Add CUDA kernel for prior_box_op.

paddle/fluid/operators/prior_box_op.cc

@@ -73,7 +73,7 @@ class PriorBoxOp : public framework::OperatorWithKernel {
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
-        platform::CPUPlace());
+        ctx.device_context());
   }
 };
 
@@ -171,6 +171,5 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker,
                   paddle::framework::EmptyGradOpMaker);
 
-REGISTER_OP_CPU_KERNEL(
-    prior_box, ops::PriorBoxOpKernel<paddle::platform::CPUPlace, float>,
-    ops::PriorBoxOpKernel<paddle::platform::CPUPlace, double>);
+REGISTER_OP_CPU_KERNEL(prior_box, ops::PriorBoxOpKernel<float>,
+                       ops::PriorBoxOpKernel<double>);

paddle/fluid/operators/prior_box_op.cu

@@ -0,0 +1,167 @@
+/* Copyright (c) 2016 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/prior_box_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__device__ inline T clip(T in) {
+  return min(max(in, 0.), 1.);
+}
+
+template <typename T>
+__global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
+                            const int width, const int im_height,
+                            const int im_width, const int as_num,
+                            const T offset, const T step_width,
+                            const T step_height, const T* min_sizes,
+                            const T* max_sizes, const int min_num,
+                            bool is_clip) {
+  int num_priors = max_sizes ? as_num * min_num + min_num : as_num * min_num;
+  int box_num = height * width * num_priors;
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < box_num;
+       i += blockDim.x * gridDim.x) {
+    int h = i / (num_priors * width);
+    int w = (i / num_priors) % width;
+    int p = i % num_priors;
+    int m = max_sizes ? p / (as_num + 1) : p / as_num;
+    T cx = (w + offset) * step_width;
+    T cy = (h + offset) * step_height;
+    T bw, bh;
+    T min_size = min_sizes[m];
+    if (max_sizes) {
+      int s = p % (as_num + 1);
+      if (s < as_num) {
+        T ar = aspect_ratios[s];
+        bw = min_size * sqrt(ar) / 2.;
+        bh = min_size / sqrt(ar) / 2.;
+      } else {
+        T max_size = max_sizes[m];
+        bw = sqrt(min_size * max_size) / 2.;
+        bh = bw;
+      }
+    } else {
+      int s = p % as_num;
+      T ar = aspect_ratios[s];
+      bw = min_size * sqrt(ar) / 2.;
+      bh = min_size / sqrt(ar) / 2.;
+    }
+    T xmin = (cx - bw) / im_width;
+    T ymin = (cy - bh) / im_height;
+    T xmax = (cx + bw) / im_width;
+    T ymax = (cy + bh) / im_height;
+    out[i * 4] = is_clip ? clip<T>(xmin) : xmin;
+    out[i * 4 + 1] = is_clip ? clip<T>(ymin) : ymin;
+    out[i * 4 + 2] = is_clip ? clip<T>(xmax) : xmax;
+    out[i * 4 + 3] = is_clip ? clip<T>(ymax) : ymax;
+  }
+}
+
+template <typename T>
+__global__ void SetVariance(T* out, const T* var, const int vnum,
+                            const int num) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
+       i += blockDim.x * gridDim.x) {
+    out[i] = var[i % vnum];
+  }
+}
+
+template <typename T>
+class PriorBoxOpCUDAKernel : 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 min_sizes = ctx.Attr<std::vector<float>>("min_sizes");
+    auto max_sizes = ctx.Attr<std::vector<float>>("max_sizes");
+    auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+    auto flip = ctx.Attr<bool>("flip");
+    auto clip = ctx.Attr<bool>("clip");
+
+    std::vector<float> aspect_ratios;
+    ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios);
+
+    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 im_width = image->dims()[3];
+    auto im_height = image->dims()[2];
+
+    auto width = input->dims()[3];
+    auto height = input->dims()[2];
+
+    T step_width, step_height;
+    if (step_w == 0 || step_h == 0) {
+      step_width = static_cast<T>(im_width) / width;
+      step_height = static_cast<T>(im_height) / height;
+    } else {
+      step_width = step_w;
+      step_height = step_h;
+    }
+
+    int num_priors = aspect_ratios.size() * min_sizes.size();
+    if (max_sizes.size() > 0) {
+      num_priors += max_sizes.size();
+    }
+    int min_num = static_cast<int>(min_sizes.size());
+    int box_num = width * height * num_priors;
+
+    int block = 512;
+    int grid = (box_num + block - 1) / block;
+
+    auto stream =
+        ctx.template device_context<platform::CUDADeviceContext>().stream();
+
+    boxes->mutable_data<T>(ctx.GetPlace());
+    vars->mutable_data<T>(ctx.GetPlace());
+
+    framework::Tensor r;
+    framework::TensorFromVector(aspect_ratios, ctx.device_context(), &r);
+
+    framework::Tensor min;
+    framework::TensorFromVector(min_sizes, ctx.device_context(), &min);
+
+    T* max_data = nullptr;
+    framework::Tensor max;
+    if (max_sizes.size() > 0) {
+      framework::TensorFromVector(max_sizes, ctx.device_context(), &max);
+      max_data = max.data<T>();
+    }
+
+    GenPriorBox<T><<<grid, block, 0, stream>>>(
+        boxes->data<T>(), r.data<T>(), height, width, im_height, im_width,
+        aspect_ratios.size(), offset, step_width, step_height, min.data<T>(),
+        max_data, min_num, clip);
+
+    framework::Tensor v;
+    framework::TensorFromVector(variances, ctx.device_context(), &v);
+    grid = (box_num * 4 + block - 1) / block;
+    SetVariance<T><<<grid, block, 0, stream>>>(vars->data<T>(), v.data<T>(),
+                                               variances.size(), box_num * 4);
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(prior_box, ops::PriorBoxOpCUDAKernel<float>,
+                        ops::PriorBoxOpCUDAKernel<double>);

paddle/fluid/operators/prior_box_op.h

@@ -51,7 +51,7 @@ struct ClipFunctor {
   }
 };
 
-template <typename Place, typename T>
+template <typename T>
 class PriorBoxOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
@@ -106,49 +106,24 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
         int idx = 0;
         for (size_t s = 0; s < min_sizes.size(); ++s) {
           auto min_size = min_sizes[s];
-          // first prior: aspect_ratio = 1, size = min_size
-          box_width = box_height = min_size / 2.;
-          // xmin
-          e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-          // ymin
-          e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-          // xmax
-          e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-          // ymax
-          e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
-
-          idx++;
-          if (max_sizes.size() > 0) {
-            auto max_size = max_sizes[s];
-            // second prior: aspect_ratio = 1,
-            // size = sqrt(min_size * max_size)
-            box_width = box_height = sqrt(min_size * max_size) / 2.;
-            // xmin
+          // priors with different aspect ratios
+          for (size_t r = 0; r < aspect_ratios.size(); ++r) {
+            float ar = aspect_ratios[r];
+            box_width = min_size * sqrt(ar) / 2.;
+            box_height = min_size / sqrt(ar) / 2.;
             e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-            // ymin
             e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-            // xmax
             e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-            // ymax
             e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
             idx++;
           }
-
-          // rest of priors
-          for (size_t r = 0; r < aspect_ratios.size(); ++r) {
-            float ar = aspect_ratios[r];
-            if (fabs(ar - 1.) < 1e-6) {
-              continue;
-            }
-            box_width = min_size * sqrt(ar) / 2.;
-            box_height = min_size / sqrt(ar) / 2.;
-            // xmin
+          if (max_sizes.size() > 0) {
+            auto max_size = max_sizes[s];
+            // square prior with size sqrt(minSize * maxSize)
+            box_width = box_height = sqrt(min_size * max_size) / 2.;
             e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
-            // ymin
             e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
-            // xmax
             e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
-            // ymax
             e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
             idx++;
           }

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

@@ -28,7 +28,6 @@ def set_data(self):
 
         self.attrs = {
             'min_sizes': self.min_sizes,
-            'max_sizes': self.max_sizes,
             'aspect_ratios': self.aspect_ratios,
             'variances': self.variances,
             'flip': self.flip,
@@ -37,25 +36,28 @@ def set_data(self):
             'step_h': self.step_h,
             'offset': self.offset
         }
+        if len(self.max_sizes) > 0:
+            self.attrs['max_sizes'] = self.max_sizes
 
         self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
 
     def test_check_output(self):
         self.check_output()
 
-    def test_check_grad(self):
-        return
-
     def setUp(self):
         self.op_type = "prior_box"
         self.set_data()
 
+    def set_max_sizes(self):
+        max_sizes = [5, 10]
+        self.max_sizes = np.array(max_sizes).astype('float32').tolist()
+
     def init_test_params(self):
-        self.layer_w = 4
-        self.layer_h = 4
+        self.layer_w = 32
+        self.layer_h = 32
 
-        self.image_w = 20
-        self.image_h = 20
+        self.image_w = 40
+        self.image_h = 40
 
         self.step_w = float(self.image_w) / float(self.layer_w)
         self.step_h = float(self.image_h) / float(self.layer_h)
@@ -66,8 +68,7 @@ def init_test_params(self):
 
         self.min_sizes = [2, 4]
         self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
-        self.max_sizes = [5, 10]
-        self.max_sizes = np.array(self.max_sizes).astype('float32').tolist()
+        self.set_max_sizes()
         self.aspect_ratios = [2.0, 3.0]
         self.flip = True
         self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
@@ -79,7 +80,7 @@ def init_test_params(self):
         self.clip = True
 
         self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
-        if len(self.max_sizes) > 1:
+        if len(self.max_sizes) > 0:
             self.num_priors += len(self.max_sizes)
         self.offset = 0.5
 
@@ -105,35 +106,27 @@ def init_test_output(self):
                 idx = 0
                 for s in range(len(self.min_sizes)):
                     min_size = self.min_sizes[s]
-                    c_w = c_h = min_size / 2.
-                    out_boxes[h, w, idx, :] = [
-                        (c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h,
-                        (c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h
-                    ]
-                    idx += 1
-
-                    if len(self.max_sizes) > 0:
-                        max_size = self.max_sizes[s]
-                        # second prior: aspect_ratio = 1,
-                        c_w = c_h = math.sqrt(min_size * max_size) / 2
+                    # rest of priors
+                    for r in range(len(self.real_aspect_ratios)):
+                        ar = self.real_aspect_ratios[r]
+                        c_w = min_size * math.sqrt(ar) / 2
+                        c_h = (min_size / math.sqrt(ar)) / 2
                         out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
                                                    (c_y - c_h) / self.image_h,
                                                    (c_x + c_w) / self.image_w,
                                                    (c_y + c_h) / self.image_h]
                         idx += 1
 
-                    # rest of priors
-                    for r in range(len(self.real_aspect_ratios)):
-                        ar = self.real_aspect_ratios[r]
-                        if math.fabs(ar - 1.) < 1e-6:
-                            continue
-                        c_w = min_size * math.sqrt(ar) / 2
-                        c_h = (min_size / math.sqrt(ar)) / 2
+                    if len(self.max_sizes) > 0:
+                        max_size = self.max_sizes[s]
+                        # second prior: aspect_ratio = 1,
+                        c_w = c_h = math.sqrt(min_size * max_size) / 2
                         out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
                                                    (c_y - c_h) / self.image_h,
                                                    (c_x + c_w) / self.image_w,
                                                    (c_y + c_h) / self.image_h]
                         idx += 1
+
         # clip the prior's coordidate such that it is within[0, 1]
         if self.clip:
             out_boxes = np.clip(out_boxes, 0.0, 1.0)
@@ -144,5 +137,10 @@ def init_test_output(self):
         self.out_var = out_var.astype('float32')
 
 
+class TestPriorBoxOpWithMaxSize(TestPriorBoxOp):
+    def set_max_sizes(self):
+        self.max_sizes = []
+
+
 if __name__ == '__main__':
     unittest.main()