diff --git a/src/framework/executor.cpp b/src/framework/executor.cpp
index d511ad48ca3cf794ea473ab5bc0d791d4c72eded..97957f02ede60036c828c849eb779cfd14a6468e 100644
--- a/src/framework/executor.cpp
+++ b/src/framework/executor.cpp
@@ -474,12 +474,14 @@ PMStatus Executor<Device, T>::Predict() {
   struct timespec ts;
   int op_index = 0;
 #endif
-  for (auto &op_handler : ops_of_block0_) {
+  for (int i = 0; i < ops_of_block0_.size(); ++i) {
+    auto &op_handler = ops_of_block0_[i];
 #ifdef PADDLE_MOBILE_PROFILE
     clock_gettime(CLOCK_MONOTONIC, &ts);
     profile[op_index].runBegin = (uint64_t)ts.tv_sec * 1e9 + ts.tv_nsec;
 #endif
-    DLOG << "run op: " << op_handler->Type();
+    DLOG << i << "th, "
+         << "run op: " << op_handler->Type();
     if (lod_mode_ && input_dim_has_changed_) {
       op_handler->InferShape();
     }
diff --git a/src/framework/program/program_desc.cpp b/src/framework/program/program_desc.cpp
index 88cac11d283cd0c5b72a6809deb34df8206c3e25..7ec74874b678d564529c3d6d86e4db439a94cba9 100644
--- a/src/framework/program/program_desc.cpp
+++ b/src/framework/program/program_desc.cpp
@@ -58,7 +58,7 @@ void ProgramDesc::Description(std::string header) const {
     LOG(kLOG_INFO) << "block ops size: " << block->Ops().size();
     for (int j = 0; j < block->Ops().size(); ++j) {
       auto op = block->Ops()[j];
-      LOG(kLOG_DEBUG1) << "op: " << op->Type();
+      LOG(kLOG_DEBUG1) << j << "th, op: " << op->Type();
       for (auto &input : op->GetInputs()) {
         LOG(kLOG_DEBUG2) << "input parameter: " << input.first;
         for (auto &n : input.second) {
diff --git a/src/operators/kernel/arm/slice_kernel.cpp b/src/operators/kernel/arm/slice_kernel.cpp
index e373b569870c81587377ac02e578397518513a85..db3718612be94e78a38d412b52579c7abc1d98c8 100644
--- a/src/operators/kernel/arm/slice_kernel.cpp
+++ b/src/operators/kernel/arm/slice_kernel.cpp
@@ -19,12 +19,54 @@ limitations under the License. */
 namespace paddle_mobile {
 namespace operators {
 
+void SliceCompute(const SliceParam<CPU>& param) {
+  auto input = param.input_;
+  auto output = param.output_;
+  auto* input_ptr = input->data<float>();
+  auto* output_ptr = output->mutable_data<float>();
+  auto out_dims = output->dims();
+  auto in_dims = input->dims();
+  auto starts = param.starts_;
+  auto ends = param.ends_;
+  int axes = param.axes_[0];
+  int HW = input->dims()[axes + 1] * input->dims()[axes + 2];
+  int batch_size = out_dims[axes - 1];
+  int input_channel = in_dims[axes];
+  int output_channel = out_dims[axes];
+
+  for (int c1 = 0; c1 < batch_size; ++c1) {
+    for (int c2 = starts[0], c3 = 0; c2 < ends[0]; ++c2, ++c3) {
+      size_t out_offset = c1 * output_channel * HW + c3 * HW;
+      size_t in_offset = c1 * input_channel * HW + c2 * HW;
+      memcpy(output_ptr + out_offset, input_ptr + in_offset,
+             HW * sizeof(float));
+    }
+  }
+}
+
 template <>
 bool SliceKernel<CPU, float>::Init(SliceParam<CPU>* param) {
   return true;
 }
+
 template <>
-void SliceKernel<CPU, float>::Compute(const SliceParam<CPU>& param) {}
+void SliceKernel<CPU, float>::Compute(const SliceParam<CPU>& param) {
+  int rank = param.input_->dims().size();
+  switch (rank) {
+    case 4:
+      SliceCompute(param);
+      break;
+    case 5:
+      if (param.input_->dims()[0] == 1) {
+        SliceCompute(param);
+      }
+      break;
+    default:
+      PADDLE_MOBILE_ENFORCE(0, "input dims not support now");
+      break;
+  }
+}
+
 }  // namespace operators
 }  // namespace paddle_mobile
 #endif
diff --git a/src/operators/slice_op.cpp b/src/operators/slice_op.cpp
index 85b2fea07f8bbe1ce3452566abb0c41111a03d88..14316d37eb3ea69557e47f47f3d8563523184490 100644
--- a/src/operators/slice_op.cpp
+++ b/src/operators/slice_op.cpp
@@ -21,7 +21,14 @@ namespace operators {
 
 template <typename Dtype, typename T>
 void SliceOp<Dtype, T>::InferShape() const {
-  /// todo: add InputShape() detection.
+  auto axes = this->param_.axes_;
+  auto input = this->param_.input_;
+  auto output = this->param_.output_;
+  PADDLE_MOBILE_ENFORCE(axes.size() == 1, "axes size should equals 1");
+  PADDLE_MOBILE_ENFORCE(input->dims().size() == output->dims().size(),
+                        "input dim size should equals output dim size");
+  PADDLE_MOBILE_ENFORCE(input->dims().size() - axes[0] == 3,
+                        "op only support slice channel now");
 }
 
 }  // namespace operators
diff --git a/tools/python/fluidtools/run.py b/tools/python/fluidtools/run.py
index f519f97208b0c6b49cce3910e602d62c3c8f7a9f..03efa209e2482bbc11cbbfdbe91b5fd1a9f4b159 100644
--- a/tools/python/fluidtools/run.py
+++ b/tools/python/fluidtools/run.py
@@ -368,10 +368,11 @@ def check_mobile_results(args, fuse, mem_opt):
         error_values1 = np.array(error_values1)
         error_values2 = np.array(error_values2)
         # pp_red("mobile op is not correct, error occurs at {}th op, op's type is {}")
-        pp_red("corresponding fluid op is {}th op, op's type is {}".format(error_index, op_cache[error_index][1].type), 1)
+        pp_red("corresponding fluid op is {}th op, op's type is {}, wrong var name is {}".format(
+            error_index,op_cache[error_index][1].type,op_output_var_name), 1)
         pp_red("fluid results are : ", 1)
         pp_red(str(error_values1).replace("\n", "\n" + "\t" * 1), 1)
-        pp_red("paddle mobile results are : ", 1)
+        pp_yellow("paddle mobile results are : ", 1)
         pp_red(str(error_values2).replace("\n", "\n" + "\t" * 1), 1)
     # print(output_var_cache)
     # print(mobile_var_cache)