์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

์ฐธ์กฐ ultralytics/nn/modules/head.py

์ฐธ๊ณ 

์ด ํŒŒ์ผ์€ https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/nn/modules/head .py์—์„œ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฌธ์ œ๋ฅผ ๋ฐœ๊ฒฌํ•˜๋ฉด ํ’€ ๋ฆฌํ€˜์ŠคํŠธ ๐Ÿ› ๏ธ ์— ๊ธฐ์—ฌํ•˜์—ฌ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋„๋ก ๋„์™€์ฃผ์„ธ์š”. ๊ฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค ๐Ÿ™!



ultralytics.nn.modules.head.Detect

๊ธฐ์ง€: Module

YOLOv8 ๊ฐ์ง€ ๋ชจ๋ธ์— ๋Œ€ํ•œ ํ—ค๋“œ ๊ฐ์ง€.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
class Detect(nn.Module):
    """YOLOv8 Detect head for detection models."""

    dynamic = False  # force grid reconstruction
    export = False  # export mode
    shape = None
    anchors = torch.empty(0)  # init
    strides = torch.empty(0)  # init

    def __init__(self, nc=80, ch=()):
        """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
        super().__init__()
        self.nc = nc  # number of classes
        self.nl = len(ch)  # number of detection layers
        self.reg_max = 16  # DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x)
        self.no = nc + self.reg_max * 4  # number of outputs per anchor
        self.stride = torch.zeros(self.nl)  # strides computed during build
        c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], min(self.nc, 100))  # channels
        self.cv2 = nn.ModuleList(
            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
        )
        self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
        self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()

    def forward(self, x):
        """Concatenates and returns predicted bounding boxes and class probabilities."""
        for i in range(self.nl):
            x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
        if self.training:  # Training path
            return x

        # Inference path
        shape = x[0].shape  # BCHW
        x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
        if self.dynamic or self.shape != shape:
            self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
            self.shape = shape

        if self.export and self.format in ("saved_model", "pb", "tflite", "edgetpu", "tfjs"):  # avoid TF FlexSplitV ops
            box = x_cat[:, : self.reg_max * 4]
            cls = x_cat[:, self.reg_max * 4 :]
        else:
            box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)

        if self.export and self.format in ("tflite", "edgetpu"):
            # Precompute normalization factor to increase numerical stability
            # See https://github.com/ultralytics/ultralytics/issues/7371
            grid_h = shape[2]
            grid_w = shape[3]
            grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
            norm = self.strides / (self.stride[0] * grid_size)
            dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
        else:
            dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides

        y = torch.cat((dbox, cls.sigmoid()), 1)
        return y if self.export else (y, x)

    def bias_init(self):
        """Initialize Detect() biases, WARNING: requires stride availability."""
        m = self  # self.model[-1]  # Detect() module
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
        # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
        for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
            a[-1].bias.data[:] = 1.0  # box
            b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)

    def decode_bboxes(self, bboxes, anchors):
        """Decode bounding boxes."""
        return dist2bbox(bboxes, anchors, xywh=True, dim=1)

__init__(nc=80, ch=())

์ง€์ •๋œ ์ˆ˜์˜ ํด๋ž˜์Šค ๋ฐ ์ฑ„๋„๋กœ YOLOv8 ํƒ์ง€ ๊ณ„์ธต์„ ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def __init__(self, nc=80, ch=()):
    """Initializes the YOLOv8 detection layer with specified number of classes and channels."""
    super().__init__()
    self.nc = nc  # number of classes
    self.nl = len(ch)  # number of detection layers
    self.reg_max = 16  # DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x)
    self.no = nc + self.reg_max * 4  # number of outputs per anchor
    self.stride = torch.zeros(self.nl)  # strides computed during build
    c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], min(self.nc, 100))  # channels
    self.cv2 = nn.ModuleList(
        nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
    )
    self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
    self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()

bias_init()

Detect() ํŽธํ–ฅ ์ดˆ๊ธฐํ™”, ๊ฒฝ๊ณ : ๋ณดํญ ๊ฐ€์šฉ์„ฑ์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def bias_init(self):
    """Initialize Detect() biases, WARNING: requires stride availability."""
    m = self  # self.model[-1]  # Detect() module
    # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1
    # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # nominal class frequency
    for a, b, s in zip(m.cv2, m.cv3, m.stride):  # from
        a[-1].bias.data[:] = 1.0  # box
        b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2)  # cls (.01 objects, 80 classes, 640 img)

decode_bboxes(bboxes, anchors)

๋ฐ”์šด๋”ฉ ๋ฐ•์Šค๋ฅผ ๋””์ฝ”๋”ฉํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def decode_bboxes(self, bboxes, anchors):
    """Decode bounding boxes."""
    return dist2bbox(bboxes, anchors, xywh=True, dim=1)

forward(x)

์˜ˆ์ธก๋œ ๋ฐ”์šด๋”ฉ ๋ฐ•์Šค์™€ ํด๋ž˜์Šค ํ™•๋ฅ ์„ ์—ฐ๊ฒฐํ•˜์—ฌ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def forward(self, x):
    """Concatenates and returns predicted bounding boxes and class probabilities."""
    for i in range(self.nl):
        x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
    if self.training:  # Training path
        return x

    # Inference path
    shape = x[0].shape  # BCHW
    x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
    if self.dynamic or self.shape != shape:
        self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
        self.shape = shape

    if self.export and self.format in ("saved_model", "pb", "tflite", "edgetpu", "tfjs"):  # avoid TF FlexSplitV ops
        box = x_cat[:, : self.reg_max * 4]
        cls = x_cat[:, self.reg_max * 4 :]
    else:
        box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)

    if self.export and self.format in ("tflite", "edgetpu"):
        # Precompute normalization factor to increase numerical stability
        # See https://github.com/ultralytics/ultralytics/issues/7371
        grid_h = shape[2]
        grid_w = shape[3]
        grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
        norm = self.strides / (self.stride[0] * grid_size)
        dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
    else:
        dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides

    y = torch.cat((dbox, cls.sigmoid()), 1)
    return y if self.export else (y, x)



ultralytics.nn.modules.head.Segment

๊ธฐ์ง€: Detect

YOLOv8 ์„ธ๋ถ„ํ™” ๋ชจ๋ธ์˜ ์„ธ๊ทธ๋จผํŠธ ํ—ค๋“œ์ž…๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
class Segment(Detect):
    """YOLOv8 Segment head for segmentation models."""

    def __init__(self, nc=80, nm=32, npr=256, ch=()):
        """Initialize the YOLO model attributes such as the number of masks, prototypes, and the convolution layers."""
        super().__init__(nc, ch)
        self.nm = nm  # number of masks
        self.npr = npr  # number of protos
        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
        self.detect = Detect.forward

        c4 = max(ch[0] // 4, self.nm)
        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)

    def forward(self, x):
        """Return model outputs and mask coefficients if training, otherwise return outputs and mask coefficients."""
        p = self.proto(x[0])  # mask protos
        bs = p.shape[0]  # batch size

        mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
        x = self.detect(self, x)
        if self.training:
            return x, mc, p
        return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))

__init__(nc=80, nm=32, npr=256, ch=())

๋งˆ์Šคํฌ ์ˆ˜, ํ”„๋กœํ† ํƒ€์ž… ๋ฐ ์ปจ๋ณผ๋ฃจ์…˜ ๋ ˆ์ด์–ด์™€ ๊ฐ™์€ YOLO ๋ชจ๋ธ ์†์„ฑ์„ ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def __init__(self, nc=80, nm=32, npr=256, ch=()):
    """Initialize the YOLO model attributes such as the number of masks, prototypes, and the convolution layers."""
    super().__init__(nc, ch)
    self.nm = nm  # number of masks
    self.npr = npr  # number of protos
    self.proto = Proto(ch[0], self.npr, self.nm)  # protos
    self.detect = Detect.forward

    c4 = max(ch[0] // 4, self.nm)
    self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch)

forward(x)

ํ›ˆ๋ จํ•  ๊ฒฝ์šฐ ๋ชจ๋ธ ์ถœ๋ ฅ๊ณผ ๋งˆ์Šคํฌ ๊ณ„์ˆ˜๋ฅผ ๋ฐ˜ํ™˜ํ•˜๊ณ , ๊ทธ๋ ‡์ง€ ์•Š์œผ๋ฉด ์ถœ๋ ฅ๊ณผ ๋งˆ์Šคํฌ ๊ณ„์ˆ˜๋ฅผ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def forward(self, x):
    """Return model outputs and mask coefficients if training, otherwise return outputs and mask coefficients."""
    p = self.proto(x[0])  # mask protos
    bs = p.shape[0]  # batch size

    mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2)  # mask coefficients
    x = self.detect(self, x)
    if self.training:
        return x, mc, p
    return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p))



ultralytics.nn.modules.head.OBB

๊ธฐ์ง€: Detect

YOLOv8 ํšŒ์ „ ๋ชจ๋ธ์—์„œ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” OBB ๊ฐ์ง€ ํ—ค๋“œ์ž…๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
class OBB(Detect):
    """YOLOv8 OBB detection head for detection with rotation models."""

    def __init__(self, nc=80, ne=1, ch=()):
        """Initialize OBB with number of classes `nc` and layer channels `ch`."""
        super().__init__(nc, ch)
        self.ne = ne  # number of extra parameters
        self.detect = Detect.forward

        c4 = max(ch[0] // 4, self.ne)
        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.ne, 1)) for x in ch)

    def forward(self, x):
        """Concatenates and returns predicted bounding boxes and class probabilities."""
        bs = x[0].shape[0]  # batch size
        angle = torch.cat([self.cv4[i](x[i]).view(bs, self.ne, -1) for i in range(self.nl)], 2)  # OBB theta logits
        # NOTE: set `angle` as an attribute so that `decode_bboxes` could use it.
        angle = (angle.sigmoid() - 0.25) * math.pi  # [-pi/4, 3pi/4]
        # angle = angle.sigmoid() * math.pi / 2  # [0, pi/2]
        if not self.training:
            self.angle = angle
        x = self.detect(self, x)
        if self.training:
            return x, angle
        return torch.cat([x, angle], 1) if self.export else (torch.cat([x[0], angle], 1), (x[1], angle))

    def decode_bboxes(self, bboxes, anchors):
        """Decode rotated bounding boxes."""
        return dist2rbox(bboxes, self.angle, anchors, dim=1)

__init__(nc=80, ne=1, ch=())

ํด๋ž˜์Šค ์ˆ˜๋กœ OBB ์ดˆ๊ธฐํ™” nc ๋ฐ ๋ ˆ์ด์–ด ์ฑ„๋„ ch.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def __init__(self, nc=80, ne=1, ch=()):
    """Initialize OBB with number of classes `nc` and layer channels `ch`."""
    super().__init__(nc, ch)
    self.ne = ne  # number of extra parameters
    self.detect = Detect.forward

    c4 = max(ch[0] // 4, self.ne)
    self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.ne, 1)) for x in ch)

decode_bboxes(bboxes, anchors)

ํšŒ์ „๋œ ๋ฐ”์šด๋”ฉ ๋ฐ•์Šค๋ฅผ ๋””์ฝ”๋”ฉํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def decode_bboxes(self, bboxes, anchors):
    """Decode rotated bounding boxes."""
    return dist2rbox(bboxes, self.angle, anchors, dim=1)

forward(x)

์˜ˆ์ธก๋œ ๋ฐ”์šด๋”ฉ ๋ฐ•์Šค์™€ ํด๋ž˜์Šค ํ™•๋ฅ ์„ ์—ฐ๊ฒฐํ•˜์—ฌ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def forward(self, x):
    """Concatenates and returns predicted bounding boxes and class probabilities."""
    bs = x[0].shape[0]  # batch size
    angle = torch.cat([self.cv4[i](x[i]).view(bs, self.ne, -1) for i in range(self.nl)], 2)  # OBB theta logits
    # NOTE: set `angle` as an attribute so that `decode_bboxes` could use it.
    angle = (angle.sigmoid() - 0.25) * math.pi  # [-pi/4, 3pi/4]
    # angle = angle.sigmoid() * math.pi / 2  # [0, pi/2]
    if not self.training:
        self.angle = angle
    x = self.detect(self, x)
    if self.training:
        return x, angle
    return torch.cat([x, angle], 1) if self.export else (torch.cat([x[0], angle], 1), (x[1], angle))



ultralytics.nn.modules.head.Pose

๊ธฐ์ง€: Detect

YOLOv8 ํ‚คํฌ์ธํŠธ ๋ชจ๋ธ์˜ ๋จธ๋ฆฌ ํฌ์ฆˆ๋ฅผ ์ทจํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
class Pose(Detect):
    """YOLOv8 Pose head for keypoints models."""

    def __init__(self, nc=80, kpt_shape=(17, 3), ch=()):
        """Initialize YOLO network with default parameters and Convolutional Layers."""
        super().__init__(nc, ch)
        self.kpt_shape = kpt_shape  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
        self.nk = kpt_shape[0] * kpt_shape[1]  # number of keypoints total
        self.detect = Detect.forward

        c4 = max(ch[0] // 4, self.nk)
        self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nk, 1)) for x in ch)

    def forward(self, x):
        """Perform forward pass through YOLO model and return predictions."""
        bs = x[0].shape[0]  # batch size
        kpt = torch.cat([self.cv4[i](x[i]).view(bs, self.nk, -1) for i in range(self.nl)], -1)  # (bs, 17*3, h*w)
        x = self.detect(self, x)
        if self.training:
            return x, kpt
        pred_kpt = self.kpts_decode(bs, kpt)
        return torch.cat([x, pred_kpt], 1) if self.export else (torch.cat([x[0], pred_kpt], 1), (x[1], kpt))

    def kpts_decode(self, bs, kpts):
        """Decodes keypoints."""
        ndim = self.kpt_shape[1]
        if self.export:  # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
            y = kpts.view(bs, *self.kpt_shape, -1)
            a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
            if ndim == 3:
                a = torch.cat((a, y[:, :, 2:3].sigmoid()), 2)
            return a.view(bs, self.nk, -1)
        else:
            y = kpts.clone()
            if ndim == 3:
                y[:, 2::3] = y[:, 2::3].sigmoid()  # sigmoid (WARNING: inplace .sigmoid_() Apple MPS bug)
            y[:, 0::ndim] = (y[:, 0::ndim] * 2.0 + (self.anchors[0] - 0.5)) * self.strides
            y[:, 1::ndim] = (y[:, 1::ndim] * 2.0 + (self.anchors[1] - 0.5)) * self.strides
            return y

__init__(nc=80, kpt_shape=(17, 3), ch=())

๊ธฐ๋ณธ ํŒŒ๋ผ๋ฏธํ„ฐ์™€ ์ปจ๋ณผ๋ฃจ์…˜ ๋ ˆ์ด์–ด๋กœ YOLO ๋„คํŠธ์›Œํฌ๋ฅผ ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def __init__(self, nc=80, kpt_shape=(17, 3), ch=()):
    """Initialize YOLO network with default parameters and Convolutional Layers."""
    super().__init__(nc, ch)
    self.kpt_shape = kpt_shape  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
    self.nk = kpt_shape[0] * kpt_shape[1]  # number of keypoints total
    self.detect = Detect.forward

    c4 = max(ch[0] // 4, self.nk)
    self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nk, 1)) for x in ch)

forward(x)

YOLO ๋ชจ๋ธ์„ ํ†ตํ•ด ํฌ์›Œ๋“œ ํŒจ์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ณ  ์˜ˆ์ธก์„ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def forward(self, x):
    """Perform forward pass through YOLO model and return predictions."""
    bs = x[0].shape[0]  # batch size
    kpt = torch.cat([self.cv4[i](x[i]).view(bs, self.nk, -1) for i in range(self.nl)], -1)  # (bs, 17*3, h*w)
    x = self.detect(self, x)
    if self.training:
        return x, kpt
    pred_kpt = self.kpts_decode(bs, kpt)
    return torch.cat([x, pred_kpt], 1) if self.export else (torch.cat([x[0], pred_kpt], 1), (x[1], kpt))

kpts_decode(bs, kpts)

ํ‚คํฌ์ธํŠธ๋ฅผ ๋””์ฝ”๋”ฉํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def kpts_decode(self, bs, kpts):
    """Decodes keypoints."""
    ndim = self.kpt_shape[1]
    if self.export:  # required for TFLite export to avoid 'PLACEHOLDER_FOR_GREATER_OP_CODES' bug
        y = kpts.view(bs, *self.kpt_shape, -1)
        a = (y[:, :, :2] * 2.0 + (self.anchors - 0.5)) * self.strides
        if ndim == 3:
            a = torch.cat((a, y[:, :, 2:3].sigmoid()), 2)
        return a.view(bs, self.nk, -1)
    else:
        y = kpts.clone()
        if ndim == 3:
            y[:, 2::3] = y[:, 2::3].sigmoid()  # sigmoid (WARNING: inplace .sigmoid_() Apple MPS bug)
        y[:, 0::ndim] = (y[:, 0::ndim] * 2.0 + (self.anchors[0] - 0.5)) * self.strides
        y[:, 1::ndim] = (y[:, 1::ndim] * 2.0 + (self.anchors[1] - 0.5)) * self.strides
        return y



ultralytics.nn.modules.head.Classify

๊ธฐ์ง€: Module

YOLOv8 ๋ถ„๋ฅ˜ ํ—ค๋“œ, ์ฆ‰ X(B,C1,20,20)์—์„œ X(B,C2)๋กœ ๋ฐ”๋€๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
class Classify(nn.Module):
    """YOLOv8 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):
        """Initializes YOLOv8 classification head with specified input and output channels, kernel size, stride,
        padding, and groups.
        """
        super().__init__()
        c_ = 1280  # efficientnet_b0 size
        self.conv = Conv(c1, c_, k, s, p, g)
        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
        self.drop = nn.Dropout(p=0.0, inplace=True)
        self.linear = nn.Linear(c_, c2)  # to x(b,c2)

    def forward(self, x):
        """Performs a forward pass of the YOLO model on input image data."""
        if isinstance(x, list):
            x = torch.cat(x, 1)
        x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
        return x if self.training else x.softmax(1)

__init__(c1, c2, k=1, s=1, p=None, g=1)

์ง€์ •๋œ ์ž…๋ ฅ ๋ฐ ์ถœ๋ ฅ ์ฑ„๋„, ์ปค๋„ ํฌ๊ธฐ, ๋ณดํญ์œผ๋กœ YOLOv8 ๋ถ„๋ฅ˜ ํ—ค๋“œ๋ฅผ ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค, ํŒจ๋”ฉ, ๊ทธ๋ฃน์œผ๋กœ ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
    """Initializes YOLOv8 classification head with specified input and output channels, kernel size, stride,
    padding, and groups.
    """
    super().__init__()
    c_ = 1280  # efficientnet_b0 size
    self.conv = Conv(c1, c_, k, s, p, g)
    self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
    self.drop = nn.Dropout(p=0.0, inplace=True)
    self.linear = nn.Linear(c_, c2)  # to x(b,c2)

forward(x)

์ž…๋ ฅ ์ด๋ฏธ์ง€ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด YOLO ๋ชจ๋ธ์˜ ์ •๋ฐฉํ–ฅ ํŒจ์Šค๋ฅผ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def forward(self, x):
    """Performs a forward pass of the YOLO model on input image data."""
    if isinstance(x, list):
        x = torch.cat(x, 1)
    x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
    return x if self.training else x.softmax(1)



ultralytics.nn.modules.head.RTDETRDecoder

๊ธฐ์ง€: Module

๋ฌผ์ฒด ๊ฐ์ง€๋ฅผ ์œ„ํ•œ ์‹ค์‹œ๊ฐ„ ๋ณ€ํ˜• ๊ฐ€๋Šฅ ๋ณ€์••๊ธฐ ๋””์ฝ”๋”(RTDETRDecoder) ๋ชจ๋“ˆ์ž…๋‹ˆ๋‹ค.

์ด ๋””์ฝ”๋” ๋ชจ๋“ˆ์€ ๋ณ€ํ˜• ๊ฐ€๋Šฅํ•œ ์ปจ๋ณผ๋ฃจ์…˜๊ณผ ํ•จ๊ป˜ ํŠธ๋žœ์Šคํฌ๋จธ ์•„ํ‚คํ…์ฒ˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ด๋ฏธ์ง€์˜ ๋ฐ”์šด๋”ฉ ๋ฐ•์Šค ๋ฐ ์ด๋ฏธ์ง€ ๋‚ด ์˜ค๋ธŒ์ ํŠธ์˜ ํด๋ž˜์Šค ๋ ˆ์ด๋ธ”์„ ์˜ˆ์ธกํ•ฉ๋‹ˆ๋‹ค. ์ด ๋ชจ๋“ˆ์€ ์—ฌ๋Ÿฌ ๋ ˆ์ด์–ด์˜ ๊ธฐ๋Šฅ์„ ํ†ตํ•ฉํ•˜๊ณ  ์ผ๋ จ์˜ ์ผ๋ จ์˜ ํŠธ๋žœ์Šคํฌ๋จธ ๋””์ฝ”๋” ๋ ˆ์ด์–ด๋ฅผ ๊ฑฐ์ณ ์ตœ์ข… ์˜ˆ์ธก์„ ์ถœ๋ ฅํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
class RTDETRDecoder(nn.Module):
    """
    Real-Time Deformable Transformer Decoder (RTDETRDecoder) module for object detection.

    This decoder module utilizes Transformer architecture along with deformable convolutions to predict bounding boxes
    and class labels for objects in an image. It integrates features from multiple layers and runs through a series of
    Transformer decoder layers to output the final predictions.
    """

    export = False  # export mode

    def __init__(
        self,
        nc=80,
        ch=(512, 1024, 2048),
        hd=256,  # hidden dim
        nq=300,  # num queries
        ndp=4,  # num decoder points
        nh=8,  # num head
        ndl=6,  # num decoder layers
        d_ffn=1024,  # dim of feedforward
        dropout=0.0,
        act=nn.ReLU(),
        eval_idx=-1,
        # Training args
        nd=100,  # num denoising
        label_noise_ratio=0.5,
        box_noise_scale=1.0,
        learnt_init_query=False,
    ):
        """
        Initializes the RTDETRDecoder module with the given parameters.

        Args:
            nc (int): Number of classes. Default is 80.
            ch (tuple): Channels in the backbone feature maps. Default is (512, 1024, 2048).
            hd (int): Dimension of hidden layers. Default is 256.
            nq (int): Number of query points. Default is 300.
            ndp (int): Number of decoder points. Default is 4.
            nh (int): Number of heads in multi-head attention. Default is 8.
            ndl (int): Number of decoder layers. Default is 6.
            d_ffn (int): Dimension of the feed-forward networks. Default is 1024.
            dropout (float): Dropout rate. Default is 0.
            act (nn.Module): Activation function. Default is nn.ReLU.
            eval_idx (int): Evaluation index. Default is -1.
            nd (int): Number of denoising. Default is 100.
            label_noise_ratio (float): Label noise ratio. Default is 0.5.
            box_noise_scale (float): Box noise scale. Default is 1.0.
            learnt_init_query (bool): Whether to learn initial query embeddings. Default is False.
        """
        super().__init__()
        self.hidden_dim = hd
        self.nhead = nh
        self.nl = len(ch)  # num level
        self.nc = nc
        self.num_queries = nq
        self.num_decoder_layers = ndl

        # Backbone feature projection
        self.input_proj = nn.ModuleList(nn.Sequential(nn.Conv2d(x, hd, 1, bias=False), nn.BatchNorm2d(hd)) for x in ch)
        # NOTE: simplified version but it's not consistent with .pt weights.
        # self.input_proj = nn.ModuleList(Conv(x, hd, act=False) for x in ch)

        # Transformer module
        decoder_layer = DeformableTransformerDecoderLayer(hd, nh, d_ffn, dropout, act, self.nl, ndp)
        self.decoder = DeformableTransformerDecoder(hd, decoder_layer, ndl, eval_idx)

        # Denoising part
        self.denoising_class_embed = nn.Embedding(nc, hd)
        self.num_denoising = nd
        self.label_noise_ratio = label_noise_ratio
        self.box_noise_scale = box_noise_scale

        # Decoder embedding
        self.learnt_init_query = learnt_init_query
        if learnt_init_query:
            self.tgt_embed = nn.Embedding(nq, hd)
        self.query_pos_head = MLP(4, 2 * hd, hd, num_layers=2)

        # Encoder head
        self.enc_output = nn.Sequential(nn.Linear(hd, hd), nn.LayerNorm(hd))
        self.enc_score_head = nn.Linear(hd, nc)
        self.enc_bbox_head = MLP(hd, hd, 4, num_layers=3)

        # Decoder head
        self.dec_score_head = nn.ModuleList([nn.Linear(hd, nc) for _ in range(ndl)])
        self.dec_bbox_head = nn.ModuleList([MLP(hd, hd, 4, num_layers=3) for _ in range(ndl)])

        self._reset_parameters()

    def forward(self, x, batch=None):
        """Runs the forward pass of the module, returning bounding box and classification scores for the input."""
        from ultralytics.models.utils.ops import get_cdn_group

        # Input projection and embedding
        feats, shapes = self._get_encoder_input(x)

        # Prepare denoising training
        dn_embed, dn_bbox, attn_mask, dn_meta = get_cdn_group(
            batch,
            self.nc,
            self.num_queries,
            self.denoising_class_embed.weight,
            self.num_denoising,
            self.label_noise_ratio,
            self.box_noise_scale,
            self.training,
        )

        embed, refer_bbox, enc_bboxes, enc_scores = self._get_decoder_input(feats, shapes, dn_embed, dn_bbox)

        # Decoder
        dec_bboxes, dec_scores = self.decoder(
            embed,
            refer_bbox,
            feats,
            shapes,
            self.dec_bbox_head,
            self.dec_score_head,
            self.query_pos_head,
            attn_mask=attn_mask,
        )
        x = dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta
        if self.training:
            return x
        # (bs, 300, 4+nc)
        y = torch.cat((dec_bboxes.squeeze(0), dec_scores.squeeze(0).sigmoid()), -1)
        return y if self.export else (y, x)

    def _generate_anchors(self, shapes, grid_size=0.05, dtype=torch.float32, device="cpu", eps=1e-2):
        """Generates anchor bounding boxes for given shapes with specific grid size and validates them."""
        anchors = []
        for i, (h, w) in enumerate(shapes):
            sy = torch.arange(end=h, dtype=dtype, device=device)
            sx = torch.arange(end=w, dtype=dtype, device=device)
            grid_y, grid_x = torch.meshgrid(sy, sx, indexing="ij") if TORCH_1_10 else torch.meshgrid(sy, sx)
            grid_xy = torch.stack([grid_x, grid_y], -1)  # (h, w, 2)

            valid_WH = torch.tensor([w, h], dtype=dtype, device=device)
            grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_WH  # (1, h, w, 2)
            wh = torch.ones_like(grid_xy, dtype=dtype, device=device) * grid_size * (2.0**i)
            anchors.append(torch.cat([grid_xy, wh], -1).view(-1, h * w, 4))  # (1, h*w, 4)

        anchors = torch.cat(anchors, 1)  # (1, h*w*nl, 4)
        valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True)  # 1, h*w*nl, 1
        anchors = torch.log(anchors / (1 - anchors))
        anchors = anchors.masked_fill(~valid_mask, float("inf"))
        return anchors, valid_mask

    def _get_encoder_input(self, x):
        """Processes and returns encoder inputs by getting projection features from input and concatenating them."""
        # Get projection features
        x = [self.input_proj[i](feat) for i, feat in enumerate(x)]
        # Get encoder inputs
        feats = []
        shapes = []
        for feat in x:
            h, w = feat.shape[2:]
            # [b, c, h, w] -> [b, h*w, c]
            feats.append(feat.flatten(2).permute(0, 2, 1))
            # [nl, 2]
            shapes.append([h, w])

        # [b, h*w, c]
        feats = torch.cat(feats, 1)
        return feats, shapes

    def _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):
        """Generates and prepares the input required for the decoder from the provided features and shapes."""
        bs = feats.shape[0]
        # Prepare input for decoder
        anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
        features = self.enc_output(valid_mask * feats)  # bs, h*w, 256

        enc_outputs_scores = self.enc_score_head(features)  # (bs, h*w, nc)

        # Query selection
        # (bs, num_queries)
        topk_ind = torch.topk(enc_outputs_scores.max(-1).values, self.num_queries, dim=1).indices.view(-1)
        # (bs, num_queries)
        batch_ind = torch.arange(end=bs, dtype=topk_ind.dtype).unsqueeze(-1).repeat(1, self.num_queries).view(-1)

        # (bs, num_queries, 256)
        top_k_features = features[batch_ind, topk_ind].view(bs, self.num_queries, -1)
        # (bs, num_queries, 4)
        top_k_anchors = anchors[:, topk_ind].view(bs, self.num_queries, -1)

        # Dynamic anchors + static content
        refer_bbox = self.enc_bbox_head(top_k_features) + top_k_anchors

        enc_bboxes = refer_bbox.sigmoid()
        if dn_bbox is not None:
            refer_bbox = torch.cat([dn_bbox, refer_bbox], 1)
        enc_scores = enc_outputs_scores[batch_ind, topk_ind].view(bs, self.num_queries, -1)

        embeddings = self.tgt_embed.weight.unsqueeze(0).repeat(bs, 1, 1) if self.learnt_init_query else top_k_features
        if self.training:
            refer_bbox = refer_bbox.detach()
            if not self.learnt_init_query:
                embeddings = embeddings.detach()
        if dn_embed is not None:
            embeddings = torch.cat([dn_embed, embeddings], 1)

        return embeddings, refer_bbox, enc_bboxes, enc_scores

    # TODO
    def _reset_parameters(self):
        """Initializes or resets the parameters of the model's various components with predefined weights and biases."""
        # Class and bbox head init
        bias_cls = bias_init_with_prob(0.01) / 80 * self.nc
        # NOTE: the weight initialization in `linear_init` would cause NaN when training with custom datasets.
        # linear_init(self.enc_score_head)
        constant_(self.enc_score_head.bias, bias_cls)
        constant_(self.enc_bbox_head.layers[-1].weight, 0.0)
        constant_(self.enc_bbox_head.layers[-1].bias, 0.0)
        for cls_, reg_ in zip(self.dec_score_head, self.dec_bbox_head):
            # linear_init(cls_)
            constant_(cls_.bias, bias_cls)
            constant_(reg_.layers[-1].weight, 0.0)
            constant_(reg_.layers[-1].bias, 0.0)

        linear_init(self.enc_output[0])
        xavier_uniform_(self.enc_output[0].weight)
        if self.learnt_init_query:
            xavier_uniform_(self.tgt_embed.weight)
        xavier_uniform_(self.query_pos_head.layers[0].weight)
        xavier_uniform_(self.query_pos_head.layers[1].weight)
        for layer in self.input_proj:
            xavier_uniform_(layer[0].weight)

__init__(nc=80, ch=(512, 1024, 2048), hd=256, nq=300, ndp=4, nh=8, ndl=6, d_ffn=1024, dropout=0.0, act=nn.ReLU(), eval_idx=-1, nd=100, label_noise_ratio=0.5, box_noise_scale=1.0, learnt_init_query=False)

์ฃผ์–ด์ง„ ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ RTDETRDecoder ๋ชจ๋“ˆ์„ ์ดˆ๊ธฐํ™”ํ•ฉ๋‹ˆ๋‹ค.

๋งค๊ฐœ๋ณ€์ˆ˜:

์ด๋ฆ„ ์œ ํ˜• ์„ค๋ช… ๊ธฐ๋ณธ๊ฐ’
nc int

ํด๋ž˜์Šค ์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 80์ž…๋‹ˆ๋‹ค.

80
ch tuple

๋ฐฑ๋ณธ ๊ธฐ๋Šฅ ๋งต์˜ ์ฑ„๋„์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ (512, 1024, 2048)์ž…๋‹ˆ๋‹ค.

(512, 1024, 2048)
hd int

์ˆจ๊ฒจ์ง„ ๋ ˆ์ด์–ด์˜ ํฌ๊ธฐ์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 256์ž…๋‹ˆ๋‹ค.

256
nq int

์ฟผ๋ฆฌ ํฌ์ธํŠธ ์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 300์ž…๋‹ˆ๋‹ค.

300
ndp int

๋””์ฝ”๋” ํฌ์ธํŠธ ์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 4์ž…๋‹ˆ๋‹ค.

4
nh int

๋‹ค์ค‘ ํ—ค๋“œ ์ฃผ์˜์˜ ํ—ค๋“œ ์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 8์ž…๋‹ˆ๋‹ค.

8
ndl int

๋””์ฝ”๋” ๋ ˆ์ด์–ด ์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 6์ž…๋‹ˆ๋‹ค.

6
d_ffn int

ํ”ผ๋“œ ํฌ์›Œ๋“œ ๋„คํŠธ์›Œํฌ์˜ ํฌ๊ธฐ์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 1024์ž…๋‹ˆ๋‹ค.

1024
dropout float

ํƒˆ๋ฝ๋ฅ . ๊ธฐ๋ณธ๊ฐ’์€ 0์ž…๋‹ˆ๋‹ค.

0.0
act Module

ํ™œ์„ฑํ™” ๊ธฐ๋Šฅ. ๊ธฐ๋ณธ๊ฐ’์€ nn.ReLU์ž…๋‹ˆ๋‹ค.

ReLU()
eval_idx int

ํ‰๊ฐ€ ์ง€์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ -1์ž…๋‹ˆ๋‹ค.

-1
nd int

๋…ธ์ด์ฆˆ ์ œ๊ฑฐ ํšŸ์ˆ˜์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ 100์ž…๋‹ˆ๋‹ค.

100
label_noise_ratio float

๋ผ๋ฒจ ๋…ธ์ด์ฆˆ ๋น„์œจ. ๊ธฐ๋ณธ๊ฐ’์€ 0.5์ž…๋‹ˆ๋‹ค.

0.5
box_noise_scale float

๋ฐ•์Šค ๋…ธ์ด์ฆˆ ์Šค์ผ€์ผ. ๊ธฐ๋ณธ๊ฐ’์€ 1.0์ž…๋‹ˆ๋‹ค.

1.0
learnt_init_query bool

์ดˆ๊ธฐ ์ฟผ๋ฆฌ ์ž„๋ฒ ๋”ฉ์„ ํ•™์Šตํ• ์ง€ ์—ฌ๋ถ€์ž…๋‹ˆ๋‹ค. ๊ธฐ๋ณธ๊ฐ’์€ False์ž…๋‹ˆ๋‹ค.

False
์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def __init__(
    self,
    nc=80,
    ch=(512, 1024, 2048),
    hd=256,  # hidden dim
    nq=300,  # num queries
    ndp=4,  # num decoder points
    nh=8,  # num head
    ndl=6,  # num decoder layers
    d_ffn=1024,  # dim of feedforward
    dropout=0.0,
    act=nn.ReLU(),
    eval_idx=-1,
    # Training args
    nd=100,  # num denoising
    label_noise_ratio=0.5,
    box_noise_scale=1.0,
    learnt_init_query=False,
):
    """
    Initializes the RTDETRDecoder module with the given parameters.

    Args:
        nc (int): Number of classes. Default is 80.
        ch (tuple): Channels in the backbone feature maps. Default is (512, 1024, 2048).
        hd (int): Dimension of hidden layers. Default is 256.
        nq (int): Number of query points. Default is 300.
        ndp (int): Number of decoder points. Default is 4.
        nh (int): Number of heads in multi-head attention. Default is 8.
        ndl (int): Number of decoder layers. Default is 6.
        d_ffn (int): Dimension of the feed-forward networks. Default is 1024.
        dropout (float): Dropout rate. Default is 0.
        act (nn.Module): Activation function. Default is nn.ReLU.
        eval_idx (int): Evaluation index. Default is -1.
        nd (int): Number of denoising. Default is 100.
        label_noise_ratio (float): Label noise ratio. Default is 0.5.
        box_noise_scale (float): Box noise scale. Default is 1.0.
        learnt_init_query (bool): Whether to learn initial query embeddings. Default is False.
    """
    super().__init__()
    self.hidden_dim = hd
    self.nhead = nh
    self.nl = len(ch)  # num level
    self.nc = nc
    self.num_queries = nq
    self.num_decoder_layers = ndl

    # Backbone feature projection
    self.input_proj = nn.ModuleList(nn.Sequential(nn.Conv2d(x, hd, 1, bias=False), nn.BatchNorm2d(hd)) for x in ch)
    # NOTE: simplified version but it's not consistent with .pt weights.
    # self.input_proj = nn.ModuleList(Conv(x, hd, act=False) for x in ch)

    # Transformer module
    decoder_layer = DeformableTransformerDecoderLayer(hd, nh, d_ffn, dropout, act, self.nl, ndp)
    self.decoder = DeformableTransformerDecoder(hd, decoder_layer, ndl, eval_idx)

    # Denoising part
    self.denoising_class_embed = nn.Embedding(nc, hd)
    self.num_denoising = nd
    self.label_noise_ratio = label_noise_ratio
    self.box_noise_scale = box_noise_scale

    # Decoder embedding
    self.learnt_init_query = learnt_init_query
    if learnt_init_query:
        self.tgt_embed = nn.Embedding(nq, hd)
    self.query_pos_head = MLP(4, 2 * hd, hd, num_layers=2)

    # Encoder head
    self.enc_output = nn.Sequential(nn.Linear(hd, hd), nn.LayerNorm(hd))
    self.enc_score_head = nn.Linear(hd, nc)
    self.enc_bbox_head = MLP(hd, hd, 4, num_layers=3)

    # Decoder head
    self.dec_score_head = nn.ModuleList([nn.Linear(hd, nc) for _ in range(ndl)])
    self.dec_bbox_head = nn.ModuleList([MLP(hd, hd, 4, num_layers=3) for _ in range(ndl)])

    self._reset_parameters()

forward(x, batch=None)

๋ชจ๋“ˆ์˜ ํฌ์›Œ๋“œ ํŒจ์Šค๋ฅผ ์‹คํ–‰ํ•˜์—ฌ ์ž…๋ ฅ์— ๋Œ€ํ•œ ๋ฐ”์šด๋”ฉ ๋ฐ•์Šค ๋ฐ ๋ถ„๋ฅ˜ ์ ์ˆ˜๋ฅผ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/nn/modules/head.py
def forward(self, x, batch=None):
    """Runs the forward pass of the module, returning bounding box and classification scores for the input."""
    from ultralytics.models.utils.ops import get_cdn_group

    # Input projection and embedding
    feats, shapes = self._get_encoder_input(x)

    # Prepare denoising training
    dn_embed, dn_bbox, attn_mask, dn_meta = get_cdn_group(
        batch,
        self.nc,
        self.num_queries,
        self.denoising_class_embed.weight,
        self.num_denoising,
        self.label_noise_ratio,
        self.box_noise_scale,
        self.training,
    )

    embed, refer_bbox, enc_bboxes, enc_scores = self._get_decoder_input(feats, shapes, dn_embed, dn_bbox)

    # Decoder
    dec_bboxes, dec_scores = self.decoder(
        embed,
        refer_bbox,
        feats,
        shapes,
        self.dec_bbox_head,
        self.dec_score_head,
        self.query_pos_head,
        attn_mask=attn_mask,
    )
    x = dec_bboxes, dec_scores, enc_bboxes, enc_scores, dn_meta
    if self.training:
        return x
    # (bs, 300, 4+nc)
    y = torch.cat((dec_bboxes.squeeze(0), dec_scores.squeeze(0).sigmoid()), -1)
    return y if self.export else (y, x)





์ƒ์„ฑ๋จ 2023-11-12, ์—…๋ฐ์ดํŠธ๋จ 2024-01-05
์ž‘์„ฑ์ž: glenn-jocher (4)