fix picodet error in det_keypoint_unite_infer (#4561)

* fix picodet error in det_keypoint_unite_infer

* Optimize configuration file path

configs/keypoint/tiny_pose/README.md

@@ -11,14 +11,14 @@ PP-TinyPose是PaddleDetecion针对移动端设备优化的实时姿态检测模
 ### 姿态检测模型
 | 模型  | 输入尺寸 | AP (coco val) | 单人推理耗时 (FP32)| 单人推理耗时（FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型（FP32) | Paddle-Lite部署模型（FP16)|
 | :------------------------ | :-------:  | :------: | :------: |:---: | :---: | :---: | :---: | :---: | :---: |
-| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./keypoint/tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
-| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./keypoint/tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.nb) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
+| PP-TinyPose | 128*96 | 58.1 | 4.57ms | 3.27ms | [Config](./tinypose_128x96.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96_fp16.nb) |
+| PP-TinyPose | 256*192 | 68.8 | 14.07ms | 8.33ms | [Config](./tinypose_256x192.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.nb) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.tar) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192_fp16.nb) |
 
 ### 行人检测模型
 | 模型  | 输入尺寸 | mAP (coco val) | 平均推理耗时 (FP32) | 平均推理耗时 (FP16) | 配置文件 | 模型权重 | 预测部署模型 | Paddle-Lite部署模型（FP32) | Paddle-Lite部署模型（FP16)|
 | :------------------------ | :-------:  | :------: | :------: | :---: | :---: | :---: | :---: | :---: | :---: |
-| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms |  2.37ms | [Config](./pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb) |
-| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms |  6.30ms | [Config](./pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb) |
+| PicoDet-S-Pedestrian | 192*192 | 29.0 | 4.30ms |  2.37ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml) |[Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_192_pedestrian_fp16.nb) |
+| PicoDet-S-Pedestrian | 320*320 | 38.5 | 10.26ms |  6.30ms | [Config](../../picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml) | [Model](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.pdparams) | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.tar) | [Lite部署模型](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian.nb) | [Lite部署模型(FP16)](https://bj.bcebos.com/v1/paddledet/models/keypoint/picodet_s_320_pedestrian_fp16.nb) |
 
 
 **说明**
@@ -86,19 +86,19 @@ AI Challenger Description:
 利用转换为`COCO`形式的合并数据标注，执行模型训练：
 ```bash
 # 姿态检测模型
-python3 -m paddle.distributed.launch tools/train.py -c keypoint/tinypose_128x96.yml
+python3 -m paddle.distributed.launch tools/train.py -c configs/keypoint/tiny_pose/tinypose_128x96.yml
 
 # 行人检测模型
-python3 -m paddle.distributed.launch tools/train.py -c pedestrian_detection/picodet_s_320_pedestrian.yml
+python3 -m paddle.distributed.launch tools/train.py -c configs/picodet/application/pedestrian_detection/picodet_s_320_pedestrian.yml
 ```
 
 ## 部署流程
 ### 实现部署预测
 1. 通过以下命令将训练得到的模型导出：
 ```bash
-python3 tools/export_model.py -c keypoint/picodet_s_192_pedestrian.yml --output_dir=outut_inference -o weights=output/picodet_s_192_pedestrian/model_final
+python3 tools/export_model.py -c configs/picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml --output_dir=outut_inference -o weights=output/picodet_s_192_pedestrian/model_final
 
-python3 tools/export_model.py -c keypoint/tinypose_128x96.yml --output_dir=outut_inference -o weights=output/tinypose_128x96/model_final
+python3 tools/export_model.py -c configs/keypoint/tiny_pose/tinypose_128x96.yml --output_dir=outut_inference -o weights=output/tinypose_128x96/model_final
 ```
 导出后的模型如：
 ```
@@ -147,9 +147,9 @@ python3 deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inferen
 如果您希望将自己训练的模型应用于部署，可以参考以下步骤：
 1. 将训练的模型导出
 ```bash
-python3 tools/export_model.py -c keypoint/picodet_s_192_pedestrian.yml --output_dir=outut_inference -o weights=output/picodet_s_192_pedestrian/model_final TestReader.fuse_normalize=true
+python3 tools/export_model.py -c configs/picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml --output_dir=outut_inference -o weights=output/picodet_s_192_pedestrian/model_final TestReader.fuse_normalize=true
 
-python3 tools/export_model.py -c keypoint/tinypose_128x96.yml --output_dir=outut_inference -o weights=output/tinypose_128x96/model_final TestReader.fuse_normalize=true
+python3 tools/export_model.py -c configs/keypoint/tiny_pose/tinypose_128x96.yml --output_dir=outut_inference -o weights=output/tinypose_128x96/model_final TestReader.fuse_normalize=true
 ```
 2. 转换为Lite模型（依赖[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite))
 

configs/keypoint/tiny_pose/keypoint/tinypose_128x96.yml → configs/keypoint/tiny_pose/tinypose_128x96.yml

@@ -77,7 +77,7 @@ EvalDataset:
     trainsize: *trainsize
     pixel_std: *pixel_std
     use_gt_bbox: True
-    image_thre: 0.0
+    image_thre: 0.5
 
 TestDataset:
   !ImageFolder

configs/keypoint/tiny_pose/keypoint/tinypose_256x192.yml → configs/keypoint/tiny_pose/tinypose_256x192.yml

@@ -77,7 +77,7 @@ EvalDataset:
     trainsize: *trainsize
     pixel_std: *pixel_std
     use_gt_bbox: True
-    image_thre: 0.0
+    image_thre: 0.5
 
 TestDataset:
   !ImageFolder

configs/picodet/application/pedestrian_detection/picodet_s_192_pedestrian.yml

+use_gpu: true
+log_iter: 20
+save_dir: output
+snapshot_epoch: 1
+print_flops: false
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
+weights: output/picodet_s_192_pedestrian/model_final
+find_unused_parameters: True
+use_ema: true
+cycle_epoch: 40
+snapshot_epoch: 10
+epoch: 300
+metric: COCO
+num_classes: 1
+
+architecture: PicoDet
+
+PicoDet:
+  backbone: ESNet
+  neck: CSPPAN
+  head: PicoHead
+
+ESNet:
+  scale: 0.75
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+
+CSPPAN:
+  out_channels: 96
+  use_depthwise: True
+  num_csp_blocks: 1
+  num_features: 4
+
+PicoHead:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+  fpn_stride: [8, 16, 32, 64]
+  feat_in_chan: 96
+  prior_prob: 0.01
+  reg_max: 7
+  cell_offset: 0.5
+  loss_class:
+    name: VarifocalLoss
+    use_sigmoid: True
+    iou_weighted: True
+    loss_weight: 1.0
+  loss_dfl:
+    name: DistributionFocalLoss
+    loss_weight: 0.25
+  loss_bbox:
+    name: GIoULoss
+    loss_weight: 2.0
+  assigner:
+    name: SimOTAAssigner
+    candidate_topk: 10
+    iou_weight: 6
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6
+
+LearningRate:
+  base_lr: 0.4
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
+
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: aic_coco_train_cocoformat.json
+    dataset_dir: dataset
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/instances_val2017.json
+
+worker_num: 8
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [128, 160, 192, 224, 256], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_size: 128
+  shuffle: true
+  drop_last: true
+  collate_batch: false
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [192, 192], 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}
+  batch_size: 8
+  shuffle: false
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, 192, 192]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [192, 192], 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}
+  batch_size: 1
+  shuffle: false
+  fuse_normalize: true

deploy/python/det_keypoint_unite_infer.py

@@ -21,7 +21,7 @@ import paddle
 
 from det_keypoint_unite_utils import argsparser
 from preprocess import decode_image
-from infer import Detector, PredictConfig, print_arguments, get_test_images
+from infer import Detector, DetectorPicoDet, PredictConfig, print_arguments, get_test_images
 from keypoint_infer import KeyPoint_Detector, PredictConfig_KeyPoint
 from visualize import draw_pose
 from benchmark_utils import PaddleInferBenchmark
@@ -217,17 +217,20 @@ def topdown_unite_predict_video(detector,
 
 def main():
     pred_config = PredictConfig(FLAGS.det_model_dir)
-    detector = Detector(
-        pred_config,
-        FLAGS.det_model_dir,
-        device=FLAGS.device,
-        run_mode=FLAGS.run_mode,
-        trt_min_shape=FLAGS.trt_min_shape,
-        trt_max_shape=FLAGS.trt_max_shape,
-        trt_opt_shape=FLAGS.trt_opt_shape,
-        trt_calib_mode=FLAGS.trt_calib_mode,
-        cpu_threads=FLAGS.cpu_threads,
-        enable_mkldnn=FLAGS.enable_mkldnn)
+    detector_func = 'Detector'
+    if pred_config.arch == 'PicoDet':
+        detector_func = 'DetectorPicoDet'
+
+    detector = eval(detector_func)(pred_config,
+                                   FLAGS.det_model_dir,
+                                   device=FLAGS.device,
+                                   run_mode=FLAGS.run_mode,
+                                   trt_min_shape=FLAGS.trt_min_shape,
+                                   trt_max_shape=FLAGS.trt_max_shape,
+                                   trt_opt_shape=FLAGS.trt_opt_shape,
+                                   trt_calib_mode=FLAGS.trt_calib_mode,
+                                   cpu_threads=FLAGS.cpu_threads,
+                                   enable_mkldnn=FLAGS.enable_mkldnn)
 
     pred_config = PredictConfig_KeyPoint(FLAGS.keypoint_model_dir)
     assert KEYPOINT_SUPPORT_MODELS[