File size: 6,431 Bytes
80288b5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/

Usage:
    import torch
    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
    model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx')  # file from branch
"""

import torch


def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    """Creates or loads a YOLOv5 model

    Arguments:
        name (str): model name 'yolov5s' or path 'path/to/best.pt'
        pretrained (bool): load pretrained weights into the model
        channels (int): number of input channels
        classes (int): number of model classes
        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
        verbose (bool): print all information to screen
        device (str, torch.device, None): device to use for model parameters

    Returns:
        YOLOv5 model
    """
    from pathlib import Path

    from models.common import AutoShape, DetectMultiBackend
    from models.yolo import Model
    #from utils.downloads import attempt_download
    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
    from utils.torch_utils import select_device

    if not verbose:
        LOGGER.setLevel(logging.WARNING)
    check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
    name = Path(name)
    path = name.with_suffix('.pt') if name.suffix == '' else name  # checkpoint path
    try:
        device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
        #device = 'mps'
        if pretrained and channels == 3 and classes == 80:
            model = DetectMultiBackend(path, device=device)  # download/load FP32 model
            # model = models.experimental.attempt_load(path, map_location=device)  # download/load FP32 model
        else:
            cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0]  # model.yaml path
            model = Model(cfg, channels, classes)  # create model
            if pretrained:
                ckpt = torch.load(path, map_location=device)  # load
                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
                csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors'])  # intersect
                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 = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
        return model.to(device)

    except Exception as e:
        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
        s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
        raise Exception(s) from e


def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
    # YOLOv5 custom or local model
    return _create(path, autoshape=autoshape, verbose=verbose, device=device)


def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-nano model https://github.com/ultralytics/yolov5
    return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)


def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-small model https://github.com/ultralytics/yolov5
    return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)


def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-medium model https://github.com/ultralytics/yolov5
    return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)


def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-large model https://github.com/ultralytics/yolov5
    return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)


def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
    return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)


def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)


def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)


def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)


def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)


def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
    return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)


if __name__ == '__main__':
    model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)  # pretrained
    # model = custom(path='path/to/model.pt')  # custom

    # Verify inference
    from pathlib import Path

    import cv2
    import numpy as np
    from PIL import Image

    imgs = ['data/images/zidane.jpg',  # filename
            Path('data/images/zidane.jpg'),  # Path
            'https://ultralytics.com/images/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, size=320)  # batched inference
    results.print()
    results.save()