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      1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 #ifndef TENSORFLOW_CONTRIB_LITE_KERNELS_KERNEL_UTIL_H_
     16 #define TENSORFLOW_CONTRIB_LITE_KERNELS_KERNEL_UTIL_H_
     17 
     18 #include "tensorflow/contrib/lite/builtin_op_data.h"
     19 #include "tensorflow/contrib/lite/context.h"
     20 
     21 namespace tflite {
     22 
     23 inline int NumDimensions(const TfLiteTensor* t) { return t->dims->size; }
     24 inline int SizeOfDimension(const TfLiteTensor* t, int dim) {
     25   return t->dims->data[dim];
     26 }
     27 inline TfLiteTensor* GetInput(TfLiteContext* context, TfLiteNode* node,
     28                               int index) {
     29   return &context->tensors[node->inputs->data[index]];
     30 }
     31 inline TfLiteTensor* GetOutput(TfLiteContext* context, TfLiteNode* node,
     32                                int index) {
     33   return &context->tensors[node->outputs->data[index]];
     34 }
     35 inline int NumInputs(const TfLiteNode* node) { return node->inputs->size; }
     36 inline int NumOutputs(const TfLiteNode* node) { return node->outputs->size; }
     37 
     38 inline int64_t NumElements(const TfLiteTensor* t) {
     39   int64_t count = 1;
     40   for (int i = 0; i < NumDimensions(t); ++i) {
     41     count *= SizeOfDimension(t, i);
     42   }
     43   return count;
     44 }
     45 
     46 inline TfLiteTensor* GetOptionalInputTensor(TfLiteContext* context,
     47                                             const TfLiteNode* node, int index) {
     48   const bool use_tensor = node->inputs->data[index] != kOptionalTensor;
     49   if (use_tensor) {
     50     return &context->tensors[node->inputs->data[index]];
     51   }
     52   return nullptr;
     53 }
     54 
     55 // Determines whether tensor is constant.
     56 inline bool IsConstantTensor(TfLiteTensor* tensor) {
     57   return tensor->allocation_type == kTfLiteMmapRo;
     58 }
     59 
     60 // Determines whether tensor is dynamic. Note that a tensor can be non-const and
     61 // not dynamic. This function specificially checks for a dynamic tensor.
     62 inline bool IsDynamicTensor(TfLiteTensor* tensor) {
     63   return tensor->allocation_type == kTfLiteDynamic;
     64 }
     65 
     66 // Sets tensor to dynamic.
     67 inline void SetTensorToDynamic(TfLiteTensor* tensor) {
     68   if (tensor->allocation_type != kTfLiteDynamic) {
     69     tensor->allocation_type = kTfLiteDynamic;
     70     tensor->data.raw = nullptr;
     71   }
     72 }
     73 
     74 // Calculates the multiplication factor for a quantized convolution (or
     75 // quantized depthwise convolution) involving the given tensors. Returns an
     76 // error if the scales of the tensors are not compatible.
     77 TfLiteStatus GetQuantizedConvolutionMultipler(
     78     TfLiteContext* context, TfLiteTensor* input, TfLiteTensor* filter,
     79     TfLiteTensor* bias, TfLiteTensor* output, double* multiplier);
     80 
     81 // Calculates the useful range of an activation layer given its activation
     82 // tensor.
     83 void CalculateActivationRangeUint8(TfLiteFusedActivation activation,
     84                                    TfLiteTensor* output, int32_t* act_min,
     85                                    int32_t* act_max);
     86 void CalculateActivationRangeFloat(TfLiteFusedActivation activation,
     87                                    float* activation_min,
     88                                    float* activation_max);
     89 
     90 // Return true if the given tensors have the same shape.
     91 bool HaveSameShapes(TfLiteTensor* input1, TfLiteTensor* input2);
     92 
     93 // Calculate the output_shape that is necessary for element-wise operations
     94 // with broadcasting involving the two input tensors.
     95 TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context,
     96                                         TfLiteTensor* input1,
     97                                         TfLiteTensor* input2,
     98                                         TfLiteIntArray** output_shape);
     99 }  // namespace tflite
    100 
    101 #endif  // TENSORFLOW_CONTRIB_LITE_KERNELS_KERNEL_UTIL_H_
    102