feat(ocr,det,slide): 重构配置解析流程，移除非必要的生命周期方法

- 优化 规范化模型目录
- 重构 Ocr，Detector配置解析流程

src/models/ocr/session.rs

@@ -0,0 +1,53 @@
+use crate::models::ocr::model_metadata::ModelMetadata;
+use crate::models::loader::{ModelLoader, ModelSession, ModelType};
+use anyhow::Context;
+use anyhow::Result;
+use std::path::Path;
+use tract_onnx::prelude::{tvec, Graph, IntoTensor, RunnableModel, Tensor, TypedFact, TypedOp};
+
+pub struct OcrSession {
+    pub session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+    pub model_metadata: ModelMetadata,
+}
+impl ModelSession for OcrSession {
+    fn get_model_type(&self) -> ModelType {
+        todo!("使用thiserror作为错误处理的库,thiserror 专门用于开发库（Library）");
+    }
+    fn desc(&self) -> String {
+        "Ocr Model 加载成功".to_string()
+    }
+}
+impl OcrSession {
+    pub fn new<P>(model_path: P, model_metadata: ModelMetadata) -> Result<Self, anyhow::Error>
+    where
+        P: AsRef<Path>,
+    {
+        let session = ModelLoader::model_for_path(model_path)?.session;
+        Ok(Self {
+            session,
+            model_metadata,
+        })
+    }
+
+    pub fn model_from_bytes(
+        model_bytes: &[u8],
+        model_metadata: ModelMetadata,
+    ) -> Result<Self, anyhow::Error> {
+        let session = ModelLoader::model_from_bytes(model_bytes)?.session;
+        Ok(Self {
+            session,
+            model_metadata,
+        })
+    }
+    /// 对应 Python 的 _inference
+    pub fn inference(&self, tensor: Tensor) -> anyhow::Result<Tensor> {
+        // tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
+        // let result = self.ocr.run(tvec!(tensor.into()))?;
+        let mut result = self
+            .session
+            .run(tvec!(tensor.into()))
+            .context("执行模型推理失败")?;
+        println!("模型输出原始数据: {:?}", result);
+        Ok(result.swap_remove(0).into_tensor())
+    }
+}