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 +1,97 @@
+//
+//  ReductionTf.cpp
+//  MNNConverter
+//
+//  Created by MNN on 2019/01/31.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <string.h>
+#include "TfUtils.hpp"
+#include "tfOpConverter.hpp"
+
+#include "graph.pb.h"
+
+DECLARE_OP_CONVERTER(ReductionTf);
+
+MNN::OpType ReductionTf::opType() {
+    return MNN::OpType_Reduction;
+}
+MNN::OpParameter ReductionTf::type() {
+    return MNN::OpParameter_ReductionParam;
+}
+
+void ReductionTf::run(MNN::OpT *dstOp, TmpNode *srcNode) {
+    auto reductionParam = new MNN::ReductionParamT;
+
+    // reduction parameter
+    tensorflow::AttrValue value;
+
+    reductionParam->dType = MNN::DataType_DT_FLOAT;
+    if (find_attr_value(srcNode->tfNode, "T", value)) {
+        reductionParam->dType = (MNN::DataType)value.type();
+    }
+
+    reductionParam->keepDims = false;
+    if (find_attr_value(srcNode->tfNode, "keep_dims", value)) {
+        reductionParam->keepDims = value.b();
+    }
+#ifdef TF_CONVERT_ORIGIN
+    TmpNode *constNode = tempGraph->_getTmpNode(srcNode->inEdges[1]);
+    if (constNode->opType == "Const") {
+        if (find_attr_value(constNode->tfNode, "value", value)) {
+            const tensorflow::TensorProto &reductionIndices           = value.tensor();
+            const tensorflow::TensorShapeProto &reductionIndicesShape = reductionIndices.tensor_shape();
+            int dimSize                                               = 1;
+            if (reductionIndicesShape.dim_size() > 0) {
+                dimSize = reductionIndicesShape.dim(0).size();
+            }
+            reductionParam->dim.resize(dimSize);
+            if (reductionIndices.int_val_size() > 0) {
+                for (int i = 0; i < dimSize; ++i) {
+                    reductionParam->dim[i] = reductionIndices.int_val(i);
+                }
+            } else {
+                DCHECK((MNN::DataType)reductionIndices.dtype() == MNN::DataType_DT_INT32);
+                DCHECK(reductionIndices.tensor_content().size() > 0);
+                const int *dimData = (int *)reductionIndices.tensor_content().c_str();
+                for (int i = 0; i < dimSize; i++) {
+                    reductionParam->dim[i] = dimData[i];
+                }
+            }
+        }
+    }
+#endif
+    // reduction operation
+    if (srcNode->opType == "Mean") {
+        reductionParam->operation = MNN::ReductionType_MEAN;
+    } else if (srcNode->opType == "Max") {
+        reductionParam->operation = MNN::ReductionType_MAXIMUM;
+    } else if (srcNode->opType == "Min") {
+        reductionParam->operation = MNN::ReductionType_MINIMUM;
+    } else if (srcNode->opType == "Sum") {
+        reductionParam->operation = MNN::ReductionType_SUM;
+    } else if (srcNode->opType == "Any") {
+        reductionParam->operation = MNN::ReductionType_ANY;
+    } else if (srcNode->opType == "All") {
+        reductionParam->operation = MNN::ReductionType_ALL;
+    } else if (srcNode->opType == "Prod") {
+        reductionParam->operation = MNN::ReductionType_PROD;
+    } else {
+        DLOG(ERROR) << "MNN Converter Not "
+                       "Supported!!! ===> "
+                    << srcNode->opType;
+    }
+
+    reductionParam->coeff = 0.0f; // defalut
+
+    dstOp->main.value = reductionParam;
+}
+
+REGISTER_CONVERTER(ReductionTf, Mean);
+REGISTER_CONVERTER(ReductionTf, Max);
+REGISTER_CONVERTER(ReductionTf, Min);
+REGISTER_CONVERTER(ReductionTf, Any);
+REGISTER_CONVERTER(ReductionTf, All);
+REGISTER_CONVERTER(ReductionTf, Sum);
+REGISTER_CONVERTER(ReductionTf, Prod);