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| # MMClassification Support | ||
| # MMClassification Deployment | ||
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| [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. | ||
| [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. | ||
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| ## MMClassification installation tutorial | ||
| ## Installation | ||
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| Please refer to [install.md](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/install.md) for installation. | ||
| ### Install mmcls | ||
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| ## List of MMClassification models supported by MMDeploy | ||
| If you have already done that, move on to the next section. Otherwise, please follow this [quick guide](https://github.com/open-mmlab/mmclassification/tree/1.x#installation) to finish mmcls installation. | ||
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| | Model | ONNX Runtime | TensorRT | ncnn | PPLNN | OpenVINO | Model config | | ||
| | :---------------- | :----------: | :------: | :--: | :---: | :------: | :---------------------------------------------------------------------------------------------: | | ||
| | ResNet | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet) | | ||
| | ResNeXt | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext) | | ||
| | SE-ResNet | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet) | | ||
| | MobileNetV2 | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2) | | ||
| | ShuffleNetV1 | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1) | | ||
| | ShuffleNetV2 | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2) | | ||
| | VisionTransformer | Y | Y | Y | ? | Y | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer) | | ||
| ### Install mmdeploy | ||
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| There are several methods to install mmdeploy, among which you can choose an appropriate one according to your target platform and device. | ||
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| **Method I:** Install precompiled package | ||
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| If your target device is NVIDIA GPU, it is recommended to follow [this](../01-how-to-build/install_prebuilt_package.md) guide to install the precompiled package and its corresponding inference engine - `TensorRT`. | ||
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| <!-- The supported platform and device and inference engine matrix by precompiled package is presented as following: | ||
| | **Platform** | **Device** | **Inference Engine** | | ||
| | -------------- | :--------: | :------------------: | | ||
| | Linux-x86_64 | CPU | ONNX Runtime | | ||
| | Linux-x86_64 | CUDA | TensorRT | | ||
| | Windows-x86_64 | CPU | ONNX Runtime | | ||
| | Windows-x86_64 | CUDA | TensorRT | | ||
| --> | ||
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| **Method II:** Build using scripts | ||
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| 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), by which mmdeploy as well as the inference engines, such as ONNX Runtime, ncnn, pplnn and torchscript, can be installed automatically. | ||
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| **Method III:** Build from source | ||
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| If neither **I** nor **II** meets your requirements, [building mmdeploy from source](../01-how-to-build/build_from_source.md) is the last option. | ||
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| ## Convert model | ||
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| You can use `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). | ||
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| The following shows an example about converting `resnet18` model to onnx model that can be inferred by ONNX Runtime. | ||
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| ```shell | ||
| git clone --recursive https://github.com/open-mmlab/mmdeploy.git | ||
| cd mmdeploy | ||
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| # download mmcls model | ||
| pip install openmim | ||
| mim download mmcls --config resnet18_8xb16_cifar10 --dest . | ||
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| # convert mmcls model to onnxruntime model with dynamic shape | ||
| python tools/deploy.py \ | ||
| configs/mmcls/classification_onnxruntime_dynamic.py \ | ||
| resnet18_8xb16_cifar10.py \ | ||
| resnet18_b16x8_cifar10_20210528-bd6371c8.pth \ | ||
| tests/data/tiger.jpeg \ | ||
| --work-dir mmdeploy_models \ | ||
| --device cpu \ | ||
| --show \ | ||
| --dump-info | ||
| ``` | ||
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| 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/master/configs/mmcls) of all supported backends for mmclassification. The config filename pattern is: | ||
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| ``` | ||
| classification_{backend}-{precision}_{static | dynamic}_{shape}.py | ||
| ``` | ||
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| - **{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 | ||
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| Therefore, in the above example, you can also convert `resnet18` to other backend models by changing the deployment config file `mmclassification_onnxruntime_dynamic.py` to [others](https://github.com/open-mmlab/mmdeploy/tree/master/configs/mmcls), e.g., converting to tensorrt model by `classification_tensorrt-fp16_dynamic-224x224-224x224.py`. | ||
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| ```{tip} | ||
| When converting mmcls models to tensorrt models, --device should be set to "cuda" | ||
| ``` | ||
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| ## Inference model | ||
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| After the installation, you can perform inference by mmdeploy inference SDK. | ||
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| ```python | ||
| from mmdeploy_python import Classifier | ||
| import cv2 | ||
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| img = cv2.imread('tests/data/tiger.jpeg') | ||
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| # create a classifier | ||
| classifier = Classifier(model_path='./mmdeploy_models', device_name='cpu', device_id=0) | ||
| # perform inference | ||
| result = classifier(img) | ||
| # show inference result | ||
| indices = [i for i in range(len(bboxes))] | ||
| for label_id, score in result: | ||
| print(label_id, score) | ||
| ``` | ||
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| Besides python API, mmdeploy Inference SDK also provides other FFI (Foreign Function Interface), such as C, C++, C#, Java and so on. You can learn the usage from the [demos](https://github.com/open-mmlab/mmdeploy/tree/master/demo). | ||
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| ## Supported models | ||
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| | Model | ONNX Runtime | TensorRT | ncnn | PPLNN | OpenVINO | | ||
| | :--------------------------------------------------------------------------------------------------------: | :----------: | :------: | :--: | :---: | :------: | | ||
| | [ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet) | Y | Y | Y | Y | Y | | ||
| | [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext) | Y | Y | Y | Y | Y | | ||
| | [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet) | Y | Y | Y | Y | Y | | ||
| | [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2) | Y | Y | Y | Y | Y | | ||
| | [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1) | Y | Y | Y | Y | Y | | ||
| | [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2) | Y | Y | Y | Y | Y | | ||
| | [VisionTransformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer) | Y | Y | Y | ? | Y | |