Add pytorch to onnx and onnx to tensorrt doc (#180)
* add pytorch2onnx and onnx2trt doc * fix the typo in deploy doc * fix bug of onnx2trt.md * Remove redundant install steps in onnx2trt.md * update doc based on code review * update index.rstpull/208/head
QingChuanWS
GitHub
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1f6549eeee
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d259306013
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@ -34,6 +34,8 @@ Welcome to MMClassification's documentation!
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tutorials/new_dataset.md
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tutorials/data_pipeline.md
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tutorials/new_modules.md
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tutorials/pytorch2onnx.md
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tutorials/onnx2tensorrt.md
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.. toctree::
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@ -0,0 +1,76 @@
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# Tutorial 6: ONNX to TensorRT (Experimental)
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<!-- TOC -->
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- [Tutorial 6: ONNX to TensorRT (Experimental)](#tutorial-6-onnx-to-tensorrt-experimental)
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- [How to convert models from ONNX to TensorRT](#how-to-convert-models-from-onnx-to-tensorrt)
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- [Prerequisite](#prerequisite)
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- [Usage](#usage)
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- [List of supported models convertable to TensorRT](#list-of-supported-models-convertable-to-tensorrt)
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- [Reminders](#reminders)
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- [FAQs](#faqs)
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<!-- TOC -->
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## How to convert models from ONNX to TensorRT
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### Prerequisite
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1. Please refer to [install.md](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification from source.
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2. Use our tool [pytorch2onnx.md](./pytorch2onnx.md) to convert the model from PyTorch to ONNX.
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### Usage
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```bash
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python tools/deployment/onnx2tensorrt.py \
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${MODEL} \
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--trt-file ${TRT_FILE} \
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--shape ${IMAGE_SHAPE} \
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--workspace-size {WORKSPACE_SIZE} \
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--show \
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--verify \
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```
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Description of all arguments:
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- `model` : The path of an ONNX model file.
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- `--trt-file`: The Path of output TensorRT engine file. If not specified, it will be set to `tmp.trt`.
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- `--shape`: The height and width of model input. If not specified, it will be set to `224 224`.
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- `--workspace-size` : The required GPU workspace size in GiB to build TensorRT engine. If not specified, it will be set to `1` GiB.
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- `--show`: Determines whether to show the outputs of the model. If not specified, it will be set to `False`.
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- `--verify`: Determines whether to verify the correctness of models between ONNXRuntime and TensorRT. If not specified, it will be set to `False`.
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Example:
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```bash
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python tools/onnx2tensorrt.py \
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checkpoints/resnet/resnet18_b16x8_cifar10.onnx \
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--trt-file checkpoints/resnet/resnet18_b16x8_cifar10.trt \
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--shape 224 224 \
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--show \
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--verify \
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```
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## List of supported models convertable to TensorRT
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The table below lists the models that are guaranteed to be convertable to TensorRT.
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| Model | Config | Status |
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| :----------: | :----------------------------------------------------------: | :----: |
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| MobileNetV2 | `configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py` | Y |
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| ResNet | `configs/resnet/resnet18_b16x8_cifar10.py` | Y |
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| ResNeXt | `configs/resnext/resnext50_32x4d_b32x8_imagenet.py` | Y |
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| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py` | Y |
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| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py` | Y |
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Notes:
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- *All models above are tested with Pytorch==1.6.0 and TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0*
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## Reminders
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- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, we may not provide much help here due to the limited resources. Please try to dig a little deeper and debug by yourself.
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## FAQs
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- None
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@ -0,0 +1,86 @@
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# Tutorial 5: Pytorch to ONNX (Experimental)
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<!-- TOC -->
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- [Tutorial 5: Pytorch to ONNX (Experimental)](#tutorial-5-pytorch-to-onnx-experimental)
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- [How to convert models from Pytorch to ONNX](#how-to-convert-models-from-pytorch-to-onnx)
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- [Prerequisite](#prerequisite)
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- [Usage](#usage)
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- [List of supported models exportable to ONNX](#list-of-supported-models-exportable-to-onnx)
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- [Reminders](#reminders)
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- [FAQs](#faqs)
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<!-- TOC -->
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## How to convert models from Pytorch to ONNX
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### Prerequisite
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1. Please refer to [install](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification.
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2. Install onnx and onnxruntime
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```shell
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pip install onnx onnxruntime==1.5.1
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```
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### Usage
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```bash
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python tools/pytorch2onnx.py \
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${CONFIG_FILE} \
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--checkpoint ${CHECKPOINT_FILE} \
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--output-file ${OUTPUT_FILE} \
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--shape ${IMAGE_SHAPE} \
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--opset-version ${OPSET_VERSION} \
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--dynamic-shape \
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--show \
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--verify \
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```
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Description of all arguments:
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- `config` : The path of a model config file.
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- `--checkpoint` : The path of a model checkpoint file.
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- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`.
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- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `224 224`.
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- `--opset-version` : The opset version of ONNX. If not specified, it will be set to `11`.
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- `--dynamic-shape` : Determines whether to export ONNX with dynamic input shape. If not specified, it will be set to `False`.
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- `--show`: Determines whether to print the architecture of the exported model. If not specified, it will be set to `False`.
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- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`.
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Example:
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```bash
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python tools/pytorch2onnx.py \
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configs/resnet/resnet18_b16x8_cifar10.py \
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--checkpoint checkpoints/resnet/resnet18_b16x8_cifar10.pth \
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--output-file checkpoints/resnet/resnet18_b16x8_cifar10.onnx \
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--dynamic-shape \
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--show \
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--verify \
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```
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## List of supported models exportable to ONNX
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The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime.
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| Model | Config | Note |
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| :----------: | :----------------------------------------------------------: | :--: |
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| MobileNetV2 | `configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py` | |
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| ResNet | `configs/resnet/resnet18_b16x8_cifar10.py` | |
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| ResNeXt | `configs/resnext/resnext50_32x4d_b32x8_imagenet.py` | |
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| SE-ResNet | `configs/seresnet/seresnet50_b32x8_imagenet.py` | |
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| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py` | |
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| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py` | |
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Notes:
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- *All models above are tested with Pytorch==1.6.0*
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## Reminders
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- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to dig a little deeper and debug a little bit more and hopefully solve them by yourself.
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## FAQs
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- None
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