Modules and utils for YOLOv5

class yolort.v5.AutoShape(model)

YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS

classes = None

conf = 0.25

forward(imgs, size=640, augment=False, profile=False)

Inference from various sources. For height=640, width=1280, RGB images example inputs are:

• file: imgs = ‘data/images/zidane.jpg’ # str or PosixPath

• URI: = ‘https://ultralytics.com/images/zidane.jpg’

• OpenCV: = cv2.imread(‘image.jpg’)[:,:,::-1] # HWC BGR to RGB x(640,1280,3)

• PIL: = Image.open(‘image.jpg’) or ImageGrab.grab() # 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

iou = 0.45

max_det = 1000

multi_label = False

class yolort.v5.Bottleneck(c1, c2, shortcut=True, g=1, e=0.5, version='r4.0')

Standard bottleneck

Parameters

• c1 (int) – ch_in

• c2 (int) – ch_out

• shortcut (bool) – shortcut

• g (int) – groups

• e (float) – expansion

• version (str) – Module version released by ultralytics. Possible values are [“r3.1”, “r4.0”]. Default: “r4.0”.

class yolort.v5.BottleneckCSP(c1, c2, n=1, shortcut=True, g=1, e=0.5)

CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks

Parameters

• c1 (int) – ch_in

• c2 (int) – ch_out

• n (int) – number

• shortcut (bool) – shortcut

• g (int) – groups

• e (float) – expansion

class yolort.v5.C3(c1, c2, n=1, shortcut=True, g=1, e=0.5, version='r4.0')

CSP Bottleneck with 3 convolutions

Parameters

• c1 (int) – ch_in

• c2 (int) – ch_out

• n (int) – number

• shortcut (bool) – shortcut

• g (int) – groups

• e (float) – expansion

• version (str) – Module version released by ultralytics. Possible values are [“r4.0”]. Default: “r4.0”.

class yolort.v5.C3TR(c1, c2, n=1, shortcut=True, g=1, e=0.5)

class yolort.v5.Concat(dimension: int = 1)

class yolort.v5.Contract(gain=2)

class yolort.v5.Conv(c1, c2, k=1, s=1, p=None, g=1, act=True, version='r4.0')

Standard convolution

Parameters

• c1 (int) – ch_in

• c2 (int) – ch_out

• k (int) – kernel

• s (int) – stride

• p (Optional[int]) – padding

• g (int) – groups

• act (bool or nn.Module) – determine the activation function

• version (str) – Module version released by ultralytics. Possible values are [“r3.1”, “r4.0”]. Default: “r4.0”.

fuseforward(x)

class yolort.v5.DWConv(c1, c2, k=1, s=1, act=True, version='r4.0')

Depth-wise convolution class.

Parameters

• c1 (int) – ch_in

• c2 (int) – ch_out

• k (int) – kernel

• s (int) – stride

• act (bool or nn.Module) – determine the activation function

• version (str) – Module version released by ultralytics. Possible values are [“r3.1”, “r4.0”]. Default: “r4.0”.

class yolort.v5.Detect(nc=80, anchors=(), ch=(), inplace=True)

onnx_dynamic = False

stride = None

class yolort.v5.Ensemble

class yolort.v5.Expand(gain=2)

class yolort.v5.Focus(c1, c2, k=1, s=1, p=None, g=1, act=True, version='r4.0')

Focus wh information into c-space

Parameters

• c1 (int) – ch_in

• c2 (int) – ch_out

• k (int) – kernel

• s (int) – stride

• p (Optional[int]) – padding

• g (int) – groups

• act (bool or nn.Module) – determine the activation function

• version (str) – Module version released by ultralytics. Possible values are [“r3.1”, “r4.0”]. Default: “r4.0”.

class yolort.v5.GhostBottleneck(c1, c2, k=3, s=1)

class yolort.v5.GhostConv(c1, c2, k=1, s=1, g=1, act=True)

class yolort.v5.Model(cfg='yolov5s.yaml', ch=3, nc=None, anchors=None)

fuse()

info(verbose=False, img_size=640)

class yolort.v5.SPP(c1, c2, k=(5, 9, 13), version='r4.0')

class yolort.v5.SPPF(c1, c2, k=5, version='r4.0')

Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher

yolort.v5.add_yolov5_context()

Temporarily add yolov5 folder to sys.path. Adapted from https://github.com/fcakyon/yolov5-pip/blob/0d03de6/yolov5/utils/general.py#L739-L754

torch.hub handles it in the same way: https://github.com/pytorch/pytorch/blob/d3e36fa/torch/hub.py#L387-L416

yolort.v5.attempt_download(file, repo='ultralytics/yolov5', hash_prefix=None)

yolort.v5.focus_transform(x: torch.Tensor) → torch.Tensor

x(b,c,w,h) -> y(b,4c,w/2,h/2)

yolort.v5.get_yolov5_size(depth_multiple, width_multiple)

yolort.v5.intersect_dicts(dict1, dict2, exclude=())

Dictionary intersection of matching keys and shapes, omitting ‘exclude’ keys, using dict1 values

yolort.v5.letterbox(im: numpy.ndarray, new_shape: Tuple[int, int] = (640, 640), color: Tuple[int, int, int] = (114, 114, 114), auto: bool = True, scale_fill: bool = False, scaleup: bool = True, stride: int = 32)

yolort.v5.load_yolov5_model(checkpoint_path: str, autoshape: bool = False, verbose: bool = True)

Creates a specified YOLOv5 model

Parameters

• checkpoint_path (str) – path of the YOLOv5 model, i.e. ‘yolov5s.pt’

• autoshape (bool) – apply YOLOv5 .autoshape() wrapper to model. Default: False.

• verbose (bool) – print all information to screen. Default: True.

Returns

YOLOv5 pytorch model

yolort.v5.non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, labels=(), max_det=300)

Runs Non-Maximum Suppression (NMS) on inference results

Returns

list of detections, on (n,6) tensor per image [xyxy, conf, cls]

yolort.v5.scale_coords(img1_shape, coords, img0_shape, ratio_pad=None)

yolort.v5.select_device(device='', batch_size=None, newline=True)

yolort.v5.set_logging(name=None, verbose=True)

yolort.v5.space_to_depth(x: torch.Tensor) → torch.Tensor

x(b,c,w,h) -> y(b,4c,w/2,h/2)