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[CMSIS-NN] Fixed rank support in pad and min/max #3563

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[CMSIS-NN] Fixed rank support in pad and min/max #3563

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50 changes: 16 additions & 34 deletions tensorflow/lite/micro/kernels/cmsis_nn/maximum_minimum.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -30,16 +30,10 @@ namespace tflite {

namespace {

cmsis_nn_dims FillVariableShape(int32_t rank, int32_t* tensor_dims) {
if (rank == 4) {
return {tensor_dims[0], tensor_dims[1], tensor_dims[2], tensor_dims[3]};
} else if (rank == 3) {
return {1, tensor_dims[0], tensor_dims[1], tensor_dims[2]};
} else if (rank == 2) {
return {1, 1, tensor_dims[0], tensor_dims[1]};
} else {
return {1, 1, 1, 1};
}
cmsis_nn_dims FillVariableShape(const RuntimeShape& shape) {
RuntimeShape extended_shape = RuntimeShape::ExtendedShape(4, shape);
return {extended_shape.Dims(0), extended_shape.Dims(1),
extended_shape.Dims(2), extended_shape.Dims(3)};
}

TfLiteStatus EvalMaximum(TfLiteContext* context, TfLiteNode* node) {
Expand All @@ -55,12 +49,9 @@ TfLiteStatus EvalMaximum(TfLiteContext* context, TfLiteNode* node) {
RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
RuntimeShape output_shape = tflite::micro::GetTensorShape(output);

cmsis_nn_dims input_1_dims = FillVariableShape(
input_1_shape.DimensionsCount(), input_1_shape.DimsData());
cmsis_nn_dims input_2_dims = FillVariableShape(
input_2_shape.DimensionsCount(), input_2_shape.DimsData());
cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
output_shape.DimsData());
cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
cmsis_nn_dims output_dims = FillVariableShape(output_shape);

switch (op_context.output->type) {
case kTfLiteInt8:
Expand Down Expand Up @@ -107,12 +98,9 @@ TfLiteStatus EvalMaximumInt8(TfLiteContext* context, TfLiteNode* node) {
RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
RuntimeShape output_shape = tflite::micro::GetTensorShape(output);

cmsis_nn_dims input_1_dims = FillVariableShape(
input_1_shape.DimensionsCount(), input_1_shape.DimsData());
cmsis_nn_dims input_2_dims = FillVariableShape(
input_2_shape.DimensionsCount(), input_2_shape.DimsData());
cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
output_shape.DimsData());
cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
cmsis_nn_dims output_dims = FillVariableShape(output_shape);

switch (op_context.output->type) {
case kTfLiteInt8:
Expand Down Expand Up @@ -147,12 +135,9 @@ TfLiteStatus EvalMinimum(TfLiteContext* context, TfLiteNode* node) {
RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
RuntimeShape output_shape = tflite::micro::GetTensorShape(output);

cmsis_nn_dims input_1_dims = FillVariableShape(
input_1_shape.DimensionsCount(), input_1_shape.DimsData());
cmsis_nn_dims input_2_dims = FillVariableShape(
input_2_shape.DimensionsCount(), input_2_shape.DimsData());
cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
output_shape.DimsData());
cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
cmsis_nn_dims output_dims = FillVariableShape(output_shape);

switch (op_context.output->type) {
case kTfLiteInt8:
Expand Down Expand Up @@ -199,12 +184,9 @@ TfLiteStatus EvalMinimumInt8(TfLiteContext* context, TfLiteNode* node) {
RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2);
RuntimeShape output_shape = tflite::micro::GetTensorShape(output);

cmsis_nn_dims input_1_dims = FillVariableShape(
input_1_shape.DimensionsCount(), input_1_shape.DimsData());
cmsis_nn_dims input_2_dims = FillVariableShape(
input_2_shape.DimensionsCount(), input_2_shape.DimsData());
cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(),
output_shape.DimsData());
cmsis_nn_dims input_1_dims = FillVariableShape(input_1_shape);
cmsis_nn_dims input_2_dims = FillVariableShape(input_2_shape);
cmsis_nn_dims output_dims = FillVariableShape(output_shape);

switch (op_context.output->type) {
case kTfLiteInt8:
Expand Down
18 changes: 13 additions & 5 deletions tensorflow/lite/micro/kernels/cmsis_nn/pad.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -49,13 +49,21 @@ TfLiteStatus PadEvalInt8(TfLiteContext* context, TfLiteNode* node) {
int8_t* output_ptr = tflite::micro::GetTensorData<int8_t>(output);

const RuntimeShape d = tflite::micro::GetTensorShape(input);
const cmsis_nn_dims input_size = {d.Dims(0), d.Dims(1), d.Dims(2), d.Dims(3)};
const int rank = d.DimensionsCount();

cmsis_nn_dims input_size = {
rank >= 4 ? d.Dims(rank - 4) : 1, rank >= 3 ? d.Dims(rank - 3) : 1,
rank >= 2 ? d.Dims(rank - 2) : 1, rank >= 1 ? d.Dims(rank - 1) : 1};

const PadParams p = data->params;
const cmsis_nn_dims pre_pad = {p.left_padding[0], p.left_padding[1],
p.left_padding[2], p.left_padding[3]};
const cmsis_nn_dims post_pad = {p.right_padding[0], p.right_padding[1],
p.right_padding[2], p.right_padding[3]};
cmsis_nn_dims pre_pad = {rank >= 4 ? p.left_padding[rank - 4] : 0,
rank >= 3 ? p.left_padding[rank - 3] : 0,
rank >= 2 ? p.left_padding[rank - 2] : 0,
rank >= 1 ? p.left_padding[rank - 1] : 0};
cmsis_nn_dims post_pad = {rank >= 4 ? p.right_padding[rank - 4] : 0,
rank >= 3 ? p.right_padding[rank - 3] : 0,
rank >= 2 ? p.right_padding[rank - 2] : 0,
rank >= 1 ? p.right_padding[rank - 1] : 0};

arm_pad_s8(input_ptr, output_ptr, pad_value, &input_size, &pre_pad,
&post_pad);
Expand Down
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