Modify some infer-shape about detection operators in compile-time. (#14483)

* Modify some infer-shape in compile-time.

paddle/fluid/operators/detection/box_coder_op.cc

@@ -30,15 +30,18 @@ class BoxCoderOp : public framework::OperatorWithKernel {
     auto prior_box_dims = ctx->GetInputDim("PriorBox");
     auto target_box_dims = ctx->GetInputDim("TargetBox");
 
+    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]");
+      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"));
+      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");
@@ -50,7 +53,7 @@ class BoxCoderOp : public framework::OperatorWithKernel {
         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}));

paddle/fluid/operators/detection/multiclass_nms_op.cc

@@ -36,12 +36,14 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
     auto box_dims = ctx->GetInputDim("BBoxes");
     auto score_dims = ctx->GetInputDim("Scores");
 
+    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,
+      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 "
@@ -53,7 +55,7 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
                         "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});

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(