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[new api] add func/class API psroi_pool and UT (#35352)
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* add func/class API psroi_pool and UT

* add UT in static mode

* Remove redundant type checks in static mode

* More detailed description for test_psroi_pool_op

* fix code format of UT

* fix en-doc
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@zoooo0820
zoooo0820 authored Sep 26, 2021
1 parent 991ae3b commit e45d64e
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Showing 6 changed files with 526 additions and 120 deletions.
22 changes: 16 additions & 6 deletions paddle/fluid/operators/psroi_pool_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -25,22 +25,26 @@ class PSROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"Tensor, "
"(Tensor), "
"the input of PSROIPoolOp. "
"The format of input tensor is NCHW. Where N is the batch size, "
"C is the number of input channels, "
"H is the height of the input feature map, and "
"W is the width. The data type can be float32 or float64");
AddInput("ROIs",
"LoDTensor, "
"(LoDTensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]. "
"where (x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates. "
"The roi batch index can be calculated from LoD.");
AddInput("RoisNum",
"(Tensor), "
"The number of RoIs in each image.")
.AsDispensable();
AddOutput("Out",
"Tensor, "
"(Tensor), "
"the output of PSROIPoolOp is a 4-D Tensor with shape "
"(num_rois, output_channels, pooled_h, pooled_w). "
"The data type is the same as `x` ");
Expand All @@ -65,8 +69,6 @@ class PSROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"the pooled output width.")
.SetDefault(1);
AddComment(R"Doc(
**PSROIPool Operator,** `rois` **of this op should be a LoDTensor**
Position sensitive region of interest pooling (also known as PSROIPooling) is to perform
position-sensitive average pooling on regions of interest specified by input, takes as
input N position-sensitive score maps and a list of num_rois regions of interest.
Expand Down Expand Up @@ -106,7 +108,14 @@ class PSROIPoolOp : public framework::OperatorWithKernel {
platform::errors::InvalidArgument(
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4) "
"given as [(x1, y1, x2, y2), ...]"));

if (ctx->HasInput("RoisNum")) {
auto rois_num_dims = ctx->GetInputDim("RoisNum");
PADDLE_ENFORCE_EQ(rois_num_dims.size(), 1,
platform::errors::InvalidArgument(
"The second dimension of RoisNum should "
"be 1, but received dimension is %d",
rois_num_dims.size()));
}
int pooled_height = ctx->Attrs().Get<int>("pooled_height");
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
int output_channels = ctx->Attrs().Get<int>("output_channels");
Expand Down Expand Up @@ -184,6 +193,7 @@ class PSROIPoolGradMaker : public framework::SingleGradOpMaker<T> {
op->SetType("psroi_pool_grad");
op->SetInput("X", this->Input("X"));
op->SetInput("ROIs", this->Input("ROIs"));
op->SetInput("RoisNum", this->Input("RoisNum"));
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
op->SetAttrMap(this->Attrs());
Expand Down
105 changes: 77 additions & 28 deletions paddle/fluid/operators/psroi_pool_op.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -185,34 +185,67 @@ class GPUPSROIPoolOpKernel : public framework::OpKernel<T> {

int rois_num = rois->dims()[0];
if (rois_num == 0) return;

auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(rois_batch_size, batch_size,
platform::errors::InvalidArgument(
"The batch size of input(ROIs) and input(X) must be "
"the same but received batch size of input(ROIs) and "
"input(X) is %d and %d respectively.",
rois_batch_size, batch_size));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
platform::errors::InvalidArgument(
"The number of rois from input(ROIs) and its LOD "
"must be the same. Received rois %d of input(ROIs) "
"but the number of rois %d from its LOD is %d",
rois_num, rois_num_with_lod));

// set rois batch id
int rois_batch_size;
framework::Tensor rois_batch_id_list;
rois_batch_id_list.Resize({rois_num});
int* rois_batch_id_data =
rois_batch_id_list.mutable_data<int>(platform::CPUPlace());
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;

if (ctx.HasInput("RoisNum")) {
auto* rois_num_t = ctx.Input<Tensor>("RoisNum");
rois_batch_size = rois_num_t->numel();
auto* rois_num_data = rois_num_t->data<int>();
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::errors::InvalidArgument(
"The batch size of input(ROIs) and input(X) must be "
"the same but received batch size of input(ROIs) and "
"input(X) is %d and %d respectively.",
rois_batch_size, batch_size));
std::vector<int> rois_num_list(rois_batch_size);
memory::Copy(platform::CPUPlace(), rois_num_list.data(),
BOOST_GET_CONST(platform::CUDAPlace, ctx.GetPlace()),
rois_num_data, sizeof(int) * rois_batch_size, 0);
int rois_num_count = 0;
for (int i = 0; i < rois_batch_size; ++i) {
rois_num_count += rois_num_list[i];
}
PADDLE_ENFORCE_EQ(
rois_num_count, rois_num,
platform::errors::InvalidArgument(
"the rois_num from input and RoisNum must be the same"));
int start = 0;
for (int n = 0; n < rois_batch_size; ++n) {
for (int i = start; i < start + rois_num_list[n]; ++i) {
rois_batch_id_data[i] = n;
}
start += rois_num_list[n];
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::errors::InvalidArgument(
"The batch size of input(ROIs) and input(X) must be "
"the same but received batch size of input(ROIs) and "
"input(X) is %d and %d respectively.",
rois_batch_size, batch_size));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
platform::errors::InvalidArgument(
"The number of rois from input(ROIs) and its LOD "
"must be the same. Received rois %d of input(ROIs) "
"but the number of rois %d from its LOD is %d",
rois_num, rois_num_with_lod));

// set rois batch id
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;
}
}
}

framework::Tensor rois_batch_id_list_gpu;
framework::TensorCopy(rois_batch_id_list, ctx.GetPlace(),
ctx.device_context(), &rois_batch_id_list_gpu);
Expand Down Expand Up @@ -257,14 +290,30 @@ class GPUPSROIPoolGradOpKernel : public framework::OpKernel<T> {
rois_batch_id_list.Resize({rois_num});
int* rois_batch_id_data =
rois_batch_id_list.mutable_data<int>(platform::CPUPlace());
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;
int rois_batch_size;
if (ctx.HasInput("RoisNum")) {
auto* rois_num_t = ctx.Input<Tensor>("RoisNum");
rois_batch_size = rois_num_t->numel();
std::vector<int> rois_num_list(rois_batch_size);
memory::Copy(platform::CPUPlace(), rois_num_list.data(),
BOOST_GET_CONST(platform::CUDAPlace, ctx.GetPlace()),
rois_num_t->data<int>(), sizeof(int) * rois_batch_size, 0);
int start = 0;
for (int n = 0; n < rois_batch_size; ++n) {
for (int i = start; i < start + rois_num_list[n]; ++i) {
rois_batch_id_data[i] = n;
}
start += rois_num_list[n];
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;
}
}
}

framework::Tensor rois_batch_id_list_gpu;
framework::TensorCopy(rois_batch_id_list, ctx.GetPlace(),
ctx.device_context(), &rois_batch_id_list_gpu);
Expand Down
103 changes: 71 additions & 32 deletions paddle/fluid/operators/psroi_pool_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,13 @@ class CPUPSROIPoolOpKernel : public framework::OpKernel<T> {
int width = in_dims[3];
int rois_num = rois->dims()[0];

PADDLE_ENFORCE_EQ(input_channels,
output_channels * pooled_height * pooled_width,
platform::errors::InvalidArgument(
"the channels of input "
"X should equal the product of "
"output_channels x pooled_height x pooled_width"));

auto in_stride = framework::stride(in_dims);
auto out_stride = framework::stride(out->dims());

Expand All @@ -49,32 +56,52 @@ class CPUPSROIPoolOpKernel : public framework::OpKernel<T> {
rois_batch_id_list.Resize({rois_num});
int* rois_batch_id_data =
rois_batch_id_list.mutable_data<int>(ctx.GetPlace());

auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::errors::InvalidArgument("the rois_batch_size and input(X) "
"batch_size should be the same."));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num_with_lod, rois_num,
platform::errors::InvalidArgument(
"the rois_num from input and lod must be the same"));

PADDLE_ENFORCE_EQ(input_channels,
output_channels * pooled_height * pooled_width,
platform::errors::InvalidArgument(
"the channels of input "
"X should equal the product of "
"output_channels x pooled_height x pooled_width"));

// calculate batch id index for each roi according to LoD
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;
int rois_batch_size;
if (ctx.HasInput("RoisNum")) {
auto* rois_num_t = ctx.Input<framework::Tensor>("RoisNum");
rois_batch_size = rois_num_t->numel();
auto* rois_num_data = rois_num_t->data<int>();
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::errors::InvalidArgument(
"The batch size of rois and the batch size of images "
" must be the same. But received the batch size of rois is %d, "
"and the batch size of images is %d",
rois_batch_size, batch_size));
int rois_num_count = 0;
for (int i = 0; i < rois_batch_size; ++i) {
rois_num_count += rois_num_data[i];
}
PADDLE_ENFORCE_EQ(
rois_num_count, rois_num,
platform::errors::InvalidArgument(
"the rois_num from input and RoisNum must be the same"));
int start = 0;
for (int n = 0; n < rois_batch_size; ++n) {
for (int i = start; i < start + rois_num_data[n]; ++i) {
rois_batch_id_data[i] = n;
}
start += rois_num_data[n];
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::errors::InvalidArgument("the rois_batch_size and input(X) "
"batch_size should be the same."));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(
rois_num_with_lod, rois_num,
platform::errors::InvalidArgument(
"the rois_num from input and lod must be the same"));
// calculate batch id index for each roi according to LoD
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;
}
}
}

T* output_data = out->mutable_data<T>(ctx.GetPlace());
const T* input_rois = rois->data<T>();

Expand All @@ -93,7 +120,6 @@ class CPUPSROIPoolOpKernel : public framework::OpKernel<T> {
static_cast<T>(round(offset_input_rois[2]) + 1.) * spatial_scale;
T roi_end_h =
static_cast<T>(round(offset_input_rois[3]) + 1.) * spatial_scale;

// Force too small rois to be 1 x 1
T roi_height = std::max(roi_end_h - roi_start_h, (T)0.1); // avoid 0
T roi_width = std::max(roi_end_w - roi_start_w, (T)0.1);
Expand Down Expand Up @@ -172,15 +198,28 @@ class CPUPSROIPoolGradOpKernel : public framework::OpKernel<T> {
rois_batch_id_list.Resize({rois_num});
int* rois_batch_id_data =
rois_batch_id_list.mutable_data<int>(ctx.GetPlace());
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
// calculate batch id index for each roi according to LoD
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;
int rois_batch_size;
if (ctx.HasInput("RoisNum")) {
auto* rois_num_t = ctx.Input<framework::Tensor>("RoisNum");
rois_batch_size = rois_num_t->numel();
auto* rois_num_data = rois_num_t->data<int>();
int start = 0;
for (int n = 0; n < rois_batch_size; ++n) {
for (int i = start; i < start + rois_num_data[n]; ++i) {
rois_batch_id_data[i] = n;
}
start += rois_num_data[n];
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
// calculate batch id index for each roi according to LoD
for (int n = 0; n < rois_batch_size; ++n) {
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
rois_batch_id_data[i] = n;
}
}
}

const T* input_rois = rois->data<T>();
const T* output_grad_data = output_grad->data<T>();
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
Expand Down
1 change: 1 addition & 0 deletions paddle/fluid/pybind/op_function_generator.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@ std::map<std::string, std::set<std::string>> op_ins_map = {
{"gather", {"X", "Index", "Axis"}},
{"roi_pool", {"X", "ROIs", "RoisNum"}},
{"roi_align", {"X", "ROIs", "RoisNum"}},
{"psroi_pool", {"X", "ROIs", "RoisNum"}},
{"collect_fpn_proposals",
{"MultiLevelRois", "MultiLevelScores", "MultiLevelRoIsNum"}},
{"distribute_fpn_proposals", {"FpnRois", "RoisNum"}},
Expand Down
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