1 files changed, 37 insertions, 37 deletions

diff --git a/tensorflow/contrib/lite/kernels/internal/depthwiseconv_float_test.cc b/tensorflow/contrib/lite/kernels/internal/depthwiseconv_float_test.cc
index 7600b26f5c..41862a21a6 100644
--- a/tensorflow/contrib/lite/kernels/internal/depthwiseconv_float_test.cc
+++ b/tensorflow/contrib/lite/kernels/internal/depthwiseconv_float_test.cc
@@ -29,28 +29,20 @@ namespace tflite {
 namespace {
 
 // Runs the DepthwiseConv and compares against the reference implementation.
-void TestOneDepthwiseConv(const float* input_data, const Dims<4>& input_dims,
-                          const float* filter_data, const Dims<4>& filter_dims,
-                          const float* bias_data, const Dims<4>& bias_dims,
-                          int stride, int dilation_width_factor,
-                          int dilation_height_factor, int pad_width,
-                          int pad_height, int depth_multiplier,
-                          float output_activation_min,
-                          float output_activation_max,
-                          const Dims<4>& output_dims) {
-  const int output_buffer_size = RequiredBufferSizeForDims(output_dims);
+void TestOneDepthwiseConv(
+    const DepthwiseParams& params, const RuntimeShape& input_shape,
+    const float* input_data, const RuntimeShape& filter_shape,
+    const float* filter_data, const RuntimeShape& bias_shape,
+    const float* bias_data, const RuntimeShape& output_shape) {
+  const int output_buffer_size = output_shape.FlatSize();
   std::vector<float> output_data(output_buffer_size);
   std::vector<float> reference_output_data(output_buffer_size);
-  reference_ops::DepthwiseConv(
-      input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims,
-      stride, stride, dilation_width_factor, dilation_height_factor, pad_width,
-      pad_height, depth_multiplier, output_activation_min,
-      output_activation_max, reference_output_data.data(), output_dims);
-  optimized_ops::DepthwiseConv(
-      input_data, input_dims, filter_data, filter_dims, bias_data, bias_dims,
-      stride, stride, dilation_width_factor, dilation_height_factor, pad_width,
-      pad_height, depth_multiplier, output_activation_min,
-      output_activation_max, output_data.data(), output_dims);
+  reference_ops::DepthwiseConv(params, input_shape, input_data, filter_shape,
+                               filter_data, bias_shape, bias_data, output_shape,
+                               reference_output_data.data());
+  optimized_ops::DepthwiseConv(params, input_shape, input_data, filter_shape,
+                               filter_data, bias_shape, bias_data, output_shape,
+                               output_data.data());
 
   double sum_abs_diff = 0;
   float max_abs_val = 0;
@@ -105,24 +97,23 @@ bool TryTestOneDepthwiseConv() {
   if (output_depth > kMaxSupportedOutputDepth) {
     return false;
   }
-  Dims<4> input_dims_inference =
-      MakeDimsForInference(input_depth, input_width, input_height, batch);
-  Dims<4> output_dims_inference;
+  RuntimeShape input_shape_inference(
+      {batch, input_height, input_width, input_depth});
+  RuntimeShape output_shape_inference;
   int pad_width, pad_height;
   const auto padding_type =
       UniformRandomInt(0, 1) ? PaddingType::kSame : PaddingType::kValid;
-  if (!ComputeConvSizes(input_dims_inference, output_depth, filter_width,
+  if (!ComputeConvSizes(input_shape_inference, output_depth, filter_width,
                         filter_height, stride, dilation_width_factor,
                         dilation_height_factor, padding_type,
-                        &output_dims_inference, &pad_width, &pad_height)) {
+                        &output_shape_inference, &pad_width, &pad_height)) {
     return false;
   }
-  Dims<4> filter_dims_inference =
-      MakeDimsForInference(output_depth, filter_width, filter_height, 1);
-  Dims<4> bias_dims_inference = MakeDimsForInference(output_depth, 1, 1, 1);
-  const int input_buffer_size = RequiredBufferSizeForDims(input_dims_inference);
-  const int filter_buffer_size =
-      RequiredBufferSizeForDims(filter_dims_inference);
+  RuntimeShape filter_shape_inference(
+      {1, filter_height, filter_width, output_depth});
+  RuntimeShape bias_shape_inference({1, 1, 1, output_depth});
+  const int input_buffer_size = input_shape_inference.FlatSize();
+  const int filter_buffer_size = filter_shape_inference.FlatSize();
   std::vector<float> input_data(input_buffer_size);
   std::vector<float> filter_data(filter_buffer_size);
   std::vector<float> bias_data(output_depth);
@@ -133,12 +124,21 @@ bool TryTestOneDepthwiseConv() {
   FillRandom(&input_data, -input_amplitude, input_amplitude);
   FillRandom(&filter_data, -filter_amplitude, filter_amplitude);
   FillRandom(&bias_data, -bias_amplitude, bias_amplitude);
-  TestOneDepthwiseConv(input_data.data(), input_dims_inference,
-                       filter_data.data(), filter_dims_inference,
-                       bias_data.data(), bias_dims_inference, stride,
-                       dilation_width_factor, dilation_height_factor, pad_width,
-                       pad_height, depth_multiplier, output_activation_min,
-                       output_activation_max, output_dims_inference);
+  DepthwiseParams op_params;
+  op_params.padding_type = PaddingType::kSame;
+  op_params.padding_values.width = pad_width;
+  op_params.padding_values.height = pad_height;
+  op_params.stride_width = stride;
+  op_params.stride_height = stride;
+  op_params.dilation_width_factor = dilation_width_factor;
+  op_params.dilation_height_factor = dilation_height_factor;
+  op_params.depth_multiplier = depth_multiplier;
+  op_params.float_activation_min = output_activation_min;
+  op_params.float_activation_max = output_activation_max;
+  TestOneDepthwiseConv(op_params, input_shape_inference, input_data.data(),
+                       filter_shape_inference, filter_data.data(),
+                       bias_shape_inference, bias_data.data(),
+                       output_shape_inference);
   return true;
 }