Quantized Dilated Convolution support.

Also add tests for float and quantized dilated support, which seemed to be missing.

PiperOrigin-RevId: 208766032

1 files changed, 31 insertions, 17 deletions

diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc
index 04c0263b78..50fe5c2e04 100644
--- a/tensorflow/contrib/lite/kernels/conv.cc
+++ b/tensorflow/contrib/lite/kernels/conv.cc
@@ -334,18 +334,31 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
   auto filter_offset = -filter->params.zero_point;
   auto output_offset = output->params.zero_point;
 
-  switch (kernel_type) {
+  KernelType effective_kernel_type;
+  if ((kernel_type == kMultithreadOptimized ||
+       kernel_type == kCblasOptimized) &&
+      (params->dilation_width_factor != 1 ||
+       params->dilation_height_factor != 1)) {
+    // kMultithreadOptimized and kCblasOptimized do not support dilation.
+    // Therefore, fallback to optimized.
+    effective_kernel_type = kGenericOptimized;
+  } else {
+    effective_kernel_type = kernel_type;
+  }
+
+  switch (effective_kernel_type) {
     case kReference:
       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, 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);
+          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);
       break;
     case kGenericOptimized:
     case kMultithreadOptimized:
@@ -355,12 +368,13 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
           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, 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);
+          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);
       break;
   }
 }
@@ -374,10 +388,10 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
   CalculateActivationRange(params->activation, &output_activation_min,
                            &output_activation_max);
   KernelType effective_kernel_type;
-  if (((kernel_type == kMultithreadOptimized) ||
-       (kernel_type == kCblasOptimized)) &&
-      ((params->dilation_width_factor != 1) ||
-       (params->dilation_height_factor != 1))) {
+  if ((kernel_type == kMultithreadOptimized ||
+       kernel_type == kCblasOptimized) &&
+      (params->dilation_width_factor != 1 ||
+       params->dilation_height_factor != 1)) {
     // kMultithreadOptimized and kCblasOptimized do not support dilation.
     // Therefore, fallback to optimized.
     effective_kernel_type = kGenericOptimized;