Reference for 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

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 __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 __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 __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 __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 __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 __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initializes a CSP Bottleneck with 2 convolutions and optional shortcut connection."""
    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 __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
    """Initializes a CSP bottleneck with 2 convolutions and n Bottleneck blocks for faster processing."""
    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 __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 __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)))

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 __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)

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)))

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 __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
    """Initializes a standard bottleneck module with optional shortcut connection and configurable parameters."""
    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 __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 __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 __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 __init__(self, c1, c2, nh=1, ec=128, gc=512, scale=False):
    """Initializes MaxSigmoidAttnBlock with specified arguments."""
    super().__init__()
    self.nh = nh
    self.hc = c2 // nh
    self.ec = Conv(c1, ec, k=1, act=False) if c1 != ec else None
    self.gl = nn.Linear(gc, ec)
    self.bias = nn.Parameter(torch.zeros(nh))
    self.proj_conv = Conv(c1, c2, k=3, s=1, act=False)
    self.scale = nn.Parameter(torch.ones(1, nh, 1, 1)) if scale else 1.0

def __init__(self, c1, c2, n=1, ec=128, nh=1, gc=512, shortcut=False, g=1, e=0.5):
    """Initializes C2f module with attention mechanism for enhanced feature extraction and processing."""
    super().__init__()
    self.c = int(c2 * e)  # hidden channels
    self.cv1 = Conv(c1, 2 * self.c, 1, 1)
    self.cv2 = Conv((3 + 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))
    self.attn = MaxSigmoidAttnBlock(self.c, self.c, gc=gc, ec=ec, nh=nh)

def __init__(self, ec=256, ch=(), ct=512, nh=8, k=3, scale=False):
    """Initializes ImagePoolingAttn with specified arguments."""
    super().__init__()

    nf = len(ch)
    self.query = nn.Sequential(nn.LayerNorm(ct), nn.Linear(ct, ec))
    self.key = nn.Sequential(nn.LayerNorm(ec), nn.Linear(ec, ec))
    self.value = nn.Sequential(nn.LayerNorm(ec), nn.Linear(ec, ec))
    self.proj = nn.Linear(ec, ct)
    self.scale = nn.Parameter(torch.tensor([0.0]), requires_grad=True) if scale else 1.0
    self.projections = nn.ModuleList([nn.Conv2d(in_channels, ec, kernel_size=1) for in_channels in ch])
    self.im_pools = nn.ModuleList([nn.AdaptiveMaxPool2d((k, k)) for _ in range(nf)])
    self.ec = ec
    self.nh = nh
    self.nf = nf
    self.hc = ec // nh
    self.k = k

def __init__(self):
    """Initializes ContrastiveHead with specified region-text similarity parameters."""
    super().__init__()
    # NOTE: use -10.0 to keep the init cls loss consistency with other losses
    self.bias = nn.Parameter(torch.tensor([-10.0]))
    self.logit_scale = nn.Parameter(torch.ones([]) * torch.tensor(1 / 0.07).log())

def __init__(self, embed_dims: int):
    """Initialize ContrastiveHead with region-text similarity parameters."""
    super().__init__()
    self.norm = nn.BatchNorm2d(embed_dims)
    # NOTE: use -10.0 to keep the init cls loss consistency with other losses
    self.bias = nn.Parameter(torch.tensor([-10.0]))
    # use -1.0 is more stable
    self.logit_scale = nn.Parameter(-1.0 * torch.ones([]))

def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
    """Initializes a RepBottleneck module with customizable in/out channels, shortcuts, groups and expansion."""
    super().__init__(c1, c2, shortcut, g, k, e)
    c_ = int(c2 * e)  # hidden channels
    self.cv1 = RepConv(c1, c_, k[0], 1)

def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
    """Initializes RepCSP layer with given channels, repetitions, shortcut, groups and expansion ratio."""
    super().__init__(c1, c2, n, shortcut, g, e)
    c_ = int(c2 * e)  # hidden channels
    self.m = nn.Sequential(*(RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

def __init__(self, c1, c2, c3, c4, n=1):
    """Initializes CSP-ELAN layer with specified channel sizes, repetitions, and convolutions."""
    super().__init__()
    self.c = c3 // 2
    self.cv1 = Conv(c1, c3, 1, 1)
    self.cv2 = nn.Sequential(RepCSP(c3 // 2, c4, n), Conv(c4, c4, 3, 1))
    self.cv3 = nn.Sequential(RepCSP(c4, c4, n), Conv(c4, c4, 3, 1))
    self.cv4 = Conv(c3 + (2 * c4), c2, 1, 1)