Fix the compiling error because of the modification of DDimLite's function.

test=develop

lite/kernels/arm/scale_compute_test.cc

@@ -28,7 +28,7 @@ void scale_compute_ref(const operators::ScaleParam& param) {
   dtype* output_data = param.output->mutable_data<dtype>();
   DDim x_dims = param.x->dims();
   DDim output_dims = param.output->dims();
-  ASSERT_EQ(x_dims.data(), output_dims.data());
+  ASSERT_EQ(x_dims, output_dims);
   bool bias_after_scale = param.bias_after_scale;
   float scale = param.scale;
   float bias = param.bias;

lite/kernels/arm/slice_compute.cc

@@ -157,8 +157,9 @@ void SliceCompute<T, PType>::Run() {
   auto new_out_dims = out->dims();
   const auto* x_data = in->template data<T>();
   auto* o_data = out->template mutable_data<T>();
+  std::vector<int64_t> in_dims_data = in_dims.Vectorize();
   lite::arm::math::slice(
-      x_data, in_dims.data(), axes, starts, ends, o_data, &ctx);
+      x_data, in_dims_data, axes, starts, ends, o_data, &ctx);
 }
 
 }  // namespace arm

lite/kernels/arm/softmax_compute_test.cc

@@ -29,7 +29,7 @@ void softmax_compute_ref(const operators::SoftmaxParam& param) {
   const dtype* x_data = param.x->mutable_data<const dtype>();
   dtype* output_data = param.output->mutable_data<dtype>();
   DDim x_dims = param.x->dims();
-  ASSERT_EQ(x_dims.data(), param.output->dims().data());
+  ASSERT_EQ(x_dims, param.output->dims());
   auto x_rank = x_dims.size();
   int axis = param.axis;
   if (axis < 0) {

lite/kernels/cuda/softmax_compute_test.cc

@@ -33,7 +33,7 @@ static void softmax_compute_ref(const operators::SoftmaxParam& param) {
   const dtype* x_data = param.x->mutable_data<const dtype>();
   dtype* output_data = param.output->mutable_data<dtype>();
   DDim x_dims = param.x->dims();
-  ASSERT_EQ(x_dims.data(), param.output->dims().data());
+  ASSERT_EQ(x_dims, param.output->dims());
   auto x_rank = x_dims.size();
   int axis = param.axis;
   if (axis < 0) {

lite/kernels/cuda/transpose_compute.cu

@@ -35,7 +35,7 @@ void TransposeCompute::Run() {
   float* out = Out->mutable_data<float>(TARGET(kCUDA));
 
   int ndim = X->dims().size();
-  std::vector<int64_t> dims = X->dims().data();
+  std::vector<int64_t> dims = X->dims().Vectorize();
 
   // NCHW -> NHWC
   if (axes.size() == 4 && axes[0] == 0 && axes[1] == 2 && axes[2] == 3 &&

lite/kernels/fpga/activation_compute_test.cc

@@ -30,7 +30,7 @@ void activation_compute_ref(const operators::ActivationParam& param) {
   auto output_data = param.Out->mutable_data<dtype>();
   DDim x_dims = param.X->dims();
   DDim output_dims = param.Out->dims();
-  ASSERT_EQ(x_dims.data(), output_dims.data());
+  ASSERT_EQ(x_dims, output_dims);
   for (int i = 0; i < output_dims.production(); i++) {
     output_data[i] = std::max(0.f, x_data[i]);
   }

lite/kernels/fpga/softmax_compute_test.cc

@@ -29,7 +29,7 @@ void softmax_compute_ref(const operators::SoftmaxParam& param) {
   const dtype* x_data = param.x->mutable_data<const dtype>();
   dtype* output_data = param.output->mutable_data<dtype>();
   DDim x_dims = param.x->dims();
-  ASSERT_EQ(x_dims.data(), param.output->dims().data());
+  ASSERT_EQ(x_dims, param.output->dims());
   auto x_rank = x_dims.size();
   int axis = param.axis;
   if (axis < 0) {