66 lines
2.5 KiB
Rust
66 lines
2.5 KiB
Rust
use crate::types::Session;
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use ddddocr_core::DetOutput;
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use ddddocr_core::error::{Result, TensorError};
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use ddddocr_core::traits::{DetEngine, InferenceEngine};
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use ndarray::Ix3;
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use ort::inputs;
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use ort::value::TensorRef;
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// use tract_onnx::prelude::{tvec, IntoTensor, Tensor};
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#[derive(Debug)]
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pub struct DetRuntime {
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pub session: Session,
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}
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impl DetRuntime {
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pub fn new(session: Session) -> Self {
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Self { session }
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}
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}
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impl InferenceEngine for DetRuntime {
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type Output = DetOutput; // 明确绑定 OCR 小枚举
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fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
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// tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
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// let result = self.ocr.run(tvec!(tensor.into()))?;
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let mut session_guard = self
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.session
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.lock()
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.map_err(|_| TensorError::Engine("获取 Session 锁失败 (Poisoned)".to_string()))?;
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let result = session_guard
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.run(inputs![TensorRef::from_array_view(&input_array).map_err(
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|e| TensorError::Engine(format!("构建输入失败: {e}"))
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)?])
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.map_err(|e| TensorError::Engine(format!("执行模型推理失败: {e}")))?;
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// .context("执行模型推理失败")?;
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println!("模型输出原始数据: {:?}", result);
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// Ok(result.swap_remove(0).into_tensor())
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let raw_value = &result[0];
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// raw_tensor.into_plain_array()?
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let (shape_ref, slice) = raw_value.try_extract_tensor::<f32>().map_err(|_| {
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TensorError::Engine("Tract 实体张量无法转换为 ndarray::ArrayD".to_string())
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})?;
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// 提前利用克隆(Clone)备份好当前未转维度前的真实 shape (Vec<usize>)
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let shape_vec: Vec<usize> = shape_ref.to_vec().iter().map(|v| *v as usize).collect();
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let shape_vec_slice = shape_vec.as_slice();
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let view = ndarray::ArrayViewD::from_shape(shape_vec_slice, slice)
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.map_err(|_| TensorError::Engine("构建 ndarray ArrayViewD 失败".to_string()))?;
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let array3 = view.to_owned().into_dimensionality::<Ix3>().map_err(|_| {
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TensorError::DimensionMismatch {
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expected: "3D 检测矩阵 [Batch, Box_Count, Box_Attributes]".to_string(),
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actual: shape_vec, // 优雅降维失败时动态捕获
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}
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})?;
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Ok(DetOutput::Detection(array3))
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// 在引擎内部消化掉 DatumType 强耦合
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}
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}
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impl DetEngine for DetRuntime {}
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