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 +1,68 @@
+//
+//  ShapeSpaceToDepth.cpp
+//  MNN
+//
+//  Created by MNN on 2019/07/16.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+#include "core/TensorUtils.hpp"
+
+namespace MNN {
+
+class SpaceToDepthSizeComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs, 
+                                const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(inputs.size() == 1);
+        MNN_ASSERT(outputs.size() == 1);
+        if (inputs[0]->buffer().dimensions != 4) {
+            MNN_ERROR("SpaceToDepth requires 4D input, got %d dimensions\n", inputs[0]->buffer().dimensions);
+            return false;
+        }
+
+        const int blockSize = op->main_as_DepthSpaceParam()->blockSize();
+        if (blockSize <= 0) {
+            return false;
+        }
+
+        auto& ib = inputs[0]->buffer();
+        auto& ob = outputs[0]->buffer();
+
+        auto format = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        if (MNN_DATA_FORMAT_NHWC == format) {
+            if (ib.dim[1].extent % blockSize != 0 || ib.dim[2].extent % blockSize != 0) {
+                MNN_ERROR("SpaceToDepth: H(%d) and W(%d) must be divisible by blockSize(%d)\n",
+                          ib.dim[1].extent, ib.dim[2].extent, blockSize);
+                return false;
+            }
+        } else {
+            if (ib.dim[2].extent % blockSize != 0 || ib.dim[3].extent % blockSize != 0) {
+                MNN_ERROR("SpaceToDepth: H(%d) and W(%d) must be divisible by blockSize(%d)\n",
+                          ib.dim[2].extent, ib.dim[3].extent, blockSize);
+                return false;
+            }
+        }
+
+        ob.dimensions = ib.dimensions;
+        ob.type = ib.type;
+        ob.dim[0].extent = ib.dim[0].extent;
+        if (MNN_DATA_FORMAT_NHWC == format) {
+            ob.dim[1].extent = ib.dim[1].extent / blockSize;
+            ob.dim[2].extent = ib.dim[2].extent / blockSize;
+            ob.dim[3].extent = ib.dim[3].extent * (blockSize * blockSize);
+        } else {
+            ob.dim[3].extent = ib.dim[3].extent / blockSize;
+            ob.dim[2].extent = ib.dim[2].extent / blockSize;
+            ob.dim[1].extent = ib.dim[1].extent * (blockSize * blockSize);
+        }
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+
+        return true;
+    }
+};
+
+REGISTER_SHAPE(SpaceToDepthSizeComputer, OpType_SpaceToDepth);
+
+} // namespace MNN