Use a trait to abstract the inference engine and mock it in tests

In the next rten version some of the current APIs used to construct models are
going to be removed (see robertknight/rten#1014 and in
particular robertknight/rten#1018). Hence this project
needs to use a different way to create the fake models for unit tests.

This commit introduces a `Model` trait as an abstraction of the runtime. This is
implemented by `rten::Model` for non-test usage and by fake model types in
tests. The new fakes have the same behavior as the previous "real" model.

Author: robertknight

ocrs/src/detection.rs

@@ -1,9 +1,10 @@
 use anyhow::anyhow;
-use rten::{Dimension, FloatOperators, Model, Operators, RunOptions};
+use rten::{Dimension, FloatOperators, Operators, RunOptions};
 use rten_imageproc::{find_contours, min_area_rect, simplify_polygon, RetrievalMode, RotatedRect};
 use rten_tensor::prelude::*;
 use rten_tensor::{NdTensor, NdTensorView, Tensor};
 
+use crate::model::Model;
 use crate::preprocess::BLACK_VALUE;
 
 /// Parameters that control post-processing of text detection model outputs.
@@ -63,7 +64,7 @@ fn find_connected_component_rects(
 /// Text detector which finds the oriented bounding boxes of words in an input
 /// image.
 pub struct TextDetector {
-    model: Model,
+    model: Box<dyn Model>,
     params: TextDetectorParams,
     input_shape: Vec<Dimension>,
 }
@@ -72,19 +73,13 @@ impl TextDetector {
     /// Initialize a DetectionModel from a trained RTen model.
     ///
     /// This will fail if the model doesn't have the expected inputs or outputs.
-    pub fn from_model(model: Model, params: TextDetectorParams) -> anyhow::Result<TextDetector> {
-        let input_id = model
-            .input_ids()
-            .first()
-            .copied()
-            .ok_or(anyhow!("model has no inputs"))?;
-        let input_shape = model
-            .node_info(input_id)
-            .and_then(|info| info.shape())
-            .ok_or(anyhow!("model does not specify expected input shape"))?;
-
+    pub fn from_model(
+        model: impl Model + 'static,
+        params: TextDetectorParams,
+    ) -> anyhow::Result<TextDetector> {
+        let input_shape = model.input_shape()?;
         Ok(TextDetector {
-            model,
+            model: Box::new(model),
             params,
             input_shape,
         })
@@ -177,21 +172,18 @@ impl TextDetector {
 
         // Run text detection model to compute a probability mask indicating whether
         // each pixel is part of a text word or not.
-        let text_mask: Tensor<f32> = self
-            .model
-            .run_one(
-                image.view().into(),
-                if debug {
-                    Some(RunOptions {
-                        timing: true,
-                        verbose: false,
-                        ..Default::default()
-                    })
-                } else {
-                    None
-                },
-            )?
-            .try_into()?;
+        let text_mask: Tensor<f32> = self.model.run(
+            image.view(),
+            if debug {
+                Some(RunOptions {
+                    timing: true,
+                    verbose: false,
+                    ..Default::default()
+                })
+            } else {
+                None
+            },
+        )?;
 
         // Resize probability mask to original input size and apply threshold to get a
         // binary text/not-text mask.