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 +1,80 @@
+//
+//  NNAPIPool.cpp
+//  MNN
+//
+//  Created by MNN on 2022/09/06.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "NNAPIPool.hpp"
+
+namespace MNN {
+
+
+NNAPIPool::NNAPIPool(MNN::Backend *b, const MNN::Op *op, const std::vector<Tensor *> &inputs, const std::vector<MNN::Tensor *> &outputs) : NNAPICommonExecution(b, op) {
+}
+
+ErrorCode NNAPIPool::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
+    auto pool      = mOp->main_as_Pool();
+    auto strideX   = pool->strideX();
+    auto strideY   = pool->strideY();
+    auto kernelX   = pool->kernelX();
+    auto kernelY   = pool->kernelY();
+    auto padMod    = pool->padType();
+    auto global    = pool->isGlobal();
+    auto ceilModel = pool->ceilModel();
+    int top, left, bottom, right;
+    if (nullptr != pool->pads()) {
+        MNN_ASSERT(pool->pads()->size() >= 4);
+        top = pool->pads()->Get(0);
+        left = pool->pads()->Get(1);
+        bottom = pool->pads()->Get(2);
+        right = pool->pads()->Get(3);
+    } else {
+        top = pool->padY();
+        left = pool->padX();
+        bottom = pool->padY();
+        right = pool->padX();
+    }
+    if (padMod == PoolPadType_SAME || (ceilModel && (top + bottom + left + right) == 0)) {
+        int inputY = (outputs[0]->height() - 1) * strideY + (kernelY - 1) + 1;
+        int inputX = (outputs[0]->width() - 1) * strideX + (kernelX - 1) + 1;
+        int padY = std::max(inputY - inputs[0]->height(), 0);
+        int padX = std::max(inputY - inputs[0]->width(), 0);
+        top = bottom = padY / 2;
+        left = right = padX / 2;
+        top += padY % 2;
+        left += padX % 2;
+    }
+    if (global) {
+        strideX = 1;
+        strideY = 1;
+        kernelX = inputs[0]->width();
+        kernelY = inputs[0]->height();
+    }
+    // NNAPI Pool inputs: [input, pad_left, pad_right, pad_top, pad_bottom, stride_w, stride_h, kernel_w, kernel_h, fusecode, NCHW/NHWC]
+    auto inputIdxs = getTensorIdxs(inputs);
+    // pad
+    inputIdxs.push_back(buildScalar(left));
+    inputIdxs.push_back(buildScalar(right));
+    inputIdxs.push_back(buildScalar(top));
+    inputIdxs.push_back(buildScalar(bottom));
+    // stride
+    inputIdxs.push_back(buildScalar(strideX));
+    inputIdxs.push_back(buildScalar(strideY));
+    // kernel
+    inputIdxs.push_back(buildScalar(kernelX));
+    inputIdxs.push_back(buildScalar(kernelY));
+    // fusecode
+    inputIdxs.push_back(buildScalar(ANEURALNETWORKS_FUSED_NONE));
+    // NCHW/NHWC
+    inputIdxs.push_back(buildScalar(mNCHW));
+    auto op = ANEURALNETWORKS_MAX_POOL_2D;
+    if (pool->type() == PoolType_AVEPOOL) {
+        op = ANEURALNETWORKS_AVERAGE_POOL_2D;
+    }
+    return buildOperation(op, inputIdxs, getTensorIdxs(outputs));
+}
+
+REGISTER_NNAPI_OP_CREATOR(NNAPIPool, OpType_Pooling)
+} // namespace MNN