diff --git a/lite/kernels/arm/unsqueeze_compute.cc b/lite/kernels/arm/unsqueeze_compute.cc
index e623407c2e718a51b51e880a4d81df4ee0d96f87..91c8c0423b6fcc5bade5751985f190b3395b0779 100644
--- a/lite/kernels/arm/unsqueeze_compute.cc
+++ b/lite/kernels/arm/unsqueeze_compute.cc
@@ -54,12 +54,12 @@ REGISTER_LITE_KERNEL(unsqueeze,
                      kNCHW,
                      paddle::lite::kernels::host::UnsqueezeCompute,
                      def)
-    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
     .BindInput("AxesTensor",
                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
     .BindInput("AxesTensorList",
                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
-    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
     .Finalize();
 
 REGISTER_LITE_KERNEL(unsqueeze2,
@@ -68,11 +68,11 @@ REGISTER_LITE_KERNEL(unsqueeze2,
                      kNCHW,
                      paddle::lite::kernels::host::Unsqueeze2Compute,
                      def)
-    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
     .BindInput("AxesTensor",
                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
     .BindInput("AxesTensorList",
                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
-    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kAny))})
     .BindOutput("XShape", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
diff --git a/lite/kernels/arm/yolo_box_compute.cc b/lite/kernels/arm/yolo_box_compute.cc
index 1336e5e1e0a6438a08f542d299eddc30d15dad15..ad8a630b8c0064af7358674d1b7424eff25a194a 100644
--- a/lite/kernels/arm/yolo_box_compute.cc
+++ b/lite/kernels/arm/yolo_box_compute.cc
@@ -54,7 +54,8 @@ REGISTER_LITE_KERNEL(yolo_box,
                      paddle::lite::kernels::arm::YoloBoxCompute,
                      def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindInput("ImgSize", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("ImgSize",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
     .BindOutput("Boxes", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Scores", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
diff --git a/lite/tests/kernels/unsqueeze_compute_test.cc b/lite/tests/kernels/unsqueeze_compute_test.cc
index 22e475672a87dafee29d68a3824e4f8ac0c15615..590d3fd29c37e16cfeec53557a825a4acf9684ca 100644
--- a/lite/tests/kernels/unsqueeze_compute_test.cc
+++ b/lite/tests/kernels/unsqueeze_compute_test.cc
@@ -107,6 +107,7 @@ class UnsqueezeComputeTester : public arena::TestCase {
   }
 
   void PrepareData() override {
+    SetPrecisionType(out_, PRECISION(kFloat));
     std::vector<float> in_data(dims_.production());
     for (int i = 0; i < dims_.production(); ++i) {
       in_data[i] = i;
@@ -213,6 +214,7 @@ class Unsqueeze2ComputeTester : public arena::TestCase {
   }
 
   void PrepareData() override {
+    SetPrecisionType(out_, PRECISION(kFloat));
     std::vector<float> in_data(dims_.production());
     for (int i = 0; i < dims_.production(); ++i) {
       in_data[i] = i;