refactor(load,tract):将 ModelMetadata JSON 加载逻辑解耦至 ddddocr-tract, 优化 Error 枚举结构与错误透传

- 在 load 模块中精简 Error 与 Result 别名定义
- 增加 ParseError 子类型区分路径与字节流加载失败
- 支持通过 #[from] 自动转换 Tract 引擎底层错误
- 移出 core 中的 serde 依赖，保持核心库纯洁
- 在 tract 中实现 TractModelMetadata 扩展 trait 加载解析配置

ddddocr-tract/src/det/session.rs

@@ -1,68 +1,45 @@
-use crate::loader::ModelLoader;
-use anyhow::Context;
-use ddddocr_core::error::{DdddError, Result, TensorErrorReason};
+use crate::types::Session;
+use ddddocr_core::error::{Result, TensorError};
 use ddddocr_core::{DetEngine, DetOutput, InferenceEngine};
 use ndarray::Ix3;
-use std::path::Path;
-use tract_onnx::prelude::{Graph, IntoTensor, RunnableModel, Tensor, TypedFact, TypedOp, tvec};
+use tract_onnx::prelude::{tvec, IntoTensor, Tensor};
 #[derive(Debug)]
 pub struct DetSession {
-    pub session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+    pub session: Session,
 }
 
 impl DetSession {
-    pub fn new<P>(model_path: P) -> Result<Self>
-    where
-        P: AsRef<Path>,
-    {
-        let session = ModelLoader::model_for_path(&model_path)?.session;
-        Ok(Self { session })
+    pub fn new(session: Session) -> Self {
+        Self { session }
     }
 
-    pub fn model_from_bytes(model_bytes: &[u8]) -> Result<Self> {
-        let session = ModelLoader::model_from_bytes(model_bytes)?.session;
-        Ok(Self { session })
-    }
-    // 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())
-    // }
 }
 
 impl InferenceEngine for DetSession {
     type Output = DetOutput; // 明确绑定 OCR 小枚举
-    fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output> {
+    fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
         // 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 tensor = Tensor::from(input_array);
 
-        let mut result = self.session.run(tvec!(tensor.into())).map_err(|_| {
-            DdddError::Inference(TensorErrorReason::EngineError(
-                "执行模型推理失败".to_string(),
-            ))
-        })?;
+        let mut result = self
+            .session
+            .run(tvec!(tensor.into()))
+            .map_err(|_| TensorError::Engine("执行模型推理失败".to_string()))?;
         println!("模型输出原始数据: {:?}", result);
         // Ok(result.swap_remove(0).into_tensor())
         let raw_tensor = result.swap_remove(0).into_tensor();
         let array_d = raw_tensor.into_array::<f32>().map_err(|_| {
-            DdddError::Inference(TensorErrorReason::EngineError(
-                "Tract 实体张量无法转换为 ndarray::ArrayD".to_string(),
-            ))
+            TensorError::Engine("Tract 实体张量无法转换为 ndarray::ArrayD".to_string())
         })?;
         // 提前利用克隆(Clone)备份好当前未转维度前的真实 shape (Vec<usize>)
         let actual_shape = array_d.shape().to_vec();
 
         let array3 = array_d.into_dimensionality::<Ix3>().map_err(|_| {
-            DdddError::Inference(TensorErrorReason::TensorDimensionMismatch {
+            TensorError::DimensionMismatch {
                 expected: "3D 检测矩阵 [Batch, Box_Count, Box_Attributes]".to_string(),
                 actual: actual_shape, // 优雅降维失败时动态捕获
-            })
+            }
         })?;
         Ok(DetOutput::Detection(array3))