remove csrc/README.md (#8)

csrc/README.md

@@ -1,132 +0,0 @@
-## Introduction
-
-## Installation
-
-### Dependencies
-
-MMDeploy requires a compiler supporting C++17, e.g. GCC 7+, and CMake 3.14+ to build. Currently, it's tested on Linux
-x86-64, more platforms will be added in the future. The following packages are required to build MMDeploy SDK
-
-- OpenCV 3+
-- spdlog 0.16+
-
-Make sure they can be found by `find_package` in cmake. If they are not installed by your OS's package manager, you
-probably need to pass their locations via `CMAKE_PREFIX_PATH` or as `*_DIR` variable.
-
-On Ubuntu 16.04, please use the following command to install spdlog instead of `apt-get install libspdlog-dev`
-```bash
-wget http://archive.ubuntu.com/ubuntu/pool/universe/s/spdlog/libspdlog-dev_0.16.3-1_amd64.deb
-sudo dpkg -i libspdlog-dev_0.16.3-1_amd64.deb
-```
-
-### Enabling devices
-
-By default, only CPU device is included in the target devices. You can enable device support for other devices by
-passing a semicolon separated list of device names to `MMDEPLOY_TARGET_DEVICES` variable, e.g. `"cpu;cuda"`. Currently,
-the following devices are supported.
-
-| device |  name | path setter |
-|--------|-------|-------------|
-|  Host  |  cpu  |    N/A      |
-|  CUDA  |  cuda | CUDA_TOOLKIT_ROOT_DIR |
-
-If you have multiple CUDA versions installed on your system, you will need to pass `CUDA_TOOLKIT_ROOT_DIR` to cmake to
-specify the version.
-
-### Enabling inference engines
-
-**By default, no target inference engines are set**, since it's highly dependent on the use
-case. `MMDEPLOY_TARGET_BACKENDS`
-must be set to a semicolon separated list of inference engine names. A path to the inference engine library is also
-needed. The following backends are currently supported
-
-|   library   |  name    |   path setter   |
-|-------------|----------|-----------------|
-| PPL.nn      | pplnn    | pplnn_DIR       |
-| ncnn        | ncnn     | ncnn_DIR        |
-| ONNXRuntime | ort      | ONNXRUNTIME_DIR |
-| TensorRT    | trt      | TENSORRT_DIR & CUDNN_DIR |
-| OpenVINO    | openvino | InferenceEngine_DIR |
-
-### Put it all together
-
-The following is a recipe for building MMDeploy SDK with CPU device and ONNXRuntime support
-
-```Bash
-mkdir build && cd build
-cmake \
-    -DMMDEPLOY_BUILD_SDK=ON \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DCMAKE_CXX_COMPILER=g++-7 \
-    -DOpenCV_DIR=/path/to/OpenCV/lib/cmake/OpenCV \
-    -Dspdlog_DIR=/path/to/spdlog/lib/cmake/spdlog \
-    -DONNXRUNTIME_DIR=/path/to/onnxruntime \
-    -DMMDEPLOY_TARGET_DEVICES=cpu \
-    -DMMDEPLOY_TARGET_BACKENDS=ort \
-    -DMMDEPLOY_CODEBASES=all
-    ..
-cmake --build . -- -j$(nproc) && cmake --install .
-```
-
-## Getting Started
-
-After building & installing, the installation folder should have the following structure
-
-```
-.
-└── Release
-    ├── example
-    │   ├── CMakeLists.txt
-    │   ├── image_classification.cpp
-    │   └── object_detection.cpp
-    ├── include
-    │   ├── c
-    │   │   ├── classifier.h
-    │   │   ├── common.h
-    │   │   ├── detector.h
-    │   │   ├── restorer.h
-    │   │   ├── segmentor.h
-    │   │   ├── text_detector.h
-    │   │   └── text_recognizer.h
-    │   └── cpp
-    │       ├── archive
-    │       ├── core
-    │       └── experimental
-    └── lib
-```
-
-where `include/c` and `include/cpp` correspond to C and C++ API respectively.
-
-**Caution: The C++ API is highly volatile and not recommended at the moment.**
-
-In the example directory, there are 2 examples involving classification and object detection. The examples are tested
-with ONNXRuntime on CPU. More examples on more devices/backends will come once our cmake packaging code is ready.
-
-To start with, put the corresponding ONNX model file exported for ONNXRuntime in `demo/config/resnet50_ort`
-and `demo/config/retinanet_ort`. The models should be renamed as `end2end.onnx` to match the configs. The models can
-be exported using [MMDeploy](https://github.com/open-mmlab/mmdeploy) or corresponding OpenMMLab codebases.
-This can be done automatically when the model conversion to SDK model packaging script is ready in the future.
-
-
-Here is a recipe for building & running the examples
-
-```Bash
-cd build/install/example
-
-# path to onnxruntime ** libraries **
-export LD_LIBRARY_PATH=/path/to/onnxruntime/lib
-
-mkdir build && cd build
-cmake -DOpenCV_DIR=path/to/OpenCV/lib/cmake/OpenCV \
-      -DMMDeploy_DIR=${DMMDeploy_SOURCE_ROOT_DIR}/build/install/lib/cmake/MMDeploy ..
-cmake --build .
-
-# suppress verbose logs
-export SPDLOG_LEVEL=warn
-
-# running the image classification example
-./image_classification ../config/resnet50_ort ${path/to/an/image}
-
-# running the object detection example
-./object_detection ../config/retinanet_ort ${path/to/an/image}
-```