Format code with pre-commit

Author: triple-Mu

README.md

@@ -14,14 +14,14 @@ Implementation of paper - [YOLOv6: A Single-Stage Object Detection Framework for
 
 YOLOv6 has a series of models for various industrial scenarios, including N/T/S/M/L, which the architectures vary considering the model size for better accuracy-speed trade-off. And some Bag-of-freebies methods are introduced to further improve the performance, such as self-distillation and more training epochs. For industrial deployment, we adopt QAT with channel-wise distillation and graph optimization to pursue extreme performance.
 
-YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.5% AP with 495 FPS, and the quantized YOLOv6-S model achieves 43.3% AP at a accelerated speed of 869 FPS on T4. YOLOv6-T/M/L also have excellent performance, which show higher accuracy than other detectors with the similar inference speed. 
+YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.5% AP with 495 FPS, and the quantized YOLOv6-S model achieves 43.3% AP at a accelerated speed of 869 FPS on T4. YOLOv6-T/M/L also have excellent performance, which show higher accuracy than other detectors with the similar inference speed.
 
 
 ## What's New
 
 - Release M/L models and update N/T/S models with enhanced performance.⭐️ [Benchmark](#Benchmark)
 - 2x faster training time.
-- Fix the degration of performance when evaluating on 640x640 inputs. 
+- Fix the degration of performance when evaluating on 640x640 inputs.
 - Customized quantization methods. 🚀 [Quantization Tutorial](./tools/qat/README.md)
 
 ## Benchmark
@@ -34,7 +34,7 @@ YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.
 | [**YOLOv6-L-ReLU**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l_relu.pt) | 640  | 51.7                                  | 113                                     | 149                                      | 58.5                 | 144.0               |
 | [**YOLOv6-L**](https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6l.pt) | 640  | 52.5                                  | 98                                      | 121                                      | 58.5                 | 144.0               |
 
-### Quantized model 🚀 
+### Quantized model 🚀
 
 | Model                 | Size | Precision | mAP<sup>val<br/>0.5:0.95 | Speed<sup>T4<br/>trt b1 <br/>(fps) | Speed<sup>T4<br/>trt b32 <br/>(fps) |
 | :-------------------- | ---- | --------- | :----------------------- | ---------------------------------- | ----------------------------------- |
@@ -135,19 +135,19 @@ python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
 									--epoch 300 \
 									--device 0,1,2,3,4,5,6,7 \
 									--name yolov6m_coco # yolov6l_coco
-									
-                                                                                      
+
+
 # Step 2: Self-distillation training
 python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \
-									--batch 256 \ # 128 for distillation of yolov6l 
+									--batch 256 \ # 128 for distillation of yolov6l
 									--conf configs/yolov6m.py \ # configs/yolov6l.py
 									--data data/coco.yaml \
 									--epoch 300 \
 									--device 0,1,2,3,4,5,6,7 \
 									--distill \
 									--teacher_model_path runs/train/yolov6m_coco/weights/best_ckpt.pt \ # # yolov6l_coco
 									--name yolov6m_coco # yolov6l_coco
-							
+
 ```
 </details>
 
@@ -242,5 +242,5 @@ python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir / video.mp4
  * Tutorial: [How to train YOLOv6 on a custom dataset](https://blog.roboflow.com/how-to-train-yolov6-on-a-custom-dataset/) <a href="https://colab.research.google.com/drive/1YnbqOinBZV-c9I7fk_UL6acgnnmkXDMM"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
  * Demo of YOLOv6 inference on Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mahdilamb/YOLOv6/blob/main/inference.ipynb)
 </details>
-  
+
 ### [FAQ（Continuously updated）](https://github.com/meituan/YOLOv6/wiki/FAQ%EF%BC%88Continuously-updated%EF%BC%89)

configs/experiment/eval_640_repro.py

@@ -57,4 +57,4 @@
         force_no_pad=True,
         not_infer_on_rect=True,
     )
-)
+)

configs/experiment/yolov6n_with_eval_params.py

@@ -31,7 +31,7 @@
 solver = dict(
     optim='SGD',
     lr_scheduler='Cosine',
-    lr0=0.02, #0.01 # 0.02 
+    lr0=0.02, #0.01 # 0.02
     lrf=0.01,
     momentum=0.937,
     weight_decay=0.0005,
@@ -55,15 +55,15 @@
 )
 
 # Eval params when eval model.
-# If eval_params item is list, eg conf_thres=[0.03, 0.03], 
+# If eval_params item is list, eg conf_thres=[0.03, 0.03],
 # first will be used in train.py and second will be used in eval.py.
 eval_params = dict(
     batch_size=None,  #None mean will be the same as batch on one device * 2
     img_size=None,  #None mean will be the same as train image size
     conf_thres=0.03,
     iou_thres=0.65,
 
-    #pading and scale coord 
+    #pading and scale coord
     test_load_size=None, #None mean will be the same as test image size
     letterbox_return_int=False,
     force_no_pad=False,

configs/experiment/yolov6s_csp_scaled.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6s_csp',
     pretrained=None,
-    depth_multiple=0.70,  
+    depth_multiple=0.70,
     width_multiple=0.50,
     backbone=dict(
         type='CSPBepBackbone',
@@ -39,12 +39,12 @@
     weight_decay=0.0005,
     warmup_epochs=3.0,
     warmup_momentum=0.8,
-    warmup_bias_lr=0.1       
+    warmup_bias_lr=0.1
 )
- 
+
 data_aug = dict(
-    hsv_h=0.015,  
-    hsv_s=0.7, 
+    hsv_h=0.015,
+    hsv_s=0.7,
     hsv_v=0.4,
     degrees=0.0,
     translate=0.1,

configs/experiment/yolov6t_csp_scaled.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6n_csp',
     pretrained=None,
-    depth_multiple=0.60,  
+    depth_multiple=0.60,
     width_multiple=0.50,
     backbone=dict(
         type='CSPBepBackbone',
@@ -39,12 +39,12 @@
     weight_decay=0.0005,
     warmup_epochs=3.0,
     warmup_momentum=0.8,
-    warmup_bias_lr=0.1       
+    warmup_bias_lr=0.1
 )
- 
+
 data_aug = dict(
-    hsv_h=0.015,  
-    hsv_s=0.7, 
+    hsv_h=0.015,
+    hsv_s=0.7,
     hsv_v=0.4,
     degrees=0.0,
     translate=0.1,

configs/repopt/yolov6_tiny_hs.py

@@ -55,4 +55,4 @@
 )
 
 # Choose Rep-block by the training Mode, choices=["repvgg", "hyper-search", "repopt"]
-training_mode='hyper_search'
+training_mode='hyper_search'

configs/repopt/yolov6_tiny_opt.py

@@ -55,4 +55,4 @@
     mixup=0.0,
 )
 # Choose Rep-block by the training Mode, choices=["repvgg", "hyper-search", "repopt"]
-training_mode='repopt'
+training_mode='repopt'

configs/repopt/yolov6n_hs.py

@@ -31,7 +31,7 @@
 solver = dict(
     optim='SGD',
     lr_scheduler='Cosine',
-    lr0=0.02, #0.01 # 0.02 
+    lr0=0.02, #0.01 # 0.02
     lrf=0.01,
     momentum=0.937,
     weight_decay=0.0005,
@@ -55,4 +55,4 @@
 )
 
 # Choose Rep-block by the training Mode, choices=["repvgg", "hyper-search", "repopt"]
-training_mode='hyper_search'
+training_mode='hyper_search'

configs/repopt/yolov6n_opt.py

@@ -32,7 +32,7 @@
 solver = dict(
     optim='SGD',
     lr_scheduler='Cosine',
-    lr0=0.02, #0.01 # 0.02 
+    lr0=0.02, #0.01 # 0.02
     lrf=0.01,
     momentum=0.937,
     weight_decay=0.0005,
@@ -55,4 +55,4 @@
     mixup=0.0,
 )
 # Choose Rep-block by the training Mode, choices=["repvgg", "hyper-search", "repopt"]
-training_mode='repopt'
+training_mode='repopt'

configs/yolov6l.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6l',
     pretrained=None,
-    depth_multiple=1.0,  
+    depth_multiple=1.0,
     width_multiple=1.0,
     backbone=dict(
         type='CSPBepBackbone',
@@ -43,12 +43,12 @@
     weight_decay=0.0005,
     warmup_epochs=3.0,
     warmup_momentum=0.8,
-    warmup_bias_lr=0.1       
+    warmup_bias_lr=0.1
 )
- 
+
 data_aug = dict(
-    hsv_h=0.015,  
-    hsv_s=0.7, 
+    hsv_h=0.015,
+    hsv_s=0.7,
     hsv_v=0.4,
     degrees=0.0,
     translate=0.1,
@@ -59,5 +59,5 @@
     mosaic=1.0,
     mixup=0.1,
 )
-training_mode = "conv_silu" 
-# use normal conv to speed up training and further improve accuracy.
+training_mode = "conv_silu"
+# use normal conv to speed up training and further improve accuracy.

configs/yolov6l_finetune.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6l',
     pretrained='weights/yolov6l.pt',
-    depth_multiple=1.0,  
+    depth_multiple=1.0,
     width_multiple=1.0,
     backbone=dict(
         type='CSPBepBackbone',
@@ -59,5 +59,5 @@
     mosaic=1.0,
     mixup=0.243,
 )
-training_mode = "conv_silu" 
-# use normal conv to speed up training and further improve accuracy.
+training_mode = "conv_silu"
+# use normal conv to speed up training and further improve accuracy.

configs/yolov6l_relu.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6l_relu',
     pretrained=None,
-    depth_multiple=1.0,  
+    depth_multiple=1.0,
     width_multiple=1.0,
     backbone=dict(
         type='CSPBepBackbone',
@@ -43,12 +43,12 @@
     weight_decay=0.0005,
     warmup_epochs=3.0,
     warmup_momentum=0.8,
-    warmup_bias_lr=0.1       
+    warmup_bias_lr=0.1
 )
- 
+
 data_aug = dict(
-    hsv_h=0.015,  
-    hsv_s=0.7, 
+    hsv_h=0.015,
+    hsv_s=0.7,
     hsv_v=0.4,
     degrees=0.0,
     translate=0.1,
@@ -58,4 +58,4 @@
     fliplr=0.5,
     mosaic=1.0,
     mixup=0.1,
-)
+)

configs/yolov6l_relu_finetune.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6l_relu',
     pretrained='weights/yolov6l_relu.pt',
-    depth_multiple=1.0,  
+    depth_multiple=1.0,
     width_multiple=1.0,
     backbone=dict(
         type='CSPBepBackbone',
@@ -58,4 +58,4 @@
     fliplr=0.5,
     mosaic=1.0,
     mixup=0.243,
-)
+)

configs/yolov6m.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6m',
     pretrained=None,
-    depth_multiple=0.60,  
+    depth_multiple=0.60,
     width_multiple=0.75,
     backbone=dict(
         type='CSPBepBackbone',
@@ -43,12 +43,12 @@
     weight_decay=0.0005,
     warmup_epochs=3.0,
     warmup_momentum=0.8,
-    warmup_bias_lr=0.1       
+    warmup_bias_lr=0.1
 )
- 
+
 data_aug = dict(
-    hsv_h=0.015,  
-    hsv_s=0.7, 
+    hsv_h=0.015,
+    hsv_s=0.7,
     hsv_v=0.4,
     degrees=0.0,
     translate=0.1,
@@ -58,4 +58,4 @@
     fliplr=0.5,
     mosaic=1.0,
     mixup=0.1,
-)
+)

configs/yolov6m_finetune.py

@@ -2,7 +2,7 @@
 model = dict(
     type='YOLOv6m',
     pretrained='weights/yolov6m.pt',
-    depth_multiple=0.60,  
+    depth_multiple=0.60,
     width_multiple=0.75,
     backbone=dict(
         type='CSPBepBackbone',
@@ -58,4 +58,4 @@
     fliplr=0.5,
     mosaic=1.0,
     mixup=0.243,
-)
+)

configs/yolov6n.py

@@ -31,7 +31,7 @@
 solver = dict(
     optim='SGD',
     lr_scheduler='Cosine',
-    lr0=0.02, #0.01 # 0.02 
+    lr0=0.02, #0.01 # 0.02
     lrf=0.01,
     momentum=0.937,
     weight_decay=0.0005,

deploy/ONNX/OpenCV/yolo.py

@@ -25,11 +25,11 @@
 
 def draw_label(input_image, label, left, top):
     """Draw text onto image at location."""
-    
+
     # Get text size.
     text_size = cv2.getTextSize(label, FONT_FACE, FONT_SCALE, THICKNESS)
     dim, baseline = text_size[0], text_size[1]
-    # Use text size to create a BLACK rectangle. 
+    # Use text size to create a BLACK rectangle.
     cv2.rectangle(input_image, (left, top), (left + dim[0], top + dim[1] + baseline), BLACK, cv2.FILLED)
     # Display text inside the rectangle.
     cv2.putText(input_image, label, (left, top + dim[1]), FONT_FACE, FONT_SCALE, YELLOW, THICKNESS, cv2.LINE_AA)
@@ -88,7 +88,7 @@ def post_process(input_image, outputs):
 				top = int((cy - h/2) * y_factor)
 				width = int(w * x_factor)
 				height = int(h * y_factor)
-			  
+
 				box = np.array([left, top, width, height])
 				boxes.append(box)
 
@@ -129,7 +129,7 @@ def post_process(input_image, outputs):
 	# Give the weight files to the model and load the network using them.
 	net = cv2.dnn.readNet(model_path)
 
-	# Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the 
+	# Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the
 	# timings for each of the layers(in layersTimes)
 	# Process image.
 	cycles = 300
@@ -140,10 +140,10 @@ def post_process(input_image, outputs):
 		t, _ = net.getPerfProfile()
 		total_time += t
 		print(f'Cycle [{i + 1}]:\t{t * 1000.0 / cv2.getTickFrequency():.2f}\tms')
-	
+
 	avg_time = total_time / cycles
 	label = 'Average Inference time: %.2f ms' % (avg_time * 1000.0 / cv2.getTickFrequency())
 	print(f'Model: {window_name}\n{label}')
 	cv2.putText(img, label, (20, 40), FONT_FACE, FONT_SCALE, RED, THICKNESS, cv2.LINE_AA)
 	cv2.imshow(window_name, img)
-	cv2.waitKey(0)
+	cv2.waitKey(0)