diff --git a/lite/kernels/x86/conv_compute.h b/lite/kernels/x86/conv_compute.h
index 48cb3c74ef3c05675115ab7cec09f16322d1410a..62f5887947f81c05e996ebcab7d625eef1740404 100644
--- a/lite/kernels/x86/conv_compute.h
+++ b/lite/kernels/x86/conv_compute.h
@@ -65,7 +65,7 @@ class Conv2dCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
       col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
     }
     lite::DDim col_shape(col_shape_vec);
-    lite::DDim col_matrix_shape = col_shape.Flatten2D(data_dim + 1);
+    lite::DDim col_matrix_shape = col_shape.Flatten2D(data_dim);
     bool is_expand = IsExpand(
         filter_shape_vec, param.strides, param.paddings, param.dilations);
     lite::Tensor col;
@@ -95,19 +95,14 @@ class Conv2dCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
     auto blas =
         paddle::lite::x86::math::GetBlas<lite::TargetType::kX86, T>(context);
     for (int i = 0; i < batch_size; i++) {
-      lite::Tensor in_batch;
-      lite::Tensor tmp_in_batch = param.x->Slice<T>(i, i + 1);
-      tmp_in_batch.Resize(input_shape);
-      in_batch.ShareDataWith(tmp_in_batch);
-      lite::Tensor out_batch;
-      lite::Tensor tmp_out_batch = param.output->Slice<T>(i, i + 1);
-      tmp_out_batch.Resize(output_matrix_shape);
-      out_batch.ShareDataWith(tmp_out_batch);
+      lite::Tensor in_batch = param.x->Slice<T>(i, i + 1);
+      in_batch.Resize(input_shape);
+      lite::Tensor out_batch = param.output->Slice<T>(i, i + 1);
+      out_batch.Resize(output_matrix_shape);
       for (int g = 0; g < param.groups; g++) {
-        lite::Tensor in_slice;
-        in_slice.ShareDataWith(
+        lite::Tensor in_slice =
             in_batch.Slice<T>(static_cast<int64_t>(g * in_step),
-                              static_cast<int64_t>((g + 1) * in_step)));
+                              static_cast<int64_t>((g + 1) * in_step));
 
         if (!is_expand) {
           col.ShareDataWith(in_slice);
@@ -136,13 +131,13 @@ class Conv2dCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
 
         // gemm
         lite::Tensor out_slice;
-        out_slice.ShareDataWith(
+        out_slice =
             out_batch.Slice<T>(static_cast<int64_t>(g * out_step),
-                               static_cast<int64_t>((g + 1) * out_step)));
+                               static_cast<int64_t>((g + 1) * out_step));
         lite::Tensor filter_slice;
-        filter_slice.ShareDataWith(
+        filter_slice =
             filter.Slice<T>(static_cast<int64_t>(g * out_step),
-                            static_cast<int64_t>((g + 1) * out_step)));
+                            static_cast<int64_t>((g + 1) * out_step));
         blas.MatMul(filter_slice,
                     false,
                     col_matrix,
diff --git a/lite/tools/ci_build.sh b/lite/tools/ci_build.sh
index 8be8e6e6b6da1e2aa38b6fcbcf95b23a8543a5be..6c3750e69fa6eb1ecc36a1fc7e42fe32bead377a 100755
--- a/lite/tools/ci_build.sh
+++ b/lite/tools/ci_build.sh
@@ -195,7 +195,6 @@ function test_server {
     # Due to the missing of x86 kernels, we skip the following tests temporarily.
     # TODO(xxx) clear the skip list latter
     local skip_list=("test_paddle_api" "test_cxx_api"
-                     "test_mobilenetv1_lite_x86" "test_mobilenetv2_lite_x86"
                      "test_light_api"
                      "test_apis" "test_model_bin"
                     )