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
学涯乐码堂主1 小时前
有趣的“打擂台算法”
c++·算法·青少年编程·gesp
云栖梦泽2 小时前
Linux内核与驱动：14.SPI子系统
linux·运维·服务器·c++
Gary Studio2 小时前
安卓HAL C++基础-智能指针
开发语言·c++
还是阿落呀2 小时前
基本控制结构2
c++
多思考少编码3 小时前
PAT甲级真题1001 - 1005题详细题解（C++）（个人题解）
c++·python·最短路·pat·算法竞赛
极客智造4 小时前
C++ 标准 IO 流全详解：cin /cout/get /getline 原理、用法、区别与避坑
c++·io
charlie1145141914 小时前
嵌入式C++工程实践第20篇：GPIO 输入模式内部电路 —— 芯片是如何“听“到外部信号的
开发语言·c++·stm32·单片机
样例过了就是过了6 小时前
LeetCode热题100 分割等和子集
数据结构·c++·算法·leetcode·动态规划
麦兜和小可的舅舅7 小时前
ClickHouse 列管理机制解析：从 COW、IColumn 到 CRTP
c++·clickhouse
九成宫7 小时前
动手学深度学习PyTorch版初步安装过程
人工智能·pytorch·深度学习
热门推荐
01GitHub 镜像站点02要裂开了！ChatGPT要手机号验证了？注册Codex要求验证电话号码怎么办？2026年登陆Codex要手机号验证的解决办法03裂开！ChatGPT 居然开始要手机号验证，附详细解决方法04Codex 接入 DeepSeek API 完整配置文档05【AI】2026 年具身智能模型和世界模型总结062026年4月AI大事件深度解读：大模型竞争进入“深水区“07实测可用｜小米 MiMo 百万亿 Token 免费领，开发者速冲08近期有什么ai的新消息，新动态？ 2026.4月092026年AI编程工具终极横评：Cursor vs Claude Code vs Copilot10在Windows 11上安装Docker的踩坑记录