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 +1,91 @@
+//
+//  ShapePool3D.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 Pool3DSizeComputer : 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(1 == outputs.size());
+
+        auto input  = inputs[0];
+        auto output = outputs[0];
+        
+        auto layer = op->main_as_Pool3D();
+        auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        // only check channel dim when global pool
+        int maxCheckDim = (layer->isGlobal() ? 1 :input->buffer().dimensions - 1);
+        for (unsigned int i = 1; i <= maxCheckDim; ++i) {
+            if (input->buffer().dim[i].extent <= 0) {
+                return false;
+            }
+        }
+        output->buffer().dimensions = input->buffer().dimensions;
+        ::memcpy(output->buffer().dim, input->buffer().dim, input->buffer().dimensions * sizeof(halide_dimension_t));
+
+        if (layer->isGlobal()) {
+            if (format == MNN_DATA_FORMAT_NHWC) {
+                // N [1...] C
+                for (int d = 1; d < output->dimensions() - 1; d++) {
+                    output->buffer().dim[d].extent = 1;
+                }
+            } else {
+                // N C [1...]
+                for (int d = 2; d < output->dimensions(); d++) {
+                    output->buffer().dim[d].extent = 1;
+                }
+            }
+        } else {
+            int offset = format == MNN_DATA_FORMAT_NHWC ? 1 : 2;
+            for (unsigned int i = 0; i < input->dimensions() - 2; ++i) {
+                int inputLength = input->buffer().dim[i + 2].extent, outputLength = 0;
+                const int kernel = (*layer->kernels())[i], stride = (*layer->strides())[i];
+                if (layer->padType() == PoolPadType_CAFFE) {
+                    int pad = (*layer->pads())[i];
+                    outputLength = (inputLength + 2 * pad - kernel) / stride + 1;
+                } else if (layer->padType() == PoolPadType_SAME) {
+                    outputLength = UP_DIV(inputLength, stride);
+                } else if (layer->padType() == PoolPadType_VALID) {
+                    outputLength = (inputLength - kernel) / stride + 1;
+                } else {
+                    MNN_ERROR("PoolPadType %d not support\n", layer->padType());
+                }
+                if (outputLength <= 0) {
+                    return false;
+                }
+                output->buffer().dim[i + offset].extent = outputLength;
+            }
+        }
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        output->buffer().type          = input->buffer().type;
+        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_Pool3D();
+        float flopsPerElement = 1;
+        if (layer->kernels() == nullptr) {
+            return size * flopsPerElement;
+        }
+        for (auto kernel: *layer->kernels()) {
+            flopsPerElement *= kernel;
+        }
+        return size * flopsPerElement;
+    }
+};
+
+REGISTER_SHAPE(Pool3DSizeComputer, OpType_Pooling3D);
+} // namespace MNN