假设我们已经得到了存储模型输入参数ids和mask向量的两个vector对象:input_tensor_values和mask_tensor_values,我们可以先这样获得表示各自Tensor的Ort::Value对象: Ort::Value input_tensor = Ort::Value::CreateTensor<int64_t>(memory_info, input_tensor_values.data(), input_tensor_values.size(), input_node...
YOLO(Net_config config);//构造函数voiddetect(Mat& frame);//通过图像参数,进行目标检测private:float* anchors;intnum_stride;intinpWidth;intinpHeight;vector<string> class_names;intnum_class;floatconfThreshold;floatnmsThreshold;floatobjThreshold;constboolkeep_ratio =true; Net net;voiddrawPred(floatconf...
在本paper中,我们提供了一个基于NN的模型,以显式、vector-wise的方式来学习特征交叉。论文的方法基于DCN(Deep&Cross Network)之上,该方法能有效捕获有限阶数(bounded degree)的特征交叉,然而,DCN将带来一种特殊形式的交叉。论文设计了一种新的压缩交叉网络CIN(compressed interaction network)来替换在DCN中的cross ...
int minMalwareSpread(vector<vector<int>>& graph, vector<int>& initial) { int n = graph.size(); vector<bool> virus(n, false); for (auto i : initial) { virus[i] = true; } UnionFind uf(n); for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { if (...
void doInference(const std::vectorcv::String& image_paths); private: IRuntime* runtime; ICudaEngine* engine_; IExecutionContext* context_; void* buffers_[5]; int inputIndex1_; int inputIndex2_; int outputIndex1_; int outputIndex2_; ...
(oppath: [Compile /usr/local/Ascend/ascend-toolkit/7.0.RC1/opp/built-in/op_impl/ai_core/tbe/impl/fix_pipe.py failed with errormsg/stack: File "/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/tbe/common/utils/errormgr/error_manager_vector.py", line 284, in raise_err_...
(i); // ShuffleChannel <= Reshape - Transpose - Reshape // ShuffleChannel <= Reshape - Transpose - Constant - Reshape if (node->op_type() == "Reshape") { if (node_reference[node->output(0)] != 1) continue; std::vector<int> shape; if (node->input_size() == 1) { shape ...
·TextureFilename 纹理的名称，字符串类型，加双引号如：“C:\\demo.dds”·TimedFloatKeys 时间值，用于Animaterkey中定义时间间隔 ·Vector 三维向量，三个浮点值 ·VertexDuplicationIndices 保留副本，用于精简Mesh的操作 1、原始顶点数 2、实际顶点数 3、数组(数组中元素为顶点的索引值)·XSkinMeshHeader 描述...
vector = np.zeros([label_name_len, char_set_len]) for i, c in enumerate(text): idx = char_set.index(c) vector[i][idx] = 1.0 return vector all_labels = [text2vec(i) for i in all_label_names] 1. 2. 3. 4. 5. 6. ...