update backend docs (#319)

docs/en/backends/ncnn.md

@@ -5,10 +5,14 @@
 #### Install ncnn
 
 - Download VulkanTools for the compilation of ncnn.
+
     ```bash
     wget https://sdk.lunarg.com/sdk/download/1.2.176.1/linux/vulkansdk-linux-x86_64-1.2.176.1.tar.gz?Human=true -O vulkansdk-linux-x86_64-1.2.176.1.tar.gz
     tar -xf vulkansdk-linux-x86_64-1.2.176.1.tar.gz
     export VULKAN_SDK=$(pwd)/1.2.176.1/x86_64
+    export LD_LIBRARY_PATH=$VULKAN_SDK/lib:$LD_LIBRARY_PATH
+    ```
+
 - Check your gcc version.
 You should ensure your gcc satisfies `gcc >= 6`.
 
@@ -27,17 +31,18 @@ You should ensure your gcc satisfies `gcc >= 6`.
     - <font color=red>Make install</font> ncnn library
         ```bash
         cd ncnn
+        export NCNN_DIR=$(pwd)
         git submodule update --init
-        mkdir build
+        mkdir -p build && cd build
-        cd build
         cmake -DNCNN_VULKAN=ON -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=ON -DNCNN_PYTHON=ON -DNCNN_BUILD_TOOLS=ON -DNCNN_BUILD_BENCHMARK=ON -DNCNN_BUILD_TESTS=ON ..
         make install
         ```
 
     - Install pyncnn module
         ```bash
-        cd ncnn/python
+        cd ${NCNN_DIR} # To NCNN root directory
-        pip install .
+        cd python
+        pip install -e .
         ```
 
 #### Build custom ops
@@ -46,8 +51,7 @@ Some custom ops are created to support models in OpenMMLab, the custom ops can b
 
 ```bash
 cd ${MMDEPLOY_DIR}
-mkdir build
+mkdir -p build && cd build
-cd build
 cmake -DMMDEPLOY_TARGET_BACKENDS=ncnn ..
 make -j$(nproc)
 ```
@@ -55,7 +59,7 @@ make -j$(nproc)
 If you haven't installed NCNN in the default path, please add `-Dncnn_DIR` flag in cmake.
 
 ```bash
- cmake -DMMDEPLOY_TARGET_BACKENDS=ncnn -Dncnn_DIR={path/of/ncnn}/build/install/lib/cmake/ncnn ..
+ cmake -DMMDEPLOY_TARGET_BACKENDS=ncnn -Dncnn_DIR=${NCNN_DIR}/build/install/lib/cmake/ncnn ..
  make -j$(nproc)
 ```
 

docs/en/backends/onnxruntime.md

@@ -44,10 +44,9 @@ Note:
 
 ```bash
 cd ${MMDEPLOY_DIR} # To MMDeploy root directory
-mkdir build
+mkdir -p build && cd build
-cd build
 cmake -DMMDEPLOY_TARGET_BACKENDS=ort -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} ..
-make -j10
+make -j$(nproc)
 ```
 
 ### How to convert a model
@@ -73,8 +72,8 @@ make -j10
 
 Take custom operator `roi_align` for example.
 
-1. Create a `roi_align` directory in ONNX Runtime source directory `backend_ops/onnxruntime/`
+1. Create a `roi_align` directory in ONNX Runtime source directory `${MMDEPLOY_DIR}/csrc/backend_ops/onnxruntime/`
-2. Add header and source file into `roi_align` directory `backend_ops/onnxruntime/roi_align/`
+2. Add header and source file into `roi_align` directory `${MMDEPLOY_DIR}/csrc/backend_ops/onnxruntime/roi_align/`
 3. Add unit test into `tests/test_ops/test_ops.py`
    Check [here](../../../tests/test_ops/test_ops.py) for examples.
 

docs/en/backends/openvino.md

@@ -63,8 +63,8 @@ The table below lists the models that are guaranteed to be exportable to OpenVIN
 | Faster R-CNN + DCN |         `configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py`          |       Y       |
 |       VFNet        |                 `configs/vfnet/vfnet_r50_fpn_1x_coco.py`                  |       Y       |
 
-
 Notes:
+
 - Custom operations from OpenVINO use the domain `org.openvinotoolkit`.
 - For faster work in OpenVINO in the Faster-RCNN, Mask-RCNN, Cascade-RCNN, Cascade-Mask-RCNN models
 the RoiAlign operation is replaced with the [ExperimentalDetectronROIFeatureExtractor](https://docs.openvinotoolkit.org/latest/openvino_docs_ops_detection_ExperimentalDetectronROIFeatureExtractor_6.html) operation in the ONNX graph.

docs/en/backends/tensorrt.md

@@ -25,9 +25,8 @@ Please install TensorRT 8 follow [install-guide](https://docs.nvidia.com/deeplea
 Some custom ops are created to support models in OpenMMLab, and the custom ops can be built as follow:
 
 ```bash
-cd ${MMDEPLOY_DIR}
+cd ${MMDEPLOY_DIR} # To MMDeploy root directory
-mkdir build
+mkdir -p build && cd build
-cd build
 cmake -DMMDEPLOY_TARGET_BACKENDS=trt ..
 make -j$(nproc)
 ```
@@ -73,10 +72,6 @@ test_pipeline = [
 data = dict(
     samples_per_gpu=2,
     workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'train_annotations.json',
-        pipeline=train_pipeline),
     val=dict(
         type=dataset_type,
         ann_file=data_root + 'val_annotations.json',