diff --git a/mace/kernels/opencl/cl/conv_2d_1x1.cl b/mace/kernels/opencl/cl/conv_2d_1x1.cl
index 6007cdf6ddc183cc7e8ba8b258079f4637a32ec2..c9d49525723285a25410b2a976d0b95de0319c05 100644
--- a/mace/kernels/opencl/cl/conv_2d_1x1.cl
+++ b/mace/kernels/opencl/cl/conv_2d_1x1.cl
@@ -1,29 +1,5 @@
 #include <common.h>
 
-__kernel void conv_2d_1x1_naive(__global const float *input, /* n, c, h, w */
-                                __global const float *filter, /* o, i, kh, kw */
-                                __global const float *bias, /* o */
-                                __global float *output, /* n, c, h, w */
-                                __private const int in_chan_num) {
-  const int batch = get_global_id(0);
-  const int channel = get_global_id(1);
-  const int channels = get_global_size(1);
-  const int pixel = get_global_id(2);
-  const int pixels = get_global_size(2);
-
-  float *output_ptr = output + (batch * channels + channel) * pixels;
-  output_ptr[pixel] = bias[channel];
-
-  for (int inc = 0; inc < in_chan_num; ++inc) {
-    const float *input_ptr = input + (batch * in_chan_num + inc) * pixels + pixel;
-    const float weights = filter[channel * in_chan_num + inc];
-    float in = input_ptr[0];
-    float out = output_ptr[0];
-    out += in * weights;
-    output_ptr[0] = out;
-  }
-}
-
 #define vec_conv_2d_1x1_s1                    \
   VEC_DATA_TYPE(DATA_TYPE,4) in0 = vload4(0, input_ptr);                   \
   VEC_DATA_TYPE(DATA_TYPE,4) in1 = vload4(0, input_ptr + in_pixel);        \
diff --git a/mace/ops/ops_test_util.h b/mace/ops/ops_test_util.h
index 16b1158850f83043d828a1a93ae677f5b0c29ae1..bbe2d1ab37109cdae5618fee35080a89be5ae951 100644
--- a/mace/ops/ops_test_util.h
+++ b/mace/ops/ops_test_util.h
@@ -107,32 +107,6 @@ class OpsTestNet {
     memcpy(input_data, data.data(), data.size() * sizeof(T));
   }
 
-  template <DeviceType D, typename T>
-  void AddInputImageFromArray(const std::string &name,
-                              const std::vector<index_t> &shape,
-                              const std::vector<T> &data) {
-    Tensor *input =
-        ws_.CreateTensor(name, GetDeviceAllocator(D), DataTypeToEnum<T>::v());
-    std::vector<size_t> image_shape;
-    input->ResizeImage(shape, image_shape);
-    Tensor::MappingGuard input_mapper(input);
-    T *input_data = input->mutable_data<T>();
-    MACE_CHECK(static_cast<size_t>(input->size()) == data.size());
-    const T *data_ptr = data.data();
-    const int type_size = sizeof(T);
-    const int row_pitch = shape[3];
-    auto mapped_image_pitch = input_mapper.mapped_image_pitch();
-    const size_t slice_size = mapped_image_pitch[1] / sizeof(T);
-    for (int c = 0; c < shape[0] * shape[1]; ++c) {
-      T *input_ptr = input_data + c * slice_size;
-      for (int h = 0; h < shape[2]; ++h) {
-        memcpy(input_ptr, data_ptr, row_pitch * type_size);
-        input_ptr += mapped_image_pitch[0] / sizeof(T);
-        data_ptr += row_pitch;
-      }
-    }
-  }
-
   template <DeviceType D, typename T>
   void AddRepeatedInput(const std::string &name,
                         const std::vector<index_t> &shape,