Add PP-YOLOE+ support

README.md

@@ -7,7 +7,6 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * DeepStream tutorials
 * YOLOv6 support
 * Dynamic batch-size
-* PP-YOLOE+ support
 
 ### Improvements on this repository
 
@@ -29,6 +28,7 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * Models benchmarks
 * **YOLOv8 support**
 * **YOLOX support**
+* **PP-YOLOE+ support**
 
 ##
 
@@ -44,7 +44,7 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * [INT8 calibration](#int8-calibration)
 * [YOLOv5 usage](docs/YOLOv5.md)
 * [YOLOR usage](docs/YOLOR.md)
-* [PP-YOLOE usage](docs/PPYOLOE.md)
+* [PP-YOLOE / PP-YOLOE+ usage](docs/PPYOLOE.md)
 * [YOLOv7 usage](docs/YOLOv7.md)
 * [YOLOv8 usage](docs/YOLOv8.md)
 * [YOLOX usage](docs/YOLOX.md)
@@ -110,7 +110,7 @@ NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
 * [Darknet YOLO](https://github.com/AlexeyAB/darknet)
 * [YOLOv5 >= 2.0](https://github.com/ultralytics/yolov5)
 * [YOLOR](https://github.com/WongKinYiu/yolor)
-* [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
+* [PP-YOLOE / PP-YOLOE+](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ppyoloe)
 * [YOLOv7](https://github.com/WongKinYiu/yolov7)
 * [YOLOv8](https://github.com/ultralytics/ultralytics)
 * [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)

config_infer_primary_ppyoloe_plus.txt

@@ -0,0 +1,26 @@
+[property]
+gpu-id=0
+net-scale-factor=0.0039215697906911373
+model-color-format=0
+custom-network-config=ppyoloe_plus_crn_s_80e_coco.cfg
+model-file=ppyoloe_plus_crn_s_80e_coco.wts
+model-engine-file=model_b1_gpu0_fp32.engine
+#int8-calib-file=calib.table
+labelfile-path=labels.txt
+batch-size=1
+network-mode=0
+num-detected-classes=80
+interval=0
+gie-unique-id=1
+process-mode=1
+network-type=0
+cluster-mode=2
+maintain-aspect-ratio=0
+parse-bbox-func-name=NvDsInferParseYolo
+custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
+engine-create-func-name=NvDsInferYoloCudaEngineGet
+
+[class-attrs-all]
+nms-iou-threshold=0.7
+pre-cluster-threshold=0.25
+topk=300

docs/PPYOLOE.md

@@ -1,8 +1,8 @@
-# PP-YOLOE usage
+# PP-YOLOE / PP-YOLOE+ usage
 
 * [Convert model](#convert-model)
 * [Compile the lib](#compile-the-lib)
-* [Edit the config_infer_primary_ppyoloe file](#edit-the-config_infer_primary_ppyoloe-file)
+* [Edit the config_infer_primary_ppyoloe_plus file](#edit-the-config_infer_primary_ppyoloe_plus-file)
 * [Edit the deepstream_app_config file](#edit-the-deepstream_app_config-file)
 * [Testing the model](#testing-the-model)
 
@@ -12,7 +12,7 @@
 
 #### 1. Download the PaddleDetection repo and install the requirements
 
-https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/INSTALL.md
+https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/INSTALL.md
 
 **NOTE**: It is recommended to use Python virtualenv.
 
@@ -22,20 +22,20 @@ Copy the `gen_wts_ppyoloe.py` file from `DeepStream-Yolo/utils` directory to the
 
 #### 3. Download the model
 
-Download the `pdparams` file from [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe) releases (example for PP-YOLOE-s)
+Download the `pdparams` file from [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ppyoloe) releases (example for PP-YOLOE+_s)
 
 ```
-wget https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams
+wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams
 ```
 
 **NOTE**: You can use your custom model, but it is important to keep the YOLO model reference (`ppyoloe_`) in you `cfg` and `weights`/`wts` filenames to generate the engine correctly.
 
 #### 4. Convert model
 
-Generate the `cfg` and `wts` files (example for PP-YOLOE-s)
+Generate the `cfg` and `wts` files (example for PP-YOLOE+_s)
 
 ```
-python3 gen_wts_ppyoloe.py -w ppyoloe_crn_s_400e_coco.pdparams -c configs/ppyoloe/ppyoloe_crn_s_400e_coco.yml
+python3 gen_wts_ppyoloe.py -w ppyoloe_plus_crn_s_80e_coco.pdparams -c configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml
 ```
 
 #### 5. Copy generated files
@@ -80,19 +80,27 @@ Open the `DeepStream-Yolo` folder and compile the lib
 
 ##
 
-### Edit the config_infer_primary_ppyoloe file
+### Edit the config_infer_primary_ppyoloe_plus file
 
-Edit the `config_infer_primary_ppyoloe.txt` file according to your model (example for PP-YOLOE-s)
+Edit the `config_infer_primary_ppyoloe_plus.txt` file according to your model (example for PP-YOLOE+_s)
 
 ```
 [property]
 ...
-custom-network-config=ppyoloe_crn_s_400e_coco.cfg
+custom-network-config=ppyoloe_plus_crn_s_80e_coco.cfg
-model-file=ppyoloe_crn_s_400e_coco.wts
+model-file=ppyoloe_plus_crn_s_80e_coco.wts
 ...
 ```
 
-**NOTE**: The PP-YOLOE uses normalization on the image preprocess. It is important to change the `net-scale-factor` and `offsets` according to the trained values.
+**NOTE**: If you use the **legacy** model, you should edit the `config_infer_primary_ppyoloe.txt` file.
+
+**NOTE**: The **PP-YOLOE+** uses zero mean normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
+
+```
+net-scale-factor=0.0039215697906911373
+```
+
+**NOTE**: The **PP-YOLOE (legacy)** uses normalization on the image preprocess. It is important to change the `net-scale-factor` and `offsets` according to the trained values.
 
 Default: `mean = 0.485, 0.456, 0.406` and `std = 0.229, 0.224, 0.225`
 
@@ -109,9 +117,11 @@ offsets=123.675;116.28;103.53
 ...
 [primary-gie]
 ...
-config-file=config_infer_primary_ppyoloe.txt
+config-file=config_infer_primary_ppyoloe_plus.txt
 ```
 
+**NOTE**: If you use the **legacy** model, you should edit it to `config_infer_primary_ppyoloe.txt`.
+
 ##
 
 ### Testing the model

utils/gen_wts_ppyoloe.py

@@ -3,7 +3,7 @@ import struct
 import paddle
 import numpy as np
 from ppdet.core.workspace import load_config, merge_config
-from ppdet.utils.check import check_gpu, check_version, check_config
+from ppdet.utils.check import check_version, check_config
 from ppdet.utils.cli import ArgsParser
 from ppdet.engine import Trainer
 from ppdet.slim import build_slim_model
@@ -273,6 +273,7 @@ class Layers(object):
 
     def get_state_dict(self, state_dict):
         for k, v in state_dict.items():
+            if 'alpha' not in k:
                 vr = v.reshape([-1]).numpy()
                 self.fw.write('{} {} '.format(k, len(vr)))
                 for vv in vr: