Add prior_box output order control (#12032)

* Add flag to set prior_box output order.

paddle/fluid/operators/detection/prior_box_op.cc

@@ -149,6 +149,13 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
                    "(float) "
                    "Prior boxes center offset.")
         .SetDefault(0.5);
+    AddAttr<bool>(
+        "min_max_aspect_ratios_order",
+        "(bool) If set True, the output prior box is in order of"
+        "[min, max, aspect_ratios], which is consistent with Caffe."
+        "Please note, this order affects the weights order of convolution layer"
+        "followed by and does not affect the final detection results.")
+        .SetDefault(false);
     AddComment(R"DOC(
 Prior box operator
 Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.

paddle/fluid/operators/detection/prior_box_op.cu

@@ -28,8 +28,8 @@ __global__ void GenPriorBox(T* out, const T* aspect_ratios, const int 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) {
+                            const T* max_sizes, const int min_num, bool is_clip,
+                            bool min_max_aspect_ratios_order) {
   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;
@@ -44,6 +44,7 @@ __global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
     T min_size = min_sizes[m];
     if (max_sizes) {
       int s = p % (as_num + 1);
+      if (!min_max_aspect_ratios_order) {
         if (s < as_num) {
           T ar = aspect_ratios[s];
           bw = min_size * sqrt(ar) / 2.;
@@ -53,6 +54,19 @@ __global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
           bw = sqrt(min_size * max_size) / 2.;
           bh = bw;
         }
+      } else {
+        if (s == 0) {
+          bw = bh = min_size / 2.;
+        } else if (s == 1) {
+          T max_size = max_sizes[m];
+          bw = sqrt(min_size * max_size) / 2.;
+          bh = bw;
+        } else {
+          T ar = aspect_ratios[s - 1];
+          bw = min_size * sqrt(ar) / 2.;
+          bh = min_size / sqrt(ar) / 2.;
+        }
+      }
     } else {
       int s = p % as_num;
       T ar = aspect_ratios[s];
@@ -94,6 +108,8 @@ class PriorBoxOpCUDAKernel : public framework::OpKernel<T> {
     auto variances = ctx.Attr<std::vector<float>>("variances");
     auto flip = ctx.Attr<bool>("flip");
     auto clip = ctx.Attr<bool>("clip");
+    auto min_max_aspect_ratios_order =
+        ctx.Attr<bool>("min_max_aspect_ratios_order");
 
     std::vector<float> aspect_ratios;
     ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);
@@ -149,7 +165,7 @@ class PriorBoxOpCUDAKernel : public framework::OpKernel<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);
+        max_data, min_num, clip, min_max_aspect_ratios_order);
 
     framework::Tensor v;
     framework::TensorFromVector(variances, ctx.device_context(), &v);

paddle/fluid/operators/detection/prior_box_op.h

@@ -68,6 +68,8 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
     auto variances = ctx.Attr<std::vector<float>>("variances");
     auto flip = ctx.Attr<bool>("flip");
     auto clip = ctx.Attr<bool>("clip");
+    auto min_max_aspect_ratios_order =
+        ctx.Attr<bool>("min_max_aspect_ratios_order");
 
     std::vector<float> aspect_ratios;
     ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);
@@ -108,6 +110,38 @@ 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];
+          if (min_max_aspect_ratios_order) {
+            box_width = box_height = min_size / 2.;
+            e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
+            e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
+            e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
+            e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
+            idx++;
+            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;
+              e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
+              e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
+              e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
+              idx++;
+            }
+            // priors with different aspect ratios
+            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.;
+              e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
+              e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
+              e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
+              e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
+              idx++;
+            }
+          } else {
             // priors with different aspect ratios
             for (size_t r = 0; r < aspect_ratios.size(); ++r) {
               float ar = aspect_ratios[r];
@@ -132,6 +166,7 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
           }
         }
       }
+    }
 
     if (clip) {
       platform::Transform<platform::CPUDeviceContext> trans;

python/paddle/fluid/layers/detection.py

@@ -789,7 +789,8 @@ def prior_box(input,
               clip=False,
               steps=[0.0, 0.0],
               offset=0.5,
-              name=None):
+              name=None,
+              min_max_aspect_ratios_order=False):
     """
     **Prior Box Operator**
 
@@ -818,6 +819,11 @@ def prior_box(input,
             Default: [0., 0.]
        offset(float): Prior boxes center offset. Default: 0.5
        name(str): Name of the prior box op. Default: None.
+       min_max_aspect_ratios_order(bool): If set True, the output prior box is
+            in order of [min, max, aspect_ratios], which is consistent with 
+            Caffe. Please note, this order affects the weights order of
+            convolution layer followed by and does not affect the final
+            detection results. Default: False.
 
     Returns:
         tuple: A tuple with two Variable (boxes, variances)
@@ -871,7 +877,8 @@ def prior_box(input,
         'clip': clip,
         'step_w': steps[0],
         'step_h': steps[1],
-        'offset': offset
+        'offset': offset,
+        'min_max_aspect_ratios_order': min_max_aspect_ratios_order
     }
     if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
         if not _is_list_or_tuple_(max_sizes):
@@ -911,7 +918,8 @@ def multi_box_head(inputs,
                    kernel_size=1,
                    pad=0,
                    stride=1,
-                   name=None):
+                   name=None,
+                   min_max_aspect_ratios_order=False):
     """
     Generate prior boxes for SSD(Single Shot MultiBox Detector)
     algorithm. The details of this algorithm, please refer the
@@ -954,6 +962,11 @@ def multi_box_head(inputs,
        pad(int|list|tuple): The padding of conv2d. Default:0.
        stride(int|list|tuple): The stride of conv2d. Default:1,
        name(str): Name of the prior box layer. Default: None.
+       min_max_aspect_ratios_order(bool): If set True, the output prior box is
+            in order of [min, max, aspect_ratios], which is consistent with 
+            Caffe. Please note, this order affects the weights order of
+            convolution layer followed by and does not affect the fininal
+            detection results. Default: False.
 
     Returns:
         tuple: A tuple with four Variables. (mbox_loc, mbox_conf, boxes, variances)
@@ -1068,7 +1081,8 @@ def multi_box_head(inputs,
         step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0]
 
         box, var = prior_box(input, image, min_size, max_size, aspect_ratio,
-                             variance, flip, clip, step, offset)
+                             variance, flip, clip, step, offset, None,
+                             min_max_aspect_ratios_order)
 
         box_results.append(box)
         var_results.append(var)

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

@@ -32,6 +32,7 @@ class TestPriorBoxOp(OpTest):
             'variances': self.variances,
             'flip': self.flip,
             'clip': self.clip,
+            'min_max_aspect_ratios_order': self.min_max_aspect_ratios_order,
             'step_w': self.step_w,
             'step_h': self.step_h,
             'offset': self.offset
@@ -52,6 +53,9 @@ class TestPriorBoxOp(OpTest):
         max_sizes = [5, 10]
         self.max_sizes = np.array(max_sizes).astype('float32').tolist()
 
+    def set_min_max_aspect_ratios_order(self):
+        self.min_max_aspect_ratios_order = False
+
     def init_test_params(self):
         self.layer_w = 32
         self.layer_h = 32
@@ -71,6 +75,7 @@ class TestPriorBoxOp(OpTest):
         self.set_max_sizes()
         self.aspect_ratios = [2.0, 3.0]
         self.flip = True
+        self.set_min_max_aspect_ratios_order()
         self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
         self.aspect_ratios = np.array(
             self.aspect_ratios, dtype=np.float).flatten()
@@ -78,7 +83,6 @@ class TestPriorBoxOp(OpTest):
         self.variances = np.array(self.variances, dtype=np.float).flatten()
 
         self.clip = True
-
         self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
         if len(self.max_sizes) > 0:
             self.num_priors += len(self.max_sizes)
@@ -106,26 +110,60 @@ class TestPriorBoxOp(OpTest):
                 idx = 0
                 for s in range(len(self.min_sizes)):
                     min_size = self.min_sizes[s]
+                    if not self.min_max_aspect_ratios_order:
                         # 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]
+                            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
+                            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
+                    else:
+                        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
+                            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 abs(ar - 1.) < 1e-6:
+                                continue
+                            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
 
         # clip the prior's coordidate such that it is within[0, 1]
         if self.clip:
@@ -137,10 +175,15 @@ class TestPriorBoxOp(OpTest):
         self.out_var = out_var.astype('float32')
 
 
-class TestPriorBoxOpWithMaxSize(TestPriorBoxOp):
+class TestPriorBoxOpWithoutMaxSize(TestPriorBoxOp):
     def set_max_sizes(self):
         self.max_sizes = []
 
 
+class TestPriorBoxOpWithSpecifiedOutOrder(TestPriorBoxOp):
+    def set_min_max_aspect_ratios_order(self):
+        self.min_max_aspect_ratios_order = True
+
+
 if __name__ == '__main__':
     unittest.main()