add normalize switch to box_coder_op (#11129)

paddle/fluid/operators/detection/box_coder_op.cc

@@ -91,6 +91,10 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
                          "the code type used with the target box")
         .SetDefault("encode_center_size")
         .InEnum({"encode_center_size", "decode_center_size"});
+    AddAttr<bool>("box_normalized",
+                  "(bool, default true) "
+                  "whether treat the priorbox as a noramlized box")
+        .SetDefault(true);
     AddOutput("OutputBox",
               "(LoDTensor or Tensor) "
               "When code_type is 'encode_center_size', the output tensor of "

paddle/fluid/operators/detection/box_coder_op.cu

@@ -20,15 +20,16 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
                                        const T* prior_box_var_data,
                                        const T* target_box_data, const int row,
                                        const int col, const int len,
-                                       T* output) {
+                                       const bool normalized, T* output) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < row * col) {
     const int row_idx = idx / col;
     const int col_idx = idx % col;
-    T prior_box_width =
-        prior_box_data[col_idx * len + 2] - prior_box_data[col_idx * len];
-    T prior_box_height =
-        prior_box_data[col_idx * len + 3] - prior_box_data[col_idx * len + 1];
+    T prior_box_width = prior_box_data[col_idx * len + 2] -
+                        prior_box_data[col_idx * len] + (normalized == false);
+    T prior_box_height = prior_box_data[col_idx * len + 3] -
+                         prior_box_data[col_idx * len + 1] +
+                         (normalized == false);
     T prior_box_center_x =
         (prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
     T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
@@ -41,10 +42,11 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
     T target_box_center_y = (target_box_data[row_idx * len + 3] +
                              target_box_data[row_idx * len + 1]) /
                             2;
-    T target_box_width =
-        target_box_data[row_idx * len + 2] - target_box_data[row_idx * len];
-    T target_box_height =
-        target_box_data[row_idx * len + 3] - target_box_data[row_idx * len + 1];
+    T target_box_width = target_box_data[row_idx * len + 2] -
+                         target_box_data[row_idx * len] + (normalized == false);
+    T target_box_height = target_box_data[row_idx * len + 3] -
+                          target_box_data[row_idx * len + 1] +
+                          (normalized == false);
 
     output[idx * len] = (target_box_center_x - prior_box_center_x) /
                         prior_box_width / prior_box_var_data[col_idx * len];
@@ -63,14 +65,15 @@ __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
                                        const T* prior_box_var_data,
                                        const T* target_box_data, const int row,
                                        const int col, const int len,
-                                       T* output) {
+                                       const bool normalized, T* output) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < row * col) {
     const int col_idx = idx % col;
-    T prior_box_width =
-        prior_box_data[col_idx * len + 2] - prior_box_data[col_idx * len];
-    T prior_box_height =
-        prior_box_data[col_idx * len + 3] - prior_box_data[col_idx * len + 1];
+    T prior_box_width = prior_box_data[col_idx * len + 2] -
+                        prior_box_data[col_idx * len] + (normalized == false);
+    T prior_box_height = prior_box_data[col_idx * len + 3] -
+                         prior_box_data[col_idx * len + 1] +
+                         (normalized == false);
     T prior_box_center_x =
         (prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
     T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
@@ -93,8 +96,10 @@ __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
 
     output[idx * len] = target_box_center_x - target_box_width / 2;
     output[idx * len + 1] = target_box_center_y - target_box_height / 2;
-    output[idx * len + 2] = target_box_center_x + target_box_width / 2;
-    output[idx * len + 3] = target_box_center_y + target_box_height / 2;
+    output[idx * len + 2] =
+        target_box_center_x + target_box_width / 2 - (normalized == false);
+    output[idx * len + 3] =
+        target_box_center_y + target_box_height / 2 - (normalized == false);
   }
 }
 
@@ -128,14 +133,15 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     T* output = output_box->data<T>();
 
     auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
+    bool normalized = context.Attr<bool>("box_normalized");
     if (code_type == BoxCodeType::kEncodeCenterSize) {
       EncodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
           prior_box_data, prior_box_var_data, target_box_data, row, col, len,
-          output);
+          normalized, output);
     } else if (code_type == BoxCodeType::kDecodeCenterSize) {
       DecodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
           prior_box_data, prior_box_var_data, target_box_data, row, col, len,
-          output);
+          normalized, output);
     }
   }
 };

paddle/fluid/operators/detection/box_coder_op.h

@@ -34,7 +34,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
   void EncodeCenterSize(const framework::Tensor& target_box,
                         const framework::Tensor& prior_box,
                         const framework::Tensor& prior_box_var,
-                        T* output) const {
+                        const bool normalized, T* output) const {
     int64_t row = target_box.dims()[0];
     int64_t col = prior_box.dims()[0];
     int64_t len = prior_box.dims()[1];
@@ -44,10 +44,11 @@ class BoxCoderKernel : public framework::OpKernel<T> {
 
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
-        T prior_box_width =
-            prior_box_data[j * len + 2] - prior_box_data[j * len];
-        T prior_box_height =
-            prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
+        T prior_box_width = prior_box_data[j * len + 2] -
+                            prior_box_data[j * len] + (normalized == false);
+        T prior_box_height = prior_box_data[j * len + 3] -
+                             prior_box_data[j * len + 1] +
+                             (normalized == false);
         T prior_box_center_x =
             (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
         T prior_box_center_y =
@@ -57,10 +58,11 @@ class BoxCoderKernel : public framework::OpKernel<T> {
             (target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
         T target_box_center_y =
             (target_box_data[i * len + 3] + target_box_data[i * len + 1]) / 2;
-        T target_box_width =
-            target_box_data[i * len + 2] - target_box_data[i * len];
-        T target_box_height =
-            target_box_data[i * len + 3] - target_box_data[i * len + 1];
+        T target_box_width = target_box_data[i * len + 2] -
+                             target_box_data[i * len] + (normalized == false);
+        T target_box_height = target_box_data[i * len + 3] -
+                              target_box_data[i * len + 1] +
+                              (normalized == false);
 
         size_t offset = i * col * len + j * len;
         output[offset] = (target_box_center_x - prior_box_center_x) /
@@ -79,7 +81,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
   void DecodeCenterSize(const framework::Tensor& target_box,
                         const framework::Tensor& prior_box,
                         const framework::Tensor& prior_box_var,
-                        T* output) const {
+                        const bool normalized, T* output) const {
     int64_t row = target_box.dims()[0];
     int64_t col = prior_box.dims()[0];
     int64_t len = prior_box.dims()[1];
@@ -91,10 +93,11 @@ class BoxCoderKernel : public framework::OpKernel<T> {
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
         size_t offset = i * col * len + j * len;
-        T prior_box_width =
-            prior_box_data[j * len + 2] - prior_box_data[j * len];
-        T prior_box_height =
-            prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
+        T prior_box_width = prior_box_data[j * len + 2] -
+                            prior_box_data[j * len] + (normalized == false);
+        T prior_box_height = prior_box_data[j * len + 3] -
+                             prior_box_data[j * len + 1] +
+                             (normalized == false);
         T prior_box_center_x =
             (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
         T prior_box_center_y =
@@ -116,8 +119,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
 
         output[offset] = target_box_center_x - target_box_width / 2;
         output[offset + 1] = target_box_center_y - target_box_height / 2;
-        output[offset + 2] = target_box_center_x + target_box_width / 2;
-        output[offset + 3] = target_box_center_y + target_box_height / 2;
+        output[offset + 2] =
+            target_box_center_x + target_box_width / 2 - (normalized == false);
+        output[offset + 3] =
+            target_box_center_y + target_box_height / 2 - (normalized == false);
       }
     }
   }
@@ -139,11 +144,14 @@ class BoxCoderKernel : public framework::OpKernel<T> {
     output_box->mutable_data<T>({row, col, len}, context.GetPlace());
 
     auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
+    bool normalized = context.Attr<bool>("box_normalized");
     T* output = output_box->data<T>();
     if (code_type == BoxCodeType::kEncodeCenterSize) {
-      EncodeCenterSize(*target_box, *prior_box, *prior_box_var, output);
+      EncodeCenterSize(*target_box, *prior_box, *prior_box_var, normalized,
+                       output);
     } else if (code_type == BoxCodeType::kDecodeCenterSize) {
-      DecodeCenterSize(*target_box, *prior_box, *prior_box_var, output);
+      DecodeCenterSize(*target_box, *prior_box, *prior_box_var, normalized,
+                       output);
     }
   }
 };

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

@@ -19,7 +19,8 @@ import math
 from op_test import OpTest
 
 
-def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
+def box_coder(target_box, prior_box, prior_box_var, output_box, code_type,
+              box_normalized):
     prior_box_x = (
         (prior_box[:, 2] + prior_box[:, 0]) / 2).reshape(1, prior_box.shape[0])
     prior_box_y = (
@@ -30,6 +31,9 @@ def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
         (prior_box[:, 3] - prior_box[:, 1])).reshape(1, prior_box.shape[0])
     prior_box_var = prior_box_var.reshape(1, prior_box_var.shape[0],
                                           prior_box_var.shape[1])
+    if not box_normalized:
+        prior_box_height = prior_box_height + 1
+        prior_box_width = prior_box_width + 1
 
     if (code_type == "EncodeCenterSize"):
         target_box_x = ((target_box[:, 2] + target_box[:, 0]) / 2).reshape(
@@ -40,6 +44,9 @@ def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
             target_box.shape[0], 1)
         target_box_height = ((target_box[:, 3] - target_box[:, 1])).reshape(
             target_box.shape[0], 1)
+        if not box_normalized:
+            target_box_height = target_box_height + 1
+            target_box_width = target_box_width + 1
 
         output_box[:,:,0] = (target_box_x - prior_box_x) / prior_box_width / \
                 prior_box_var[:,:,0]
@@ -64,9 +71,13 @@ def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
         output_box[:, :, 1] = target_box_y - target_box_height / 2
         output_box[:, :, 2] = target_box_x + target_box_width / 2
         output_box[:, :, 3] = target_box_y + target_box_height / 2
+        if not box_normalized:
+            output_box[:, :, 2] = output_box[:, :, 2] - 1
+            output_box[:, :, 3] = output_box[:, :, 3] - 1
 
 
-def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type):
+def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type,
+                    box_normalized):
     n = target_box.shape[0]
     m = prior_box.shape[0]
     output_box = np.zeros((n, m, 4), dtype=np.float32)
@@ -74,11 +85,11 @@ def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type):
         if (code_type == "EncodeCenterSize"):
             box_coder(target_box[lod[i]:lod[i + 1], :], prior_box,
                       prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
-                      code_type)
+                      code_type, box_normalized)
         elif (code_type == "DecodeCenterSize"):
             box_coder(target_box[lod[i]:lod[i + 1], :, :], prior_box,
                       prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
-                      code_type)
+                      code_type, box_normalized)
     return output_box
 
 
@@ -93,15 +104,19 @@ class TestBoxCoderOp(OpTest):
         prior_box_var = np.random.random((10, 4)).astype('float32')
         target_box = np.random.random((5, 10, 4)).astype('float32')
         code_type = "DecodeCenterSize"
+        box_normalized = False
         output_box = batch_box_coder(prior_box, prior_box_var, target_box,
-                                     lod[0], code_type)
+                                     lod[0], code_type, box_normalized)
 
         self.inputs = {
             'PriorBox': prior_box,
             'PriorBoxVar': prior_box_var,
             'TargetBox': target_box,
         }
-        self.attrs = {'code_type': 'decode_center_size'}
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False
+        }
         self.outputs = {'OutputBox': output_box}
 
 
@@ -116,15 +131,16 @@ class TestBoxCoderOpWithLoD(OpTest):
         prior_box_var = np.random.random((10, 4)).astype('float32')
         target_box = np.random.random((20, 4)).astype('float32')
         code_type = "EncodeCenterSize"
+        box_normalized = True
         output_box = batch_box_coder(prior_box, prior_box_var, target_box,
-                                     lod[0], code_type)
+                                     lod[0], code_type, box_normalized)
 
         self.inputs = {
             'PriorBox': prior_box,
             'PriorBoxVar': prior_box_var,
             'TargetBox': (target_box, lod),
         }
-        self.attrs = {'code_type': 'encode_center_size'}
+        self.attrs = {'code_type': 'encode_center_size', 'box_normalized': True}
         self.outputs = {'OutputBox': output_box}