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# Global Wheat 2020 dataset http://www.global-wheat.com/
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# Train command: python train.py --data GlobalWheat2020.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /datasets/GlobalWheat2020
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# /yolov5
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: # 3422 images
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- ../datasets/GlobalWheat2020/images/arvalis_1
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- ../datasets/GlobalWheat2020/images/arvalis_2
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- ../datasets/GlobalWheat2020/images/arvalis_3
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- ../datasets/GlobalWheat2020/images/ethz_1
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- ../datasets/GlobalWheat2020/images/rres_1
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- ../datasets/GlobalWheat2020/images/inrae_1
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- ../datasets/GlobalWheat2020/images/usask_1
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val: # 748 images (WARNING: train set contains ethz_1)
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- ../datasets/GlobalWheat2020/images/ethz_1
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test: # 1276 images
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- ../datasets/GlobalWheat2020/images/utokyo_1
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- ../datasets/GlobalWheat2020/images/utokyo_2
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- ../datasets/GlobalWheat2020/images/nau_1
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- ../datasets/GlobalWheat2020/images/uq_1
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# number of classes
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nc: 1
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# class names
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names: [ 'wheat_head' ]
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# download command/URL (optional) --------------------------------------------------------------------------------------
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download: |
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from utils.general import download, Path
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# Download
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dir = Path('../datasets/GlobalWheat2020') # dataset directory
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urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
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download(urls, dir=dir)
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# Make Directories
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for p in 'annotations', 'images', 'labels':
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(dir / p).mkdir(parents=True, exist_ok=True)
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# Move
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for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
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'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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(dir / p).rename(dir / 'images' / p) # move to /images
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f = (dir / p).with_suffix('.json') # json file
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if f.exists():
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f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
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# Train command: python train.py --data VisDrone.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /VisDrone
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# /yolov5
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../VisDrone/VisDrone2019-DET-train/images # 6471 images
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val: ../VisDrone/VisDrone2019-DET-val/images # 548 images
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test: ../VisDrone/VisDrone2019-DET-test-dev/images # 1610 images
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# number of classes
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nc: 10
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# class names
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names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
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# download command/URL (optional) --------------------------------------------------------------------------------------
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download: |
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from utils.general import download, os, Path
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def visdrone2yolo(dir):
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from PIL import Image
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from tqdm import tqdm
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def convert_box(size, box):
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# Convert VisDrone box to YOLO xywh box
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dw = 1. / size[0]
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dh = 1. / size[1]
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return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
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(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
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pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
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for f in pbar:
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img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
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lines = []
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with open(f, 'r') as file: # read annotation.txt
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for row in [x.split(',') for x in file.read().strip().splitlines()]:
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if row[4] == '0': # VisDrone 'ignored regions' class 0
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continue
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cls = int(row[5]) - 1
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box = convert_box(img_size, tuple(map(int, row[:4])))
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lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
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with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
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fl.writelines(lines) # write label.txt
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# Download
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dir = Path('../VisDrone') # dataset directory
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urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
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'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
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download(urls, dir=dir)
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# Convert
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for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
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visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
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# Train command: python train.py --data argoverse_hd.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /argoverse
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# /yolov5
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# download command/URL (optional)
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download: bash data/scripts/get_argoverse_hd.sh
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images
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val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges
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test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview
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# number of classes
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nc: 8
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# class names
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ]
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# COCO 2017 dataset http://cocodataset.org
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# Train command: python train.py --data coco.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /coco
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# /yolov5
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# download command/URL (optional)
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download: bash data/scripts/get_coco.sh
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../coco/train2017.txt # 118287 images
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val: ../coco/val2017.txt # 5000 images
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test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
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# number of classes
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nc: 80
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# class names
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush' ]
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# Print classes
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# with open('data/coco.yaml') as f:
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# d = yaml.safe_load(f) # dict
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# for i, x in enumerate(d['names']):
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# print(i, x)
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# COCO 2017 dataset http://cocodataset.org - first 128 training images
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# Train command: python train.py --data coco128.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /coco128
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# /yolov5
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# download command/URL (optional)
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download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: ../coco128/images/train2017/ # 128 images
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val: ../coco128/images/train2017/ # 128 images
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# number of classes
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nc: 80
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# class names
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names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
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'hair drier', 'toothbrush' ]
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# Global Wheat 2020 dataset http://www.global-wheat.com/
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# Train command: python train.py --data GlobalWheat2020.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /datasets/GlobalWheat2020
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# /yolov5
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: # 3422 images
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- ../../data/exitPP/train/images/
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val: # 748 images (WARNING: train set contains ethz_1)
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- ../../data/exitPP/val/images/
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test: # 1276 images
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- ../../data/exitPP/val/images/
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# number of classes
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nc: 5
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# class names
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names: [ 'ex','dEx','qV','wF','oth']
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# download command/URL (optional) --------------------------------------------------------------------------------------
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# download: |
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# from utils.general import download, Path
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# # Download
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# dir = Path('../datasets/GlobalWheat2020') # dataset directory
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# urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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# 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
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# download(urls, dir=dir)
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# # Make Directories
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# for p in 'annotations', 'images', 'labels':
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# (dir / p).mkdir(parents=True, exist_ok=True)
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# # Move
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# for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
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# 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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# (dir / p).rename(dir / 'images' / p) # move to /images
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# f = (dir / p).with_suffix('.json') # json file
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# if f.exists():
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# f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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# Global Wheat 2020 dataset http://www.global-wheat.com/
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# Train command: python train.py --data GlobalWheat2020.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /datasets/GlobalWheat2020
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# /yolov5
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
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train: # 3422 images
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- ../../data/exitPPS/train/images/
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val: # 748 images (WARNING: train set contains ethz_1)
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- ../../data/exitPPS/val/images/
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test: # 1276 images
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- ../../data/exitPPS/val/images/
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# number of classes
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nc: 9
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# class names
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names: [ 'ex','dEx','qV','wF','oth','bdg','rsh','fam','st']
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# download command/URL (optional) --------------------------------------------------------------------------------------
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# download: |
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# from utils.general import download, Path
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# # Download
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# dir = Path('../datasets/GlobalWheat2020') # dataset directory
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# urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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# 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
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# download(urls, dir=dir)
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# # Make Directories
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# for p in 'annotations', 'images', 'labels':
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# (dir / p).mkdir(parents=True, exist_ok=True)
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# # Move
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# for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
|
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# 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
|
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# (dir / p).rename(dir / 'images' / p) # move to /images
|
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# f = (dir / p).with_suffix('.json') # json file
|
||||
# if f.exists():
|
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# f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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|
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@ -0,0 +1,47 @@
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# Global Wheat 2020 dataset http://www.global-wheat.com/
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# Train command: python train.py --data GlobalWheat2020.yaml
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# Default dataset location is next to YOLOv5:
|
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# /parent_folder
|
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# /datasets/GlobalWheat2020
|
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# /yolov5
|
||||
|
||||
|
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: # 3422 images
|
||||
- ../../data/exitPlus/train/images/
|
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|
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val: # 748 images (WARNING: train set contains ethz_1)
|
||||
- ../../data/exitPlus/val/images/
|
||||
|
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test: # 1276 images
|
||||
- ../../data/exitPlus/val/images/
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||||
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# number of classes
|
||||
nc: 4
|
||||
|
||||
# class names
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||||
names: [ 'exit','dirtyExit','quaticVegetation','waterFloater']
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||||
|
||||
|
||||
# download command/URL (optional) --------------------------------------------------------------------------------------
|
||||
# download: |
|
||||
# from utils.general import download, Path
|
||||
|
||||
# # Download
|
||||
# dir = Path('../datasets/GlobalWheat2020') # dataset directory
|
||||
# urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
|
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# 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
|
||||
# download(urls, dir=dir)
|
||||
|
||||
# # Make Directories
|
||||
# for p in 'annotations', 'images', 'labels':
|
||||
# (dir / p).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# # Move
|
||||
# for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
|
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# 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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# (dir / p).rename(dir / 'images' / p) # move to /images
|
||||
# f = (dir / p).with_suffix('.json') # json file
|
||||
# if f.exists():
|
||||
# f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
# Global Wheat 2020 dataset http://www.global-wheat.com/
|
||||
# Train command: python train.py --data GlobalWheat2020.yaml
|
||||
# Default dataset location is next to YOLOv5:
|
||||
# /parent_folder
|
||||
# /datasets/GlobalWheat2020
|
||||
# /yolov5
|
||||
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: # 3422 images
|
||||
data/train.txt
|
||||
val: # 748 images (WARNING: train set contains ethz_1)
|
||||
data/val.txt
|
||||
test: # 1276 images
|
||||
data/val.txt
|
||||
|
||||
# number of classes
|
||||
nc: 9
|
||||
|
||||
# class names
|
||||
names: [ 'ex','dEx','qV','wF','oth','bdg','rsh','fam','st']
|
||||
|
||||
|
||||
# download command/URL (optional) --------------------------------------------------------------------------------------
|
||||
# download: |
|
||||
# from utils.general import download, Path
|
||||
|
||||
# # Download
|
||||
# dir = Path('../datasets/GlobalWheat2020') # dataset directory
|
||||
# urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
|
||||
# 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
|
||||
# download(urls, dir=dir)
|
||||
|
||||
# # Make Directories
|
||||
# for p in 'annotations', 'images', 'labels':
|
||||
# (dir / p).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# # Move
|
||||
# for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
|
||||
# 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
|
||||
# (dir / p).rename(dir / 'images' / p) # move to /images
|
||||
# f = (dir / p).with_suffix('.json') # json file
|
||||
# if f.exists():
|
||||
# f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
# Hyperparameters for VOC finetuning
|
||||
# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
|
||||
# Hyperparameter Evolution Results
|
||||
# Generations: 306
|
||||
# P R mAP.5 mAP.5:.95 box obj cls
|
||||
# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146
|
||||
|
||||
lr0: 0.0032
|
||||
lrf: 0.12
|
||||
momentum: 0.843
|
||||
weight_decay: 0.00036
|
||||
warmup_epochs: 2.0
|
||||
warmup_momentum: 0.5
|
||||
warmup_bias_lr: 0.05
|
||||
box: 0.0296
|
||||
cls: 0.243
|
||||
cls_pw: 0.631
|
||||
obj: 0.301
|
||||
obj_pw: 0.911
|
||||
iou_t: 0.2
|
||||
anchor_t: 2.91
|
||||
# anchors: 3.63
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.0138
|
||||
hsv_s: 0.664
|
||||
hsv_v: 0.464
|
||||
degrees: 0.373
|
||||
translate: 0.245
|
||||
scale: 0.898
|
||||
shear: 0.602
|
||||
perspective: 0.0
|
||||
flipud: 0.00856
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.243
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
lr0: 0.00258
|
||||
lrf: 0.17
|
||||
momentum: 0.779
|
||||
weight_decay: 0.00058
|
||||
warmup_epochs: 1.33
|
||||
warmup_momentum: 0.86
|
||||
warmup_bias_lr: 0.0711
|
||||
box: 0.0539
|
||||
cls: 0.299
|
||||
cls_pw: 0.825
|
||||
obj: 0.632
|
||||
obj_pw: 1.0
|
||||
iou_t: 0.2
|
||||
anchor_t: 3.44
|
||||
anchors: 3.2
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.0188
|
||||
hsv_s: 0.704
|
||||
hsv_v: 0.36
|
||||
degrees: 0.0
|
||||
translate: 0.0902
|
||||
scale: 0.491
|
||||
shear: 0.0
|
||||
perspective: 0.0
|
||||
flipud: 0.0
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.0
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
# Hyperparameters for COCO training from scratch
|
||||
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.5 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 1.0 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.5 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 476 KiB |
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|
After Width: | Height: | Size: 165 KiB |
|
|
@ -0,0 +1,102 @@
|
|||
# Objects365 dataset https://www.objects365.org/
|
||||
# Train command: python train.py --data objects365.yaml
|
||||
# Default dataset location is next to YOLOv5:
|
||||
# /parent_folder
|
||||
# /datasets/objects365
|
||||
# /yolov5
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../datasets/objects365/images/train # 1742289 images
|
||||
val: ../datasets/objects365/images/val # 5570 images
|
||||
|
||||
# number of classes
|
||||
nc: 365
|
||||
|
||||
# class names
|
||||
names: [ 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
|
||||
'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
|
||||
'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
|
||||
'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
|
||||
'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
|
||||
'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
|
||||
'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
|
||||
'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
|
||||
'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
|
||||
'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
|
||||
'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
|
||||
'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
|
||||
'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
|
||||
'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
|
||||
'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
|
||||
'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
|
||||
'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
|
||||
'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
|
||||
'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
|
||||
'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
|
||||
'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
|
||||
'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
|
||||
'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
|
||||
'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
|
||||
'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
|
||||
'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
|
||||
'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
|
||||
'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
|
||||
'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
|
||||
'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
|
||||
'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
|
||||
'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
|
||||
'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
|
||||
'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
|
||||
'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
|
||||
'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
|
||||
'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
|
||||
'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
|
||||
'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
|
||||
'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
|
||||
'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis' ]
|
||||
|
||||
|
||||
# download command/URL (optional) --------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from pycocotools.coco import COCO
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.general import download, Path
|
||||
|
||||
# Make Directories
|
||||
dir = Path('../datasets/objects365') # dataset directory
|
||||
for p in 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
for q in 'train', 'val':
|
||||
(dir / p / q).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Download
|
||||
url = "https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/"
|
||||
download([url + 'zhiyuan_objv2_train.tar.gz'], dir=dir, delete=False) # annotations json
|
||||
download([url + f for f in [f'patch{i}.tar.gz' for i in range(51)]], dir=dir / 'images' / 'train',
|
||||
curl=True, delete=False, threads=8)
|
||||
|
||||
# Move
|
||||
train = dir / 'images' / 'train'
|
||||
for f in tqdm(train.rglob('*.jpg'), desc=f'Moving images'):
|
||||
f.rename(train / f.name) # move to /images/train
|
||||
|
||||
# Labels
|
||||
coco = COCO(dir / 'zhiyuan_objv2_train.json')
|
||||
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
|
||||
for cid, cat in enumerate(names):
|
||||
catIds = coco.getCatIds(catNms=[cat])
|
||||
imgIds = coco.getImgIds(catIds=catIds)
|
||||
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
|
||||
width, height = im["width"], im["height"]
|
||||
path = Path(im["file_name"]) # image filename
|
||||
try:
|
||||
with open(dir / 'labels' / 'train' / path.with_suffix('.txt').name, 'a') as file:
|
||||
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
|
||||
for a in coco.loadAnns(annIds):
|
||||
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
|
||||
x, y = x + w / 2, y + h / 2 # xy to center
|
||||
file.write(f"{cid} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
|
@ -0,0 +1,47 @@
|
|||
# Global Wheat 2020 dataset http://www.global-wheat.com/
|
||||
# Train command: python train.py --data GlobalWheat2020.yaml
|
||||
# Default dataset location is next to YOLOv5:
|
||||
# /parent_folder
|
||||
# /datasets/GlobalWheat2020
|
||||
# /yolov5
|
||||
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: # 3422 images
|
||||
- ../../data/THexit/train/images/
|
||||
|
||||
val: # 748 images (WARNING: train set contains ethz_1)
|
||||
- ../../data/THexit/val/images/
|
||||
|
||||
test: # 1276 images
|
||||
- ../../data/THexit/val/images/
|
||||
|
||||
|
||||
# number of classes
|
||||
nc: 2
|
||||
|
||||
# class names
|
||||
names: [ 'outlet','no' ]
|
||||
|
||||
|
||||
# download command/URL (optional) --------------------------------------------------------------------------------------
|
||||
# download: |
|
||||
# from utils.general import download, Path
|
||||
|
||||
# # Download
|
||||
# dir = Path('../datasets/GlobalWheat2020') # dataset directory
|
||||
# urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
|
||||
# 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
|
||||
# download(urls, dir=dir)
|
||||
|
||||
# # Make Directories
|
||||
# for p in 'annotations', 'images', 'labels':
|
||||
# (dir / p).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# # Move
|
||||
# for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
|
||||
# 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
|
||||
# (dir / p).rename(dir / 'images' / p) # move to /images
|
||||
# f = (dir / p).with_suffix('.json') # json file
|
||||
# if f.exists():
|
||||
# f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
#!/bin/bash
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
|
||||
# Download command: bash data/scripts/get_argoverse_hd.sh
|
||||
# Train command: python train.py --data argoverse_hd.yaml
|
||||
# Default dataset location is next to YOLOv5:
|
||||
# /parent_folder
|
||||
# /argoverse
|
||||
# /yolov5
|
||||
|
||||
# Download/unzip images
|
||||
d='../argoverse/' # unzip directory
|
||||
mkdir $d
|
||||
url=https://argoverse-hd.s3.us-east-2.amazonaws.com/
|
||||
f=Argoverse-HD-Full.zip
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background
|
||||
wait # finish background tasks
|
||||
|
||||
cd ../argoverse/Argoverse-1.1/
|
||||
ln -s tracking images
|
||||
|
||||
cd ../Argoverse-HD/annotations/
|
||||
|
||||
python3 - "$@" <<END
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
annotation_files = ["train.json", "val.json"]
|
||||
print("Converting annotations to YOLOv5 format...")
|
||||
|
||||
for val in annotation_files:
|
||||
a = json.load(open(val, "rb"))
|
||||
|
||||
label_dict = {}
|
||||
for annot in a['annotations']:
|
||||
img_id = annot['image_id']
|
||||
img_name = a['images'][img_id]['name']
|
||||
img_label_name = img_name[:-3] + "txt"
|
||||
|
||||
cls = annot['category_id'] # instance class id
|
||||
x_center, y_center, width, height = annot['bbox']
|
||||
x_center = (x_center + width / 2) / 1920. # offset and scale
|
||||
y_center = (y_center + height / 2) / 1200. # offset and scale
|
||||
width /= 1920. # scale
|
||||
height /= 1200. # scale
|
||||
|
||||
img_dir = "./labels/" + a['seq_dirs'][a['images'][annot['image_id']]['sid']]
|
||||
|
||||
Path(img_dir).mkdir(parents=True, exist_ok=True)
|
||||
if img_dir + "/" + img_label_name not in label_dict:
|
||||
label_dict[img_dir + "/" + img_label_name] = []
|
||||
|
||||
label_dict[img_dir + "/" + img_label_name].append(f"{cls} {x_center} {y_center} {width} {height}\n")
|
||||
|
||||
for filename in label_dict:
|
||||
with open(filename, "w") as file:
|
||||
for string in label_dict[filename]:
|
||||
file.write(string)
|
||||
|
||||
END
|
||||
|
||||
mv ./labels ../../Argoverse-1.1/
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
#!/bin/bash
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Download command: bash data/scripts/get_coco.sh
|
||||
# Train command: python train.py --data coco.yaml
|
||||
# Default dataset location is next to YOLOv5:
|
||||
# /parent_folder
|
||||
# /coco
|
||||
# /yolov5
|
||||
|
||||
# Download/unzip labels
|
||||
d='../' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
|
||||
# Download/unzip images
|
||||
d='../coco/images' # unzip directory
|
||||
url=http://images.cocodataset.org/zips/
|
||||
f1='train2017.zip' # 19G, 118k images
|
||||
f2='val2017.zip' # 1G, 5k images
|
||||
f3='test2017.zip' # 7G, 41k images (optional)
|
||||
for f in $f1 $f2; do
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
done
|
||||
wait # finish background tasks
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
#!/bin/bash
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128
|
||||
# Download command: bash data/scripts/get_coco128.sh
|
||||
# Train command: python train.py --data coco128.yaml
|
||||
# Default dataset location is next to /yolov5:
|
||||
# /parent_folder
|
||||
# /coco128
|
||||
# /yolov5
|
||||
|
||||
# Download/unzip images and labels
|
||||
d='../' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco128.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
|
||||
wait # finish background tasks
|
||||
|
|
@ -0,0 +1,116 @@
|
|||
#!/bin/bash
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||
# Download command: bash data/scripts/get_voc.sh
|
||||
# Train command: python train.py --data voc.yaml
|
||||
# Default dataset location is next to YOLOv5:
|
||||
# /parent_folder
|
||||
# /VOC
|
||||
# /yolov5
|
||||
|
||||
start=$(date +%s)
|
||||
mkdir -p ../tmp
|
||||
cd ../tmp/
|
||||
|
||||
# Download/unzip images and labels
|
||||
d='.' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
|
||||
f2=VOCtest_06-Nov-2007.zip # 438MB, 4953 images
|
||||
f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
|
||||
for f in $f3 $f2 $f1; do
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
|
||||
done
|
||||
wait # finish background tasks
|
||||
|
||||
end=$(date +%s)
|
||||
runtime=$((end - start))
|
||||
echo "Completed in" $runtime "seconds"
|
||||
|
||||
echo "Splitting dataset..."
|
||||
python3 - "$@" <<END
|
||||
import os
|
||||
import xml.etree.ElementTree as ET
|
||||
from os import getcwd
|
||||
|
||||
sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
|
||||
|
||||
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
|
||||
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
|
||||
|
||||
|
||||
def convert_box(size, box):
|
||||
dw = 1. / (size[0])
|
||||
dh = 1. / (size[1])
|
||||
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
|
||||
return x * dw, y * dh, w * dw, h * dh
|
||||
|
||||
|
||||
def convert_annotation(year, image_id):
|
||||
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
|
||||
out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')
|
||||
tree = ET.parse(in_file)
|
||||
root = tree.getroot()
|
||||
size = root.find('size')
|
||||
w = int(size.find('width').text)
|
||||
h = int(size.find('height').text)
|
||||
|
||||
for obj in root.iter('object'):
|
||||
difficult = obj.find('difficult').text
|
||||
cls = obj.find('name').text
|
||||
if cls not in classes or int(difficult) == 1:
|
||||
continue
|
||||
cls_id = classes.index(cls)
|
||||
xmlbox = obj.find('bndbox')
|
||||
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
|
||||
float(xmlbox.find('ymax').text))
|
||||
bb = convert_box((w, h), b)
|
||||
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
|
||||
|
||||
|
||||
cwd = getcwd()
|
||||
for year, image_set in sets:
|
||||
if not os.path.exists('VOCdevkit/VOC%s/labels/' % year):
|
||||
os.makedirs('VOCdevkit/VOC%s/labels/' % year)
|
||||
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()
|
||||
list_file = open('%s_%s.txt' % (year, image_set), 'w')
|
||||
for image_id in image_ids:
|
||||
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (cwd, year, image_id))
|
||||
convert_annotation(year, image_id)
|
||||
list_file.close()
|
||||
END
|
||||
|
||||
cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
|
||||
cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
|
||||
|
||||
mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val
|
||||
mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val
|
||||
|
||||
python3 - "$@" <<END
|
||||
import os
|
||||
|
||||
print(os.path.exists('../tmp/train.txt'))
|
||||
with open('../tmp/train.txt', 'r') as f:
|
||||
for line in f.readlines():
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/images/train")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/labels/train")
|
||||
|
||||
print(os.path.exists('../tmp/2007_test.txt'))
|
||||
with open('../tmp/2007_test.txt', 'r') as f:
|
||||
for line in f.readlines():
|
||||
line = "/".join(line.split('/')[-5:]).strip()
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/images/val")
|
||||
|
||||
line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
|
||||
if os.path.exists("../" + line):
|
||||
os.system("cp ../" + line + " ../VOC/labels/val")
|
||||
END
|
||||
|
||||
rm -rf ../tmp # remove temporary directory
|
||||
echo "VOC download done."
|
||||
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
|
@ -0,0 +1,263 @@
|
|||
/home/thsw/WJ/data/exitPPS/images/00000626.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000480.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/30.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000882.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000749.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/79.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000085.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000754.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000837.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001005.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000744.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000147.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000170.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000264.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000747.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000379.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000451.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001140.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000488.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001167.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000231.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000528.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000743.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000535.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000718.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/63.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000157.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/16.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000629.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000905.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001065.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/18.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001014.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000025.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000700.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000822.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000674.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000361.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000220.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000549.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000909.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000438.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000097.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000250.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001078.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/39.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000112.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001194.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000037.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001181.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/15.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000015.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001097.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000079.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000892.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000374.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000984.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000785.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000440.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000107.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000918.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000920.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000054.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000228.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000824.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/51.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000478.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000763.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000242.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000757.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000617.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001122.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/72.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000960.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000871.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000122.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000782.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000603.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000843.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000742.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000795.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000049.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001161.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000985.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000452.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001093.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000521.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000269.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000391.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/5.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000935.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000006.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000132.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000434.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000302.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000354.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000551.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001143.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000355.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000968.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000411.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/37.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000858.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000162.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000343.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/32.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000919.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000336.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000857.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000787.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001050.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000796.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000155.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000115.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000750.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000694.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000249.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000697.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000675.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000460.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000459.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001164.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000324.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000292.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001125.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001004.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000772.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001104.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000518.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000377.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000262.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000522.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000316.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000550.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000143.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000876.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000225.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000712.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000561.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000650.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/2.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000828.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000863.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000356.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/74.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000619.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000152.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000600.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001062.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/75.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000720.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000903.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000375.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000400.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000010.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001128.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000094.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000586.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000078.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000313.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000270.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000719.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000332.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000031.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000676.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001114.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000707.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000976.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000055.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/102.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000738.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000666.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000422.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000116.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001153.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000353.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000430.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001012.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000021.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000065.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000166.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000544.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000593.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001134.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/93.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000516.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000279.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000360.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001045.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000059.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/55.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000877.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000267.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000042.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000627.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/65.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000853.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000511.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000043.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000383.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000739.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000182.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001060.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/107.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000253.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001082.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000952.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000427.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000624.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000599.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000622.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000883.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000380.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000731.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000180.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000204.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000653.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000542.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000118.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001141.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001032.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000789.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000477.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000068.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000221.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001071.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000408.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000385.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000734.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000436.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/78.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001142.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000886.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000523.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/34.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000321.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000163.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000818.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000656.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000347.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000084.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000202.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000474.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000168.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/67.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001041.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000340.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000668.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000604.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000187.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000456.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001150.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001025.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000206.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000533.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000441.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000873.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000490.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001086.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001096.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00001008.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000426.jpg
|
||||
/home/thsw/WJ/data/exitPPS/images/00000388.jpg
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
|
||||
# Train command: python train.py --data voc.yaml
|
||||
# Default dataset location is next to YOLOv5:
|
||||
# /parent_folder
|
||||
# /VOC
|
||||
# /yolov5
|
||||
|
||||
|
||||
# download command/URL (optional)
|
||||
download: bash data/scripts/get_voc.sh
|
||||
|
||||
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
|
||||
train: ../VOC/images/train/ # 16551 images
|
||||
val: ../VOC/images/val/ # 4952 images
|
||||
|
||||
# number of classes
|
||||
nc: 20
|
||||
|
||||
# class names
|
||||
names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
|
||||
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ]
|
||||
|
|
@ -0,0 +1,443 @@
|
|||
import argparse
|
||||
import time
|
||||
from pathlib import Path
|
||||
import random,string
|
||||
import cv2
|
||||
import torch
|
||||
import torch.backends.cudnn as cudnn
|
||||
|
||||
import base64
|
||||
import requests
|
||||
|
||||
|
||||
from models.experimental import attempt_load
|
||||
from utils.datasets import LoadStreams, LoadImages
|
||||
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
|
||||
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
|
||||
from utils.plots import plot_one_box,plot_one_box_PIL,draw_painting_joint,get_label_arrays,get_websource,smooth_outline_auto
|
||||
from utils.get_offline_url import update_websource_offAndLive,platurlToJsonfile, get_websource_fromTxt
|
||||
from utils.torch_utils import select_device, load_classifier, time_synchronized
|
||||
import cv2
|
||||
import queue
|
||||
import os,json,sys
|
||||
import numpy as np
|
||||
from threading import Thread
|
||||
import datetime,_thread
|
||||
import subprocess as sp
|
||||
import time
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
from segutils.segmodel import SegModel,get_largest_contours
|
||||
sys.path.extend(['/home/thsw2/WJ/src/yolov5/segutils'])
|
||||
#from segutils.segWaterBuilding import SegModel,get_largest_contours,illBuildings
|
||||
from segutils.core.models.bisenet import BiSeNet_MultiOutput
|
||||
from collections import Counter
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
platform_query_url='http://47.96.182.154:9051/api/suanfa/getPlatformInfo'
|
||||
offlineFile='mintors/offlines/doneCodes.txt'
|
||||
|
||||
def get_cls(array):
|
||||
dcs = Counter(array)
|
||||
keys = list(dcs.keys())
|
||||
values = list(dcs.values())
|
||||
max_index = values.index(max(values))
|
||||
cls = int(keys[max_index])
|
||||
return cls
|
||||
|
||||
##9月3日之后，需要新增语义分割模型。
|
||||
##10月22日， source 改成文件输入
|
||||
##12月31日，支持从platform读取离线视频
|
||||
# 使用线程锁，防止线程死锁
|
||||
mutex = _thread.allocate_lock()
|
||||
# 存图片的队列
|
||||
frame_queue = queue.Queue()
|
||||
|
||||
# 推流的地址，前端通过这个地址拉流，主机的IP，2019是ffmpeg在nginx中设置的端口号
|
||||
|
||||
|
||||
#camera_path='rtmp://58.200.131.2:1935/livetv/cctv1'
|
||||
camera_path='/data/WJ/data/THexit/vedio/XiYuDaiHe4.MP4'
|
||||
###lables colors (BGR)#####
|
||||
rainbows=[
|
||||
(0,0,255),(0,255,0),(255,0,0),(255,0,255),(255,255,0),(255,127,0),(255,0,127),
|
||||
(127,255,0),(0,255,127),(0,127,255),(127,0,255),(255,127,255),(255,255,127),
|
||||
(127,255,255),(0,255,255),(255,127,255),(127,255,255),
|
||||
(0,127,0),(0,0,127),(0,255,255)
|
||||
]
|
||||
|
||||
def detect(save_img=False):
|
||||
rtmpUrl = "rtmp://127.0.0.1:1935/live/test"
|
||||
OutVideoW,OutVideoH,OutVideoFps=int(opt.OutVideoW),int(opt.OutVideoH),int(opt.OutVideoFps)
|
||||
command=['ffmpeg',
|
||||
'-y',
|
||||
#'-re',' ',
|
||||
'-f', 'rawvideo',
|
||||
'-vcodec','rawvideo',
|
||||
'-pix_fmt', 'bgr24',
|
||||
'-s', "{}x{}".format(OutVideoW,OutVideoH),# 图片分辨率
|
||||
#'-vcodec','libx264',
|
||||
#'-b','2500k',
|
||||
'-r', str(OutVideoFps),# 视频帧率
|
||||
'-i', '-',
|
||||
'-c:v', 'libx264',
|
||||
'-pix_fmt', 'yuv420p',
|
||||
#'-preset', 'ultrafast',
|
||||
'-f', 'flv',
|
||||
rtmpUrl]
|
||||
|
||||
|
||||
|
||||
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
|
||||
#save_img = not opt.nosave and not source.endswith('.txt') # save inference images
|
||||
save_img = not opt.nosave
|
||||
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
|
||||
('rtsp://', 'rtmp://', 'http://', 'https://'))
|
||||
sourceTxt=source
|
||||
if source.endswith('.txt'):
|
||||
#source_list,port_list,streamName_list = get_websource(source)
|
||||
source_infos = get_websource_fromTxt(source)
|
||||
else:
|
||||
#source_list,port_list,streamName_list = [source],[1935],['demo']
|
||||
source_infos = [{'url':source,'port':1935,'name':'demo' }]
|
||||
# Directories
|
||||
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
|
||||
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Initialize
|
||||
set_logging()
|
||||
device = select_device(opt.device)
|
||||
half = device.type != 'cpu' # half precision only supported on CUDA
|
||||
# Load model
|
||||
model = attempt_load(weights, map_location=device) # load FP32 model
|
||||
stride = int(model.stride.max()) # model stride
|
||||
imgsz = check_img_size(imgsz, s=stride) # check img_size
|
||||
#print('###'*20,imgsz,stride)
|
||||
|
||||
##加载分割模型###
|
||||
seg_nclass = 2
|
||||
weights = 'weights/segmentation/BiSeNet/checkpoint.pth'
|
||||
segmodel = SegModel(nclass=seg_nclass,weights=weights,device=device)
|
||||
|
||||
'''nclass = [2,2]
|
||||
Segmodel = BiSeNet_MultiOutput(nclass)
|
||||
weights='weights/segmentation/WaterBuilding.pth'
|
||||
segmodel = SegModel(model=Segmodel,nclass=nclass,weights=weights,device='cuda:0',multiOutput=True)'''
|
||||
|
||||
|
||||
|
||||
if half:
|
||||
model.half() # to FP16
|
||||
# Second-stage classifier
|
||||
classify = False
|
||||
if classify:
|
||||
modelc = load_classifier(name='resnet101', n=2) # initialize
|
||||
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
|
||||
if webcam:
|
||||
# create file pointer
|
||||
fp_out=open('mintors/%s.txt'%(time.strftime("Start-%Y-%m-%d-%H-%M-%S", time.localtime())) ,'w')
|
||||
# Set Dataloader
|
||||
vid_path, vid_writer = None, None
|
||||
stream_id = 0
|
||||
platurlToJsonfile(platform_query_url)
|
||||
while True:
|
||||
#for isource in range(len(source_list)):
|
||||
for isource in range(len(source_infos)):
|
||||
#source , port ,streamName = source_list[isource],port_list[isource],streamName_list[isource]
|
||||
source , port ,streamName = source_infos[isource]['url'],source_infos[isource]['port'],source_infos[isource]['name']
|
||||
print('########## detect.py line129 souce informations:',isource, source , port ,streamName,webcam )
|
||||
Push_Flag = False
|
||||
#for wang in ['test']:
|
||||
try:
|
||||
if webcam:
|
||||
#view_img = check_imshow()
|
||||
cudnn.benchmark = True # set True to speed up constant image size inference
|
||||
print('#########Using web cam#################')
|
||||
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
|
||||
|
||||
# Get names and colors,fp_log,fp_out都是日志文件
|
||||
fp_log=open('mintors/detection/stream_%s_%d-%s.txt'%(streamName,stream_id,time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())) ,'w')
|
||||
fp_out.write(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())+ ' rtmp stream-%s-%d starts \n'%(streamName,stream_id) )
|
||||
fp_out.flush()
|
||||
problem_image = [[],[],[],[],[]];
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, stride=stride)
|
||||
|
||||
|
||||
####
|
||||
iimage_cnt = 0
|
||||
names = model.module.names if hasattr(model, 'module') else model.names
|
||||
EngLish_label = True
|
||||
if os.path.exists(opt.labelnames):
|
||||
with open(opt.labelnames,'r') as fp:
|
||||
namesjson=json.load(fp)
|
||||
names_fromfile=namesjson['labelnames']
|
||||
if len(names_fromfile) == len(names):
|
||||
names = names_fromfile
|
||||
EngLish_label = False
|
||||
else:
|
||||
print('******Warning 文件:%s读取的类别数目与模型中的数目不一致，使用模型的类别********'%(opt.labelnames))
|
||||
|
||||
colors = rainbows
|
||||
label_arraylist = get_label_arrays(names,colors,outfontsize=40)
|
||||
|
||||
# Run inference
|
||||
if device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
|
||||
t00 = time.time();
|
||||
|
||||
|
||||
if webcam:
|
||||
while True:
|
||||
if len(command) > 0:
|
||||
rtmpUrl = "rtmp://127.0.0.1:%s/live/test"%(port)
|
||||
command[-1] = rtmpUrl
|
||||
# 管道配置，其中用到管道
|
||||
print(command)
|
||||
ppipe = sp.Popen(command, stdin=sp.PIPE)
|
||||
Push_Flag = True
|
||||
break
|
||||
time00=time.time()
|
||||
|
||||
for path, img, im0s, vid_cap in dataset:
|
||||
t0= time_synchronized()
|
||||
img = torch.from_numpy(img).to(device)
|
||||
img = img.half() if half else img.float() # uint8 to fp16/32
|
||||
img /= 255.0 # 0 - 255 to 0.0 - 1.0
|
||||
timeseg0 = time.time()
|
||||
if segmodel:
|
||||
if webcam:
|
||||
seg_pred,segstr = segmodel.eval(im0s[0] )
|
||||
#seg_pred = segmodel.eval(im0s[0],outsize=None,smooth_kernel=20)
|
||||
else:
|
||||
seg_pred,segstr = segmodel.eval(im0s )
|
||||
#seg_pred = segmodel.eval(im0s[0],outsize=None,smooth_kernel=20)
|
||||
|
||||
|
||||
timeseg1 = time.time()
|
||||
if img.ndimension() == 3:
|
||||
img = img.unsqueeze(0)
|
||||
|
||||
# Inference
|
||||
t1 = time_synchronized()
|
||||
|
||||
pred = model(img, augment=opt.augment)[0]
|
||||
#print('###','line197:',img.shape,opt.augment,opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms)
|
||||
# Apply NMS
|
||||
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
|
||||
t2 = time_synchronized()
|
||||
# Apply Classifier
|
||||
if classify:
|
||||
pred = apply_classifier(pred, modelc, img, im0s)
|
||||
|
||||
# Process detections
|
||||
for i, det in enumerate(pred): # detections per image
|
||||
if webcam: # batch_size >= 1
|
||||
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
|
||||
im0_bak = im0.copy()
|
||||
else:
|
||||
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
|
||||
|
||||
iimage_cnt += 1
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # img.jpg
|
||||
|
||||
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
|
||||
s += '%gx%g ' % img.shape[2:] # print string
|
||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
|
||||
|
||||
if segmodel:
|
||||
contours, hierarchy = cv2.findContours(seg_pred,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
|
||||
water = seg_pred.copy();
|
||||
if len(contours)>0:
|
||||
max_id = get_largest_contours(contours)
|
||||
water[:,:]=0
|
||||
cv2.fillPoly(water, [contours[max_id][:,0,:]], 1)
|
||||
|
||||
cv2.drawContours(im0,contours,max_id,(0,255,255),3)
|
||||
|
||||
else:
|
||||
water[:,:] = 0
|
||||
#im0,water = illBuildings(seg_pred,im0)
|
||||
|
||||
if len(det):
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
|
||||
#check weather the box is inside of water area
|
||||
if segmodel:
|
||||
det_c = det.clone(); det_c=det_c.cpu().numpy()
|
||||
#area_factors = np.array([np.sum(water[int(x[1]):int(x[3]), int(x[0]):int(x[2])] )/((x[2]-x[0])*(x[3]-x[1])) for x in det] )
|
||||
area_factors = np.array([np.sum(water[int(x[1]):int(x[3]), int(x[0]):int(x[2])] )/((x[2]-x[0])*(x[3]-x[1])) for x in det_c] )
|
||||
#det = det[area_factors>0.1]
|
||||
det = det[area_factors>0.03]
|
||||
###联通要求，临时屏蔽掉水生植被
|
||||
'''if len(det):
|
||||
clss = det[:,5]
|
||||
det = det[clss!=2]'''
|
||||
|
||||
if len(det)>0:
|
||||
# Print results
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
|
||||
# Write results
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
if save_txt: # Write to file
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
|
||||
with open(txt_path + '.txt', 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
|
||||
if save_img or view_img: # Add bbox to image
|
||||
label = f'{names[int(cls)]} {conf:.2f}'
|
||||
if EngLish_label:
|
||||
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)%20], line_thickness=3)
|
||||
else:
|
||||
#im0=plot_one_box_PIL(xyxy, im0, label=label, color=colors[int(cls)%20], line_thickness=3)
|
||||
im0 = draw_painting_joint(xyxy,im0,label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],line_thickness=None)
|
||||
|
||||
###处理问题图片，每fpsample帧，上报一张图片。以平均得分最大的为最佳图片
|
||||
if webcam:
|
||||
problem_image[0].append( det[:,4].mean()); problem_image[1].append(det);
|
||||
problem_image[2].append(im0);problem_image[3].append(iimage_cnt);problem_image[4].append(im0_bak)
|
||||
|
||||
if webcam & (iimage_cnt % opt.fpsample == 0) & (dataset.mode != 'image') & (len(problem_image[0])>0):
|
||||
best_index = problem_image[0].index(max(problem_image[0]))
|
||||
best_frame = problem_image[3][ best_index]
|
||||
img_send = problem_image[2][ best_index]
|
||||
img_bak = problem_image[4][ best_index]
|
||||
dets = problem_image[1][best_index]
|
||||
cls_max = get_cls(dets[:,5].cpu().detach().numpy())
|
||||
|
||||
time_str = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
|
||||
uid=''.join(random.sample(string.ascii_letters + string.digits, 16))
|
||||
AIUrl='problems/images_tmp/%s_frame-%d-%d_type-%s_%s_s-%s_AI.jpg'%(time_str,best_frame,iimage_cnt,names[cls_max],uid,streamName)
|
||||
ORIUrl='problems/images_tmp/%s_frame-%d-%d_type-%s_%s_s-%s_OR.jpg'%(time_str,best_frame,iimage_cnt,names[cls_max],uid,streamName)
|
||||
cv2.imwrite(AIUrl,img_send); cv2.imwrite(ORIUrl,img_bak)
|
||||
outstr='%s save images to %s \n'%(time_str, AIUrl)
|
||||
fp_log.write(outstr )
|
||||
problem_image=[[],[],[],[],[]]
|
||||
|
||||
# Print time (inference + NMS)
|
||||
t3 = time_synchronized()
|
||||
|
||||
# Save results (image with detections)
|
||||
if save_img:
|
||||
if dataset.mode == 'image':
|
||||
cv2.imwrite(save_path, im0)
|
||||
else: # 'video' or 'stream'
|
||||
if vid_path != save_path: # new video
|
||||
vid_path = save_path
|
||||
if isinstance(vid_writer, cv2.VideoWriter):
|
||||
vid_writer.release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path += '.mp4'
|
||||
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
im0 = cv2.resize(im0,(OutVideoW,OutVideoH))
|
||||
|
||||
if dataset.mode == 'stream':
|
||||
ppipe.stdin.write(im0.tostring())
|
||||
|
||||
t4 = time_synchronized()
|
||||
#outstr='%s Done.read:%.1f ms, infer:%.1f ms, seginfer:%.1f ms,draw:%.1f ms, save:%.1f ms total:%.1f ms \n'%(s,(t1 - t0)*1000, (t2 - t1)*1000,(timeseg1-timeseg0)*1000, (t3 - t2)*1000,(t4-t3)*1000, (t4-t00)*1000)
|
||||
timestr=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||||
outstr='%s ,%s,iframe:%d,,read:%.1f ms,copy:%.1f, infer:%.1f ms, detinfer:%.1f ms,draw:%.1f ms, save:%.1f ms total:%.1f ms \n'%(s,timestr,iimage_cnt,(t0 - t00)*1000,(timeseg0-t0)*1000, (t1 - timeseg0)*1000,(t2-t1)*1000, (t3 - t2)*1000,(t4-t3)*1000, (t4-t00)*1000)
|
||||
if webcam:
|
||||
if len(det):
|
||||
fp_log.write(outstr )
|
||||
else:
|
||||
print(outstr)
|
||||
print(segstr)
|
||||
sys.stdout.flush()
|
||||
|
||||
t00=t4
|
||||
|
||||
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||
print(f"Results saved to {save_dir}{s}")
|
||||
|
||||
print(f'Done. ({time.time() - t0:.3f}s)')
|
||||
if not webcam:
|
||||
break;
|
||||
|
||||
except Exception as e:
|
||||
print('#######reading souce:%s ,error :%s:'%(source,e ))
|
||||
if Push_Flag and webcam:
|
||||
####source end 推流or视频结束 ###
|
||||
ppipe.kill()
|
||||
|
||||
fp_out.write(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) +' rtmp stream-%s-%d ends \n'%(streamName,stream_id) );fp_out.flush()
|
||||
stream_id += 1
|
||||
time_str = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
|
||||
if 'off' in streamName:##只有离线视频时，才会写结束文件。
|
||||
EndUrl='problems/images_tmp/%s_frame-9999-9999_type-结束_9999999999999999_s-%s_AI.jpg'%(time_str,streamName)
|
||||
img_end=np.zeros((100,100),dtype=np.uint8);cv2.imwrite(EndUrl,img_end)
|
||||
EndUrl='problems/images_tmp/%s_frame-9999-9999_type-结束_9999999999999999_s-%s_OR.jpg'%(time_str,streamName)
|
||||
cv2.imwrite(EndUrl,img_end)
|
||||
time_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
|
||||
with open( 'mintors/offlines/doneCodes.txt','a+' ) as fp:
|
||||
fp.write('%s %s\n'%(time_str,streamName ))
|
||||
|
||||
#source_infos=update_websource_offAndLive(platform_query_url,sourceTxt,offlineFile)
|
||||
fp_log.close()
|
||||
if webcam:
|
||||
fp_out.write( time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())+' GPU server sleep 10s \n' ) ;fp_out.flush()
|
||||
time.sleep(10)
|
||||
|
||||
if not webcam:
|
||||
break;
|
||||
|
||||
###update source (online or offline)
|
||||
source_infos=update_websource_offAndLive(platform_query_url,sourceTxt,offlineFile)
|
||||
if len(source_infos)==0:
|
||||
print('######NO valid source sleep 10s#####')
|
||||
time.sleep(10)
|
||||
if webcam:
|
||||
fp_out.close()
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
|
||||
parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam
|
||||
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--view-img', action='store_true', help='display results')
|
||||
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||||
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
|
||||
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
|
||||
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
|
||||
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||||
parser.add_argument('--update', action='store_true', help='update all models')
|
||||
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save results to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--labelnames', type=str,default=None, help='labes nams')
|
||||
|
||||
parser.add_argument('--fpsample', type=int, default=240, help='fpsample')
|
||||
parser.add_argument('--OutVideoW', type=int, default=1920, help='out video width size')
|
||||
parser.add_argument('--OutVideoH', type=int, default=1080, help='out video height size')
|
||||
parser.add_argument('--OutVideoFps', type=int, default=30, help='out video fps ')
|
||||
|
||||
opt = parser.parse_args()
|
||||
print(opt)
|
||||
check_requirements(exclude=('pycocotools', 'thop'))
|
||||
|
||||
with torch.no_grad():
|
||||
if opt.update: # update all models (to fix SourceChangeWarning)
|
||||
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
|
||||
detect()
|
||||
strip_optimizer(opt.weights)
|
||||
else:
|
||||
detect()
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
|
||||
#OutVideoW=3840 OutVideoH=2160 OutVideoFps=30 source=/home/thsw2/WJ/data/video3840
|
||||
#OutVideoW=1920 OutVideoH=1080 OutVideoFps=30 source=/home/thsw2/WJ/data/video1920
|
||||
#OutVideoW=3840 OutVideoH=2160
|
||||
OutVideoW=1920 OutVideoH=1080
|
||||
OutVideoFps=30
|
||||
#source='rtmp://demoplay.yunhengzhizao.cn/live/test' #address2
|
||||
#source='rtmp://liveplay.yunhengzhizao.cn/live/demo' #address1
|
||||
#source='rtmp://liveplay.yunhengzhizao.cn/live/demo_HD5M' #address1_HD5M
|
||||
|
||||
source=config/source.txt
|
||||
#source='/home/thsw2/WJ/data/THexit/val/images/'
|
||||
|
||||
#model=runs/train/exp10/weights/best.pt
|
||||
#model=weights/best_5classes.pt ###before 1228
|
||||
#model=weights/best_5classes_1228.pt
|
||||
model=weights/1230_last.pt
|
||||
labelnames=config/labelnames.json
|
||||
fpsample=720 confthres=0.4
|
||||
|
||||
#python detect_zhuanbo.py --weights ${model} --source ${source} --OutVideoW ${OutVideoW} --OutVideoH ${OutVideoH} --OutVideoFps ${OutVideoFps} --labelnames ${labelnames} --fpsample ${fpsample} --conf-thres ${confthres}
|
||||
|
||||
python detect.py --weights ${model} --source ${source} --OutVideoW ${OutVideoW} --OutVideoH ${OutVideoH} --OutVideoFps ${OutVideoFps} --labelnames ${labelnames} --fpsample ${fpsample} --conf-thres ${confthres} &
|
||||
python Send_tranfer.py &
|
||||
|
|
@ -0,0 +1,139 @@
|
|||
"""YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
|
||||
|
||||
Usage:
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
from models.yolo import Model
|
||||
from utils.general import check_requirements, set_logging
|
||||
from utils.google_utils import attempt_download
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
dependencies = ['torch', 'yaml']
|
||||
check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('pycocotools', 'thop'))
|
||||
set_logging()
|
||||
|
||||
|
||||
def create(name, pretrained, channels, classes, autoshape):
|
||||
"""Creates a specified YOLOv5 model
|
||||
|
||||
Arguments:
|
||||
name (str): name of model, i.e. 'yolov5s'
|
||||
pretrained (bool): load pretrained weights into the model
|
||||
channels (int): number of input channels
|
||||
classes (int): number of model classes
|
||||
|
||||
Returns:
|
||||
pytorch model
|
||||
"""
|
||||
try:
|
||||
cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
|
||||
model = Model(cfg, channels, classes)
|
||||
if pretrained:
|
||||
fname = f'{name}.pt' # checkpoint filename
|
||||
attempt_download(fname) # download if not found locally
|
||||
ckpt = torch.load(fname, map_location=torch.device('cpu')) # load
|
||||
msd = model.state_dict() # model state_dict
|
||||
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
|
||||
csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
if len(ckpt['model'].names) == classes:
|
||||
model.names = ckpt['model'].names # set class names attribute
|
||||
if autoshape:
|
||||
model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
|
||||
device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available
|
||||
return model.to(device)
|
||||
|
||||
except Exception as e:
|
||||
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
|
||||
s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url
|
||||
raise Exception(s) from e
|
||||
|
||||
|
||||
def custom(path_or_model='path/to/model.pt', autoshape=True):
|
||||
"""YOLOv5-custom model https://github.com/ultralytics/yolov5
|
||||
|
||||
Arguments (3 options):
|
||||
path_or_model (str): 'path/to/model.pt'
|
||||
path_or_model (dict): torch.load('path/to/model.pt')
|
||||
path_or_model (nn.Module): torch.load('path/to/model.pt')['model']
|
||||
|
||||
Returns:
|
||||
pytorch model
|
||||
"""
|
||||
model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint
|
||||
if isinstance(model, dict):
|
||||
model = model['ema' if model.get('ema') else 'model'] # load model
|
||||
|
||||
hub_model = Model(model.yaml).to(next(model.parameters()).device) # create
|
||||
hub_model.load_state_dict(model.float().state_dict()) # load state_dict
|
||||
hub_model.names = model.names # class names
|
||||
if autoshape:
|
||||
hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
|
||||
device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available
|
||||
return hub_model.to(device)
|
||||
|
||||
|
||||
def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-small model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5s', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-medium model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5m', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-large model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5l', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-xlarge model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5x', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-small model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5s6', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-medium model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5m6', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-large model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5l6', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True):
|
||||
# YOLOv5-xlarge model https://github.com/ultralytics/yolov5
|
||||
return create('yolov5x6', pretrained, channels, classes, autoshape)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example
|
||||
# model = custom(path_or_model='path/to/model.pt') # custom example
|
||||
|
||||
# Verify inference
|
||||
import cv2
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
imgs = ['data/images/zidane.jpg', # filename
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg', # URI
|
||||
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
|
||||
Image.open('data/images/bus.jpg'), # PIL
|
||||
np.zeros((320, 640, 3))] # numpy
|
||||
|
||||
results = model(imgs) # batched inference
|
||||
results.print()
|
||||
results.save()
|
||||
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|
|
@ -0,0 +1,386 @@
|
|||
# YOLOv5 common modules
|
||||
|
||||
import math
|
||||
from copy import copy
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import requests
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from PIL import Image
|
||||
from torch.cuda import amp
|
||||
|
||||
from utils.datasets import letterbox
|
||||
from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
|
||||
from utils.plots import color_list, plot_one_box
|
||||
from utils.torch_utils import time_synchronized
|
||||
|
||||
|
||||
def autopad(k, p=None): # kernel, padding
|
||||
# Pad to 'same'
|
||||
if p is None:
|
||||
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
|
||||
return p
|
||||
|
||||
|
||||
def DWConv(c1, c2, k=1, s=1, act=True):
|
||||
# Depthwise convolution
|
||||
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
|
||||
|
||||
|
||||
class Conv(nn.Module):
|
||||
# Standard convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super(Conv, self).__init__()
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
|
||||
|
||||
def forward(self, x):
|
||||
return self.act(self.bn(self.conv(x)))
|
||||
|
||||
def fuseforward(self, x):
|
||||
return self.act(self.conv(x))
|
||||
|
||||
|
||||
class TransformerLayer(nn.Module):
|
||||
# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
|
||||
def __init__(self, c, num_heads):
|
||||
super().__init__()
|
||||
self.q = nn.Linear(c, c, bias=False)
|
||||
self.k = nn.Linear(c, c, bias=False)
|
||||
self.v = nn.Linear(c, c, bias=False)
|
||||
self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
|
||||
self.fc1 = nn.Linear(c, c, bias=False)
|
||||
self.fc2 = nn.Linear(c, c, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
|
||||
x = self.fc2(self.fc1(x)) + x
|
||||
return x
|
||||
|
||||
|
||||
class TransformerBlock(nn.Module):
|
||||
# Vision Transformer https://arxiv.org/abs/2010.11929
|
||||
def __init__(self, c1, c2, num_heads, num_layers):
|
||||
super().__init__()
|
||||
self.conv = None
|
||||
if c1 != c2:
|
||||
self.conv = Conv(c1, c2)
|
||||
self.linear = nn.Linear(c2, c2) # learnable position embedding
|
||||
self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
|
||||
self.c2 = c2
|
||||
|
||||
def forward(self, x):
|
||||
if self.conv is not None:
|
||||
x = self.conv(x)
|
||||
b, _, w, h = x.shape
|
||||
p = x.flatten(2)
|
||||
p = p.unsqueeze(0)
|
||||
p = p.transpose(0, 3)
|
||||
p = p.squeeze(3)
|
||||
e = self.linear(p)
|
||||
x = p + e
|
||||
|
||||
x = self.tr(x)
|
||||
x = x.unsqueeze(3)
|
||||
x = x.transpose(0, 3)
|
||||
x = x.reshape(b, self.c2, w, h)
|
||||
return x
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
# Standard bottleneck
|
||||
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
|
||||
super(Bottleneck, self).__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c_, c2, 3, 1, g=g)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
|
||||
|
||||
class BottleneckCSP(nn.Module):
|
||||
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super(BottleneckCSP, self).__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
|
||||
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
|
||||
self.cv4 = Conv(2 * c_, c2, 1, 1)
|
||||
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
|
||||
self.act = nn.LeakyReLU(0.1, inplace=True)
|
||||
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||||
|
||||
def forward(self, x):
|
||||
y1 = self.cv3(self.m(self.cv1(x)))
|
||||
y2 = self.cv2(x)
|
||||
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
|
||||
|
||||
|
||||
class C3(nn.Module):
|
||||
# CSP Bottleneck with 3 convolutions
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super(C3, self).__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c1, c_, 1, 1)
|
||||
self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
|
||||
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||||
# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
|
||||
|
||||
def forward(self, x):
|
||||
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
|
||||
|
||||
|
||||
class C3TR(C3):
|
||||
# C3 module with TransformerBlock()
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
|
||||
super().__init__(c1, c2, n, shortcut, g, e)
|
||||
c_ = int(c2 * e)
|
||||
self.m = TransformerBlock(c_, c_, 4, n)
|
||||
|
||||
|
||||
class SPP(nn.Module):
|
||||
# Spatial pyramid pooling layer used in YOLOv3-SPP
|
||||
def __init__(self, c1, c2, k=(5, 9, 13)):
|
||||
super(SPP, self).__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
|
||||
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
|
||||
|
||||
def forward(self, x):
|
||||
x = self.cv1(x)
|
||||
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
|
||||
|
||||
|
||||
class Focus(nn.Module):
|
||||
# Focus wh information into c-space
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super(Focus, self).__init__()
|
||||
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
|
||||
# self.contract = Contract(gain=2)
|
||||
|
||||
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
|
||||
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
|
||||
# return self.conv(self.contract(x))
|
||||
|
||||
|
||||
class Contract(nn.Module):
|
||||
# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
|
||||
def __init__(self, gain=2):
|
||||
super().__init__()
|
||||
self.gain = gain
|
||||
|
||||
def forward(self, x):
|
||||
N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
|
||||
s = self.gain
|
||||
x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
|
||||
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
|
||||
return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
|
||||
|
||||
|
||||
class Expand(nn.Module):
|
||||
# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
|
||||
def __init__(self, gain=2):
|
||||
super().__init__()
|
||||
self.gain = gain
|
||||
|
||||
def forward(self, x):
|
||||
N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
|
||||
s = self.gain
|
||||
x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
|
||||
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
|
||||
return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
|
||||
|
||||
|
||||
class Concat(nn.Module):
|
||||
# Concatenate a list of tensors along dimension
|
||||
def __init__(self, dimension=1):
|
||||
super(Concat, self).__init__()
|
||||
self.d = dimension
|
||||
|
||||
def forward(self, x):
|
||||
return torch.cat(x, self.d)
|
||||
|
||||
|
||||
class NMS(nn.Module):
|
||||
# Non-Maximum Suppression (NMS) module
|
||||
conf = 0.25 # confidence threshold
|
||||
iou = 0.45 # IoU threshold
|
||||
classes = None # (optional list) filter by class
|
||||
|
||||
def __init__(self):
|
||||
super(NMS, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
|
||||
|
||||
|
||||
class autoShape(nn.Module):
|
||||
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
||||
conf = 0.25 # NMS confidence threshold
|
||||
iou = 0.45 # NMS IoU threshold
|
||||
classes = None # (optional list) filter by class
|
||||
|
||||
def __init__(self, model):
|
||||
super(autoShape, self).__init__()
|
||||
self.model = model.eval()
|
||||
|
||||
def autoshape(self):
|
||||
print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
|
||||
return self
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, imgs, size=640, augment=False, profile=False):
|
||||
# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
|
||||
# filename: imgs = 'data/images/zidane.jpg'
|
||||
# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
|
||||
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
|
||||
# PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
|
||||
# numpy: = np.zeros((640,1280,3)) # HWC
|
||||
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
|
||||
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
|
||||
|
||||
t = [time_synchronized()]
|
||||
p = next(self.model.parameters()) # for device and type
|
||||
if isinstance(imgs, torch.Tensor): # torch
|
||||
with amp.autocast(enabled=p.device.type != 'cpu'):
|
||||
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
|
||||
|
||||
# Pre-process
|
||||
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
|
||||
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
|
||||
for i, im in enumerate(imgs):
|
||||
f = f'image{i}' # filename
|
||||
if isinstance(im, str): # filename or uri
|
||||
im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im
|
||||
elif isinstance(im, Image.Image): # PIL Image
|
||||
im, f = np.asarray(im), getattr(im, 'filename', f) or f
|
||||
files.append(Path(f).with_suffix('.jpg').name)
|
||||
if im.shape[0] < 5: # image in CHW
|
||||
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
||||
im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
|
||||
s = im.shape[:2] # HWC
|
||||
shape0.append(s) # image shape
|
||||
g = (size / max(s)) # gain
|
||||
shape1.append([y * g for y in s])
|
||||
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
|
||||
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
|
||||
x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
|
||||
x = np.stack(x, 0) if n > 1 else x[0][None] # stack
|
||||
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
|
||||
x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
|
||||
t.append(time_synchronized())
|
||||
|
||||
with amp.autocast(enabled=p.device.type != 'cpu'):
|
||||
# Inference
|
||||
y = self.model(x, augment, profile)[0] # forward
|
||||
t.append(time_synchronized())
|
||||
|
||||
# Post-process
|
||||
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
|
||||
for i in range(n):
|
||||
scale_coords(shape1, y[i][:, :4], shape0[i])
|
||||
|
||||
t.append(time_synchronized())
|
||||
return Detections(imgs, y, files, t, self.names, x.shape)
|
||||
|
||||
|
||||
class Detections:
|
||||
# detections class for YOLOv5 inference results
|
||||
def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
|
||||
super(Detections, self).__init__()
|
||||
d = pred[0].device # device
|
||||
gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
|
||||
self.imgs = imgs # list of images as numpy arrays
|
||||
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
|
||||
self.names = names # class names
|
||||
self.files = files # image filenames
|
||||
self.xyxy = pred # xyxy pixels
|
||||
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
|
||||
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
|
||||
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
|
||||
self.n = len(self.pred) # number of images (batch size)
|
||||
self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
|
||||
self.s = shape # inference BCHW shape
|
||||
|
||||
def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
|
||||
colors = color_list()
|
||||
for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
|
||||
str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
|
||||
if pred is not None:
|
||||
for c in pred[:, -1].unique():
|
||||
n = (pred[:, -1] == c).sum() # detections per class
|
||||
str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
if show or save or render:
|
||||
for *box, conf, cls in pred: # xyxy, confidence, class
|
||||
label = f'{self.names[int(cls)]} {conf:.2f}'
|
||||
plot_one_box(box, img, label=label, color=colors[int(cls) % 10])
|
||||
img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
|
||||
if pprint:
|
||||
print(str.rstrip(', '))
|
||||
if show:
|
||||
img.show(self.files[i]) # show
|
||||
if save:
|
||||
f = self.files[i]
|
||||
img.save(Path(save_dir) / f) # save
|
||||
print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
|
||||
if render:
|
||||
self.imgs[i] = np.asarray(img)
|
||||
|
||||
def print(self):
|
||||
self.display(pprint=True) # print results
|
||||
print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
|
||||
|
||||
def show(self):
|
||||
self.display(show=True) # show results
|
||||
|
||||
def save(self, save_dir='runs/hub/exp'):
|
||||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir
|
||||
Path(save_dir).mkdir(parents=True, exist_ok=True)
|
||||
self.display(save=True, save_dir=save_dir) # save results
|
||||
|
||||
def render(self):
|
||||
self.display(render=True) # render results
|
||||
return self.imgs
|
||||
|
||||
def pandas(self):
|
||||
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
|
||||
new = copy(self) # return copy
|
||||
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
|
||||
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
|
||||
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
|
||||
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
|
||||
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
|
||||
return new
|
||||
|
||||
def tolist(self):
|
||||
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||||
x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
|
||||
for d in x:
|
||||
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||||
setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||||
return x
|
||||
|
||||
def __len__(self):
|
||||
return self.n
|
||||
|
||||
|
||||
class Classify(nn.Module):
|
||||
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super(Classify, self).__init__()
|
||||
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
|
||||
self.flat = nn.Flatten()
|
||||
|
||||
def forward(self, x):
|
||||
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
||||
return self.flat(self.conv(z)) # flatten to x(b,c2)
|
||||
|
|
@ -0,0 +1,134 @@
|
|||
# YOLOv5 experimental modules
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from models.common import Conv, DWConv
|
||||
from utils.google_utils import attempt_download
|
||||
|
||||
|
||||
class CrossConv(nn.Module):
|
||||
# Cross Convolution Downsample
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
|
||||
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
|
||||
super(CrossConv, self).__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, (1, k), (1, s))
|
||||
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
|
||||
|
||||
class Sum(nn.Module):
|
||||
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
|
||||
def __init__(self, n, weight=False): # n: number of inputs
|
||||
super(Sum, self).__init__()
|
||||
self.weight = weight # apply weights boolean
|
||||
self.iter = range(n - 1) # iter object
|
||||
if weight:
|
||||
self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
|
||||
|
||||
def forward(self, x):
|
||||
y = x[0] # no weight
|
||||
if self.weight:
|
||||
w = torch.sigmoid(self.w) * 2
|
||||
for i in self.iter:
|
||||
y = y + x[i + 1] * w[i]
|
||||
else:
|
||||
for i in self.iter:
|
||||
y = y + x[i + 1]
|
||||
return y
|
||||
|
||||
|
||||
class GhostConv(nn.Module):
|
||||
# Ghost Convolution https://github.com/huawei-noah/ghostnet
|
||||
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
|
||||
super(GhostConv, self).__init__()
|
||||
c_ = c2 // 2 # hidden channels
|
||||
self.cv1 = Conv(c1, c_, k, s, None, g, act)
|
||||
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.cv1(x)
|
||||
return torch.cat([y, self.cv2(y)], 1)
|
||||
|
||||
|
||||
class GhostBottleneck(nn.Module):
|
||||
# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
|
||||
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
|
||||
super(GhostBottleneck, self).__init__()
|
||||
c_ = c2 // 2
|
||||
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
|
||||
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
|
||||
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
|
||||
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
|
||||
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv(x) + self.shortcut(x)
|
||||
|
||||
|
||||
class MixConv2d(nn.Module):
|
||||
# Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
|
||||
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
|
||||
super(MixConv2d, self).__init__()
|
||||
groups = len(k)
|
||||
if equal_ch: # equal c_ per group
|
||||
i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
|
||||
c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
|
||||
else: # equal weight.numel() per group
|
||||
b = [c2] + [0] * groups
|
||||
a = np.eye(groups + 1, groups, k=-1)
|
||||
a -= np.roll(a, 1, axis=1)
|
||||
a *= np.array(k) ** 2
|
||||
a[0] = 1
|
||||
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
|
||||
|
||||
self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.LeakyReLU(0.1, inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
|
||||
|
||||
|
||||
class Ensemble(nn.ModuleList):
|
||||
# Ensemble of models
|
||||
def __init__(self):
|
||||
super(Ensemble, self).__init__()
|
||||
|
||||
def forward(self, x, augment=False):
|
||||
y = []
|
||||
for module in self:
|
||||
y.append(module(x, augment)[0])
|
||||
# y = torch.stack(y).max(0)[0] # max ensemble
|
||||
# y = torch.stack(y).mean(0) # mean ensemble
|
||||
y = torch.cat(y, 1) # nms ensemble
|
||||
return y, None # inference, train output
|
||||
|
||||
|
||||
def attempt_load(weights, map_location=None):
|
||||
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
|
||||
model = Ensemble()
|
||||
for w in weights if isinstance(weights, list) else [weights]:
|
||||
attempt_download(w)
|
||||
ckpt = torch.load(w, map_location=map_location) # load
|
||||
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model
|
||||
|
||||
# Compatibility updates
|
||||
for m in model.modules():
|
||||
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
|
||||
m.inplace = True # pytorch 1.7.0 compatibility
|
||||
elif type(m) is Conv:
|
||||
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
|
||||
|
||||
if len(model) == 1:
|
||||
return model[-1] # return model
|
||||
else:
|
||||
print('Ensemble created with %s\n' % weights)
|
||||
for k in ['names', 'stride']:
|
||||
setattr(model, k, getattr(model[-1], k))
|
||||
return model # return ensemble
|
||||
|
|
@ -0,0 +1,123 @@
|
|||
"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
|
||||
|
||||
Usage:
|
||||
$ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import time
|
||||
|
||||
sys.path.append('./') # to run '$ python *.py' files in subdirectories
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import models
|
||||
from models.experimental import attempt_load
|
||||
from utils.activations import Hardswish, SiLU
|
||||
from utils.general import colorstr, check_img_size, check_requirements, set_logging
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
|
||||
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
|
||||
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
||||
parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
|
||||
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only
|
||||
parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only
|
||||
opt = parser.parse_args()
|
||||
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
|
||||
print(opt)
|
||||
set_logging()
|
||||
t = time.time()
|
||||
|
||||
# Load PyTorch model
|
||||
device = select_device(opt.device)
|
||||
model = attempt_load(opt.weights, map_location=device) # load FP32 model
|
||||
labels = model.names
|
||||
|
||||
# Checks
|
||||
gs = int(max(model.stride)) # grid size (max stride)
|
||||
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
|
||||
|
||||
# Input
|
||||
img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
|
||||
|
||||
# Update model
|
||||
for k, m in model.named_modules():
|
||||
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
|
||||
if isinstance(m, models.common.Conv): # assign export-friendly activations
|
||||
if isinstance(m.act, nn.Hardswish):
|
||||
m.act = Hardswish()
|
||||
elif isinstance(m.act, nn.SiLU):
|
||||
m.act = SiLU()
|
||||
# elif isinstance(m, models.yolo.Detect):
|
||||
# m.forward = m.forward_export # assign forward (optional)
|
||||
model.model[-1].export = not opt.grid # set Detect() layer grid export
|
||||
y = model(img) # dry run
|
||||
|
||||
# TorchScript export -----------------------------------------------------------------------------------------------
|
||||
prefix = colorstr('TorchScript:')
|
||||
try:
|
||||
print(f'\n{prefix} starting export with torch {torch.__version__}...')
|
||||
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
|
||||
ts = torch.jit.trace(model, img, strict=False)
|
||||
ts.save(f)
|
||||
print(f'{prefix} export success, saved as {f}')
|
||||
except Exception as e:
|
||||
print(f'{prefix} export failure: {e}')
|
||||
|
||||
# ONNX export ------------------------------------------------------------------------------------------------------
|
||||
prefix = colorstr('ONNX:')
|
||||
try:
|
||||
import onnx
|
||||
|
||||
print(f'{prefix} starting export with onnx {onnx.__version__}...')
|
||||
f = opt.weights.replace('.pt', '.onnx') # filename
|
||||
torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
|
||||
output_names=['classes', 'boxes'] if y is None else ['output'],
|
||||
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640)
|
||||
'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
|
||||
|
||||
# Checks
|
||||
model_onnx = onnx.load(f) # load onnx model
|
||||
onnx.checker.check_model(model_onnx) # check onnx model
|
||||
# print(onnx.helper.printable_graph(model_onnx.graph)) # print
|
||||
|
||||
# Simplify
|
||||
if opt.simplify:
|
||||
try:
|
||||
check_requirements(['onnx-simplifier'])
|
||||
import onnxsim
|
||||
|
||||
print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
|
||||
model_onnx, check = onnxsim.simplify(model_onnx,
|
||||
dynamic_input_shape=opt.dynamic,
|
||||
input_shapes={'images': list(img.shape)} if opt.dynamic else None)
|
||||
assert check, 'assert check failed'
|
||||
onnx.save(model_onnx, f)
|
||||
except Exception as e:
|
||||
print(f'{prefix} simplifier failure: {e}')
|
||||
print(f'{prefix} export success, saved as {f}')
|
||||
except Exception as e:
|
||||
print(f'{prefix} export failure: {e}')
|
||||
|
||||
# CoreML export ----------------------------------------------------------------------------------------------------
|
||||
prefix = colorstr('CoreML:')
|
||||
try:
|
||||
import coremltools as ct
|
||||
|
||||
print(f'{prefix} starting export with coremltools {onnx.__version__}...')
|
||||
# convert model from torchscript and apply pixel scaling as per detect.py
|
||||
model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
|
||||
f = opt.weights.replace('.pt', '.mlmodel') # filename
|
||||
model.save(f)
|
||||
print(f'{prefix} export success, saved as {f}')
|
||||
except Exception as e:
|
||||
print(f'{prefix} export failure: {e}')
|
||||
|
||||
# Finish
|
||||
print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
# Default YOLOv5 anchors for COCO data
|
||||
|
||||
|
||||
# P5 -------------------------------------------------------------------------------------------------------------------
|
||||
# P5-640:
|
||||
anchors_p5_640:
|
||||
- [ 10,13, 16,30, 33,23 ] # P3/8
|
||||
- [ 30,61, 62,45, 59,119 ] # P4/16
|
||||
- [ 116,90, 156,198, 373,326 ] # P5/32
|
||||
|
||||
|
||||
# P6 -------------------------------------------------------------------------------------------------------------------
|
||||
# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387
|
||||
anchors_p6_640:
|
||||
- [ 9,11, 21,19, 17,41 ] # P3/8
|
||||
- [ 43,32, 39,70, 86,64 ] # P4/16
|
||||
- [ 65,131, 134,130, 120,265 ] # P5/32
|
||||
- [ 282,180, 247,354, 512,387 ] # P6/64
|
||||
|
||||
# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792
|
||||
anchors_p6_1280:
|
||||
- [ 19,27, 44,40, 38,94 ] # P3/8
|
||||
- [ 96,68, 86,152, 180,137 ] # P4/16
|
||||
- [ 140,301, 303,264, 238,542 ] # P5/32
|
||||
- [ 436,615, 739,380, 925,792 ] # P6/64
|
||||
|
||||
# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187
|
||||
anchors_p6_1920:
|
||||
- [ 28,41, 67,59, 57,141 ] # P3/8
|
||||
- [ 144,103, 129,227, 270,205 ] # P4/16
|
||||
- [ 209,452, 455,396, 358,812 ] # P5/32
|
||||
- [ 653,922, 1109,570, 1387,1187 ] # P6/64
|
||||
|
||||
|
||||
# P7 -------------------------------------------------------------------------------------------------------------------
|
||||
# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372
|
||||
anchors_p7_640:
|
||||
- [ 11,11, 13,30, 29,20 ] # P3/8
|
||||
- [ 30,46, 61,38, 39,92 ] # P4/16
|
||||
- [ 78,80, 146,66, 79,163 ] # P5/32
|
||||
- [ 149,150, 321,143, 157,303 ] # P6/64
|
||||
- [ 257,402, 359,290, 524,372 ] # P7/128
|
||||
|
||||
# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818
|
||||
anchors_p7_1280:
|
||||
- [ 19,22, 54,36, 32,77 ] # P3/8
|
||||
- [ 70,83, 138,71, 75,173 ] # P4/16
|
||||
- [ 165,159, 148,334, 375,151 ] # P5/32
|
||||
- [ 334,317, 251,626, 499,474 ] # P6/64
|
||||
- [ 750,326, 534,814, 1079,818 ] # P7/128
|
||||
|
||||
# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227
|
||||
anchors_p7_1920:
|
||||
- [ 29,34, 81,55, 47,115 ] # P3/8
|
||||
- [ 105,124, 207,107, 113,259 ] # P4/16
|
||||
- [ 247,238, 222,500, 563,227 ] # P5/32
|
||||
- [ 501,476, 376,939, 749,711 ] # P6/64
|
||||
- [ 1126,489, 801,1222, 1618,1227 ] # P7/128
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
|
||||
# YOLOv3-SPP head
|
||||
head:
|
||||
[[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, SPP, [512, [5, 9, 13]]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,14, 23,27, 37,58] # P4/16
|
||||
- [81,82, 135,169, 344,319] # P5/32
|
||||
|
||||
# YOLOv3-tiny backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [16, 3, 1]], # 0
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
|
||||
[-1, 1, Conv, [32, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
|
||||
[-1, 1, Conv, [64, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
|
||||
[-1, 1, Conv, [128, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
|
||||
[-1, 1, Conv, [256, 3, 1]],
|
||||
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
|
||||
[-1, 1, Conv, [512, 3, 1]],
|
||||
[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
|
||||
[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
|
||||
]
|
||||
|
||||
# YOLOv3-tiny head
|
||||
head:
|
||||
[[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
|
||||
|
||||
[[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [32, 3, 1]], # 0
|
||||
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
|
||||
[-1, 1, Bottleneck, [64]],
|
||||
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
|
||||
[-1, 2, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
|
||||
[-1, 8, Bottleneck, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
|
||||
[-1, 8, Bottleneck, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
|
||||
[-1, 4, Bottleneck, [1024]], # 10
|
||||
]
|
||||
|
||||
# YOLOv3 head
|
||||
head:
|
||||
[[-1, 1, Bottleneck, [1024, False]],
|
||||
[-1, 1, Conv, [512, [1, 1]]],
|
||||
[-1, 1, Conv, [1024, 3, 1]],
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
|
||||
|
||||
[-2, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 8], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Bottleneck, [512, False]],
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
|
||||
|
||||
[-2, 1, Conv, [128, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Bottleneck, [256, False]],
|
||||
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
|
||||
|
||||
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Focus, [64, 3]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, Bottleneck, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 9, BottleneckCSP, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, BottleneckCSP, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 1, SPP, [1024, [5, 9, 13]]],
|
||||
[-1, 6, BottleneckCSP, [1024]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 FPN head
|
||||
head:
|
||||
[[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large)
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium)
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small)
|
||||
|
||||
[[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors: 3
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
|
||||
[ -1, 3, C3, [ 128 ] ],
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
|
||||
[ -1, 9, C3, [ 256 ] ],
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
|
||||
[ -1, 9, C3, [ 512 ] ],
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
|
||||
[ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[ [ -1, 1, Conv, [ 512, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 13
|
||||
|
||||
[ -1, 1, Conv, [ 256, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 128, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 2 ], 1, Concat, [ 1 ] ], # cat backbone P2
|
||||
[ -1, 1, C3, [ 128, False ] ], # 21 (P2/4-xsmall)
|
||||
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ],
|
||||
[ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 24 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ],
|
||||
[ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 27 (P4/16-medium)
|
||||
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ],
|
||||
[ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 30 (P5/32-large)
|
||||
|
||||
[ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors: 3
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
|
||||
[ -1, 3, C3, [ 128 ] ],
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
|
||||
[ -1, 9, C3, [ 256 ] ],
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
|
||||
[ -1, 9, C3, [ 512 ] ],
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
|
||||
[ -1, 3, C3, [ 768 ] ],
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
|
||||
[ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[ [ -1, 1, Conv, [ 768, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 15
|
||||
|
||||
[ -1, 1, Conv, [ 512, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 19
|
||||
|
||||
[ -1, 1, Conv, [ 256, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ],
|
||||
[ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium)
|
||||
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ],
|
||||
[ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large)
|
||||
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ],
|
||||
[ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 32 (P5/64-xlarge)
|
||||
|
||||
[ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors: 3
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
|
||||
[ -1, 3, C3, [ 128 ] ],
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
|
||||
[ -1, 9, C3, [ 256 ] ],
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
|
||||
[ -1, 9, C3, [ 512 ] ],
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
|
||||
[ -1, 3, C3, [ 768 ] ],
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
|
||||
[ -1, 3, C3, [ 1024 ] ],
|
||||
[ -1, 1, Conv, [ 1280, 3, 2 ] ], # 11-P7/128
|
||||
[ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ],
|
||||
[ -1, 3, C3, [ 1280, False ] ], # 13
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[ [ -1, 1, Conv, [ 1024, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat backbone P6
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 17
|
||||
|
||||
[ -1, 1, Conv, [ 768, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 21
|
||||
|
||||
[ -1, 1, Conv, [ 512, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 25
|
||||
|
||||
[ -1, 1, Conv, [ 256, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 29 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ],
|
||||
[ [ -1, 26 ], 1, Concat, [ 1 ] ], # cat head P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 32 (P4/16-medium)
|
||||
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ],
|
||||
[ [ -1, 22 ], 1, Concat, [ 1 ] ], # cat head P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 35 (P5/32-large)
|
||||
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ],
|
||||
[ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P6
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 38 (P6/64-xlarge)
|
||||
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ],
|
||||
[ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P7
|
||||
[ -1, 3, C3, [ 1280, False ] ], # 41 (P7/128-xxlarge)
|
||||
|
||||
[ [ 29, 32, 35, 38, 41 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6, P7)
|
||||
]
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Focus, [64, 3]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, BottleneckCSP, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 9, BottleneckCSP, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, BottleneckCSP, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 1, SPP, [1024, [5, 9, 13]]],
|
||||
[-1, 3, BottleneckCSP, [1024, False]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 PANet head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, BottleneckCSP, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [ 19,27, 44,40, 38,94 ] # P3/8
|
||||
- [ 96,68, 86,152, 180,137 ] # P4/16
|
||||
- [ 140,301, 303,264, 238,542 ] # P5/32
|
||||
- [ 436,615, 739,380, 925,792 ] # P6/64
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
|
||||
[ -1, 3, C3, [ 128 ] ],
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
|
||||
[ -1, 9, C3, [ 256 ] ],
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
|
||||
[ -1, 9, C3, [ 512 ] ],
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
|
||||
[ -1, 3, C3, [ 768 ] ],
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
|
||||
[ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[ [ -1, 1, Conv, [ 768, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 15
|
||||
|
||||
[ -1, 1, Conv, [ 512, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 19
|
||||
|
||||
[ -1, 1, Conv, [ 256, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ],
|
||||
[ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium)
|
||||
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ],
|
||||
[ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large)
|
||||
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ],
|
||||
[ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge)
|
||||
|
||||
[ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [ 19,27, 44,40, 38,94 ] # P3/8
|
||||
- [ 96,68, 86,152, 180,137 ] # P4/16
|
||||
- [ 140,301, 303,264, 238,542 ] # P5/32
|
||||
- [ 436,615, 739,380, 925,792 ] # P6/64
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
|
||||
[ -1, 3, C3, [ 128 ] ],
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
|
||||
[ -1, 9, C3, [ 256 ] ],
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
|
||||
[ -1, 9, C3, [ 512 ] ],
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
|
||||
[ -1, 3, C3, [ 768 ] ],
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
|
||||
[ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[ [ -1, 1, Conv, [ 768, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 15
|
||||
|
||||
[ -1, 1, Conv, [ 512, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 19
|
||||
|
||||
[ -1, 1, Conv, [ 256, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ],
|
||||
[ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium)
|
||||
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ],
|
||||
[ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large)
|
||||
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ],
|
||||
[ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge)
|
||||
|
||||
[ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Focus, [64, 3]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 9, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 1, SPP, [1024, [5, 9, 13]]],
|
||||
[-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [ 19,27, 44,40, 38,94 ] # P3/8
|
||||
- [ 96,68, 86,152, 180,137 ] # P4/16
|
||||
- [ 140,301, 303,264, 238,542 ] # P5/32
|
||||
- [ 436,615, 739,380, 925,792 ] # P6/64
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
|
||||
[ -1, 3, C3, [ 128 ] ],
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
|
||||
[ -1, 9, C3, [ 256 ] ],
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
|
||||
[ -1, 9, C3, [ 512 ] ],
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
|
||||
[ -1, 3, C3, [ 768 ] ],
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
|
||||
[ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[ [ -1, 1, Conv, [ 768, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 15
|
||||
|
||||
[ -1, 1, Conv, [ 512, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 19
|
||||
|
||||
[ -1, 1, Conv, [ 256, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ],
|
||||
[ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium)
|
||||
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ],
|
||||
[ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large)
|
||||
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ],
|
||||
[ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge)
|
||||
|
||||
[ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.33 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [ 19,27, 44,40, 38,94 ] # P3/8
|
||||
- [ 96,68, 86,152, 180,137 ] # P4/16
|
||||
- [ 140,301, 303,264, 238,542 ] # P5/32
|
||||
- [ 436,615, 739,380, 925,792 ] # P6/64
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
|
||||
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
|
||||
[ -1, 3, C3, [ 128 ] ],
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
|
||||
[ -1, 9, C3, [ 256 ] ],
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
|
||||
[ -1, 9, C3, [ 512 ] ],
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32
|
||||
[ -1, 3, C3, [ 768 ] ],
|
||||
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64
|
||||
[ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 11
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[ [ -1, 1, Conv, [ 768, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 15
|
||||
|
||||
[ -1, 1, Conv, [ 512, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 19
|
||||
|
||||
[ -1, 1, Conv, [ 256, 1, 1 ] ],
|
||||
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
|
||||
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
|
||||
[ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small)
|
||||
|
||||
[ -1, 1, Conv, [ 256, 3, 2 ] ],
|
||||
[ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4
|
||||
[ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium)
|
||||
|
||||
[ -1, 1, Conv, [ 512, 3, 2 ] ],
|
||||
[ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5
|
||||
[ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large)
|
||||
|
||||
[ -1, 1, Conv, [ 768, 3, 2 ] ],
|
||||
[ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6
|
||||
[ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge)
|
||||
|
||||
[ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
|
|
@ -0,0 +1,277 @@
|
|||
# YOLOv5 YOLO-specific modules
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
|
||||
sys.path.append('./') # to run '$ python *.py' files in subdirectories
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
from models.common import *
|
||||
from models.experimental import *
|
||||
from utils.autoanchor import check_anchor_order
|
||||
from utils.general import make_divisible, check_file, set_logging
|
||||
from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
|
||||
select_device, copy_attr
|
||||
|
||||
try:
|
||||
import thop # for FLOPS computation
|
||||
except ImportError:
|
||||
thop = None
|
||||
|
||||
|
||||
class Detect(nn.Module):
|
||||
stride = None # strides computed during build
|
||||
export = False # onnx export
|
||||
|
||||
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
|
||||
super(Detect, self).__init__()
|
||||
self.nc = nc # number of classes
|
||||
self.no = nc + 5 # number of outputs per anchor
|
||||
self.nl = len(anchors) # number of detection layers
|
||||
self.na = len(anchors[0]) // 2 # number of anchors
|
||||
self.grid = [torch.zeros(1)] * self.nl # init grid
|
||||
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
|
||||
self.register_buffer('anchors', a) # shape(nl,na,2)
|
||||
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
|
||||
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
||||
|
||||
def forward(self, x):
|
||||
# x = x.copy() # for profiling
|
||||
z = [] # inference output
|
||||
self.training |= self.export
|
||||
for i in range(self.nl):
|
||||
x[i] = self.m[i](x[i]) # conv
|
||||
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
|
||||
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
|
||||
|
||||
if not self.training: # inference
|
||||
if self.grid[i].shape[2:4] != x[i].shape[2:4]:
|
||||
self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
|
||||
|
||||
y = x[i].sigmoid()
|
||||
y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
|
||||
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
z.append(y.view(bs, -1, self.no))
|
||||
|
||||
return x if self.training else (torch.cat(z, 1), x)
|
||||
|
||||
@staticmethod
|
||||
def _make_grid(nx=20, ny=20):
|
||||
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
|
||||
return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
|
||||
|
||||
|
||||
class Model(nn.Module):
|
||||
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
|
||||
super(Model, self).__init__()
|
||||
if isinstance(cfg, dict):
|
||||
self.yaml = cfg # model dict
|
||||
else: # is *.yaml
|
||||
import yaml # for torch hub
|
||||
self.yaml_file = Path(cfg).name
|
||||
with open(cfg) as f:
|
||||
self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict
|
||||
|
||||
# Define model
|
||||
ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels
|
||||
if nc and nc != self.yaml['nc']:
|
||||
logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
|
||||
self.yaml['nc'] = nc # override yaml value
|
||||
if anchors:
|
||||
logger.info(f'Overriding model.yaml anchors with anchors={anchors}')
|
||||
self.yaml['anchors'] = round(anchors) # override yaml value
|
||||
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
|
||||
self.names = [str(i) for i in range(self.yaml['nc'])] # default names
|
||||
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
|
||||
|
||||
# Build strides, anchors
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, Detect):
|
||||
s = 256 # 2x min stride
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
m.anchors /= m.stride.view(-1, 1, 1)
|
||||
check_anchor_order(m)
|
||||
self.stride = m.stride
|
||||
self._initialize_biases() # only run once
|
||||
# print('Strides: %s' % m.stride.tolist())
|
||||
|
||||
# Init weights, biases
|
||||
initialize_weights(self)
|
||||
self.info()
|
||||
logger.info('')
|
||||
|
||||
def forward(self, x, augment=False, profile=False):
|
||||
if augment:
|
||||
img_size = x.shape[-2:] # height, width
|
||||
s = [1, 0.83, 0.67] # scales
|
||||
f = [None, 3, None] # flips (2-ud, 3-lr)
|
||||
y = [] # outputs
|
||||
for si, fi in zip(s, f):
|
||||
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
|
||||
yi = self.forward_once(xi)[0] # forward
|
||||
# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
|
||||
yi[..., :4] /= si # de-scale
|
||||
if fi == 2:
|
||||
yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud
|
||||
elif fi == 3:
|
||||
yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr
|
||||
y.append(yi)
|
||||
return torch.cat(y, 1), None # augmented inference, train
|
||||
else:
|
||||
return self.forward_once(x, profile) # single-scale inference, train
|
||||
|
||||
def forward_once(self, x, profile=False):
|
||||
y, dt = [], [] # outputs
|
||||
for m in self.model:
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
|
||||
if profile:
|
||||
o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS
|
||||
t = time_synchronized()
|
||||
for _ in range(10):
|
||||
_ = m(x)
|
||||
dt.append((time_synchronized() - t) * 100)
|
||||
print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
|
||||
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.save else None) # save output
|
||||
|
||||
if profile:
|
||||
print('%.1fms total' % sum(dt))
|
||||
return x
|
||||
|
||||
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
|
||||
# https://arxiv.org/abs/1708.02002 section 3.3
|
||||
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi, s in zip(m.m, m.stride): # from
|
||||
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
|
||||
b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
|
||||
b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
|
||||
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
|
||||
|
||||
def _print_biases(self):
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi in m.m: # from
|
||||
b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
|
||||
print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
|
||||
|
||||
# def _print_weights(self):
|
||||
# for m in self.model.modules():
|
||||
# if type(m) is Bottleneck:
|
||||
# print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
|
||||
|
||||
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
|
||||
print('Fusing layers... ')
|
||||
for m in self.model.modules():
|
||||
if type(m) is Conv and hasattr(m, 'bn'):
|
||||
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
||||
delattr(m, 'bn') # remove batchnorm
|
||||
m.forward = m.fuseforward # update forward
|
||||
self.info()
|
||||
return self
|
||||
|
||||
def nms(self, mode=True): # add or remove NMS module
|
||||
present = type(self.model[-1]) is NMS # last layer is NMS
|
||||
if mode and not present:
|
||||
print('Adding NMS... ')
|
||||
m = NMS() # module
|
||||
m.f = -1 # from
|
||||
m.i = self.model[-1].i + 1 # index
|
||||
self.model.add_module(name='%s' % m.i, module=m) # add
|
||||
self.eval()
|
||||
elif not mode and present:
|
||||
print('Removing NMS... ')
|
||||
self.model = self.model[:-1] # remove
|
||||
return self
|
||||
|
||||
def autoshape(self): # add autoShape module
|
||||
print('Adding autoShape... ')
|
||||
m = autoShape(self) # wrap model
|
||||
copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
|
||||
return m
|
||||
|
||||
def info(self, verbose=False, img_size=640): # print model information
|
||||
model_info(self, verbose, img_size)
|
||||
|
||||
|
||||
def parse_model(d, ch): # model_dict, input_channels(3)
|
||||
logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
|
||||
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
|
||||
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
|
||||
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
|
||||
|
||||
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
|
||||
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
|
||||
m = eval(m) if isinstance(m, str) else m # eval strings
|
||||
for j, a in enumerate(args):
|
||||
try:
|
||||
args[j] = eval(a) if isinstance(a, str) else a # eval strings
|
||||
except:
|
||||
pass
|
||||
|
||||
n = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
|
||||
C3, C3TR]:
|
||||
c1, c2 = ch[f], args[0]
|
||||
if c2 != no: # if not output
|
||||
c2 = make_divisible(c2 * gw, 8)
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in [BottleneckCSP, C3, C3TR]:
|
||||
args.insert(2, n) # number of repeats
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum([ch[x] for x in f])
|
||||
elif m is Detect:
|
||||
args.append([ch[x] for x in f])
|
||||
if isinstance(args[1], int): # number of anchors
|
||||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||||
elif m is Contract:
|
||||
c2 = ch[f] * args[0] ** 2
|
||||
elif m is Expand:
|
||||
c2 = ch[f] // args[0] ** 2
|
||||
else:
|
||||
c2 = ch[f]
|
||||
|
||||
m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
|
||||
t = str(m)[8:-2].replace('__main__.', '') # module type
|
||||
np = sum([x.numel() for x in m_.parameters()]) # number params
|
||||
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
|
||||
logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
|
||||
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
||||
layers.append(m_)
|
||||
if i == 0:
|
||||
ch = []
|
||||
ch.append(c2)
|
||||
return nn.Sequential(*layers), sorted(save)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
opt = parser.parse_args()
|
||||
opt.cfg = check_file(opt.cfg) # check file
|
||||
set_logging()
|
||||
device = select_device(opt.device)
|
||||
|
||||
# Create model
|
||||
model = Model(opt.cfg).to(device)
|
||||
model.train()
|
||||
|
||||
# Profile
|
||||
# img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
|
||||
# y = model(img, profile=True)
|
||||
|
||||
# Tensorboard
|
||||
# from torch.utils.tensorboard import SummaryWriter
|
||||
# tb_writer = SummaryWriter()
|
||||
# print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
|
||||
# tb_writer.add_graph(model.model, img) # add model to tensorboard
|
||||
# tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Focus, [64, 3]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 9, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 1, SPP, [1024, [5, 9, 13]]],
|
||||
[-1, 3, C3, [1024, False]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Focus, [64, 3]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 9, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 1, SPP, [1024, [5, 9, 13]]],
|
||||
[-1, 3, C3, [1024, False]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Focus, [64, 3]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 9, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 1, SPP, [1024, [5, 9, 13]]],
|
||||
[-1, 3, C3, [1024, False]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.33 # model depth multiple
|
||||
width_multiple: 1.25 # layer channel multiple
|
||||
|
||||
# anchors
|
||||
anchors:
|
||||
- [10,13, 16,30, 33,23] # P3/8
|
||||
- [30,61, 62,45, 59,119] # P4/16
|
||||
- [116,90, 156,198, 373,326] # P5/32
|
||||
|
||||
# YOLOv5 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Focus, [64, 3]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C3, [128]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 9, C3, [256]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 9, C3, [512]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 1, SPP, [1024, [5, 9, 13]]],
|
||||
[-1, 3, C3, [1024, False]], # 9
|
||||
]
|
||||
|
||||
# YOLOv5 head
|
||||
head:
|
||||
[[-1, 1, Conv, [512, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C3, [512, False]], # 13
|
||||
|
||||
[-1, 1, Conv, [256, 1, 1]],
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 14], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 10], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
|
||||
|
||||
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
|
||||
]
|
||||
Binary file not shown.
|
|
@ -0,0 +1,2 @@
|
|||
python train.py --img 640 --batch 32 --epochs 100 --data exitStone.yaml --weights yolov5s.pt
|
||||
|
||||
Binary file not shown.
|
|
@ -0,0 +1,27 @@
|
|||
lr0: 0.01
|
||||
lrf: 0.2
|
||||
momentum: 0.937
|
||||
weight_decay: 0.0005
|
||||
warmup_epochs: 3.0
|
||||
warmup_momentum: 0.8
|
||||
warmup_bias_lr: 0.1
|
||||
box: 0.05
|
||||
cls: 0.5
|
||||
cls_pw: 1.0
|
||||
obj: 1.0
|
||||
obj_pw: 1.0
|
||||
iou_t: 0.2
|
||||
anchor_t: 4.0
|
||||
fl_gamma: 0.0
|
||||
hsv_h: 0.015
|
||||
hsv_s: 0.7
|
||||
hsv_v: 0.4
|
||||
degrees: 0.0
|
||||
translate: 0.1
|
||||
scale: 0.5
|
||||
shear: 0.0
|
||||
perspective: 0.0
|
||||
flipud: 0.0
|
||||
fliplr: 0.5
|
||||
mosaic: 1.0
|
||||
mixup: 0.0
|
||||
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|
After Width: | Height: | Size: 156 KiB |
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|
After Width: | Height: | Size: 200 KiB |
|
|
@ -0,0 +1,40 @@
|
|||
weights: yolov5s.pt
|
||||
cfg: ''
|
||||
data: ./data/exitStone.yaml
|
||||
hyp: data/hyp.scratch.yaml
|
||||
epochs: 100
|
||||
batch_size: 32
|
||||
img_size:
|
||||
- 640
|
||||
- 640
|
||||
rect: false
|
||||
resume: false
|
||||
nosave: false
|
||||
notest: false
|
||||
noautoanchor: false
|
||||
evolve: false
|
||||
bucket: ''
|
||||
cache_images: false
|
||||
image_weights: false
|
||||
device: ''
|
||||
multi_scale: false
|
||||
single_cls: false
|
||||
adam: false
|
||||
sync_bn: false
|
||||
local_rank: -1
|
||||
workers: 8
|
||||
project: runs/train
|
||||
entity: null
|
||||
name: exp
|
||||
exist_ok: false
|
||||
quad: false
|
||||
linear_lr: false
|
||||
label_smoothing: 0.0
|
||||
upload_dataset: false
|
||||
bbox_interval: -1
|
||||
save_period: -1
|
||||
artifact_alias: latest
|
||||
world_size: 1
|
||||
global_rank: -1
|
||||
save_dir: runs/train/exp
|
||||
total_batch_size: 32
|
||||
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|
After Width: | Height: | Size: 331 KiB |
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|
After Width: | Height: | Size: 343 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 354 KiB |
|
|
@ -0,0 +1,345 @@
|
|||
import argparse
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from tqdm import tqdm
|
||||
|
||||
from models.experimental import attempt_load
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
|
||||
box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
|
||||
from utils.metrics import ap_per_class, ConfusionMatrix
|
||||
from utils.plots import plot_images, output_to_target, plot_study_txt
|
||||
from utils.torch_utils import select_device, time_synchronized
|
||||
|
||||
|
||||
def test(data,
|
||||
weights=None,
|
||||
batch_size=32,
|
||||
imgsz=640,
|
||||
conf_thres=0.001,
|
||||
iou_thres=0.6, # for NMS
|
||||
save_json=False,
|
||||
single_cls=False,
|
||||
augment=False,
|
||||
verbose=False,
|
||||
model=None,
|
||||
dataloader=None,
|
||||
save_dir=Path(''), # for saving images
|
||||
save_txt=False, # for auto-labelling
|
||||
save_hybrid=False, # for hybrid auto-labelling
|
||||
save_conf=False, # save auto-label confidences
|
||||
plots=True,
|
||||
wandb_logger=None,
|
||||
compute_loss=None,
|
||||
half_precision=True,
|
||||
is_coco=False):
|
||||
# Initialize/load model and set device
|
||||
training = model is not None
|
||||
if training: # called by train.py
|
||||
device = next(model.parameters()).device # get model device
|
||||
|
||||
else: # called directly
|
||||
set_logging()
|
||||
device = select_device(opt.device, batch_size=batch_size)
|
||||
|
||||
# Directories
|
||||
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
|
||||
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
model = attempt_load(weights, map_location=device) # load FP32 model
|
||||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||||
imgsz = check_img_size(imgsz, s=gs) # check img_size
|
||||
|
||||
# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
|
||||
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
|
||||
# model = nn.DataParallel(model)
|
||||
|
||||
# Half
|
||||
half = device.type != 'cpu' and half_precision # half precision only supported on CUDA
|
||||
if half:
|
||||
model.half()
|
||||
|
||||
# Configure
|
||||
model.eval()
|
||||
if isinstance(data, str):
|
||||
is_coco = data.endswith('coco.yaml')
|
||||
with open(data) as f:
|
||||
data = yaml.load(f, Loader=yaml.SafeLoader)
|
||||
check_dataset(data) # check
|
||||
nc = 1 if single_cls else int(data['nc']) # number of classes
|
||||
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
|
||||
niou = iouv.numel()
|
||||
|
||||
# Logging
|
||||
log_imgs = 0
|
||||
if wandb_logger and wandb_logger.wandb:
|
||||
log_imgs = min(wandb_logger.log_imgs, 100)
|
||||
# Dataloader
|
||||
if not training:
|
||||
if device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
|
||||
task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images
|
||||
dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True,
|
||||
prefix=colorstr(f'{task}: '))[0]
|
||||
|
||||
seen = 0
|
||||
confusion_matrix = ConfusionMatrix(nc=nc)
|
||||
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
|
||||
coco91class = coco80_to_coco91_class()
|
||||
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
|
||||
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
|
||||
loss = torch.zeros(3, device=device)
|
||||
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
|
||||
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
|
||||
img = img.to(device, non_blocking=True)
|
||||
img = img.half() if half else img.float() # uint8 to fp16/32
|
||||
img /= 255.0 # 0 - 255 to 0.0 - 1.0
|
||||
targets = targets.to(device)
|
||||
nb, _, height, width = img.shape # batch size, channels, height, width
|
||||
|
||||
with torch.no_grad():
|
||||
# Run model
|
||||
t = time_synchronized()
|
||||
out, train_out = model(img, augment=augment) # inference and training outputs
|
||||
t0 += time_synchronized() - t
|
||||
|
||||
# Compute loss
|
||||
if compute_loss:
|
||||
loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls
|
||||
|
||||
# Run NMS
|
||||
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
|
||||
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
|
||||
t = time_synchronized()
|
||||
out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
|
||||
t1 += time_synchronized() - t
|
||||
|
||||
# Statistics per image
|
||||
for si, pred in enumerate(out):
|
||||
labels = targets[targets[:, 0] == si, 1:]
|
||||
nl = len(labels)
|
||||
tcls = labels[:, 0].tolist() if nl else [] # target class
|
||||
path = Path(paths[si])
|
||||
seen += 1
|
||||
|
||||
if len(pred) == 0:
|
||||
if nl:
|
||||
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
|
||||
continue
|
||||
|
||||
# Predictions
|
||||
predn = pred.clone()
|
||||
scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
|
||||
|
||||
# Append to text file
|
||||
if save_txt:
|
||||
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
|
||||
for *xyxy, conf, cls in predn.tolist():
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||
with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
|
||||
# W&B logging - Media Panel Plots
|
||||
if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation
|
||||
if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
|
||||
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
|
||||
"class_id": int(cls),
|
||||
"box_caption": "%s %.3f" % (names[cls], conf),
|
||||
"scores": {"class_score": conf},
|
||||
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
|
||||
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
|
||||
wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
|
||||
wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None
|
||||
|
||||
# Append to pycocotools JSON dictionary
|
||||
if save_json:
|
||||
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
|
||||
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
|
||||
box = xyxy2xywh(predn[:, :4]) # xywh
|
||||
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
||||
for p, b in zip(pred.tolist(), box.tolist()):
|
||||
jdict.append({'image_id': image_id,
|
||||
'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
|
||||
'bbox': [round(x, 3) for x in b],
|
||||
'score': round(p[4], 5)})
|
||||
|
||||
# Assign all predictions as incorrect
|
||||
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
|
||||
if nl:
|
||||
detected = [] # target indices
|
||||
tcls_tensor = labels[:, 0]
|
||||
|
||||
# target boxes
|
||||
tbox = xywh2xyxy(labels[:, 1:5])
|
||||
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
|
||||
if plots:
|
||||
confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))
|
||||
|
||||
# Per target class
|
||||
for cls in torch.unique(tcls_tensor):
|
||||
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
|
||||
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices
|
||||
|
||||
# Search for detections
|
||||
if pi.shape[0]:
|
||||
# Prediction to target ious
|
||||
ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices
|
||||
|
||||
# Append detections
|
||||
detected_set = set()
|
||||
for j in (ious > iouv[0]).nonzero(as_tuple=False):
|
||||
d = ti[i[j]] # detected target
|
||||
if d.item() not in detected_set:
|
||||
detected_set.add(d.item())
|
||||
detected.append(d)
|
||||
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
|
||||
if len(detected) == nl: # all targets already located in image
|
||||
break
|
||||
|
||||
# Append statistics (correct, conf, pcls, tcls)
|
||||
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
|
||||
|
||||
# Plot images
|
||||
if plots and batch_i < 3:
|
||||
f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels
|
||||
Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
|
||||
f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions
|
||||
Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
|
||||
|
||||
# Compute statistics
|
||||
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
|
||||
if len(stats) and stats[0].any():
|
||||
p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
|
||||
ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95
|
||||
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
|
||||
nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
|
||||
else:
|
||||
nt = torch.zeros(1)
|
||||
|
||||
# Print results
|
||||
pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format
|
||||
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
|
||||
|
||||
# Print results per class
|
||||
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
|
||||
for i, c in enumerate(ap_class):
|
||||
print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
|
||||
|
||||
# Print speeds
|
||||
t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
|
||||
if not training:
|
||||
print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
|
||||
|
||||
# Plots
|
||||
if plots:
|
||||
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
|
||||
if wandb_logger and wandb_logger.wandb:
|
||||
val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
|
||||
wandb_logger.log({"Validation": val_batches})
|
||||
if wandb_images:
|
||||
wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
|
||||
|
||||
# Save JSON
|
||||
if save_json and len(jdict):
|
||||
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
|
||||
anno_json = '../coco/annotations/instances_val2017.json' # annotations json
|
||||
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
|
||||
print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
|
||||
with open(pred_json, 'w') as f:
|
||||
json.dump(jdict, f)
|
||||
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
|
||||
anno = COCO(anno_json) # init annotations api
|
||||
pred = anno.loadRes(pred_json) # init predictions api
|
||||
eval = COCOeval(anno, pred, 'bbox')
|
||||
if is_coco:
|
||||
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate
|
||||
eval.evaluate()
|
||||
eval.accumulate()
|
||||
eval.summarize()
|
||||
map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
|
||||
except Exception as e:
|
||||
print(f'pycocotools unable to run: {e}')
|
||||
|
||||
# Return results
|
||||
model.float() # for training
|
||||
if not training:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||
print(f"Results saved to {save_dir}{s}")
|
||||
maps = np.zeros(nc) + map
|
||||
for i, c in enumerate(ap_class):
|
||||
maps[c] = ap[i]
|
||||
return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='test.py')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
|
||||
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
|
||||
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
|
||||
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
|
||||
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||||
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
|
||||
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||||
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
|
||||
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
|
||||
parser.add_argument('--project', default='runs/test', help='save to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
opt = parser.parse_args()
|
||||
opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
opt.data = check_file(opt.data) # check file
|
||||
print(opt)
|
||||
check_requirements()
|
||||
|
||||
if opt.task in ('train', 'val', 'test'): # run normally
|
||||
test(opt.data,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
opt.conf_thres,
|
||||
opt.iou_thres,
|
||||
opt.save_json,
|
||||
opt.single_cls,
|
||||
opt.augment,
|
||||
opt.verbose,
|
||||
save_txt=opt.save_txt | opt.save_hybrid,
|
||||
save_hybrid=opt.save_hybrid,
|
||||
save_conf=opt.save_conf,
|
||||
)
|
||||
|
||||
elif opt.task == 'speed': # speed benchmarks
|
||||
for w in opt.weights:
|
||||
test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False)
|
||||
|
||||
elif opt.task == 'study': # run over a range of settings and save/plot
|
||||
# python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
|
||||
x = list(range(256, 1536 + 128, 128)) # x axis (image sizes)
|
||||
for w in opt.weights:
|
||||
f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to
|
||||
y = [] # y axis
|
||||
for i in x: # img-size
|
||||
print(f'\nRunning {f} point {i}...')
|
||||
r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
|
||||
plots=False)
|
||||
y.append(r + t) # results and times
|
||||
np.savetxt(f, y, fmt='%10.4g') # save
|
||||
os.system('zip -r study.zip study_*.txt')
|
||||
plot_study_txt(x=x) # plot
|
||||
|
|
@ -0,0 +1,627 @@
|
|||
import argparse
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import time
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
|
||||
import numpy as np
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
import torch.optim.lr_scheduler as lr_scheduler
|
||||
import torch.utils.data
|
||||
import yaml
|
||||
from torch.cuda import amp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
from tqdm import tqdm
|
||||
|
||||
import test # import test.py to get mAP after each epoch
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import Model
|
||||
from utils.autoanchor import check_anchors
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
|
||||
fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
|
||||
check_requirements, print_mutation, set_logging, one_cycle, colorstr
|
||||
from utils.google_utils import attempt_download
|
||||
from utils.loss import ComputeLoss
|
||||
from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
|
||||
from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
|
||||
from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def train(hyp, opt, device, tb_writer=None):
|
||||
logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
|
||||
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
|
||||
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
|
||||
|
||||
# Directories
|
||||
wdir = save_dir / 'weights'
|
||||
wdir.mkdir(parents=True, exist_ok=True) # make dir
|
||||
last = wdir / 'last.pt'
|
||||
best = wdir / 'best.pt'
|
||||
results_file = save_dir / 'results.txt'
|
||||
|
||||
# Save run settings
|
||||
with open(save_dir / 'hyp.yaml', 'w') as f:
|
||||
yaml.dump(hyp, f, sort_keys=False)
|
||||
with open(save_dir / 'opt.yaml', 'w') as f:
|
||||
yaml.dump(vars(opt), f, sort_keys=False)
|
||||
|
||||
# Configure
|
||||
plots = not opt.evolve # create plots
|
||||
cuda = device.type != 'cpu'
|
||||
init_seeds(2 + rank)
|
||||
with open(opt.data) as f:
|
||||
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
|
||||
is_coco = opt.data.endswith('coco.yaml')
|
||||
|
||||
# Logging- Doing this before checking the dataset. Might update data_dict
|
||||
loggers = {'wandb': None} # loggers dict
|
||||
if rank in [-1, 0]:
|
||||
opt.hyp = hyp # add hyperparameters
|
||||
run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
|
||||
wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
|
||||
loggers['wandb'] = wandb_logger.wandb
|
||||
data_dict = wandb_logger.data_dict
|
||||
if wandb_logger.wandb:
|
||||
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
|
||||
|
||||
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
|
||||
names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
|
||||
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
|
||||
|
||||
# Model
|
||||
pretrained = weights.endswith('.pt')
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(rank):
|
||||
attempt_download(weights) # download if not found locally
|
||||
ckpt = torch.load(weights, map_location=device) # load checkpoint
|
||||
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
|
||||
state_dict = ckpt['model'].float().state_dict() # to FP32
|
||||
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
|
||||
model.load_state_dict(state_dict, strict=False) # load
|
||||
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
|
||||
else:
|
||||
model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
with torch_distributed_zero_first(rank):
|
||||
check_dataset(data_dict) # check
|
||||
train_path = data_dict['train']
|
||||
test_path = data_dict['val']
|
||||
|
||||
# Freeze
|
||||
freeze = [] # parameter names to freeze (full or partial)
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = True # train all layers
|
||||
if any(x in k for x in freeze):
|
||||
print('freezing %s' % k)
|
||||
v.requires_grad = False
|
||||
|
||||
# Optimizer
|
||||
nbs = 64 # nominal batch size
|
||||
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
|
||||
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
|
||||
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
|
||||
|
||||
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
|
||||
for k, v in model.named_modules():
|
||||
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
|
||||
pg2.append(v.bias) # biases
|
||||
if isinstance(v, nn.BatchNorm2d):
|
||||
pg0.append(v.weight) # no decay
|
||||
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
|
||||
pg1.append(v.weight) # apply decay
|
||||
|
||||
if opt.adam:
|
||||
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
|
||||
else:
|
||||
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
|
||||
|
||||
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
|
||||
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
|
||||
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
|
||||
del pg0, pg1, pg2
|
||||
|
||||
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
|
||||
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
|
||||
if opt.linear_lr:
|
||||
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
|
||||
else:
|
||||
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
|
||||
# plot_lr_scheduler(optimizer, scheduler, epochs)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if rank in [-1, 0] else None
|
||||
|
||||
# Resume
|
||||
start_epoch, best_fitness = 0, 0.0
|
||||
if pretrained:
|
||||
# Optimizer
|
||||
if ckpt['optimizer'] is not None:
|
||||
optimizer.load_state_dict(ckpt['optimizer'])
|
||||
best_fitness = ckpt['best_fitness']
|
||||
|
||||
# EMA
|
||||
if ema and ckpt.get('ema'):
|
||||
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
|
||||
ema.updates = ckpt['updates']
|
||||
|
||||
# Results
|
||||
if ckpt.get('training_results') is not None:
|
||||
results_file.write_text(ckpt['training_results']) # write results.txt
|
||||
|
||||
# Epochs
|
||||
start_epoch = ckpt['epoch'] + 1
|
||||
if opt.resume:
|
||||
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
|
||||
if epochs < start_epoch:
|
||||
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
|
||||
(weights, ckpt['epoch'], epochs))
|
||||
epochs += ckpt['epoch'] # finetune additional epochs
|
||||
|
||||
del ckpt, state_dict
|
||||
|
||||
# Image sizes
|
||||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||||
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
|
||||
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
|
||||
|
||||
# DP mode
|
||||
if cuda and rank == -1 and torch.cuda.device_count() > 1:
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# SyncBatchNorm
|
||||
if opt.sync_bn and cuda and rank != -1:
|
||||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
|
||||
logger.info('Using SyncBatchNorm()')
|
||||
|
||||
# Trainloader
|
||||
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
|
||||
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
|
||||
world_size=opt.world_size, workers=opt.workers,
|
||||
image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
|
||||
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
|
||||
nb = len(dataloader) # number of batches
|
||||
logger.info('#####bs:%d steps:%d ' % (batch_size, nb))
|
||||
|
||||
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
|
||||
|
||||
# Process 0
|
||||
if rank in [-1, 0]:
|
||||
testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader
|
||||
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
|
||||
world_size=opt.world_size, workers=opt.workers,
|
||||
pad=0.5, prefix=colorstr('val: '))[0]
|
||||
|
||||
if not opt.resume:
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
c = torch.tensor(labels[:, 0]) # classes
|
||||
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
|
||||
# model._initialize_biases(cf.to(device))
|
||||
if plots:
|
||||
plot_labels(labels, names, save_dir, loggers)
|
||||
if tb_writer:
|
||||
tb_writer.add_histogram('classes', c, 0)
|
||||
|
||||
# Anchors
|
||||
if not opt.noautoanchor:
|
||||
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
|
||||
model.half().float() # pre-reduce anchor precision
|
||||
|
||||
# DDP mode
|
||||
if cuda and rank != -1:
|
||||
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank,
|
||||
# nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698
|
||||
find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules()))
|
||||
|
||||
# Model parameters
|
||||
hyp['box'] *= 3. / nl # scale to layers
|
||||
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
|
||||
hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
|
||||
hyp['label_smoothing'] = opt.label_smoothing
|
||||
model.nc = nc # attach number of classes to model
|
||||
model.hyp = hyp # attach hyperparameters to model
|
||||
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
|
||||
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
|
||||
model.names = names
|
||||
|
||||
# Start training
|
||||
t0 = time.time()
|
||||
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
|
||||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||||
maps = np.zeros(nc) # mAP per class
|
||||
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||||
scheduler.last_epoch = start_epoch - 1 # do not move
|
||||
scaler = amp.GradScaler(enabled=cuda)
|
||||
compute_loss = ComputeLoss(model) # init loss class
|
||||
logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
|
||||
f'Using {dataloader.num_workers} dataloader workers\n'
|
||||
f'Logging results to {save_dir}\n'
|
||||
f'Starting training for {epochs} epochs...')
|
||||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||||
model.train()
|
||||
|
||||
# Update image weights (optional)
|
||||
if opt.image_weights:
|
||||
# Generate indices
|
||||
if rank in [-1, 0]:
|
||||
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
|
||||
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
|
||||
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
|
||||
# Broadcast if DDP
|
||||
if rank != -1:
|
||||
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
|
||||
dist.broadcast(indices, 0)
|
||||
if rank != 0:
|
||||
dataset.indices = indices.cpu().numpy()
|
||||
|
||||
# Update mosaic border
|
||||
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
|
||||
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
|
||||
|
||||
mloss = torch.zeros(4, device=device) # mean losses
|
||||
if rank != -1:
|
||||
dataloader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(dataloader)
|
||||
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
|
||||
if rank in [-1, 0]:
|
||||
pbar = tqdm(pbar, total=nb) # progress bar
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
|
||||
ni = i + nb * epoch # number integrated batches (since train start)
|
||||
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
|
||||
|
||||
# Warmup
|
||||
if ni <= nw:
|
||||
xi = [0, nw] # x interp
|
||||
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
|
||||
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
|
||||
for j, x in enumerate(optimizer.param_groups):
|
||||
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
|
||||
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
|
||||
if 'momentum' in x:
|
||||
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
|
||||
|
||||
# Multi-scale
|
||||
if opt.multi_scale:
|
||||
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
|
||||
sf = sz / max(imgs.shape[2:]) # scale factor
|
||||
if sf != 1:
|
||||
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
|
||||
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
|
||||
|
||||
# Forward
|
||||
with amp.autocast(enabled=cuda):
|
||||
pred = model(imgs) # forward
|
||||
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
|
||||
if rank != -1:
|
||||
loss *= opt.world_size # gradient averaged between devices in DDP mode
|
||||
if opt.quad:
|
||||
loss *= 4.
|
||||
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize
|
||||
if ni % accumulate == 0:
|
||||
scaler.step(optimizer) # optimizer.step
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
if ema:
|
||||
ema.update(model)
|
||||
|
||||
# Print
|
||||
if rank in [-1, 0]:
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
|
||||
s = ('%10s' * 2 + '%10.4g' * 6) % (
|
||||
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||||
pbar.set_description(s)
|
||||
|
||||
# Plot
|
||||
if plots and ni < 3:
|
||||
f = save_dir / f'train_batch{ni}.jpg' # filename
|
||||
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
|
||||
# if tb_writer:
|
||||
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
|
||||
# tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph
|
||||
elif plots and ni == 10 and wandb_logger.wandb:
|
||||
wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
|
||||
save_dir.glob('train*.jpg') if x.exists()]})
|
||||
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
|
||||
# Scheduler
|
||||
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
|
||||
scheduler.step()
|
||||
|
||||
# DDP process 0 or single-GPU
|
||||
if rank in [-1, 0]:
|
||||
# mAP
|
||||
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
|
||||
final_epoch = epoch + 1 == epochs
|
||||
if not opt.notest or final_epoch: # Calculate mAP
|
||||
wandb_logger.current_epoch = epoch + 1
|
||||
results, maps, times = test.test(data_dict,
|
||||
batch_size=batch_size * 2,
|
||||
imgsz=imgsz_test,
|
||||
model=ema.ema,
|
||||
single_cls=opt.single_cls,
|
||||
dataloader=testloader,
|
||||
save_dir=save_dir,
|
||||
verbose=nc < 50 and final_epoch,
|
||||
plots=plots and final_epoch,
|
||||
wandb_logger=wandb_logger,
|
||||
compute_loss=compute_loss,
|
||||
is_coco=is_coco)
|
||||
|
||||
# Write
|
||||
with open(results_file, 'a') as f:
|
||||
f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss
|
||||
if len(opt.name) and opt.bucket:
|
||||
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
|
||||
|
||||
# Log
|
||||
tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
|
||||
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
|
||||
'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
|
||||
'x/lr0', 'x/lr1', 'x/lr2'] # params
|
||||
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
|
||||
if tb_writer:
|
||||
tb_writer.add_scalar(tag, x, epoch) # tensorboard
|
||||
if wandb_logger.wandb:
|
||||
wandb_logger.log({tag: x}) # W&B
|
||||
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
wandb_logger.end_epoch(best_result=best_fitness == fi)
|
||||
|
||||
# Save model
|
||||
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
|
||||
ckpt = {'epoch': epoch,
|
||||
'best_fitness': best_fitness,
|
||||
'training_results': results_file.read_text(),
|
||||
'model': deepcopy(model.module if is_parallel(model) else model).half(),
|
||||
'ema': deepcopy(ema.ema).half(),
|
||||
'updates': ema.updates,
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fi:
|
||||
torch.save(ckpt, best)
|
||||
if wandb_logger.wandb:
|
||||
if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
|
||||
wandb_logger.log_model(
|
||||
last.parent, opt, epoch, fi, best_model=best_fitness == fi)
|
||||
del ckpt
|
||||
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training
|
||||
if rank in [-1, 0]:
|
||||
# Plots
|
||||
if plots:
|
||||
plot_results(save_dir=save_dir) # save as results.png
|
||||
if wandb_logger.wandb:
|
||||
files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
|
||||
wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
|
||||
if (save_dir / f).exists()]})
|
||||
# Test best.pt
|
||||
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
|
||||
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
|
||||
for m in (last, best) if best.exists() else (last): # speed, mAP tests
|
||||
results, _, _ = test.test(opt.data,
|
||||
batch_size=batch_size * 2,
|
||||
imgsz=imgsz_test,
|
||||
conf_thres=0.001,
|
||||
iou_thres=0.7,
|
||||
model=attempt_load(m, device).half(),
|
||||
single_cls=opt.single_cls,
|
||||
dataloader=testloader,
|
||||
save_dir=save_dir,
|
||||
save_json=True,
|
||||
plots=False,
|
||||
is_coco=is_coco)
|
||||
|
||||
# Strip optimizers
|
||||
final = best if best.exists() else last # final model
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if opt.bucket:
|
||||
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
|
||||
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
|
||||
wandb_logger.wandb.log_artifact(str(final), type='model',
|
||||
name='run_' + wandb_logger.wandb_run.id + '_model',
|
||||
aliases=['last', 'best', 'stripped'])
|
||||
wandb_logger.finish_run()
|
||||
else:
|
||||
dist.destroy_process_group()
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default='yolov5s.pt', help='initial weights path')
|
||||
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
|
||||
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
|
||||
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
|
||||
parser.add_argument('--epochs', type=int, default=300)
|
||||
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
|
||||
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
|
||||
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
||||
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
|
||||
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
|
||||
parser.add_argument('--notest', action='store_true', help='only test final epoch')
|
||||
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
|
||||
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
|
||||
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
|
||||
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
|
||||
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
|
||||
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
|
||||
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
|
||||
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
|
||||
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
|
||||
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
|
||||
parser.add_argument('--project', default='runs/train', help='save to project/name')
|
||||
parser.add_argument('--entity', default=None, help='W&B entity')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--quad', action='store_true', help='quad dataloader')
|
||||
parser.add_argument('--linear-lr', action='store_true', help='linear LR')
|
||||
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
|
||||
parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')
|
||||
parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')
|
||||
parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
|
||||
parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
|
||||
opt = parser.parse_args()
|
||||
|
||||
# Set DDP variables
|
||||
opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
|
||||
opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
|
||||
set_logging(opt.global_rank)
|
||||
if opt.global_rank in [-1, 0]:
|
||||
check_git_status()
|
||||
check_requirements()
|
||||
|
||||
# Resume
|
||||
wandb_run = check_wandb_resume(opt)
|
||||
if opt.resume and not wandb_run: # resume an interrupted run
|
||||
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
|
||||
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
|
||||
apriori = opt.global_rank, opt.local_rank
|
||||
with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
|
||||
opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader)) # replace
|
||||
opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate
|
||||
logger.info('Resuming training from %s' % ckpt)
|
||||
else:
|
||||
# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
|
||||
opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
|
||||
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
|
||||
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
|
||||
opt.name = 'evolve' if opt.evolve else opt.name
|
||||
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run
|
||||
|
||||
# DDP mode
|
||||
opt.total_batch_size = opt.batch_size
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if opt.local_rank != -1:
|
||||
assert torch.cuda.device_count() > opt.local_rank
|
||||
torch.cuda.set_device(opt.local_rank)
|
||||
device = torch.device('cuda', opt.local_rank)
|
||||
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
|
||||
assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
|
||||
opt.batch_size = opt.total_batch_size // opt.world_size
|
||||
|
||||
# Hyperparameters
|
||||
with open(opt.hyp) as f:
|
||||
hyp = yaml.load(f, Loader=yaml.SafeLoader) # load hyps
|
||||
|
||||
# Train
|
||||
logger.info(opt)
|
||||
if not opt.evolve:
|
||||
tb_writer = None # init loggers
|
||||
if opt.global_rank in [-1, 0]:
|
||||
prefix = colorstr('tensorboard: ')
|
||||
logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
|
||||
tb_writer = SummaryWriter(opt.save_dir) # Tensorboard
|
||||
train(hyp, opt, device, tb_writer)
|
||||
|
||||
# Evolve hyperparameters (optional)
|
||||
else:
|
||||
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
|
||||
meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
|
||||
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
|
||||
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
|
||||
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
|
||||
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
|
||||
'box': (1, 0.02, 0.2), # box loss gain
|
||||
'cls': (1, 0.2, 4.0), # cls loss gain
|
||||
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
|
||||
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||||
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
|
||||
'iou_t': (0, 0.1, 0.7), # IoU training threshold
|
||||
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
|
||||
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||||
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||||
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
|
||||
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
|
||||
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
|
||||
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
|
||||
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
|
||||
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
|
||||
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
|
||||
'mixup': (1, 0.0, 1.0)} # image mixup (probability)
|
||||
|
||||
assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
|
||||
opt.notest, opt.nosave = True, True # only test/save final epoch
|
||||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||||
yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
|
||||
if opt.bucket:
|
||||
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
|
||||
|
||||
for _ in range(300): # generations to evolve
|
||||
if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate
|
||||
# Select parent(s)
|
||||
parent = 'single' # parent selection method: 'single' or 'weighted'
|
||||
x = np.loadtxt('evolve.txt', ndmin=2)
|
||||
n = min(5, len(x)) # number of previous results to consider
|
||||
x = x[np.argsort(-fitness(x))][:n] # top n mutations
|
||||
w = fitness(x) - fitness(x).min() # weights
|
||||
if parent == 'single' or len(x) == 1:
|
||||
# x = x[random.randint(0, n - 1)] # random selection
|
||||
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
|
||||
elif parent == 'weighted':
|
||||
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
|
||||
|
||||
# Mutate
|
||||
mp, s = 0.8, 0.2 # mutation probability, sigma
|
||||
npr = np.random
|
||||
npr.seed(int(time.time()))
|
||||
g = np.array([x[0] for x in meta.values()]) # gains 0-1
|
||||
ng = len(meta)
|
||||
v = np.ones(ng)
|
||||
while all(v == 1): # mutate until a change occurs (prevent duplicates)
|
||||
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
|
||||
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
|
||||
hyp[k] = float(x[i + 7] * v[i]) # mutate
|
||||
|
||||
# Constrain to limits
|
||||
for k, v in meta.items():
|
||||
hyp[k] = max(hyp[k], v[1]) # lower limit
|
||||
hyp[k] = min(hyp[k], v[2]) # upper limit
|
||||
hyp[k] = round(hyp[k], 5) # significant digits
|
||||
|
||||
# Train mutation
|
||||
results = train(hyp.copy(), opt, device)
|
||||
|
||||
# Write mutation results
|
||||
print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
|
||||
|
||||
# Plot results
|
||||
plot_evolution(yaml_file)
|
||||
print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
|
||||
f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
|
||||
|
|
@ -0,0 +1 @@
|
|||
python train.py --img 640 --batch 64 --epochs 100 --data exitPPS.yaml --resume ../../cby/yolov5-master/runs/train/outlet-x11/weights/best.pt
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"code": 0, "msg": "操作成功", "data": []}
|
||||
Binary file not shown.
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|
|
@ -0,0 +1,72 @@
|
|||
# Activation functions
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
|
||||
class SiLU(nn.Module): # export-friendly version of nn.SiLU()
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
# return x * F.hardsigmoid(x) # for torchscript and CoreML
|
||||
return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX
|
||||
|
||||
|
||||
class MemoryEfficientSwish(nn.Module):
|
||||
class F(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
ctx.save_for_backward(x)
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
x = ctx.saved_tensors[0]
|
||||
sx = torch.sigmoid(x)
|
||||
return grad_output * (sx * (1 + x * (1 - sx)))
|
||||
|
||||
def forward(self, x):
|
||||
return self.F.apply(x)
|
||||
|
||||
|
||||
# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
|
||||
class Mish(nn.Module):
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return x * F.softplus(x).tanh()
|
||||
|
||||
|
||||
class MemoryEfficientMish(nn.Module):
|
||||
class F(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
ctx.save_for_backward(x)
|
||||
return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
x = ctx.saved_tensors[0]
|
||||
sx = torch.sigmoid(x)
|
||||
fx = F.softplus(x).tanh()
|
||||
return grad_output * (fx + x * sx * (1 - fx * fx))
|
||||
|
||||
def forward(self, x):
|
||||
return self.F.apply(x)
|
||||
|
||||
|
||||
# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
|
||||
class FReLU(nn.Module):
|
||||
def __init__(self, c1, k=3): # ch_in, kernel
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
|
||||
self.bn = nn.BatchNorm2d(c1)
|
||||
|
||||
def forward(self, x):
|
||||
return torch.max(x, self.bn(self.conv(x)))
|
||||
|
|
@ -0,0 +1,160 @@
|
|||
# Auto-anchor utils
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import yaml
|
||||
from scipy.cluster.vq import kmeans
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.general import colorstr
|
||||
|
||||
|
||||
def check_anchor_order(m):
|
||||
# Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
|
||||
a = m.anchor_grid.prod(-1).view(-1) # anchor area
|
||||
da = a[-1] - a[0] # delta a
|
||||
ds = m.stride[-1] - m.stride[0] # delta s
|
||||
if da.sign() != ds.sign(): # same order
|
||||
print('Reversing anchor order')
|
||||
m.anchors[:] = m.anchors.flip(0)
|
||||
m.anchor_grid[:] = m.anchor_grid.flip(0)
|
||||
|
||||
|
||||
def check_anchors(dataset, model, thr=4.0, imgsz=640):
|
||||
# Check anchor fit to data, recompute if necessary
|
||||
prefix = colorstr('autoanchor: ')
|
||||
print(f'\n{prefix}Analyzing anchors... ', end='')
|
||||
m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect()
|
||||
shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
|
||||
scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale
|
||||
wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh
|
||||
|
||||
def metric(k): # compute metric
|
||||
r = wh[:, None] / k[None]
|
||||
x = torch.min(r, 1. / r).min(2)[0] # ratio metric
|
||||
best = x.max(1)[0] # best_x
|
||||
aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold
|
||||
bpr = (best > 1. / thr).float().mean() # best possible recall
|
||||
return bpr, aat
|
||||
|
||||
anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors
|
||||
bpr, aat = metric(anchors)
|
||||
print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
|
||||
if bpr < 0.98: # threshold to recompute
|
||||
print('. Attempting to improve anchors, please wait...')
|
||||
na = m.anchor_grid.numel() // 2 # number of anchors
|
||||
try:
|
||||
anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
|
||||
except Exception as e:
|
||||
print(f'{prefix}ERROR: {e}')
|
||||
new_bpr = metric(anchors)[0]
|
||||
if new_bpr > bpr: # replace anchors
|
||||
anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
|
||||
m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference
|
||||
m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss
|
||||
check_anchor_order(m)
|
||||
print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
|
||||
else:
|
||||
print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
|
||||
print('') # newline
|
||||
|
||||
|
||||
def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
|
||||
""" Creates kmeans-evolved anchors from training dataset
|
||||
|
||||
Arguments:
|
||||
path: path to dataset *.yaml, or a loaded dataset
|
||||
n: number of anchors
|
||||
img_size: image size used for training
|
||||
thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
|
||||
gen: generations to evolve anchors using genetic algorithm
|
||||
verbose: print all results
|
||||
|
||||
Return:
|
||||
k: kmeans evolved anchors
|
||||
|
||||
Usage:
|
||||
from utils.autoanchor import *; _ = kmean_anchors()
|
||||
"""
|
||||
thr = 1. / thr
|
||||
prefix = colorstr('autoanchor: ')
|
||||
|
||||
def metric(k, wh): # compute metrics
|
||||
r = wh[:, None] / k[None]
|
||||
x = torch.min(r, 1. / r).min(2)[0] # ratio metric
|
||||
# x = wh_iou(wh, torch.tensor(k)) # iou metric
|
||||
return x, x.max(1)[0] # x, best_x
|
||||
|
||||
def anchor_fitness(k): # mutation fitness
|
||||
_, best = metric(torch.tensor(k, dtype=torch.float32), wh)
|
||||
return (best * (best > thr).float()).mean() # fitness
|
||||
|
||||
def print_results(k):
|
||||
k = k[np.argsort(k.prod(1))] # sort small to large
|
||||
x, best = metric(k, wh0)
|
||||
bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
|
||||
print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
|
||||
print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
|
||||
f'past_thr={x[x > thr].mean():.3f}-mean: ', end='')
|
||||
for i, x in enumerate(k):
|
||||
print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg
|
||||
return k
|
||||
|
||||
if isinstance(path, str): # *.yaml file
|
||||
with open(path) as f:
|
||||
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict
|
||||
from utils.datasets import LoadImagesAndLabels
|
||||
dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
|
||||
else:
|
||||
dataset = path # dataset
|
||||
|
||||
# Get label wh
|
||||
shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
|
||||
wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh
|
||||
|
||||
# Filter
|
||||
i = (wh0 < 3.0).any(1).sum()
|
||||
if i:
|
||||
print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
|
||||
wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
|
||||
# wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1
|
||||
|
||||
# Kmeans calculation
|
||||
print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
|
||||
s = wh.std(0) # sigmas for whitening
|
||||
k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
|
||||
assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
|
||||
k *= s
|
||||
wh = torch.tensor(wh, dtype=torch.float32) # filtered
|
||||
wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered
|
||||
k = print_results(k)
|
||||
|
||||
# Plot
|
||||
# k, d = [None] * 20, [None] * 20
|
||||
# for i in tqdm(range(1, 21)):
|
||||
# k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance
|
||||
# fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
|
||||
# ax = ax.ravel()
|
||||
# ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
|
||||
# fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh
|
||||
# ax[0].hist(wh[wh[:, 0]<100, 0],400)
|
||||
# ax[1].hist(wh[wh[:, 1]<100, 1],400)
|
||||
# fig.savefig('wh.png', dpi=200)
|
||||
|
||||
# Evolve
|
||||
npr = np.random
|
||||
f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
|
||||
pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar
|
||||
for _ in pbar:
|
||||
v = np.ones(sh)
|
||||
while (v == 1).all(): # mutate until a change occurs (prevent duplicates)
|
||||
v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
|
||||
kg = (k.copy() * v).clip(min=2.0)
|
||||
fg = anchor_fitness(kg)
|
||||
if fg > f:
|
||||
f, k = fg, kg.copy()
|
||||
pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
|
||||
if verbose:
|
||||
print_results(k)
|
||||
|
||||
return print_results(k)
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/
|
||||
# This script will run on every instance restart, not only on first start
|
||||
# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---
|
||||
|
||||
Content-Type: multipart/mixed; boundary="//"
|
||||
MIME-Version: 1.0
|
||||
|
||||
--//
|
||||
Content-Type: text/cloud-config; charset="us-ascii"
|
||||
MIME-Version: 1.0
|
||||
Content-Transfer-Encoding: 7bit
|
||||
Content-Disposition: attachment; filename="cloud-config.txt"
|
||||
|
||||
#cloud-config
|
||||
cloud_final_modules:
|
||||
- [scripts-user, always]
|
||||
|
||||
--//
|
||||
Content-Type: text/x-shellscript; charset="us-ascii"
|
||||
MIME-Version: 1.0
|
||||
Content-Transfer-Encoding: 7bit
|
||||
Content-Disposition: attachment; filename="userdata.txt"
|
||||
|
||||
#!/bin/bash
|
||||
# --- paste contents of userdata.sh here ---
|
||||
--//
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
# Resume all interrupted trainings in yolov5/ dir including DDP trainings
|
||||
# Usage: $ python utils/aws/resume.py
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import yaml
|
||||
|
||||
sys.path.append('./') # to run '$ python *.py' files in subdirectories
|
||||
|
||||
port = 0 # --master_port
|
||||
path = Path('').resolve()
|
||||
for last in path.rglob('*/**/last.pt'):
|
||||
ckpt = torch.load(last)
|
||||
if ckpt['optimizer'] is None:
|
||||
continue
|
||||
|
||||
# Load opt.yaml
|
||||
with open(last.parent.parent / 'opt.yaml') as f:
|
||||
opt = yaml.load(f, Loader=yaml.SafeLoader)
|
||||
|
||||
# Get device count
|
||||
d = opt['device'].split(',') # devices
|
||||
nd = len(d) # number of devices
|
||||
ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel
|
||||
|
||||
if ddp: # multi-GPU
|
||||
port += 1
|
||||
cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}'
|
||||
else: # single-GPU
|
||||
cmd = f'python train.py --resume {last}'
|
||||
|
||||
cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread
|
||||
print(cmd)
|
||||
os.system(cmd)
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
#!/bin/bash
|
||||
# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
|
||||
# This script will run only once on first instance start (for a re-start script see mime.sh)
|
||||
# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
|
||||
# Use >300 GB SSD
|
||||
|
||||
cd home/ubuntu
|
||||
if [ ! -d yolov5 ]; then
|
||||
echo "Running first-time script." # install dependencies, download COCO, pull Docker
|
||||
git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5
|
||||
cd yolov5
|
||||
bash data/scripts/get_coco.sh && echo "Data done." &
|
||||
sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
|
||||
python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
|
||||
wait && echo "All tasks done." # finish background tasks
|
||||
else
|
||||
echo "Running re-start script." # resume interrupted runs
|
||||
i=0
|
||||
list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
|
||||
while IFS= read -r id; do
|
||||
((i++))
|
||||
echo "restarting container $i: $id"
|
||||
sudo docker start $id
|
||||
# sudo docker exec -it $id python train.py --resume # single-GPU
|
||||
sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
|
||||
done <<<"$list"
|
||||
fi
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,51 @@
|
|||
# Flask REST API
|
||||
[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the `yolov5s` model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
|
||||
|
||||
## Requirements
|
||||
|
||||
[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
|
||||
```shell
|
||||
$ pip install Flask
|
||||
```
|
||||
|
||||
## Run
|
||||
|
||||
After Flask installation run:
|
||||
|
||||
```shell
|
||||
$ python3 restapi.py --port 5000
|
||||
```
|
||||
|
||||
Then use [curl](https://curl.se/) to perform a request:
|
||||
|
||||
```shell
|
||||
$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'`
|
||||
```
|
||||
|
||||
The model inference results are returned:
|
||||
|
||||
```shell
|
||||
[{'class': 0,
|
||||
'confidence': 0.8197850585,
|
||||
'name': 'person',
|
||||
'xmax': 1159.1403808594,
|
||||
'xmin': 750.912902832,
|
||||
'ymax': 711.2583007812,
|
||||
'ymin': 44.0350036621},
|
||||
{'class': 0,
|
||||
'confidence': 0.5667674541,
|
||||
'name': 'person',
|
||||
'xmax': 1065.5523681641,
|
||||
'xmin': 116.0448303223,
|
||||
'ymax': 713.8904418945,
|
||||
'ymin': 198.4603881836},
|
||||
{'class': 27,
|
||||
'confidence': 0.5661227107,
|
||||
'name': 'tie',
|
||||
'xmax': 516.7975463867,
|
||||
'xmin': 416.6880187988,
|
||||
'ymax': 717.0524902344,
|
||||
'ymin': 429.2020568848}]
|
||||
```
|
||||
|
||||
An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
"""Perform test request"""
|
||||
import pprint
|
||||
|
||||
import requests
|
||||
|
||||
DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
|
||||
TEST_IMAGE = "zidane.jpg"
|
||||
|
||||
image_data = open(TEST_IMAGE, "rb").read()
|
||||
|
||||
response = requests.post(DETECTION_URL, files={"image": image_data}).json()
|
||||
|
||||
pprint.pprint(response)
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
"""
|
||||
Run a rest API exposing the yolov5s object detection model
|
||||
"""
|
||||
import argparse
|
||||
import io
|
||||
|
||||
import torch
|
||||
from PIL import Image
|
||||
from flask import Flask, request
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
DETECTION_URL = "/v1/object-detection/yolov5s"
|
||||
|
||||
|
||||
@app.route(DETECTION_URL, methods=["POST"])
|
||||
def predict():
|
||||
if not request.method == "POST":
|
||||
return
|
||||
|
||||
if request.files.get("image"):
|
||||
image_file = request.files["image"]
|
||||
image_bytes = image_file.read()
|
||||
|
||||
img = Image.open(io.BytesIO(image_bytes))
|
||||
|
||||
results = model(img, size=640)
|
||||
data = results.pandas().xyxy[0].to_json(orient="records")
|
||||
return data
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Flask api exposing yolov5 model")
|
||||
parser.add_argument("--port", default=5000, type=int, help="port number")
|
||||
args = parser.parse_args()
|
||||
|
||||
model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True).autoshape() # force_reload to recache
|
||||
app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat
|
||||
|
|
@ -0,0 +1,604 @@
|
|||
# YOLOv5 general utils
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
import random
|
||||
import re
|
||||
import subprocess
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
import torchvision
|
||||
import yaml
|
||||
|
||||
from utils.google_utils import gsutil_getsize
|
||||
from utils.metrics import fitness
|
||||
from utils.torch_utils import init_torch_seeds
|
||||
|
||||
# Settings
|
||||
torch.set_printoptions(linewidth=320, precision=5, profile='long')
|
||||
np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
|
||||
pd.options.display.max_columns = 10
|
||||
cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
|
||||
os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads
|
||||
|
||||
|
||||
def set_logging(rank=-1):
|
||||
logging.basicConfig(
|
||||
format="%(message)s",
|
||||
level=logging.INFO if rank in [-1, 0] else logging.WARN)
|
||||
|
||||
|
||||
def init_seeds(seed=0):
|
||||
# Initialize random number generator (RNG) seeds
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
init_torch_seeds(seed)
|
||||
|
||||
|
||||
def get_latest_run(search_dir='.'):
|
||||
# Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
|
||||
last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
|
||||
return max(last_list, key=os.path.getctime) if last_list else ''
|
||||
|
||||
|
||||
def isdocker():
|
||||
# Is environment a Docker container
|
||||
return Path('/workspace').exists() # or Path('/.dockerenv').exists()
|
||||
|
||||
|
||||
def emojis(str=''):
|
||||
# Return platform-dependent emoji-safe version of string
|
||||
return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
|
||||
|
||||
|
||||
def check_online():
|
||||
# Check internet connectivity
|
||||
import socket
|
||||
try:
|
||||
socket.create_connection(("1.1.1.1", 443), 5) # check host accesability
|
||||
return True
|
||||
except OSError:
|
||||
return False
|
||||
|
||||
|
||||
def check_git_status():
|
||||
# Recommend 'git pull' if code is out of date
|
||||
print(colorstr('github: '), end='')
|
||||
try:
|
||||
assert Path('.git').exists(), 'skipping check (not a git repository)'
|
||||
assert not isdocker(), 'skipping check (Docker image)'
|
||||
assert check_online(), 'skipping check (offline)'
|
||||
|
||||
cmd = 'git fetch && git config --get remote.origin.url'
|
||||
url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url
|
||||
branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
|
||||
n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind
|
||||
if n > 0:
|
||||
s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \
|
||||
f"Use 'git pull' to update or 'git clone {url}' to download latest."
|
||||
else:
|
||||
s = f'up to date with {url} ✅'
|
||||
print(emojis(s)) # emoji-safe
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
|
||||
def check_requirements(requirements='requirements.txt', exclude=()):
|
||||
# Check installed dependencies meet requirements (pass *.txt file or list of packages)
|
||||
import pkg_resources as pkg
|
||||
prefix = colorstr('red', 'bold', 'requirements:')
|
||||
if isinstance(requirements, (str, Path)): # requirements.txt file
|
||||
file = Path(requirements)
|
||||
if not file.exists():
|
||||
print(f"{prefix} {file.resolve()} not found, check failed.")
|
||||
return
|
||||
requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude]
|
||||
else: # list or tuple of packages
|
||||
requirements = [x for x in requirements if x not in exclude]
|
||||
|
||||
n = 0 # number of packages updates
|
||||
for r in requirements:
|
||||
try:
|
||||
pkg.require(r)
|
||||
except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
|
||||
n += 1
|
||||
print(f"{prefix} {e.req} not found and is required by YOLOv5, attempting auto-update...")
|
||||
print(subprocess.check_output(f"pip install {e.req}", shell=True).decode())
|
||||
|
||||
if n: # if packages updated
|
||||
source = file.resolve() if 'file' in locals() else requirements
|
||||
s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
|
||||
f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
|
||||
print(emojis(s)) # emoji-safe
|
||||
|
||||
|
||||
def check_img_size(img_size, s=32):
|
||||
# Verify img_size is a multiple of stride s
|
||||
new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
|
||||
if new_size != img_size:
|
||||
print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
|
||||
return new_size
|
||||
|
||||
|
||||
def check_imshow():
|
||||
# Check if environment supports image displays
|
||||
try:
|
||||
assert not isdocker(), 'cv2.imshow() is disabled in Docker environments'
|
||||
cv2.imshow('test', np.zeros((1, 1, 3)))
|
||||
cv2.waitKey(1)
|
||||
cv2.destroyAllWindows()
|
||||
cv2.waitKey(1)
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
|
||||
return False
|
||||
|
||||
|
||||
def check_file(file):
|
||||
# Search for file if not found
|
||||
if Path(file).is_file() or file == '':
|
||||
return file
|
||||
else:
|
||||
files = glob.glob('./**/' + file, recursive=True) # find file
|
||||
assert len(files), f'File Not Found: {file}' # assert file was found
|
||||
assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
|
||||
return files[0] # return file
|
||||
|
||||
|
||||
def check_dataset(dict):
|
||||
# Download dataset if not found locally
|
||||
val, s = dict.get('val'), dict.get('download')
|
||||
if val and len(val):
|
||||
val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
|
||||
if not all(x.exists() for x in val):
|
||||
print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
|
||||
if s and len(s): # download script
|
||||
print('Downloading %s ...' % s)
|
||||
if s.startswith('http') and s.endswith('.zip'): # URL
|
||||
f = Path(s).name # filename
|
||||
torch.hub.download_url_to_file(s, f)
|
||||
r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip
|
||||
else: # bash script
|
||||
r = os.system(s)
|
||||
print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value
|
||||
else:
|
||||
raise Exception('Dataset not found.')
|
||||
|
||||
|
||||
def make_divisible(x, divisor):
|
||||
# Returns x evenly divisible by divisor
|
||||
return math.ceil(x / divisor) * divisor
|
||||
|
||||
|
||||
def clean_str(s):
|
||||
# Cleans a string by replacing special characters with underscore _
|
||||
return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
|
||||
|
||||
|
||||
def one_cycle(y1=0.0, y2=1.0, steps=100):
|
||||
# lambda function for sinusoidal ramp from y1 to y2
|
||||
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
|
||||
|
||||
|
||||
def colorstr(*input):
|
||||
# Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')
|
||||
*args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
|
||||
colors = {'black': '\033[30m', # basic colors
|
||||
'red': '\033[31m',
|
||||
'green': '\033[32m',
|
||||
'yellow': '\033[33m',
|
||||
'blue': '\033[34m',
|
||||
'magenta': '\033[35m',
|
||||
'cyan': '\033[36m',
|
||||
'white': '\033[37m',
|
||||
'bright_black': '\033[90m', # bright colors
|
||||
'bright_red': '\033[91m',
|
||||
'bright_green': '\033[92m',
|
||||
'bright_yellow': '\033[93m',
|
||||
'bright_blue': '\033[94m',
|
||||
'bright_magenta': '\033[95m',
|
||||
'bright_cyan': '\033[96m',
|
||||
'bright_white': '\033[97m',
|
||||
'end': '\033[0m', # misc
|
||||
'bold': '\033[1m',
|
||||
'underline': '\033[4m'}
|
||||
return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
|
||||
|
||||
|
||||
def labels_to_class_weights(labels, nc=80):
|
||||
# Get class weights (inverse frequency) from training labels
|
||||
if labels[0] is None: # no labels loaded
|
||||
return torch.Tensor()
|
||||
|
||||
labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
|
||||
classes = labels[:, 0].astype(np.int) # labels = [class xywh]
|
||||
weights = np.bincount(classes, minlength=nc) # occurrences per class
|
||||
|
||||
# Prepend gridpoint count (for uCE training)
|
||||
# gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
|
||||
# weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
|
||||
|
||||
weights[weights == 0] = 1 # replace empty bins with 1
|
||||
weights = 1 / weights # number of targets per class
|
||||
weights /= weights.sum() # normalize
|
||||
return torch.from_numpy(weights)
|
||||
|
||||
|
||||
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
|
||||
# Produces image weights based on class_weights and image contents
|
||||
class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
|
||||
image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
|
||||
# index = random.choices(range(n), weights=image_weights, k=1) # weight image sample
|
||||
return image_weights
|
||||
|
||||
|
||||
def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper)
|
||||
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
|
||||
# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
|
||||
# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
|
||||
# x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
|
||||
# x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
|
||||
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
|
||||
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
|
||||
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
|
||||
return x
|
||||
|
||||
|
||||
def xyxy2xywh(x):
|
||||
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
|
||||
y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
|
||||
y[:, 2] = x[:, 2] - x[:, 0] # width
|
||||
y[:, 3] = x[:, 3] - x[:, 1] # height
|
||||
return y
|
||||
|
||||
|
||||
def xywh2xyxy(x):
|
||||
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
|
||||
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
|
||||
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
|
||||
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
|
||||
return y
|
||||
|
||||
|
||||
def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
|
||||
# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
|
||||
y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y
|
||||
y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x
|
||||
y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y
|
||||
return y
|
||||
|
||||
|
||||
def xyn2xy(x, w=640, h=640, padw=0, padh=0):
|
||||
# Convert normalized segments into pixel segments, shape (n,2)
|
||||
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
||||
y[:, 0] = w * x[:, 0] + padw # top left x
|
||||
y[:, 1] = h * x[:, 1] + padh # top left y
|
||||
return y
|
||||
|
||||
|
||||
def segment2box(segment, width=640, height=640):
|
||||
# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
|
||||
x, y = segment.T # segment xy
|
||||
inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
|
||||
x, y, = x[inside], y[inside]
|
||||
return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
|
||||
|
||||
|
||||
def segments2boxes(segments):
|
||||
# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
|
||||
boxes = []
|
||||
for s in segments:
|
||||
x, y = s.T # segment xy
|
||||
boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
|
||||
return xyxy2xywh(np.array(boxes)) # cls, xywh
|
||||
|
||||
|
||||
def resample_segments(segments, n=1000):
|
||||
# Up-sample an (n,2) segment
|
||||
for i, s in enumerate(segments):
|
||||
x = np.linspace(0, len(s) - 1, n)
|
||||
xp = np.arange(len(s))
|
||||
segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
|
||||
return segments
|
||||
|
||||
|
||||
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
|
||||
# Rescale coords (xyxy) from img1_shape to img0_shape
|
||||
if ratio_pad is None: # calculate from img0_shape
|
||||
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
|
||||
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
|
||||
else:
|
||||
gain = ratio_pad[0][0]
|
||||
pad = ratio_pad[1]
|
||||
|
||||
coords[:, [0, 2]] -= pad[0] # x padding
|
||||
coords[:, [1, 3]] -= pad[1] # y padding
|
||||
coords[:, :4] /= gain
|
||||
clip_coords(coords, img0_shape)
|
||||
return coords
|
||||
|
||||
|
||||
def clip_coords(boxes, img_shape):
|
||||
# Clip bounding xyxy bounding boxes to image shape (height, width)
|
||||
boxes[:, 0].clamp_(0, img_shape[1]) # x1
|
||||
boxes[:, 1].clamp_(0, img_shape[0]) # y1
|
||||
boxes[:, 2].clamp_(0, img_shape[1]) # x2
|
||||
boxes[:, 3].clamp_(0, img_shape[0]) # y2
|
||||
|
||||
|
||||
def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
|
||||
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
|
||||
box2 = box2.T
|
||||
|
||||
# Get the coordinates of bounding boxes
|
||||
if x1y1x2y2: # x1, y1, x2, y2 = box1
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
|
||||
else: # transform from xywh to xyxy
|
||||
b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
|
||||
b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
|
||||
b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
|
||||
b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
|
||||
|
||||
# Intersection area
|
||||
inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
|
||||
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
|
||||
|
||||
# Union Area
|
||||
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
|
||||
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
|
||||
union = w1 * h1 + w2 * h2 - inter + eps
|
||||
|
||||
iou = inter / union
|
||||
if GIoU or DIoU or CIoU:
|
||||
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
|
||||
ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
|
||||
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
|
||||
c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
|
||||
rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
|
||||
(b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared
|
||||
if DIoU:
|
||||
return iou - rho2 / c2 # DIoU
|
||||
elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
|
||||
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
|
||||
with torch.no_grad():
|
||||
alpha = v / (v - iou + (1 + eps))
|
||||
return iou - (rho2 / c2 + v * alpha) # CIoU
|
||||
else: # GIoU https://arxiv.org/pdf/1902.09630.pdf
|
||||
c_area = cw * ch + eps # convex area
|
||||
return iou - (c_area - union) / c_area # GIoU
|
||||
else:
|
||||
return iou # IoU
|
||||
|
||||
|
||||
def box_iou(box1, box2):
|
||||
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
|
||||
"""
|
||||
Return intersection-over-union (Jaccard index) of boxes.
|
||||
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
|
||||
Arguments:
|
||||
box1 (Tensor[N, 4])
|
||||
box2 (Tensor[M, 4])
|
||||
Returns:
|
||||
iou (Tensor[N, M]): the NxM matrix containing the pairwise
|
||||
IoU values for every element in boxes1 and boxes2
|
||||
"""
|
||||
|
||||
def box_area(box):
|
||||
# box = 4xn
|
||||
return (box[2] - box[0]) * (box[3] - box[1])
|
||||
|
||||
area1 = box_area(box1.T)
|
||||
area2 = box_area(box2.T)
|
||||
|
||||
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
|
||||
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
|
||||
return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
|
||||
|
||||
|
||||
def wh_iou(wh1, wh2):
|
||||
# Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
|
||||
wh1 = wh1[:, None] # [N,1,2]
|
||||
wh2 = wh2[None] # [1,M,2]
|
||||
inter = torch.min(wh1, wh2).prod(2) # [N,M]
|
||||
return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter)
|
||||
|
||||
|
||||
def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
|
||||
labels=()):
|
||||
"""Runs Non-Maximum Suppression (NMS) on inference results
|
||||
|
||||
Returns:
|
||||
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
|
||||
"""
|
||||
|
||||
nc = prediction.shape[2] - 5 # number of classes
|
||||
xc = prediction[..., 4] > conf_thres # candidates
|
||||
|
||||
# Settings
|
||||
min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height
|
||||
max_det = 300 # maximum number of detections per image
|
||||
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
|
||||
time_limit = 10.0 # seconds to quit after
|
||||
redundant = True # require redundant detections
|
||||
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
|
||||
merge = False # use merge-NMS
|
||||
|
||||
t = time.time()
|
||||
output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
|
||||
for xi, x in enumerate(prediction): # image index, image inference
|
||||
# Apply constraints
|
||||
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
|
||||
x = x[xc[xi]] # confidence
|
||||
|
||||
# Cat apriori labels if autolabelling
|
||||
if labels and len(labels[xi]):
|
||||
l = labels[xi]
|
||||
v = torch.zeros((len(l), nc + 5), device=x.device)
|
||||
v[:, :4] = l[:, 1:5] # box
|
||||
v[:, 4] = 1.0 # conf
|
||||
v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls
|
||||
x = torch.cat((x, v), 0)
|
||||
|
||||
# If none remain process next image
|
||||
if not x.shape[0]:
|
||||
continue
|
||||
|
||||
# Compute conf
|
||||
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
|
||||
|
||||
# Box (center x, center y, width, height) to (x1, y1, x2, y2)
|
||||
box = xywh2xyxy(x[:, :4])
|
||||
|
||||
# Detections matrix nx6 (xyxy, conf, cls)
|
||||
if multi_label:
|
||||
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
|
||||
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
|
||||
else: # best class only
|
||||
conf, j = x[:, 5:].max(1, keepdim=True)
|
||||
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
|
||||
|
||||
# Filter by class
|
||||
if classes is not None:
|
||||
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
|
||||
|
||||
# Apply finite constraint
|
||||
# if not torch.isfinite(x).all():
|
||||
# x = x[torch.isfinite(x).all(1)]
|
||||
|
||||
# Check shape
|
||||
n = x.shape[0] # number of boxes
|
||||
if not n: # no boxes
|
||||
continue
|
||||
elif n > max_nms: # excess boxes
|
||||
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
|
||||
|
||||
# Batched NMS
|
||||
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
|
||||
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
|
||||
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
|
||||
if i.shape[0] > max_det: # limit detections
|
||||
i = i[:max_det]
|
||||
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
|
||||
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
|
||||
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
|
||||
weights = iou * scores[None] # box weights
|
||||
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
|
||||
if redundant:
|
||||
i = i[iou.sum(1) > 1] # require redundancy
|
||||
|
||||
output[xi] = x[i]
|
||||
if (time.time() - t) > time_limit:
|
||||
print(f'WARNING: NMS time limit {time_limit}s exceeded')
|
||||
break # time limit exceeded
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer()
|
||||
# Strip optimizer from 'f' to finalize training, optionally save as 's'
|
||||
x = torch.load(f, map_location=torch.device('cpu'))
|
||||
if x.get('ema'):
|
||||
x['model'] = x['ema'] # replace model with ema
|
||||
for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys
|
||||
x[k] = None
|
||||
x['epoch'] = -1
|
||||
x['model'].half() # to FP16
|
||||
for p in x['model'].parameters():
|
||||
p.requires_grad = False
|
||||
torch.save(x, s or f)
|
||||
mb = os.path.getsize(s or f) / 1E6 # filesize
|
||||
print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
|
||||
|
||||
|
||||
def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
|
||||
# Print mutation results to evolve.txt (for use with train.py --evolve)
|
||||
a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
|
||||
b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
|
||||
c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
|
||||
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
|
||||
|
||||
if bucket:
|
||||
url = 'gs://%s/evolve.txt' % bucket
|
||||
if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
|
||||
os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local
|
||||
|
||||
with open('evolve.txt', 'a') as f: # append result
|
||||
f.write(c + b + '\n')
|
||||
x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
|
||||
x = x[np.argsort(-fitness(x))] # sort
|
||||
np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness
|
||||
|
||||
# Save yaml
|
||||
for i, k in enumerate(hyp.keys()):
|
||||
hyp[k] = float(x[0, i + 7])
|
||||
with open(yaml_file, 'w') as f:
|
||||
results = tuple(x[0, :7])
|
||||
c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
|
||||
f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
|
||||
yaml.dump(hyp, f, sort_keys=False)
|
||||
|
||||
if bucket:
|
||||
os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload
|
||||
|
||||
|
||||
def apply_classifier(x, model, img, im0):
|
||||
# applies a second stage classifier to yolo outputs
|
||||
im0 = [im0] if isinstance(im0, np.ndarray) else im0
|
||||
for i, d in enumerate(x): # per image
|
||||
if d is not None and len(d):
|
||||
d = d.clone()
|
||||
|
||||
# Reshape and pad cutouts
|
||||
b = xyxy2xywh(d[:, :4]) # boxes
|
||||
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
|
||||
b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
|
||||
d[:, :4] = xywh2xyxy(b).long()
|
||||
|
||||
# Rescale boxes from img_size to im0 size
|
||||
scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
|
||||
|
||||
# Classes
|
||||
pred_cls1 = d[:, 5].long()
|
||||
ims = []
|
||||
for j, a in enumerate(d): # per item
|
||||
cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
|
||||
im = cv2.resize(cutout, (224, 224)) # BGR
|
||||
# cv2.imwrite('test%i.jpg' % j, cutout)
|
||||
|
||||
im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
|
||||
im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
|
||||
im /= 255.0 # 0 - 255 to 0.0 - 1.0
|
||||
ims.append(im)
|
||||
|
||||
pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
|
||||
x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def increment_path(path, exist_ok=True, sep=''):
|
||||
# Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
|
||||
path = Path(path) # os-agnostic
|
||||
if (path.exists() and exist_ok) or (not path.exists()):
|
||||
return str(path)
|
||||
else:
|
||||
dirs = glob.glob(f"{path}{sep}*") # similar paths
|
||||
matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
|
||||
i = [int(m.groups()[0]) for m in matches if m] # indices
|
||||
n = max(i) + 1 if i else 2 # increment number
|
||||
return f"{path}{sep}{n}" # update path
|
||||
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Reference in New Issue