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 @@
+//
+//  ShapePriorbox.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+#include "core/TensorUtils.hpp"
+
+namespace MNN {
+class PriorBoxComputer : public SizeComputer {
+public:
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(2 == inputs.size());
+        MNN_ASSERT(1 == outputs.size());
+
+        auto layer = op->main_as_PriorBox();
+
+        auto inputTensor  = inputs[0];
+        auto inputTensor1 = inputs[1];
+
+        int w = inputTensor->width();
+        int h = inputTensor->height();
+
+        auto minSizes     = layer->minSizes();
+        auto maxSizes     = layer->maxSizes();
+        auto aspectRatios = layer->aspectRatios();
+
+        int flip         = layer->flip();
+        int imageWidth   = layer->imageWidth();
+        int imageHeight  = layer->imageHeight();
+        float stepWidth  = layer->stepWidth();
+        float stepHeight = layer->stepHeight();
+
+        int imageW = imageWidth;
+        int imageH = imageHeight;
+        if (imageW <= 0) {
+            imageW = inputTensor1->width();
+        }
+        if (imageH <= 0) {
+            imageH = inputTensor1->height();
+        }
+
+        float stepW = stepWidth;
+        float stepH = stepHeight;
+        if (stepW <= 0) {
+            stepW = (float)imageW / w;
+        }
+
+        if (stepH <= 0) {
+            stepH = (float)imageH / h;
+        }
+
+        int minSizeCount = minSizes ? (int)minSizes->size() : 0;
+        int maxSizeCount = maxSizes ? (int)maxSizes->size() : 0;
+        std::vector<float> aspectRatiosValue{1.0f};
+        if (aspectRatios != nullptr) {
+            for (int i = 0; i < aspectRatios->size(); ++i) {
+                auto ratio = aspectRatios->data()[i];
+                bool exist = false;
+                for (auto v : aspectRatiosValue) {
+                    auto diff = v - ratio;
+                    if (diff < 0) {
+                        diff = -diff;
+                    }
+                    if (diff < 1e-6) {
+                        exist = true;
+                        break;
+                    }
+                }
+                if (!exist) {
+                    aspectRatiosValue.emplace_back(ratio);
+                    if (flip) {
+                        aspectRatiosValue.emplace_back(1.0f / ratio);
+                    }
+                }
+            }
+        }
+        int priorCount = minSizeCount * aspectRatiosValue.size() + maxSizeCount;
+
+        auto& outputTensorBuffer                              = outputs[0]->buffer();
+        outputTensorBuffer.dim[0].extent                      = 1;
+        outputTensorBuffer.dim[1].extent                      = 2;
+        outputTensorBuffer.dim[2].extent                      = 4 * w * h * priorCount;
+        outputTensorBuffer.dim[3].extent                      = 1;
+        outputTensorBuffer.type = halide_type_of<float>();
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
+
+        return true;
+    }
+};
+
+REGISTER_SHAPE(PriorBoxComputer, OpType_PriorBox);
+} // namespace MNN