fix the precise roi poop op test=develop (#20126)

* fix the precise roi poop op test=develop
add roi backward implementation, fix the output-channel

paddle/fluid/API.spec

@@ -290,7 +290,7 @@ paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], vararg
 paddle.fluid.layers.temporal_shift (ArgSpec(args=['x', 'seg_num', 'shift_ratio', 'name'], varargs=None, keywords=None, defaults=(0.25, None)), ('document', 'd5945431cdcae3cda21914db5bbf383e'))
 paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '231f91231430f5dae2b757df22317c67'))
 paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9bf0cc6b0717010b8ceec5dc2541d566'))
-paddle.fluid.layers.prroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(1.0, 1, 1, None)), ('document', '454c7ea8c73313dd41513929d7526303'))
+paddle.fluid.layers.prroi_pool (ArgSpec(args=['input', 'rois', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(1.0, 1, 1, None)), ('document', '466be691ac4c1cd7b88fccb40846afce'))
 paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', 'b0e07aa41caae04b07a8e8217cc96020'))
 paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '9d93ee81f7a3e526d68bb280bc695d6c'))
 paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '45f3ebbcb766fca84cb2fe6307086573'))

paddle/fluid/operators/prroi_pool_op.cc

@@ -43,12 +43,6 @@ class PRROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
               "(Tensor), "
               "the output of PRROIPoolOp is a 4-D Tensor with shape "
               "(num_rois, output_channels, pooled_h, pooled_w).");
-    AddAttr<int>(
-        "output_channels",
-        "(int), "
-        "the number of channels of the output feature map. "
-        "For a task of C classes of objects, output_channels should be "
-        "(C + 1) for classification only.");
     AddAttr<float>("spatial_scale",
                    "(float, default 1.0), "
                    "Multiplicative spatial scale factor "
@@ -100,28 +94,18 @@ class PRROIPoolOp : public framework::OperatorWithKernel {
 
     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");
     float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
 
-    PADDLE_ENFORCE_EQ(
-        input_dims[1], output_channels * pooled_height * pooled_width,
-        "the channel of X(%d) should be equal to the product of "
-        "output_channels(%d), pooled_height(%d) and pooled_width(%d)",
-        input_dims[1], output_channels, pooled_height, pooled_width);
-
     PADDLE_ENFORCE_GT(pooled_height, 0,
                       "The pooled output height must be greater than 0");
     PADDLE_ENFORCE_GT(pooled_width, 0,
                       "The pooled output width must be greater than 0");
-    PADDLE_ENFORCE_GT(output_channels, 1,
-                      "The pooled output channels must greater than 1");
     PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
                       "The spatial scale must greater than 0.");
 
     auto out_dims = input_dims;
     out_dims[0] = rois_dims[0];
-    out_dims[1] =
-        output_channels;  // input_dims[1] / (pooled_height * pooled_width);
+    out_dims[1] = input_dims[1];
     out_dims[2] = pooled_height;
     out_dims[3] = pooled_width;
     ctx->SetOutputDim("Out", out_dims);
@@ -145,6 +129,7 @@ class PRROIPoolGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(ctx->HasOutput(framework::GradVarName("X")), true,
                       "The gradient of X should not be null.");
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    ctx->SetOutputDim(framework::GradVarName("ROIs"), ctx->GetInputDim("ROIs"));
   }
 
  protected:
@@ -164,9 +149,11 @@ class PRROIPoolGradDescMaker : public framework::SingleGradOpDescMaker {
     std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
     op->SetType("prroi_pool_grad");
     op->SetInput("X", Input("X"));
+    op->SetInput("Out", Output("Out"));
     op->SetInput("ROIs", Input("ROIs"));
     op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
     op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
+    op->SetOutput(framework::GradVarName("ROIs"), InputGrad("ROIs"));
     op->SetAttrMap(Attrs());
     return op;
   }

paddle/fluid/operators/prroi_pool_op.cu

@@ -40,6 +40,11 @@ DEVICE void PrRoIPoolingDistributeDiffCUDA(T* diff, const T top_diff,
   }
 }
 
+template <typename T>
+DEVICE void GPUAccumulateRois(T* offset, T data) {
+  paddle::platform::CudaAtomicAdd(offset, data);
+}
+
 template <typename T>
 __global__ void GPUPRROIPoolForward(
     const int nthreads, const T* input_data, const T* input_rois,
@@ -78,7 +83,7 @@ __global__ void GPUPRROIPoolForward(
     T win_end_h = win_start_h + bin_size_h;
 
     T win_size = max(static_cast<T>(0.0), bin_size_w * bin_size_h);
-    int input_channel = (c * pooled_height + ph) * pooled_width + pw;
+    int input_channel = c;
     const T* offset_input_data =
         input_data +
         (roi_batch_id * input_channels + input_channel) * height * width;
@@ -110,10 +115,12 @@ __global__ void GPUPRROIPoolForward(
 
 template <typename T>
 __global__ void GPUPRROIPoolBackward(
-    const int nthreads, const T* input_rois, const T* output_grad_data,
-    const float spatial_scale, const int input_channels, const int height,
-    const int width, const int output_channels, const int pooled_height,
-    const int pooled_width, const int* rois_batch_id_data, T* input_grad_data) {
+    const int nthreads, const T* in_data, const T* input_rois,
+    const T* output_grad_data, const float spatial_scale,
+    const int input_channels, const int height, const int width,
+    const int output_channels, const int pooled_height, const int pooled_width,
+    const int* rois_batch_id_data, T* input_grad_data, const T* out_data,
+    T* input_roi_grad_data) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int offset = blockDim.x * gridDim.x;
   for (int i = index; i < nthreads; i += offset) {
@@ -125,7 +132,7 @@ __global__ void GPUPRROIPoolBackward(
 
     // set roi_batch_id
     int roi_batch_id = rois_batch_id_data[n];
-    int input_channel = (c * pooled_height + ph) * pooled_width + pw;
+    int input_channel = c;
     int input_offset =
         (roi_batch_id * input_channels + input_channel) * height * width;
     T* offset_input_grad_data = input_grad_data + input_offset;
@@ -137,6 +144,7 @@ __global__ void GPUPRROIPoolBackward(
     T roi_start_h = static_cast<T>(offset_input_rois[1]) * spatial_scale;
     T roi_end_w = static_cast<T>(offset_input_rois[2]) * spatial_scale;
     T roi_end_h = static_cast<T>(offset_input_rois[3]) * spatial_scale;
+    T* offset_input_roi_grad_data = input_roi_grad_data + n * 4;
 
     T roi_width = max(roi_end_w - roi_start_w, static_cast<T>(0.0));
     T roi_height = max(roi_end_h - roi_start_h, static_cast<T>(0.0));
@@ -171,6 +179,16 @@ __global__ void GPUPRROIPoolBackward(
             height, width, PrRoIPoolingDistributeDiffCUDA<T>);
       }
     }
+
+    const T* offset_out_data = out_data + i;
+    const T* offset_in_data = in_data + input_offset;
+    PrRoIPoolingCoorBackward(
+        s_w, e_w, s_h, e_h, width, height, win_start_w, win_start_h, win_end_w,
+        win_end_h, pw, ph, pooled_width, pooled_height, win_size, spatial_scale,
+        offset_in_data, offset_out_data, offset_input_grad_data,
+        offset_input_roi_grad_data, GPUAccumulateRois<T>,
+        [](const T x, const T y) { return max(x, y); },
+        [](const T x, const T y) { return min(x, y); });
   }
 }
 
@@ -184,20 +202,15 @@ class GPUPRROIPoolOpKernel : public framework::OpKernel<T> {
 
     auto pooled_height = ctx.Attr<int>("pooled_height");
     auto pooled_width = ctx.Attr<int>("pooled_width");
-    auto output_channels = ctx.Attr<int>("output_channels");
     auto spatial_scale = ctx.Attr<float>("spatial_scale");
 
     auto in_dims = in->dims();
     int batch_size = in_dims[0];
     int input_channels = in_dims[1];
+    auto output_channels = input_channels;
     int height = in_dims[2];
     int width = in_dims[3];
 
-    PADDLE_ENFORCE_EQ(input_channels,
-                      output_channels * pooled_height * pooled_width,
-                      "the channels of input X should equal the product of "
-                      "output_channels x pooled_height x pooled_width");
-
     int rois_num = rois->dims()[0];
     if (rois_num == 0) return;
 
@@ -245,17 +258,20 @@ class GPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<Tensor>("X");
     auto* rois = ctx.Input<LoDTensor>("ROIs");
+    auto* out = ctx.Input<framework::Tensor>("Out");
 
     auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* input_roi_grad =
+        ctx.Output<LoDTensor>(framework::GradVarName("ROIs"));
 
     auto pooled_height = ctx.Attr<int>("pooled_height");
     auto pooled_width = ctx.Attr<int>("pooled_width");
-    auto output_channels = ctx.Attr<int>("output_channels");
     auto spatial_scale = ctx.Attr<float>("spatial_scale");
 
     int rois_num = rois->dims()[0];
     int input_channels = in->dims()[1];
+    auto output_channels = input_channels;
     int height = in->dims()[2];
     int width = in->dims()[3];
 
@@ -280,6 +296,8 @@ class GPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
       input_grad->mutable_data<T>(ctx.GetPlace());
       math::SetConstant<DeviceContext, T> set_zero;
       set_zero(ctx.cuda_device_context(), input_grad, static_cast<T>(0));
+      input_roi_grad->mutable_data<T>(ctx.GetPlace());
+      set_zero(ctx.cuda_device_context(), input_roi_grad, static_cast<T>(0));
 
       int output_grad_size = output_grad->numel();
       int blocks = NumBlocks(output_grad_size);
@@ -288,10 +306,12 @@ class GPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
       if (output_grad_size > 0) {
         GPUPRROIPoolBackward<
             T><<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
-            output_grad_size, rois->data<T>(), output_grad->data<T>(),
-            spatial_scale, input_channels, height, width, output_channels,
-            pooled_height, pooled_width, rois_batch_id_list_gpu.data<int>(),
-            input_grad->mutable_data<T>(ctx.GetPlace()));
+            output_grad_size, in->data<T>(), rois->data<T>(),
+            output_grad->data<T>(), spatial_scale, input_channels, height,
+            width, output_channels, pooled_height, pooled_width,
+            rois_batch_id_list_gpu.data<int>(),
+            input_grad->mutable_data<T>(ctx.GetPlace()), out->data<T>(),
+            input_roi_grad->mutable_data<T>(ctx.GetPlace()));
       }
     }
   }

paddle/fluid/operators/prroi_pool_op.h

@@ -21,7 +21,7 @@ namespace paddle {
 namespace operators {
 
 template <typename T>
-HOSTDEVICE T PrRoIPoolingGetData(const T* data, const int h, const int w,
+inline HOSTDEVICE T PrRoIPoolingGetData(const T* data, const int h, const int w,
                                         const int height, const int width) {
   bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width);
   T retVal = overflow ? 0.0f : data[h * width + w];
@@ -29,11 +29,12 @@ HOSTDEVICE T PrRoIPoolingGetData(const T* data, const int h, const int w,
 }
 
 template <typename T>
-HOSTDEVICE T PrRoIPoolingMatCalculation(const T* this_data, const int s_h,
-                                        const int s_w, const int e_h,
-                                        const int e_w, const T y0, const T x0,
-                                        const T y1, const T x1, const int h0,
-                                        const int w0) {
+inline HOSTDEVICE T PrRoIPoolingMatCalculation(const T* this_data,
+                                               const int s_h, const int s_w,
+                                               const int e_h, const int e_w,
+                                               const T y0, const T x0,
+                                               const T y1, const T x1,
+                                               const int h0, const int w0) {
   T alpha, beta, lim_alpha, lim_beta, tmp;
   T sum_out = 0;
 
@@ -73,9 +74,10 @@ HOSTDEVICE T PrRoIPoolingMatCalculation(const T* this_data, const int s_h,
 }
 
 template <typename T>
-HOSTDEVICE void PrRoIPoolingDistributeDiff(T* diff, const T top_diff,
+inline HOSTDEVICE void PrRoIPoolingDistributeDiff(T* diff, const T top_diff,
                                                   const int h, const int w,
-                                           const int height, const int width,
+                                                  const int height,
+                                                  const int width,
                                                   const T coeff) {
   bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width);
   if (!overflow) {
@@ -123,6 +125,132 @@ HOSTDEVICE void PrRoIPoolingMatDistributeDiff(
   functor(diff, top_diff, e_h, e_w, h0, w0, tmp);
 }
 
+template <typename T>
+inline HOSTDEVICE void CPUAccumulateRois(T* offset, T data) {
+  *offset += data;
+}
+
+template <typename T>
+inline HOSTDEVICE static T PrRoIPoolingGetCoeff(T dh, T dw) {
+  dw = dw > 0 ? dw : -dw;
+  dh = dh > 0 ? dh : -dh;
+  return (1.0f - dh) * (1.0f - dw);
+}
+
+template <typename T, typename H, typename W>
+inline HOSTDEVICE static T PrRoIPoolingInterpolation(const T* data, const H h,
+                                                     const W w,
+                                                     const int height,
+                                                     const int width) {
+  T retVal = 0.0f;
+  int h1 = floorf(h);
+  int w1 = floorf(w);
+  retVal +=
+      PrRoIPoolingGetData(data, h1, w1, height, width) *
+      PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
+  h1 = floorf(h) + 1;
+  w1 = floorf(w);
+  retVal +=
+      PrRoIPoolingGetData(data, h1, w1, height, width) *
+      PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
+  h1 = floorf(h);
+  w1 = floorf(w) + 1;
+  retVal +=
+      PrRoIPoolingGetData(data, h1, w1, height, width) *
+      PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
+  h1 = floorf(h) + 1;
+  w1 = floorf(w) + 1;
+  retVal +=
+      PrRoIPoolingGetData(data, h1, w1, height, width) *
+      PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
+  return retVal;
+}
+
+template <typename T>
+inline HOSTDEVICE T PrRoIPoolingSingleCoorIntegral(T s, T t, T c1, T c2) {
+  return 0.5f * (t * t - s * s) * c2 +
+         (t - 0.5f * t * t - s + 0.5f * s * s) * c1;
+}
+
+template <typename T, typename Functor, typename MaxFunctor,
+          typename MinFunctor>
+inline HOSTDEVICE void PrRoIPoolingCoorBackward(
+    int s_w, int e_w, int s_h, int e_h, int width, int height, T win_start_w,
+    T win_start_h, T win_end_w, T win_end_h, int pw, int ph,
+    const int pooled_width, const int pooled_height, T win_size,
+    const float spatial_scale, const T* this_bottom_data,
+    const T* this_top_data, T* this_data_grad, T* this_out_grad,
+    Functor functor, MaxFunctor maxFunctor, MinFunctor minFunctor) {
+  T g_x1_y = 0.f;
+  T g_x2_y = 0.f;
+  T g_x_y1 = 0.f;
+  T g_x_y2 = 0.f;
+
+  for (int h_iter = s_h; h_iter < e_h; ++h_iter) {
+    g_x1_y += PrRoIPoolingSingleCoorIntegral(
+        maxFunctor(win_start_h, static_cast<T>(h_iter)) - h_iter,
+        minFunctor(win_end_h, static_cast<T>(h_iter + 1)) - h_iter,
+        PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height,
+                                  width),
+        PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w,
+                                  height, width));
+
+    g_x2_y += PrRoIPoolingSingleCoorIntegral(
+        maxFunctor(win_start_h, static_cast<T>(h_iter)) - h_iter,
+        minFunctor(win_end_h, static_cast<T>(h_iter + 1)) - h_iter,
+        PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height,
+                                  width),
+        PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w,
+                                  height, width));
+  }
+
+  for (int w_iter = s_w; w_iter < e_w; ++w_iter) {
+    g_x_y1 += PrRoIPoolingSingleCoorIntegral(
+        maxFunctor(win_start_w, static_cast<T>(w_iter)) - w_iter,
+        minFunctor(win_end_w, static_cast<T>(w_iter + 1)) - w_iter,
+        PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height,
+                                  width),
+        PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1,
+                                  height, width));
+
+    g_x_y2 += PrRoIPoolingSingleCoorIntegral(
+        maxFunctor(win_start_w, static_cast<T>(w_iter)) - w_iter,
+        minFunctor(win_end_w, static_cast<T>(w_iter + 1)) - w_iter,
+        PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height,
+                                  width),
+        PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1,
+                                  height, width));
+  }
+
+  float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data);
+  float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data);
+  float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data);
+  float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data);
+
+  partial_x1 = partial_x1 / win_size * spatial_scale;
+  partial_x2 = partial_x2 / win_size * spatial_scale;
+  partial_y1 = partial_y1 / win_size * spatial_scale;
+  partial_y2 = partial_y2 / win_size * spatial_scale;
+
+  this_data_grad[0] = 0;
+  functor(this_data_grad + 1,
+          (partial_x1 * (1.0 - static_cast<T>(pw) / pooled_width) +
+           partial_x2 * (1.0 - static_cast<T>(pw + 1) / pooled_width)) *
+              (*this_out_grad));
+  functor(this_data_grad + 2,
+          (partial_y1 * (1.0 - static_cast<T>(ph) / pooled_height) +
+           partial_y2 * (1.0 - static_cast<T>(ph + 1) / pooled_height)) *
+              (*this_out_grad));
+  functor(this_data_grad + 3,
+          (partial_x2 * static_cast<T>(pw + 1) / pooled_width +
+           partial_x1 * static_cast<T>(pw) / pooled_width) *
+              (*this_out_grad));
+  functor(this_data_grad + 4,
+          (partial_y2 * static_cast<T>(ph + 1) / pooled_height +
+           partial_y1 * static_cast<T>(ph) / pooled_height) *
+              (*this_out_grad));
+}
+
 template <typename DeviceContext, typename T>
 class CPUPRROIPoolOpKernel : public framework::OpKernel<T> {
  public:
@@ -134,11 +262,11 @@ class CPUPRROIPoolOpKernel : public framework::OpKernel<T> {
     auto pooled_height = ctx.Attr<int>("pooled_height");
     auto pooled_width = ctx.Attr<int>("pooled_width");
     auto spatial_scale = ctx.Attr<float>("spatial_scale");
-    auto output_channels = ctx.Attr<int>("output_channels");
 
     auto in_dims = in->dims();
     int batch_size = in_dims[0];
     int input_channels = in_dims[1];
+    auto output_channels = input_channels;
     int height = in_dims[2];
     int width = in_dims[3];
     int rois_num = rois->dims()[0];
@@ -162,11 +290,6 @@ class CPUPRROIPoolOpKernel : public framework::OpKernel<T> {
     PADDLE_ENFORCE_EQ(rois_num_with_lod, rois_num,
                       "the rois_num from input and lod must be the same");
 
-    PADDLE_ENFORCE_EQ(input_channels,
-                      output_channels * pooled_height * pooled_width,
-                      "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) {
@@ -217,7 +340,7 @@ class CPUPRROIPoolOpKernel : public framework::OpKernel<T> {
             int e_h = std::ceil(win_end_h);
 
             int output_index = out_row_offset + pw;
-            int input_channel = (c * pooled_height + ph) * pooled_width + pw;
+            int input_channel = c;
             int input_plane_offset =
                 roi_batch_id * in_stride[0] + input_channel * in_stride[1];
             const T* offset_input_data = input_data + input_plane_offset;
@@ -254,20 +377,26 @@ class CPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<framework::Tensor>("X");
+    auto* out = ctx.Input<framework::Tensor>("Out");
     auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
     auto* output_grad =
         ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
     auto* input_grad =
         ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* input_roi_grad =
+        ctx.Output<framework::Tensor>(framework::GradVarName("ROIs"));
 
     auto pooled_height = ctx.Attr<int>("pooled_height");
     auto pooled_width = ctx.Attr<int>("pooled_width");
-    auto output_channels = ctx.Attr<int>("output_channels");
     auto spatial_scale = ctx.Attr<float>("spatial_scale");
 
-    if (input_grad) {
+    if (input_grad && input_roi_grad) {
       auto in_dims = in->dims();
+      auto* in_data = in->data<T>();
+      auto* out_data = out->data<T>();
+
       int input_channels = in_dims[1];
+      auto output_channels = input_channels;
       int height = in_dims[2];
       int width = in_dims[3];
       int rois_num = rois->dims()[0];
@@ -289,6 +418,7 @@ class CPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
       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());
+      T* input_roi_grad_data = input_roi_grad->mutable_data<T>(ctx.GetPlace());
 
       // set gradient of X to be 0. before backpropagate.
       math::SetConstant<DeviceContext, T> set_zero;
@@ -306,11 +436,12 @@ class CPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
 
         // set roi_batch_id
         int roi_batch_id = rois_batch_id_data[n];
-        int input_channel = (c * pooled_height + ph) * pooled_width + pw;
+        int input_channel = c;
         int input_offset =
             (roi_batch_id * input_channels + input_channel) * height * width;
         T* offset_input_grad_data = input_grad_data + input_offset;
         const T* offset_output_grad_data = output_grad_data + i;
+        const T* offset_out_data = out_data + i;
 
         // [start, end) interval for spatial sampling
         const T* offset_input_rois = input_rois + n * 4;
@@ -318,6 +449,7 @@ class CPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
         T roi_start_h = static_cast<T>(offset_input_rois[1]) * spatial_scale;
         T roi_end_w = static_cast<T>(offset_input_rois[2]) * spatial_scale;
         T roi_end_h = static_cast<T>(offset_input_rois[3]) * spatial_scale;
+        T* offset_input_roi_grad_data = input_roi_grad_data + n * 4;
 
         T roi_width = std::max(roi_end_w - roi_start_w, static_cast<T>(0.0));
         T roi_height = std::max(roi_end_h - roi_start_h, static_cast<T>(0.0));
@@ -355,6 +487,16 @@ class CPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
                 height, width, PrRoIPoolingDistributeDiff<T>);
           }
         }
+
+        const T* offset_in_data = in_data + input_offset;
+        PrRoIPoolingCoorBackward(
+            s_w, e_w, s_h, e_h, width, height, win_start_w, win_start_h,
+            win_end_w, win_end_h, pw, ph, pooled_width, pooled_height, win_size,
+            spatial_scale, offset_in_data, offset_out_data,
+            offset_input_grad_data, offset_input_roi_grad_data,
+            CPUAccumulateRois<T>,
+            [](const T x, const T y) { return std::max(x, y); },
+            [](const T x, const T y) { return std::min(x, y); });
       }
     }
   }

python/paddle/fluid/layers/nn.py

@@ -15442,7 +15442,6 @@ def psroi_pool(input,
 @templatedoc()
 def prroi_pool(input,
                rois,
-               output_channels,
                spatial_scale=1.0,
                pooled_height=1,
                pooled_width=1,
@@ -15459,7 +15458,6 @@ def prroi_pool(input,
                         is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is
                         the top left coordinates, and (x2, y2) is the bottom
                         right coordinates.
-        output_channels (integer): The output's channel.
         spatial_scale (float): Ratio of input feature map height (or width) to raw image height (or width).
                              Equals the reciprocal of total stride in convolutional layers, Default: 1.0.
         pooled_height (integer): The pooled output height. Default: 1.
@@ -15475,12 +15473,10 @@ def prroi_pool(input,
             import paddle.fluid as fluid
             x = fluid.layers.data(name='x', shape=[490, 28, 28], dtype='float32')
             rois = fluid.layers.data(name='rois', shape=[4], lod_level=1, dtype='float32')
-            pool_out = fluid.layers.prroi_pool(x, rois, 10, 1.0, 7, 7)
+            pool_out = fluid.layers.prroi_pool(x, rois, 1.0, 7, 7)
     """
     helper = LayerHelper('prroi_pool', **locals())
     # check attrs
-    if not isinstance(output_channels, int):
-        raise TypeError("output_channels must be int type")
     if not isinstance(spatial_scale, float):
         raise TypeError("spatial_scale must be float type")
     if not isinstance(pooled_height, int):
@@ -15495,7 +15491,6 @@ def prroi_pool(input,
                 'ROIs': rois},
         outputs={'Out': out},
         attrs={
-            'output_channels': output_channels,
             'spatial_scale': spatial_scale,
             'pooled_height': pooled_height,
             'pooled_width': pooled_width

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

@@ -133,8 +133,7 @@ class PyPrRoIPool(object):
                             s_h = math.floor(win_start_h)
                             e_h = math.ceil(win_end_h)
 
-                            c_in = (c * pooled_height + ph) * pooled_width + pw
-
+                            c_in = c
                             for w_iter in range(int(s_w), int(e_w)):
                                 for h_iter in range(int(s_h), int(e_h)):
                                     sum_out += self._PrRoIPoolingMatCalculation(

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

@@ -48,7 +48,7 @@ class TestPRROIPoolOp(OpTest):
         self.x_dim = [self.batch_size, self.channels, self.height, self.width]
 
         self.spatial_scale = 1.0 / 4.0
-        self.output_channels = 3
+        self.output_channels = self.channels
         self.pooled_height = 2
         self.pooled_width = 2
 
@@ -60,15 +60,15 @@ class TestPRROIPoolOp(OpTest):
         for bno in range(self.batch_size):
             self.rois_lod[0].append(bno + 1)
             for i in range(bno + 1):
-                x1 = np.random.random_integers(
+                x1 = np.random.uniform(
                     0, self.width // self.spatial_scale - self.pooled_width)
-                y1 = np.random.random_integers(
+                y1 = np.random.uniform(
                     0, self.height // self.spatial_scale - self.pooled_height)
 
-                x2 = np.random.random_integers(x1 + self.pooled_width,
+                x2 = np.random.uniform(x1 + self.pooled_width,
                                        self.width // self.spatial_scale)
-                y2 = np.random.random_integers(
-                    y1 + self.pooled_height, self.height // self.spatial_scale)
+                y2 = np.random.uniform(y1 + self.pooled_height,
+                                       self.height // self.spatial_scale)
                 roi = [bno, x1, y1, x2, y2]
                 rois.append(roi)
         self.rois_num = len(rois)
@@ -93,8 +93,7 @@ class TestPRROIPoolOp(OpTest):
                 dtype="float32")
             rois = fluid.layers.data(
                 name="ROIs", shape=[4], dtype="float32", lod_level=1)
-            output = fluid.layers.prroi_pool(x, rois, self.output_channels,
-                                             0.25, 2, 2)
+            output = fluid.layers.prroi_pool(x, rois, 0.25, 2, 2)
             loss = fluid.layers.mean(output)
             optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
             optimizer.minimize(loss)
@@ -120,18 +119,15 @@ class TestPRROIPoolOp(OpTest):
                 name="x", shape=[245, 30, 30], dtype="float32")
             rois = fluid.layers.data(
                 name="rois", shape=[4], dtype="float32", lod_level=1)
-            # channel must be int type
-            self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.5,
-                              0.25, 7, 7)
             # spatial_scale must be float type
-            self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 5, 2,
-                              7, 7)
+            self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7,
+                              7)
             # pooled_height must be int type
-            self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 5,
-                              0.25, 0.7, 7)
+            self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25,
+                              0.7, 7)
             # pooled_width must be int type
-            self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 5,
-                              0.25, 7, 0.7)
+            self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25,
+                              7, 0.7)
 
 
 if __name__ == '__main__':