experiments/STT-tensorflow
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internal cleanup functions

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PiperOrigin-RevId: 261820730
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A. Unique TensorFlower 2019-08-05 19:19:13 -07:00 committed by TensorFlower Gardener
parent 9239c61f20
commit 476fd9f8da
1 changed files with 11 additions and 11 deletions
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22
tensorflow/lite/kernels/reshape.cc
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@ -31,8 +31,10 @@ constexpr int kInputTensor = 0;
constexpr int kShapeTensor = 1; constexpr int kShapeTensor = 1;
constexpr int kOutputTensor = 0; constexpr int kOutputTensor = 0;
TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node, TfLiteIntArray* GetOutputShape(TfLiteContext*, TfLiteNode*);
TfLiteIntArray* output_shape) {
TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
TfLiteIntArray* output_shape = GetOutputShape(context, node);
std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)> std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)>
scoped_output_shape(output_shape, TfLiteIntArrayFree); scoped_output_shape(output_shape, TfLiteIntArrayFree);
@ -65,8 +67,8 @@ TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node,
return context->ResizeTensor(context, output, scoped_output_shape.release()); return context->ResizeTensor(context, output, scoped_output_shape.release());
} }
TfLiteIntArray* GetOutputShapeFromTensor(TfLiteContext* context, inline TfLiteIntArray* GetOutputShapeFromTensor(TfLiteContext* context,
TfLiteNode* node) { TfLiteNode* node) {
const TfLiteTensor* shape = GetInput(context, node, kShapeTensor); const TfLiteTensor* shape = GetInput(context, node, kShapeTensor);
TfLiteIntArray* output_shape = TfLiteIntArrayCreate(shape->dims->data[0]); TfLiteIntArray* output_shape = TfLiteIntArrayCreate(shape->dims->data[0]);
@ -77,8 +79,8 @@ TfLiteIntArray* GetOutputShapeFromTensor(TfLiteContext* context,
return output_shape; return output_shape;
} }
TfLiteIntArray* GetOutputShapeFromParam(TfLiteContext* context, inline TfLiteIntArray* GetOutputShapeFromParam(TfLiteContext* context,
TfLiteNode* node) { TfLiteNode* node) {
auto* params = reinterpret_cast<TfLiteReshapeParams*>(node->builtin_data); auto* params = reinterpret_cast<TfLiteReshapeParams*>(node->builtin_data);
// The function is returned above this line if the shape tensor is usable. // The function is returned above this line if the shape tensor is usable.
@ -99,7 +101,7 @@ TfLiteIntArray* GetOutputShapeFromParam(TfLiteContext* context,
} }
// Check if the shape tensor is valid. Shapes should be int32 vectors. // Check if the shape tensor is valid. Shapes should be int32 vectors.
bool ShapeIsVector(TfLiteContext* context, TfLiteNode* node) { inline bool ShapeIsVector(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* shape = GetInput(context, node, kShapeTensor); const TfLiteTensor* shape = GetInput(context, node, kShapeTensor);
return (shape->dims->size == 1 && shape->type == kTfLiteInt32); return (shape->dims->size == 1 && shape->type == kTfLiteInt32);
} }
@ -124,8 +126,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
if (output->type != kTfLiteString) { if (output->type != kTfLiteString) {
if (NumInputs(node) == 1 || if (NumInputs(node) == 1 ||
IsConstantTensor(GetInput(context, node, kShapeTensor))) { IsConstantTensor(GetInput(context, node, kShapeTensor))) {
TF_LITE_ENSURE_OK( TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
context, ResizeOutput(context, node, GetOutputShape(context, node)));
} else { } else {
SetTensorToDynamic(output); SetTensorToDynamic(output);
} }
@ -141,8 +142,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
// a string tensor, or its shape cannot be calculated during Prepare(). In // a string tensor, or its shape cannot be calculated during Prepare(). In
// either case, we now have all the information to calculate its shape. // either case, we now have all the information to calculate its shape.
if (IsDynamicTensor(output)) { if (IsDynamicTensor(output)) {
TF_LITE_ENSURE_OK( TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
context, ResizeOutput(context, node, GetOutputShape(context, node)));
} }
// Note that string tensors are always "dynamic" in the sense that their size // Note that string tensors are always "dynamic" in the sense that their size