fix YOLO series act demo some bug (#1359)

example/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml

@@ -13,6 +13,7 @@ Distillation:
   loss: soft_label
 
 Quantization:
+  onnx_format: true
   use_pact: true
   activation_quantize_type: 'moving_average_abs_max'
   quantize_op_types:

example/auto_compression/detection/configs/ppyoloe_s_qat_dis.yaml

@@ -13,6 +13,7 @@ Distillation:
   loss: soft_label
 
 Quantization:
+  onnx_format: true
   use_pact: true
   activation_quantize_type: 'moving_average_abs_max'
   quantize_op_types:

example/auto_compression/pytorch_yolo_series/README.md

@@ -33,7 +33,7 @@
 | YOLOv7 |  ACT量化训练 | 640*640  |  **50.9**   | 36MB |   - |   -   |  **4.55ms**  |  [config](./configs/yolov7_qat_dis.yaml) | [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_quant.tar) | [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_quant.onnx) |
 |  |  |  |  |  |  |  |  |  |
 | YOLOv7-Tiny |  Base模型 | 640*640  |  37.3   | 24MB  |  5.06ms  |   2.32ms   |  -  |  - | [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov7-tiny.onnx) |
-| YOLOv7-Tiny |  离线量化 | 640*640  |  -   | 6.1MB  |   - |   -   |  1.68ms  |  - | - |
+| YOLOv7-Tiny |  离线量化 | 640*640  |  35.8   | 6.1MB  |   - |   -   |  1.68ms  |  - | - |
 | YOLOv7-Tiny |  ACT量化训练 | 640*640  |  **37.0**   | 6.1MB  |  - |   -   |  **1.68ms**  |  [config](./configs/yolov7_tiny_qat_dis.yaml) | [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_tiny_quant.tar) | [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_tiny_quant.onnx) |
 
 说明：
@@ -43,15 +43,15 @@
 ## 3. 自动压缩流程
 
 #### 3.1 准备环境
-- PaddlePaddle develop每日版本 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html)下载安装）
+- PaddlePaddle >= 2.3.2版本 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)根据相应环境的安装指令进行安装）
 - PaddleSlim develop 版本
 
-（1）安装paddlepaddle：
-```shell
+（1）安装paddlepaddle
+```
 # CPU
-pip install paddlepaddle
+pip install paddlepaddle==2.3.2
 # GPU
-pip install paddlepaddle-gpu
+pip install paddlepaddle-gpu==2.3.2
 ```
 
 （2）安装paddleslim：
@@ -90,22 +90,22 @@ dataset/coco/
 
 - YOLOv5:
 
-可通过[ultralytics/yolov5](https://github.com/ultralytics/yolov5) 官方的[导出教程](https://github.com/ultralytics/yolov5/issues/251)来准备ONNX模型。也可以下载准备好的[yolov5s.onnx](https://paddle-slim-models.bj.bcebos.com/act/yolov5s.onnx)。
-```shell
-python export.py --weights yolov5s.pt --include onnx
-```
+  本示例模型使用[ultralytics/yolov5](https://github.com/ultralytics/yolov5)的master分支导出，要求v6.1之后的ONNX模型，可以根据官方的[导出教程](https://github.com/ultralytics/yolov5/issues/251)来准备ONNX模型。也可以下载准备好的[yolov5s.onnx](https://paddle-slim-models.bj.bcebos.com/act/yolov5s.onnx)。
+  ```shell
+  python export.py --weights yolov5s.pt --include onnx
+  ```
 
 - YOLOv6:
 
-可通过[meituan/YOLOv6](https://github.com/meituan/YOLOv6)官方的[导出教程](https://github.com/meituan/YOLOv6/blob/main/deploy/ONNX/README.md)来准备ONNX模型。也可以下载已经准备好的[yolov6s.onnx](https://paddle-slim-models.bj.bcebos.com/act/yolov6s.onnx)。
+  可通过[meituan/YOLOv6](https://github.com/meituan/YOLOv6)官方的[导出教程](https://github.com/meituan/YOLOv6/blob/main/deploy/ONNX/README.md)来准备ONNX模型。也可以下载已经准备好的[yolov6s.onnx](https://paddle-slim-models.bj.bcebos.com/act/yolov6s.onnx)。
 
 - YOLOv7: 可通过[WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7)的导出脚本来准备ONNX模型，具体步骤如下：
-```shell
-git clone https://github.com/WongKinYiu/yolov7.git
-python export.py --weights yolov7-tiny.pt --grid
-```
+  ```shell
+  git clone https://github.com/WongKinYiu/yolov7.git
+  python export.py --weights yolov7-tiny.pt --grid
+  ```
 
-**注意**：目前ACT支持不带NMS模型，使用如上命令导出即可。也可以直接下载我们已经准备好的[yolov7.onnx](https://paddle-slim-models.bj.bcebos.com/act/yolov7-tiny.onnx)。
+  **注意**：目前ACT支持**不带NMS**模型，使用如上命令导出即可。也可以直接下载我们已经准备好的[yolov7.onnx](https://paddle-slim-models.bj.bcebos.com/act/yolov7-tiny.onnx)。
 
 #### 3.4 自动压缩并产出模型
 
@@ -138,25 +138,11 @@ python eval.py --config_path=./configs/yolov7_tiny_qat_dis.yaml
 
 #### 导出至ONNX使用TensorRT部署
 
-- 首先安装Paddle2onnx：
-```shell
-pip install paddle2onnx==1.0.0rc3
-```
-
-- 然后将量化模型导出至ONNX：
-```shell
-paddle2onnx --model_dir output/ \
-            --model_filename model.pdmodel \
-            --params_filename model.pdiparams \
-            --opset_version 13 \
-            --enable_onnx_checker True \
-            --save_file yolov7_quant.onnx \
-            --deploy_backend tensorrt
-```
+执行完自动压缩后会默认在`save_dir`中生成`quant_model.onnx`的ONNX模型文件，可以直接使用TensorRT测试脚本进行验证。
 
 - 进行测试：
 ```shell
-python yolov7_onnx_trt.py --model_path=yolov7_quant.onnx --image_file=images/000000570688.jpg --precision=int8
+python yolov7_onnx_trt.py --model_path=output/quant_model.onnx --image_file=images/000000570688.jpg --precision=int8
 ```
 
 #### Paddle-TensorRT部署

example/auto_compression/pytorch_yolo_series/configs/yolov5s_qat_dis.yaml

@@ -15,7 +15,6 @@ Distillation:
 Quantization:
   onnx_format: true
   use_pact: true
-  onnx_format: False
   activation_quantize_type: 'moving_average_abs_max'
   quantize_op_types:
   - conv2d

example/auto_compression/pytorch_yolo_series/eval.py

@@ -32,6 +32,8 @@ def argsparser():
         default=None,
         help="path of compression strategy config.",
         required=True)
+    parser.add_argument(
+        '--batch_size', type=int, default=1, help="Batch size of model input.")
     parser.add_argument(
         '--devices',
         type=str,
@@ -83,7 +85,8 @@ def main():
         anno_path=global_config['val_anno_path'])
     global anno_file
     anno_file = dataset.ann_file
-    val_loader = paddle.io.DataLoader(dataset, batch_size=1)
+    val_loader = paddle.io.DataLoader(
+        dataset, batch_size=FLAGS.batch_size, drop_last=True)
 
     eval()
 

example/auto_compression/pytorch_yolo_series/post_process.py

@@ -159,8 +159,8 @@ class YOLOPostProcess(object):
             if len(pred.shape) == 1:
                 pred = pred[np.newaxis, :]
             pred_bboxes = pred[:, :4]
-            scale_factor = np.tile(scale_factor[i][::-1], (1, 2))
-            pred_bboxes /= scale_factor
+            scale = np.tile(scale_factor[i][::-1], (2))
+            pred_bboxes /= scale
             bbox = np.concatenate(
                 [
                     pred[:, -1][:, np.newaxis], pred[:, -2][:, np.newaxis],

example/post_training_quantization/pytorch_yolo_series/configs/yolov5s_ptq.yaml

 arch: YOLOv5
 model_dir: ./yolov5s.onnx
-dataset_dir: /dataset/coco/
+dataset_dir: dataset/coco/
 train_image_dir: train2017
 val_image_dir: val2017
 train_anno_path: annotations/instances_train2017.json

example/post_training_quantization/pytorch_yolo_series/eval.py

@@ -20,7 +20,7 @@ from tqdm import tqdm
 import paddle
 from paddleslim.common import load_config as load_slim_config
 from paddleslim.common import load_inference_model
-from post_process import YOLOv6PostProcess, coco_metric
+from post_process import YOLOPostProcess, coco_metric
 from dataset import COCOValDataset
 
 
@@ -32,6 +32,8 @@ def argsparser():
         default=None,
         help="path of compression strategy config.",
         required=True)
+    parser.add_argument(
+        '--batch_size', type=int, default=1, help="Batch size of model input.")
     parser.add_argument(
         '--devices',
         type=str,
@@ -60,8 +62,7 @@ def eval():
                            feed={feed_target_names[0]: data_all['image']},
                            fetch_list=fetch_targets,
                            return_numpy=False)
-            res = {}
-            postprocess = YOLOv6PostProcess(
+            postprocess = YOLOPostProcess(
                 score_threshold=0.001, nms_threshold=0.65, multi_label=True)
             res = postprocess(np.array(outs[0]), data_all['scale_factor'])
             bboxes_list.append(res['bbox'])
@@ -83,7 +84,8 @@ def main():
         anno_path=config['val_anno_path'])
     global anno_file
     anno_file = dataset.ann_file
-    val_loader = paddle.io.DataLoader(dataset, batch_size=1)
+    val_loader = paddle.io.DataLoader(
+        dataset, batch_size=FLAGS.batch_size, drop_last=True)
 
     eval()
 

example/post_training_quantization/pytorch_yolo_series/post_process.py

@@ -70,9 +70,9 @@ def nms(boxes, scores, iou_threshold):
     return keep
 
 
-class YOLOv6PostProcess(object):
+class YOLOPostProcess(object):
     """
-    Post process of YOLOv6 network.
+    Post process of YOLO serise network.
     args:
         score_threshold(float): Threshold to filter out bounding boxes with low 
                 confidence score. If not provided, consider all boxes.
@@ -159,8 +159,8 @@ class YOLOv6PostProcess(object):
             if len(pred.shape) == 1:
                 pred = pred[np.newaxis, :]
             pred_bboxes = pred[:, :4]
-            scale_factor = np.tile(scale_factor[i][::-1], (1, 2))
-            pred_bboxes /= scale_factor
+            scale = np.tile(scale_factor[i][::-1], (2))
+            pred_bboxes /= scale
             bbox = np.concatenate(
                 [
                     pred[:, -1][:, np.newaxis], pred[:, -2][:, np.newaxis],