duan8/rcnn

Rcnn

The Pytorch implementation is facebookresearch/detectron2.

Models

• Faster R-CNN(R50-C4)

• Mask R-CNN(R50-C4)

Test Environment

• GTX2080Ti / Ubuntu16.04 / cuda10.2 / cudnn8.0.4 / TensorRT7.0.0 / 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)

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.

2. 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] // serialize model to plan file
sudo ./rcnn -d [.engine] [image folder]  // deserialize and run inference, the images in [image folder] will be processed.
// For example
sudo ./rcnn -s faster.wts faster.engine
sudo ./rcnn -d faster.engine ../samples

3. check the images generated, as follows. _zidane.jpg and _bus.jpg

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(rcnn.cpp line 61), 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(rcnn.cpp line 113)

• 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.

Quantization

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

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

Plugins

• 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

• RoiAlignPlugin: implement of RoiAlign(align=True). see f50ec07cf2/detectron2/layers/roi_align.py (L7) for detail

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

• BatchedNmsPlugin: apply nms to predicted boxes with different classes. same with https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/nms.py#L19

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