support yolov6s_v2_qat_dis (PaddlePaddle#1419)

lizexu123 · Sep 13, 2022 · b747596 · b747596
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diff --git a/example/auto_compression/pytorch_yolo_series/README.md b/example/auto_compression/pytorch_yolo_series/README.md
@@ -18,23 +18,26 @@
 
 ## 2.Benchmark
 
-| 模型  |  策略  | 输入尺寸 | mAP<sup>val<br>0.5:0.95 | 模型体积 | 预测时延<sup><small>FP32</small><sup><br><sup> |预测时延<sup><small>FP16</small><sup><br><sup> | 预测时延<sup><small>INT8</small><sup><br><sup> | 内存占用 | 显存占用  |  配置文件 | Inference模型  |
-| :-------- |:-------- |:--------: | :--------: | :---------------------: | :----------------: | :----------------: |:----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: |
-| YOLOv5s |  Base模型 | 640*640  |  37.4   | 28.1MB  | 5.95ms  |   2.44ms   |  -  | 1718MB | 705MB | - |  [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov5s.onnx) |
-| YOLOv5s |  离线量化 | 640*640  |  36.0   | 7.4MB |   - |   -   |  1.87ms  | 736MB | 315MB  |   [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/post_training_quantization/pytorch_yolo_series) | - |
-| YOLOv5s |  ACT量化训练  | 640*640  |  **36.9**   | 7.4MB |    - |   -   |  **1.87ms**  | 736MB | 315MB |  [config](./configs/yolov5s_qat_dis.yaml) | [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov5s_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov5s_quant_onnx.tar) |
-|  |  |  |  |  |  |  |  |  |
-| YOLOv6s |  Base模型 | 640*640  |  42.4   | 65.9MB |   9.06ms  |   2.90ms   |  - | 1208MB   | 555MB  | -  | [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov6s.onnx) |
-| YOLOv6s |  KL离线量化 | 640*640  |  30.3   | 16.8MB |   - |   -   |  1.83ms  | 736MB   | 315MB | [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/post_training_quantization/pytorch_yolo_series) | - |
-| YOLOv6s |  量化蒸馏训练 | 640*640  |  **41.3**   | 16.8MB |   - |   -   |  **1.83ms**  | 736MB   | 315MB |  [config](./configs/yolov6s_qat_dis.yaml) | [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov6s_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov6s_quant_onnx.tar) |
-|  |  |  |  |  |  |  |  |  |
-| YOLOv7 |  Base模型 | 640*640  |  51.1   | 141MB  |  26.84ms  |   7.44ms   |  -  | 1722MB  | 917MB | -  | [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov7.onnx) |
-| YOLOv7 |  离线量化 | 640*640  |  50.2   | 36MB |   - |   -   |  4.55ms  | 827MB  | 363MB  |  [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/post_training_quantization/pytorch_yolo_series) | - |
-| YOLOv7 |  ACT量化训练 | 640*640  |  **50.9**   | 36MB |   - |   -   |  **4.55ms**  | 827MB  | 363MB |  [config](./configs/yolov7_qat_dis.yaml) | [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_quant_onnx.tar) |
-|  |  |  |  |  |  |  |  |  |
-| YOLOv7-Tiny |  Base模型 | 640*640  |  37.3   | 24MB  |  5.06ms  |   2.32ms   |  - | 738MB  | 349MB  | -  | [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov7-tiny.onnx) |
-| YOLOv7-Tiny |  离线量化 | 640*640  |  35.8   | 6.1MB  |   - |   -   |  1.68ms  | 729MB  | 315MB  |  - | - |
-| YOLOv7-Tiny |  ACT量化训练 | 640*640  |  **37.0**   | 6.1MB  |  - |   -   |  **1.68ms**  | 729MB  | 315MB |   [config](./configs/yolov7_tiny_qat_dis.yaml) | [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_tiny_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_tiny_quant_onnx.tar) |
+| 模型            |  策略  | 输入尺寸 | mAP<sup>val<br>0.5:0.95 |  模型体积  | 预测时延<sup><small>FP32</small><sup><br><sup> |预测时延<sup><small>FP16</small><sup><br><sup> | 预测时延<sup><small>INT8</small><sup><br><sup> | 内存占用 | 显存占用  |                                                           配置文件                                                           |                                                                                      Inference模型                                                                                       |
+|:--------------|:-------- |:--------: |:-----------------------:|:------:| :----------------: | :----------------: |:----------------: | :----------------: | :---------------: |:------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| YOLOv5s       |  Base模型 | 640*640  |          37.4           | 28.1MB | 5.95ms  |   2.44ms   |  -  | 1718MB | 705MB |                                                            -                                                             |                                                           [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov5s.onnx)                                                           |
+| YOLOv5s       |  离线量化 | 640*640  |          36.0           | 7.4MB  |   - |   -   |  1.87ms  | 736MB | 315MB  | [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/post_training_quantization/pytorch_yolo_series) |                                                                                           -                                                                                            |
+| YOLOv5s       |  ACT量化训练  | 640*640  |        **36.9**         | 7.4MB  |    - |   -   |  **1.87ms**  | 736MB | 315MB |                                         [config](./configs/yolov5s_qat_dis.yaml)                                         |      [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov5s_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov5s_quant_onnx.tar)      |
+|               |  |  |                         |        |  |  |  |  |
+| YOLOv6s       |  Base模型 | 640*640  |          42.4           | 65.9MB |   9.06ms  |   2.90ms   |  - | 1208MB   | 555MB  |                                                            -                                                             |                                                           [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov6s.onnx)                                                           |
+| YOLOv6s       |  KL离线量化 | 640*640  |          30.3           | 16.8MB |   - |   -   |  1.83ms  | 736MB   | 315MB | [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/post_training_quantization/pytorch_yolo_series) |                                                                                           -                                                                                            |
+| YOLOv6s       |  量化蒸馏训练 | 640*640  |        **41.3**         | 16.8MB |   - |   -   |  **1.83ms**  | 736MB   | 315MB |                                         [config](./configs/yolov6s_qat_dis.yaml)                                         |      [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov6s_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov6s_quant_onnx.tar)      |
+|               |  |  |                         |        |  |  |  |  |
+| YOLOv6s_v2    |  Base模型 | 640*640  |          43.4           | 67.4MB |   9.06ms  |   2.90ms   |  - | 1208MB   | 555MB  |                                                            -                                                             |                                                           [Model](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.onnx)                                                           |
+| YOLOv6s_v2    |  量化蒸馏训练 | 640*640  |        **43.0**         | 16.8MB |   - |   -   |  **1.83ms**  | 736MB   | 315MB |                                       [config](./configs/yolov6s_v2_qat_dis.yaml)                                        | [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov6s_v2_0_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov6s_v2_0_quant_onnx.tar) |
+|               |  |  |                         |        |  |  |  |  |
+| YOLOv7        |  Base模型 | 640*640  |          51.1           | 141MB  |  26.84ms  |   7.44ms   |  -  | 1722MB  | 917MB |                                                            -                                                             |                                                           [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov7.onnx)                                                            |
+| YOLOv7        |  离线量化 | 640*640  |          50.2           |  36MB  |   - |   -   |  4.55ms  | 827MB  | 363MB  | [config](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/post_training_quantization/pytorch_yolo_series) |                                                                                           -                                                                                            |
+| YOLOv7        |  ACT量化训练 | 640*640  |        **50.9**         |  36MB  |   - |   -   |  **4.55ms**  | 827MB  | 363MB |                                         [config](./configs/yolov7_qat_dis.yaml)                                          |       [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_quant_onnx.tar)       |
+|               |  |  |                         |        |  |  |  |  |
+| YOLOv7-Tiny   |  Base模型 | 640*640  |          37.3           |  24MB  |  5.06ms  |   2.32ms   |  - | 738MB  | 349MB  |                                                            -                                                             |                                                         [Model](https://paddle-slim-models.bj.bcebos.com/act/yolov7-tiny.onnx)                                                         |
+| YOLOv7-Tiny   |  离线量化 | 640*640  |          35.8           | 6.1MB  |   - |   -   |  1.68ms  | 729MB  | 315MB  |                                                            -                                                             |                                                                                           -                                                                                            |
+| YOLOv7-Tiny   |  ACT量化训练 | 640*640  |        **37.0**         | 6.1MB  |  - |   -   |  **1.68ms**  | 729MB  | 315MB |                                       [config](./configs/yolov7_tiny_qat_dis.yaml)                                       |  [Infer Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_tiny_quant.tar) &#124; [ONNX Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov7_tiny_quant_onnx.tar)  |
 
 说明：
 - mAP的指标均在COCO val2017数据集中评测得到。

diff --git a/example/auto_compression/pytorch_yolo_series/configs/yolov6s_v2_qat_dis.yaml b/example/auto_compression/pytorch_yolo_series/configs/yolov6s_v2_qat_dis.yaml
@@ -0,0 +1,33 @@
+Global:
+  model_dir: ./yolov6s.onnx
+  image_path: None   # If image_path is set, it will be trained directly based on unlabeled images, no need to set the COCO dataset path.
+  coco_dataset_dir: dataset/coco/
+  coco_train_image_dir: train2017
+  coco_train_anno_path: annotations/instances_train2017.json
+  coco_val_image_dir: val2017
+  coco_val_anno_path: annotations/instances_val2017.json
+  arch: YOLOv6
+  nms_num_top_k: 1000
+
+Distillation:
+  alpha: 1.0
+  loss: soft_label
+
+Quantization:
+  onnx_format: true
+  activation_quantize_type: 'moving_average_abs_max'
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+
+TrainConfig:
+  train_iter: 8000
+  eval_iter: 1000
+  learning_rate:
+    type: CosineAnnealingDecay
+    learning_rate: 0.00003
+    T_max: 8000
+  optimizer_builder:
+    optimizer:
+      type: SGD
+    weight_decay: 0.00004
diff --git a/example/auto_compression/pytorch_yolo_series/eval.py b/example/auto_compression/pytorch_yolo_series/eval.py
@@ -51,6 +51,12 @@ def eval():
     val_program, feed_target_names, fetch_targets = load_inference_model(
         global_config["model_dir"], exe)
 
+    postprocess = YOLOPostProcess(
+        score_threshold=0.001,
+        nms_threshold=0.65,
+        multi_label=True,
+        num_top_k=global_config.get('nms_num_top_k', 30000))
+
     bboxes_list, bbox_nums_list, image_id_list = [], [], []
     with tqdm(
             total=len(val_loader),
@@ -62,8 +68,6 @@ def eval():
                            feed={feed_target_names[0]: data_all['image']},
                            fetch_list=fetch_targets,
                            return_numpy=False)
-            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'])
             bbox_nums_list.append(res['bbox_num'])

diff --git a/example/auto_compression/pytorch_yolo_series/post_process.py b/example/auto_compression/pytorch_yolo_series/post_process.py
@@ -80,17 +80,20 @@ class YOLOPostProcess(object):
         multi_label(bool): Whether keep multi label in boxes.
         keep_top_k(int): Number of total bboxes to be kept per image after NMS
                 step. -1 means keeping all bboxes after NMS step.
+        num_top_k(int): Maximum number of boxes put into torchvision.ops.nums()
     """
 
     def __init__(self,
                  score_threshold=0.25,
                  nms_threshold=0.5,
                  multi_label=False,
-                 keep_top_k=300):
+                 keep_top_k=300,
+                 num_top_k=30000):
         self.score_threshold = score_threshold
         self.nms_threshold = nms_threshold
         self.multi_label = multi_label
         self.keep_top_k = keep_top_k
+        self.num_top_k = num_top_k
 
     def _xywh2xyxy(self, x):
         # Convert from [x, y, w, h] to [x1, y1, x2, y2]
@@ -103,7 +106,6 @@ def _xywh2xyxy(self, x):
 
     def _non_max_suppression(self, prediction):
         max_wh = 4096  # (pixels) minimum and maximum box width and height
-        nms_top_k = 30000
 
         cand_boxes = prediction[..., 4] > self.score_threshold  # candidates
         output = [np.zeros((0, 6))] * prediction.shape[0]
@@ -137,8 +139,8 @@ def _non_max_suppression(self, prediction):
             num_box = boxes.shape[0]
             if not num_box:
                 continue
-            elif num_box > nms_top_k:
-                boxes = boxes[boxes[:, 4].argsort()[::-1][:nms_top_k]]
+            elif num_box > self.nms_top_k:
+                boxes = boxes[boxes[:, 4].argsort()[::-1][:self.nms_top_k]]
 
             # Batched NMS
             c = boxes[:, 5:6] * max_wh

diff --git a/example/auto_compression/pytorch_yolo_series/run.py b/example/auto_compression/pytorch_yolo_series/run.py
@@ -56,6 +56,11 @@ def gen():
 
 def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
     bboxes_list, bbox_nums_list, image_id_list = [], [], []
+    postprocess = YOLOPostProcess(
+        score_threshold=0.001,
+        nms_threshold=0.65,
+        multi_label=True,
+        num_top_k=global_config.get('nms_num_top_k', 30000))
     with tqdm(
             total=len(val_loader),
             bar_format='Evaluation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
@@ -66,9 +71,6 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
                            feed={test_feed_names[0]: data_all['image']},
                            fetch_list=test_fetch_list,
                            return_numpy=False)
-            res = {}
-            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'])
             bbox_nums_list.append(res['bbox_num'])