[Cherry pick] Enhance yolo_box & yolov3_loss (#25696)

* add scale_x_y for yolo_box, test=develop

* add scale_x_y for yolo_box, test=develop

* refine eps in iou_similarity, test=develop

paddle/fluid/operators/detection/iou_similarity_op.h

@@ -18,7 +18,8 @@ limitations under the License. */
 
 template <typename T>
 inline HOSTDEVICE T IOUSimilarity(T xmin1, T ymin1, T xmax1, T ymax1, T xmin2,
-                                  T ymin2, T xmax2, T ymax2, bool normalized) {
+                                  T ymin2, T xmax2, T ymax2, bool normalized,
+                                  T eps) {
   constexpr T zero = static_cast<T>(0);
   T area1;
   T area2;
@@ -43,19 +44,21 @@ inline HOSTDEVICE T IOUSimilarity(T xmin1, T ymin1, T xmax1, T ymax1, T xmin2,
   inter_height = inter_height > zero ? inter_height : zero;
   inter_width = inter_width > zero ? inter_width : zero;
   T inter_area = inter_width * inter_height;
-  T union_area = area1 + area2 - inter_area;
+  T union_area = area1 + area2 - inter_area + eps;
   T sim_score = inter_area / union_area;
   return sim_score;
 }
 
 template <typename T>
 struct IOUSimilarityFunctor {
-  IOUSimilarityFunctor(const T* x, const T* y, T* z, int cols, bool normalized)
+  IOUSimilarityFunctor(const T* x, const T* y, T* z, int cols, bool normalized,
+                       T eps)
       : x_(x),
         y_(y),
         z_(z),
         cols_(static_cast<size_t>(cols)),
-        normalized_(normalized) {}
+        normalized_(normalized),
+        eps_(eps) {}
 
   inline HOSTDEVICE void operator()(size_t tid) const {
     size_t row_id = tid / cols_;
@@ -72,7 +75,7 @@ struct IOUSimilarityFunctor {
     T y_max2 = y_[col_id * 4 + 3];
 
     T sim = IOUSimilarity(x_min1, y_min1, x_max1, y_max1, x_min2, y_min2,
-                          x_max2, y_max2, normalized_);
+                          x_max2, y_max2, normalized_, eps_);
 
     z_[row_id * cols_ + col_id] = sim;
   }
@@ -81,6 +84,7 @@ struct IOUSimilarityFunctor {
   T* z_;
   const size_t cols_;
   bool normalized_;
+  T eps_;
 };
 
 namespace paddle {
@@ -97,9 +101,10 @@ class IOUSimilarityKernel : public framework::OpKernel<T> {
 
     int x_n = in_x->dims()[0];
     int y_n = in_y->dims()[0];
+    T eps = static_cast<T>(1e-10);
     IOUSimilarityFunctor<T> functor(in_x->data<T>(), in_y->data<T>(),
                                     out->mutable_data<T>(ctx.GetPlace()), y_n,
-                                    normalized);
+                                    normalized, eps);
 
     platform::ForRange<DeviceContext> for_range(
         static_cast<const DeviceContext&>(ctx.device_context()), x_n * y_n);

paddle/fluid/operators/detection/yolo_box_op.cc

@@ -136,6 +136,10 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
                   "Whether clip output bonding box in Input(ImgSize) "
                   "boundary. Default true.")
         .SetDefault(true);
+    AddAttr<float>("scale_x_y",
+                   "Scale the center point of decoded bounding "
+                   "box. Default 1.0")
+        .SetDefault(1.);
     AddComment(R"DOC(
          This operator generates YOLO detection boxes from output of YOLOv3 network.
          

paddle/fluid/operators/detection/yolo_box_op.cu

@@ -26,7 +26,8 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
                             T* scores, const float conf_thresh,
                             const int* anchors, const int n, const int h,
                             const int w, const int an_num, const int class_num,
-                            const int box_num, int input_size, bool clip_bbox) {
+                            const int box_num, int input_size, bool clip_bbox,
+                            const float scale, const float bias) {
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = blockDim.x * gridDim.x;
   T box[4];
@@ -51,7 +52,7 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
     int box_idx =
         GetEntryIndex(i, j, k * w + l, an_num, an_stride, grid_num, 0);
     GetYoloBox<T>(box, input, anchors, l, k, j, h, input_size, box_idx,
-                  grid_num, img_height, img_width);
+                  grid_num, img_height, img_width, scale, bias);
     box_idx = (i * box_num + j * grid_num + k * w + l) * 4;
     CalcDetectionBox<T>(boxes, box, box_idx, img_height, img_width, clip_bbox);
 
@@ -77,6 +78,8 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
     float conf_thresh = ctx.Attr<float>("conf_thresh");
     int downsample_ratio = ctx.Attr<int>("downsample_ratio");
     bool clip_bbox = ctx.Attr<bool>("clip_bbox");
+    float scale = ctx.Attr<float>("scale_x_y");
+    float bias = -0.5 * (scale - 1.);
 
     const int n = input->dims()[0];
     const int h = input->dims()[2];
@@ -109,7 +112,7 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
     KeYoloBoxFw<T><<<grid_dim, 512, 0, ctx.cuda_device_context().stream()>>>(
         input_data, imgsize_data, boxes_data, scores_data, conf_thresh,
         anchors_data, n, h, w, an_num, class_num, box_num, input_size,
-        clip_bbox);
+        clip_bbox, scale, bias);
   }
 };
 

paddle/fluid/operators/detection/yolo_box_op.h

@@ -29,9 +29,11 @@ template <typename T>
 HOSTDEVICE inline void GetYoloBox(T* box, const T* x, const int* anchors, int i,
                                   int j, int an_idx, int grid_size,
                                   int input_size, int index, int stride,
-                                  int img_height, int img_width) {
-  box[0] = (i + sigmoid<T>(x[index])) * img_width / grid_size;
-  box[1] = (j + sigmoid<T>(x[index + stride])) * img_height / grid_size;
+                                  int img_height, int img_width, float scale,
+                                  float bias) {
+  box[0] = (i + sigmoid<T>(x[index]) * scale + bias) * img_width / grid_size;
+  box[1] = (j + sigmoid<T>(x[index + stride]) * scale + bias) * img_height /
+           grid_size;
   box[2] = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] * img_width /
            input_size;
   box[3] = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] *
@@ -89,6 +91,8 @@ class YoloBoxKernel : public framework::OpKernel<T> {
     float conf_thresh = ctx.Attr<float>("conf_thresh");
     int downsample_ratio = ctx.Attr<int>("downsample_ratio");
     bool clip_bbox = ctx.Attr<bool>("clip_bbox");
+    float scale = ctx.Attr<float>("scale_x_y");
+    float bias = -0.5 * (scale - 1.);
 
     const int n = input->dims()[0];
     const int h = input->dims()[2];
@@ -131,7 +135,7 @@ class YoloBoxKernel : public framework::OpKernel<T> {
             int box_idx =
                 GetEntryIndex(i, j, k * w + l, an_num, an_stride, stride, 0);
             GetYoloBox<T>(box, input_data, anchors_data, l, k, j, h, input_size,
-                          box_idx, stride, img_height, img_width);
+                          box_idx, stride, img_height, img_width, scale, bias);
             box_idx = (i * box_num + j * stride + k * w + l) * 4;
             CalcDetectionBox<T>(boxes_data, box, box_idx, img_height, img_width,
                                 clip_bbox);

paddle/fluid/operators/detection/yolov3_loss_op.cc

@@ -215,6 +215,10 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<bool>("use_label_smooth",
                   "Whether to use label smooth. Default True.")
         .SetDefault(true);
+    AddAttr<float>("scale_x_y",
+                   "Scale the center point of decoded bounding "
+                   "box. Default 1.0")
+        .SetDefault(1.);
     AddComment(R"DOC(
          This operator generates yolov3 loss based on given predict result and ground
          truth boxes.

paddle/fluid/operators/detection/yolov3_loss_op.h

@@ -73,10 +73,11 @@ static inline T sigmoid(T x) {
 template <typename T>
 static inline Box<T> GetYoloBox(const T* x, std::vector<int> anchors, int i,
                                 int j, int an_idx, int grid_size,
-                                int input_size, int index, int stride) {
+                                int input_size, int index, int stride,
+                                float scale, float bias) {
   Box<T> b;
-  b.x = (i + sigmoid<T>(x[index])) / grid_size;
-  b.y = (j + sigmoid<T>(x[index + stride])) / grid_size;
+  b.x = (i + sigmoid<T>(x[index]) * scale + bias) / grid_size;
+  b.y = (j + sigmoid<T>(x[index + stride]) * scale + bias) / grid_size;
   b.w = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] / input_size;
   b.h = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] / input_size;
   return b;
@@ -267,6 +268,8 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
     float ignore_thresh = ctx.Attr<float>("ignore_thresh");
     int downsample_ratio = ctx.Attr<int>("downsample_ratio");
     bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
+    float scale = ctx.Attr<float>("scale_x_y");
+    float bias = -0.5 * (scale - 1.);
 
     const int n = input->dims()[0];
     const int h = input->dims()[2];
@@ -325,8 +328,9 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
             // then ignore_thresh, ignore the objectness loss.
             int box_idx =
                 GetEntryIndex(i, j, k * w + l, mask_num, an_stride, stride, 0);
-            Box<T> pred = GetYoloBox(input_data, anchors, l, k, anchor_mask[j],
-                                     h, input_size, box_idx, stride);
+            Box<T> pred =
+                GetYoloBox(input_data, anchors, l, k, anchor_mask[j], h,
+                           input_size, box_idx, stride, scale, bias);
             T best_iou = 0;
             for (int t = 0; t < b; t++) {
               if (!gt_valid_mask_data[i * b + t]) {

python/paddle/fluid/layers/detection.py

@@ -932,7 +932,8 @@ def yolov3_loss(x,
                 downsample_ratio,
                 gt_score=None,
                 use_label_smooth=True,
-                name=None):
+                name=None,
+                scale_x_y=1.):
     """
     ${comment}
 
@@ -958,6 +959,7 @@ def yolov3_loss(x,
         gt_score (Variable): mixup score of ground truth boxes, should be in shape
                             of [N, B]. Default None.
         use_label_smooth (bool): ${use_label_smooth_comment}
+        scale_x_y (float): ${scale_x_y_comment}
 
     Returns:
         Variable: A 1-D tensor with shape [N], the value of yolov3 loss
@@ -1030,6 +1032,7 @@ def yolov3_loss(x,
         "ignore_thresh": ignore_thresh,
         "downsample_ratio": downsample_ratio,
         "use_label_smooth": use_label_smooth,
+        "scale_x_y": scale_x_y,
     }
 
     helper.append_op(
@@ -1052,7 +1055,8 @@ def yolo_box(x,
              conf_thresh,
              downsample_ratio,
              clip_bbox=True,
-             name=None):
+             name=None,
+             scale_x_y=1.):
     """
     ${comment}
 
@@ -1064,6 +1068,7 @@ def yolo_box(x,
         conf_thresh (float): ${conf_thresh_comment}
         downsample_ratio (int): ${downsample_ratio_comment}
         clip_bbox (bool): ${clip_bbox_comment}
+        scale_x_y (float): ${scale_x_y_comment}
         name (string): The default value is None.  Normally there is no need 
                        for user to set this property.  For more information, 
                        please refer to :ref:`api_guide_Name`
@@ -1112,6 +1117,7 @@ def yolo_box(x,
         "conf_thresh": conf_thresh,
         "downsample_ratio": downsample_ratio,
         "clip_bbox": clip_bbox,
+        "scale_x_y": scale_x_y,
     }
 
     helper.append_op(

python/paddle/fluid/tests/test_detection.py

@@ -527,6 +527,36 @@ class TestYoloDetection(unittest.TestCase):
             self.assertIsNotNone(boxes)
             self.assertIsNotNone(scores)
 
+    def test_yolov3_loss_with_scale(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
+            gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
+            gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
+            gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
+            loss = layers.yolov3_loss(
+                x,
+                gt_box,
+                gt_label, [10, 13, 30, 13], [0, 1],
+                10,
+                0.7,
+                32,
+                gt_score=gt_score,
+                use_label_smooth=False,
+                scale_x_y=1.2)
+
+            self.assertIsNotNone(loss)
+
+    def test_yolo_box_with_scale(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
+            img_size = layers.data(name='img_size', shape=[2], dtype='int32')
+            boxes, scores = layers.yolo_box(
+                x, img_size, [10, 13, 30, 13], 10, 0.01, 32, scale_x_y=1.2)
+            self.assertIsNotNone(boxes)
+            self.assertIsNotNone(scores)
+
 
 class TestBoxClip(unittest.TestCase):
     def test_box_clip(self):

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

@@ -33,6 +33,8 @@ def YoloBox(x, img_size, attrs):
     conf_thresh = attrs['conf_thresh']
     downsample = attrs['downsample']
     clip_bbox = attrs['clip_bbox']
+    scale_x_y = attrs['scale_x_y']
+    bias_x_y = -0.5 * (scale_x_y - 1.)
     input_size = downsample * h
 
     x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
@@ -40,8 +42,10 @@ def YoloBox(x, img_size, attrs):
     pred_box = x[:, :, :, :, :4].copy()
     grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
     grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
-    pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0])) / w
-    pred_box[:, :, :, :, 1] = (grid_y + sigmoid(pred_box[:, :, :, :, 1])) / h
+    pred_box[:, :, :, :, 0] = (
+        grid_x + sigmoid(pred_box[:, :, :, :, 0]) * scale_x_y + bias_x_y) / w
+    pred_box[:, :, :, :, 1] = (
+        grid_y + sigmoid(pred_box[:, :, :, :, 1]) * scale_x_y + bias_x_y) / h
 
     anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
     anchors_s = np.array(
@@ -90,6 +94,7 @@ class TestYoloBoxOp(OpTest):
             "conf_thresh": self.conf_thresh,
             "downsample": self.downsample,
             "clip_bbox": self.clip_bbox,
+            "scale_x_y": self.scale_x_y,
         }
 
         self.inputs = {
@@ -115,6 +120,7 @@ class TestYoloBoxOp(OpTest):
         self.clip_bbox = True
         self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
         self.imgsize_shape = (self.batch_size, 2)
+        self.scale_x_y = 1.
 
 
 class TestYoloBoxOpNoClipBbox(TestYoloBoxOp):
@@ -128,6 +134,21 @@ class TestYoloBoxOpNoClipBbox(TestYoloBoxOp):
         self.clip_bbox = False
         self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
         self.imgsize_shape = (self.batch_size, 2)
+        self.scale_x_y = 1.
+
+
+class TestYoloBoxOpScaleXY(TestYoloBoxOp):
+    def initTestCase(self):
+        self.anchors = [10, 13, 16, 30, 33, 23]
+        an_num = int(len(self.anchors) // 2)
+        self.batch_size = 32
+        self.class_num = 2
+        self.conf_thresh = 0.5
+        self.downsample = 32
+        self.clip_bbox = True
+        self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
+        self.imgsize_shape = (self.batch_size, 2)
+        self.scale_x_y = 1.2
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py

@@ -77,6 +77,8 @@ def YOLOv3Loss(x, gtbox, gtlabel, gtscore, attrs):
     ignore_thresh = attrs['ignore_thresh']
     downsample_ratio = attrs['downsample_ratio']
     use_label_smooth = attrs['use_label_smooth']
+    scale_x_y = attrs['scale_x_y']
+    bias_x_y = -0.5 * (scale_x_y - 1.)
     input_size = downsample_ratio * h
     x = x.reshape((n, mask_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
     loss = np.zeros((n)).astype('float64')
@@ -88,8 +90,10 @@ def YOLOv3Loss(x, gtbox, gtlabel, gtscore, attrs):
     pred_box = x[:, :, :, :, :4].copy()
     grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
     grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
-    pred_box[:, :, :, :, 0] = (grid_x + sigmoid(pred_box[:, :, :, :, 0])) / w
-    pred_box[:, :, :, :, 1] = (grid_y + sigmoid(pred_box[:, :, :, :, 1])) / h
+    pred_box[:, :, :, :, 0] = (
+        grid_x + sigmoid(pred_box[:, :, :, :, 0]) * scale_x_y + bias_x_y) / w
+    pred_box[:, :, :, :, 1] = (
+        grid_y + sigmoid(pred_box[:, :, :, :, 1]) * scale_x_y + bias_x_y) / h
 
     mask_anchors = []
     for m in anchor_mask:
@@ -180,6 +184,7 @@ class TestYolov3LossOp(OpTest):
             "ignore_thresh": self.ignore_thresh,
             "downsample_ratio": self.downsample_ratio,
             "use_label_smooth": self.use_label_smooth,
+            "scale_x_y": self.scale_x_y,
         }
 
         self.inputs = {
@@ -222,6 +227,7 @@ class TestYolov3LossOp(OpTest):
         self.gtbox_shape = (3, 5, 4)
         self.gtscore = True
         self.use_label_smooth = True
+        self.scale_x_y = 1.
 
 
 class TestYolov3LossWithoutLabelSmooth(TestYolov3LossOp):
@@ -238,6 +244,7 @@ class TestYolov3LossWithoutLabelSmooth(TestYolov3LossOp):
         self.gtbox_shape = (3, 5, 4)
         self.gtscore = True
         self.use_label_smooth = False
+        self.scale_x_y = 1.
 
 
 class TestYolov3LossNoGTScore(TestYolov3LossOp):
@@ -254,6 +261,24 @@ class TestYolov3LossNoGTScore(TestYolov3LossOp):
         self.gtbox_shape = (3, 5, 4)
         self.gtscore = False
         self.use_label_smooth = True
+        self.scale_x_y = 1.
+
+
+class TestYolov3LossWithScaleXY(TestYolov3LossOp):
+    def initTestCase(self):
+        self.anchors = [
+            10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198,
+            373, 326
+        ]
+        self.anchor_mask = [0, 1, 2]
+        self.class_num = 5
+        self.ignore_thresh = 0.7
+        self.downsample_ratio = 32
+        self.x_shape = (3, len(self.anchor_mask) * (5 + self.class_num), 5, 5)
+        self.gtbox_shape = (3, 5, 4)
+        self.gtscore = True
+        self.use_label_smooth = True
+        self.scale_x_y = 1.2
 
 
 if __name__ == "__main__":