diff --git a/tensorflow/lite/experimental/micro/kernels/depthwise_conv.cc b/tensorflow/lite/experimental/micro/kernels/depthwise_conv.cc
index ce821a94787..2fdd18f93b7 100644
--- a/tensorflow/lite/experimental/micro/kernels/depthwise_conv.cc
+++ b/tensorflow/lite/experimental/micro/kernels/depthwise_conv.cc
@@ -50,12 +50,12 @@ struct OpData {
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
TfLiteDepthwiseConvParams* params, int width,
int height, int filter_width, int filter_height,
- int out_width, int out_height,
const TfLiteType data_type, OpData* data) {
- data->padding.height = ComputePadding(params->stride_height, 1, height,
- filter_height, out_height);
- data->padding.width =
- ComputePadding(params->stride_width, 1, width, filter_width, out_width);
+ int unused_output_height, unused_output_width;
+ data->padding = ComputePaddingHeightWidth(
+ params->stride_height, params->stride_width, 1, 1, height, width,
+ filter_height, filter_width, params->padding, &unused_output_height,
+ &unused_output_width);
// Note that quantized inference requires that all tensors have their
// parameters set. This is usually done during quantized training.
@@ -168,15 +168,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
int height = SizeOfDimension(input, 1);
int filter_width = SizeOfDimension(filter, 2);
int filter_height = SizeOfDimension(filter, 1);
- int out_width = ComputeOutSize(params->padding, width, filter_width,
- params->stride_width);
- int out_height = ComputeOutSize(params->padding, height, filter_height,
- params->stride_height);
OpData local_data_object;
OpData* data = &local_data_object;
TF_LITE_ENSURE_STATUS(CalculateOpData(context, node, params, width, height,
- filter_width, filter_height, out_width,
- out_height, data_type, data));
+ filter_width, filter_height, data_type,
+ data));
// TODO(aselle): Consider whether float conv and quantized conv should be
// separate ops to avoid dispatch overhead here.
diff --git a/tensorflow/lite/kernels/conv.cc b/tensorflow/lite/kernels/conv.cc
index 8da8bd1d145..2d058aaf2e3 100644
--- a/tensorflow/lite/kernels/conv.cc
+++ b/tensorflow/lite/kernels/conv.cc
@@ -284,28 +284,11 @@ TfLiteStatus Prepare(KernelType kernel_type, TfLiteContext* context,
// Matching GetWindowedOutputSize in TensorFlow.
auto padding = params->padding;
- auto compute_out_size = [padding](int image_size, int filter_size, int stride,
- int dilation_rate) -> int {
- int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
- return padding == kTfLitePaddingSame
- ? (image_size + stride - 1) / stride
- : padding == kTfLitePaddingValid
- ? (image_size - effective_filter_size + stride) / stride
- : 0;
- };
-
- int out_width = compute_out_size(width, filter_width, params->stride_width,
- params->dilation_width_factor);
- int out_height =
- compute_out_size(height, filter_height, params->stride_height,
- params->dilation_height_factor);
-
- data->padding.height =
- ComputePadding(params->stride_height, params->dilation_height_factor,
- height, filter_height, out_height);
- data->padding.width =
- ComputePadding(params->stride_width, params->dilation_width_factor, width,
- filter_width, out_width);
+ int out_width, out_height;
+ data->padding = ComputePaddingHeightWidth(
+ params->stride_height, params->stride_width,
+ params->dilation_height_factor, params->dilation_width_factor, height,
+ width, filter_height, filter_width, padding, &out_height, &out_width);
TF_LITE_ENSURE(context, has_bias);
diff --git a/tensorflow/lite/kernels/depthwise_conv.cc b/tensorflow/lite/kernels/depthwise_conv.cc
index 93c5bcb2d7e..488dcf3cedc 100644
--- a/tensorflow/lite/kernels/depthwise_conv.cc
+++ b/tensorflow/lite/kernels/depthwise_conv.cc
@@ -138,28 +138,12 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
// Matching GetWindowedOutputSize in TensorFlow.
auto padding = params->padding;
- auto compute_out_size = [padding](int image_size, int filter_size, int stride,
- int dilation_rate) -> int {
- int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
- return padding == kTfLitePaddingSame
- ? (image_size + stride - 1) / stride
- : padding == kTfLitePaddingValid
- ? (image_size - effective_filter_size + stride) / stride
- : 0;
- };
+ int out_width, out_height;
- int out_width = compute_out_size(width, filter_width, params->stride_width,
- params->dilation_width_factor);
- int out_height =
- compute_out_size(height, filter_height, params->stride_height,
- params->dilation_height_factor);
-
- data->padding.height =
- ComputePadding(params->stride_height, params->dilation_height_factor,
- height, filter_height, out_height);
- data->padding.width =
- ComputePadding(params->stride_width, params->dilation_width_factor, width,
- filter_width, out_width);
+ data->padding = ComputePaddingHeightWidth(
+ params->stride_height, params->stride_width,
+ params->dilation_height_factor, params->dilation_width_factor, height,
+ width, filter_height, filter_width, padding, &out_height, &out_width);
// Note that quantized inference requires that all tensors have their
// parameters set. This is usually done during quantized training or
diff --git a/tensorflow/lite/kernels/padding.h b/tensorflow/lite/kernels/padding.h
index 1aecf1054b3..1116b1da852 100644
--- a/tensorflow/lite/kernels/padding.h
+++ b/tensorflow/lite/kernels/padding.h
@@ -42,31 +42,36 @@ inline int ComputePaddingWithOffset(int stride, int dilation_rate, int in_size,
// Matching GetWindowedOutputSize in TensorFlow.
inline int ComputeOutSize(TfLitePadding padding, int image_size,
- int filter_size, int stride) {
+ int filter_size, int stride, int dilation_rate = 1) {
+ int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
switch (padding) {
case kTfLitePaddingSame:
return (image_size + stride - 1) / stride;
case kTfLitePaddingValid:
- return (image_size + stride - filter_size) / stride;
+ return (image_size + stride - effective_filter_size) / stride;
default:
return 0;
}
}
inline TfLitePaddingValues ComputePaddingHeightWidth(
- int stride_height, int stride_width, int dilation_rate, int in_height,
- int in_width, int filter_height, int filter_width, TfLitePadding padding) {
- int out_width = ComputeOutSize(padding, in_width, filter_width, stride_width);
- int out_height =
- ComputeOutSize(padding, in_height, filter_height, stride_height);
+ int stride_height, int stride_width, int dilation_rate_height,
+ int dilation_rate_width, int in_height, int in_width, int filter_height,
+ int filter_width, TfLitePadding padding, int* out_height, int* out_width) {
+ *out_width = ComputeOutSize(padding, in_width, filter_width, stride_width,
+ dilation_rate_width);
+ *out_height = ComputeOutSize(padding, in_height, filter_height, stride_height,
+ dilation_rate_height);
TfLitePaddingValues padding_values;
int offset = 0;
- padding_values.height = ComputePaddingWithOffset(
- stride_height, 1, in_height, filter_height, out_height, &offset);
+ padding_values.height =
+ ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height,
+ filter_height, *out_height, &offset);
padding_values.height_offset = offset;
- padding_values.width = ComputePaddingWithOffset(
- stride_width, 1, in_width, filter_width, out_width, &offset);
+ padding_values.width =
+ ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width,
+ filter_width, *out_width, &offset);
padding_values.width_offset = offset;
return padding_values;
}
diff --git a/tensorflow/lite/kernels/pooling.cc b/tensorflow/lite/kernels/pooling.cc
index 9a90fa3d4fe..71dd349481c 100644
--- a/tensorflow/lite/kernels/pooling.cc
+++ b/tensorflow/lite/kernels/pooling.cc
@@ -81,24 +81,12 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
// Matching GetWindowedOutputSize in TensorFlow.
auto padding = params->padding;
- auto compute_out_size = [padding](int image_size, int filter_size,
- int stride) -> int {
- return padding == kTfLitePaddingSame
- ? (image_size + stride - 1) / stride
- : padding == kTfLitePaddingValid
- ? (image_size - filter_size + stride) / stride
- : 0;
- };
+ int out_width, out_height;
- int out_width =
- compute_out_size(width, params->filter_width, params->stride_width);
- int out_height =
- compute_out_size(height, params->filter_height, params->stride_height);
-
- data->padding.height = ComputePadding(params->stride_height, 1, height,
- params->filter_height, out_height);
- data->padding.width = ComputePadding(params->stride_width, 1, width,
- params->filter_width, out_width);
+ data->padding = ComputePaddingHeightWidth(
+ params->stride_height, params->stride_width, 1, 1, height, width,
+ params->filter_height, params->filter_width, padding, &out_height,
+ &out_width);
if (input->type == kTfLiteUInt8 || input->type == kTfLiteInt8) {
if (pool_type == kAverage || pool_type == kMax) {
diff --git a/tensorflow/lite/kernels/transpose_conv.cc b/tensorflow/lite/kernels/transpose_conv.cc
index cc55671fa93..094b8c5a05c 100644
--- a/tensorflow/lite/kernels/transpose_conv.cc
+++ b/tensorflow/lite/kernels/transpose_conv.cc
@@ -404,9 +404,11 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const int filter_width = SizeOfDimension(weights, 2);
const int filter_height = SizeOfDimension(weights, 1);
+ int unused_output_height, unused_output_width;
data->padding = ComputePaddingHeightWidth(
- params->stride_height, params->stride_width, 1, height, width,
- filter_height, filter_width, params->padding);
+ params->stride_height, params->stride_width, 1, 1, height, width,
+ filter_height, filter_width, params->padding, &unused_output_height,
+ &unused_output_width);
// Currently support float32 and uint8.
switch (input->type) {