Update kernel evals to use new kernel signatures.

PiperOrigin-RevId: 214377809

1 files changed, 93 insertions, 48 deletions

diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index ab6bdaecaa..101b4fc961 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -414,35 +414,57 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
   }
 
   switch (effective_kernel_type) {
-    case kReference:
+    case kReference: {
+      ConvParams op_params;
+      op_params.padding_type = PaddingType::kSame;
+      op_params.padding_values.width = data->padding.width;
+      op_params.padding_values.height = data->padding.height;
+      op_params.stride_width = params->stride_width;
+      op_params.stride_height = params->stride_height;
+      op_params.dilation_width_factor = params->dilation_width_factor;
+      op_params.dilation_height_factor = params->dilation_height_factor;
+      op_params.input_offset = input_offset;
+      op_params.weights_offset = filter_offset;
+      op_params.output_offset = output_offset;
+      op_params.output_multiplier = data->output_multiplier;
+      op_params.output_shift = -data->output_shift;
+      op_params.quantized_activation_min = data->output_activation_min;
+      op_params.quantized_activation_max = data->output_activation_max;
       reference_ops::Conv(
-          GetTensorData<uint8_t>(input), GetTensorDims(input), input_offset,
-          GetTensorData<uint8_t>(filter), GetTensorDims(filter), filter_offset,
-          GetTensorData<int32_t>(bias), GetTensorDims(bias),
-          params->stride_width, params->stride_height,
-          params->dilation_width_factor, params->dilation_height_factor,
-          data->padding.width, data->padding.height, output_offset,
-          data->output_multiplier, data->output_shift,
-          data->output_activation_min, data->output_activation_max,
-          GetTensorData<uint8_t>(output), GetTensorDims(output),
-          GetTensorData<uint8_t>(im2col), GetTensorDims(im2col), gemm_context);
+          op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
+          GetTensorShape(filter), GetTensorData<uint8_t>(filter),
+          GetTensorShape(bias), GetTensorData<int32_t>(bias),
+          GetTensorShape(output), GetTensorData<uint8_t>(output),
+          GetTensorShape(im2col), GetTensorData<uint8_t>(im2col), gemm_context);
       break;
+    }
     case kGenericOptimized:
     case kMultithreadOptimized:
-    case kCblasOptimized:
+    case kCblasOptimized: {
       // There is only one optimized implementation for Quantized Conv.
+      ConvParams op_params;
+      op_params.padding_type = PaddingType::kSame;
+      op_params.padding_values.width = data->padding.width;
+      op_params.padding_values.height = data->padding.height;
+      op_params.stride_width = params->stride_width;
+      op_params.stride_height = params->stride_height;
+      op_params.dilation_width_factor = params->dilation_width_factor;
+      op_params.dilation_height_factor = params->dilation_height_factor;
+      op_params.input_offset = input_offset;
+      op_params.weights_offset = filter_offset;
+      op_params.output_offset = output_offset;
+      op_params.output_multiplier = data->output_multiplier;
+      op_params.output_shift = -data->output_shift;
+      op_params.quantized_activation_min = data->output_activation_min;
+      op_params.quantized_activation_max = data->output_activation_max;
       optimized_ops::Conv(
-          GetTensorData<uint8_t>(input), GetTensorDims(input), input_offset,
-          GetTensorData<uint8_t>(filter), GetTensorDims(filter), filter_offset,
-          GetTensorData<int32_t>(bias), GetTensorDims(bias),
-          params->stride_width, params->stride_height,
-          params->dilation_width_factor, params->dilation_height_factor,
-          data->padding.width, data->padding.height, output_offset,
-          data->output_multiplier, data->output_shift,
-          data->output_activation_min, data->output_activation_max,
-          GetTensorData<uint8_t>(output), GetTensorDims(output),
-          GetTensorData<uint8_t>(im2col), GetTensorDims(im2col), gemm_context);
+          op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
+          GetTensorShape(filter), GetTensorData<uint8_t>(filter),
+          GetTensorShape(bias), GetTensorData<int32_t>(bias),
+          GetTensorShape(output), GetTensorData<uint8_t>(output),
+          GetTensorShape(im2col), GetTensorData<uint8_t>(im2col), gemm_context);
       break;
+    }
   }
 }
 
@@ -467,27 +489,41 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
   }
   switch (effective_kernel_type) {
     case kReference: {
-      reference_ops::Conv(
-          GetTensorData<float>(input), GetTensorDims(input),
-          GetTensorData<float>(filter), GetTensorDims(filter),
-          GetTensorData<float>(bias), GetTensorDims(bias), params->stride_width,
-          params->stride_height, params->dilation_width_factor,
-          params->dilation_height_factor, data->padding.width,
-          data->padding.height, output_activation_min, output_activation_max,
-          GetTensorData<float>(output), GetTensorDims(output),
-          GetTensorData<float>(im2col), GetTensorDims(im2col));
+      ConvParams op_params;
+      op_params.padding_type = PaddingType::kSame;
+      op_params.padding_values.width = data->padding.width;
+      op_params.padding_values.height = data->padding.height;
+      op_params.stride_width = params->stride_width;
+      op_params.stride_height = params->stride_height;
+      op_params.dilation_width_factor = params->dilation_width_factor;
+      op_params.dilation_height_factor = params->dilation_height_factor;
+      op_params.float_activation_min = output_activation_min;
+      op_params.float_activation_max = output_activation_max;
+      reference_ops::Conv(op_params, GetTensorShape(input),
+                          GetTensorData<float>(input), GetTensorShape(filter),
+                          GetTensorData<float>(filter), GetTensorShape(bias),
+                          GetTensorData<float>(bias), GetTensorShape(output),
+                          GetTensorData<float>(output), GetTensorShape(im2col),
+                          GetTensorData<float>(im2col));
       break;
     }
     case kGenericOptimized: {
-      optimized_ops::Conv(
-          GetTensorData<float>(input), GetTensorDims(input),
-          GetTensorData<float>(filter), GetTensorDims(filter),
-          GetTensorData<float>(bias), GetTensorDims(bias), params->stride_width,
-          params->stride_height, params->dilation_width_factor,
-          params->dilation_height_factor, data->padding.width,
-          data->padding.height, output_activation_min, output_activation_max,
-          GetTensorData<float>(output), GetTensorDims(output),
-          GetTensorData<float>(im2col), GetTensorDims(im2col));
+      ConvParams op_params;
+      op_params.padding_type = PaddingType::kSame;
+      op_params.padding_values.width = data->padding.width;
+      op_params.padding_values.height = data->padding.height;
+      op_params.stride_width = params->stride_width;
+      op_params.stride_height = params->stride_height;
+      op_params.dilation_width_factor = params->dilation_width_factor;
+      op_params.dilation_height_factor = params->dilation_height_factor;
+      op_params.float_activation_min = output_activation_min;
+      op_params.float_activation_max = output_activation_max;
+      optimized_ops::Conv(op_params, GetTensorShape(input),
+                          GetTensorData<float>(input), GetTensorShape(filter),
+                          GetTensorData<float>(filter), GetTensorShape(bias),
+                          GetTensorData<float>(bias), GetTensorShape(output),
+                          GetTensorData<float>(output), GetTensorShape(im2col),
+                          GetTensorData<float>(im2col));
       break;
     }
     case kMultithreadOptimized: {
@@ -561,18 +597,27 @@ void EvalHybrid(TfLiteContext* context, TfLiteNode* node,
     case kReference:
     case kGenericOptimized:
     case kMultithreadOptimized:
-    case kCblasOptimized:
+    case kCblasOptimized: {
       // There is only one implementation for hybrid kernel. Note
       // this does not make use of gemmlowp nor supports multithreading.
+      ConvParams op_params;
+      op_params.padding_type = PaddingType::kSame;
+      op_params.padding_values.width = data->padding.width;
+      op_params.padding_values.height = data->padding.height;
+      op_params.stride_width = params->stride_width;
+      op_params.stride_height = params->stride_height;
+      op_params.dilation_width_factor = 1;
+      op_params.dilation_height_factor = 1;
+      op_params.float_activation_min = output_activation_min;
+      op_params.float_activation_max = output_activation_max;
       optimized_ops::HybridConv(
-          quantized_input_ptr_batch, GetTensorDims(input), filter_ptr,
-          GetTensorDims(filter), GetTensorData<float>(bias),
-          GetTensorDims(bias), params->stride_width, params->stride_height,
-          data->padding.width, data->padding.height, scaling_factors_ptr,
-          output_activation_min, output_activation_max,
-          GetTensorData<float>(output), GetTensorDims(output), im2col_ptr,
-          GetTensorDims(im2col));
+          op_params, scaling_factors_ptr, GetTensorShape(input),
+          quantized_input_ptr_batch, GetTensorShape(filter), filter_ptr,
+          GetTensorShape(bias), GetTensorData<float>(bias),
+          GetTensorShape(output), GetTensorData<float>(output),
+          GetTensorShape(im2col), im2col_ptr);
       break;
+    }
   }
 }