CUDA kernel for density_prior_box_op. (#14513)

* CUDA kernel for density_prior_box_op.
* Support flatten to 2D.

paddle/fluid/API.spec

@@ -276,7 +276,7 @@ paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, k
 paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.prior_box ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False))
-paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, None))
+paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'flatten_to_2d', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, False, None))
 paddle.fluid.layers.multi_box_head ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False))
 paddle.fluid.layers.bipartite_match ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None))

paddle/fluid/framework/op_desc.cc

@@ -252,6 +252,12 @@ void OpDesc::SetAttr(const std::string &name, const Attribute &v) {
         this->attrs_[name] = std::vector<int>();
         break;
       }
+      case proto::AttrType::LONGS: {
+        VLOG(110) << "SetAttr: " << Type() << ", " << name
+                  << " from LONGS to LONGS";
+        this->attrs_[name] = std::vector<int64_t>();
+        break;
+      }
       case proto::AttrType::FLOATS: {
         VLOG(110) << "SetAttr: " << Type() << ", " << name
                   << " from INTS to FLOATS";

paddle/fluid/operators/detection/CMakeLists.txt

@@ -22,7 +22,7 @@ iou_similarity_op.cu)
 detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
 detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc poly_util.cc gpc.cc)
 detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
-detection_library(density_prior_box_op SRCS density_prior_box_op.cc)
+detection_library(density_prior_box_op SRCS density_prior_box_op.cc density_prior_box_op.cu)
 detection_library(anchor_generator_op SRCS anchor_generator_op.cc
 anchor_generator_op.cu)
 detection_library(target_assign_op SRCS target_assign_op.cc

paddle/fluid/operators/detection/density_prior_box_op.cc

@@ -39,24 +39,27 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
     auto fixed_sizes = ctx->Attrs().Get<std::vector<float>>("fixed_sizes");
     auto fixed_ratios = ctx->Attrs().Get<std::vector<float>>("fixed_ratios");
     auto densities = ctx->Attrs().Get<std::vector<int>>("densities");
+    bool flatten = ctx->Attrs().Get<bool>("flatten_to_2d");
 
     PADDLE_ENFORCE_EQ(fixed_sizes.size(), densities.size(),
                       "The number of fixed_sizes and densities must be equal.");
     size_t num_priors = 0;
-    if ((fixed_sizes.size() > 0) && (densities.size() > 0)) {
-      for (size_t i = 0; i < densities.size(); ++i) {
-        if (fixed_ratios.size() > 0) {
-          num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
-        }
-      }
+    for (size_t i = 0; i < densities.size(); ++i) {
+      num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
+    }
+    if (!flatten) {
+      std::vector<int64_t> dim_vec(4);
+      dim_vec[0] = input_dims[2];
+      dim_vec[1] = input_dims[3];
+      dim_vec[2] = num_priors;
+      dim_vec[3] = 4;
+      ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
+      ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
+    } else {
+      int64_t dim0 = input_dims[2] * input_dims[3] * num_priors;
+      ctx->SetOutputDim("Boxes", {dim0, 4});
+      ctx->SetOutputDim("Variances", {dim0, 4});
     }
-    std::vector<int64_t> dim_vec(4);
-    dim_vec[0] = input_dims[2];
-    dim_vec[1] = input_dims[3];
-    dim_vec[2] = num_priors;
-    dim_vec[3] = 4;
-    ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
-    ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
   }
 
  protected:
@@ -64,7 +67,7 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel {
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
-        platform::CPUPlace());
+        ctx.GetPlace());
   }
 };
 
@@ -101,7 +104,10 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
         });
     AddAttr<bool>("clip", "(bool) Whether to clip out-of-boundary boxes.")
         .SetDefault(true);
-
+    AddAttr<bool>("flatten_to_2d",
+                  "(bool) Whether to flatten to 2D and "
+                  "the second dim is 4.")
+        .SetDefault(false);
     AddAttr<float>(
         "step_w",
         "Density prior boxes step across width, 0.0 for auto calculation.")

paddle/fluid/operators/detection/density_prior_box_op.cu

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/detection/density_prior_box_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+static __device__ inline T Clip(T in) {
+  return min(max(in, 0.), 1.);
+}
+
+template <typename T>
+static __global__ void GenDensityPriorBox(
+    const int height, const int width, const int im_height, const int im_width,
+    const T offset, const T step_width, const T step_height,
+    const int num_priors, const T* ratios_shift, bool is_clip, const T var_xmin,
+    const T var_ymin, const T var_xmax, const T var_ymax, T* out, T* var) {
+  int gidx = blockIdx.x * blockDim.x + threadIdx.x;
+  int gidy = blockIdx.y * blockDim.y + threadIdx.y;
+  int step_x = blockDim.x * gridDim.x;
+  int step_y = blockDim.y * gridDim.y;
+
+  const T* width_ratio = ratios_shift;
+  const T* height_ratio = ratios_shift + num_priors;
+  const T* width_shift = ratios_shift + 2 * num_priors;
+  const T* height_shift = ratios_shift + 3 * num_priors;
+
+  for (int j = gidy; j < height; j += step_y) {
+    for (int i = gidx; i < width * num_priors; i += step_x) {
+      int h = j;
+      int w = i / num_priors;
+      int k = i % num_priors;
+
+      T center_x = (w + offset) * step_width;
+      T center_y = (h + offset) * step_height;
+
+      T center_x_temp = center_x + width_shift[k];
+      T center_y_temp = center_y + height_shift[k];
+
+      T box_width_ratio = width_ratio[k] / 2.;
+      T box_height_ratio = height_ratio[k] / 2.;
+
+      T xmin = max((center_x_temp - box_width_ratio) / im_width, 0.);
+      T ymin = max((center_y_temp - box_height_ratio) / im_height, 0.);
+      T xmax = min((center_x_temp + box_width_ratio) / im_width, 1.);
+      T ymax = min((center_y_temp + box_height_ratio) / im_height, 1.);
+
+      int out_offset = (j * width * num_priors + i) * 4;
+      out[out_offset] = is_clip ? Clip<T>(xmin) : xmin;
+      out[out_offset + 1] = is_clip ? Clip<T>(ymin) : ymin;
+      out[out_offset + 2] = is_clip ? Clip<T>(xmax) : xmax;
+      out[out_offset + 3] = is_clip ? Clip<T>(ymax) : ymax;
+
+      var[out_offset] = var_xmin;
+      var[out_offset + 1] = var_ymin;
+      var[out_offset + 2] = var_xmax;
+      var[out_offset + 3] = var_ymax;
+    }
+  }
+}
+
+template <typename T>
+class DensityPriorBoxOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<paddle::framework::Tensor>("Input");
+    auto* image = ctx.Input<paddle::framework::Tensor>("Image");
+    auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
+    auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
+
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+    auto is_clip = ctx.Attr<bool>("clip");
+
+    auto fixed_sizes = ctx.Attr<std::vector<float>>("fixed_sizes");
+    auto fixed_ratios = ctx.Attr<std::vector<float>>("fixed_ratios");
+    auto densities = ctx.Attr<std::vector<int>>("densities");
+
+    T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
+    T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
+    T offset = static_cast<T>(ctx.Attr<float>("offset"));
+
+    auto img_width = image->dims()[3];
+    auto img_height = image->dims()[2];
+
+    auto feature_width = input->dims()[3];
+    auto feature_height = input->dims()[2];
+
+    T step_width, step_height;
+    if (step_w == 0 || step_h == 0) {
+      step_width = static_cast<T>(img_width) / feature_width;
+      step_height = static_cast<T>(img_height) / feature_height;
+    } else {
+      step_width = step_w;
+      step_height = step_h;
+    }
+
+    int num_priors = 0;
+    for (size_t i = 0; i < densities.size(); ++i) {
+      num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
+    }
+    int step_average = static_cast<int>((step_width + step_height) * 0.5);
+
+    framework::Tensor h_temp;
+    T* tdata = h_temp.mutable_data<T>({num_priors * 4}, platform::CPUPlace());
+    int idx = 0;
+    for (size_t s = 0; s < fixed_sizes.size(); ++s) {
+      auto fixed_size = fixed_sizes[s];
+      int density = densities[s];
+      for (size_t r = 0; r < fixed_ratios.size(); ++r) {
+        float ar = fixed_ratios[r];
+        int shift = step_average / density;
+        float box_width_ratio = fixed_size * sqrt(ar);
+        float box_height_ratio = fixed_size / sqrt(ar);
+        for (int di = 0; di < density; ++di) {
+          for (int dj = 0; dj < density; ++dj) {
+            float center_x_temp = shift / 2. + dj * shift - step_average / 2.;
+            float center_y_temp = shift / 2. + di * shift - step_average / 2.;
+            tdata[idx] = box_width_ratio;
+            tdata[num_priors + idx] = box_height_ratio;
+            tdata[2 * num_priors + idx] = center_x_temp;
+            tdata[3 * num_priors + idx] = center_y_temp;
+            idx++;
+          }
+        }
+      }
+    }
+
+    boxes->mutable_data<T>(ctx.GetPlace());
+    vars->mutable_data<T>(ctx.GetPlace());
+
+    framework::Tensor d_temp;
+    framework::TensorCopySync(h_temp, ctx.GetPlace(), &d_temp);
+
+    // At least use 32 threads, at most 512 threads.
+    // blockx is multiple of 32.
+    int blockx = std::min(((feature_width * num_priors + 31) >> 5) << 5, 512L);
+    int gridx = (feature_width * num_priors + blockx - 1) / blockx;
+    dim3 threads(blockx, 1);
+    dim3 grids(gridx, feature_height);
+
+    auto stream =
+        ctx.template device_context<platform::CUDADeviceContext>().stream();
+    GenDensityPriorBox<T><<<grids, threads, 0, stream>>>(
+        feature_height, feature_width, img_height, img_width, offset,
+        step_width, step_height, num_priors, d_temp.data<T>(), is_clip,
+        variances[0], variances[1], variances[2], variances[3],
+        boxes->data<T>(), vars->data<T>());
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(density_prior_box,
+                        ops::DensityPriorBoxOpCUDAKernel<float>,
+                        ops::DensityPriorBoxOpCUDAKernel<double>);

paddle/fluid/operators/detection/density_prior_box_op.h

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
@@ -52,18 +52,16 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
       step_height = step_h;
     }
     int num_priors = 0;
-    if (fixed_sizes.size() > 0 && densities.size() > 0) {
-      for (size_t i = 0; i < densities.size(); ++i) {
-        if (fixed_ratios.size() > 0) {
-          num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
-        }
-      }
+    for (size_t i = 0; i < densities.size(); ++i) {
+      num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
     }
 
     boxes->mutable_data<T>(ctx.GetPlace());
     vars->mutable_data<T>(ctx.GetPlace());
-    auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
 
+    auto box_dim = vars->dims();
+    boxes->Resize({feature_height, feature_width, num_priors, 4});
+    auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
     int step_average = static_cast<int>((step_width + step_height) * 0.5);
 
     for (int h = 0; h < feature_height; ++h) {
@@ -76,36 +74,34 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
           auto fixed_size = fixed_sizes[s];
           int density = densities[s];
           // Generate density prior boxes with fixed ratios.
-          if (fixed_ratios.size() > 0) {
-            for (size_t r = 0; r < fixed_ratios.size(); ++r) {
-              float ar = fixed_ratios[r];
-              int shift = step_average / density;
-              float box_width_ratio = fixed_size * sqrt(ar);
-              float box_height_ratio = fixed_size / sqrt(ar);
-              for (int di = 0; di < density; ++di) {
-                for (int dj = 0; dj < density; ++dj) {
-                  float center_x_temp =
-                      center_x - step_average / 2. + shift / 2. + dj * shift;
-                  float center_y_temp =
-                      center_y - step_average / 2. + shift / 2. + di * shift;
-                  e_boxes(h, w, idx, 0) =
-                      (center_x_temp - box_width_ratio / 2.) / img_width >= 0
-                          ? (center_x_temp - box_width_ratio / 2.) / img_width
-                          : 0;
-                  e_boxes(h, w, idx, 1) =
-                      (center_y_temp - box_height_ratio / 2.) / img_height >= 0
-                          ? (center_y_temp - box_height_ratio / 2.) / img_height
-                          : 0;
-                  e_boxes(h, w, idx, 2) =
-                      (center_x_temp + box_width_ratio / 2.) / img_width <= 1
-                          ? (center_x_temp + box_width_ratio / 2.) / img_width
-                          : 1;
-                  e_boxes(h, w, idx, 3) =
-                      (center_y_temp + box_height_ratio / 2.) / img_height <= 1
-                          ? (center_y_temp + box_height_ratio / 2.) / img_height
-                          : 1;
-                  idx++;
-                }
+          for (size_t r = 0; r < fixed_ratios.size(); ++r) {
+            float ar = fixed_ratios[r];
+            int shift = step_average / density;
+            float box_width_ratio = fixed_size * sqrt(ar);
+            float box_height_ratio = fixed_size / sqrt(ar);
+            for (int di = 0; di < density; ++di) {
+              for (int dj = 0; dj < density; ++dj) {
+                float center_x_temp =
+                    center_x - step_average / 2. + shift / 2. + dj * shift;
+                float center_y_temp =
+                    center_y - step_average / 2. + shift / 2. + di * shift;
+                e_boxes(h, w, idx, 0) =
+                    (center_x_temp - box_width_ratio / 2.) / img_width >= 0
+                        ? (center_x_temp - box_width_ratio / 2.) / img_width
+                        : 0;
+                e_boxes(h, w, idx, 1) =
+                    (center_y_temp - box_height_ratio / 2.) / img_height >= 0
+                        ? (center_y_temp - box_height_ratio / 2.) / img_height
+                        : 0;
+                e_boxes(h, w, idx, 2) =
+                    (center_x_temp + box_width_ratio / 2.) / img_width <= 1
+                        ? (center_x_temp + box_width_ratio / 2.) / img_width
+                        : 1;
+                e_boxes(h, w, idx, 3) =
+                    (center_y_temp + box_height_ratio / 2.) / img_height <= 1
+                        ? (center_y_temp + box_height_ratio / 2.) / img_height
+                        : 1;
+                idx++;
               }
             }
           }
@@ -139,6 +135,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
     e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
 
     vars->Resize(var_dim);
+    boxes->Resize(box_dim);
   }
 };  // namespace operators
 

python/paddle/fluid/layers/detection.py

@@ -1029,6 +1029,7 @@ def density_prior_box(input,
                       clip=False,
                       steps=[0.0, 0.0],
                       offset=0.5,
+                      flatten_to_2d=False,
                       name=None):
     """
     **Density Prior Box Operator**
@@ -1065,22 +1066,24 @@ def density_prior_box(input,
             height/weight of the input will be automatically calculated.
             Default: [0., 0.]
        offset(float): Prior boxes center offset. Default: 0.5
+       flatten_to_2d(bool): Whether to flatten output prior boxes and variance
+           to 2D shape, the second dim is 4. Default: False.
        name(str): Name of the density prior box op. Default: None.
 
     Returns:
         tuple: A tuple with two Variable (boxes, variances)
 
         boxes: the output density prior boxes of PriorBox.
-        The layout is [H, W, num_priors, 4].
-        H is the height of input, W is the width of input,
-        num_priors is the total
-        box count of each position of input.
+            The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
+            The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
+            H is the height of input, W is the width of input,
+            num_priors is the total box count of each position of input.
 
         variances: the expanded variances of PriorBox.
-        The layout is [H, W, num_priors, 4].
-        H is the height of input, W is the width of input
-        num_priors is the total
-        box count of each position of input
+            The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
+            The layout is [H * W * num_priors, 4] when flatten_to_2d is True.
+            H is the height of input, W is the width of input
+            num_priors is the total box count of each position of input.
 
 
     Examples:
@@ -1089,14 +1092,11 @@ def density_prior_box(input,
             box, var = fluid.layers.density_prior_box(
                 input=conv1,
                 image=images,
-                min_sizes=[100.],
-                max_sizes=[200.],
-                aspect_ratios=[1.0, 1.0 / 2.0, 2.0],
-                densities=[3, 4],
-                fixed_sizes=[50., 60.],
-                fixed_ratios=[1.0, 3.0, 1.0 / 3.0],
-                flip=True,
-                clip=True)
+                densities=[4, 2, 1],
+                fixed_sizes=[32.0, 64.0, 128.0],
+                fixed_ratios=[1.],
+                clip=True,
+                flatten_to_2d=True)
     """
     helper = LayerHelper("density_prior_box", **locals())
     dtype = helper.input_dtype()
@@ -1127,14 +1127,11 @@ def density_prior_box(input,
         'step_w': steps[0],
         'step_h': steps[1],
         'offset': offset,
+        'densities': densities,
+        'fixed_sizes': fixed_sizes,
+        'fixed_ratios': fixed_ratios,
+        'flatten_to_2d': flatten_to_2d,
     }
-    if densities is not None and len(densities) > 0:
-        attrs['densities'] = densities
-    if fixed_sizes is not None and len(fixed_sizes) > 0:
-        attrs['fixed_sizes'] = fixed_sizes
-    if fixed_ratios is not None and len(fixed_ratios) > 0:
-        attrs['fixed_ratios'] = fixed_ratios
-
     box = helper.create_variable_for_type_inference(dtype)
     var = helper.create_variable_for_type_inference(dtype)
     helper.append_op(

python/paddle/fluid/tests/test_detection.py

@@ -112,38 +112,42 @@ class TestDetection(unittest.TestCase):
 
 class TestPriorBox(unittest.TestCase):
     def test_prior_box(self):
-        data_shape = [3, 224, 224]
-        images = fluid.layers.data(
-            name='pixel', shape=data_shape, dtype='float32')
-        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
-        box, var = layers.prior_box(
-            input=conv1,
-            image=images,
-            min_sizes=[100.0],
-            aspect_ratios=[1.],
-            flip=True,
-            clip=True)
-        assert len(box.shape) == 4
-        assert box.shape == var.shape
-        assert box.shape[3] == 4
+        program = Program()
+        with program_guard(program):
+            data_shape = [3, 224, 224]
+            images = fluid.layers.data(
+                name='pixel', shape=data_shape, dtype='float32')
+            conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+            box, var = layers.prior_box(
+                input=conv1,
+                image=images,
+                min_sizes=[100.0],
+                aspect_ratios=[1.],
+                flip=True,
+                clip=True)
+            assert len(box.shape) == 4
+            assert box.shape == var.shape
+            assert box.shape[3] == 4
 
 
 class TestDensityPriorBox(unittest.TestCase):
     def test_density_prior_box(self):
-        data_shape = [3, 224, 224]
-        images = fluid.layers.data(
-            name='pixel', shape=data_shape, dtype='float32')
-        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
-        box, var = layers.density_prior_box(
-            input=conv1,
-            image=images,
-            densities=[3, 4],
-            fixed_sizes=[50., 60.],
-            fixed_ratios=[1.0],
-            clip=True)
-        assert len(box.shape) == 4
-        assert box.shape == var.shape
-        assert box.shape[3] == 4
+        program = Program()
+        with program_guard(program):
+            data_shape = [3, 224, 224]
+            images = fluid.layers.data(
+                name='pixel', shape=data_shape, dtype='float32')
+            conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+            box, var = layers.density_prior_box(
+                input=conv1,
+                image=images,
+                densities=[3, 4],
+                fixed_sizes=[50., 60.],
+                fixed_ratios=[1.0],
+                clip=True)
+            assert len(box.shape) == 4
+            assert box.shape == var.shape
+            assert box.shape[-1] == 4
 
 
 class TestAnchorGenerator(unittest.TestCase):

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

@@ -36,7 +36,8 @@ class TestDensityPriorBoxOp(OpTest):
             'offset': self.offset,
             'densities': self.densities,
             'fixed_sizes': self.fixed_sizes,
-            'fixed_ratios': self.fixed_ratios
+            'fixed_ratios': self.fixed_ratios,
+            'flatten_to_2d': self.flatten_to_2d
         }
         self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
 
@@ -48,16 +49,17 @@ class TestDensityPriorBoxOp(OpTest):
         self.set_data()
 
     def set_density(self):
-        self.densities = []
-        self.fixed_sizes = []
-        self.fixed_ratios = []
+        self.densities = [4, 2, 1]
+        self.fixed_sizes = [32.0, 64.0, 128.0]
+        self.fixed_ratios = [1.0]
+        self.layer_w = 17
+        self.layer_h = 17
+        self.image_w = 533
+        self.image_h = 533
+        self.flatten_to_2d = False
 
     def init_test_params(self):
-        self.layer_w = 32
-        self.layer_h = 32
-
-        self.image_w = 40
-        self.image_h = 40
+        self.set_density()
 
         self.step_w = float(self.image_w) / float(self.layer_w)
         self.step_h = float(self.image_h) / float(self.layer_h)
@@ -69,8 +71,6 @@ class TestDensityPriorBoxOp(OpTest):
         self.variances = [0.1, 0.1, 0.2, 0.2]
         self.variances = np.array(self.variances, dtype=np.float).flatten()
 
-        self.set_density()
-
         self.clip = True
         self.num_priors = 0
         if len(self.fixed_sizes) > 0 and len(self.densities) > 0:
@@ -129,6 +129,9 @@ class TestDensityPriorBoxOp(OpTest):
                           (self.layer_h, self.layer_w, self.num_priors, 1))
         self.out_boxes = out_boxes.astype('float32')
         self.out_var = out_var.astype('float32')
+        if self.flatten_to_2d:
+            self.out_boxes = self.out_boxes.reshape((-1, 4))
+            self.out_var = self.out_var.reshape((-1, 4))
 
 
 class TestDensityPriorBox(TestDensityPriorBoxOp):
@@ -136,6 +139,11 @@ class TestDensityPriorBox(TestDensityPriorBoxOp):
         self.densities = [3, 4]
         self.fixed_sizes = [1.0, 2.0]
         self.fixed_ratios = [1.0]
+        self.layer_w = 32
+        self.layer_h = 32
+        self.image_w = 40
+        self.image_h = 40
+        self.flatten_to_2d = True
 
 
 if __name__ == '__main__':