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Referenz fĂŒr ultralytics/nn/modules/block.py

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ultralytics.nn.modules.block.DFL

Basen: Module

Integraler Baustein der Distribution Focal Loss (DFL).

Vorgeschlagen in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391

Quellcode in ultralytics/nn/modules/block.py
class DFL(nn.Module):
    """
    Integral module of Distribution Focal Loss (DFL).

    Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
    """

    def __init__(self, c1=16):
        """Initialize a convolutional layer with a given number of input channels."""
        super().__init__()
        self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
        x = torch.arange(c1, dtype=torch.float)
        self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1))
        self.c1 = c1

    def forward(self, x):
        """Applies a transformer layer on input tensor 'x' and returns a tensor."""
        b, c, a = x.shape  # batch, channels, anchors
        return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a)

__init__(c1=16)

Initialisiere eine Faltungsschicht mit einer bestimmten Anzahl von EingangskanÀlen.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1=16):
    """Initialize a convolutional layer with a given number of input channels."""
    super().__init__()
    self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)
    x = torch.arange(c1, dtype=torch.float)
    self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1))
    self.c1 = c1

forward(x)

Wendet eine Transformatorschicht auf die Eingabe tensor 'x' an und gibt tensor zurĂŒck.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Applies a transformer layer on input tensor 'x' and returns a tensor."""
    b, c, a = x.shape  # batch, channels, anchors
    return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a)



ultralytics.nn.modules.block.Proto

Basen: Module

YOLOv8 Maske Proto-Modul fĂŒr Segmentierungsmodelle.

Quellcode in ultralytics/nn/modules/block.py
class Proto(nn.Module):
    """YOLOv8 mask Proto module for segmentation models."""

    def __init__(self, c1, c_=256, c2=32):
        """
        Initializes the YOLOv8 mask Proto module with specified number of protos and masks.

        Input arguments are ch_in, number of protos, number of masks.
        """
        super().__init__()
        self.cv1 = Conv(c1, c_, k=3)
        self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True)  # nn.Upsample(scale_factor=2, mode='nearest')
        self.cv2 = Conv(c_, c_, k=3)
        self.cv3 = Conv(c_, c2)

    def forward(self, x):
        """Performs a forward pass through layers using an upsampled input image."""
        return self.cv3(self.cv2(self.upsample(self.cv1(x))))

__init__(c1, c_=256, c2=32)

Initialisiert das Modul YOLOv8 mask Proto mit der angegebenen Anzahl von Protos und Masken.

Eingangsargumente sind ch_in, Anzahl der Protos, Anzahl der Masken.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c_=256, c2=32):
    """
    Initializes the YOLOv8 mask Proto module with specified number of protos and masks.

    Input arguments are ch_in, number of protos, number of masks.
    """
    super().__init__()
    self.cv1 = Conv(c1, c_, k=3)
    self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True)  # nn.Upsample(scale_factor=2, mode='nearest')
    self.cv2 = Conv(c_, c_, k=3)
    self.cv3 = Conv(c_, c2)

forward(x)

FĂŒhrt einen VorwĂ€rtsdurchlauf durch die Ebenen mit einem hochabgetasteten Eingangsbild durch.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Performs a forward pass through layers using an upsampled input image."""
    return self.cv3(self.cv2(self.upsample(self.cv1(x))))



ultralytics.nn.modules.block.HGStem

Basen: Module

StemBlock von PPHGNetV2 mit 5 Convolutions und einem maxpool2d.

https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py

Quellcode in ultralytics/nn/modules/block.py
class HGStem(nn.Module):
    """
    StemBlock of PPHGNetV2 with 5 convolutions and one maxpool2d.

    https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
    """

    def __init__(self, c1, cm, c2):
        """Initialize the SPP layer with input/output channels and specified kernel sizes for max pooling."""
        super().__init__()
        self.stem1 = Conv(c1, cm, 3, 2, act=nn.ReLU())
        self.stem2a = Conv(cm, cm // 2, 2, 1, 0, act=nn.ReLU())
        self.stem2b = Conv(cm // 2, cm, 2, 1, 0, act=nn.ReLU())
        self.stem3 = Conv(cm * 2, cm, 3, 2, act=nn.ReLU())
        self.stem4 = Conv(cm, c2, 1, 1, act=nn.ReLU())
        self.pool = nn.MaxPool2d(kernel_size=2, stride=1, padding=0, ceil_mode=True)

    def forward(self, x):
        """Forward pass of a PPHGNetV2 backbone layer."""
        x = self.stem1(x)
        x = F.pad(x, [0, 1, 0, 1])
        x2 = self.stem2a(x)
        x2 = F.pad(x2, [0, 1, 0, 1])
        x2 = self.stem2b(x2)
        x1 = self.pool(x)
        x = torch.cat([x1, x2], dim=1)
        x = self.stem3(x)
        x = self.stem4(x)
        return x

__init__(c1, cm, c2)

Initialisiere die SPP-Schicht mit Eingangs-/AusgangskanĂ€len und festgelegten KernelgrĂ¶ĂŸen fĂŒr das maximale Pooling.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, cm, c2):
    """Initialize the SPP layer with input/output channels and specified kernel sizes for max pooling."""
    super().__init__()
    self.stem1 = Conv(c1, cm, 3, 2, act=nn.ReLU())
    self.stem2a = Conv(cm, cm // 2, 2, 1, 0, act=nn.ReLU())
    self.stem2b = Conv(cm // 2, cm, 2, 1, 0, act=nn.ReLU())
    self.stem3 = Conv(cm * 2, cm, 3, 2, act=nn.ReLU())
    self.stem4 = Conv(cm, c2, 1, 1, act=nn.ReLU())
    self.pool = nn.MaxPool2d(kernel_size=2, stride=1, padding=0, ceil_mode=True)

forward(x)

VorwÀrtspass einer PPHGNetV2-Backbone-Schicht.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass of a PPHGNetV2 backbone layer."""
    x = self.stem1(x)
    x = F.pad(x, [0, 1, 0, 1])
    x2 = self.stem2a(x)
    x2 = F.pad(x2, [0, 1, 0, 1])
    x2 = self.stem2b(x2)
    x1 = self.pool(x)
    x = torch.cat([x1, x2], dim=1)
    x = self.stem3(x)
    x = self.stem4(x)
    return x



ultralytics.nn.modules.block.HGBlock

Basen: Module

HG_Block von PPHGNetV2 mit 2 Convolutions und LightConv.

https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py

Quellcode in ultralytics/nn/modules/block.py
class HGBlock(nn.Module):
    """
    HG_Block of PPHGNetV2 with 2 convolutions and LightConv.

    https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py
    """

    def __init__(self, c1, cm, c2, k=3, n=6, lightconv=False, shortcut=False, act=nn.ReLU()):
        """Initializes a CSP Bottleneck with 1 convolution using specified input and output channels."""
        super().__init__()
        block = LightConv if lightconv else Conv
        self.m = nn.ModuleList(block(c1 if i == 0 else cm, cm, k=k, act=act) for i in range(n))
        self.sc = Conv(c1 + n * cm, c2 // 2, 1, 1, act=act)  # squeeze conv
        self.ec = Conv(c2 // 2, c2, 1, 1, act=act)  # excitation conv
        self.add = shortcut and c1 == c2

    def forward(self, x):
        """Forward pass of a PPHGNetV2 backbone layer."""
        y = [x]
        y.extend(m(y[-1]) for m in self.m)
        y = self.ec(self.sc(torch.cat(y, 1)))
        return y + x if self.add else y

__init__(c1, cm, c2, k=3, n=6, lightconv=False, shortcut=False, act=nn.ReLU())

Initialisiert einen CSP Bottleneck mit 1 Faltung unter Verwendung der angegebenen Eingangs- und AusgangskanÀle.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, cm, c2, k=3, n=6, lightconv=False, shortcut=False, act=nn.ReLU()):
    """Initializes a CSP Bottleneck with 1 convolution using specified input and output channels."""
    super().__init__()
    block = LightConv if lightconv else Conv
    self.m = nn.ModuleList(block(c1 if i == 0 else cm, cm, k=k, act=act) for i in range(n))
    self.sc = Conv(c1 + n * cm, c2 // 2, 1, 1, act=act)  # squeeze conv
    self.ec = Conv(c2 // 2, c2, 1, 1, act=act)  # excitation conv
    self.add = shortcut and c1 == c2

forward(x)

VorwÀrtspass einer PPHGNetV2-Backbone-Schicht.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass of a PPHGNetV2 backbone layer."""
    y = [x]
    y.extend(m(y[-1]) for m in self.m)
    y = self.ec(self.sc(torch.cat(y, 1)))
    return y + x if self.add else y



ultralytics.nn.modules.block.SPP

Basen: Module

Spatial Pyramid Pooling (SPP) Ebene https://arxiv.org/abs/1406.4729.

Quellcode in ultralytics/nn/modules/block.py
class SPP(nn.Module):
    """Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729."""

    def __init__(self, c1, c2, k=(5, 9, 13)):
        """Initialize the SPP layer with input/output channels and pooling kernel sizes."""
        super().__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):
        """Forward pass of the SPP layer, performing spatial pyramid pooling."""
        x = self.cv1(x)
        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))

__init__(c1, c2, k=(5, 9, 13))

Initialisiere die SPP-Schicht mit Eingangs-/AusgangskanĂ€len und Pooling-KernelgrĂ¶ĂŸen.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, k=(5, 9, 13)):
    """Initialize the SPP layer with input/output channels and pooling kernel sizes."""
    super().__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])

forward(x)

VorwĂ€rtsdurchlauf der SPP-Schicht, die das rĂ€umliche Pyramiden-Pooling durchfĂŒhrt.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass of the SPP layer, performing spatial pyramid pooling."""
    x = self.cv1(x)
    return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))



ultralytics.nn.modules.block.SPPF

Basen: Module

Spatial Pyramid Pooling - Fast (SPPF) Layer fĂŒr YOLOv5 von Glenn Jocher.

Quellcode in ultralytics/nn/modules/block.py
class SPPF(nn.Module):
    """Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher."""

    def __init__(self, c1, c2, k=5):
        """
        Initializes the SPPF layer with given input/output channels and kernel size.

        This module is equivalent to SPP(k=(5, 9, 13)).
        """
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * 4, c2, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)

    def forward(self, x):
        """Forward pass through Ghost Convolution block."""
        x = self.cv1(x)
        y1 = self.m(x)
        y2 = self.m(y1)
        return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))

__init__(c1, c2, k=5)

Initialisiert die SPPF-Schicht mit den angegebenen Eingangs-/AusgangskanĂ€len und der KernelgrĂ¶ĂŸe.

Dieses Modul ist gleichwertig mit SPP(k=(5, 9, 13)).

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, k=5):
    """
    Initializes the SPPF layer with given input/output channels and kernel size.

    This module is equivalent to SPP(k=(5, 9, 13)).
    """
    super().__init__()
    c_ = c1 // 2  # hidden channels
    self.cv1 = Conv(c1, c_, 1, 1)
    self.cv2 = Conv(c_ * 4, c2, 1, 1)
    self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)

forward(x)

VorwÀrtsdurchlauf durch den Ghost Convolution Block.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass through Ghost Convolution block."""
    x = self.cv1(x)
    y1 = self.m(x)
    y2 = self.m(y1)
    return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))



ultralytics.nn.modules.block.C1

Basen: Module

CSP Bottleneck mit 1 Faltung.

Quellcode in ultralytics/nn/modules/block.py
class C1(nn.Module):
    """CSP Bottleneck with 1 convolution."""

    def __init__(self, c1, c2, n=1):
        """Initializes the CSP Bottleneck with configurations for 1 convolution with arguments ch_in, ch_out, number."""
        super().__init__()
        self.cv1 = Conv(c1, c2, 1, 1)
        self.m = nn.Sequential(*(Conv(c2, c2, 3) for _ in range(n)))

    def forward(self, x):
        """Applies cross-convolutions to input in the C3 module."""
        y = self.cv1(x)
        return self.m(y) + y

__init__(c1, c2, n=1)

Initialisiert den CSP Bottleneck mit Konfigurationen fĂŒr 1 Faltung mit den Argumenten ch_in, ch_out, number.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1):
    """Initializes the CSP Bottleneck with configurations for 1 convolution with arguments ch_in, ch_out, number."""
    super().__init__()
    self.cv1 = Conv(c1, c2, 1, 1)
    self.m = nn.Sequential(*(Conv(c2, c2, 3) for _ in range(n)))

forward(x)

Wendet Querverbiegungen auf die Eingaben im C3-Modul an.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Applies cross-convolutions to input in the C3 module."""
    y = self.cv1(x)
    return self.m(y) + y



ultralytics.nn.modules.block.C2

Basen: Module

CSP Bottleneck mit 2 Faltungen.

Quellcode in ultralytics/nn/modules/block.py
class C2(nn.Module):
    """CSP Bottleneck with 2 convolutions."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initializes the CSP Bottleneck with 2 convolutions module with arguments ch_in, ch_out, number, shortcut,
        groups, expansion.
        """
        super().__init__()
        self.c = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
        self.cv2 = Conv(2 * self.c, c2, 1)  # optional act=FReLU(c2)
        # self.attention = ChannelAttention(2 * self.c)  # or SpatialAttention()
        self.m = nn.Sequential(*(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n)))

    def forward(self, x):
        """Forward pass through the CSP bottleneck with 2 convolutions."""
        a, b = self.cv1(x).chunk(2, 1)
        return self.cv2(torch.cat((self.m(a), b), 1))

__init__(c1, c2, n=1, shortcut=True, g=1, e=0.5)

Initialisiert das Modul CSP Bottleneck with 2 convolutions mit den Argumenten ch_in, ch_out, number, shortcut, Gruppen, Erweiterung.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initializes the CSP Bottleneck with 2 convolutions module with arguments ch_in, ch_out, number, shortcut,
    groups, expansion.
    """
    super().__init__()
    self.c = int(c2 * e)  # hidden channels
    self.cv1 = Conv(c1, 2 * self.c, 1, 1)
    self.cv2 = Conv(2 * self.c, c2, 1)  # optional act=FReLU(c2)
    # self.attention = ChannelAttention(2 * self.c)  # or SpatialAttention()
    self.m = nn.Sequential(*(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n)))

forward(x)

VorwÀrtsdurchlauf durch den CSP-Engpass mit 2 Faltungen.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass through the CSP bottleneck with 2 convolutions."""
    a, b = self.cv1(x).chunk(2, 1)
    return self.cv2(torch.cat((self.m(a), b), 1))



ultralytics.nn.modules.block.C2f

Basen: Module

Schnellere Implementierung von CSP Bottleneck mit 2 Faltungen.

Quellcode in ultralytics/nn/modules/block.py
class C2f(nn.Module):
    """Faster Implementation of CSP Bottleneck with 2 convolutions."""

    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
        """Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,
        expansion.
        """
        super().__init__()
        self.c = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
        self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))

    def forward(self, x):
        """Forward pass through C2f layer."""
        y = list(self.cv1(x).chunk(2, 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))

    def forward_split(self, x):
        """Forward pass using split() instead of chunk()."""
        y = list(self.cv1(x).split((self.c, self.c), 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))

__init__(c1, c2, n=1, shortcut=False, g=1, e=0.5)

Initialisiere die CSP-Engpassschicht mit zwei Faltungen mit den Argumenten ch_in, ch_out, number, shortcut, groups, Erweiterung.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
    """Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,
    expansion.
    """
    super().__init__()
    self.c = int(c2 * e)  # hidden channels
    self.cv1 = Conv(c1, 2 * self.c, 1, 1)
    self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)
    self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))

forward(x)

VorwÀrtsgang durch die C2f-Schicht.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass through C2f layer."""
    y = list(self.cv1(x).chunk(2, 1))
    y.extend(m(y[-1]) for m in self.m)
    return self.cv2(torch.cat(y, 1))

forward_split(x)

Weiterleitung mit split() anstelle von chunk().

Quellcode in ultralytics/nn/modules/block.py
def forward_split(self, x):
    """Forward pass using split() instead of chunk()."""
    y = list(self.cv1(x).split((self.c, self.c), 1))
    y.extend(m(y[-1]) for m in self.m)
    return self.cv2(torch.cat(y, 1))



ultralytics.nn.modules.block.C3

Basen: Module

CSP Bottleneck mit 3 Faltungen.

Quellcode in ultralytics/nn/modules/block.py
class C3(nn.Module):
    """CSP Bottleneck with 3 convolutions."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initialize the CSP Bottleneck with given channels, number, shortcut, groups, and expansion values."""
        super().__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)  # optional act=FReLU(c2)
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3, 3)), e=1.0) for _ in range(n)))

    def forward(self, x):
        """Forward pass through the CSP bottleneck with 2 convolutions."""
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))

__init__(c1, c2, n=1, shortcut=True, g=1, e=0.5)

Initialisiere den CSP Bottleneck mit den angegebenen Werten fĂŒr KanĂ€le, Anzahl, AbkĂŒrzung, Gruppen und Erweiterung.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initialize the CSP Bottleneck with given channels, number, shortcut, groups, and expansion values."""
    super().__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)  # optional act=FReLU(c2)
    self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3, 3)), e=1.0) for _ in range(n)))

forward(x)

VorwÀrtsdurchlauf durch den CSP-Engpass mit 2 Faltungen.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass through the CSP bottleneck with 2 convolutions."""
    return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))



ultralytics.nn.modules.block.C3x

Basen: C3

C3-Modul mit Querverbiegungen.

Quellcode in ultralytics/nn/modules/block.py
class C3x(C3):
    """C3 module with cross-convolutions."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initialize C3TR instance and set default parameters."""
        super().__init__(c1, c2, n, shortcut, g, e)
        self.c_ = int(c2 * e)
        self.m = nn.Sequential(*(Bottleneck(self.c_, self.c_, shortcut, g, k=((1, 3), (3, 1)), e=1) for _ in range(n)))

__init__(c1, c2, n=1, shortcut=True, g=1, e=0.5)

Initialisiere die C3TR-Instanz und setze die Standardparameter.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initialize C3TR instance and set default parameters."""
    super().__init__(c1, c2, n, shortcut, g, e)
    self.c_ = int(c2 * e)
    self.m = nn.Sequential(*(Bottleneck(self.c_, self.c_, shortcut, g, k=((1, 3), (3, 1)), e=1) for _ in range(n)))



ultralytics.nn.modules.block.RepC3

Basen: Module

Vertreter C3.

Quellcode in ultralytics/nn/modules/block.py
class RepC3(nn.Module):
    """Rep C3."""

    def __init__(self, c1, c2, n=3, e=1.0):
        """Initialize CSP Bottleneck with a single convolution using input channels, output channels, and number."""
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c2, 1, 1)
        self.cv2 = Conv(c1, c2, 1, 1)
        self.m = nn.Sequential(*[RepConv(c_, c_) for _ in range(n)])
        self.cv3 = Conv(c_, c2, 1, 1) if c_ != c2 else nn.Identity()

    def forward(self, x):
        """Forward pass of RT-DETR neck layer."""
        return self.cv3(self.m(self.cv1(x)) + self.cv2(x))

__init__(c1, c2, n=3, e=1.0)

Initialisiere CSP Bottleneck mit einer einzelnen Faltung unter Verwendung von EingangskanÀlen, AusgangskanÀlen und Anzahl.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=3, e=1.0):
    """Initialize CSP Bottleneck with a single convolution using input channels, output channels, and number."""
    super().__init__()
    c_ = int(c2 * e)  # hidden channels
    self.cv1 = Conv(c1, c2, 1, 1)
    self.cv2 = Conv(c1, c2, 1, 1)
    self.m = nn.Sequential(*[RepConv(c_, c_) for _ in range(n)])
    self.cv3 = Conv(c_, c2, 1, 1) if c_ != c2 else nn.Identity()

forward(x)

VorwÀrtspass der RT-DETR Halsschicht.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass of RT-DETR neck layer."""
    return self.cv3(self.m(self.cv1(x)) + self.cv2(x))



ultralytics.nn.modules.block.C3TR

Basen: C3

C3-Modul mit TransformerBlock().

Quellcode in ultralytics/nn/modules/block.py
class C3TR(C3):
    """C3 module with TransformerBlock()."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initialize C3Ghost module with GhostBottleneck()."""
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)
        self.m = TransformerBlock(c_, c_, 4, n)

__init__(c1, c2, n=1, shortcut=True, g=1, e=0.5)

Initialisiere das C3Ghost-Modul mit GhostBottleneck().

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initialize C3Ghost module with GhostBottleneck()."""
    super().__init__(c1, c2, n, shortcut, g, e)
    c_ = int(c2 * e)
    self.m = TransformerBlock(c_, c_, 4, n)



ultralytics.nn.modules.block.C3Ghost

Basen: C3

C3-Modul mit GhostBottleneck().

Quellcode in ultralytics/nn/modules/block.py
class C3Ghost(C3):
    """C3 module with GhostBottleneck()."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initialize 'SPP' module with various pooling sizes for spatial pyramid pooling."""
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))

__init__(c1, c2, n=1, shortcut=True, g=1, e=0.5)

Initialisiere das Modul "SPP" mit verschiedenen Pooling-GrĂ¶ĂŸen fĂŒr das rĂ€umliche Pyramiden-Pooling.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initialize 'SPP' module with various pooling sizes for spatial pyramid pooling."""
    super().__init__(c1, c2, n, shortcut, g, e)
    c_ = int(c2 * e)  # hidden channels
    self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))



ultralytics.nn.modules.block.GhostBottleneck

Basen: Module

Ghost Bottleneck https://github.com/huawei-noah/ghostnet.

Quellcode in ultralytics/nn/modules/block.py
class GhostBottleneck(nn.Module):
    """Ghost Bottleneck https://github.com/huawei-noah/ghostnet."""

    def __init__(self, c1, c2, k=3, s=1):
        """Initializes GhostBottleneck module with arguments ch_in, ch_out, kernel, stride."""
        super().__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):
        """Applies skip connection and concatenation to input tensor."""
        return self.conv(x) + self.shortcut(x)

__init__(c1, c2, k=3, s=1)

Initialisiert das GhostBottleneck-Modul mit den Argumenten ch_in, ch_out, kernel, stride.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, k=3, s=1):
    """Initializes GhostBottleneck module with arguments ch_in, ch_out, kernel, stride."""
    super().__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()
    )

forward(x)

Wendet eine Skip-Verbindung und Verkettung auf die Eingabe tensor an.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Applies skip connection and concatenation to input tensor."""
    return self.conv(x) + self.shortcut(x)



ultralytics.nn.modules.block.Bottleneck

Basen: Module

Standard-Engpass.

Quellcode in ultralytics/nn/modules/block.py
class Bottleneck(nn.Module):
    """Standard bottleneck."""

    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
        """Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, and
        expansion.
        """
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, k[0], 1)
        self.cv2 = Conv(c_, c2, k[1], 1, g=g)
        self.add = shortcut and c1 == c2

    def forward(self, x):
        """'forward()' applies the YOLO FPN to input data."""
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

__init__(c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5)

Initialisiert ein Engpassmodul mit den angegebenen Ein-/AusgangskanÀlen, der Shortcut-Option, der Gruppe, den Kerneln und der Erweiterung.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
    """Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, and
    expansion.
    """
    super().__init__()
    c_ = int(c2 * e)  # hidden channels
    self.cv1 = Conv(c1, c_, k[0], 1)
    self.cv2 = Conv(c_, c2, k[1], 1, g=g)
    self.add = shortcut and c1 == c2

forward(x)

'forward()' wendet die YOLO FPN auf die Eingabedaten an.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """'forward()' applies the YOLO FPN to input data."""
    return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))



ultralytics.nn.modules.block.BottleneckCSP

Basen: Module

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

Quellcode in ultralytics/nn/modules/block.py
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):
        """Initializes the CSP Bottleneck given arguments for ch_in, ch_out, number, shortcut, groups, expansion."""
        super().__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.SiLU()
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        """Applies a CSP bottleneck with 3 convolutions."""
        y1 = self.cv3(self.m(self.cv1(x)))
        y2 = self.cv2(x)
        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))

__init__(c1, c2, n=1, shortcut=True, g=1, e=0.5)

Initialisiert den CSP Bottleneck mit Argumenten fĂŒr ch_in, ch_out, number, shortcut, groups, expansion.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initializes the CSP Bottleneck given arguments for ch_in, ch_out, number, shortcut, groups, expansion."""
    super().__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.SiLU()
    self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

forward(x)

Wendet einen CSP-Engpass mit 3 Faltungen an.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Applies a CSP bottleneck with 3 convolutions."""
    y1 = self.cv3(self.m(self.cv1(x)))
    y2 = self.cv2(x)
    return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))



ultralytics.nn.modules.block.ResNetBlock

Basen: Module

ResNet-Block mit Standard-Faltungsschichten.

Quellcode in ultralytics/nn/modules/block.py
class ResNetBlock(nn.Module):
    """ResNet block with standard convolution layers."""

    def __init__(self, c1, c2, s=1, e=4):
        """Initialize convolution with given parameters."""
        super().__init__()
        c3 = e * c2
        self.cv1 = Conv(c1, c2, k=1, s=1, act=True)
        self.cv2 = Conv(c2, c2, k=3, s=s, p=1, act=True)
        self.cv3 = Conv(c2, c3, k=1, act=False)
        self.shortcut = nn.Sequential(Conv(c1, c3, k=1, s=s, act=False)) if s != 1 or c1 != c3 else nn.Identity()

    def forward(self, x):
        """Forward pass through the ResNet block."""
        return F.relu(self.cv3(self.cv2(self.cv1(x))) + self.shortcut(x))

__init__(c1, c2, s=1, e=4)

Initialisiere die Faltung mit den angegebenen Parametern.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, s=1, e=4):
    """Initialize convolution with given parameters."""
    super().__init__()
    c3 = e * c2
    self.cv1 = Conv(c1, c2, k=1, s=1, act=True)
    self.cv2 = Conv(c2, c2, k=3, s=s, p=1, act=True)
    self.cv3 = Conv(c2, c3, k=1, act=False)
    self.shortcut = nn.Sequential(Conv(c1, c3, k=1, s=s, act=False)) if s != 1 or c1 != c3 else nn.Identity()

forward(x)

VorwÀrtsdurchlauf durch den ResNet-Block.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass through the ResNet block."""
    return F.relu(self.cv3(self.cv2(self.cv1(x))) + self.shortcut(x))



ultralytics.nn.modules.block.ResNetLayer

Basen: Module

ResNet-Schicht mit mehreren ResNet-Blöcken.

Quellcode in ultralytics/nn/modules/block.py
class ResNetLayer(nn.Module):
    """ResNet layer with multiple ResNet blocks."""

    def __init__(self, c1, c2, s=1, is_first=False, n=1, e=4):
        """Initializes the ResNetLayer given arguments."""
        super().__init__()
        self.is_first = is_first

        if self.is_first:
            self.layer = nn.Sequential(
                Conv(c1, c2, k=7, s=2, p=3, act=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
            )
        else:
            blocks = [ResNetBlock(c1, c2, s, e=e)]
            blocks.extend([ResNetBlock(e * c2, c2, 1, e=e) for _ in range(n - 1)])
            self.layer = nn.Sequential(*blocks)

    def forward(self, x):
        """Forward pass through the ResNet layer."""
        return self.layer(x)

__init__(c1, c2, s=1, is_first=False, n=1, e=4)

Initialisiert den ResNetLayer mit den angegebenen Argumenten.

Quellcode in ultralytics/nn/modules/block.py
def __init__(self, c1, c2, s=1, is_first=False, n=1, e=4):
    """Initializes the ResNetLayer given arguments."""
    super().__init__()
    self.is_first = is_first

    if self.is_first:
        self.layer = nn.Sequential(
            Conv(c1, c2, k=7, s=2, p=3, act=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
    else:
        blocks = [ResNetBlock(c1, c2, s, e=e)]
        blocks.extend([ResNetBlock(e * c2, c2, 1, e=e) for _ in range(n - 1)])
        self.layer = nn.Sequential(*blocks)

forward(x)

Weiterleitung durch die ResNet-Schicht.

Quellcode in ultralytics/nn/modules/block.py
def forward(self, x):
    """Forward pass through the ResNet layer."""
    return self.layer(x)





Erstellt am 2023-11-12, Aktualisiert am 2023-12-08
Autoren: glenn-jocher (4)