yanghaitao1 00f7bdb40b | ||
---|---|---|
.. | ||
scripts | ||
src | ||
README.md | ||
eval.py | ||
train.py |
README.md
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.
-
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
-
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 inANNO_PATH
(the TXT file path),IMAGE_DIR
andANNO_PATH
are setting inconfig.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 [MINDSPORE_HCCL_CONFIG_PATH] [PRETRAINED_MODEL]
# standalone training
sh run_standalone_train.sh [PRETRAINED_MODEL]
About rank_table.json, you can refer to the distributed training tutorial.
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_infer.sh [VALIDATION_DATASET_PATH] [CHECKPOINT_PATH]
checkpoint can be produced in training process.
Result
Inference result will be stored in the example path, whose folder name is "infer". 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