Add notes about the exported ONNX model (#186)

* Add notes about the exported ONNX model * Minor fixes
zhiqwang · Oct 3, 2021 · ad7862b · ad7862b
1 parent 845d1b2
commit ad7862b
Showing 1 changed file with 9 additions and 1 deletion.
diff --git a/deployment/onnxruntime/README.md b/deployment/onnxruntime/README.md
@@ -11,6 +11,14 @@ The ONNXRuntime inference for `yolort`, both GPU and CPU are supported.
 
 *We didn't impose too strong restrictions on the versions of dependencies.*
 
+## Features
+
+The `ONNX` model exported with `yolort` differs from the official one in the following three ways.
+
+- The exported `ONNX` graph now supports dynamic shapes, and we use `(3, H, W)` as the input shape (for example `(3, 640, 640)`).
+- We embed the pre-processing ([`letterbox`](https://github.com/ultralytics/yolov5/blob/9ef94940aa5e9618e7e804f0758f9a6cebfc63a9/utils/augmentations.py#L88-L118)) into the graph as well. We only require the input image to be in the `RGB` channel, and to be rescaled to `float32 [0-1]` from general `uint [0-255]`. The main logic we use to implement this mechanism is below. (And [this](https://github.com/zhiqwang/yolov5-rt-stack/blob/b9c67205a61fa0e9d7e6696372c133ea0d36d9db/yolort/models/transform.py#L210-L234) plays the same role of the official `letterbox`, but there will be a little difference in accuracy now.)
+- We embed the post-processing (`nms`) into the model graph, which performs the same task as [`non_max_suppression`](https://github.com/ultralytics/yolov5/blob/fad57c29cd27c0fcbc0038b7b7312b9b6ef922a8/utils/general.py#L532-L623) except for the format of the inputs. (And here the `ONNX` graph is required to be dynamic.)
+
 ## Usage
 
 1. First, Setup the environment variable.
@@ -35,7 +43,7 @@ The ONNXRuntime inference for `yolort`, both GPU and CPU are supported.
                                 [--simplify]
    ```
 
-   Afterwards, you can see that a new pair of ONNX models ("best.onnx" and "best.sim.onnx") has been generated in the directory of "best.pt".
+   And then, you can find that a new pair of ONNX models ("best.onnx" and "best.sim.onnx") has been generated in the directory of "best.pt".
 
 1. \[Optional\] Quick test with the ONNXRuntime Python interface.