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 +1,77 @@
+//
+//  ConvolutionTorch.cpp
+//  MNNConverter
+//
+//  Created by MNN on 2021/05/08.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <stdio.h>
+#include "torchOpConverter.hpp"
+
+DECLARE_OP_CONVERTER(ConvolutionTorch);
+
+MNN::OpType ConvolutionTorch::opType() {
+    return MNN::OpType_Convolution;
+}
+MNN::OpParameter ConvolutionTorch::type() {
+    return MNN::OpParameter_Convolution2D;
+}
+std::vector<int> ConvolutionTorch::inputTensorIdx() {
+    return {0};
+}
+
+void ConvolutionTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) {
+    auto param = new MNN::Convolution2DT;
+    param->common.reset(new MNN::Convolution2DCommonT);
+    auto common = param->common.get();
+    // input, weight, bias, stride, padding, dialation
+    const auto& inputs = node->inputs();
+    const auto weight = inputs[1];
+    const auto bias = inputs[2];
+    const auto stride = getValue<std::vector<int64_t>>(inputs[3]);
+    const auto padding = getValue<std::vector<int64_t>>(inputs[4]);
+    const auto dialation = getValue<std::vector<int64_t>>(inputs[5]);
+    std::vector<int> weightShape, biasShape;
+    param->weight = getValue<float>(weight, weightShape);
+    param->bias = getValue<float>(bias, biasShape);
+    if (param->bias.empty()) {
+        param->bias = std::vector<float>(weightShape[0], 0.f);
+    }
+    std::string opType = getRealOpType(node);
+    if (opType == "conv2d") {
+        common->group   = static_cast<int>(getValue<int64_t>(inputs[6]));
+    } else if (opType == "convolution") {
+        common->group   = static_cast<int>(getValue<int64_t>(inputs[8]));
+    }
+    bool conv1d = (stride.size() == 1 && weightShape.size() == 3);
+    if (conv1d) {
+        common->strideX = 1;
+        common->strideY = stride[0];
+        common->padX = 0;
+        common->padY = padding[0];
+        common->dilateX = 1;
+        common->dilateY = dialation[0];
+        // weight format : NCH
+        common->outputCount = weightShape[0];
+        common->inputCount = weightShape[1] * common->group;
+        common->kernelY = weightShape[2];
+        common->kernelX = 1;
+    } else {
+        common->strideY = stride[0];
+        common->strideX = stride[1];
+        common->padY = padding[0];
+        common->padX = padding[1];
+        common->dilateY = dialation[0];
+        common->dilateX = dialation[1];
+        // weight format : NCHW
+        common->outputCount = weightShape[0];
+        common->inputCount = weightShape[1] * common->group;
+        common->kernelY = weightShape[2];
+        common->kernelX = weightShape[3];
+    }
+    dstOp->main.value = param;
+}
+
+REGISTER_CONVERTER(ConvolutionTorch, conv2d);
+REGISTER_CONVERTER(ConvolutionTorch, convolution);