chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
@@ -0,0 +1,98 @@
+//
+//  CPUQuantizedAvgPool.cpp
+//  MNN
+//
+//  Created by MNN on 2018/08/14.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+#include "backend/cpu/CPUBackend.hpp"
+#ifdef MNN_SUPPORT_DEPRECATED_OP
+#include "backend/cpu/CPUQuantizedAvgPool.hpp"
+#include "backend/cpu/CPUQuantizationUtils.hpp"
+#include "backend/cpu/compute/CommonOptFunction.h"
+#include "core/Macro.h"
+#include "backend/cpu/compute/OptimizedComputer.hpp"
+
+namespace MNN {
+
+CPUQuantizedAvgPool::CPUQuantizedAvgPool(Backend *backend, const Op *CPUQuantizedAvgPoolOp) : Execution(backend) {
+    auto CPUQuantizedAvgPool = CPUQuantizedAvgPoolOp->main_as_QuantizedAvgPool();
+    mIstflite                = (CPUQuantizedAvgPool->modelFormat() == ModeFormat_TFLITE);
+    mKernelWidth             = CPUQuantizedAvgPool->kernelX();
+    mKernelHeight            = CPUQuantizedAvgPool->kernelY();
+    mPadWidth                = CPUQuantizedAvgPool->padX();
+    mPadHeight               = CPUQuantizedAvgPool->padY();
+    mStrideWidth             = CPUQuantizedAvgPool->strideX();
+    mStrideHeight            = CPUQuantizedAvgPool->strideY();
+    mPadMode                 = CPUQuantizedAvgPool->padType();
+    mOutputActivationMin     = CPUQuantizedAvgPool->outputActivationMin();
+    mOutputActivationMax     = CPUQuantizedAvgPool->outputActivationMax();
+}
+
+
+ErrorCode CPUQuantizedAvgPool::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
+
+    auto input  = inputs[0];
+    auto output = outputs[0];
+
+    MNN_ASSERT(input->buffer().dimensions == 4);
+
+    int32_t inBatch   = input->buffer().dim[0].extent;
+    int32_t inRows    = input->buffer().dim[2].extent;
+    int32_t inCols    = input->buffer().dim[3].extent;
+    int32_t inChannel = input->buffer().dim[1].extent;
+
+    const int32_t windowRows = mKernelHeight;
+    const int32_t windowCols = mKernelWidth;
+    const int32_t rowStride  = mStrideHeight;
+    const int32_t colStride  = mStrideWidth;
+    int32_t outHeight  = output->buffer().dim[2].extent;
+    int32_t outWidth   = output->buffer().dim[3].extent;
+
+    switch (mPadMode) {
+        case PoolPadType_CAFFE:
+            MNN_ASSERT(false);
+            break;
+        case PoolPadType_VALID:
+            mPadHeight = mPadWidth = 0;
+            break;
+        case PoolPadType_SAME:
+            auto widthNeeded  = (outWidth - 1) * colStride + windowCols - inCols;
+            auto heightNeeded = (outHeight - 1) * rowStride + windowRows - inRows;
+            mPadWidth         = widthNeeded > 0 ? widthNeeded / 2 : 0;
+            mPadHeight        = heightNeeded > 0 ? heightNeeded / 2 : 0;
+            break;
+    }
+
+    mInputDims = {inBatch, inRows, inCols, inChannel};
+    mOutputDims = {output->batch(), output->height(), output->width(), output->channel()};
+
+    return NO_ERROR;
+}
+
+
+ErrorCode CPUQuantizedAvgPool::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
+
+
+    uint8_t *inputPtr  = inputs[0]->host<uint8_t>();
+    uint8_t *outputPtr = outputs[0]->host<uint8_t>();
+
+    Optimized::AveragePool(inputPtr, mInputDims, mStrideWidth, mStrideHeight, mPadWidth, mPadHeight, mKernelWidth,
+                               mKernelHeight, mOutputActivationMin, mOutputActivationMax, outputPtr, mOutputDims);
+
+    return NO_ERROR;
+}
+
+class CPUQuantizedAvgPoolCreator : public CPUBackend::Creator {
+public:
+    virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
+                                const MNN::Op *op, Backend *backend) const {
+        return new CPUQuantizedAvgPool(backend, op);
+    }
+};
+} // namespace MNN
+#endif
+
+namespace MNN {
+REGISTER_CPU_OP_CREATOR_OLD(CPUQuantizedAvgPoolCreator, OpType_QuantizedAvgPool);
+};