engine/deim/deim_utils.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init

from .utils import get_activation, bias_init_with_prob


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.scale = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        output = output * self.scale
        return output

    def extra_repr(self) -> str:
        return f'dim={self.dim}, eps={self.eps}'

# default 3-layer MLP
class MLP(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, num_layers=3, act='relu'):
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
        self.act = get_activation(act)

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)
        return x

# Taken from: https://github.com/facebookresearch/dinov2/blob/main/dinov2/layers/swiglu_ffn.py#L14-L34
class SwiGLUFFN(nn.Module):
    def __init__(
        self,
        in_features: int,
        hidden_features: int,
        out_features: int,
        bias: bool = True,
    ) -> None:
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
        self._reset_parameters()

    def _reset_parameters(self):
        init.xavier_uniform_(self.w12.weight)
        init.constant_(self.w12.bias, 0)
        init.xavier_uniform_(self.w3.weight)
        init.constant_(self.w3.bias, 0)

    def forward(self, x):
        x12 = self.w12(x)
        x1, x2 = x12.chunk(2, dim=-1)
        hidden = F.silu(x1) * x2
        return self.w3(hidden)


class Gate(nn.Module):
    def __init__(self, d_model, use_rmsnorm=False):
        super(Gate, self).__init__()
        self.gate = nn.Linear(2 * d_model, 2 * d_model)
        bias = bias_init_with_prob(0.5)
        init.constant_(self.gate.bias, bias)
        init.constant_(self.gate.weight, 0)
        self.norm = RMSNorm(d_model) if use_rmsnorm else nn.LayerNorm(d_model)

    def forward(self, x1, x2):
        gate_input = torch.cat([x1, x2], dim=-1)
        gates = torch.sigmoid(self.gate(gate_input))
        gate1, gate2 = gates.chunk(2, dim=-1)
        return self.norm(gate1 * x1 + gate2 * x2)