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rcnn

README.md

Rcnn

The Pytorch implementation is facebookresearch/detectron2.

Models

  • Faster R-CNN(C4)

  • Mask R-CNN(C4)

Test Environment

  • GTX2080Ti / Ubuntu16.04 / cuda10.2 / cudnn8.0.4 / TensorRT7.2.1 / OpenCV4.2
  • GTX2080Ti / win10 / cuda10.2 / cudnn8.0.4 / TensorRT7.2.1 / OpenCV4.2 / VS2017 (need to replace function corresponding to the dirent.h and add "--extended-lambda" in CUDA C/C++ -> Command Line -> Other options)

TensorRT7.2 is recomended because Resize layer in 7.0 with kLINEAR mode is a little different with opencv. You can also implement data preprocess out of tensorrt if you want to use TensorRT7.0 or more previous version.

The result under fp32 is same to pytorch about 4 decimal places!

How to Run

  1. generate .wts from pytorch with .pkl or .pth
// git clone -b v0.4 https://github.com/facebookresearch/detectron2.git
// go to facebookresearch/detectron2
python setup.py build develop // more install information see https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md
// download https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/model_final_721ade.pkl
// copy tensorrtx/rcnn/(gen_wts.py,demo.jpg) into facebookresearch/detectron2
// ensure cfg.MODEL.WEIGHTS in gen_wts.py is correct
// go to facebookresearch/detectron2
python gen_wts.py
// a file 'faster.wts' will be generated.
  1. build tensorrtx/rcnn and run
// put faster.wts into tensorrtx/rcnn
// go to tensorrtx/rcnn
// update parameters in rcnn.cpp if your model is trained on custom dataset.The parameters are corresponding to config in detectron2.
mkdir build
cd build
cmake ..
make
sudo ./rcnn -s [.wts] [m] // serialize model to plan file, add m for maskrcnn
sudo ./rcnn -d [.engine] [image folder] [m] // deserialize and run inference, the images in [image folder] will be processed. add m for maskrcnn
// For example
sudo ./rcnn -s faster.wts faster.engine
sudo ./rcnn -d faster.engine ../samples
// sudo ./rcnn -s mask.wts mask.engine m
// sudo ./rcnn -d mask.engine ../samples m
  1. check the images generated, as follows. _zidane.jpg and _bus.jpg

Backbone

R18, R34, R152

1.download pretrained model
  R18: https://download.pytorch.org/models/resnet18-f37072fd.pth
  R34: https://download.pytorch.org/models/resnet34-b627a593.pth
  R152: https://download.pytorch.org/models/resnet152-394f9c45.pth
2.convert pth to pkl by facebookresearch/detectron2/tools/convert-torchvision-to-d2.py
3.set merge_from_file in gen_wts.py
  ./configs/COCO-Detections/faster_rcnn_R_50_C4_1x.yaml for fasterRcnn
  ./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml for maskRcnn
4.set cfg.MODEL.RESNETS.DEPTH = 18(34,152),
      cfg.MODEL.RESNETS.STRIDE_IN_1X1 = False,
      cfg.MODEL.RESNETS.RES2_OUT_CHANNELS = 64, // for R18, R34
      cfg.MODEL.PIXEL_MEAN = [123.675, 116.280, 103.530],
      cfg.MODEL.PIXEL_STD = [58.395, 57.120, 57.375],
      cfg.INPUT.FORMAT = "RGB"
  and then train your own model
5.set BACKBONE_RESNETTYPE = R18(R34, R152) in rcnn.cpp line 13
6.modify PIXEL_MEAN and PIXEL_STD in rcnn.cpp
7.set res2_out_channels=64 in BuildResNet in rcnn.cpp line 239 // for R18, R34
8.generate wts file from your own model and build your engine, refer to how to run
9.convert your image to RGB before inference

R50, R101

1.download pretrained model
  R50: https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/model_final_721ade.pkl for fasterRcnn
       https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/model_final_9243eb.pkl for maskRcnn
  R101: https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/model_final_298dad.pkl for fasterRcnn
        https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/model_final_a2914c.pkl for maskRcnn
2.set merge_from_file in gen_wts.py
  R50-faster: ./configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml
  R101-faster: ./configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml
  R50-mask: ./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml
  R101-mask: ./configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml
3.set BACKBONE_RESNETTYPE = R50(R101) rcnn.cpp line 13
4.follow how to run

NOTE

  • if you meet the error below, just try to make again. The flag has been added in CMakeLists.txt

    error: __host__ or __device__ annotation on lambda requires --extended-lambda nvcc flag

  • the image preprocess was moved into tensorrt, see DataPreprocess in rcnn.cpp, so the input data is {H, W, C}

  • the predicted boxes is corresponding to new image size, so the final boxes need to multiply with the ratio, see calculateRatio in rcnn.cpp

  • tensorrt use fixed input size, if the size of your data is different from the engine, you need to adjust your data and the result.

  • if you want to use maskrcnn with cuda10.2, please be sure that you have upgraded cuda to the latest patch. see NVIDIA/TensorRT#1151 for detail.

  • you can only build fasterRcnn part with maskRcnn weights file.

Quantization

  1. quantizationType:fp32,fp16,int8. see BuildRcnnModel(rcnn.cpp line 276) for detail.

  2. the usage of int8 is same with tensorrtx/yolov5, but it has no improvement comparing to fp16.

Plugins

decode and nms plugins are modified from retinanet-examples

  • RpnDecodePlugin: calculate coordinates of proposals which is the first n
parameters:
  top_n: num of proposals to select
  anchors: coordinates of all anchors
  stride: stride of current feature map
  image_height: iamge height after DataPreprocess for clipping the box beyond the boundary
  image_width: iamge width after DataPreprocess for clipping the box beyond the boundary

Inputs:
  scores{C,H,W} C is number of anchors, H and W are the size of feature map
  boxes{C,H,W} C is 4*number of anchors, H and W are the size of feature map
Outputs:
  scores{C,1} C is equal to top_n
  boxes{C,4} C is equal to top_n
  • RpnNmsPlugin: apply nms to proposals
parameters:
  nms_thresh: thresh of nms
  post_nms_topk: number of proposals to select
  
Inputs:
  scores{C,1} C is equal to top_n
  boxes{C,4} C is equal to top_n
Outputs:
  boxes{C,4} C is equal to post_nms_topk

parameters:
  pooler_resolution: output size
  spatial_scale: scale the input boxes by this number
  sampling_ratio: number of inputs samples to take for each output
  num_proposals: number of proposals
  
Inputs:
  boxes{N,4} N is number of boxes
  features{C,H,W} C is channels of feature map, H and W are sizes of feature map
Outputs:
  features{N,C,H,W} N is number of boxes, C is channels of feature map, H and W are equal to pooler_resolution
  • PredictorDecodePlugin: calculate coordinates of predicted boxes by applying delta to proposals
parameters:
  num_boxes: num of proposals
  image_height: iamge height after DataPreprocess for clipping the box beyond the boundary
  image_width: iamge width after DataPreprocess for clipping the box beyond the boundary
  bbox_reg_weights: the weights for dx,dy,dw,dh. see https://github.com/facebookresearch/detectron2/blob/master/detectron2/config/defaults.py#L292 for detail

Inputs:
  scores{N,C,1,1} N is euqal to num_boxes, C is the num of classes
  boxes{N,C,1,1} N is euqal to num_boxes, C is the num of classes
  proposals{N,4} N is equal to num_boxes
Outputs:
  scores{N,1} N is equal to num_boxes
  boxes{N,4} N is equal to num_boxes
  classes{N,1} N is equal to num_boxes

parameters:
  nms_thresh: thresh of nms
  detections_per_im: number of detections to return per image

Inputs:
  scores{N,1} N is the number of the boxes
  boxes{N,4} N is the number of the boxes
  classes{N,1} N is the number of the boxes
Outputs:
  scores{N,1} N is equal to detections_per_im
  boxes{N,4} N is equal to detections_per_im
  classes{N,1} N is equal to detections_per_im

parameters:
  detections_per_im: number of detections to return per image
  output_size: same with output size of RoiAlign

Inputs:
  indices{N,1} N is the number of the predicted boxes
  masks{N,C,H,W} N is the number of the predicted boxes
Outputs:
  selected_masks{N,1,H,W} N is the number of the predicted boxes, H and W is equal to output_size