[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v5.0.0
     hooks:
       - id: check-docstring-first
       - id: check-toml
@@ -9,21 +9,21 @@ repos:
       - id: end-of-file-fixer
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 7.1.1
     hooks:
       - id: flake8
         args: [--config=setup.cfg]
 
   - repo: https://github.com/omnilib/ufmt
-    rev: v2.0.1
+    rev: v2.8.0
     hooks:
       - id: ufmt
         additional_dependencies:
           - black == 22.3.0
           - usort == 1.0.2
 
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.16
+    rev: 0.7.21
     hooks:
       - id: mdformat
         additional_dependencies:

deployment/libtorch/README.md

@@ -8,7 +8,7 @@ The LibTorch inference for `yolort`, both GPU and CPU are supported.
 
 - LibTorch 1.8.0+ together with corresponding TorchVision 0.9.0+
 - OpenCV
-- CUDA 10.2+ \[Optional\]
+- CUDA 10.2+ [Optional]
 
 *We didn't impose too strong restrictions on the version of CUDA.*
 

deployment/onnxruntime/README.md

@@ -8,7 +8,7 @@ The ONNX Runtime inference for `yolort`, both CPU and GPU are supported.
 
 - ONNX Runtime 1.7+
 - OpenCV
-- CUDA \[Optional\]
+- CUDA [Optional]
 
 *We didn't impose too strong restrictions on the versions of dependencies.*
 
@@ -30,7 +30,7 @@ The ONNX model exported by yolort differs from other pipeline in the following t
 
    And then, you can find that a ONNX model ("best.onnx") have been generated in the directory of "best.pt". Set the `size_divisible` here according to your model, 32 for P5 ("yolov5s.pt" for instance) and 64 for P6 ("yolov5s6.pt" for instance).
 
-1. \[Optional\] Quick test with the ONNX Runtime Python interface.
+1. [Optional] Quick test with the ONNX Runtime Python interface.
 
    ```python
    from yolort.runtime import PredictorORT

deployment/ppq/README.md

@@ -13,7 +13,7 @@ The ppq int8 ptq example of `yolort`.
 
 ## Usage
 
-Here we will mainly discuss how to use the ppq interface, we recommend that you check out  [tutorial](https://github.com/openppl-public/ppq/tree/master/ppq/samples) first. This code can be used to do the following stuff:
+Here we will mainly discuss how to use the ppq interface, we recommend that you check out [tutorial](https://github.com/openppl-public/ppq/tree/master/ppq/samples) first. This code can be used to do the following stuff:
 
 1. Distill your calibration data (Optional: If you don't have images for calibration and bn is in your model, you can use this)
 

deployment/tensorrt/README.md

@@ -27,7 +27,7 @@ Here we will mainly discuss how to use the C++ interface, we recommend that you
    trtexec --onnx=best.trt.onnx --saveEngine=best.engine --workspace=8192
    ```
 
-1. \[Optional\] Quick test with the TensorRT Python interface.
+1. [Optional] Quick test with the TensorRT Python interface.
 
    ```python
    import torch
@@ -58,7 +58,7 @@ Here we will mainly discuss how to use the C++ interface, we recommend that you
      cmake --build .  # Can also use the yolort_trt.sln to build on Windows System
      ```
 
-   - \[Windows System Only\] Copy following dependent dynamic link libraries (xxx.dll) to Release/Debug directory
+   - [Windows System Only] Copy following dependent dynamic link libraries (xxx.dll) to Release/Debug directory
 
      - cudnn_cnn_infer64_8.dll, cudnn_ops_infer64_8.dll, cudnn64_8.dll, nvinfer.dll, nvinfer_plugin.dll, nvonnxparser.dll, zlibwapi.dll (On which CUDA and cudnn depend)
      - opencv_corexxx.dll opencv_imgcodecsxxx.dll opencv_imgprocxxx.dll (Subsequent dependencies by OpenCV or you can also use Static OpenCV Library)