retinanetv1

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pytorch implement of retinanet,37.4 mAp(coco) at 640px(max side) ,44.24fps(RTX2080TI)

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// repository documentation

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# RetinaNet
This is an unofficial pytorch implementation of RetinaNet object detection as described in [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár.

## requirement
```text
tqdm
pyyaml
numpy
opencv-python
pycocotools
torch >= 1.5
torchvision >=0.6.0
```
## result
we trained this repo on 4 GPUs with batch size 16(4 image per node).the total epoch is 18(about 130k iter),Adam with cosine lr decay is used for optimizing.
finally, this repo achieves 37.4 mAp at 640px(max side) resolution with resnet50 backbone.(about 44.24fps)
```shell script
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.374
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.551
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.400
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.180
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.439
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.533
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.309
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.477
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.246
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.595
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.702
```

## difference from original implement
the main difference is about the input resolution.the original implement use *min_thresh* and *max_thresh* to keep the short
side of the input image larger than *min_thresh* while keep the long side smaller than *max_thresh*.for simplicity we fix the long
side a certain size, then we resize the input image while **keep the width/height ratio**, next we pad the short side.the final
width and height of the input are same.

## training
for now we only support coco detection data.

### COCO
* modify main.py (modify config file path)
```python
from solver.ddp_mix_solver import DDPMixSolver
if __name__ == '__main__':
    processor = DDPMixSolver(cfg_path="your own config path") 
    processor.run()
```
* custom some parameters in *config.yaml*
```yaml
model_name: retinanet
data:
  train_annotation_path: data/annotations/instances_train2017.json
#  train_annotation_path: data/annotations/instances_val2017.json
  val_annotation_path: data/annotations/instances_val2017.json
  train_img_root: data/train2017
#  train_img_root:data/val2017
  val_img_root:data/val2017
  max_thresh: 640
  use_crowd: False
  batch_size: 8
  num_workers: 4
  debug: False
  remove_blank: Ture

model:
  num_cls: 80
  anchor_sizes: [32, 64, 128, 256, 512]
  strides: [8, 16, 32, 64, 128]
  backbone: resnet18
  pretrained: True
  iou_thresh: 0.5
  ignore_thresh: 0.4
  alpha: 0.25
  gamma: 2.0
  allow_low_quality_matches: True
  iou_type: giou
  conf_thresh: 0.05
  nms_iou_thresh: 0.5
  max_det: 300

optim:
  optimizer: Adam
  lr: 0.0001
  milestones: [12,16]
  warm_up_epoch: 0
  weight_decay: 0.0001
  epochs: 18
  sync_bn: True
  amp: True
val:
  interval: 1
  weight_path: weights

gpus: 0,1,2,3
```
* run train scripts
```shell script
nohup python -m torch.distributed.launch --nproc_per_node=4 main.py >>train.log 2>&1 &
```

## TODO
- [x] Color Jitter
- [x] Perspective Transform
- [x] Mosaic Augment
- [x] MixUp Augment
- [x] IOU GIOU DIOU CIOU
- [x] Warming UP
- [x] Cosine Lr Decay
- [x] EMA(Exponential Moving Average)
- [x] Mixed Precision Training (supported by apex)
- [x] Sync Batch Normalize
- [ ] PANet(neck)
- [ ] BiFPN(EfficientDet neck)
- [ ] VOC data train\test scripts
- [ ] custom data train\test scripts
- [ ] MobileNet Backbone support

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