[pre-commit.ci] auto fixes from pre-commit.com hooks

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zhiqwang · Oct 1, 2021 · aee957f · aee957f
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diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
@@ -14,22 +14,22 @@ appearance, race, religion, or sexual identity and orientation.
 Examples of behavior that contributes to creating a positive environment
 include:
 
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
+- Using welcoming and inclusive language
+- Being respectful of differing viewpoints and experiences
+- Gracefully accepting constructive criticism
+- Focusing on what is best for the community
+- Showing empathy towards other community members
 
 Examples of unacceptable behavior by participants include:
 
-* The use of sexualized language or imagery and unwelcome sexual attention or
- advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
- address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
- professional setting
+- The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+- Trolling, insulting/derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
 
 ## Our Responsibilities
 
@@ -70,7 +70,7 @@ members of the project's leadership.
 This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
 available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
 
-[homepage]: https://www.contributor-covenant.org
-
 For answers to common questions about this code of conduct, see
 https://www.contributor-covenant.org/faq
+
+[homepage]: https://www.contributor-covenant.org
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -1,17 +1,17 @@
 # Contributing
 
 When contributing to this repository, please first discuss the change you wish to make via issue,
-email, or any other method with the owners of this repository before making a change. 
+email, or any other method with the owners of this repository before making a change.
 
 Please note we have a code of conduct, please follow it in all your interactions with the project.
 
 ## Pull Request Process
 
-1. Ensure any install or build dependencies are removed before the end of the layer when doing a 
+1. Ensure any install or build dependencies are removed before the end of the layer when doing a
    build.
-2. Update the README.md with details of changes to the interface, this includes new environment 
+1. Update the README.md with details of changes to the interface, this includes new environment
    variables, exposed ports, useful file locations and container parameters.
-3. Increase the version numbers in any examples files and the README.md to the new version that this
-   Pull Request would represent. The versioning scheme we use is <[SemVer](http://semver.org/)>.
-4. You may merge the Pull Request in once you have the sign-off of two other developers, or if you 
+1. Increase the version numbers in any examples files and the README.md to the new version that this
+   Pull Request would represent. The versioning scheme we use is \<[SemVer](http://semver.org/)>.
+1. You may merge the Pull Request in once you have the sign-off of two other developers, or if you
    do not have permission to do that, you may request the second reviewer to merge it for you
diff --git a/LICENSE b/LICENSE
@@ -671,4 +671,4 @@ into proprietary programs.  If your program is a subroutine library, you
 may consider it more useful to permit linking proprietary applications with
 the library.  If this is what you want to do, use the GNU Lesser General
 Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/README.md b/README.md
@@ -92,17 +92,17 @@ There are no extra compiled components in `yolort` and package dependencies are
   model.eval()
 
   # Perform inference on an image file
-  predictions = model.predict('bus.jpg')
+  predictions = model.predict("bus.jpg")
   # Perform inference on a list of image files
-  predictions = model.predict(['bus.jpg', 'zidane.jpg'])
+  predictions = model.predict(["bus.jpg", "zidane.jpg"])
   ```
 
 ### Loading via `torch.hub`
 
 The models are also available via torch hub, to load `yolov5s` with pretrained weights simply do:
 
 ```python
-model = torch.hub.load('zhiqwang/yolov5-rt-stack', 'yolov5s', pretrained=True)
+model = torch.hub.load("zhiqwang/yolov5-rt-stack", "yolov5s", pretrained=True)
 ```
 
 ### Loading checkpoint from official yolov5
@@ -113,11 +113,11 @@ The following is the interface for loading the checkpoint weights trained with `
 from yolort.models import YOLOv5
 
 # 'yolov5s.pt' is downloaded from https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt
-ckpt_path_from_ultralytics = 'yolov5s.pt'
+ckpt_path_from_ultralytics = "yolov5s.pt"
 model = YOLOv5.load_from_yolov5(ckpt_path_from_ultralytics, score_thresh=0.25)
 
 model.eval()
-img_path = 'test/assets/bus.jpg'
+img_path = "test/assets/bus.jpg"
 predictions = model.predict(img_path)
 ```
 

diff --git a/deployment/libtorch/README.md b/deployment/libtorch/README.md
@@ -8,55 +8,55 @@ The LibTorch inference for `yolort`, both GPU and CPU are supported.
 - LibTorch 1.8.0 / 1.9.0
 - TorchVision 0.9.0 / 0.10.0
 - OpenCV 3.4+
-- CUDA 10.2 [Optional]
+- CUDA 10.2 \[Optional\]
 
 *We didn't impose too strong restrictions on the version of CUDA and Ubuntu systems.*
 
 ## Usage
 
 1. First, Setup the environment variables.
 
-    ```bash
-    export TORCH_PATH=$(dirname $(python -c "import torch; print(torch.__file__)"))
-    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TORCH_PATH/lib/
-    ```
+   ```bash
+   export TORCH_PATH=$(dirname $(python -c "import torch; print(torch.__file__)"))
+   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TORCH_PATH/lib/
+   ```
 
 1. Don't forget to compile `TorchVision` using the following scripts.
 
-    ```bash
-    git clone https://github.com/pytorch/vision.git
-    cd vision
-    git checkout release/0.9  # Double check the version of TorchVision currently in use
-    mkdir build && cd build
-    cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch  # Set `-DWITH_CUDA=ON` if you're using GPU
-    make -j4
-    sudo make install
-    ```
+   ```bash
+   git clone https://github.com/pytorch/vision.git
+   cd vision
+   git checkout release/0.9  # Double check the version of TorchVision currently in use
+   mkdir build && cd build
+   cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch  # Set `-DWITH_CUDA=ON` if you're using GPU
+   make -j4
+   sudo make install
+   ```
 
 1. Generate `TorchScript` model
 
-    Unlike [ultralytics's](https://github.com/ultralytics/yolov5/blob/8ee9fd1/export.py) `torch.jit.trace` mechanism, We're using `torch.jit.script` to trace the YOLOv5 models which containing the whole pre-processing (especially with the [`letterbox`](https://github.com/ultralytics/yolov5/blob/8ee9fd1/utils/augmentations.py#L85-L115) ops) and post-processing (especially with the `nms` ops) procedures, as such you don't need to rewrite manually the C++ codes of pre-processing and post-processing.
+   Unlike [ultralytics's](https://github.com/ultralytics/yolov5/blob/8ee9fd1/export.py) `torch.jit.trace` mechanism, We're using `torch.jit.script` to trace the YOLOv5 models which containing the whole pre-processing (especially with the [`letterbox`](https://github.com/ultralytics/yolov5/blob/8ee9fd1/utils/augmentations.py#L85-L115) ops) and post-processing (especially with the `nms` ops) procedures, as such you don't need to rewrite manually the C++ codes of pre-processing and post-processing.
 
-    ```bash
-    git clone https://github.com/zhiqwang/yolov5-rt-stack.git
-    cd yolov5-rt-stack
-    python -m test.tracing.trace_model
-    ```
+   ```bash
+   git clone https://github.com/zhiqwang/yolov5-rt-stack.git
+   cd yolov5-rt-stack
+   python -m test.tracing.trace_model
+   ```
 
 1. Then compile the source code.
 
-    ```bash
-    cd deployment/libtorch
-    mkdir build && cd build
-    cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch
-    make
-    ```
+   ```bash
+   cd deployment/libtorch
+   mkdir build && cd build
+   cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch
+   make
+   ```
 
 1. Now, you can infer your own images.
 
-    ```bash
-    ./yolo_inference [--input_source ../../../test/assets/zidane.jpg]
-                     [--checkpoint ../../../test/tracing/yolov5s.torchscript.pt]
-                     [--labelmap ../../../notebooks/assets/coco.names]
-                     [--gpu]  # GPU switch, which is optional, and set False as default
-    ```
+   ```bash
+   ./yolo_inference [--input_source ../../../test/assets/zidane.jpg]
+                    [--checkpoint ../../../test/tracing/yolov5s.torchscript.pt]
+                    [--labelmap ../../../notebooks/assets/coco.names]
+                    [--gpu]  # GPU switch, which is optional, and set False as default
+   ```
diff --git a/deployment/libtorch/cmdline.h b/deployment/libtorch/cmdline.h
@@ -27,7 +27,7 @@
 
 #pragma once
 
-//#define USE_DEMANGLING 
+//#define USE_DEMANGLING
 
 #include <iostream>
 #include <sstream>
@@ -55,7 +55,7 @@ class lexical_cast_t{
     std::stringstream ss;
     if (!(ss<<arg && ss>>ret && ss.eof()))
       throw std::bad_cast();
-    
+
     return ret;
   }
 };
@@ -65,7 +65,7 @@ class lexical_cast_t<Target, Source, true>{
 public:
   static Target cast(const Source &arg){
     return arg;
-  }  
+  }
 };
 
 template <typename Source>
@@ -568,7 +568,7 @@ class parser{
       if (ordered[i]->must())
         oss<<ordered[i]->short_description()<<" ";
     }
-    
+
     oss<<"[options] ... "<<ftr<<std::endl;
     oss<<"options:"<<std::endl;
 

diff --git a/deployment/libtorch/main.cpp b/deployment/libtorch/main.cpp
@@ -30,7 +30,7 @@ std::vector<std::string> LoadNames(const std::string& path) {
   }
   else {
     std::cerr << ">>> ERROR: Failed to access class name path: " << path
-              << "\n>>>\tDoes the file exist? Permission to read it?\n"; 
+              << "\n>>>\tDoes the file exist? Permission to read it?\n";
   }
 
   return class_names;
@@ -126,8 +126,8 @@ int main(int argc, char* argv[]) {
   std::string image_path = cmd.get<std::string>("input_source");
   if (std::ifstream(image_path).fail()) {
     std::cerr << ">>> ERROR: Failed to access image file path: " << image_path
-              << "\n>>>\tDoes the file exist? Permission to read it?\n"; 
-    return -1; 
+              << "\n>>>\tDoes the file exist? Permission to read it?\n";
+    return -1;
   }
 
   torch::jit::script::Module module;
@@ -137,8 +137,8 @@ int main(int argc, char* argv[]) {
     std::string weights = cmd.get<std::string>("checkpoint");
     if (std::ifstream(weights).fail()) {
       std::cerr << ">>> ERROR: Failed to access checkpoint file path: " << weights
-                << "\n>>>\tDoes the file exist? Permission to read it?\n"; 
-      return -1; 
+                << "\n>>>\tDoes the file exist? Permission to read it?\n";
+      return -1;
     }
 
     module = torch::jit::load(weights);

diff --git a/deployment/ncnn/README.md b/deployment/ncnn/README.md
@@ -12,37 +12,37 @@ The ncnn inference for `yolort`, both GPU and CPU are supported.
 
 1. First, Setup the environment variables.
 
-    ```bash
-    export TORCH_PATH=$(dirname $(python -c "import torch; print(torch.__file__)"))
-    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TORCH_PATH/lib/
-    ```
+   ```bash
+   export TORCH_PATH=$(dirname $(python -c "import torch; print(torch.__file__)"))
+   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TORCH_PATH/lib/
+   ```
 
 1. First, compile `ncnn` using the following scripts.
 
-    ```bash
-    git clone --recursive git@github.com:Tencent/ncnn.git
-    cd ncnn
-    mkdir build && cd build
-    cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=ON .. # Set -DNCNN_VULKAN=ON if you're using VULKAN
-    make -j4
-    make install
-    ```
+   ```bash
+   git clone --recursive git@github.com:Tencent/ncnn.git
+   cd ncnn
+   mkdir build && cd build
+   cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=ON .. # Set -DNCNN_VULKAN=ON if you're using VULKAN
+   make -j4
+   make install
+   ```
 
-    Or follow the [official instructions](https://github.com/Tencent/ncnn/wiki/how-to-build) to install ncnn.
+   Or follow the [official instructions](https://github.com/Tencent/ncnn/wiki/how-to-build) to install ncnn.
 
 1. Then compile the source code.
 
-    ```bash
-    cd deployment/ncnn
-    mkdir build && cd build
-    cmake .. -Dncnn_DIR=<ncnn_install_dir>/lib/cmake/ncnn/
-    make
-    ```
+   ```bash
+   cd deployment/ncnn
+   mkdir build && cd build
+   cmake .. -Dncnn_DIR=<ncnn_install_dir>/lib/cmake/ncnn/
+   make
+   ```
 
-_Note: you have to change <ncnn_install_dir> to your machine's directory, it is the directory that contains ncnnConfig.cmake, if you are following the above operations, you should set it to <./ncnn/build/install>_
+_Note: you have to change \<ncnn_install_dir> to your machine's directory, it is the directory that contains ncnnConfig.cmake, if you are following the above operations, you should set it to \<./ncnn/build/install>_
 
 1. Now, you can infer your own images with ncnn.
 
-    ```bash
-    ./yolort_ncnn ../../../test/assets/zidane.jpg
-    ```
+   ```bash
+   ./yolort_ncnn ../../../test/assets/zidane.jpg
+   ```
diff --git a/deployment/ncnn/export_onnx.py b/deployment/ncnn/export_onnx.py
@@ -1,31 +1,40 @@
 # Copyright (c) 2021, Zhiqiang Wang. All Rights Reserved.
 import argparse
+
 import torch
+
 from tools.yolort_deploy_friendly import yolov5s_r40_deploy_ncnn
 
 
 def get_parser():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolov5s.pt',
-                        help='weights path')
-    parser.add_argument('--output_path', type=str, default='./yolov5s.onnx',
-                        help='path of exported onnx')
-    parser.add_argument('--img_size', nargs='+', type=int, default=[640, 640],
-                        help='image (height, width)')
-    parser.add_argument('--num_classes', type=int, default=80,
-                        help='number of classes')
-    parser.add_argument('--batch_size', type=int, default=1,
-                        help='batch size')
-    parser.add_argument('--device', default='cpu',
-                        help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--half', action='store_true',
-                        help='FP16 half-precision export')
-    parser.add_argument('--dynamic', action='store_true',
-                        help='ONNX: dynamic axes')
-    parser.add_argument('--simplify', action='store_true',
-                        help='ONNX: simplify model')
-    parser.add_argument('--opset', type=int, default=11,
-                        help='ONNX: opset version')
+    parser.add_argument(
+        "--weights", type=str, default="./yolov5s.pt", help="weights path"
+    )
+    parser.add_argument(
+        "--output_path",
+        type=str,
+        default="./yolov5s.onnx",
+        help="path of exported onnx",
+    )
+    parser.add_argument(
+        "--img_size",
+        nargs="+",
+        type=int,
+        default=[640, 640],
+        help="image (height, width)",
+    )
+    parser.add_argument("--num_classes", type=int, default=80, help="number of classes")
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+    parser.add_argument(
+        "--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu"
+    )
+    parser.add_argument(
+        "--half", action="store_true", help="FP16 half-precision export"
+    )
+    parser.add_argument("--dynamic", action="store_true", help="ONNX: dynamic axes")
+    parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model")
+    parser.add_argument("--opset", type=int, default=11, help="ONNX: opset version")
     return parser
 
 
@@ -53,8 +62,8 @@ def export_onnx(args):
         verbose=False,
         opset_version=args.opset,
         do_constant_folding=True,
-        input_names=['images'],
-        output_names=['h1', 'h2', 'h3'],
+        input_names=["images"],
+        output_names=["h1", "h2", "h3"],
     )