1 /*
2 * Copyright (C) 2017 The Android Open Source Project
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17 // Contains the implementation of the operations.
18
19 #define LOG_TAG "Operations"
20
21 #include <tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h>
22 #include <tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h>
23
24 #include <vector>
25
26 #include "CpuOperationUtils.h"
27 #include "Operations.h"
28 #include "Tracing.h"
29
30 namespace android {
31 namespace nn {
32
meanFloat16(_Float16 * inputData,const Shape & inputShape,const int32_t * axis,const Shape & axisShape,bool keepDims,_Float16 * outputData,const Shape & outputShape)33 bool meanFloat16(_Float16* inputData, const Shape& inputShape, const int32_t* axis,
34 const Shape& axisShape, bool keepDims, _Float16* outputData,
35 const Shape& outputShape) {
36 NNTRACE_TRANS("meanFloat16");
37 std::vector<float> inputDataFloat32(getNumberOfElements(inputShape));
38 convertFloat16ToFloat32(inputData, &inputDataFloat32);
39
40 std::vector<float> outputDataFloat32(getNumberOfElements(outputShape));
41 meanGeneric<float, float>(inputDataFloat32.data(), inputShape, axis, axisShape, keepDims,
42 outputDataFloat32.data(), outputShape);
43 convertFloat32ToFloat16(outputDataFloat32, outputData);
44 return true;
45 }
46
47 template <typename T, typename U>
meanGeneric(T * inputData,const Shape & inputShape,const int32_t * axis,const Shape & axisShape,bool keepDims,T * outputData,const Shape & outputShape)48 bool meanGeneric(T* inputData, const Shape& inputShape, const int32_t* axis, const Shape& axisShape,
49 bool keepDims, T* outputData, const Shape& outputShape) {
50 NNTRACE_TRANS("meanGeneric");
51 // Creates a temp index to iterate through input data.
52 int32_t* scratchBuffer = new int32_t[getNumberOfDimensions(inputShape)];
53
54 // Creates a temp tensor to store resolved axis given input data.
55 int32_t axisSize = static_cast<int32_t>(getSizeOfDimension(axisShape, 0));
56 int32_t* resolvedAxis = new int32_t[axisSize];
57
58 bool result = true;
59 U* tempSumBuffer = new (std::nothrow) U[getNumberOfElements(outputShape)];
60 if (!tempSumBuffer) {
61 LOG(ERROR) << "Failed to allocate tempSumBuffer for MEAN";
62 result = false;
63 } else {
64 NNTRACE_COMP_SWITCH("optimized_ops::Mean");
65 tflite::reference_ops::Mean<T, U>(
66 inputData, reinterpret_cast<const int*>(inputShape.dimensions.data()),
67 getNumberOfDimensions(inputShape), outputData,
68 reinterpret_cast<const int*>(outputShape.dimensions.data()),
69 getNumberOfDimensions(outputShape), axis, axisSize, keepDims, scratchBuffer,
70 resolvedAxis, tempSumBuffer);
71 delete[] tempSumBuffer;
72 }
73 delete[] scratchBuffer;
74 delete[] resolvedAxis;
75 return result;
76 }
77 template bool meanGeneric<float, float>(float* inputData, const Shape& inputShape,
78 const int32_t* axis, const Shape& axisShape, bool keepDims,
79 float* outputData, const Shape& outputShape);
80 template bool meanGeneric<uint8_t, int32_t>(uint8_t* inputData, const Shape& inputShape,
81 const int32_t* axis, const Shape& axisShape,
82 bool keepDims, uint8_t* outputData,
83 const Shape& outputShape);
84 template bool meanGeneric<int8_t, int32_t>(int8_t* inputData, const Shape& inputShape,
85 const int32_t* axis, const Shape& axisShape,
86 bool keepDims, int8_t* outputData,
87 const Shape& outputShape);
88
89 } // namespace nn
90 } // namespace android
91