[*] gender

boot/main/src/cli/CLI.cpp

@@ -13,7 +13,7 @@ bool lifuren::cli(const int argc, const char* const argv[]) {
         SPDLOG_DEBUG("命令参数：{}", argv[i]);
     }
     const char* const command = argv[0];
-    if(command == "poetize") {
+    if(std::strcmp(command, "poetize") == 0) {
         if(argc <= 2) {
             SPDLOG_WARN("缺少提示内容");
             return true;
@@ -23,7 +23,7 @@ bool lifuren::cli(const int argc, const char* const argv[]) {
             prompt.push_back(argv[i]);
         }
         // TODO: 实现
-    } else if(command == "embedding") {
+    } else if(std::strcmp(command, "embedding") == 0) {
         // TODO: 实现
     } else {
         SPDLOG_WARN("不支持的命令：{}", command);

boot/test/src/cv/TestImage.cpp

@@ -1,6 +1,5 @@
 #include "lifuren/Test.hpp"
 
-#include "lifuren/CV.hpp"
 #include "lifuren/File.hpp"
 #include "lifuren/Image.hpp"
 

boot/test/src/model/TestGender.cpp

@@ -56,7 +56,7 @@ class GenderModuleImpl : public torch::nn::Module {
     torch::Tensor forward(torch::Tensor x) {
         x = this->features->forward(x);
         x = this->avgPool->forward(x);
-        x = x.flatten();
+        x = x.flatten(1);
         x = this->classifier->forward(x);
         return torch::log_softmax(x, 1);
     }
@@ -67,14 +67,16 @@ class GenderModuleImpl : public torch::nn::Module {
 
 TORCH_MODULE(GenderModule);
 
-class GenderModel : public lifuren::Model<lifuren::dataset::ImageFileDatasetLoader, float, std::string, torch::nn::CrossEntropyLoss, GenderModule> {
+class GenderModel : public lifuren::Model<lifuren::dataset::ImageFileDatasetLoader, float, std::string, torch::nn::CrossEntropyLoss, GenderModule, torch::optim::Adam> {
 
 public:
     GenderModel(lifuren::ModelParams params = {
         .batch_size = 10LL,
         .epoch_count = 10LL,
         .classify = true
-    }) : Model(torch::nn::CrossEntropyLoss{}, GenderModule{}, params) {
+    }) : Model(params, [](auto tensor) {
+        return tensor.squeeze().to(torch::kInt64);
+    }) {
     }
     virtual ~GenderModel() {
     }
@@ -92,12 +94,8 @@ class GenderModel : public lifuren::Model<lifuren::dataset::ImageFileDatasetLoad
         this->trainDataset = std::move(lifuren::dataset::loadImageFileDataset(200, 200, this->params.batch_size, path_train, ".jpg", mapping));
         return true;
     }
-    std::shared_ptr<torch::optim::Optimizer> defineOptimizer() override {
-        // return std::make_shared<torch::optim::SGD>(this->model->parameters(), this->params.lr);
-        return std::make_shared<torch::optim::Adam>(this->model->parameters(), this->params.lr);
-    }
     float eval(std::string i) {
-         cv::Mat image = cv::imread(i);
+        cv::Mat image = cv::imread(i);
         cv::resize(image, image, cv::Size(200, 200));
         torch::Tensor image_tensor = torch::from_blob(image.data, { image.rows, image.cols, 3 }, torch::kByte).permute({ 2, 0, 1 }).unsqueeze(0).to(torch::kF32).div(255.0);
         auto prediction = this->model->forward(image_tensor);

boot/test/src/model/TestLibtorch.cpp

@@ -23,6 +23,8 @@ LFR_FORMAT_LOG_STREAM(at::Tensor);
     // torch::Tensor a = torch::rand({4, 6});
     SPDLOG_DEBUG("\n{}", a);
     SPDLOG_DEBUG("\n{}", a.t());
+    SPDLOG_DEBUG("\n{}", a.numel());
+    SPDLOG_DEBUG("\n{}", a.element_size());
     SPDLOG_DEBUG("\n{}", a.flatten());
     SPDLOG_DEBUG("\n{}", a.reshape({6, 4}));
     SPDLOG_DEBUG("\n{}", a.permute({1, 0}));
@@ -52,6 +54,6 @@ LFR_FORMAT_LOG_STREAM(at::Tensor);
 
 LFR_TEST(
     // testPrint();
-    // testTensor();
-    testNorm();
+    testTensor();
+    // testNorm();
 );

boot/test/src/model/TestLinear.cpp

@@ -28,12 +28,14 @@ class LinearModuleImpl : public torch::nn::Module {
 
 TORCH_MODULE(LinearModule);
 
-class LinearModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, float, torch::Tensor, torch::nn::MSELoss, LinearModule> {
+class LinearModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, float, torch::Tensor, torch::nn::MSELoss, LinearModule, torch::optim::SGD> {
 
 public:
     LinearModel(lifuren::ModelParams params = {
-        .batch_size = 10LL
-    }) : Model(torch::nn::MSELoss{}, LinearModule{}, params) {
+        .lr = 0.001F,
+        .batch_size = 10,
+        .epoch_count = 256
+    }) : Model(params) {
     }
     virtual ~LinearModel() {
     }
@@ -46,21 +48,17 @@ class LinearModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, fl
         std::normal_distribution<float> b(0.5, 0.2);
         std::vector<float> labels;
         std::vector<std::vector<float>> features;
-        labels.reserve(100);
-        features.reserve(100);
+        labels.reserve(200);
+        features.reserve(200);
         // w * 15.4 + 4 + r
-        for(int index = 0; index < 100; ++index) {
+        for(int index = 0; index < 200; ++index) {
             float f = w(rand);
-            features.push_back(std::vector<float>{ f });
             labels.push_back(15.4 * f + 4 + b(rand));
+            features.push_back(std::vector<float>{ f });
         }
         this->trainDataset = std::move(lifuren::dataset::loadRawDataset(this->params.batch_size, labels, features));
         return true;
     }
-    std::shared_ptr<torch::optim::Optimizer> defineOptimizer() override {
-        return std::make_shared<torch::optim::SGD>(this->model->parameters(), this->params.lr);
-        // return std::make_shared<torch::optim::Adam>(this->model->parameters(), this->params.lr);
-    }
     float eval(torch::Tensor i) {
         auto o = this->model->forward(i);
         return o.template item<float>();
@@ -72,7 +70,7 @@ class LinearModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, fl
     LinearModel linear;
     linear.define();
     linear.trainValAndTest(false, false);
-    float pred = linear.eval(torch::tensor({3.0F}, torch::kFloat32));
+    float pred = linear.eval(torch::tensor({ 3.0F }, torch::kFloat32));
     SPDLOG_DEBUG("当前预测：{}", pred);
     linear.print();
     linear.save();
@@ -85,11 +83,11 @@ class LinearModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, fl
     // model.load(lifuren::config::CONFIG.tmp);
     model.print();
     // w * 15.4 + 4 + r
-    float pred = model.eval(torch::tensor({3.0F}, torch::kFloat32));
+    float pred = model.eval(torch::tensor({ 3.0F }, torch::kFloat32));
     SPDLOG_DEBUG("当前预测：{}", pred);
 }
 
 LFR_TEST(
-    // testLine();
-    testLoad();
+    testLine();
+    // testLoad();
 );

boot/test/src/model/TestModel.cpp

@@ -25,10 +25,10 @@ class SimpleModuleImpl : public torch::nn::Module {
 
 TORCH_MODULE(SimpleModule);
 
-class SimpleModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, float, torch::Tensor, torch::nn::MSELoss, SimpleModule> {
+class SimpleModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, float, torch::Tensor, torch::nn::MSELoss, SimpleModule, torch::optim::Adam> {
 
 public:
-    SimpleModel(lifuren::ModelParams params = {}) : Model(torch::nn::MSELoss{}, SimpleModule{}, params) {
+    SimpleModel(lifuren::ModelParams params = {}) : Model(params) {
     }
     virtual ~SimpleModel() {
     }
@@ -53,9 +53,6 @@ class SimpleModel : public lifuren::Model<lifuren::dataset::RawDatasetLoader, fl
         this->trainDataset = std::move(lifuren::dataset::loadRawDataset(5LL, labels, features));
         return true;
     }
-    std::shared_ptr<torch::optim::Optimizer> defineOptimizer() override {
-        return std::make_shared<torch::optim::Adam>(this->model->parameters(), this->params.lr);
-    }
     float eval(torch::Tensor i) {
         auto o = this->model->forward(i);
         return o.template item<float>();

cv/src/OpenCV.cpp

@@ -1,6 +1,6 @@
 #include "lifuren/Logger.hpp"
 
-#include "opencv2/opencv.hpp"
+#include "opencv2/core/utils/logger.hpp"
 
 void lifuren::logger::opencv::init() {
     #if defined(_DEBUG) || !defined(NDEBUG)