mindspore/model_zoo/official/cv/faster_rcnn

• FasterRcnn Example
• • Description
  • Requirements
  • Example structure
  • Running the example
  • • Train
    • • Usage
      • Result
    • Infer
    • • Usage
      • Result

FasterRcnn Example

Description

FasterRcnn is a two-stage target detection network,This network uses a region proposal network (RPN), which can share the convolution features of the whole image with the detection network, so that the calculation of region proposal is almost cost free. The whole network further combines RPN and FastRcnn into a network by sharing the convolution features.

Requirements

• Install MindSpore.

• Download the dataset COCO2017.

• We use coco2017 as training dataset in this example by default, and you can also use your own datasets.

  1. If coco dataset is used. Select dataset to coco when run script.
     Install Cython and pycocotool, and you can also install mmcv to process data.

     pip install Cython
     
     pip install pycocotools
     
     pip install mmcv
     

     And change the COCO_ROOT and other settings you need in config.py. The directory structure is as follows:

     .
     └─cocodataset
       ├─annotations
         ├─instance_train2017.json
         └─instance_val2017.json
       ├─val2017
       └─train2017
     
     

  2. If your own dataset is used. Select dataset to other when run script.
     Organize the dataset infomation into a TXT file, each row in the file is as follows:

     train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
     

     Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the IMAGE_DIR(dataset directory) and the relative path in ANNO_PATH(the TXT file path), IMAGE_DIR and ANNO_PATH are setting in config.py.

Example structure

.
└─FasterRcnn      
  ├─README.md
  ├─scripts
    ├─run_download_process_data.sh       
    ├─run_standalone_train.sh
    ├─run_train.sh
    └─run_eval.sh
  ├─src
    ├─FasterRcnn
      ├─__init__.py
      ├─anchor_generator.py
      ├─bbox_assign_sample.py
      ├─bbox_assign_sample_stage2.py
      ├─faster_rcnn_r50.py
      ├─fpn_neck.py
      ├─proposal_generator.py
      ├─rcnn.py
      ├─resnet50.py
      ├─roi_align.py
      └─rpn.py
    ├─config.py
    ├─dataset.py
    ├─lr_schedule.py
    ├─network_define.py
    └─util.py
  ├─eval.py
  └─train.py

Running the example

Train

Usage

# distributed training
sh run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]
 
# standalone training
sh run_standalone_train.sh [PRETRAINED_MODEL]

Rank_table.json which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it by using the hccl_tools.
As for PRETRAINED_MODEL，it should be a ResNet50 checkpoint that trained over ImageNet2012. Ready-made pretrained_models are not available now. Stay tuned.

Result

Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the followings in loss.log.

# distribute training result(8p)
epoch: 1 step: 7393, rpn_loss: 0.12054, rcnn_loss: 0.40601, rpn_cls_loss: 0.04025, rpn_reg_loss: 0.08032, rcnn_cls_loss: 0.25854, rcnn_reg_loss: 0.14746, total_loss: 0.52655
epoch: 2 step: 7393, rpn_loss: 0.06561, rcnn_loss: 0.50293, rpn_cls_loss: 0.02587, rpn_reg_loss: 0.03967, rcnn_cls_loss: 0.35669, rcnn_reg_loss: 0.14624, total_loss: 0.56854
epoch: 3 step: 7393, rpn_loss: 0.06940, rcnn_loss: 0.49658, rpn_cls_loss: 0.03769, rpn_reg_loss: 0.03165, rcnn_cls_loss: 0.36353, rcnn_reg_loss: 0.13318, total_loss: 0.56598
...
epoch: 10 step: 7393, rpn_loss: 0.03555, rcnn_loss: 0.32666, rpn_cls_loss: 0.00697, rpn_reg_loss: 0.02859, rcnn_cls_loss: 0.16125, rcnn_reg_loss: 0.16541, total_loss: 0.36221
epoch: 11 step: 7393, rpn_loss: 0.19849, rcnn_loss: 0.47827, rpn_cls_loss: 0.11639, rpn_reg_loss: 0.08209, rcnn_cls_loss: 0.29712, rcnn_reg_loss: 0.18115, total_loss: 0.67676
epoch: 12 step: 7393, rpn_loss: 0.00691, rcnn_loss: 0.10168, rpn_cls_loss: 0.00529, rpn_reg_loss: 0.00162, rcnn_cls_loss: 0.05426, rcnn_reg_loss: 0.04745, total_loss: 0.10859

Infer

Usage

# infer
sh run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]

checkpoint can be produced in training process.

Result

Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.360
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.586
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.385
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.229
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.402
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.299
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.487
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.515
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.631