1 //
2 // CPUQuantizedAvgPool.cpp
3 // MNN
4 //
5 // Created by MNN on 2018/08/14.
6 // Copyright © 2018, Alibaba Group Holding Limited
7 //
8 #ifdef MNN_SUPPORT_TFLITE_QUAN
9 #include "backend/cpu/CPUQuantizedAvgPool.hpp"
10 #include "backend/cpu/CPUBackend.hpp"
11 #include "backend/cpu/CPUQuantizationUtils.hpp"
12 #include "backend/cpu/compute/CommonOptFunction.h"
13 #include "core/Macro.h"
14 #include "backend/cpu/compute/OptimizedComputer.hpp"
15
16 namespace MNN {
17
CPUQuantizedAvgPool(Backend * backend,const Op * CPUQuantizedAvgPoolOp)18 CPUQuantizedAvgPool::CPUQuantizedAvgPool(Backend *backend, const Op *CPUQuantizedAvgPoolOp) : Execution(backend) {
19 auto CPUQuantizedAvgPool = CPUQuantizedAvgPoolOp->main_as_QuantizedAvgPool();
20 mIstflite = (CPUQuantizedAvgPool->modelFormat() == ModeFormat_TFLITE);
21 mKernelWidth = CPUQuantizedAvgPool->kernelX();
22 mKernelHeight = CPUQuantizedAvgPool->kernelY();
23 mPadWidth = CPUQuantizedAvgPool->padX();
24 mPadHeight = CPUQuantizedAvgPool->padY();
25 mStrideWidth = CPUQuantizedAvgPool->strideX();
26 mStrideHeight = CPUQuantizedAvgPool->strideY();
27 mPadMode = CPUQuantizedAvgPool->padType();
28 mOutputActivationMin = CPUQuantizedAvgPool->outputActivationMin();
29 mOutputActivationMax = CPUQuantizedAvgPool->outputActivationMax();
30 }
31
32
onResize(const std::vector<Tensor * > & inputs,const std::vector<Tensor * > & outputs)33 ErrorCode CPUQuantizedAvgPool::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
34
35 auto input = inputs[0];
36 auto output = outputs[0];
37
38 MNN_ASSERT(input->buffer().dimensions == 4);
39
40 int32_t inBatch = input->buffer().dim[0].extent;
41 int32_t inRows = input->buffer().dim[2].extent;
42 int32_t inCols = input->buffer().dim[3].extent;
43 int32_t inChannel = input->buffer().dim[1].extent;
44
45 const int32_t windowRows = mKernelHeight;
46 const int32_t windowCols = mKernelWidth;
47 const int32_t rowStride = mStrideHeight;
48 const int32_t colStride = mStrideWidth;
49 int32_t outHeight = output->buffer().dim[2].extent;
50 int32_t outWidth = output->buffer().dim[3].extent;
51
52 switch (mPadMode) {
53 case PoolPadType_CAFFE:
54 MNN_ASSERT(false);
55 break;
56 case PoolPadType_VALID:
57 mPadHeight = mPadWidth = 0;
58 break;
59 case PoolPadType_SAME:
60 auto widthNeeded = (outWidth - 1) * colStride + windowCols - inCols;
61 auto heightNeeded = (outHeight - 1) * rowStride + windowRows - inRows;
62 mPadWidth = widthNeeded > 0 ? widthNeeded / 2 : 0;
63 mPadHeight = heightNeeded > 0 ? heightNeeded / 2 : 0;
64 break;
65 }
66
67 mInputDims = {inBatch, inRows, inCols, inChannel};
68 mOutputDims = {output->batch(), output->height(), output->width(), output->channel()};
69
70 return NO_ERROR;
71 }
72
73
onExecute(const std::vector<Tensor * > & inputs,const std::vector<Tensor * > & outputs)74 ErrorCode CPUQuantizedAvgPool::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
75
76
77 uint8_t *inputPtr = inputs[0]->host<uint8_t>();
78 uint8_t *outputPtr = outputs[0]->host<uint8_t>();
79
80 Optimized::AveragePool(inputPtr, mInputDims, mStrideWidth, mStrideHeight, mPadWidth, mPadHeight, mKernelWidth,
81 mKernelHeight, mOutputActivationMin, mOutputActivationMax, outputPtr, mOutputDims);
82
83 return NO_ERROR;
84 }
85
86 class CPUQuantizedAvgPoolCreator : public CPUBackend::Creator {
87 public:
onCreate(const std::vector<Tensor * > & inputs,const std::vector<Tensor * > & outputs,const MNN::Op * op,Backend * backend) const88 virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
89 const MNN::Op *op, Backend *backend) const {
90 return new CPUQuantizedAvgPool(backend, op);
91 }
92 };
93 REGISTER_CPU_OP_CREATOR(CPUQuantizedAvgPoolCreator, OpType_QuantizedAvgPool);
94 } // namespace MNN
95 #endif
96