[Dygraph]yolo series (#2148)

* add ppyolo r18vd mbv3, yolov3 r50vd

* modify TestReader of ppyolo mbv3 r18vd

* add clip to avoid nan, modify ema to apply ema on bn mean and bn var

* fix code resulting in nan

* add yolov3 r50vd dcn configs

* finish yolo_series and fix some problems

* hide --bias flag and modify docs

* modify --bias and fix deploy/python/infer

dygraph/configs/ppyolo/README.md

@@ -37,9 +37,18 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 ### PP-YOLO
 
 |          Model           | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
-|:------------------------:|:----------:|:----------:|:----------:| :----------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :-----: |
+|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
 | PP-YOLO                  |     8      |     24     | ResNet50vd |     608     |         44.8         |         45.2          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     512     |         43.9         |         44.4          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     416     |         42.1         |         42.5          |      109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     320     |         38.9         |         39.3          |      132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
 | PP-YOLO_2x               |     8      |     24     | ResNet50vd |     608     |         45.3         |         45.9          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     512     |         44.4         |         45.0          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     416     |         42.7         |         43.2          |      109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     320     |         39.5         |         40.1          |      132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_ResNet18vd               |     4      |     32     | ResNet18vd |     512     |         29.3         |         29.5          |      357.1      |          657.9          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml)                   |
+| PP-YOLO_ResNet18vd               |     4      |     32     | ResNet18vd |     416     |         28.6         |         28.9          |      409.8      |          719.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml)                   |
+| PP-YOLO_ResNet18vd               |     4      |     32     | ResNet18vd |     320     |         26.2         |         26.4          |      480.7      |          763.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml)                   |
 
 **Notes:**
 
@@ -49,6 +58,29 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 - PP-YOLO FP32 inference speed testing uses inference model exported by `tools/export_model.py` and benchmarked by running `depoly/python/infer.py` with `--run_benchmark`. All testing results do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method.
 - TensorRT FP16 inference speed testing exclude the time cost of bounding-box decoding(`yolo_box`) part comparing with FP32 testing above, which means that data reading, bounding-box decoding and post-processing(NMS) is excluded(test method same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) too)
 
+### PP-YOLO for mobile
+
+|            Model             | GPU number | images/GPU | Model Size | input shape | Box AP<sup>val</sup> |  Box AP50<sup>val</sup> | Kirin 990 1xCore(FPS) | download | config  |
+|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: |  :--------------------: | :--------------------: | :------: | :------: |
+| PP-YOLO_MobileNetV3_large    |    4    |      32       |    28MB    |   320    |         23.2         |           42.6          |           14.1         | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_mbv3_large_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml)                   |
+| PP-YOLO_MobileNetV3_small    |    4    |      32       |    16MB    |   320    |         17.2         |           33.8          |           21.5         | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_mbv3_small_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml)                   |
+
+**Notes:**
+
+- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`, Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
+- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../../docs/FAQ.md).
+- PP-YOLO_MobileNetV3 inference speed is tested on Kirin 990 with 1 thread.
+
+### PP-YOLO on Pascal VOC
+
+PP-YOLO trained on Pascal VOC dataset as follows:
+
+|       Model        | GPU number | images/GPU |  backbone  | input shape | Box AP50<sup>val</sup> | download | config  |
+|:------------------:|:----------:|:----------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
+| PP-YOLO            |    8    |       12      | ResNet50vd |     608     |          84.9          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml)                   |
+| PP-YOLO            |    8    |       12      | ResNet50vd |     416     |          84.3          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml)                   |
+| PP-YOLO            |    8    |       12      | ResNet50vd |     320     |          82.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml)                   |
+
 ## Getting Start
 
 ### 1. Training

dygraph/configs/ppyolo/README_cn.md

@@ -39,7 +39,16 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 |          模型            | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP<sup>val</sup> | Box AP<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
 |:------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------------: | :------------: | :---------------------: | :------: | :------: |
 | PP-YOLO                  |     8      |     24     | ResNet50vd |     608     |         44.8         |         45.2          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     512     |         43.9         |         44.4          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     416     |         42.1         |         42.5          |      109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     320     |         38.9         |         39.3          |      132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml)                   |
 | PP-YOLO_2x               |     8      |     24     | ResNet50vd |     608     |         45.3         |         45.9          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     512     |         44.4         |         45.0          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     416     |         42.7         |         43.2          |      109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     320     |         39.5         |         40.1          |      132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml)                   |
+| PP-YOLO_ResNet18vd               |     4      |     32     | ResNet18vd |     512     |         29.3         |         29.5          |      357.1      |          657.9          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml)                   |
+| PP-YOLO_ResNet18vd               |     4      |     32     | ResNet18vd |     416     |         28.6         |         28.9          |      409.8      |          719.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml)                   |
+| PP-YOLO_ResNet18vd               |     4      |     32     | ResNet18vd |     320     |         26.2         |         26.4          |      480.7      |          763.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r18vd_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml)                   |
 
 **注意:**
 
@@ -50,6 +59,27 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 - TensorRT FP16的速度测试相比于FP32去除了`yolo_box`(bbox解码)部分耗时，即不包含数据预处理，bbox解码和NMS(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
 - PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
 
+### PP-YOLO 轻量级模型
+
+|          模型                | GPU个数 | 每GPU图片个数 |  模型体积  | 输入尺寸 | Box AP<sup>val</sup> |  Box AP50<sup>val</sup> | Kirin 990 1xCore (FPS) | 模型下载 |  配置文件 |
+|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: |  :--------------------: | :--------------------: | :------: | :------: |
+| PP-YOLO_MobileNetV3_large    |    4    |      32       |    28MB    |   320    |         23.2         |           42.6          |           14.1         | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_mbv3_large_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml)                   |
+| PP-YOLO_MobileNetV3_small    |    4    |      32       |    16MB    |   320    |         17.2         |           33.8          |           21.5         | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_mbv3_small_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml)                   |
+
+- PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP<sup>val</sup>为`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
+- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../../docs/FAQ.md)调整学习率和迭代次数。
+- PP-YOLO_MobileNetV3 模型推理速度测试环境配置为麒麟990芯片单线程。
+
+### Pascal VOC数据集上的PP-YOLO
+
+PP-YOLO在Pascal VOC数据集上训练模型如下:
+
+|       模型         | GPU个数 | 每GPU图片个数 |  骨干网络  |   输入尺寸  | Box AP50<sup>val</sup> | 模型下载 | 配置文件 |
+|:------------------:|:-------:|:-------------:|:----------:| :----------:| :--------------------: | :------: | :-----: |
+| PP-YOLO            |    8    |       12      | ResNet50vd |     608     |          84.9          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml)                   |
+| PP-YOLO            |    8    |       12      | ResNet50vd |     416     |          84.3          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml)                   |
+| PP-YOLO            |    8    |       12      | ResNet50vd |     320     |          82.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/ppyolo_r50vd_dcn_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml)                   |
+
 ## 使用说明
 
 ### 1. 训练

dygraph/configs/ppyolo/_base_/optimizer_1x.yml

@@ -13,7 +13,6 @@ LearningRate:
     steps: 4000
 
 OptimizerBuilder:
-  clip_grad_by_norm: 35.
   optimizer:
     momentum: 0.9
     type: Momentum

dygraph/configs/ppyolo/_base_/optimizer_2x.yml

@@ -13,7 +13,6 @@ LearningRate:
     steps: 4000
 
 OptimizerBuilder:
-  clip_grad_by_norm: 35.
   optimizer:
     momentum: 0.9
     type: Momentum

dygraph/configs/ppyolo/_base_/ppyolo_mbv3_large.yml

+architecture: YOLOv3
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar
+load_static_weights: true
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+
+YOLOv3:
+  backbone: MobileNetV3
+  neck: PPYOLOFPN
+  yolo_head: YOLOv3Head
+  post_process: BBoxPostProcess
+
+MobileNetV3:
+  model_name: large
+  scale: 1.
+  with_extra_blocks: false
+  extra_block_filters: []
+  feature_maps: [13, 16]
+
+PPYOLOFPN:
+  feat_channels: [160, 368]
+  coord_conv: true
+  conv_block_num: 0
+  spp: true
+  drop_block: true
+
+YOLOv3Head:
+  anchors: [[11, 18], [34, 47], [51, 126],
+            [115, 71], [120, 195], [254, 235]]
+  anchor_masks: [[3, 4, 5], [0, 1, 2]]
+  loss: YOLOv3Loss
+
+YOLOv3Loss:
+  ignore_thresh: 0.5
+  downsample: [32, 16]
+  label_smooth: false
+  scale_x_y: 1.05
+  iou_loss: IouLoss
+
+IouLoss:
+  loss_weight: 2.5
+  loss_square: true
+
+BBoxPostProcess:
+  decode:
+    name: YOLOBox
+    conf_thresh: 0.005
+    downsample_ratio: 32
+    clip_bbox: true
+    scale_x_y: 1.05
+  nms:
+    name: MultiClassNMS
+    keep_top_k: 100
+    nms_threshold: 0.45
+    nms_top_k: 1000
+    score_threshold: 0.005
+    normalized: false

dygraph/configs/ppyolo/_base_/ppyolo_mbv3_small.yml

+architecture: YOLOv3
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar
+load_static_weights: true
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+
+YOLOv3:
+  backbone: MobileNetV3
+  neck: PPYOLOFPN
+  yolo_head: YOLOv3Head
+  post_process: BBoxPostProcess
+
+MobileNetV3:
+  model_name: small
+  scale: 1.
+  with_extra_blocks: false
+  extra_block_filters: []
+  feature_maps: [9, 12]
+
+PPYOLOFPN:
+  feat_channels: [96, 304]
+  coord_conv: true
+  conv_block_num: 0
+  spp: true
+  drop_block: true
+
+YOLOv3Head:
+  anchors: [[11, 18], [34, 47], [51, 126],
+            [115, 71], [120, 195], [254, 235]]
+  anchor_masks: [[3, 4, 5], [0, 1, 2]]
+  loss: YOLOv3Loss
+
+YOLOv3Loss:
+  ignore_thresh: 0.5
+  downsample: [32, 16]
+  label_smooth: false
+  scale_x_y: 1.05
+  iou_loss: IouLoss
+
+IouLoss:
+  loss_weight: 2.5
+  loss_square: true
+
+BBoxPostProcess:
+  decode:
+    name: YOLOBox
+    conf_thresh: 0.005
+    downsample_ratio: 32
+    clip_bbox: true
+    scale_x_y: 1.05
+  nms:
+    name: MultiClassNMS
+    keep_top_k: 100
+    nms_threshold: 0.45
+    nms_top_k: 1000
+    score_threshold: 0.005
+    normalized: false

dygraph/configs/ppyolo/_base_/ppyolo_r18vd.yml

+architecture: YOLOv3
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_vd_pretrained.tar
+load_static_weights: true
+norm_type: sync_bn
+use_ema: true
+ema_decay: 0.9998
+
+YOLOv3:
+  backbone: ResNet
+  neck: PPYOLOFPN
+  yolo_head: YOLOv3Head
+  post_process: BBoxPostProcess
+
+ResNet:
+  depth: 18
+  variant: d
+  return_idx: [2, 3]
+  freeze_at: -1
+  freeze_norm: false
+  norm_decay: 0.
+
+PPYOLOFPN:
+  feat_channels: [512, 512]
+  drop_block: true
+  block_size: 3
+  keep_prob: 0.9
+  conv_block_num: 0
+
+YOLOv3Head:
+  anchor_masks: [[3, 4, 5], [0, 1, 2]]
+  anchors: [[10, 14], [23, 27], [37, 58],
+            [81, 82], [135, 169], [344, 319]]
+  loss: YOLOv3Loss
+
+YOLOv3Loss:
+  ignore_thresh: 0.7
+  downsample: [32, 16]
+  label_smooth: false
+  scale_x_y: 1.05
+  iou_loss: IouLoss
+
+IouLoss:
+  loss_weight: 2.5
+  loss_square: true
+
+BBoxPostProcess:
+  decode:
+    name: YOLOBox
+    conf_thresh: 0.01
+    downsample_ratio: 32
+    clip_bbox: true
+    scale_x_y: 1.05
+  nms:
+    name: MatrixNMS
+    keep_top_k: 100
+    score_threshold: 0.01
+    post_threshold: 0.01
+    nms_top_k: -1
+    normalized: false
+    background_label: -1

dygraph/configs/ppyolo/_base_/ppyolo_r50vd_dcn.yml

 architecture: YOLOv3
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
-weights: output/ppyolo_r50vd_dcn/model_final
 load_static_weights: true
 norm_type: sync_bn
 use_ema: true
@@ -55,7 +54,7 @@ IouAwareLoss:
 BBoxPostProcess:
   decode:
     name: YOLOBox
-    conf_thresh: 0.005
+    conf_thresh: 0.01
     downsample_ratio: 32
     clip_bbox: true
     scale_x_y: 1.05
@@ -66,3 +65,4 @@ BBoxPostProcess:
     post_threshold: 0.01
     nms_top_k: -1
     normalized: false
+    background_label: -1

dygraph/configs/ppyolo/ppyolo_mbv3_large_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/ppyolo_mbv3_large.yml',
+  './_base_/optimizer_1x.yml',
+  './_base_/ppyolo_reader.yml',
+]
+
+snapshot_epoch: 10
+weights: output/ppyolo_mbv3_large_coco/model_final
+
+TrainReader:
+  inputs_def:
+    num_max_boxes: 90
+  sample_transforms:
+    - DecodeOp: {}
+    - MixupOp: {alpha: 1.5, beta: 1.5}
+    - RandomDistortOp: {}
+    - RandomExpandOp: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCropOp: {}
+    - RandomFlipOp: {}
+  batch_transforms:
+    - BatchRandomResizeOp:
+        target_size: [224, 256, 288, 320, 352, 384, 416, 448, 480, 512]
+        random_size: True
+        random_interp: True
+        keep_ratio: False
+    - NormalizeBoxOp: {}
+    - PadBoxOp: {num_max_boxes: 90}
+    - BboxXYXY2XYWHOp: {}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+    - Gt2YoloTargetOp:
+        anchor_masks: [[3, 4, 5], [0, 1, 2]]
+        anchors: [[11, 18], [34, 47], [51, 126], [115, 71], [120, 195], [254, 235]]
+        downsample_ratios: [32, 16]
+        iou_thresh: 0.25
+        num_classes: 80
+  batch_size: 32
+  mixup_epoch: 200
+  shuffle: true
+
+EvalReader:
+  sample_transforms:
+    - DecodeOp: {}
+    - ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+  batch_size: 8
+  drop_empty: false
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 320, 320]
+  sample_transforms:
+    - DecodeOp: {}
+    - ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+  batch_size: 1
+
+epoch: 270
+
+LearningRate:
+  base_lr: 0.005
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones:
+    - 162
+    - 216
+  - !LinearWarmup
+    start_factor: 0.
+    steps: 4000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

dygraph/configs/ppyolo/ppyolo_mbv3_small_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/ppyolo_mbv3_small.yml',
+  './_base_/optimizer_1x.yml',
+  './_base_/ppyolo_reader.yml',
+]
+
+snapshot_epoch: 10
+weights: output/ppyolo_mbv3_small_coco/model_final
+
+TrainReader:
+  inputs_def:
+    num_max_boxes: 90
+  sample_transforms:
+    - DecodeOp: {}
+    - MixupOp: {alpha: 1.5, beta: 1.5}
+    - RandomDistortOp: {}
+    - RandomExpandOp: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCropOp: {}
+    - RandomFlipOp: {}
+  batch_transforms:
+    - BatchRandomResizeOp:
+        target_size: [224, 256, 288, 320, 352, 384, 416, 448, 480, 512]
+        random_size: True
+        random_interp: True
+        keep_ratio: False
+    - NormalizeBoxOp: {}
+    - PadBoxOp: {num_max_boxes: 90}
+    - BboxXYXY2XYWHOp: {}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+    - Gt2YoloTargetOp:
+        anchor_masks: [[3, 4, 5], [0, 1, 2]]
+        anchors: [[11, 18], [34, 47], [51, 126], [115, 71], [120, 195], [254, 235]]
+        downsample_ratios: [32, 16]
+        iou_thresh: 0.25
+        num_classes: 80
+  batch_size: 32
+  mixup_epoch: 200
+  shuffle: true
+
+EvalReader:
+  sample_transforms:
+    - DecodeOp: {}
+    - ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+  batch_size: 8
+  drop_empty: false
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 320, 320]
+  sample_transforms:
+    - DecodeOp: {}
+    - ResizeOp: {target_size: [320, 320], keep_ratio: False, interp: 2}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+  batch_size: 1
+
+epoch: 270
+
+LearningRate:
+  base_lr: 0.005
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones:
+    - 162
+    - 216
+  - !LinearWarmup
+    start_factor: 0.
+    steps: 4000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

dygraph/configs/ppyolo/ppyolo_r18vd_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/ppyolo_r18vd.yml',
+  './_base_/optimizer_1x.yml',
+  './_base_/ppyolo_reader.yml',
+]
+
+snapshot_epoch: 10
+weights: output/ppyolo_r18vd_coco/model_final
+
+TrainReader:
+  sample_transforms:
+    - DecodeOp: {}
+    - MixupOp: {alpha: 1.5, beta: 1.5}
+    - RandomDistortOp: {}
+    - RandomExpandOp: {fill_value: [123.675, 116.28, 103.53]}
+    - RandomCropOp: {}
+    - RandomFlipOp: {}
+  batch_transforms:
+    - BatchRandomResizeOp:
+        target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
+        random_size: True
+        random_interp: True
+        keep_ratio: False
+    - NormalizeBoxOp: {}
+    - PadBoxOp: {num_max_boxes: 50}
+    - BboxXYXY2XYWHOp: {}
+    - NormalizeImageOp:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        is_scale: True
+    - PermuteOp: {}
+    - Gt2YoloTargetOp:
+        anchor_masks: [[3, 4, 5], [0, 1, 2]]
+        anchors: [[10, 14], [23, 27], [37, 58], [81, 82], [135, 169], [344, 319]]
+        downsample_ratios: [32, 16]
+
+  batch_size: 32
+  mixup_epoch: 500
+  shuffle: true
+
+EvalReader:
+  sample_transforms:
+    - DecodeOp: {}
+    - ResizeOp: {target_size: [512, 512], keep_ratio: False, interp: 2}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+  batch_size: 8
+  drop_empty: false
+
+TestReader:
+  inputs_def:
+    image_shape: [3, 512, 512]
+  sample_transforms:
+    - DecodeOp: {}
+    - ResizeOp: {target_size: [512, 512], keep_ratio: False, interp: 2}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+  batch_size: 1
+
+epoch: 270
+
+LearningRate:
+  base_lr: 0.004
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones:
+    - 162
+    - 216
+  - !LinearWarmup
+    start_factor: 0.
+    steps: 4000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml

@@ -7,3 +7,4 @@ _BASE_: [
 ]
 
 snapshot_epoch: 16
+weights: output/ppyolo_r50vd_dcn_1x_coco/model_final

dygraph/configs/ppyolo/ppyolo_r50vd_dcn_1x_minicoco.yml

@@ -7,7 +7,8 @@ _BASE_: [
 ]
 
 snapshot_epoch: 8
-use_ema: false
+use_ema: true
+weights: output/ppyolo_r50vd_dcn_1x_minicoco/model_final
 
 TrainReader:
   batch_size: 12
@@ -33,3 +34,11 @@ LearningRate:
   - !LinearWarmup
     start_factor: 0.
     steps: 4000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

dygraph/configs/ppyolo/ppyolo_r50vd_dcn_2x_coco.yml

@@ -7,3 +7,4 @@ _BASE_: [
 ]
 
 snapshot_epoch: 16
+weights: output/ppyolo_r50vd_dcn_2x_coco/model_final

dygraph/configs/ppyolo/ppyolo_r50vd_dcn_voc.yml

+_BASE_: [
+  '../datasets/voc.yml',
+  '../runtime.yml',
+  './_base_/ppyolo_r50vd_dcn.yml',
+  './_base_/optimizer_1x.yml',
+  './_base_/ppyolo_reader.yml',
+]
+
+snapshot_epoch: 83
+weights: output/ppyolo_r50vd_dcn_voc/model_final
+
+TrainReader:
+  batch_transforms:
+    - BatchRandomResizeOp: {target_size: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608], random_size: True, random_interp: True, keep_ratio: False}
+    - NormalizeBoxOp: {}
+    - PadBoxOp: {num_max_boxes: 50}
+    - BboxXYXY2XYWHOp: {}
+    - NormalizeImageOp: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - PermuteOp: {}
+    - Gt2YoloTargetOp: {anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]], anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], downsample_ratios: [32, 16, 8], num_classes: 20}
+  mixup_epoch: 350
+  batch_size: 12
+
+epoch: 583
+
+LearningRate:
+  base_lr: 0.00333
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones:
+    - 466
+    - 516
+  - !LinearWarmup
+    start_factor: 0.
+    steps: 4000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2

dygraph/configs/yolov3/README.md

@@ -12,6 +12,7 @@
 | DarkNet53         | 608         |    8    |   270e    |     ----     |  39.0  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
 | DarkNet53         | 416         |    8    |   270e    |     ----     |  37.5  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
 | DarkNet53         | 320         |    8    |   270e    |     ----     |  34.6  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_darknet53_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml) |
+|   ResNet50_vd        | 608        |    8    |   270e    |     ----     |  39.1  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_r50vd_dcn_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml) |
 | MobileNet-V1         | 608         |    8    |   270e    |     ----     |  28.8  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
 | MobileNet-V1         | 416         |    8    |   270e    |     ----     |  28.7  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |
 | MobileNet-V1         | 320         |    8    |   270e    |     ----     |  26.5  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/yolov3_mobilenet_v1_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml) |

dygraph/configs/yolov3/_base_/yolov3_darknet53.yml

 architecture: YOLOv3
 pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar
-use_fine_grained_loss: false
 load_static_weights: True
 norm_type: sync_bn
 

dygraph/configs/yolov3/_base_/yolov3_r50vd_dcn.yml

+architecture: YOLOv3
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
+load_static_weights: True
+norm_type: sync_bn
+
+YOLOv3:
+  backbone: ResNet
+  neck: YOLOv3FPN
+  yolo_head: YOLOv3Head
+  post_process: BBoxPostProcess
+
+ResNet:
+  depth: 50
+  variant: d
+  return_idx: [1, 2, 3]
+  dcn_v2_stages: [3]
+  freeze_at: -1
+  freeze_norm: false
+  norm_decay: 0.
+
+# YOLOv3FPN:
+
+YOLOv3Head:
+  anchors: [[10, 13], [16, 30], [33, 23],
+            [30, 61], [62, 45], [59, 119],
+            [116, 90], [156, 198], [373, 326]]
+  anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+  loss: YOLOv3Loss
+
+YOLOv3Loss:
+  ignore_thresh: 0.7
+  downsample: [32, 16, 8]
+  label_smooth: false
+
+BBoxPostProcess:
+  decode:
+    name: YOLOBox
+    conf_thresh: 0.005
+    downsample_ratio: 32
+    clip_bbox: true
+  nms:
+    name: MultiClassNMS
+    keep_top_k: 100
+    score_threshold: 0.01
+    nms_threshold: 0.45
+    nms_top_k: 1000
+    normalized: false

dygraph/configs/yolov3/yolov3_darknet53_270e_coco.yml

@@ -5,4 +5,6 @@ _BASE_: [
   '_base_/yolov3_darknet53.yml',
   '_base_/yolov3_reader.yml',
 ]
-weights: output/yolov3_darknet53_coco/model_final
+
+snapshot_epoch: 5
+weights: output/yolov3_darknet53_270e_coco/model_final

dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_coco.yml

@@ -5,4 +5,6 @@ _BASE_: [
   '_base_/yolov3_mobilenet_v1.yml',
   '_base_/yolov3_reader.yml',
 ]
-weights: output/yolov3_mobilenet_v1_coco/model_final
+
+snapshot_epoch: 5
+weights: output/yolov3_mobilenet_v1_270e_coco/model_final

dygraph/configs/yolov3/yolov3_mobilenet_v1_270e_voc.yml

@@ -5,7 +5,9 @@ _BASE_: [
   '_base_/yolov3_mobilenet_v1.yml',
   '_base_/yolov3_reader.yml',
 ]
-weights: output/yolov3_mobilenet_v1_voc/model_final
+
+snapshot_epoch: 5
+weights: output/yolov3_mobilenet_v1_270e_voc/model_final
 
 TrainReader:
   inputs_def:

dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_coco.yml

@@ -5,4 +5,6 @@ _BASE_: [
   '_base_/yolov3_mobilenet_v3_large.yml',
   '_base_/yolov3_reader.yml',
 ]
-weights: output/yolov3_mobilenet_v3_large_coco/model_final
+
+snapshot_epoch: 5
+weights: output/yolov3_mobilenet_v3_large_270e_coco/model_final

dygraph/configs/yolov3/yolov3_mobilenet_v3_large_270e_voc.yml

@@ -5,7 +5,9 @@ _BASE_: [
   '_base_/yolov3_mobilenet_v3_large.yml',
   '_base_/yolov3_reader.yml',
 ]
-weights: output/yolov3_mobilenet_v3_large_voc/model_final
+
+snapshot_epoch: 5
+weights: output/yolov3_mobilenet_v3_large_270e_voc/model_final
 
 TrainReader:
   inputs_def:

dygraph/configs/yolov3/yolov3_r50vd_dcn_270e_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_270e.yml',
+  '_base_/yolov3_r50vd_dcn.yml',
+  '_base_/yolov3_reader.yml',
+]
+
+snapshot_epoch: 5
+weights: output/yolov3_r50vd_dcn_270e_coco/model_final

dygraph/deploy/python/infer.py

@@ -135,6 +135,13 @@ class Detector(object):
             output_names = self.predictor.get_output_names()
             boxes_tensor = self.predictor.get_output_handle(output_names[0])
             np_boxes = boxes_tensor.copy_to_cpu()
+            score_tensor = self.predictor.get_output_handle(output_names[3])
+            np_score = score_tensor.copy_to_cpu()
+            label_tensor = self.predictor.get_output_handle(output_names[2])
+            np_label = label_tensor.copy_to_cpu()
+            np_boxes = np.concatenate(
+                [np_label[:, np.newaxis], np_score[:, np.newaxis], np_boxes],
+                axis=-1)
             if self.pred_config.mask_resolution is not None:
                 masks_tensor = self.predictor.get_output_handle(output_names[2])
                 np_masks = masks_tensor.copy_to_cpu()

dygraph/ppdet/data/transform/batch_operator.py

@@ -290,7 +290,8 @@ class Gt2YoloTargetOp(BaseOperator):
                             iou = jaccard_overlap(
                                 [0., 0., gw, gh],
                                 [0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
-                            if iou > self.iou_thresh:
+                            if iou > self.iou_thresh and target[idx, 5, gj,
+                                                                gi] == 0.:
                                 # x, y, w, h, scale
                                 target[idx, 0, gj, gi] = gx * grid_w - gi
                                 target[idx, 1, gj, gi] = gy * grid_h - gj

dygraph/ppdet/data/transform/batch_operators.py

@@ -319,7 +319,8 @@ class Gt2YoloTarget(BaseOperator):
                             iou = jaccard_overlap(
                                 [0., 0., gw, gh],
                                 [0., 0., an_hw[mask_i, 0], an_hw[mask_i, 1]])
-                            if iou > self.iou_thresh:
+                            if iou > self.iou_thresh and target[idx, 5, gj,
+                                                                gi] == 0.:
                                 # x, y, w, h, scale
                                 target[idx, 0, gj, gi] = gx * grid_w - gi
                                 target[idx, 1, gj, gi] = gy * grid_h - gj

dygraph/ppdet/engine/trainer.py

@@ -114,7 +114,11 @@ class Trainer(object):
             self._metrics = []
             return
         if self.cfg.metric == 'COCO':
-            self._metrics = [COCOMetric(anno_file=self.dataset.get_anno())]
+            # TODO: bias should be unified
+            self._metrics = [
+                COCOMetric(
+                    anno_file=self.dataset.get_anno(), bias=self.cfg.bias)
+            ]
         elif self.cfg.metric == 'VOC':
             self._metrics = [
                 VOCMetric(

dygraph/ppdet/metrics/coco_utils.py

@@ -24,7 +24,7 @@ from ppdet.utils.logger import setup_logger
 logger = setup_logger(__name__)
 
 
-def get_infer_results(outs, catid):
+def get_infer_results(outs, catid, bias=0):
     """
     Get result at the stage of inference.
     The output format is dictionary containing bbox or mask result.
@@ -41,9 +41,14 @@ def get_infer_results(outs, catid):
 
     infer_res = {}
     if 'bbox' in outs:
-        infer_res['bbox'] = get_det_res(outs['bbox'], outs['score'],
-                                        outs['label'], outs['bbox_num'], im_id,
-                                        catid)
+        infer_res['bbox'] = get_det_res(
+            outs['bbox'],
+            outs['score'],
+            outs['label'],
+            outs['bbox_num'],
+            im_id,
+            catid,
+            bias=bias)
 
     if 'mask' in outs:
         # mask post process

dygraph/ppdet/metrics/metrics.py

@@ -49,12 +49,13 @@ class Metric(paddle.metric.Metric):
 
 
 class COCOMetric(Metric):
-    def __init__(self, anno_file):
+    def __init__(self, anno_file, **kwargs):
         assert os.path.isfile(anno_file), \
                 "anno_file {} not a file".format(anno_file)
         self.anno_file = anno_file
         self.clsid2catid, self.catid2name = get_categories('COCO', anno_file)
-
+        # TODO: bias should be unified
+        self.bias = kwargs.get('bias', 0)
         self.reset()
 
     def reset(self):
@@ -72,7 +73,8 @@ class COCOMetric(Metric):
         outs['im_id'] = im_id.numpy() if isinstance(im_id,
                                                     paddle.Tensor) else im_id
 
-        infer_results = get_infer_results(outs, self.clsid2catid)
+        infer_results = get_infer_results(
+            outs, self.clsid2catid, bias=self.bias)
         self.results['bbox'] += infer_results[
             'bbox'] if 'bbox' in infer_results else []
         self.results['mask'] += infer_results[

dygraph/ppdet/modeling/heads/yolo_head.py

@@ -39,15 +39,16 @@ class YOLOv3Head(nn.Layer):
 
         self.yolo_outputs = []
         for i in range(len(self.anchors)):
+
             if self.iou_aware:
-                num_filters = self.num_outputs * (self.num_classes + 6)
+                num_filters = len(self.anchors[i]) * (self.num_classes + 6)
             else:
-                num_filters = self.num_outputs * (self.num_classes + 5)
+                num_filters = len(self.anchors[i]) * (self.num_classes + 5)
             name = 'yolo_output.{}'.format(i)
             yolo_output = self.add_sublayer(
                 name,
                 nn.Conv2D(
-                    in_channels=1024 // (2**i),
+                    in_channels=128 * (2**self.num_outputs) // (2**i),
                     out_channels=num_filters,
                     kernel_size=1,
                     stride=1,

dygraph/ppdet/modeling/necks/yolo_fpn.py

@@ -249,6 +249,7 @@ class PPYOLOFPN(nn.Layer):
             self.keep_prob = kwargs.get('keep_prob', 0.9)
 
         self.spp = kwargs.get('spp', False)
+        self.conv_block_num = kwargs.get('conv_block_num', 2)
         if self.coord_conv:
             ConvLayer = CoordConv
         else:
@@ -269,25 +270,37 @@ class PPYOLOFPN(nn.Layer):
             if i > 0:
                 ch_in += 512 // (2**i)
             channel = 64 * (2**self.num_blocks) // (2**i)
-            base_cfg = [
-                # name of layer, Layer, args
-                ['conv0', ConvLayer, [ch_in, channel, 1]],
-                ['conv1', ConvBNLayer, [channel, channel * 2, 3]],
-                ['conv2', ConvLayer, [channel * 2, channel, 1]],
-                ['conv3', ConvBNLayer, [channel, channel * 2, 3]],
-                ['route', ConvLayer, [channel * 2, channel, 1]],
-                ['tip', ConvLayer, [channel, channel * 2, 3]]
+            base_cfg = []
+            c_in, c_out = ch_in, channel
+            for j in range(self.conv_block_num):
+                base_cfg += [
+                    [
+                        'conv{}'.format(2 * j), ConvLayer, [c_in, c_out, 1],
+                        dict(
+                            padding=0, norm_type=norm_type)
+                    ],
+                    [
+                        'conv{}'.format(2 * j + 1), ConvBNLayer,
+                        [c_out, c_out * 2, 3], dict(
+                            padding=1, norm_type=norm_type)
+                    ],
                 ]
-            for conf in base_cfg:
-                filter_size = conf[-1][-1]
-                conf.append(dict(padding=filter_size // 2, norm_type=norm_type))
+                c_in, c_out = c_out * 2, c_out
+
+            base_cfg += [[
+                'route', ConvLayer, [c_in, c_out, 1], dict(
+                    padding=0, norm_type=norm_type)
+            ], [
+                'tip', ConvLayer, [c_out, c_out * 2, 3], dict(
+                    padding=1, norm_type=norm_type)
+            ]]
+
+            if self.conv_block_num == 2:
                 if i == 0:
                     if self.spp:
-                    pool_size = [5, 9, 13]
                         spp_cfg = [[
-                        'spp', SPP,
-                        [channel * (len(pool_size) + 1), channel, 1], dict(
-                            pool_size=pool_size, norm_type=norm_type)
+                            'spp', SPP, [channel * 4, channel, 1], dict(
+                                pool_size=[5, 9, 13], norm_type=norm_type)
                         ]]
                     else:
                         spp_cfg = []
@@ -295,6 +308,15 @@ class PPYOLOFPN(nn.Layer):
                         3:4] + dropblock_cfg + base_cfg[4:6]
                 else:
                     cfg = base_cfg[0:2] + dropblock_cfg + base_cfg[2:6]
+            elif self.conv_block_num == 0:
+                if self.spp and i == 0:
+                    spp_cfg = [[
+                        'spp', SPP, [c_in * 4, c_in, 1], dict(
+                            pool_size=[5, 9, 13], norm_type=norm_type)
+                    ]]
+                else:
+                    spp_cfg = []
+                cfg = spp_cfg + dropblock_cfg + base_cfg
             name = 'yolo_block.{}'.format(i)
             yolo_block = self.add_sublayer(name, PPYOLODetBlock(cfg, name))
             self.yolo_blocks.append(yolo_block)
@@ -305,7 +327,7 @@ class PPYOLOFPN(nn.Layer):
                     name,
                     ConvBNLayer(
                         ch_in=channel,
-                        ch_out=channel // 2,
+                        ch_out=256 // (2**i),
                         filter_size=1,
                         stride=1,
                         padding=0,

dygraph/ppdet/modeling/shape_spec.py

@@ -28,5 +28,6 @@ class ShapeSpec(
         stride:
     """
 
-    def __new__(cls, *, channels=None, height=None, width=None, stride=None):
-        return super().__new__(cls, channels, height, width, stride)
+    def __new__(cls, channels=None, height=None, width=None, stride=None):
+        return super(ShapeSpec, cls).__new__(cls, channels, height, width,
+                                             stride)

dygraph/ppdet/modeling/utils/bbox_util.py

@@ -106,8 +106,7 @@ def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
     x2 = paddle.minimum(px2, gx2)
     y2 = paddle.minimum(py2, gy2)
 
-    overlap = (x2 - x1) * (y2 - y1)
-    overlap = overlap.clip(0)
+    overlap = ((x2 - x1).clip(0)) * ((y2 - y1).clip(0))
 
     area1 = (px2 - px1) * (py2 - py1)
     area1 = area1.clip(0)

dygraph/ppdet/optimizer.py

@@ -243,18 +243,14 @@ class ModelEMA(object):
         self._decay = decay
         model_dict = model.state_dict()
         for k, v in self.state_dict.items():
-            if '_mean' not in k and '_variance' not in k:
             v = decay * v + (1 - decay) * model_dict[k]
             v.stop_gradient = True
             self.state_dict[k] = v
-            else:
-                self.state_dict[k] = model_dict[k]
         self.step += 1
 
     def apply(self):
         state_dict = dict()
         for k, v in self.state_dict.items():
-            if '_mean' not in k and '_variance' not in k:
             v = v / (1 - self._decay**self.step)
             v.stop_gradient = True
             state_dict[k] = v

dygraph/ppdet/py_op/post_process.py

@@ -4,8 +4,13 @@ import numpy as np
 import cv2
 
 
-def get_det_res(bboxes, scores, labels, bbox_nums, image_id,
-                label_to_cat_id_map):
+def get_det_res(bboxes,
+                scores,
+                labels,
+                bbox_nums,
+                image_id,
+                label_to_cat_id_map,
+                bias=0):
     det_res = []
     k = 0
     for i in range(len(bbox_nums)):
@@ -19,8 +24,8 @@ def get_det_res(bboxes, scores, labels, bbox_nums, image_id,
             k = k + 1
             xmin, ymin, xmax, ymax = box.tolist()
             category_id = label_to_cat_id_map[label]
-            w = xmax - xmin
-            h = ymax - ymin
+            w = xmax - xmin + bias
+            h = ymax - ymin + bias
             bbox = [xmin, ymin, w, h]
             dt_res = {
                 'image_id': cur_image_id,

dygraph/ppdet/utils/checkpoint.py

@@ -163,7 +163,7 @@ def load_pretrain_weight(model,
         model.backbone.set_dict(param_state_dict)
     else:
         ignore_set = set()
-        for name, weight in model_dict:
+        for name, weight in model_dict.items():
             if name in param_state_dict:
                 if weight.shape != param_state_dict[name].shape:
                     param_state_dict.pop(name, None)

dygraph/tools/eval.py

@@ -47,7 +47,10 @@ def parse_args():
         help="Evaluation directory, default is current directory.")
 
     parser.add_argument(
-        '--json_eval', action='store_true', default=False, help='')
+        '--json_eval',
+        action='store_true',
+        default=False,
+        help='Whether to re eval with already exists bbox.json or mask.json')
 
     parser.add_argument(
         "--slim_config",
@@ -55,6 +58,12 @@ def parse_args():
         type=str,
         help="Configuration file of slim method.")
 
+    # TODO: bias should be unified
+    parser.add_argument(
+        "--bias",
+        action="store_true",
+        help="whether add bias or not while getting w and h")
+
     args = parser.parse_args()
     return args
 
@@ -77,6 +86,8 @@ def main():
     FLAGS = parse_args()
 
     cfg = load_config(FLAGS.config)
+    # TODO: bias should be unified
+    cfg['bias'] = 1 if FLAGS.bias else 0
     merge_config(FLAGS.opt)
     if FLAGS.slim_config:
         slim_cfg = load_config(FLAGS.slim_config)