diff --git a/lite/core/tensor.cc b/lite/core/tensor.cc
index ecfdcf3d1107953f1c41ea57b6f12187b29686c6..38a6be6767eae62f9d91c9c11811bc49639331bf 100644
--- a/lite/core/tensor.cc
+++ b/lite/core/tensor.cc
@@ -25,21 +25,17 @@ using value_type = int64_t;
 
 value_type DDimLite::production() const {
   value_type res = 1;
-  for (size_t i = 0; i < this->size(); i++) {
-    res *= (*this)[i];
+  for (size_t i = 0; i < data_.size(); i++) {
+    res *= data_[i];
   }
   return res;
 }
 
 value_type DDimLite::count(int start, int end) const {
-  if (start < 0) {
-    start = 0;
-  }
-  if (end > size()) {
-    end = size();
-  }
+  start = std::max(start, 0);
+  end = std::min(end, static_cast<int>(data_.size()));
   if (end < start) {
-    end = start;
+    return 0;
   }
   value_type sum = 1;
   for (auto i = start; i < end; ++i) {
@@ -49,11 +45,13 @@ value_type DDimLite::count(int start, int end) const {
 }
 
 DDimLite DDimLite::Slice(int start, int end) const {
-  std::vector<value_type> vec;
+  start = std::max(start, 0);
+  end = std::min(end, static_cast<int>(data_.size()));
+  std::vector<value_type> new_dim(end - start);
   for (int i = start; i < end; i++) {
-    vec.push_back((*this)[i]);
+    new_dim[i - start] = data_[i];
   }
-  return DDimLite(vec);
+  return DDim(new_dim);
 }
 
 std::string DDimLite::repr() const {
diff --git a/lite/core/tensor.h b/lite/core/tensor.h
index 3e334048fabb0a26951dadd173e4cf42ee4d8099..04e540002b553a0e0f7db0144fd970bdb6a4d9ed 100644
--- a/lite/core/tensor.h
+++ b/lite/core/tensor.h
@@ -85,7 +85,11 @@ class DDimLite {
   }
 
   friend bool operator!=(const DDimLite &a, const DDimLite &b) {
-    return !(a == b);
+    if (a.size() != b.size()) return true;
+    for (size_t i = 0; i < a.size(); i++) {
+      if (a[i] != b[i]) return true;
+    }
+    return false;
   }
 
  private:
@@ -118,7 +122,7 @@ class TensorLite {
   }
 
   void Resize(const DDimLite &ddim) { dims_ = ddim; }
-  void Resize(const std::vector<int64_t> &x) { dims_ = DDimLite(x); }
+  void Resize(const std::vector<int64_t> &x) { dims_.ConstructFrom(x); }
 
   const DDimLite &dims() const { return dims_; }
   int64_t numel() const { return dims_.production(); }
diff --git a/lite/kernels/x86/fc_compute.h b/lite/kernels/x86/fc_compute.h
index 971f5dfa2f407ac3bd87c47c02af1750eaa21acf..e719b8d2216949746f612bca0689c22be0606031 100644
--- a/lite/kernels/x86/fc_compute.h
+++ b/lite/kernels/x86/fc_compute.h
@@ -31,20 +31,6 @@ namespace lite {
 namespace kernels {
 namespace x86 {
 
-inline void FCOutputSize(const lite::DDim& in_dims,
-                         const lite::DDim& w_dims,
-                         std::vector<int64_t>& out_dims,  // NOLINT
-                         int in_num_col_dims,
-                         bool padding_weights) {
-  auto w_dims1 = padding_weights ? w_dims[1] - 4 : w_dims[1];
-
-  out_dims.reserve(static_cast<size_t>(in_num_col_dims + 1));
-  for (int i = 0; i < in_num_col_dims; ++i) {
-    out_dims.push_back(in_dims[i]);
-  }
-  out_dims.push_back(w_dims1);
-}
-
 template <lite::TargetType Target, typename T>
 class FCFunctor {
  public:
@@ -84,11 +70,11 @@ class FCFunctor {
       // NOTE: here need to mutable_data for temporary Tensor X1 and Y1,
       //  the overhead is unmeasured.
       lite::Tensor X1;
-      X1.Resize({M * KK});
+      X1.Resize(std::vector<int64_t>{M * KK});
       T* X1_data = X1.mutable_data<T>();
 
       lite::Tensor Y1;
-      Y1.Resize({M * (N + 4)});
+      Y1.Resize(std::vector<int64_t>{M * NN});
       Y1_data = Y1.mutable_data<T>();
 
       auto parallel_memcpy_x = [&](int64_t begin, int64_t end) {
@@ -115,7 +101,7 @@ class FCFunctor {
       if (!B) {
         auto parallel_memcpy_y = [&](int64_t begin, int64_t end) {
           for (int64_t i = begin; i < end; i++) {
-            memcpy(Y + i * N, Y1_data + i * (N + 4), N * sizeof(T));
+            memcpy(Y + i * N, Y1_data + i * NN, N * sizeof(T));
           }
         };
         lite::x86::RunParallelFor(0, M, parallel_memcpy_y);
@@ -145,22 +131,14 @@ class FcCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
     auto* w = param.w;
     auto* bias = param.bias;
     auto* output = param.output;
-    int in_num_col_dims = param.in_num_col_dims;
     bool with_relu = (param.activation_type == "relu") ? true : false;
 
-    auto w_dims = w->dims();
     bool padding_weights = param.padding_weights;
-
-    std::vector<int64_t> output_dims;
-    FCOutputSize(
-        input->dims(), w_dims, output_dims, in_num_col_dims, padding_weights);
-    output->Resize(output_dims);
-    output->set_lod(input->lod());
-
-    auto out_dims = output->dims();
+    const auto& w_dims = w->dims();
     auto w_dims0 = padding_weights ? w_dims[0] - 4 : w_dims[0];
     auto w_dims1 = padding_weights ? w_dims[1] - 4 : w_dims[1];
-    int M = out_dims.production() / w_dims1;
+
+    int M = output->dims().production() / w_dims1;
 
     const T* input_data = input->data<T>();
     const T* w_data = w->data<T>();
diff --git a/lite/kernels/x86/reshape_compute.h b/lite/kernels/x86/reshape_compute.h
index 948c4ec31d7b3a7cf16f23582e6e17ea54dd081c..b06eb6eb67e281265a47e33a5d81ed290cf38ffe 100644
--- a/lite/kernels/x86/reshape_compute.h
+++ b/lite/kernels/x86/reshape_compute.h
@@ -28,8 +28,9 @@ namespace x86 {
 
 template <typename T>
 void Compute(const lite::Tensor* in, lite::Tensor* out) {
+  // In CopyDataFrom, the target tensor's dims will be set to the source
+  // tensor's dims.
   auto out_dims = out->dims();
-  auto in_dims = in->dims();
   out->CopyDataFrom(*in);
   out->Resize(out_dims);
 }
diff --git a/lite/operators/concat_op.cc b/lite/operators/concat_op.cc
index 1941a88bbf50fa0da9a8fd22db3fa9146d242957..b2f7438b64aa34787896839f020f0b056e6453fb 100644
--- a/lite/operators/concat_op.cc
+++ b/lite/operators/concat_op.cc
@@ -27,11 +27,8 @@ bool ConcatOpLite::CheckShape() const {
 }
 
 bool ConcatOpLite::InferShape() const {
-  std::vector<lite::DDim> input_dims;
-  for (auto p : param_.x) {
-    input_dims.push_back(p->dims());
-  }
-  const size_t n = input_dims.size();
+  const std::vector<Tensor *> &inputs = param_.x;
+  const size_t n = inputs.size();
   CHECK_GT_OR_FALSE(n, 0);
 
   int axis = 0;
@@ -42,17 +39,18 @@ bool ConcatOpLite::InferShape() const {
     axis = axis_tensor_val[0];
   }
   if (axis < 0) {
-    axis += input_dims[0].size();
+    axis += inputs[0]->dims().size();
   }
 
-  auto &out_dims = input_dims[0];
+  auto out_dims = inputs[0]->dims();
   size_t in_zero_dims_size = out_dims.size();
   for (size_t i = 1; i < n; i++) {
+    const auto &input_dims_i = inputs[i]->dims();
     for (size_t j = 0; j < in_zero_dims_size; j++) {
       if (j == static_cast<size_t>(axis)) {
-        out_dims[axis] += input_dims[i][j];
+        out_dims[axis] += input_dims_i[j];
       } else {
-        CHECK_EQ_OR_FALSE(out_dims[j], input_dims[i][j]);
+        CHECK_EQ_OR_FALSE(out_dims[j], input_dims_i[j]);
       }
     }
   }
@@ -60,7 +58,7 @@ bool ConcatOpLite::InferShape() const {
     out_dims[axis] = -1;
   }
   // Set output dims
-  param_.output->Resize(lite::DDim(out_dims));
+  param_.output->Resize(out_dims);
   auto out_lod = param_.output->mutable_lod();
   *out_lod = param_.x[0]->lod();
   return true;
diff --git a/lite/operators/fc_op.cc b/lite/operators/fc_op.cc
index 702950ae18db33f90073870d9cd19cd4aa7de91c..eff9300fea4caf412186bfc8d0ad136686507be5 100644
--- a/lite/operators/fc_op.cc
+++ b/lite/operators/fc_op.cc
@@ -49,23 +49,25 @@ bool FcOpLite::CheckShape() const {
 }
 
 bool FcOpLite::InferShape() const {
-  const auto input_dims = param_.input->dims();
-  const auto w_dims = param_.w->dims();
+  const auto& input_dims = param_.input->dims();
+  const auto& w_dims = param_.w->dims();
+  int in_num_col_dims = param_.in_num_col_dims;
+  int64_t w_dims_1 = param_.padding_weights ? w_dims[1] - 4 : w_dims[1];
 
   // Set output dims
-  std::vector<int64_t> output_dims(param_.in_num_col_dims + 1, 0);
-  for (int i = 0; i < param_.in_num_col_dims; ++i) {
+  std::vector<DDim::value_type> output_dims(in_num_col_dims + 1);
+  for (int i = 0; i < in_num_col_dims; ++i) {
     output_dims[i] = input_dims[i];
   }
-  output_dims.back() = w_dims[1];
-  param_.output->Resize(lite::DDim(output_dims));
+  output_dims[in_num_col_dims] = w_dims_1;
+  param_.output->Resize(output_dims);
 
   // share LoD
   param_.output->set_lod(param_.input->lod());
   return true;
 }
 
-bool FcOpLite::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
+bool FcOpLite::AttachImpl(const cpp::OpDesc& op_desc, lite::Scope* scope) {
   auto input = op_desc.Input("Input").front();
   auto W = op_desc.Input("W").front();
   auto out = op_desc.Output("Out").front();
diff --git a/lite/operators/gru_op.cc b/lite/operators/gru_op.cc
index 3ddeb5b73463ab7502e81ce82e348c08e23dc421..eb97d65a1a213e31b23087d1ca5c8e963ecf9bbb 100644
--- a/lite/operators/gru_op.cc
+++ b/lite/operators/gru_op.cc
@@ -28,8 +28,8 @@ bool GRUOpLite::CheckShape() const {
   CHECK_OR_FALSE(param_.batch_hidden)
   CHECK_OR_FALSE(param_.hidden)
 
-  auto input_dims = param_.input->dims();
-  auto weight_dims = param_.weight->dims();
+  const auto& input_dims = param_.input->dims();
+  const auto& weight_dims = param_.weight->dims();
   int input_size = input_dims[1];
   int frame_size = weight_dims[0];
   CHECK_EQ_OR_FALSE(input_size, frame_size * 3)
@@ -52,21 +52,23 @@ bool GRUOpLite::CheckShape() const {
 }
 
 bool GRUOpLite::InferShape() const {
-  auto input_dims = param_.input->dims();
-  auto weight_dims = param_.weight->dims();
+  const auto& input_dims = param_.input->dims();
+  const auto& weight_dims = param_.weight->dims();
   int frame_size = weight_dims[0];
   auto batch_size = input_dims[0];
 
   param_.batch_gate->Resize(input_dims);
-  param_.batch_reset_hidden_prev->Resize(lite::DDim({batch_size, frame_size}));
-  param_.batch_hidden->Resize(lite::DDim({batch_size, frame_size}));
-  param_.hidden->Resize(lite::DDim({batch_size, frame_size}));
+
+  DDim out_dims({batch_size, frame_size});
+  param_.batch_reset_hidden_prev->Resize(out_dims);
+  param_.batch_hidden->Resize(out_dims);
+  param_.hidden->Resize(out_dims);
 
   *(param_.hidden->mutable_lod()) = param_.input->lod();
   return true;
 }
 
-bool GRUOpLite::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
+bool GRUOpLite::AttachImpl(const cpp::OpDesc& op_desc, lite::Scope* scope) {
   auto input = op_desc.Input("Input").front();
   auto weight = op_desc.Input("Weight").front();
   auto batch_gate = op_desc.Output("BatchGate").front();
diff --git a/lite/operators/lookup_table_op.cc b/lite/operators/lookup_table_op.cc
index 3d5a71cee96adb520aeafc83156e5f37638912ad..931894d925aa9e66b34b3577304828424bfd194e 100644
--- a/lite/operators/lookup_table_op.cc
+++ b/lite/operators/lookup_table_op.cc
@@ -25,8 +25,8 @@ bool LookupTableOpLite::CheckShape() const {
   CHECK_OR_FALSE(param_.Ids)
   CHECK_OR_FALSE(param_.Out)
 
-  auto table_dims = param_.W->dims();
-  auto ids_dims = param_.Ids->dims();
+  const auto& table_dims = param_.W->dims();
+  const auto& ids_dims = param_.Ids->dims();
 
   int ids_rank = ids_dims.size();
 
@@ -37,25 +37,20 @@ bool LookupTableOpLite::CheckShape() const {
 }
 
 bool LookupTableOpLite::InferShape() const {
-  auto table_dims = param_.W->dims();
-  auto ids_dims = param_.Ids->dims();
+  const auto& table_dims = param_.W->dims();
+  const auto& ids_dims = param_.Ids->dims();
 
+  auto out_dims = ids_dims;
   int ids_rank = ids_dims.size();
+  out_dims[ids_rank - 1] = table_dims[1];
 
-  auto output_dims = ids_dims.Slice(0, ids_rank - 1);
-
-  std::vector<int64_t> out_dims;
-  for (int i = 0; i < ids_rank - 1; ++i) {
-    out_dims.push_back(ids_dims[i]);
-  }
-  out_dims.push_back(table_dims[1]);
-  param_.Out->Resize(lite::DDim{out_dims});
+  param_.Out->Resize(out_dims);
   param_.Out->set_lod(param_.Ids->lod());
   return true;
 }
 
-bool LookupTableOpLite::AttachImpl(const cpp::OpDesc &op_desc,
-                                   lite::Scope *scope) {
+bool LookupTableOpLite::AttachImpl(const cpp::OpDesc& op_desc,
+                                   lite::Scope* scope) {
   auto input = op_desc.Input("W").front();
   auto ids = op_desc.Input("Ids").front();
   auto out = op_desc.Output("Out").front();
diff --git a/lite/operators/reduce_ops.cc b/lite/operators/reduce_ops.cc
index e986b0ca5412f8380cccc9f981e5e4069ffcdabc..3f0de174715a6fd718694fb31e9d7cb7c08cf2f9 100644
--- a/lite/operators/reduce_ops.cc
+++ b/lite/operators/reduce_ops.cc
@@ -29,39 +29,43 @@ bool ReduceOp::CheckShape() const {
 }
 
 bool ReduceOp::InferShape() const {
-  auto x_dims = param_.x->dims();
+  const auto &x_dims = param_.x->dims();
   auto x_rank = x_dims.size();
   auto dims = param_.dim;
   for (size_t i = 0; i < dims.size(); ++i) {
-    if (dims[i] < 0) dims[i] = x_rank + dims[i];
+    if (dims[i] < 0) {
+      dims[i] = x_rank + dims[i];
+    }
     CHECK_LT(dims[i], x_rank)
         << "The dim should be in the range [-rank(input), rank(input).";
   }
-  sort(dims.begin(), dims.end());
   bool reduce_all = param_.reduce_all;
   bool keep_dim = param_.keep_dim;
 
   if (reduce_all) {
     if (keep_dim)
-      param_.output->Resize(lite::DDim(std::vector<int64_t>(x_rank, 1)));
+      param_.output->Resize(std::vector<int64_t>(x_rank, 1));
     else
-      param_.output->Resize(lite::DDim(std::vector<int64_t>{1}));
+      param_.output->Resize(std::vector<int64_t>{1});
   } else {
-    auto dims_vector = x_dims.Vectorize();
+    size_t out_rank = keep_dim ? x_rank : x_rank - dims.size();
+    std::vector<DDim::value_type> out_dims(out_rank);
     if (keep_dim) {
       for (size_t i = 0; i < dims.size(); ++i) {
-        dims_vector[dims[i]] = 1;
+        out_dims[dims[i]] = 1;
       }
     } else {
-      const int kDelFlag = -2;
-      for (size_t i = 0; i < dims.size(); ++i) {
-        dims_vector[dims[i]] = kDelFlag;
+      sort(dims.begin(), dims.end());
+      int dim_index = 0;
+      int out_index = 0;
+      for (size_t i = 0; i < x_rank; ++i) {
+        if (dims[dim_index] == static_cast<DDim::value_type>(i)) {
+          dim_index++;
+        } else {
+          out_dims[out_index++] = x_dims[i];
+        }
       }
-      dims_vector.erase(
-          remove(dims_vector.begin(), dims_vector.end(), kDelFlag),
-          dims_vector.end());
     }
-    auto out_dims = lite::DDim(dims_vector);
     param_.output->Resize(out_dims);
     if (dims[0] != 0) {
       param_.output->set_lod(param_.x->lod());
diff --git a/lite/operators/reshape_op.cc b/lite/operators/reshape_op.cc
index 35f38148591690b4a1e563327112807ad12e6e2b..655ac58bdcbfc0f8d9cdbb0ef0078db5eb0333fa 100644
--- a/lite/operators/reshape_op.cc
+++ b/lite/operators/reshape_op.cc
@@ -27,14 +27,15 @@ bool ReshapeOp::CheckShape() const {
 }
 
 bool ReshapeOp::InferShape() const {
-  auto shape_tensor_vct = param_.shape_tensor_vct;
+  const auto &shape_tensor_vct = param_.shape_tensor_vct;
   auto *shape_tensor = param_.shape_tensor;
-  auto shape_vct = param_.shape_vct;
-  std::vector<int> final_shape;
+  const auto &shape_vct = param_.shape_vct;
 
+  std::vector<int> final_shape;
   if (shape_tensor_vct.size() > 0) {
-    for (int i = 0; i < shape_tensor_vct.size(); i++) {
-      final_shape.push_back(shape_tensor_vct[i]->data<int>()[0]);
+    final_shape.resize(shape_tensor_vct.size());
+    for (size_t i = 0; i < shape_tensor_vct.size(); i++) {
+      final_shape[i] = shape_tensor_vct[i]->data<int>()[0];
     }
   } else if (shape_tensor != nullptr) {
     auto *shape_tensor_data = shape_tensor->data<int>();
@@ -46,7 +47,7 @@ bool ReshapeOp::InferShape() const {
     LOG(FATAL) << "input shape error";
   }
 
-  auto x_dims = param_.x->dims();
+  const auto &x_dims = param_.x->dims();
   auto output_dims = ValidateShape(final_shape, x_dims);
   param_.output->Resize(output_dims);
   auto out_lod = param_.output->mutable_lod();
@@ -98,8 +99,9 @@ bool Reshape2Op::CheckShape() const {
 
 bool Reshape2Op::InferShape() const {
   ReshapeOp::InferShape();
-  auto x_dims = param_.x->dims();
-  std::vector<DDim::value_type> xshape_dims(x_dims.size() + 1, 0);
+  const auto &x_dims = param_.x->dims();
+  std::vector<DDim::value_type> xshape_dims(x_dims.size() + 1);
+  xshape_dims[0] = 0;
   for (size_t i = 0; i < x_dims.size(); i++) {
     xshape_dims[i + 1] = x_dims[i];
   }
@@ -116,20 +118,26 @@ bool Reshape2Op::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
   return true;
 }
 
-DDim ValidateShape(const std::vector<int> &shape, const DDim &input_dims) {
-  const lite::DDim::value_type input_size = input_dims.production();
-  auto input_shape = input_dims.Vectorize();
-  bool all_positive = std::all_of(
-      input_shape.cbegin(), input_shape.cend(), [](lite::DDim::value_type i) {
-        return i > 0;
-      });
+static bool CheckPositive(const DDim &dims) {
+  for (size_t i = 0; i < dims.size(); ++i) {
+    if (dims[i] <= 0) {
+      return false;
+    }
+  }
+  return true;
+}
+
+std::vector<DDim::value_type> ValidateShape(const std::vector<int> &shape,
+                                            const DDim &input_dims) {
+  const DDim::value_type input_size = input_dims.production();
+
   // only one dimension can be set to -1, whose size will be automatically
   // infered.
   const int unk_dim_val = -1;
   const int copy_dim_val = 0;
 
-  std::vector<lite::DDim::value_type> output_shape(shape.size(), 0);
-  lite::DDim::value_type capacity = 1;
+  std::vector<DDim::value_type> output_dims(shape.size());
+  DDim::value_type capacity = 1;
   int unk_dim_idx = -1;
   for (size_t i = 0; i < shape.size(); ++i) {
     if (shape[i] == unk_dim_val) {
@@ -137,7 +145,7 @@ DDim ValidateShape(const std::vector<int> &shape, const DDim &input_dims) {
           << "Only one input dimension of Attr(shape) can be unknown.";
       unk_dim_idx = i;
     } else if (shape[i] == copy_dim_val) {
-      CHECK_LT(static_cast<int>(i), input_shape.size())
+      CHECK_LT(static_cast<int>(i), input_dims.size())
           << "The index of dimension to copy from input shape must be less "
              "than the size of input shape.";
     } else {
@@ -145,28 +153,28 @@ DDim ValidateShape(const std::vector<int> &shape, const DDim &input_dims) {
                                "be negtive except one unknown dimension.";
     }
 
-    capacity *= (shape[i] ? static_cast<lite::DDim::value_type>(shape[i])
-                          : input_shape[i]);
-    output_shape[i] = (shape[i] ? static_cast<lite::DDim::value_type>(shape[i])
-                                : input_shape[i]);
+    DDim::value_type output_dim_i =
+        shape[i] ? static_cast<DDim::value_type>(shape[i]) : input_dims[i];
+    output_dims[i] = output_dim_i;
+    capacity *= output_dim_i;
   }
 
   if (unk_dim_idx != -1) {
-    if (all_positive) {
+    if (CheckPositive(input_dims)) {
       // input_size < 0 and is un-determinate in compile time, skip the check,
       // for example, input_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
       // capacity = -24, input_size = -8, output_shape[0] = 0
       // the following check will fail.
-      output_shape[unk_dim_idx] = -input_size / capacity;
-      CHECK_EQ(output_shape[unk_dim_idx] * capacity, -input_size)
+      output_dims[unk_dim_idx] = -input_size / capacity;
+      CHECK_EQ(output_dims[unk_dim_idx] * capacity, -input_size)
           << "Invalid shape is given.";
     } else {
-      output_shape[unk_dim_idx] = -1;
+      output_dims[unk_dim_idx] = -1;
     }
   } else {
     CHECK_EQ(capacity, input_size) << "Invalid shape is given.";
   }
-  return lite::DDim(output_shape);
+  return output_dims;
 }
 
 }  // namespace operators
diff --git a/lite/operators/reshape_op.h b/lite/operators/reshape_op.h
index bd31f7f73feb16c40138a95c961f89fc777b80cb..1df49fb5f44c88978b78f17885a5ba4412aa9ab7 100644
--- a/lite/operators/reshape_op.h
+++ b/lite/operators/reshape_op.h
@@ -56,7 +56,8 @@ class Reshape2Op : public ReshapeOp {
   std::string DebugString() const override { return "reshape2"; }
 };
 
-DDim ValidateShape(const std::vector<int> &shape, const DDim &input_dims);
+std::vector<DDim::value_type> ValidateShape(const std::vector<int> &shape,
+                                            const DDim &input_dims);
 
 }  // namespace operators
 }  // namespace lite
diff --git a/lite/tests/kernels/fc_compute_test.cc b/lite/tests/kernels/fc_compute_test.cc
index bd6d86b599e3e5340c23cc072ce46cc17ddf15df..1d5adaa6cca8986b2fb302c1f480730512b458b5 100644
--- a/lite/tests/kernels/fc_compute_test.cc
+++ b/lite/tests/kernels/fc_compute_test.cc
@@ -47,7 +47,6 @@ void Relu(float* out, int num, int channel) {
 DDim ComputeOutDim(const DDim& dim_in, const DDim& wdim, int in_num_col_dim) {
   std::vector<int64_t> out_dim;
   out_dim.resize(in_num_col_dim + 1);
-  auto in_mat_dims = dim_in.Flatten2D(in_num_col_dim);
   for (int i = 0; i < in_num_col_dim; ++i) {
     out_dim[i] = dim_in[i];
   }