int* actual3 = GetValuesAfterLoadCombineOp(target3, scope, &actual_lod3);
int* actual4 = GetValuesAfterLoadCombineOp(target4, scope, &actual_lod4);
CheckValues(expect1, actual1, expect_lod1, actual_lod1, numel1);
CheckValues(expect2, actual2, expect_lod2, actual_lod2, numel2);
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或