MMSegmentation Deployment¶
MMSegmentation aka mmseg is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.
Installation¶
Install mmseg¶
Please follow the installation guide to install mmseg.
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
You can refer to get_started
Method II: Build using scripts
If your target platform is Ubuntu 18.04 or later version, we encourage you to run
scripts. For example, the following commands install mmdeploy as well as inference engine - ONNX Runtime.
git clone --recursive -b main 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
NOTE:
Adding
$(pwd)/build/libtoPYTHONPATHis for importing mmdeploy SDK python module -mmdeploy_runtime, which will be presented in chapter SDK model inference.When inference onnx model by ONNX Runtime, it requests ONNX Runtime library be found. Thus, we add it to
LD_LIBRARY_PATH.
Method III: Build from source
If neither I nor II meets your requirements, building mmdeploy from source is the last option.
Convert model¶
You can use tools/deploy.py to convert mmseg models to the specified backend models. Its detailed usage can be learned from here.
The command below shows an example about converting unet model to onnx model that can be inferred by ONNX Runtime.
cd mmdeploy
# download unet model from mmseg model zoo
mim download mmsegmentation --config unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024 --dest .
# convert mmseg model to onnxruntime model with dynamic shape
python tools/deploy.py \
configs/mmseg/segmentation_onnxruntime_dynamic.py \
unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py \
fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth \
demo/resources/cityscapes.png \
--work-dir mmdeploy_models/mmseg/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 of all supported backends for mmsegmentation. The config filename pattern is:
segmentation_{backend}-{precision}_{static | dynamic}_{shape}.py
{backend}: inference backend, such as onnxruntime, tensorrt, pplnn, ncnn, openvino, coreml 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 unet to other backend models by changing the deployment config file segmentation_onnxruntime_dynamic.py to others, e.g., converting to tensorrt-fp16 model by segmentation_tensorrt-fp16_dynamic-512x1024-2048x2048.py.
Tip
When converting mmseg 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/mmseg/ort in the previous example. It includes:
mmdeploy_models/mmseg/ort
├── deploy.json
├── detail.json
├── end2end.onnx
└── pipeline.json
in which,
end2end.onnx: backend model which can be inferred by ONNX Runtime
*.json: the necessary information for mmdeploy SDK
The whole package mmdeploy_models/mmseg/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¶
Take the previous converted end2end.onnx model as an example, you can use the following code to inference the model and visualize the results.
from mmdeploy.apis.utils import build_task_processor
from mmdeploy.utils import get_input_shape, load_config
import torch
deploy_cfg = 'configs/mmseg/segmentation_onnxruntime_dynamic.py'
model_cfg = './unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py'
device = 'cpu'
backend_model = ['./mmdeploy_models/mmseg/ort/end2end.onnx']
image = './demo/resources/cityscapes.png'
# read deploy_cfg and model_cfg
deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)
# build task and backend model
task_processor = build_task_processor(model_cfg, deploy_cfg, device)
model = task_processor.build_backend_model(backend_model)
# process input image
input_shape = get_input_shape(deploy_cfg)
model_inputs, _ = task_processor.create_input(image, input_shape)
# do model inference
with torch.no_grad():
result = model.test_step(model_inputs)
# visualize results
task_processor.visualize(
image=image,
model=model,
result=result[0],
window_name='visualize',
output_file='./output_segmentation.png')
SDK model inference¶
You can also perform SDK model inference like following,
from mmdeploy_runtime import Segmentor
import cv2
import numpy as np
img = cv2.imread('./demo/resources/cityscapes.png')
# create a classifier
segmentor = Segmentor(model_path='./mmdeploy_models/mmseg/ort', device_name='cpu', device_id=0)
# perform inference
seg = segmentor(img)
# visualize inference result
## random a palette with size 256x3
palette = np.random.randint(0, 256, size=(256, 3))
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# convert to BGR
color_seg = color_seg[..., ::-1]
img = img * 0.5 + color_seg * 0.5
img = img.astype(np.uint8)
cv2.imwrite('output_segmentation.png', img)
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.
Supported models¶
| Model | TorchScript | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVino |
|---|---|---|---|---|---|---|
| FCN | Y | Y | Y | Y | Y | Y |
| PSPNet* | Y | Y | Y | Y | Y | Y |
| DeepLabV3 | Y | Y | Y | Y | Y | Y |
| DeepLabV3+ | Y | Y | Y | Y | Y | Y |
| Fast-SCNN* | Y | Y | Y | N | Y | Y |
| UNet | Y | Y | Y | Y | Y | Y |
| ANN* | Y | Y | Y | N | N | N |
| APCNet | Y | Y | Y | Y | N | N |
| BiSeNetV1 | Y | Y | Y | Y | N | Y |
| BiSeNetV2 | Y | Y | Y | Y | N | Y |
| CGNet | Y | Y | Y | Y | N | Y |
| DMNet | ? | Y | N | N | N | N |
| DNLNet | ? | Y | Y | Y | N | Y |
| EMANet | Y | Y | Y | N | N | Y |
| EncNet | Y | Y | Y | N | N | Y |
| ERFNet | Y | Y | Y | Y | N | Y |
| FastFCN | Y | Y | Y | Y | N | Y |
| GCNet | Y | Y | Y | N | N | N |
| ICNet* | Y | Y | Y | N | N | Y |
| ISANet* | N | Y | Y | N | N | Y |
| NonLocal Net | ? | Y | Y | Y | N | Y |
| OCRNet | Y | Y | Y | Y | N | Y |
| PointRend* | Y | Y | Y | N | N | N |
| Semantic FPN | Y | Y | Y | Y | N | Y |
| STDC | Y | Y | Y | Y | N | Y |
| UPerNet* | N | Y | Y | N | N | N |
| DANet | ? | Y | Y | N | N | Y |
| Segmenter* | N | Y | Y | Y | N | Y |
| SegFormer* | Y | Y | Y | N | N | Y |
| SETR | ? | Y | N | N | N | Y |
| CCNet | ? | N | N | N | N | N |
| PSANet | ? | N | N | N | N | N |
| DPT | ? | N | N | N | N | N |
Reminder¶
Only
wholeinference mode is supported for all mmseg models.PSPNet, Fast-SCNN only support static shape, because nn.AdaptiveAvgPool2d is not supported by most inference backends.
For models that only supports static shape, you should use the deployment config file of static shape such as
configs/mmseg/segmentation_tensorrt_static-1024x2048.py.For users prefer deployed models generate probability feature map, put
codebase_config = dict(with_argmax=False)in deploy configs.