Class FeedForward

core/FlowFormer/common.py:321–333

    return torch.cat(sampled_latents_pyramid, dim=-1)

class FeedForward(nn.Module):
    def __init__(self, dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(dim, dim),
            nn.Dropout(dropout)
        )
    def forward(self, x):
        x = self.net(x)
        return x

class MLP(nn.Module):
    def __init__(self, in_dim=22, out_dim=1, innter_dim=96, depth=5):