add ppyolo_tiny (#1116)

* add ppyolo_tiny

configs/ppyolo/README.md

@@ -32,20 +32,22 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 
 ## 模型库
 
+### PP-YOLO模型
+
 |          模型            | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
 |:------------------------:|:-------:|:-------------:|:----------:| :-------:| :----: | :------------: | :---------------------: | :------: | :------: |
 | YOLOv4(AlexyAB)          |    -    |       -       | CSPDarknet |   608    |  43.5  |       62       |          105.5           | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml)                   |
 | YOLOv4(AlexyAB)          |    -    |       -       | CSPDarknet |   512    |  43.0  |       83       |          138.4           | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml)                   |
 | YOLOv4(AlexyAB)          |    -    |       -       | CSPDarknet |   416    |  41.2  |       96       |          164.0           | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml)                   |
 | YOLOv4(AlexyAB)          |    -    |       -       | CSPDarknet |   320    |  38.0  |      123       |          199.0           | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml)                   |
-| PP-YOLO                   |    8    |      24       | ResNet50vd |   608    |  45.2  |      72.9      |          155.6          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
-| PP-YOLO                   |    8    |      24       | ResNet50vd |   512    |  44.4  |      89.9      |          188.4          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
-| PP-YOLO                   |    8    |      24       | ResNet50vd |   416    |  42.5  |     109.1      |          215.4          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
-| PP-YOLO                   |    8    |      24       | ResNet50vd |   320    |  39.3  |     132.2      |          242.2          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO                   |    8    |      24      | ResNet50vd |   608    |  45.2  |      72.9      |          155.6          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO                   |    8    |      24      | ResNet50vd |   512    |  44.4  |      89.9      |          188.4          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO                   |    8    |      24      | ResNet50vd |   416    |  42.5  |     109.1      |          215.4          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO                   |    8    |      24      | ResNet50vd |   320    |  39.3  |     132.2      |          242.2          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
 
 **注意:**
 
-- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用test-dev2017左右测试集。
+- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用test-dev2017左右测试集，`Box AP`为`mAP(IoU=0.5:0.95)`评估结果。
 - PP-YOLO模型训练过程中使用8GPU，每GPU batch size为24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模型推理速度测试数据为使用`tools/export_model.py`脚本导出模型后，使用`deploy/python/infer.py`脚本中的`--run_benchnark`参数使用Paddle预测库进行推理速度benchmark测试结果, 且测试的均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
@@ -53,6 +55,16 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 - YOLOv4(AlexyAB)模型精度和V100 FP32推理速度数据使用[YOLOv4 github库](https://github.com/AlexeyAB/darknet)提供的单卡V100上精度速度测试数据，V100 TensorRT FP16推理速度为使用[AlexyAB/darknet]库中tkDNN配置于单卡V100上的测试结果。
 - YOLOv4(AlexyAB)行`模型下载`和`配置文件`为PaddleDetection复现的YOLOv4模型，目前评估精度已对齐，支持finetune，训练精度对齐中，可参见[PaddleDetection YOLOv4 模型](../yolov4/README.md)
 
+### PP-YOLO tiny模型
+
+|          模型            | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
+|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :----: | :------------: | :---------------------: | :------: | :------: |
+| PP-YOLO tiny              |    4    |      32      | ResNet18vd |   416    |  47.0  |     401.6      |          724.6          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml)                   |
+| PP-YOLO tiny              |    4    |      32      | ResNet18vd |   320    |  43.7  |     478.5      |          791.3          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml)                   |
+
+- PP-YOLO tiny模型使用COCO数据集中train2017作为训练集，使用val2017左右测试集，`Box AP`为`mAP(IoU=0.5)`评估结果。
+- PP-YOLO tiny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 tiny模型推理速度测试环境配置和测试方法与PP-YOLO模型一致。
 
 ## 使用说明
 

configs/ppyolo/ppyolo_lb.yml

-architecture: YOLOv3
-use_gpu: true
-max_iters: 250000
-log_smooth_window: 100
-log_iter: 100
-save_dir: output
-snapshot_iter: 10000
-metric: COCO
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
-weights: output/ppyolo_lb/model_final
-num_classes: 80
-use_fine_grained_loss: true
-use_ema: true
-ema_decay: 0.9998
-
-YOLOv3:
-  backbone: ResNet
-  yolo_head: YOLOv3Head
-  use_fine_grained_loss: true
-
-ResNet:
-  norm_type: sync_bn
-  freeze_at: 0
-  freeze_norm: false
-  norm_decay: 0.
-  depth: 50
-  feature_maps: [3, 4, 5]
-  variant: d
-  dcn_v2_stages: [5]
-
-YOLOv3Head:
-  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]]
-  norm_decay: 0.
-  coord_conv: true
-  iou_aware: true
-  iou_aware_factor: 0.4
-  scale_x_y: 1.05
-  spp: true
-  yolo_loss: YOLOv3Loss
-  nms: MatrixNMS
-  drop_block: true
-
-YOLOv3Loss:
-  batch_size: 24
-  ignore_thresh: 0.7
-  scale_x_y: 1.05
-  label_smooth: false
-  use_fine_grained_loss: true
-  iou_loss: IouLoss
-  iou_aware_loss: IouAwareLoss
-
-IouLoss:
-  loss_weight: 2.5
-  max_height: 608
-  max_width: 608
-
-IouAwareLoss:
-  loss_weight: 1.0
-  max_height: 608
-  max_width: 608
-
-MatrixNMS:
-    background_label: -1
-    keep_top_k: 100
-    normalized: false
-    score_threshold: 0.01
-    post_threshold: 0.01
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones:
-    - 150000
-    - 200000
-  - !LinearWarmup
-    start_factor: 0.
-    steps: 4000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0005
-    type: L2
-
-_READER_: 'ppyolo_reader_lb.yml'

configs/ppyolo/ppyolo_reader_lb.yml

-TrainReader:
-  inputs_def:
-    fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
-    num_max_boxes: 50
-  dataset:
-    !COCODataSet
-      image_dir: train2017
-      anno_path: annotations/instances_train2017.json
-      dataset_dir: dataset/coco
-      with_background: false
-  sample_transforms:
-    - !DecodeImage
-      to_rgb: True
-      with_mixup: True
-    - !MixupImage
-      alpha: 1.5
-      beta: 1.5
-    - !ColorDistort {}
-    - !RandomExpand
-      fill_value: [123.675, 116.28, 103.53]
-    - !RandomCrop {}
-    - !RandomFlipImage
-      is_normalized: false
-    - !NormalizeBox {}
-    - !PadBox
-      num_max_boxes: 50
-    - !BboxXYXY2XYWH {}
-  batch_transforms:
-  - !RandomShape
-    sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
-    random_inter: True
-  - !NormalizeImage
-    mean: [0.485, 0.456, 0.406]
-    std: [0.229, 0.224, 0.225]
-    is_scale: True
-    is_channel_first: false
-  - !Permute
-    to_bgr: false
-    channel_first: True
-  # Gt2YoloTarget is only used when use_fine_grained_loss set as true,
-  # this operator will be deleted automatically if use_fine_grained_loss
-  # is set as false
-  - !Gt2YoloTarget
-    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]
-  batch_size: 24
-  shuffle: true
-  mixup_epoch: 25000
-  drop_last: true
-  worker_num: 8
-  bufsize: 4
-  use_process: true
-
-EvalReader:
-  inputs_def:
-    fields: ['image', 'im_size', 'im_id']
-    num_max_boxes: 50
-  dataset:
-    !COCODataSet
-      image_dir: val2017
-      anno_path: annotations/instances_val2017.json
-      dataset_dir: dataset/coco
-      with_background: false
-  sample_transforms:
-    - !DecodeImage
-      to_rgb: True
-    - !ResizeImage
-      target_size: 608
-      interp: 2
-    - !NormalizeImage
-      mean: [0.485, 0.456, 0.406]
-      std: [0.229, 0.224, 0.225]
-      is_scale: True
-      is_channel_first: false
-    - !PadBox
-      num_max_boxes: 50
-    - !Permute
-      to_bgr: false
-      channel_first: True
-  batch_size: 8
-  drop_empty: false
-  worker_num: 8
-  bufsize: 4
-
-TestReader:
-  inputs_def:
-    image_shape: [3, 608, 608]
-    fields: ['image', 'im_size', 'im_id']
-  dataset:
-    !ImageFolder
-      anno_path: annotations/instances_val2017.json
-      with_background: false
-  sample_transforms:
-    - !DecodeImage
-      to_rgb: True
-    - !ResizeImage
-      target_size: 608
-      interp: 2
-    - !NormalizeImage
-      mean: [0.485, 0.456, 0.406]
-      std: [0.229, 0.224, 0.225]
-      is_scale: True
-      is_channel_first: false
-    - !Permute
-      to_bgr: false
-      channel_first: True
-  batch_size: 1

configs/ppyolo/ppyolo_tiny.yml

@@ -33,8 +33,6 @@ YOLOv3Head:
             [81, 82], [135, 169], [344, 319]]
   norm_decay: 0.
   conv_block_num: 0
-  iou_aware: true
-  iou_aware_factor: 0.4
   scale_x_y: 1.05
   yolo_loss: YOLOv3Loss
   nms: MatrixNMS
@@ -47,18 +45,12 @@ YOLOv3Loss:
   label_smooth: false
   use_fine_grained_loss: true
   iou_loss: IouLoss
-  iou_aware_loss: IouAwareLoss
 
 IouLoss:
   loss_weight: 2.5
   max_height: 608
   max_width: 608
 
-IouAwareLoss:
-  loss_weight: 1.0
-  max_height: 608
-  max_width: 608
-
 MatrixNMS:
     background_label: -1
     keep_top_k: 100