Class CXBlock

sam2_train/modeling/memory_encoder.py:62–117

r"""ConvNeXt Block. There are two equivalent implementations: (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back We use (2) as we find it

# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt)
class CXBlock(nn.Module):
    r"""ConvNeXt Block. There are two equivalent implementations:
    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
    We use (2) as we find it slightly faster in PyTorch

    Args:
        dim (int): Number of input channels.
        drop_path (float): Stochastic depth rate. Default: 0.0
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
    """

    def __init__(
        self,
        dim,
        kernel_size=7,
        padding=3,
        drop_path=0.0,
        layer_scale_init_value=1e-6,
        use_dwconv=True,
    ):
        super().__init__()
        self.dwconv = nn.Conv2d(
            dim,
            dim,
            kernel_size=kernel_size,
            padding=padding,
            groups=dim if use_dwconv else 1,
        )  # depthwise conv
        self.norm = LayerNorm2d(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(
            dim, 4 * dim
        )  # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.gamma = (
            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
            if layer_scale_init_value > 0
            else None
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()

    def forward(self, x):
        input = x
        x = self.dwconv(x)
        x = self.norm(x)
        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * x
        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)

        x = input + self.drop_path(x)
        return x