update doc about codebase deployment

docs/en/04-supported-codebases/mmcls.md

@@ -1,20 +1,163 @@
-# MMClassification Support
+# MMClassification Deployment
 
-[MMClassification](https://github.com/open-mmlab/mmclassification) is an open-source image classification toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com) project.
+- [Installation](#installation)
+  - [Install mmcls](#install-mmcls)
+  - [Install mmdeploy](#install-mmdeploy)
+- [Convert model](#convert-model)
+- [Model Specification](#model-specification)
+- [Model inference](#model-inference)
+  - [Backend model inference](#backend-model-inference)
+  - [SDK model inference](#sdk-model-inference)
+- [Supported models](#supported-models)
 
-## MMClassification installation tutorial
+______________________________________________________________________
 
-Please refer to [install.md](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/install.md) for installation.
+[MMClassification](https://github.com/open-mmlab/mmclassification) aka `mmcls` is an open-source image classification toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com) project.
 
-## List of MMClassification models supported by MMDeploy
+## Installation
 
-| Model             | TorchScript | ONNX Runtime | TensorRT | ncnn | PPLNN | OpenVINO |                                          Model config                                           |
-| :---------------- | :---------: | :----------: | :------: | :--: | :---: | :------: | :---------------------------------------------------------------------------------------------: |
-| ResNet            |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |       [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet)       |
-| ResNeXt           |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |      [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)       |
-| SE-ResNet         |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |      [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)      |
-| MobileNetV2       |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |    [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)    |
-| ShuffleNetV1      |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |   [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1)    |
-| ShuffleNetV2      |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |   [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2)    |
-| VisionTransformer |      Y      |      Y       |    Y     |  Y   |   ?   |    Y     | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer) |
-| SwinTransformer   |      Y      |      Y       |    Y     |  N   |   ?   |    N     |  [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/swin_transformer)  |
+### Install mmcls
+
+Please follow this [quick guide](https://github.com/open-mmlab/mmclassification/tree/1.x#installation) to install mmcls. If you have already done that, move on to [the next section](#install-mmdeploy).
+
+### Install mmdeploy
+
+There are several methods to install mmdeploy, among which you can choose an appropriate one according to your target platform and device.
+
+**Method I:** Install precompiled package
+
+> **TODO**. MMDeploy hasn't released based on dev-1.x branch.
+
+**Method II:** Build using scripts
+
+If your target platform is **Ubuntu 18.04 or later version**, we encourage you to run
+[scripts](../01-how-to-build/build_from_script.md). For example, the following commands install mmdeploy as well as inference engine - `ONNX Runtime`.
+
+```shell
+git clone --recursive -b dev-1.x https://github.com/open-mmlab/mmdeploy.git
+cd mmdeploy
+python3 tools/scripts/build_ubuntu_x64_ort.py $(nproc)
+export PYTHONPATH=$(pwd)/build/lib:$PYTHONPATH
+export LD_LIBRARY_PATH=$(pwd)/../mmdeploy-dep/onnxruntime-linux-x64-1.8.1/lib/:$LD_LIBRARY_PATH
+```
+
+**Method III:** Build from source
+
+If neither **I** nor **II** meets your requirements, [building mmdeploy from source](../01-how-to-build/build_from_source.md) is the last option.
+
+## Convert model
+
+You can use [tools/deploy.py](https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/tools/deploy.py) to convert mmcls models to the specified backend models. Its detailed usage can be learned from [here](https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/02-how-to-run/convert_model.md#usage).
+
+The command below shows an example about converting `resnet18` model to onnx model that can be inferred by ONNX Runtime.
+
+```shell
+cd mmdeploy
+
+# download resnet18 model from mmcls model zoo
+mim download mmcls --config resnet18_8xb32_in1k --dest .
+
+# convert mmcls model to onnxruntime model with dynamic shape
+python tools/deploy.py \
+    configs/mmcls/classification_onnxruntime_dynamic.py \
+    resnet18_8xb32_in1k.py \
+    resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
+    tests/data/tiger.jpeg \
+    --work-dir mmdeploy_models/mmcls/ort \
+    --device cpu \
+    --show \
+    --dump-info
+```
+
+It is crucial to specify the correct deployment config during model conversion. We've already provided builtin deployment config [files](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmcls) of all supported backends for mmclassification. The config filename pattern is:
+
+```
+classification_{backend}-{precision}_{static | dynamic}_{shape}.py
+```
+
+- **{backend}:** inference backend, such as onnxruntime, tensorrt, pplnn, ncnn, openvino, coreml and etc.
+- **{precision}:** fp16, int8. When it's empty, it means fp32
+- **{static | dynamic}:** static shape or dynamic shape
+- **{shape}:** input shape or shape range of a model
+
+Therefore, in the above example, you can also convert `resnet18` to other backend models by changing the deployment config file `classification_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/configs/mmcls), e.g., converting to tensorrt-fp16 model by `classification_tensorrt-fp16_dynamic-224x224-224x224.py`.
+
+```{tip}
+When converting mmcls models to tensorrt models, --device should be set to "cuda"
+```
+
+## Model Specification
+
+Before moving on to model inference chapter, let's know more about the converted model structure which is very important for model inference.
+
+The converted model locates in the working directory like `mmdeploy_models/mmcls/ort` in the previous example. It includes:
+
+```
+mmdeploy_models/mmcls/ort
+├── deploy.json
+├── detail.json
+├── end2end.onnx
+└── pipeline.json
+```
+
+in which,
+
+- **end2end.onnx**: backend model which can be inferred by ONNX Runtime
+- **deploy.json**: meta information about backend model
+- **pipeline.json**: inference pipeline of mmdeploy SDK
+- **detail.json**: conversion parameters
+
+The whole package **mmdeploy_models/mmcls/ort** is defined as **mmdeploy SDK model**, i.e., **mmdeploy SDK model** includes both backend model and inference meta information.
+
+## Model inference
+
+### Backend model inference
+
+MMDeploy provides a unified API named as `inference_model` to inference model, making all inference backends API transparent to users.
+
+Take the previous converted `end2end.onnx` model as an example, you can use the following code to inference the model.
+
+```shell
+from mmdeploy.apis import inference_model
+result = inference_model(
+  model_cfg='./resnet18_8xb32_in1k.py',
+  deploy_cfg='configs/mmcls/classification_onnxruntime_dynamic.py',
+  backend_files=['mmdeploy_models/mmcls/ort/end2end.onnx'],
+  img='tests/data/tiger.jpeg',
+  device='cpu')
+print(result)
+```
+
+### SDK model inference
+
+You can also perform SDK model inference like following,
+
+```python
+from mmdeploy_python import Classifier
+import cv2
+
+img = cv2.imread('tests/data/tiger.jpeg')
+
+# create a classifier
+classifier = Classifier(model_path='./mmdeploy_models/mmcls/ort', device_name='cpu', device_id=0)
+# perform inference
+result = classifier(img)
+# show inference result
+for label_id, score in result:
+    print(label_id, score)
+```
+
+Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn their usage from [demos](https://github.com/open-mmlab/mmdeploy/tree/dev-1.x/demo).
+
+## Supported models
+
+| Model                                                                                                      | TorchScript | ONNX Runtime | TensorRT | ncnn | PPLNN | OpenVINO |
+| :--------------------------------------------------------------------------------------------------------- | :---------: | :----------: | :------: | :--: | :---: | :------: |
+| [ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet)                        |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |
+| [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)                      |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |
+| [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)                   |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |
+| [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)             |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |
+| [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1)           |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |
+| [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2)           |      Y      |      Y       |    Y     |  Y   |   Y   |    Y     |
+| [VisionTransformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer) |      Y      |      Y       |    Y     |  Y   |   ?   |    Y     |
+| [SwinTransformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/swin_transformer)     |      Y      |      Y       |    Y     |  N   |   ?   |    N     |