[semi_det] fix save_model and rename folder (#7433)

* fix save_model epoch_id and rename folder

* fix use_simple_ema and docs

* fix docs, test=document_fix

configs/ssod/README.md → configs/semi_det/README.md

 简体中文 | [English](README_en.md)
 
-# Semi-Supervised Object Detection (SSOD) 半监督目标检测
+# Semi-Supervised Detection (Semi DET) 半监督检测
 
 ## 内容
 - [简介](#简介)
@@ -34,12 +34,12 @@
 
 ### [DenseTeacher](denseteacher)
 
-|      模型       |          基础检测器      |  监督数据比例   | Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 |  Semi Epochs (Iters)  |  模型下载  |   配置文件   |
-| :------------: | :---------------------: | :-----------: | :-------------------------: |:---------------------------: |:--------------------: | :-------: |:---------: |
-| DenseTeacher   |   [FCOS ResNet50-FPN](./baseline/fcos_r50_fpn_2x_coco_sup005.yml)  | 5% | 21.3 | 30.6  | 240 (87120) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi005.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi005.yml) |
-| DenseTeacher   |   [FCOS ResNet50-FPN](./baseline/fcos_r50_fpn_2x_coco_sup010.yml)  | 10%| 26.3 | 35.1  | 240 (174240)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml) |
-| DenseTeacher(LSJ)|   [FCOS ResNet50-FPN](./baseline/fcos_r50_fpn_2x_coco_sup010.yml) |10%| 26.3 | 37.1  | 240 (174240)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml) |
-| DenseTeacher   |   [FCOS ResNet50-FPN](./../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.ymll)  |full| 42.6 |   -   |  36 (263844)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_full.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_full.yml) |
+|      模型       |  监督数据比例 |        Sup Baseline     |    Sup Epochs (Iters)   |  Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 |  Semi Epochs (Iters)  |  模型下载  |   配置文件   |
+| :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: |
+| DenseTeacher-FCOS     |   5% |   [sup_config](./baseline/fcos_r50_fpn_2x_coco_sup005.yml)    |  24 (8712)  | 21.3 |  **30.6**  | 240 (87120)   | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi005.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi005.yml) |
+| DenseTeacher-FCOS     |   5% |   [sup_config](./baseline/fcos_r50_fpn_2x_coco_sup010.yml)    | 24 (17424)  | 26.3 |  **35.1**  | 240 (174240)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml) |
+| DenseTeacher-FCOS(LSJ)|   5% |   [sup_config](./baseline/fcos_r50_fpn_2x_coco_sup010.yml)    | 24 (17424)  | 26.3 |  **37.1(LSJ)**  | 240 (174240)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml) |
+| DenseTeacher-FCOS |100%(full)|   [sup_config](./../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.ymll) | 24 (175896) | 42.6 | **44.2** | 24 (175896)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_full.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_full.yml) |
 
 
 ## 半监督数据集准备
@@ -215,7 +215,7 @@ use_warmup: &use_warmup True
 
 ### 全局配置
 
-需要在配置文件中添加如下全局配置：
+需要在配置文件中添加如下全局配置，并且注意 DenseTeacher 模型需要使用`use_simple_ema: True`而不是`use_ema: True`：
 
 ```python
 ### global config
@@ -249,9 +249,9 @@ _BASE_: [
 ### 数据增强配置
 
 构建半监督训练集的Reader，需要在原先`TrainReader`的基础上，新增加`weak_aug`,`strong_aug`,`sup_batch_transforms`和`unsup_batch_transforms`，并且需要注意：
-- **如果有`NormalizeImage`，需要单独从`sample_transforms`中抽出来放在`weak_aug`和`strong_aug`中；
+- 如果有`NormalizeImage`，需要单独从`sample_transforms`中抽出来放在`weak_aug`和`strong_aug`中；
 - `sample_transforms`为**公用的基础数据增强**；
-- 完整的弱数据增强为``sample_transforms + weak_aug`，完整的强数据增强为`sample_transforms + strong_aug`；
+- 完整的弱数据增强为`sample_transforms + weak_aug`，完整的强数据增强为`sample_transforms + strong_aug`；
 
 如以下所示:
 
@@ -302,8 +302,6 @@ SemiTrainReader:
         object_sizes_boundary: [64, 128, 256, 512]
         center_sampling_radius: 1.5
         downsample_ratios: [8, 16, 32, 64, 128]
-        num_shift: 0.5
-        multiply_strides_reg_targets: False
         norm_reg_targets: True
   unsup_batch_transforms:
     - Permute: {}
@@ -351,22 +349,22 @@ OptimizerBuilder:
 
 ```bash
 # 单卡训练 (不推荐，需按线性比例相应地调整学习率)
-CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
+CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
 
 # 多卡训练
-python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
+python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
 ```
 
 ### 评估
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams
 ```
 
 ### 预测
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg
 ```
 
 ### 部署
@@ -375,7 +373,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ssod/denseteacher/denseteacher_f
 
 ```bash
 # 导出模型
-CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams
 
 # 导出权重预测
 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU

configs/ssod/baseline/README.md → configs/semi_det/baseline/README.md

@@ -62,7 +62,7 @@
 将以下命令写在一个脚本文件里如```run.sh```，一键运行命令为：```sh run.sh```，也可命令行一句句去运行：
 
 ```bash
-model_type=ssod/baseline
+model_type=semi_det/baseline
 job_name=ppyoloe_plus_crn_s_80e_coco_sup010 # 可修改，如 fcos_r50_fpn_2x_coco_sup010
 
 config=configs/${model_type}/${job_name}.yml

configs/ssod/denseteacher/README.md → configs/semi_det/denseteacher/README.md

 简体中文 | [English](README_en.md)
 
-# DenseTeacher (Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection)
+# Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
 
 ## 模型库
 
-|      模型       |          基础检测器      |  监督数据比例   | Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 |  Semi Epochs (Iters)  |  模型下载  |   配置文件   |
-| :------------: | :---------------------: | :-----------: | :-------------------------: |:---------------------------: |:--------------------: | :-------: |:---------: |
-| DenseTeacher   |   [FCOS R50-FPN](../baseline/fcos_r50_fpn_2x_coco_sup005.yml)  | 5% | 21.3 | 30.6  | 240 (87120) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi005.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi005.yml) |
-| DenseTeacher   |   [FCOS R50-FPN](../baseline/fcos_r50_fpn_2x_coco_sup010.yml)  | 10%| 26.3 | 35.1  | 240 (174240)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010.yml) |
-| DenseTeacher(LSJ)|   [FCOS R50-FPN](../baseline/fcos_r50_fpn_2x_coco_sup010.yml)| 10%| 26.3 | 37.1  | 240 (174240)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml) |
-| DenseTeacher   |   [FCOS R50-FPN](../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.ymll)  |full| 42.6 |   -   |  36 (263844)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_full.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_full.yml) |
+|      模型       |  监督数据比例 |        Sup Baseline     |    Sup Epochs (Iters)   |  Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 |  Semi Epochs (Iters)  |  模型下载  |   配置文件   |
+| :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: |
+| DenseTeacher-FCOS     |   5% |   [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup005.yml)    |  24 (8712)  | 21.3 |  **30.6**  | 240 (87120)   | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi005.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi005.yml) |
+| DenseTeacher-FCOS     |   5% |   [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup010.yml)    | 24 (17424)  | 26.3 |  **35.1**  | 240 (174240)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010.yml) |
+| DenseTeacher-FCOS(LSJ)|   5% |   [sup_config](../baseline/fcos_r50_fpn_2x_coco_sup010.yml)    | 24 (17424)  | 26.3 |  **37.1(LSJ)**  | 240 (174240)  | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml) |
+| DenseTeacher-FCOS |100%(full)|   [sup_config](../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.ymll) | 24 (175896) | 42.6 | **44.2** | 24 (175896)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_full.pdparams) | [config](./denseteacher_fcos_r50_fpn_coco_full.yml) |
 
 
 **注意:**
  - 以上模型训练默认使用8 GPUs，监督数据总batch_size默认为16，无监督数据总batch_size默认也为16，默认初始学习率为0.01。如果改动了总batch_size，请按线性比例相应地调整学习率；
+ - **监督数据比例**是指使用的有标签COCO数据集占 COCO train2017 全量训练集的百分比，使用的无标签COCO数据集一般也是相同比例，但具体图片和有标签数据的图片不重合；
  - `Semi Epochs (Iters)`表示**半监督训练**的模型的 Epochs (Iters)，如果使用**自定义数据集**，需自行根据Iters换算到对应的Epochs调整，最好保证总Iters 和COCO数据集的设置较为接近；
  - `Sup mAP`是**只使用有监督数据训练**的模型的精度，请参照**基础检测器的配置文件** 和 [baseline](../baseline)；
  - `Semi mAP`是**半监督训练**的模型的精度，模型下载和配置文件的链接均为**半监督模型**；
- - `LSJ`表示 large-scale jittering，表示更大范围的多尺度训练，可进一步提升精度，但训练速度也会变慢；
+ - `LSJ`表示 **large-scale jittering**，表示使用更大范围的多尺度训练，可进一步提升精度，但训练速度也会变慢；
  - 半监督检测的配置和使用，请参照[文档](../README.md/#半监督检测配置)；
+ - `Dense Teacher`原文使用`R50-va-caffe`预训练，PaddleDetection中默认使用`R50-vb`预训练，如果使用`R50-vd`结合[SSLD](../../../docs/feature_models/SSLD_PRETRAINED_MODEL.md)的预训练模型，可进一步显著提升检测精度，如：
+ ```
+  pretrain_weights:  https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
+  ResNet:
+    depth: 50
+    variant: d
+    norm_type: bn
+    freeze_at: 0
+    return_idx: [0,1,2,3]
+    num_stages: 4
+    lr_mult_list: [0.05, 0.05, 0.1, 0.15]
+ ```
 
 
 ## 使用说明
@@ -29,22 +42,22 @@
 
 ```bash
 # 单卡训练 (不推荐，需按线性比例相应地调整学习率)
-CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
+CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
 
 # 多卡训练
-python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
+python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
 ```
 
 ### 评估
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams
 ```
 
 ### 预测
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg
 ```
 
 ### 部署
@@ -53,7 +66,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ssod/denseteacher/denseteacher_f
 
 ```bash
 # 导出模型
-CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ssod/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams
 
 # 导出权重预测
 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU

configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_full.yml

+_BASE_: [
+  'denseteacher_fcos_r50_fpn_coco_semi010.yml',
+  '../_base_/coco_detection_full.yml',
+]
+log_iter: 100
+snapshot_epoch: 2
+epochs: &epochs 24
+weights: output/denseteacher_fcos_r50_fpn_coco_full/model_final
+
+
+### pretrain and warmup config, choose one and coment another
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
+semi_start_iters: 5000
+ema_start_iters: 3000
+use_warmup: &use_warmup True
+
+
+### global config
+use_simple_ema: True
+ema_decay: 0.9996
+ssod_method: DenseTeacher
+DenseTeacher:
+  train_cfg:
+    sup_weight: 1.0
+    unsup_weight: 1.0
+    loss_weight: {distill_loss_cls: 2.0, distill_loss_box: 1.0, distill_loss_quality: 1.0}
+    concat_sup_data: True
+    suppress: linear
+    ratio: 0.01
+    gamma: 2.0
+  test_cfg:
+    inference_on: teacher
+
+
+### reader config
+worker_num: 2
+SemiTrainReader:
+  sample_transforms:
+    - Decode: {}
+    - RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1}
+    - RandomFlip: {}
+  weak_aug:
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  strong_aug:
+    - StrongAugImage: {transforms: [
+        RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
+        RandomErasingCrop: {},
+        RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
+        RandomGrayscale: {prob: 0.2},
+      ]}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
+  sup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+    - Gt2FCOSTarget:
+        object_sizes_boundary: [64, 128, 256, 512]
+        center_sampling_radius: 1.5
+        downsample_ratios: [8, 16, 32, 64, 128]
+        norm_reg_targets: True
+  unsup_batch_transforms:
+    - Permute: {}
+    - PadBatch: {pad_to_stride: 32}
+  sup_batch_size: 2
+  unsup_batch_size: 2
+  shuffle: True
+  drop_last: True
+
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+
+TestReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_transforms:
+    - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  fuse_normalize: True
+
+
+### model config
+architecture: FCOS
+FCOS:
+  backbone: ResNet
+  neck: FPN
+  fcos_head: FCOSHead
+
+ResNet:
+  depth: 50
+  variant: 'b'
+  norm_type: bn
+  freeze_at: 0 # res2
+  return_idx: [1, 2, 3]
+  num_stages: 4
+
+FPN:
+  out_channel: 256
+  spatial_scales: [0.125, 0.0625, 0.03125]
+  extra_stage: 2
+  has_extra_convs: True
+  use_c5: False
+
+FCOSHead:
+  fcos_feat:
+    name: FCOSFeat
+    feat_in: 256
+    feat_out: 256
+    num_convs: 4
+    norm_type: "gn"
+    use_dcn: False
+  fpn_stride: [8, 16, 32, 64, 128]
+  prior_prob: 0.01
+  norm_reg_targets: True
+  centerness_on_reg: True
+  fcos_loss:
+    name: FCOSLoss
+    loss_alpha: 0.25
+    loss_gamma: 2.0
+    iou_loss_type: "giou"
+    reg_weights: 1.0
+    quality: "iou"
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.05
+    nms_threshold: 0.6
+
+
+### other config
+epoch: *epochs
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [*epochs]
+    use_warmup: *use_warmup
+  - !LinearWarmup
+    start_factor: 0.001
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0001
+    type: L2
+  clip_grad_by_value: 1.0

ppdet/engine/callbacks.py

@@ -159,7 +159,6 @@ class LogPrinter(Callback):
 class Checkpointer(Callback):
     def __init__(self, model):
         super(Checkpointer, self).__init__(model)
-        cfg = self.model.cfg
         self.best_ap = -1000.
         self.save_dir = os.path.join(self.model.cfg.save_dir,
                                      self.model.cfg.filename)
@@ -210,6 +209,9 @@ class Checkpointer(Callback):
                             key, eval_func, abs(self.best_ap)))
             if weight:
                 if self.model.use_ema:
+                    exchange_save_model = status.get('exchange_save_model',
+                                                     False)
+                    if not exchange_save_model:
                         # save model and ema_model
                         save_model(
                             status['weight'],
@@ -218,6 +220,21 @@ class Checkpointer(Callback):
                             save_name,
                             epoch_id + 1,
                             ema_model=weight)
+                    else:
+                        # save model(student model) and ema_model(teacher model)
+                        # in DenseTeacher SSOD, the teacher model will be higher,
+                        # so exchange when saving pdparams
+                        student_model = status['weight']  # model
+                        teacher_model = weight  # ema_model
+                        save_model(
+                            teacher_model,
+                            self.model.optimizer,
+                            self.save_dir,
+                            save_name,
+                            epoch_id + 1,
+                            ema_model=student_model)
+                        del teacher_model
+                        del student_model
                 else:
                     save_model(weight, self.model.optimizer, self.save_dir,
                                save_name, epoch_id + 1)

ppdet/engine/trainer_ssod.py

@@ -191,8 +191,11 @@ class Trainer_DenseTeacher(Trainer):
         self.status.update({
             'epoch_id': self.start_epoch,
             'step_id': 0,
-            'steps_per_epoch': len(self.loader)
+            'steps_per_epoch': len(self.loader),
+            'exchange_save_model': True,
         })
+        # Note: exchange_save_model
+        # in DenseTeacher SSOD, the teacher model will be higher, so exchange when saving pdparams
 
         self.status['batch_time'] = stats.SmoothedValue(
             self.cfg.log_iter, fmt='{avg:.4f}')
@@ -254,8 +257,12 @@ class Trainer_DenseTeacher(Trainer):
                     data_sup_w, data_sup_s = align_weak_strong_shape(data_sup_w,
                                                                      data_sup_s)
 
+                data_sup_w['epoch_id'] = epoch_id
+                data_sup_s['epoch_id'] = epoch_id
                 if concat_sup_data:
                     for k, v in data_sup_s.items():
+                        if k in ['epoch_id']:
+                            continue
                         data_sup_s[k] = paddle.concat([v, data_sup_w[k]])
                     loss_dict_sup = self.model(data_sup_s)
                 else:
@@ -264,8 +271,6 @@ class Trainer_DenseTeacher(Trainer):
                     for k, v in loss_dict_sup_w.items():
                         loss_dict_sup[k] = (loss_dict_sup[k] + v) * 0.5
 
-                data_sup_w['epoch_id'] = epoch_id
-                data_sup_s['epoch_id'] = epoch_id
                 losses_sup = loss_dict_sup['loss'] * train_cfg['sup_weight']
                 losses_sup.backward()