fix pre-commit. test=develop

paddle/fluid/operators/detection/yolo_box_op.cu

@@ -22,9 +22,9 @@ using Tensor = framework::Tensor;
 
 template <typename T>
 __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,
+                            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) {
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = blockDim.x * gridDim.x;
@@ -84,7 +84,8 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
     int input_size = downsample_ratio * h;
 
     Tensor anchors_t, cpu_anchors_t;
-    auto cpu_anchors_data = cpu_anchors_t.mutable_data<int>({an_num*2}, platform::CPUPlace());
+    auto cpu_anchors_data =
+        cpu_anchors_t.mutable_data<int>({an_num * 2}, platform::CPUPlace());
     std::copy(anchors.begin(), anchors.end(), cpu_anchors_data);
     TensorCopySync(cpu_anchors_t, ctx.GetPlace(), &anchors_t);
     auto anchors_data = anchors_t.data<int>();
@@ -112,6 +113,5 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(yolo_box,
-                        ops::YoloBoxOpCUDAKernel<float>,
+REGISTER_OP_CUDA_KERNEL(yolo_box, ops::YoloBoxOpCUDAKernel<float>,
                         ops::YoloBoxOpCUDAKernel<double>);

paddle/fluid/operators/detection/yolo_box_op.h

@@ -20,7 +20,6 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
-
 template <typename T>
 HOSTDEVICE inline T sigmoid(T x) {
   return 1.0 / (1.0 + std::exp(-x));
@@ -35,8 +34,8 @@ HOSTDEVICE inline void GetYoloBox(T* box, const T* x, const int* anchors, int i,
   box[1] = (j + sigmoid<T>(x[index + stride])) * 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] * img_height /
-        input_size;
+  box[3] = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] *
+           img_height / input_size;
 }
 
 HOSTDEVICE inline int GetEntryIndex(int batch, int an_idx, int hw_idx,
@@ -47,16 +46,22 @@ HOSTDEVICE inline int GetEntryIndex(int batch, int an_idx, int hw_idx,
 
 template <typename T>
 HOSTDEVICE inline void CalcDetectionBox(T* boxes, T* box, const int box_idx,
-                                        const int img_height, const int img_width) {
+                                        const int img_height,
+                                        const int img_width) {
   boxes[box_idx] = box[0] - box[2] / 2;
   boxes[box_idx + 1] = box[1] - box[3] / 2;
   boxes[box_idx + 2] = box[0] + box[2] / 2;
   boxes[box_idx + 3] = box[1] + box[3] / 2;
 
   boxes[box_idx] = boxes[box_idx] > 0 ? boxes[box_idx] : static_cast<T>(0);
-  boxes[box_idx + 1] = boxes[box_idx + 1] > 0 ? boxes[box_idx + 1] : static_cast<T>(0);
-  boxes[box_idx + 2] = boxes[box_idx + 2] < img_width - 1 ? boxes[box_idx + 2] : static_cast<T>(img_width - 1);
-  boxes[box_idx + 3] = boxes[box_idx + 3] < img_height - 1 ? boxes[box_idx + 3] : static_cast<T>(img_height - 1);
+  boxes[box_idx + 1] =
+      boxes[box_idx + 1] > 0 ? boxes[box_idx + 1] : static_cast<T>(0);
+  boxes[box_idx + 2] = boxes[box_idx + 2] < img_width - 1
+                           ? boxes[box_idx + 2]
+                           : static_cast<T>(img_width - 1);
+  boxes[box_idx + 3] = boxes[box_idx + 3] < img_height - 1
+                           ? boxes[box_idx + 3]
+                           : static_cast<T>(img_height - 1);
 }
 
 template <typename T>
@@ -92,8 +97,10 @@ class YoloBoxKernel : public framework::OpKernel<T> {
     const int stride = h * w;
     const int an_stride = (class_num + 5) * stride;
 
-    int anchors_[anchors.size()];
-    std::copy(anchors.begin(), anchors.end(), anchors_);
+    Tensor anchors_;
+    auto anchors_data =
+        anchors_.mutable_data<int>({an_num * 2}, ctx.GetPlace());
+    std::copy(anchors.begin(), anchors.end(), anchors_data);
 
     const T* input_data = input->data<T>();
     const int* imgsize_data = imgsize->data<int>();
@@ -120,10 +127,11 @@ 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_, l, k, j, h, input_size,
+            GetYoloBox<T>(box, input_data, anchors_data, l, k, j, h, input_size,
                           box_idx, stride, img_height, img_width);
             box_idx = (i * box_num + j * stride + k * w + l) * 4;
-            CalcDetectionBox<T>(boxes_data, box, box_idx, img_height, img_width);
+            CalcDetectionBox<T>(boxes_data, box, box_idx, img_height,
+                                img_width);
 
             int label_idx =
                 GetEntryIndex(i, j, k * w + l, an_num, an_stride, stride, 5);

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

@@ -59,12 +59,19 @@ def YoloBox(x, img_size, attrs):
     pred_box[:, :, :2], pred_box[:, :, 2:4] = \
         pred_box[:, :, :2] - pred_box[:, :, 2:4] / 2., \
         pred_box[:, :, :2] + pred_box[:, :, 2:4] / 2.0
-    # pred_box = pred_box * input_size
     pred_box[:, :, 0] = pred_box[:, :, 0] * img_size[:, 1][:, np.newaxis]
     pred_box[:, :, 1] = pred_box[:, :, 1] * img_size[:, 0][:, np.newaxis]
     pred_box[:, :, 2] = pred_box[:, :, 2] * img_size[:, 1][:, np.newaxis]
     pred_box[:, :, 3] = pred_box[:, :, 3] * img_size[:, 0][:, np.newaxis]
 
+    for i in range(len(pred_box)):
+        pred_box[i, :, 0] = np.clip(pred_box[i, :, 0], 0, np.inf)
+        pred_box[i, :, 1] = np.clip(pred_box[i, :, 1], 0, np.inf)
+        pred_box[i, :, 2] = np.clip(pred_box[i, :, 2], -np.inf,
+                                    img_size[i, 1] - 1)
+        pred_box[i, :, 3] = np.clip(pred_box[i, :, 3], -np.inf,
+                                    img_size[i, 0] - 1)
+
     return pred_box, pred_score.reshape((n, -1, class_num))
 
 
@@ -93,8 +100,7 @@ class TestYoloBoxOp(OpTest):
         }
 
     def test_check_output(self):
-	place = core.CUDAPlace(0)
-        self.check_output_with_place(place, atol=1e-3)
+        self.check_output()
 
     def initTestCase(self):
         self.anchors = [10, 13, 16, 30, 33, 23]