unidepth/layers/mlp.py

import torch
import torch.nn as nn

from unidepth.utils.misc import default

from .activation import SwiGLU


class MLP(nn.Module):
    def __init__(
        self,
        input_dim: int,
        expansion: int = 4,
        dropout: float = 0.0,
        gated: bool = False,
        output_dim: int | None = None,
    ):
        super().__init__()
        if gated:
            expansion = int(expansion * 2 / 3)
        hidden_dim = int(input_dim * expansion)
        output_dim = default(output_dim, input_dim)
        self.norm = nn.LayerNorm(input_dim)
        self.proj1 = nn.Linear(input_dim, hidden_dim)
        self.proj2 = nn.Linear(hidden_dim, output_dim)
        self.act = nn.GELU() if not gated else SwiGLU()
        self.dropout = nn.Dropout(dropout) if dropout > 0.0 else nn.Identity()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.norm(x)
        x = self.proj1(x)
        x = self.act(x)
        x = self.proj2(x)
        x = self.dropout(x)
        return x