[Cherry-pick] Add new func/class API psroi_pool and UT (#36111)

cherry-pick from #35352

Add new detection api paddle.vision.ops.psroi_pool and paddle.vision.ops.PSRoIPool

paddle/fluid/operators/psroi_pool_op.cc

@@ -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` ");
@@ -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. 
@@ -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");
@@ -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());

paddle/fluid/operators/psroi_pool_op.cu

@@ -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);
@@ -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);

paddle/fluid/operators/psroi_pool_op.h

@@ -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());
 
@@ -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>();
 
@@ -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);
@@ -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());

paddle/fluid/pybind/op_function_generator.cc

@@ -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"}},

python/paddle/fluid/tests/unittests/test_psroi_pool_op.py

@@ -14,18 +14,89 @@
 
 from __future__ import print_function
 
+import paddle
 import math
 import numpy as np
 import unittest
 from op_test import OpTest
 
 
+def calc_psroi_pool(x, rois, rois_num_per_img, output_channels, spatial_scale,
+                    pooled_height, pooled_width):
+    """
+    Psroi_pool implemented by Numpy.
+    x: 4-D as (N, C, H, W),
+    rois: 2-D as [[x1, y1, x2, y2], ...],
+    rois_num_per_img: 1-D as [nums_of_batch_0, nums_of_batch_1,  ...]
+    """
+    output_shape = (len(rois), output_channels, pooled_height, pooled_width)
+    out_data = np.zeros(output_shape)
+    batch_id = 0
+    rois_num_id = 0
+    rois_num_left = rois_num_per_img[rois_num_id]
+    for i in range(len(rois)):
+        roi = rois[i]
+        roi_batch_id = batch_id
+        rois_num_left -= 1
+        if rois_num_left == 0:
+            rois_num_id += 1
+            if rois_num_id < len(rois_num_per_img):
+                rois_num_left = rois_num_per_img[rois_num_id]
+            batch_id += 1
+        roi_start_w = round(roi[0]) * spatial_scale
+        roi_start_h = round(roi[1]) * spatial_scale
+        roi_end_w = (round(roi[2]) + 1.) * spatial_scale
+        roi_end_h = (round(roi[3]) + 1.) * spatial_scale
+
+        roi_height = max(roi_end_h - roi_start_h, 0.1)
+        roi_width = max(roi_end_w - roi_start_w, 0.1)
+
+        bin_size_h = roi_height / float(pooled_height)
+        bin_size_w = roi_width / float(pooled_width)
+
+        x_i = x[roi_batch_id]
+
+        for c in range(output_channels):
+            for ph in range(pooled_height):
+                for pw in range(pooled_width):
+                    hstart = int(
+                        math.floor(float(ph) * bin_size_h + roi_start_h))
+                    wstart = int(
+                        math.floor(float(pw) * bin_size_w + roi_start_w))
+                    hend = int(
+                        math.ceil(float(ph + 1) * bin_size_h + roi_start_h))
+                    wend = int(
+                        math.ceil(float(pw + 1) * bin_size_w + roi_start_w))
+                    hstart = min(max(hstart, 0), x.shape[2])
+                    hend = min(max(hend, 0), x.shape[2])
+                    wstart = min(max(wstart, 0), x.shape[3])
+                    wend = min(max(wend, 0), x.shape[3])
+
+                    c_in = (c * pooled_height + ph) * pooled_width + pw
+                    is_empty = (hend <= hstart) or (wend <= wstart)
+                    out_sum = 0.
+                    for ih in range(hstart, hend):
+                        for iw in range(wstart, wend):
+                            out_sum += x_i[c_in, ih, iw]
+                    bin_area = (hend - hstart) * (wend - wstart)
+                    out_data[i, c, ph, pw] = 0. if is_empty else (
+                        out_sum / float(bin_area))
+    return out_data
+
+
 class TestPSROIPoolOp(OpTest):
     def set_data(self):
+        paddle.enable_static()
         self.init_test_case()
         self.make_rois()
-        self.calc_psroi_pool()
-        self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
+        self.outs = calc_psroi_pool(self.x, self.boxes, self.boxes_num,
+                                    self.output_channels, self.spatial_scale,
+                                    self.pooled_height,
+                                    self.pooled_width).astype('float64')
+        self.inputs = {
+            'X': self.x,
+            'ROIs': (self.rois_with_batch_id[:, 1:5], self.rois_lod)
+        }
         self.attrs = {
             'output_channels': self.output_channels,
             'spatial_scale': self.spatial_scale,
@@ -67,57 +138,10 @@ class TestPSROIPoolOp(OpTest):
                 roi = [bno, x1, y1, x2, y2]
                 rois.append(roi)
         self.rois_num = len(rois)
-        self.rois = np.array(rois).astype('float64')
-
-    def calc_psroi_pool(self):
-        output_shape = (self.rois_num, self.output_channels, self.pooled_height,
-                        self.pooled_width)
-        out_data = np.zeros(output_shape)
-        for i in range(self.rois_num):
-            roi = self.rois[i]
-            roi_batch_id = int(roi[0])
-            roi_start_w = round(roi[1]) * self.spatial_scale
-            roi_start_h = round(roi[2]) * self.spatial_scale
-            roi_end_w = (round(roi[3]) + 1.) * self.spatial_scale
-            roi_end_h = (round(roi[4]) + 1.) * self.spatial_scale
-
-            roi_height = max(roi_end_h - roi_start_h, 0.1)
-            roi_width = max(roi_end_w - roi_start_w, 0.1)
-
-            bin_size_h = roi_height / float(self.pooled_height)
-            bin_size_w = roi_width / float(self.pooled_width)
-
-            x_i = self.x[roi_batch_id]
-
-            for c in range(self.output_channels):
-                for ph in range(self.pooled_height):
-                    for pw in range(self.pooled_width):
-                        hstart = int(
-                            math.floor(float(ph) * bin_size_h + roi_start_h))
-                        wstart = int(
-                            math.floor(float(pw) * bin_size_w + roi_start_w))
-                        hend = int(
-                            math.ceil(
-                                float(ph + 1) * bin_size_h + roi_start_h))
-                        wend = int(
-                            math.ceil(
-                                float(pw + 1) * bin_size_w + roi_start_w))
-                        hstart = min(max(hstart, 0), self.height)
-                        hend = min(max(hend, 0), self.height)
-                        wstart = min(max(wstart, 0), self.width)
-                        wend = min(max(wend, 0), self.width)
-
-                        c_in = (c * self.pooled_height + ph
-                                ) * self.pooled_width + pw
-                        is_empty = (hend <= hstart) or (wend <= wstart)
-                        out_sum = 0.
-                        for ih in range(hstart, hend):
-                            for iw in range(wstart, wend):
-                                out_sum += x_i[c_in, ih, iw]
-                        bin_area = (hend - hstart) * (wend - wstart)
-                        out_data[i, c, ph, pw] = 0. if is_empty else (
-                            out_sum / float(bin_area))
-        self.outs = out_data.astype('float64')
+        self.rois_with_batch_id = np.array(rois).astype('float64')
+        self.boxes = self.rois_with_batch_id[:, 1:]
+        self.boxes_num = np.array(
+            [bno + 1 for bno in range(self.batch_size)]).astype('int32')
 
     def setUp(self):
         self.op_type = 'psroi_pool'
@@ -130,5 +154,175 @@ class TestPSROIPoolOp(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestPSROIPoolDynamicFunctionAPI(unittest.TestCase):
+    def setUp(self):
+        self.x = np.random.random([2, 490, 28, 28]).astype(np.float32)
+        self.boxes = np.array(
+            [[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]]).astype(np.float32)
+        self.boxes_num = np.array([1, 2]).astype(np.int32)
+
+    def test_output_size(self):
+        def test_output_size_is_int():
+            output_size = 7
+            out = paddle.vision.ops.psroi_pool(
+                paddle.to_tensor(self.x),
+                paddle.to_tensor(self.boxes),
+                paddle.to_tensor(self.boxes_num), output_size).numpy()
+            expect_out = calc_psroi_pool(self.x, self.boxes, self.boxes_num, 10,
+                                         1.0, 7, 7)
+            self.assertTrue(np.allclose(out, expect_out))
+
+        def test_output_size_is_tuple():
+            output_size = (7, 7)
+            out = paddle.vision.ops.psroi_pool(
+                paddle.to_tensor(self.x),
+                paddle.to_tensor(self.boxes),
+                paddle.to_tensor(self.boxes_num), output_size).numpy()
+            expect_out = calc_psroi_pool(self.x, self.boxes, self.boxes_num, 10,
+                                         1.0, 7, 7)
+            self.assertTrue(np.allclose(out, expect_out))
+
+        def test_dytype_is_float64():
+            output_size = (7, 7)
+            out = paddle.vision.ops.psroi_pool(
+                paddle.to_tensor(self.x, 'float64'),
+                paddle.to_tensor(self.boxes, 'float64'),
+                paddle.to_tensor(self.boxes_num, 'int32'), output_size).numpy()
+            expect_out = calc_psroi_pool(self.x, self.boxes, self.boxes_num, 10,
+                                         1.0, 7, 7)
+            self.assertTrue(np.allclose(out, expect_out))
+
+        places = ['cpu']
+        if paddle.fluid.core.is_compiled_with_cuda():
+            places.append('gpu')
+        for place in places:
+            paddle.set_device(place)
+            test_output_size_is_int()
+            test_output_size_is_tuple()
+            test_dytype_is_float64()
+
+
+class TestPSROIPoolDynamicClassAPI(unittest.TestCase):
+    def setUp(self):
+        self.x = np.random.random([2, 128, 32, 32]).astype(np.float32)
+        self.boxes = np.array([[3, 5, 6, 13], [7, 4, 22, 18], [4, 5, 7, 10],
+                               [5, 3, 25, 21]]).astype(np.float32)
+        self.boxes_num = np.array([2, 2]).astype(np.int32)
+
+    def test_output_size(self):
+        def test_output_size_is_int():
+            psroi_module = paddle.vision.ops.PSRoIPool(8, 1.1)
+            out = psroi_module(
+                paddle.to_tensor(self.x),
+                paddle.to_tensor(self.boxes),
+                paddle.to_tensor(self.boxes_num)).numpy()
+            expect_out = calc_psroi_pool(self.x, self.boxes, self.boxes_num, 2,
+                                         1.1, 8, 8)
+            self.assertTrue(np.allclose(out, expect_out))
+
+        def test_output_size_is_tuple():
+            psroi_pool_module = paddle.vision.ops.PSRoIPool(8, 1.1)
+            out = psroi_pool_module(
+                paddle.to_tensor(self.x),
+                paddle.to_tensor(self.boxes),
+                paddle.to_tensor(self.boxes_num)).numpy()
+            expect_out = calc_psroi_pool(self.x, self.boxes, self.boxes_num, 2,
+                                         1.1, 8, 8)
+            self.assertTrue(np.allclose(out, expect_out))
+
+        def test_dytype_is_float64():
+            psroi_pool_module = paddle.vision.ops.PSRoIPool(8, 1.1)
+            out = psroi_pool_module(
+                paddle.to_tensor(self.x, 'float64'),
+                paddle.to_tensor(self.boxes, 'float64'),
+                paddle.to_tensor(self.boxes_num, 'int32')).numpy()
+            expect_out = calc_psroi_pool(self.x, self.boxes, self.boxes_num, 2,
+                                         1.1, 8, 8)
+            self.assertTrue(np.allclose(out, expect_out))
+
+        paddle.disable_static()
+        places = ['cpu']
+        if paddle.fluid.core.is_compiled_with_cuda():
+            places.append('gpu')
+        for place in places:
+            paddle.set_device(place)
+            test_output_size_is_int()
+            test_output_size_is_tuple()
+            test_dytype_is_float64()
+
+
+class TestPSROIPoolBoxesNumError(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+        self.x = paddle.uniform([2, 490, 28, 28], dtype='float32')
+        self.boxes = paddle.to_tensor(
+            [[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]], 'float32')
+
+    def test_errors(self):
+        def test_boxes_num_nums_error():
+            boxes_num = paddle.to_tensor([1, 5], 'int32')
+            out = paddle.vision.ops.psroi_pool(
+                self.x, self.boxes, boxes_num, output_size=7)
+
+        self.assertRaises(ValueError, test_boxes_num_nums_error)
+
+        def test_boxes_num_length_error():
+            boxes_num = paddle.to_tensor([1, 1, 1], 'int32')
+            out = paddle.vision.ops.psroi_pool(
+                self.x, self.boxes, boxes_num, output_size=7)
+
+        self.assertRaises(ValueError, test_boxes_num_length_error)
+
+
+class TestPSROIPoolChannelError(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+        self.x = paddle.uniform([2, 490, 28, 28], dtype='float32')
+        self.boxes = paddle.to_tensor(
+            [[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]], 'float32')
+        self.output_size = 4
+
+    def test_errors(self):
+        def test_channel_error():
+            boxes_num = paddle.to_tensor([2, 1], 'int32')
+            out = paddle.vision.ops.psroi_pool(self.x, self.boxes, boxes_num,
+                                               self.output_size)
+
+        self.assertRaises(ValueError, test_channel_error)
+
+
+class TestPSROIPoolStaticAPI(unittest.TestCase):
+    def setUp(self):
+        paddle.enable_static()
+        self.x_placeholder = paddle.static.data(
+            name='x', shape=[2, 490, 28, 28])
+        self.x = np.random.random([2, 490, 28, 28]).astype(np.float32)
+        self.boxes_placeholder = paddle.static.data(
+            name='boxes', shape=[3, 4], lod_level=1)
+        self.boxes = np.array(
+            [[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]]).astype(np.float32)
+        self.boxes_num = np.array([1, 2]).astype(np.int32)
+
+    def test_function_in_static(self):
+        output_size = 7
+        out = paddle.vision.ops.psroi_pool(self.x_placeholder,
+                                           self.boxes_placeholder,
+                                           self.boxes_num, output_size)
+        expect_out = calc_psroi_pool(self.x, self.boxes, self.boxes_num, 10,
+                                     1.0, 7, 7)
+        places = [paddle.CPUPlace()]
+        if paddle.fluid.core.is_compiled_with_cuda():
+            places.append(paddle.CUDAPlace(0))
+        for place in places:
+            exe = paddle.static.Executor(place)
+            boxes_lod_data = paddle.fluid.create_lod_tensor(self.boxes,
+                                                            [[1, 2]], place)
+            out_res = exe.run(paddle.static.default_main_program(),
+                              feed={'x': self.x,
+                                    'boxes': boxes_lod_data},
+                              fetch_list=[out.name])
+            self.assertTrue(np.allclose(out_res, expect_out))
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/vision/ops.py

@@ -29,7 +29,9 @@ __all__ = [ #noqa
     'deform_conv2d',
     'DeformConv2D',
     'read_file',
-    'decode_jpeg'
+    'decode_jpeg',
+    'psroi_pool',
+    'PSRoIPool',
 ]
 
 
@@ -900,3 +902,114 @@ def decode_jpeg(x, mode='unchanged', name=None):
         type="decode_jpeg", inputs=inputs, attrs=attrs, outputs={"Out": out})
 
     return out
+
+
+def psroi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None):
+    """
+    Position sensitive region of interest pooling (also known as PSROIPooling) is to perform
+    position-sensitive average pooling on regions of interest specified by input. It performs 
+    on inputs of nonuniform sizes to obtain fixed-size feature maps.
+
+    PSROIPooling is proposed by R-FCN. Please refer to https://arxiv.org/abs/1605.06409 for more details.
+
+    Args:
+        x (Tensor): Input features with shape (N, C, H, W). The data type can be float32 or float64.
+        boxes (Tensor): Box coordinates of ROIs (Regions of Interest) to pool over. It should be
+                         a 2-D Tensor with shape (num_rois, 4). Given as [[x1, y1, x2, y2], ...], 
+                         (x1, y1) is the top left coordinates, and (x2, y2) is the bottom
+                         right coordinates.
+        boxes_num (Tensor): The number of boxes contained in each picture in the batch.
+        output_size (int|Tuple(int, int))  The pooled output size(H, W), data type 
+                               is int32. If int, H and W are both equal to output_size.
+        spatial_scale (float): Multiplicative spatial scale factor to translate ROI coords from their 
+                               input scale to the scale used when pooling. Default: 1.0
+        name(str, optional): The default value is None.
+                             Normally there is no need for user to set this property.
+                             For more information, please refer to :ref:`api_guide_Name`
+
+    Returns:
+        4-D Tensor. The pooled ROIs with shape (num_rois, output_channels, pooled_h, pooled_w).
+        The output_channels equal to C / (pooled_h * pooled_w), where C is the channels of input.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            x = paddle.uniform([2, 490, 28, 28], dtype='float32')
+            boxes = paddle.to_tensor([[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]], dtype='float32')
+            boxes_num = paddle.to_tensor([1, 2], dtype='int32')
+            pool_out = paddle.vision.ops.psroi_pool(x, boxes, boxes_num, 7, 1.0)
+    """
+
+    check_type(output_size, 'output_size', (int, tuple, list), 'psroi_pool')
+    if isinstance(output_size, int):
+        output_size = (output_size, output_size)
+    pooled_height, pooled_width = output_size
+    assert (len(x.shape) == 4,
+            "Input features with shape should be (N, C, H, W)")
+    output_channels = int(x.shape[1] / (pooled_height * pooled_width))
+    if in_dygraph_mode():
+        return core.ops.psroi_pool(x, boxes, boxes_num, "output_channels",
+                                   output_channels, "spatial_scale",
+                                   spatial_scale, "pooled_height",
+                                   pooled_height, "pooled_width", pooled_width)
+
+    helper = LayerHelper('psroi_pool', **locals())
+    dtype = helper.input_dtype()
+    out = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type='psroi_pool',
+        inputs={'X': x,
+                'ROIs': boxes},
+        outputs={'Out': out},
+        attrs={
+            'output_channels': output_channels,
+            'spatial_scale': spatial_scale,
+            'pooled_height': pooled_height,
+            'pooled_width': pooled_width
+        })
+    return out
+
+
+class PSRoIPool(Layer):
+    """
+    This interface is used to construct a callable object of the ``PSRoIPool`` class. Please
+    refer to :ref:`api_paddle_vision_ops_psroi_pool`.
+
+    Args:
+        output_size (int|Tuple(int, int))  The pooled output size(H, W), data type 
+                               is int32. If int, H and W are both equal to output_size.
+        spatial_scale (float): Multiplicative spatial scale factor to translate ROI coords from their 
+                               input scale to the scale used when pooling. Default: 1.0.
+
+    Shape:
+        - x: 4-D Tensor with shape (N, C, H, W).
+        - boxes: 2-D Tensor with shape (num_rois, 4).
+        - boxes_num: 1-D Tensor.
+        - output: 4-D tensor with shape (num_rois, output_channels, pooled_h, pooled_w).
+              The output_channels equal to C / (pooled_h * pooled_w), where C is the channels of input.
+
+    Returns:
+        None
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            
+            psroi_module = paddle.vision.ops.PSRoIPool(7, 1.0)
+            x = paddle.uniform([2, 490, 28, 28], dtype='float32')
+            boxes = paddle.to_tensor([[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]], dtype='float32')
+            boxes_num = paddle.to_tensor([1, 2], dtype='int32')
+            pool_out = psroi_module(x, boxes, boxes_num)
+
+    """
+
+    def __init__(self, output_size, spatial_scale=1.0):
+        super(PSRoIPool, self).__init__()
+        self.output_size = output_size
+        self.spatial_scale = spatial_scale
+
+    def forward(self, x, boxes, boxes_num):
+        return psroi_pool(x, boxes, boxes_num, self.output_size,
+                          self.spatial_scale)