diff --git a/paddle/fluid/operators/detection/box_coder_op.cc b/paddle/fluid/operators/detection/box_coder_op.cc
index d0f95f727fdbc82777147e3e8ada6ad4f7a35e60..06fbb9815c52ea69e3aa9e893512e039853b9514 100644
--- a/paddle/fluid/operators/detection/box_coder_op.cc
+++ b/paddle/fluid/operators/detection/box_coder_op.cc
@@ -30,27 +30,30 @@ class BoxCoderOp : public framework::OperatorWithKernel {
     auto prior_box_dims = ctx->GetInputDim("PriorBox");
     auto target_box_dims = ctx->GetInputDim("TargetBox");
 
-    PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2,
-                      "The rank of Input of PriorBoxVar must be 2");
-    PADDLE_ENFORCE_EQ(prior_box_dims[1], 4, "The shape of PriorBox is [N, 4]");
-    if (ctx->HasInput("PriorBoxVar")) {
-      auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
-      PADDLE_ENFORCE_EQ(prior_box_dims, prior_box_var_dims);
+    if (ctx->IsRuntime()) {
+      PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2,
+                        "The rank of Input of PriorBoxVar must be 2");
+      PADDLE_ENFORCE_EQ(prior_box_dims[1], 4,
+                        "The shape of PriorBox is [N, 4]");
+      if (ctx->HasInput("PriorBoxVar")) {
+        auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
+        PADDLE_ENFORCE_EQ(prior_box_dims, prior_box_var_dims);
+      }
+
+      auto code_type =
+          GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
+      if (code_type == BoxCodeType::kEncodeCenterSize) {
+        PADDLE_ENFORCE_EQ(target_box_dims.size(), 2,
+                          "The rank of Input of TargetBox must be 2");
+        PADDLE_ENFORCE_EQ(target_box_dims[1], 4,
+                          "The shape of TargetBox is [M, 4]");
+      } else if (code_type == BoxCodeType::kDecodeCenterSize) {
+        PADDLE_ENFORCE_EQ(target_box_dims.size(), 3,
+                          "The rank of Input of TargetBox must be 3");
+        PADDLE_ENFORCE_EQ(target_box_dims[1], prior_box_dims[0]);
+        PADDLE_ENFORCE_EQ(target_box_dims[2], prior_box_dims[1]);
+      }
     }
-
-    auto code_type = GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
-    if (code_type == BoxCodeType::kEncodeCenterSize) {
-      PADDLE_ENFORCE_EQ(target_box_dims.size(), 2,
-                        "The rank of Input of TargetBox must be 2");
-      PADDLE_ENFORCE_EQ(target_box_dims[1], 4,
-                        "The shape of TargetBox is [M, 4]");
-    } else if (code_type == BoxCodeType::kDecodeCenterSize) {
-      PADDLE_ENFORCE_EQ(target_box_dims.size(), 3,
-                        "The rank of Input of TargetBox must be 3");
-      PADDLE_ENFORCE_EQ(target_box_dims[1], prior_box_dims[0]);
-      PADDLE_ENFORCE_EQ(target_box_dims[2], prior_box_dims[1]);
-    }
-
     ctx->SetOutputDim(
         "OutputBox",
         framework::make_ddim({target_box_dims[0], prior_box_dims[0], 4}));
diff --git a/paddle/fluid/operators/detection/multiclass_nms_op.cc b/paddle/fluid/operators/detection/multiclass_nms_op.cc
index 9e78b28a6011bb7bd299ca3438eb407f600d7000..f0f8851be0ec2b532c570dc82b8ed5c290981aab 100644
--- a/paddle/fluid/operators/detection/multiclass_nms_op.cc
+++ b/paddle/fluid/operators/detection/multiclass_nms_op.cc
@@ -36,24 +36,26 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
     auto box_dims = ctx->GetInputDim("BBoxes");
     auto score_dims = ctx->GetInputDim("Scores");
 
-    PADDLE_ENFORCE_EQ(box_dims.size(), 3,
-                      "The rank of Input(BBoxes) must be 3.");
-    PADDLE_ENFORCE_EQ(score_dims.size(), 3,
-                      "The rank of Input(Scores) must be 3.");
-    PADDLE_ENFORCE(box_dims[2] == 4 || box_dims[2] == 8 || box_dims[2] == 16 ||
-                       box_dims[2] == 24 || box_dims[2] == 32,
-                   "The 2nd dimension of Input(BBoxes) must be 4 or 8, "
-                   "represents the layout of coordinate "
-                   "[xmin, ymin, xmax, ymax] or "
-                   "4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
-                   "8 points: [xi, yi] i= 1,2,...,8 or "
-                   "12 points: [xi, yi] i= 1,2,...,12 or "
-                   "16 points: [xi, yi] i= 1,2,...,16");
-    PADDLE_ENFORCE_EQ(box_dims[1], score_dims[2],
-                      "The 1st dimensiong of Input(BBoxes) must be equal to "
-                      "3rd dimension of Input(Scores), which represents the "
-                      "predicted bboxes.");
-
+    if (ctx->IsRuntime()) {
+      PADDLE_ENFORCE_EQ(box_dims.size(), 3,
+                        "The rank of Input(BBoxes) must be 3.");
+      PADDLE_ENFORCE_EQ(score_dims.size(), 3,
+                        "The rank of Input(Scores) must be 3.");
+      PADDLE_ENFORCE(box_dims[2] == 4 || box_dims[2] == 8 ||
+                         box_dims[2] == 16 || box_dims[2] == 24 ||
+                         box_dims[2] == 32,
+                     "The 2nd dimension of Input(BBoxes) must be 4 or 8, "
+                     "represents the layout of coordinate "
+                     "[xmin, ymin, xmax, ymax] or "
+                     "4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
+                     "8 points: [xi, yi] i= 1,2,...,8 or "
+                     "12 points: [xi, yi] i= 1,2,...,12 or "
+                     "16 points: [xi, yi] i= 1,2,...,16");
+      PADDLE_ENFORCE_EQ(box_dims[1], score_dims[2],
+                        "The 1st dimensiong of Input(BBoxes) must be equal to "
+                        "3rd dimension of Input(Scores), which represents the "
+                        "predicted bboxes.");
+    }
     // Here the box_dims[0] is not the real dimension of output.
     // It will be rewritten in the computing kernel.
     ctx->SetOutputDim("Out", {box_dims[1], box_dims[2] + 2});
diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py
index 96b6705e26c0f8d8d223e9020192a8f330c2c727..3f17400a1432bb799e09accf2600ab6ec85e05a7 100644
--- a/python/paddle/fluid/layers/detection.py
+++ b/python/paddle/fluid/layers/detection.py
@@ -283,11 +283,7 @@ def detection_output(loc,
         prior_box_var=prior_box_var,
         target_box=loc,
         code_type='decode_center_size')
-    compile_shape = scores.shape
-    run_shape = nn.shape(scores)
-    scores = nn.flatten(x=scores, axis=2)
     scores = nn.softmax(input=scores)
-    scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
     scores = nn.transpose(scores, perm=[0, 2, 1])
     scores.stop_gradient = True
     nmsed_outs = helper.create_variable_for_type_inference(