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 +1,117 @@
+//
+//  ShapePool.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <math.h>
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+
+namespace MNN {
+class PoolSizeComputer : public SizeComputer {
+public:
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(1 == inputs.size());
+        MNN_ASSERT(2 >= outputs.size());
+
+        auto input  = inputs[0];
+        auto output = outputs[0];
+        bool returnRedice = outputs.size() == 2;
+        Tensor *indice;
+        if(returnRedice){
+            indice = outputs[1];
+            ::memcpy(indice->buffer().dim, input->buffer().dim, input->buffer().dimensions * sizeof(halide_dimension_t));
+            indice->buffer().dimensions = input->dimensions();
+        }
+
+        ::memcpy(output->buffer().dim, input->buffer().dim, input->buffer().dimensions * sizeof(halide_dimension_t));
+        output->buffer().dimensions = input->dimensions();
+
+        auto layer = op->main_as_Pool();
+        int outw   = 1;
+        int outh   = 1;
+        if (!layer->isGlobal()) {
+            // when given explicit pad value in tensorflow mode pool, size compute will fast failed to help find problem
+            if ((layer->padType() == PoolPadType_VALID || layer->padType() == PoolPadType_SAME) && (layer->padX() != 0 || layer->padY() != 0)) {
+                MNN_PRINT("tensorflow mode pool should not have explict pad value\n");
+                return false;
+            }
+            int w = input->width();
+            int h = input->height();
+            if (nullptr != layer->pads()) {
+                // pads = 2, just add padh_h, padh_l
+                if (layer->pads()->size() == 2) {
+                    h += (layer->pads()->data()[0] + layer->pads()->data()[1]);
+                }
+                // pads = 4, add padh_h, padh_l, padw_l, padw_r
+                if (layer->pads()->size() == 4) {
+                    w += (layer->pads()->data()[1] + layer->pads()->data()[3]);
+                    h += (layer->pads()->data()[0] + layer->pads()->data()[2]);
+                }
+            } else {
+                w += layer->padX() * 2;
+                h += layer->padY() * 2;
+            }
+            int kernelWidth  = std::min(layer->kernelX(), w);
+            int kernelHeight = std::min(layer->kernelY(), h);
+
+            if (layer->padType() == PoolPadType_SAME) { // Tensorflow padding mode SAME
+                outw = ceil((float)w / (float)layer->strideX());
+                outh = ceil((float)h / (float)layer->strideY());
+            } else if (layer->padType() == PoolPadType_VALID) { // Tensorflow padding mode VALID
+                outw = ceil((float)(w - kernelWidth + 1) / (float)layer->strideX());
+                outh = ceil((float)(h - kernelHeight + 1) / (float)layer->strideY());
+            } else {
+                if (layer->ceilModel()) {
+                    outw = UP_DIV(w - kernelWidth, layer->strideX()) + 1;
+                    outh = UP_DIV(h - kernelHeight, layer->strideY()) + 1;
+                } else {
+                    outw = floor((w - kernelWidth) / layer->strideX() + 1);
+                    outh = floor((h - kernelHeight) / layer->strideY() + 1);
+                }
+            }
+        }
+        if (outw <= 0 || outh <= 0) {
+            return false;
+        }
+        auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        if (format == MNN_DATA_FORMAT_NHWC) {
+            output->buffer().dim[2].extent = outw;
+            output->buffer().dim[1].extent = outh;
+            if(returnRedice){
+                indice->buffer().dim[2].extent = outw;
+                indice->buffer().dim[1].extent = outh;
+            }
+        } else {
+            output->buffer().dim[3].extent = outw;
+            output->buffer().dim[2].extent = outh;
+            if(returnRedice){
+                indice->buffer().dim[3].extent = outw;
+                indice->buffer().dim[2].extent = outh;
+            }
+        }
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = format;
+        output->buffer().type          = input->buffer().type;
+        if(returnRedice){
+            TensorUtils::getDescribe(outputs[1])->dimensionFormat = format;
+            indice->buffer().type          = halide_type_of<int>();
+        }
+
+        return true;
+    }
+
+    virtual float onComputeFlops(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                                 const std::vector<Tensor*>& outputs) const override {
+        auto size  = (float)outputs[0]->elementSize() / 1024.0f / 1024.0f;
+        auto layer = op->main_as_Pool();
+        return size * layer->kernelX() * layer->kernelY();
+    }
+};
+
+REGISTER_SHAPE(PoolSizeComputer, OpType_Pooling);
+} // namespace MNN