Update PicoDet and GFl (#4044)

* Update PicoDet adn GFl

configs/gfl/README.md

@@ -2,21 +2,22 @@
 
 ## Introduction
 
-[Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) and [Generalized Focal Loss V2](https://arxiv.org/pdf/2011.12885.pdf)
-
-
+We reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) and [Generalized Focal Loss V2](https://arxiv.org/pdf/2011.12885.pdf). And We use a better performing pre-trained model and ResNet-vd structure to improve mAP.
 
 ## Model Zoo
 
-| Backbone        | Model      | images/GPU | lr schedule |FPS | Box AP |                           download                          | config |
+| Backbone        | Model      | batch-size/GPU | lr schedule |FPS | Box AP |                           download                          | config |
 | :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
-| ResNet50-FPN    | GFL           |    2    |   1x      |     ----     |  40.1  | [download](https://paddledet.bj.bcebos.com/models/gfl_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r50_fpn_1x_coco.yml) |
-| ResNet50-FPN    | GFLv2       |    2    |   1x      |     ----     |  40.4  | [download](https://paddledet.bj.bcebos.com/models/gflv2_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gflv2_r50_fpn_1x_coco.yml) |
+| ResNet50    | GFL           |    2    |   1x      |     ----     |  41.0  | [model](https://paddledet.bj.bcebos.com/models/gfl_r50_fpn_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r50_fpn_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r50_fpn_1x_coco.yml) |
+| ResNet101-vd   | GFL           |    2    |   2x      |     ----     |  46.8  | [model](https://paddledet.bj.bcebos.com/models/gfl_r101vd_fpn_mstrain_2x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r101vd_fpn_mstrain_2x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r101vd_fpn_mstrain_2x_coco.yml) |
+| ResNet34-vd    | GFL           |    2    |   1x      |     ----     |  40.8  | [model](https://paddledet.bj.bcebos.com/models/gfl_r34vd_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r34vd_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r34vd_1x_coco.yml) |
+| ResNet18-vd   | GFL           |    2    |   1x      |     ----     |  36.6  | [model](https://paddledet.bj.bcebos.com/models/gfl_r18vd_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gfl_r18vd_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gfl_r18vd_1x_coco.yml) |
+| ResNet50    | GFLv2       |    2    |   1x      |     ----     |  41.2  | [model](https://paddledet.bj.bcebos.com/models/gflv2_r50_fpn_1x_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_gflv2_r50_fpn_1x_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gfl/gflv2_r50_fpn_1x_coco.yml) |
 
 
 **Notes:**
 
-- GFL is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.
+- GFL is trained on COCO train2017 dataset with 8 GPUs and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.
 
 ## Citations
 ```

configs/gfl/_base_/gfl_reader.yml

@@ -3,8 +3,8 @@ TrainReader:
   sample_transforms:
   - Decode: {}
   - RandomFlip: {prob: 0.5}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {target_size: [800, 1333], keep_ratio: true, interp: 1}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}
@@ -19,8 +19,8 @@ TrainReader:
 EvalReader:
   sample_transforms:
   - Decode: {}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {interp: 1, target_size: [800, 1333], keep_ratio: True}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}
@@ -31,8 +31,8 @@ EvalReader:
 TestReader:
   sample_transforms:
   - Decode: {}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {interp: 1, target_size: [800, 1333], keep_ratio: True}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}

configs/gfl/_base_/optimizer_1x.yml

@@ -7,7 +7,7 @@ LearningRate:
     gamma: 0.1
     milestones: [8, 11]
   - !LinearWarmup
-    start_factor: 0.1
+    start_factor: 0.001
     steps: 500
 
 OptimizerBuilder:

configs/gfl/gfl_r101vd_fpn_mstrain_2x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/gfl_r50_fpn.yml',
+  '_base_/optimizer_1x.yml',
+  '_base_/gfl_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
+weights: output/gfl_r101vd_fpn_mstrain_2x_coco/model_final
+find_unused_parameters: True
+use_ema: true
+ema_decay: 0.9998
+
+ResNet:
+  depth: 101
+  variant: d
+  norm_type: bn
+  freeze_at: 0
+  return_idx: [1,2,3]
+  num_stages: 4
+
+epoch: 24
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.001
+    steps: 500
+
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomResize: {target_size: [[480, 1333], [512, 1333], [544, 1333], [576, 1333], [608, 1333], [640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], interp: 2, keep_ratio: True}
+  - RandomFlip: {prob: 0.5}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  - Gt2GFLTarget:
+      downsample_ratios: [8, 16, 32, 64, 128]
+      grid_cell_scale: 8

configs/gfl/gfl_r18vd_1x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/gfl_r50_fpn.yml',
+  '_base_/optimizer_1x.yml',
+  '_base_/gfl_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet18_vd_pretrained.pdparams
+weights: output/gfl_r18vd_1x_coco/model_final
+find_unused_parameters: True
+
+ResNet:
+  depth: 18
+  variant: d
+  norm_type: bn
+  freeze_at: 0
+  return_idx: [1,2,3]
+  num_stages: 4

configs/gfl/gfl_r34vd_1x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/gfl_r50_fpn.yml',
+  '_base_/optimizer_1x.yml',
+  '_base_/gfl_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet34_vd_pretrained.pdparams
+weights: output/gfl_r34vd_1x_coco/model_final
+find_unused_parameters: True
+
+ResNet:
+  depth: 34
+  variant: d
+  norm_type: bn
+  freeze_at: 0
+  return_idx: [1,2,3]
+  num_stages: 4

configs/gfl/gflv2_r50_fpn_1x_coco.yml

@@ -6,5 +6,5 @@ _BASE_: [
   '_base_/gfl_reader.yml',
 ]
 
-weights: output/gfl_r50_fpn_1x_coco/model_final
+weights: output/gflv2_r50_fpn_1x_coco/model_final
 find_unused_parameters: True

configs/picodet/README.md

 # PicoDet
 
+![](../../docs/images/picedet_demo.jpeg)
 ## Introduction
 
-We developed a series of mobile models, which named `PicoDet`.
-Optimizing method of we use:
-- [Generalized Focal Loss V2](https://arxiv.org/pdf/2011.12885.pdf)
-- Lr Cosine Decay
-
+We developed a series of lightweight models, which named `PicoDet`. Because of its excellent performance, it is very suitable for deployment on mobile or CPU.
 
+Optimizing method of we use:
+- [ATSS](https://arxiv.org/abs/1912.02424)
+- [Generalized Focal Loss](https://arxiv.org/abs/2006.04388)
+- Lr Cosine Decay and cycle-EMA
+- lightweight head
+
+## Requirements
+- PaddlePaddle == 2.1.2
+- PaddleSlim >= 2.1.1
+
+## Comming soon
+- [ ] Benchmark of PicoDet.
+- [ ] deploy for most platforms, such as PaddleLite、MNN、ncnn、openvino etc.
+- [ ] PicoDet-XS and PicoDet-L series of model.
+- [ ] Slim for PicoDet.
+- [ ] More features in need.
 
 ## Model Zoo
 
 ### PicoDet-S
 
-| Backbone                  | Input size | images/GPU | lr schedule |Box AP | FLOPS | Inference Time |                           download                          | config |
-| :------------------------ | :-------: | :-------: | :-----------: | :---: | :-----: | :-----: | :-------------------------------------------------: | :-----: |
-| ShuffleNetv2-1x    | 320*320   |    128    |   280e      |   21.9     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_s_shufflenetv2_320_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_shufflenetv2_320_coco.yml) |
-| MobileNetv3-large-0.5x    | 320*320   |    128    |   280e      |   20.4     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_s_mbv3_320_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_mbv3_320_coco.yml) |
-| ShuffleNetv2-1x    | 416*416   |    96    |   280e      |   24.0     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_s_shufflenetv2_416_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_shufflenetv2_416_coco.yml) |
-| MobileNetv3-large-0.5x    | 416*416   |    96    |   280e      |   23.3     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_s_mbv3_416_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_mbv3_416_coco.yml) |
+| Backbone                  | Input size | lr schedule | Box AP(0.5:0.95) | Box AP(0.5) | FLOPS | Model Size | Inference Time |                           download                          | config |
+| :------------------------ | :-------: | :-------: | :------: | :---: | :---: | :---: | :------------:  | :-------------------------------------------------: | :-----: |
+| ShuffleNetv2-1x    | 320*320    |   280e      |   22.3     | 36.8 | -- | 3.8M | -- | [model](https://paddledet.bj.bcebos.com/models/picodet_s_shufflenetv2_320_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_shufflenetv2_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_shufflenetv2_320_coco.yml) |
+| ShuffleNetv2-1x    | 416*416    |   280e      |   24.6     | 44.3 | -- | 3.8M | -- | [model](https://paddledet.bj.bcebos.com/models/picodet_s_shufflenetv2_416_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_shufflenetv2_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_shufflenetv2_416_coco.yml) |
+
 
 ### PicoDet-M
 
-| Backbone                  | Input size | images/GPU | lr schedule |Box AP | FLOPS | Inference Time |                           download                          | config |
-| :------------------------ | :-------: | :-------: | :-----------: | :---: | :-----: | :-----: | :-------------------------------------------------: | :-----: |
-| ShuffleNetv2-1.5x    | 320*320   |    128    |   280e      |   24.9     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_m_shufflenetv2_320_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_shufflenetv2_320_coco.yml) |
-| MobileNetv3-large-1x    | 320*320   |    128    |   280e      |   26.4     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_m_mbv3_320_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_mbv3_320_coco.yml) |
-| ShuffleNetv2-1.5x    | 416*416   |    128    |   280e      |   27.4     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_m_shufflenetv2_416_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_shufflenetv2_416_coco.yml) |
-| MobileNetv3-large-1x    | 416*416   |    128    |   280e      |   29.2     |  -- | -- | [download](https://paddledet.bj.bcebos.com/models/picodet_m_mbv3_416_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_mbv3_416_coco.yml) |
+| Backbone                  | Input size | lr schedule | Box AP(0.5:0.95) | Box AP(0.5) | FLOPS | Model Size | Inference Time |                           download                          | config |
+| :------------------------ | :-------: | :-------: | :-----------: | :---: | :---: | :---: | :-----: | :-------------------------------------------------: | :-----: |
+| ShuffleNetv2-1.5x    | 320*320   |   280e      |   25.3     | 41.2 |  -- | 8.1M | -- | [model](https://paddledet.bj.bcebos.com/models/picodet_m_shufflenetv2_320_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_shufflenetv2_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_shufflenetv2_320_coco.yml) |
+| MobileNetv3-large-1x    | 320*320   |   280e      |   26.7     | 44.1 |  -- | 11.6M | -- | [model](https://paddledet.bj.bcebos.com/models/picodet_m_mbv3_320_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_mbv3_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_mbv3_320_coco.yml) |
+| ShuffleNetv2-1.5x    | 416*416     |   280e      |   28.0     | 44.3 |  -- | 8.1M | -- | [model](https://paddledet.bj.bcebos.com/models/picodet_m_shufflenetv2_416_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_shufflenetv2_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_shufflenetv2_416_coco.yml) |
+| MobileNetv3-large-1x    | 416*416  |   280e      |   29.3     | 47.2 |  -- | 11.6M | -- | [model](https://paddledet.bj.bcebos.com/models/picodet_m_mbv3_416_coco.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_mbv3_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_mbv3_416_coco.yml) |
 
 
 **Notes:**
 
 - PicoDet inference speed is tested on Kirin 980 with 4 threads by arm8 and with FP16.
-- PicoDet is trained on COCO train2017 dataset and evaluated on val2017 results of `mAP(IoU=0.5:0.95)`.
+- PicoDet is trained on COCO train2017 dataset and evaluated on val2017.
 - PicoDet used 4 GPUs for training and mini-batch size as 128 or 96 on each GPU.
 
 ## Citations
 ```
-@article{li2020gflv2,
-  title={Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection},
-  author={Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian},
-  journal={arXiv preprint arXiv:2011.12885},
+@article{li2020generalized,
+  title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection},
+  author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian},
+  journal={arXiv preprint arXiv:2006.04388},
   year={2020}
 }
 

configs/picodet/_base_/picodet_320_reader.yml

@@ -2,11 +2,11 @@ worker_num: 6
 TrainReader:
   sample_transforms:
   - Decode: {}
-  - RandomDistort: {}
   - RandomCrop: {}
   - RandomFlip: {prob: 0.5}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {target_size: [320, 320], keep_ratio: False, interp: 1}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - RandomDistort: {}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}
@@ -22,8 +22,8 @@ TrainReader:
 EvalReader:
   sample_transforms:
   - Decode: {}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {interp: 1, target_size: [320, 320], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}
@@ -36,8 +36,8 @@ TestReader:
     image_shape: [3, 320, 320]
   sample_transforms:
   - Decode: {}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {interp: 1, target_size: [320, 320], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}

configs/picodet/_base_/picodet_416_reader.yml

@@ -2,11 +2,11 @@ worker_num: 6
 TrainReader:
   sample_transforms:
   - Decode: {}
-  - RandomDistort: {}
   - RandomCrop: {}
   - RandomFlip: {prob: 0.5}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {target_size: [416, 416], keep_ratio: False, interp: 1}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - RandomDistort: {}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}
@@ -22,8 +22,8 @@ TrainReader:
 EvalReader:
   sample_transforms:
   - Decode: {}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {interp: 1, target_size: [416, 416], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}
@@ -36,8 +36,8 @@ TestReader:
     image_shape: [3, 416, 416]
   sample_transforms:
   - Decode: {}
-  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Resize: {interp: 1, target_size: [416, 416], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - Permute: {}
   batch_transforms:
   - PadBatch: {pad_to_stride: 32}

configs/picodet/_base_/picodet_mbv3_0_5x.yml → configs/picodet/_base_/picodet_mobilenetv3.yml

 architecture: PicoDet
-pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x0_5_pretrained.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x1_0_ssld_pretrained.pdparams
 
 PicoDet:
   backbone: MobileNetV3
@@ -8,13 +8,13 @@ PicoDet:
 
 MobileNetV3:
   model_name: large
-  scale: 0.5
+  scale: 1.0
   with_extra_blocks: false
   extra_block_filters: []
   feature_maps: [7, 13, 16]
 
 PAN:
-  out_channel: 96
+  out_channel: 128
   start_level: 0
   end_level: 3
   spatial_scales: [0.125, 0.0625, 0.03125]
@@ -22,13 +22,13 @@ PAN:
 PicoHead:
   conv_feat:
     name: PicoFeat
-    feat_in: 96
-    feat_out: 96
+    feat_in: 128
+    feat_out: 128
     num_convs: 2
     norm_type: bn
     share_cls_reg: True
   fpn_stride: [8, 16, 32]
-  feat_in_chan: 96
+  feat_in_chan: 128
   prior_prob: 0.01
   reg_max: 7
   cell_offset: 0.5

configs/picodet/picodet_m_r18_320_coco.yml → configs/picodet/picodet_l_r18_320_coco.yml

@@ -10,7 +10,7 @@ weights: output/picodet_m_r18_320_coco/model_final
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet18_vd_pretrained.pdparams
 find_unused_parameters: True
 use_ema: true
-ema_decay: 0.9998
+cycle_epoch: 40
 snapshot_epoch: 10
 
 PicoDet:

configs/picodet/picodet_m_mbv3_320_coco.yml

 _BASE_: [
   '../datasets/coco_detection.yml',
   '../runtime.yml',
-  '_base_/picodet_mbv3_0_5x.yml',
+  '_base_/picodet_mobilenetv3.yml',
   '_base_/optimizer_280e.yml',
   '_base_/picodet_320_reader.yml',
 ]
 
 weights: output/picodet_m_mbv3_320_coco/model_final
-pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x1_0_ssld_pretrained.pdparams
 find_unused_parameters: True
 use_ema: true
-ema_decay: 0.9998
+cycle_epoch: 40
 snapshot_epoch: 10
 
-MobileNetV3:
-  model_name: large
-  scale: 1.0
-  with_extra_blocks: false
-  extra_block_filters: []
-  feature_maps: [7, 13, 16]
-
-PAN:
-  out_channel: 128
-  start_level: 0
-  end_level: 3
-  spatial_scales: [0.125, 0.0625, 0.03125]
-
-PicoHead:
-  conv_feat:
-    name: PicoFeat
-    feat_in: 128
-    feat_out: 128
-    num_convs: 2
-    norm_type: bn
-    share_cls_reg: True
-  feat_in_chan: 128
-
 TrainReader:
   batch_size: 88

configs/picodet/picodet_m_mbv3_416_coco.yml

 _BASE_: [
   '../datasets/coco_detection.yml',
   '../runtime.yml',
-  '_base_/picodet_mbv3_0_5x.yml',
+  '_base_/picodet_mobilenetv3.yml',
   '_base_/optimizer_280e.yml',
   '_base_/picodet_416_reader.yml',
 ]
 
-weights: output/picodet_m_mbv3_320_coco/model_final
-pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/MobileNetV3_large_x1_0_ssld_pretrained.pdparams
+weights: output/picodet_m_mbv3_416_coco/model_final
 find_unused_parameters: True
 use_ema: true
-ema_decay: 0.9998
+cycle_epoch: 40
 snapshot_epoch: 10
 
-MobileNetV3:
-  model_name: large
-  scale: 1.0
-  with_extra_blocks: false
-  extra_block_filters: []
-  feature_maps: [7, 13, 16]
-
-PAN:
-  out_channel: 128
-  start_level: 0
-  end_level: 3
-  spatial_scales: [0.125, 0.0625, 0.03125]
-
-PicoHead:
-  conv_feat:
-    name: PicoFeat
-    feat_in: 128
-    feat_out: 128
-    num_convs: 2
-    norm_type: bn
-    share_cls_reg: True
-  feat_in_chan: 128
-
 TrainReader:
   batch_size: 56
 

configs/picodet/picodet_m_shufflenetv2_320_coco.yml

@@ -6,11 +6,11 @@ _BASE_: [
   '_base_/picodet_320_reader.yml',
 ]
 
-weights: output/picodet_s_shufflenetv2_320_coco/model_final
+weights: output/picodet_m_shufflenetv2_320_coco/model_final
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ShuffleNetV2_x1_5_pretrained.pdparams
 find_unused_parameters: True
 use_ema: true
-ema_decay: 0.9998
+cycle_epoch: 40
 snapshot_epoch: 10
 
 ShuffleNetV2:

configs/picodet/picodet_m_shufflenetv2_416_coco.yml

@@ -6,11 +6,11 @@ _BASE_: [
   '_base_/picodet_416_reader.yml',
 ]
 
-weights: output/picodet_s_shufflenetv2_320_coco/model_final
+weights: output/picodet_m_shufflenetv2_416_coco/model_final
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ShuffleNetV2_x1_5_pretrained.pdparams
 find_unused_parameters: True
 use_ema: true
-ema_decay: 0.9998
+cycle_epoch: 40
 snapshot_epoch: 10
 
 ShuffleNetV2:

configs/picodet/picodet_s_mbv3_320_coco.yml

-_BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
-  '_base_/picodet_mbv3_0_5x.yml',
-  '_base_/optimizer_280e.yml',
-  '_base_/picodet_320_reader.yml',
-]
-
-weights: output/picodet_s_mbv3_320_coco/model_final
-find_unused_parameters: True
-use_ema: true
-ema_decay: 0.9998
-snapshot_epoch: 10

configs/picodet/picodet_s_mbv3_416_coco.yml

-_BASE_: [
-  '../datasets/coco_detection.yml',
-  '../runtime.yml',
-  '_base_/picodet_mbv3_0_5x.yml',
-  '_base_/optimizer_280e.yml',
-  '_base_/picodet_416_reader.yml',
-]
-
-weights: output/picodet_s_mbv3_320_coco/model_final
-find_unused_parameters: True
-use_ema: true
-ema_decay: 0.9998
-snapshot_epoch: 10

configs/picodet/picodet_s_shufflenetv2_320_coco.yml

@@ -9,5 +9,5 @@ _BASE_: [
 weights: output/picodet_s_shufflenetv2_320_coco/model_final
 find_unused_parameters: True
 use_ema: true
-ema_decay: 0.9998
+cycle_epoch: 40
 snapshot_epoch: 10

configs/picodet/picodet_s_shufflenetv2_416_coco.yml

@@ -6,8 +6,8 @@ _BASE_: [
   '_base_/picodet_416_reader.yml',
 ]
 
-weights: output/picodet_s_shufflenetv2_320_coco/model_final
+weights: output/picodet_s_shufflenetv2_416_coco/model_final
 find_unused_parameters: True
 use_ema: true
-ema_decay: 0.9998
+cycle_epoch: 40
 snapshot_epoch: 10

configs/slim/quant/picodet_s_mbv3_320_quant_coco.yml → configs/slim/quant/picodet_s_shufflenetv2_quant.yml

-pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_s_mbv3_320_coco.pdparams
+pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_s_shufflenetv2_320_coco.pdparams
 slim: QAT
 
 QAT:
@@ -9,15 +9,18 @@ QAT:
     'quantizable_layer_type': ['Conv2D', 'Linear']}
   print_model: False
 
-epoch: 50
+epoch: 80
 LearningRate:
-  base_lr: 0.0001
+  base_lr: 0.001
   schedulers:
   - !PiecewiseDecay
     gamma: 0.1
     milestones:
-    - 35
-    - 45
+    - 60
+    - 70
   - !LinearWarmup
     start_factor: 0.
-    steps: 1000
+    steps: 100
+
+TrainReader:
+  batch_size: 96

ppdet/engine/trainer.py

@@ -90,8 +90,13 @@ class Trainer(object):
 
         self.use_ema = ('use_ema' in cfg and cfg['use_ema'])
         if self.use_ema:
+            ema_decay = self.cfg.get('ema_decay', 0.9998)
+            cycle_epoch = self.cfg.get('cycle_epoch', -1)
             self.ema = ModelEMA(
-                cfg['ema_decay'], self.model, use_thres_step=True)
+                self.model,
+                decay=ema_decay,
+                use_thres_step=True,
+                cycle_epoch=cycle_epoch)
 
         # EvalDataset build with BatchSampler to evaluate in single device
         # TODO: multi-device evaluate
@@ -547,8 +552,9 @@ class Trainer(object):
         if image_shape is None:
             image_shape = [3, -1, -1]
 
-        self.model.eval()
         if hasattr(self.model, 'deploy'): self.model.deploy = True
+        if hasattr(self.cfg, 'lite_deploy'):
+            self.model.lite_deploy = self.cfg.lite_deploy
 
         # Save infer cfg
         _dump_infer_config(self.cfg,

ppdet/modeling/architectures/picodet.py

@@ -41,7 +41,7 @@ class PicoDet(BaseArch):
         self.backbone = backbone
         self.neck = neck
         self.head = head
-        self.deploy = False
+        self.lite_deploy = False
 
     @classmethod
     def from_config(cls, cfg, *args, **kwargs):
@@ -63,7 +63,7 @@ class PicoDet(BaseArch):
         body_feats = self.backbone(self.inputs)
         fpn_feats = self.neck(body_feats)
         head_outs = self.head(fpn_feats)
-        if self.training or self.deploy:
+        if self.training or self.lite_deploy:
             return head_outs
         else:
             im_shape = self.inputs['im_shape']
@@ -83,7 +83,7 @@ class PicoDet(BaseArch):
         return loss
 
     def get_pred(self):
-        if self.deploy:
+        if self.lite_deploy:
             return {'picodet': self._forward()[0]}
         else:
             bbox_pred, bbox_num = self._forward()

ppdet/modeling/heads/gfl_head.py

@@ -245,6 +245,9 @@ class GFLHead(nn.Layer):
             if self.dgqp_module:
                 quality_score = self.dgqp_module(bbox_reg)
                 cls_logits = F.sigmoid(cls_logits) * quality_score
+            if not self.training:
+                cls_logits = F.sigmoid(cls_logits.transpose([0, 2, 3, 1]))
+                bbox_reg = bbox_reg.transpose([0, 2, 3, 1])
             cls_logits_list.append(cls_logits)
             bboxes_reg_list.append(bbox_reg)
 
@@ -288,6 +291,11 @@ class GFLHead(nn.Layer):
         bbox_targets_list = self._images_to_levels(gt_meta['bbox_targets'],
                                                    num_level_anchors)
         num_total_pos = sum(gt_meta['pos_num'])
+        try:
+            num_total_pos = paddle.distributed.all_reduce(num_total_pos.clone(
+            )) / paddle.distributed.get_world_size()
+        except:
+            num_total_pos = max(num_total_pos, 1)
 
         loss_bbox_list, loss_dfl_list, loss_qfl_list, avg_factor = [], [], [], []
         for cls_score, bbox_pred, grid_cells, labels, label_weights, bbox_targets, stride in zip(
@@ -316,7 +324,7 @@ class GFLHead(nn.Layer):
 
                 weight_targets = F.sigmoid(cls_score.detach())
                 weight_targets = paddle.gather(
-                    weight_targets.max(axis=1), pos_inds, axis=0)
+                    weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0)
                 pos_bbox_pred_corners = self.distribution_project(pos_bbox_pred)
                 pos_decode_bbox_pred = distance2bbox(pos_grid_cell_centers,
                                                      pos_bbox_pred_corners)
@@ -333,20 +341,18 @@ class GFLHead(nn.Layer):
                 # regression loss
                 loss_bbox = paddle.sum(
                     self.loss_bbox(pos_decode_bbox_pred,
-                                   pos_decode_bbox_targets) *
-                    weight_targets.mean(axis=-1))
+                                   pos_decode_bbox_targets) * weight_targets)
 
                 # dfl loss
                 loss_dfl = self.loss_dfl(
                     pred_corners,
                     target_corners,
-                    weight=weight_targets.unsqueeze(-1).expand([-1, 4]).reshape(
-                        [-1]),
+                    weight=weight_targets.expand([-1, 4]).reshape([-1]),
                     avg_factor=4.0)
             else:
                 loss_bbox = bbox_pred.sum() * 0
                 loss_dfl = bbox_pred.sum() * 0
-                weight_targets = paddle.to_tensor([0])
+                weight_targets = paddle.to_tensor([0], dtype='float32')
 
             # qfl loss
             score = paddle.to_tensor(score)
@@ -360,6 +366,12 @@ class GFLHead(nn.Layer):
             avg_factor.append(weight_targets.sum())
 
         avg_factor = sum(avg_factor)
+        try:
+            avg_factor = paddle.distributed.all_reduce(avg_factor.clone())
+            avg_factor = paddle.clip(
+                avg_factor / paddle.distributed.get_world_size(), min=1)
+        except:
+            avg_factor = max(avg_factor.item(), 1)
         if avg_factor <= 0:
             loss_qfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
             loss_bbox = paddle.to_tensor(
@@ -407,14 +419,11 @@ class GFLHead(nn.Layer):
         mlvl_scores = []
         for stride, cls_score, bbox_pred in zip(self.fpn_stride, cls_scores,
                                                 bbox_preds):
-            featmap_size = cls_score.shape[-2:]
+            featmap_size = cls_score.shape[:2]
             y, x = self.get_single_level_center_point(
                 featmap_size, stride, cell_offset=cell_offset)
             center_points = paddle.stack([x, y], axis=-1)
-            scores = F.sigmoid(
-                cls_score.transpose([1, 2, 0]).reshape(
-                    [-1, self.cls_out_channels]))
-            bbox_pred = bbox_pred.transpose([1, 2, 0])
+            scores = cls_score.reshape([-1, self.cls_out_channels])
             bbox_pred = self.distribution_project(bbox_pred) * stride
 
             if scores.shape[0] > self.nms_pre:
@@ -434,10 +443,6 @@ class GFLHead(nn.Layer):
             im_scale = paddle.concat([scale_factor[::-1], scale_factor[::-1]])
             mlvl_bboxes /= im_scale
         mlvl_scores = paddle.concat(mlvl_scores)
-        if self.use_sigmoid:
-            # add a dummy background class to the backend when use_sigmoid
-            padding = paddle.zeros([mlvl_scores.shape[0], 1])
-            mlvl_scores = paddle.concat([mlvl_scores, padding], axis=1)
         mlvl_scores = mlvl_scores.transpose([1, 0])
         return mlvl_bboxes, mlvl_scores
 

ppdet/modeling/heads/pico_head.py

@@ -66,7 +66,7 @@ class PicoFeat(nn.Layer):
                     ConvNormLayer(
                         ch_in=in_c,
                         ch_out=feat_out,
-                        filter_size=3,
+                        filter_size=5,
                         stride=1,
                         groups=feat_out,
                         norm_type=norm_type,
@@ -91,7 +91,7 @@ class PicoFeat(nn.Layer):
                         ConvNormLayer(
                             ch_in=in_c,
                             ch_out=feat_out,
-                            filter_size=3,
+                            filter_size=5,
                             stride=1,
                             groups=feat_out,
                             norm_type=norm_type,
@@ -249,80 +249,9 @@ class PicoHead(GFLHead):
 
             if not self.training:
                 cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1]))
-                bbox_pred = self.distribution_project(
-                    bbox_pred.transpose([0, 2, 3, 1])) * self.fpn_stride[i]
+                bbox_pred = bbox_pred.transpose([0, 2, 3, 1])
 
             cls_logits_list.append(cls_score)
             bboxes_reg_list.append(bbox_pred)
 
         return (cls_logits_list, bboxes_reg_list)
-
-    def get_bboxes_single(self,
-                          cls_scores,
-                          bbox_preds,
-                          img_shape,
-                          scale_factor,
-                          rescale=True,
-                          cell_offset=0):
-        assert len(cls_scores) == len(bbox_preds)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        for stride, cls_score, bbox_pred in zip(self.fpn_stride, cls_scores,
-                                                bbox_preds):
-            featmap_size = cls_score.shape[0:2]
-            y, x = self.get_single_level_center_point(
-                featmap_size, stride, cell_offset=cell_offset)
-            center_points = paddle.stack([x, y], axis=-1)
-            scores = cls_score.reshape([-1, self.cls_out_channels])
-
-            if scores.shape[0] > self.nms_pre:
-                max_scores = scores.max(axis=1)
-                _, topk_inds = max_scores.topk(self.nms_pre)
-                center_points = center_points.gather(topk_inds)
-                bbox_pred = bbox_pred.gather(topk_inds)
-                scores = scores.gather(topk_inds)
-
-            bboxes = distance2bbox(
-                center_points, bbox_pred, max_shape=img_shape)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-        mlvl_bboxes = paddle.concat(mlvl_bboxes)
-        if rescale:
-            # [h_scale, w_scale] to [w_scale, h_scale, w_scale, h_scale]
-            im_scale = paddle.concat([scale_factor[::-1], scale_factor[::-1]])
-            mlvl_bboxes /= im_scale
-        mlvl_scores = paddle.concat(mlvl_scores)
-        mlvl_scores = mlvl_scores.transpose([1, 0])
-        return mlvl_bboxes, mlvl_scores
-
-    def decode(self, cls_scores, bbox_preds, im_shape, scale_factor,
-               cell_offset):
-        batch_bboxes = []
-        batch_scores = []
-        batch_size = cls_scores[0].shape[0]
-        for img_id in range(batch_size):
-            num_levels = len(cls_scores)
-            cls_score_list = [cls_scores[i][img_id] for i in range(num_levels)]
-            bbox_pred_list = [
-                bbox_preds[i].reshape([batch_size, -1, 4])[img_id]
-                for i in range(num_levels)
-            ]
-            bboxes, scores = self.get_bboxes_single(
-                cls_score_list,
-                bbox_pred_list,
-                im_shape[img_id],
-                scale_factor[img_id],
-                cell_offset=cell_offset)
-            batch_bboxes.append(bboxes)
-            batch_scores.append(scores)
-        batch_bboxes = paddle.stack(batch_bboxes, axis=0)
-        batch_scores = paddle.stack(batch_scores, axis=0)
-
-        return batch_bboxes, batch_scores
-
-    def post_process(self, gfl_head_outs, im_shape, scale_factor):
-        cls_scores, bboxes_reg = gfl_head_outs
-        bboxes, score = self.decode(cls_scores, bboxes_reg, im_shape,
-                                    scale_factor, self.cell_offset)
-        bbox_pred, bbox_num, _ = self.nms(bboxes, score)
-        return bbox_pred, bbox_num

ppdet/modeling/necks/pan.py

@@ -39,21 +39,13 @@ class PAN(nn.Layer):
         spatial_scales (list[float]): the spatial scales between input feature
             maps and original input image which can be derived from the output
             shape of backbone by from_config
-        has_extra_convs (bool): whether to add extra conv to the last level.
-            default False
-        extra_stage (int): the number of extra stages added to the last level.
-            default 1
-        use_c5 (bool): Whether to use c5 as the input of extra stage,
-            otherwise p5 is used. default True
+        start_level (int): Index of the start input backbone level used to
+            build the feature pyramid. Default: 0.
+        end_level (int): Index of the end input backbone level (exclusive) to
+            build the feature pyramid. Default: -1, which means the last level.
         norm_type (string|None): The normalization type in FPN module. If
             norm_type is None, norm will not be used after conv and if
             norm_type is string, bn, gn, sync_bn are available. default None
-        norm_decay (float): weight decay for normalization layer weights.
-            default 0.
-        freeze_norm (bool): whether to freeze normalization layer.
-            default False
-        relu_before_extra_convs (bool): whether to add relu before extra convs.
-            default False
     """
 
     def __init__(self,

ppdet/optimizer.py

@@ -251,13 +251,40 @@ class OptimizerBuilder():
 
 
 class ModelEMA(object):
-    def __init__(self, decay, model, use_thres_step=False):
+    """
+    Exponential Weighted Average for Deep Neutal Networks
+    Args:
+        model (nn.Layer): Detector of model.
+        decay (int):  The decay used for updating ema parameter.
+            Ema's parameter are updated with the formula:
+           `ema_param = decay * ema_param + (1 - decay) * cur_param`.
+            Defaults is 0.9998.
+        use_thres_step (bool): Whether set decay by thres_step or not 
+        cycle_epoch (int): The epoch of interval to reset ema_param and 
+            step. Defaults is -1, which means not reset. Its function is to
+            add a regular effect to ema, which is set according to experience 
+            and is effective when the total training epoch is large.
+    """
+
+    def __init__(self,
+                 model,
+                 decay=0.9998,
+                 use_thres_step=False,
+                 cycle_epoch=-1):
         self.step = 0
+        self.epoch = 0
         self.decay = decay
         self.state_dict = dict()
         for k, v in model.state_dict().items():
             self.state_dict[k] = paddle.zeros_like(v)
         self.use_thres_step = use_thres_step
+        self.cycle_epoch = cycle_epoch
+
+    def reset(self):
+        self.step = 0
+        self.epoch = 0
+        for k, v in self.state_dict.items():
+            self.state_dict[k] = paddle.zeros_like(v)
 
     def update(self, model):
         if self.use_thres_step:
@@ -280,4 +307,8 @@ class ModelEMA(object):
             v = v / (1 - self._decay**self.step)
             v.stop_gradient = True
             state_dict[k] = v
+        self.epoch += 1
+        if self.cycle_epoch > 0 and self.epoch == self.cycle_epoch:
+            self.reset()
+
         return state_dict