Pytorch实现RNN预测模型并使用C++相应的ONNX模型推理

wydxry2023-09-18 23:25

Pytorch实现RNN模型

代码

python 复制代码 
import torch
import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, seq_len, input_size, hidden_size, output_size, num_layers, device):
        super(RNN, self).__init__()
        self._seq_len = seq_len
        self._input_size = input_size
        self._output_size = output_size
        self._hidden_size = hidden_size
        self._device = device
        self._num_layers = num_layers

        self.rnn = nn.RNN(
            input_size=input_size,
            hidden_size=self._hidden_size,
            num_layers=self._num_layers,
            batch_first=True
        )

        self.fc = nn.Linear(self._seq_len * self._hidden_size, self._output_size)

    def forward(self, x, hidden_prev):
        out, hidden_prev = self.rnn(x, hidden_prev)
        out = out.contiguous().view(out.shape[0], -1)
        out = self.fc(out)
        return out, hidden_prev

seq_len = 10
batch_size = 20
input_size = 10
output_size = 10
hidden_size = 32
num_layers = 2
model = RNN(seq_len, input_size, hidden_size, output_size, num_layers, "cpu")
hidden_prev = torch.zeros(num_layers, batch_size, hidden_size).to("cpu")
model.eval() 

input_names = ["input", "hidden_prev_in"]
output_names  = ["output", "hidden_prev_out"]

x = torch.randn((batch_size, seq_len, input_size))
y, hidden_prev = model(x, hidden_prev)
print(x.shape)
print(hidden_prev.shape)
print(y.shape)
print(hidden_prev.shape)

torch.onnx.export(model, (x, hidden_prev), 'RNN.onnx', verbose=True, input_names=input_names, output_names=output_names,
  dynamic_axes={'input':[0], 'hidden_prev_in':[1], 'output':[0], 'hidden_prev_out':[1]} )

import onnx
model = onnx.load("RNN.onnx")
print("load model done.")
onnx.checker.check_model(model)
print(onnx.helper.printable_graph(model.graph))
print("check model done.")

运行结果

Shell 复制代码 
torch.Size([20, 10, 10])
torch.Size([2, 20, 32])
torch.Size([20, 10])
torch.Size([2, 20, 32])
/home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/onnx/utils.py:2041: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input input
  "No names were found for specified dynamic axes of provided input."
/home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/onnx/utils.py:2041: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input hidden_prev
  "No names were found for specified dynamic axes of provided input."
/home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/onnx/utils.py:2041: UserWarning: No names were found for specified dynamic axes of provided input.Automatically generated names will be applied to each dynamic axes of input output
  "No names were found for specified dynamic axes of provided input."
/home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/onnx/symbolic_opset9.py:4322: UserWarning: Exporting a model to ONNX with a batch_size other than 1, with a variable length with RNN_TANH can cause an error when running the ONNX model with a different batch size. Make sure to save the model with a batch size of 1, or define the initial states (h0/c0) as inputs of the model. 
  + "or define the initial states (h0/c0) as inputs of the model. "
/home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/onnx/_internal/jit_utils.py:258: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
  _C._jit_pass_onnx_node_shape_type_inference(node, params_dict, opset_version)
/home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/onnx/utils.py:688: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
  graph, params_dict, GLOBALS.export_onnx_opset_version
/home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/onnx/utils.py:1179: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
  graph, params_dict, GLOBALS.export_onnx_opset_version
Exported graph: graph(%input : Float(*, 10, 10, strides=[100, 10, 1], requires_grad=0, device=cpu),
      %hidden_prev.1 : Float(2, *, 32, strides=[640, 32, 1], requires_grad=1, device=cpu),
      %fc.weight : Float(10, 320, strides=[320, 1], requires_grad=1, device=cpu),
      %fc.bias : Float(10, strides=[1], requires_grad=1, device=cpu),
      %onnx::RNN_58 : Float(1, 32, 10, strides=[320, 10, 1], requires_grad=0, device=cpu),
      %onnx::RNN_59 : Float(1, 32, 32, strides=[1024, 32, 1], requires_grad=0, device=cpu),
      %onnx::RNN_60 : Float(1, 64, strides=[64, 1], requires_grad=0, device=cpu),
      %onnx::RNN_62 : Float(1, 32, 32, strides=[1024, 32, 1], requires_grad=0, device=cpu),
      %onnx::RNN_63 : Float(1, 32, 32, strides=[1024, 32, 1], requires_grad=0, device=cpu),
      %onnx::RNN_64 : Float(1, 64, strides=[64, 1], requires_grad=0, device=cpu)):
  %/rnn/Transpose_output_0 : Float(10, *, 10, device=cpu) = onnx::Transpose[perm=[1, 0, 2], onnx_name="/rnn/Transpose"](%input), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %onnx::RNN_13 : Tensor? = prim::Constant(), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}, onnx_name="/rnn/Constant"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_1_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}, onnx_name="/rnn/Constant_1"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_2_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={1}, onnx_name="/rnn/Constant_2"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Slice_output_0 : Float(1, *, 32, device=cpu) = onnx::Slice[onnx_name="/rnn/Slice"](%hidden_prev.1, %/rnn/Constant_1_output_0, %/rnn/Constant_2_output_0, %/rnn/Constant_output_0), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/RNN_output_0 : Float(10, 1, *, 32, device=cpu), %/rnn/RNN_output_1 : Float(1, *, 32, device=cpu) = onnx::RNN[activations=["Tanh"], hidden_size=32, onnx_name="/rnn/RNN"](%/rnn/Transpose_output_0, %onnx::RNN_58, %onnx::RNN_59, %onnx::RNN_60, %onnx::RNN_13, %/rnn/Slice_output_0), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_3_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={1}, onnx_name="/rnn/Constant_3"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Squeeze_output_0 : Float(10, *, 32, device=cpu) = onnx::Squeeze[onnx_name="/rnn/Squeeze"](%/rnn/RNN_output_0, %/rnn/Constant_3_output_0), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_4_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}, onnx_name="/rnn/Constant_4"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_5_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={1}, onnx_name="/rnn/Constant_5"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_6_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={2}, onnx_name="/rnn/Constant_6"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Slice_1_output_0 : Float(1, *, 32, device=cpu) = onnx::Slice[onnx_name="/rnn/Slice_1"](%hidden_prev.1, %/rnn/Constant_5_output_0, %/rnn/Constant_6_output_0, %/rnn/Constant_4_output_0), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/RNN_1_output_0 : Float(10, 1, *, 32, device=cpu), %/rnn/RNN_1_output_1 : Float(1, *, 32, device=cpu) = onnx::RNN[activations=["Tanh"], hidden_size=32, onnx_name="/rnn/RNN_1"](%/rnn/Squeeze_output_0, %onnx::RNN_62, %onnx::RNN_63, %onnx::RNN_64, %onnx::RNN_13, %/rnn/Slice_1_output_0), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Constant_7_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={1}, onnx_name="/rnn/Constant_7"](), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Squeeze_1_output_0 : Float(10, *, 32, device=cpu) = onnx::Squeeze[onnx_name="/rnn/Squeeze_1"](%/rnn/RNN_1_output_0, %/rnn/Constant_7_output_0), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/rnn/Transpose_1_output_0 : Float(*, 10, 32, strides=[320, 32, 1], requires_grad=1, device=cpu) = onnx::Transpose[perm=[1, 0, 2], onnx_name="/rnn/Transpose_1"](%/rnn/Squeeze_1_output_0), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %hidden_prev : Float(2, *, 32, strides=[640, 32, 1], requires_grad=1, device=cpu) = onnx::Concat[axis=0, onnx_name="/rnn/Concat"](%/rnn/RNN_output_1, %/rnn/RNN_1_output_1), scope: __main__.RNN::/torch.nn.modules.rnn.RNN::rnn # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/rnn.py:478:0
  %/Shape_output_0 : Long(3, strides=[1], device=cpu) = onnx::Shape[onnx_name="/Shape"](%/rnn/Transpose_1_output_0), scope: __main__.RNN:: # /zengli/20230320/ao/test/test_onnx_rnn.py:25:0
  %/Constant_output_0 : Long(device=cpu) = onnx::Constant[value={0}, onnx_name="/Constant"](), scope: __main__.RNN:: # /zengli/20230320/ao/test/test_onnx_rnn.py:25:0
  %/Gather_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name="/Gather"](%/Shape_output_0, %/Constant_output_0), scope: __main__.RNN:: # /zengli/20230320/ao/test/test_onnx_rnn.py:25:0
  %onnx::Unsqueeze_50 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()
  %/Unsqueeze_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name="/Unsqueeze"](%/Gather_output_0, %onnx::Unsqueeze_50), scope: __main__.RNN::
  %/Constant_1_output_0 : Long(1, strides=[1], requires_grad=0, device=cpu) = onnx::Constant[value={-1}, onnx_name="/Constant_1"](), scope: __main__.RNN::
  %/Concat_output_0 : Long(2, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name="/Concat"](%/Unsqueeze_output_0, %/Constant_1_output_0), scope: __main__.RNN:: # /zengli/20230320/ao/test/test_onnx_rnn.py:25:0
  %/Reshape_output_0 : Float(*, *, strides=[320, 1], requires_grad=1, device=cpu) = onnx::Reshape[allowzero=0, onnx_name="/Reshape"](%/rnn/Transpose_1_output_0, %/Concat_output_0), scope: __main__.RNN:: # /zengli/20230320/ao/test/test_onnx_rnn.py:25:0
  %output : Float(*, 10, strides=[10, 1], requires_grad=1, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1, onnx_name="/fc/Gemm"](%/Reshape_output_0, %fc.weight, %fc.bias), scope: __main__.RNN::/torch.nn.modules.linear.Linear::fc # /home/ubuntu/anaconda3/envs/py37/lib/python3.7/site-packages/torch/nn/modules/linear.py:114:0
  return (%output, %hidden_prev)

load model done.
graph torch_jit (
  %input[FLOAT, input_dynamic_axes_1x10x10]
  %hidden_prev.1[FLOAT, 2xhidden_prev.1_dim_1x32]
) initializers (
  %fc.weight[FLOAT, 10x320]
  %fc.bias[FLOAT, 10]
  %onnx::RNN_58[FLOAT, 1x32x10]
  %onnx::RNN_59[FLOAT, 1x32x32]
  %onnx::RNN_60[FLOAT, 1x64]
  %onnx::RNN_62[FLOAT, 1x32x32]
  %onnx::RNN_63[FLOAT, 1x32x32]
  %onnx::RNN_64[FLOAT, 1x64]
) {
  %/rnn/Transpose_output_0 = Transpose[perm = [1, 0, 2]](%input)
  %/rnn/Constant_output_0 = Constant[value = <Tensor>]()
  %/rnn/Constant_1_output_0 = Constant[value = <Tensor>]()
  %/rnn/Constant_2_output_0 = Constant[value = <Tensor>]()
  %/rnn/Slice_output_0 = Slice(%hidden_prev.1, %/rnn/Constant_1_output_0, %/rnn/Constant_2_output_0, %/rnn/Constant_output_0)
  %/rnn/RNN_output_0, %/rnn/RNN_output_1 = RNN[activations = ['Tanh'], hidden_size = 32](%/rnn/Transpose_output_0, %onnx::RNN_58, %onnx::RNN_59, %onnx::RNN_60, %, %/rnn/Slice_output_0)
  %/rnn/Constant_3_output_0 = Constant[value = <Tensor>]()
  %/rnn/Squeeze_output_0 = Squeeze(%/rnn/RNN_output_0, %/rnn/Constant_3_output_0)
  %/rnn/Constant_4_output_0 = Constant[value = <Tensor>]()
  %/rnn/Constant_5_output_0 = Constant[value = <Tensor>]()
  %/rnn/Constant_6_output_0 = Constant[value = <Tensor>]()
  %/rnn/Slice_1_output_0 = Slice(%hidden_prev.1, %/rnn/Constant_5_output_0, %/rnn/Constant_6_output_0, %/rnn/Constant_4_output_0)
  %/rnn/RNN_1_output_0, %/rnn/RNN_1_output_1 = RNN[activations = ['Tanh'], hidden_size = 32](%/rnn/Squeeze_output_0, %onnx::RNN_62, %onnx::RNN_63, %onnx::RNN_64, %, %/rnn/Slice_1_output_0)
  %/rnn/Constant_7_output_0 = Constant[value = <Tensor>]()
  %/rnn/Squeeze_1_output_0 = Squeeze(%/rnn/RNN_1_output_0, %/rnn/Constant_7_output_0)
  %/rnn/Transpose_1_output_0 = Transpose[perm = [1, 0, 2]](%/rnn/Squeeze_1_output_0)
  %hidden_prev = Concat[axis = 0](%/rnn/RNN_output_1, %/rnn/RNN_1_output_1)
  %/Shape_output_0 = Shape(%/rnn/Transpose_1_output_0)
  %/Constant_output_0 = Constant[value = <Scalar Tensor []>]()
  %/Gather_output_0 = Gather[axis = 0](%/Shape_output_0, %/Constant_output_0)
  %onnx::Unsqueeze_50 = Constant[value = <Tensor>]()
  %/Unsqueeze_output_0 = Unsqueeze(%/Gather_output_0, %onnx::Unsqueeze_50)
  %/Constant_1_output_0 = Constant[value = <Tensor>]()
  %/Concat_output_0 = Concat[axis = 0](%/Unsqueeze_output_0, %/Constant_1_output_0)
  %/Reshape_output_0 = Reshape[allowzero = 0](%/rnn/Transpose_1_output_0, %/Concat_output_0)
  %output = Gemm[alpha = 1, beta = 1, transB = 1](%/Reshape_output_0, %fc.weight, %fc.bias)
  return %output, %hidden_prev
}
check model done.

C++调用ONNX

代码

cpp 复制代码 
vector<float> testOnnxRNN() {
    //设置为VERBOSE，方便控制台输出时看到是使用了cpu还是gpu执行
    //Ort::Env env(ORT_LOGGING_LEVEL_VERBOSE, "test");
    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "Default");
    Ort::SessionOptions session_options;

    session_options.SetIntraOpNumThreads(5); // 使用五个线程执行op,提升速度
    // 第二个参数代表GPU device_id = 0，注释这行就是cpu执行
    //OrtSessionOptionsAppendExecutionProvider_CUDA(session_options, 0);
    session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);

    #ifdef _WIN32
        const wchar_t* model_path = L"C:\\Users\\xxx\\Desktop\\RNN.onnx";
    #else
        const char* model_path = "C:\\Users\\xxx\\Desktop\\RNN.onnx";
    #endif

    wprintf(L"%s\n", model_path);

    Ort::Session session(env, model_path, session_options);
    Ort::AllocatorWithDefaultOptions allocator;

    size_t num_input_nodes = session.GetInputCount();
    size_t num_output_nodes = session.GetOutputCount();

    std::vector<const char*> input_node_names = { "input" , "hidden_prev_in" }; 
    std::vector<const char*> output_node_names = { "output" , "hidden_prev_out" };

    const int input_size = 10;
    const int output_size = 10;
    const int batch_size = 1;
    const int seq_len = 10;
    const int num_layers = 2;
    const int hidden_size = 32;

    std::vector<int64_t> input_node_dims = { batch_size, seq_len, input_size };
    size_t input_tensor_size = batch_size * seq_len * input_size;
    std::vector<float> input_tensor_values(input_tensor_size);
    for (unsigned int i = 0; i < input_tensor_size; i++) {
        input_tensor_values[i] = (float)i / (input_tensor_size + 1);
    }
    auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
    Ort::Value input_tensor = Ort::Value::CreateTensor<float>(memory_info, input_tensor_values.data(), input_tensor_size, input_node_dims.data(), 3);
    assert(input_tensor.IsTensor());

    std::vector<int64_t> hidden_prev_in_node_dims = { num_layers, batch_size, hidden_size };
    size_t hidden_prev_in_tensor_size = num_layers * batch_size * hidden_size;
    std::vector<float> hidden_prev_in_tensor_values(hidden_prev_in_tensor_size);
    for (unsigned int i = 0; i < hidden_prev_in_tensor_size; i++) {
        hidden_prev_in_tensor_values[i] = (float)i / (hidden_prev_in_tensor_size + 1);
    }
    auto mask_memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
    Ort::Value hidden_prev_in_tensor = Ort::Value::CreateTensor<float>(mask_memory_info, hidden_prev_in_tensor_values.data(), hidden_prev_in_tensor_size, hidden_prev_in_node_dims.data(), 3);
    assert(hidden_prev_in_tensor.IsTensor());

    std::vector<Ort::Value> ort_inputs;
    ort_inputs.push_back(std::move(input_tensor));
    ort_inputs.push_back(std::move(hidden_prev_in_tensor));

    vector<float> ret;
    try
    {
        auto output_tensors = session.Run(Ort::RunOptions{ nullptr }, input_node_names.data(), ort_inputs.data(), ort_inputs.size(), output_node_names.data(), 2);
        float* output = output_tensors[0].GetTensorMutableData<float>();
        float* hidden_prev_out = output_tensors[1].GetTensorMutableData<float>();
           
        // output
        for (int i = 0; i < output_size; i++) {
            ret.emplace_back(output[i]);
            std::cout << output[i] << " ";
        }
        std::cout << "\n";

        // hidden_prev_out
        //for (int i = 0; i < num_layers * batch_size * hidden_size; i++) {
        //    std::cout << hidden_prev_out[i] << "\t";
        //}
        //std::cout << "\n";
    }
    catch (const std::exception& e)
    {
        std::cout << e.what() << std::endl;
    }
    return ret;
}

运行结果

bash 复制代码 
C:\Users\xxx\Desktop\RNN.onnx
0.00296116 0.104443 -0.104239 0.249864 -0.155839 0.019295 0.0458037 -0.0596341 -0.129019 -0.014682
相关推荐
Predestination王瀞潞2 小时前
IO操作（Num22）
开发语言·c++
宋恩淇要努力3 小时前
C++继承
开发语言·c++
江公望5 小时前
Qt qmlRegisterSingletonType()函数浅谈
c++·qt
逆小舟6 小时前
【C/C++】指针
c语言·c++·笔记·学习
江公望7 小时前
Qt QtConcurrent使用入门浅解
c++·qt·qml
我是华为OD~HR~栗栗呀7 小时前
23届考研-Java面经（华为OD）
java·c++·python·华为od·华为·面试
爱吃喵的鲤鱼7 小时前
仿mudou——Connection模块（连接管理）
linux·运维·服务器·开发语言·网络·c++
递归不收敛7 小时前
吴恩达机器学习课程（PyTorch适配）学习笔记：1.3 特征工程与模型优化
pytorch·学习·机器学习
郝学胜-神的一滴8 小时前
使用Linux的read和write系统函数操作文件
linux·服务器·开发语言·数据库·c++·程序人生·软件工程
2301_803554528 小时前
C++联合体（Union）详解：与结构体的区别、联系与深度解析
java·c++·算法
热门推荐
01BongoCat - 跨平台键盘猫动画工具02两千字总结：Codex 国内如何安装和使用的教程，以及如何设置中文回答03GitHub 镜像站点04UV安装并设置国内源05智能库存管理的需求预测模型：从业务痛点到落地代码的完整实践0646个Nano-banana 精选提示词，持续更新中07Cursor Plan Mode：AI 终于知道先想后做了08Linux下V2Ray安装配置指南09GitLab 零基础入门指南：从安装到项目管理全流程10一文了解国产算子编程语言 TileLang，TileLang 对国产开源生态的影响与启示