Support for Detectron 2 Mask R-CNN R50-FPN 3x model in TensorRT. This script helps with converting, running and validating this model with TensorRT.
In order for scripts to work we suggest an environment with TensorRT >= 8.4.1.
Install TensorRT as per the TensorRT Install Guide. You will need to make sure the Python bindings for TensorRT are also installed correctly, these are available by installing the python3-libnvinfer and python3-libnvinfer-dev packages on your TensorRT download.
Install all dependencies listed in requirements.txt:
pip install -r requirements.txt
The workflow to convert Detectron 2 Mask R-CNN R50-FPN 3x model is basically Detectron 2 → Caffe 2 → ONNX → TensorRT, and so parts of this process require Detectron 2 to be installed. Official export to ONNX is documented here.
Deployment is done through export model script located in detectron2/tools/deploy/export_model.py of Detectron 2 github. Detectron 2 Mask R-CNN R50-FPN 3x model is dynamic with minimum testing dimension size of 800 and maximum of 1333. TensorRT plug-ins used for conversion of this model do not support dynamic shapes, as a result we have to set both height and width of the input tensor to 1344. 1344 instead of 1333 because model requires both height and width of the input tensor to be divisible by 32. In order to export this model with correct 1344x1344 resolution, we have to make a change to export_model.py. Currently lines 160-162:
aug = T.ResizeShortestEdge(
[cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST
)
have to be changed to:
aug = T.ResizeShortestEdge(
[1344, 1344], 1344
)
Export script takes --sample-image as one of the arguments. Such image is used to adjust input dimensions and dimensions of tensors for the rest of the network. This sample image has to be an image of 1344x1344 dimensions, which contains at least one detectable by model object. My recommendation is to upsample one of COCO dataset images to 1344x1344. Sample command:
python detectron2/tools/deploy/export_model.py \
--sample-image 1344x1344.jpg \
--config-file detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
--export-method caffe2_tracing \
--format onnx \
--output ./
MODEL.WEIGHTS path/to/model_final_f10217.pkl \
MODEL.DEVICE cuda
Where --sample-image is 1344x1344 image; --config-file path to Mask R-CNN R50-FPN 3x config, included with detectron2; MODEL.WEIGHTS are weights of Mask R-CNN R50-FPN 3x that can be downloaded here. Resulted model.onnx will be an input to conversion script.
This is supported Detectron 2 model:
| Model | Resolution |
|---|---|
| Mask R-CNN R50-FPN 3x | 1344x1344 |
If Detectron 2 Mask R-CNN is ready to be converted (i.e. you ran detectron2/tools/deploy/export_model.py), run:
python create_onnx.py \
--exported_onnx /path/to/model.onnx \
--onnx /path/to/converted.onnx \
--det2_config /detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
--det2_weights /model_final_f10217.pkl
--sample_image any_image.jpg
This will create the file converted.onnx which is ready to convert to TensorRT.
It is important to mention that --sample_image in this case is used for anchor generation. Detectron 2 ONNX models do not have anchor data inside the graph, so anchors have to be generated "offline". If custom model is used, make sure preprocessing of your model matches what is coded in get_anchors(self, sample_image) function.
The script has a few optional arguments, including:
--first_nms_threshold [...]allows overriding the default 1st NMS score threshold parameter, as the runtime latency of the NMS plugin is sensitive to this value. It's a good practice to set this value as high as possible, while still fulfilling your application requirements, to reduce inference latency. In Mask R-CNN this will be a score threshold for Region Proposal Network.--second_nms_threshold [...]allows overriding the default 2nd NMS score threshold parameter, further improves the runtime latency of the NMS plugin. It's a good practice to set this value as high as possible, while still fulfilling your application requirements, to reduce inference latency. It will be the second and last NMS.--batch_sizeallows selection of various batch sizes, default is 1.
Optionally, you may wish to visualize the resulting ONNX graph with a tool such as Netron.
The input to the graph is a float32 tensor with the selected input shape, containing RGB pixel data in the range of 0 to 255. All preprocessing will be performed inside the Model graph, so it is not required to further pre-process the input data.
The outputs of the graph are the same as the outputs of the EfficientNMS_TRT plugin and segmentation head output, name of the last node is detection_masks, shape is [batch_size, max_proposals, mask_height, mask_width], dtype is float32.
TensorRT engine can be built directly with trtexec using the ONNX graph generated in the previous step. If it's not already in your $PATH, the trtexec binary is usually found in /usr/src/tensorrt/bin/trtexec, depending on your TensorRT installation method. Run:
trtexec --onnx=/path/to/converted.onnx --saveEngine=/path/to/engine.trt --useCudaGraph
However, the script build_engine.py is also provided in this repository for convenience, as it has been tailored to Detectron 2 2 Mask R-CNN R50-FPN 3x engine building and INT8 calibration. Run python3 build_engine.py --help for details on available settings.
To build the TensorRT engine file with FP16 precision, run:
python3 build_engine.py \
--onnx /path/to/converted.onnx \
--engine /path/to/engine.trt \
--precision fp16
The file engine.trt will be created, which can now be used to infer with TensorRT.
For best results, make sure no other processes are using the GPU during engine build, as it may affect the optimal tactic selection process.
To build and calibrate an engine for INT8 precision, run:
python3 build_engine.py \
--onnx /path/to/converted.onnx \
--engine /path/to/engine.trt \
--precision int8 \
--calib_input /path/to/calibration/images \
--calib_cache /path/to/calibration.cache
Where --calib_input points to a directory with several thousands of images. For example, this could be a subset of the training or validation datasets that were used for the model. It's important that this data represents the runtime data distribution relatively well, therefore, the more images that are used for calibration, the better accuracy that will be achieved in INT8 precision. For models trained for the COCO dataset, we have found that 5,000 images gives a good result.
The --calib_cache is optional, and it controls where the calibration cache file will be written to. This is useful to keep a cached copy of the calibration results. Next time you need to build an int8 engine for the same network, if this file exists, the builder will skip the calibration step and use the cached values instead.
Some sample results of using all precisions:
| Precision | Latency | bbox COCO mAP | segm COCO mAP |
|---|---|---|---|
| fp32 | 34.48 ms | 0.402 | 0.368 |
| fp16 | 17.26 ms | 0.402 | 0.368 |
| int8 | 9.90 ms | 0.398 | 0.366 |
Optionally, you can obtain execution timing information for the built engine by using the trtexec utility, as:
trtexec \
--loadEngine=/path/to/engine.trt \
--useCudaGraph --noDataTransfers \
--iterations=100 --avgRuns=100
An inference benchmark will run, with GPU Compute latency times printed out to the console. Depending on your environment, you should see something similar to:
GPU Compute Time: min = 30.1864 ms, max = 37.0945 ms, mean = 34.481 ms, median = 34.4187 ms, percentile(99%) = 37.0945 ms
For optimal performance, inference should be done in a C++ application that takes advantage of CUDA Graphs to launch the inference request. Alternatively, the TensorRT engine built with this process can also be executed through either Triton Inference Server or DeepStream SDK.
However, for convenience, a python inference script is provided here for quick testing of the built TensorRT engine.
To perform object detection on a set of images with TensorRT, run:
python infer.py \
--engine /path/to/engine.trt \
--input /path/to/images \
--det2_config /detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
--output /path/to/output \
Where the input path can be either a single image file, or a directory of jpg/png/bmp images.
The script has a few optional arguments, including:
--nms_thresholdallows overriding the default second NMS score threshold parameter.--iou_thresholdallows to set IoU threshold for the mask segmentation, default is 0.5.
The detection results will be written out to the specified output directory, consisting of a visualization image, and a tab-separated results file for each input image processed.
Given a validation dataset (such as COCO val2017 data), you can get the mAP metrics for the built TensorRT engine. This will use the mAP metrics tools functions from the Detectron 2 evaluation repository. Make sure you follow Use Builtin Datasets guide to correctly setup COCO or custom dataset. Additionally, run eval_coco.py in the same folder where /datasets is present, otherwise this error will appear:
FileNotFoundError: [Errno 2] No such file or directory: 'datasets/coco/annotations/instances_val2017.json'
To run evalutions, run:
python eval_coco.py \
--engine /path/to/engine.trt \
--input /path/to/coco/val2017 \
--det2_config /detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
--det2_weights /model_final_f10217.pkl
The script has a few optional arguments, including:
--nms_thresholdallows overriding the default second NMS score threshold parameter.--iou_thresholdallows to set IoU threshold for the mask segmentation, default is 0.5.
The mAP metric is sensitive to the NMS score threshold used, as using a high threshold will reduce the model recall, resulting in a lower mAP value. It may be a good idea to build separate TensorRT engines for different purposes. That is, one engine with a default threshold (like 0.5) dedicated for mAP validation, and another engine with your application specific threshold (like 0.8) for deployment. This is why we keep the NMS threshold as a configurable parameter in the create_onnx.py script.