lam/models/rendering/utils/utils.py

import torch
import torch.nn as nn
from torch.autograd import Function
from torch.cuda.amp import custom_bwd, custom_fwd

from lam.models.rendering.utils.typing import *

def get_activation(name):
    if name is None:
        return lambda x: x
    name = name.lower()
    if name == "none":
        return lambda x: x
    elif name == "lin2srgb":
        return lambda x: torch.where(
            x > 0.0031308,
            torch.pow(torch.clamp(x, min=0.0031308), 1.0 / 2.4) * 1.055 - 0.055,
            12.92 * x,
        ).clamp(0.0, 1.0)
    elif name == "exp":
        return lambda x: torch.exp(x)
    elif name == "shifted_exp":
        return lambda x: torch.exp(x - 1.0)
    elif name == "trunc_exp":
        return trunc_exp
    elif name == "shifted_trunc_exp":
        return lambda x: trunc_exp(x - 1.0)
    elif name == "sigmoid":
        return lambda x: torch.sigmoid(x)
    elif name == "tanh":
        return lambda x: torch.tanh(x)
    elif name == "shifted_softplus":
        return lambda x: F.softplus(x - 1.0)
    elif name == "scale_-11_01":
        return lambda x: x * 0.5 + 0.5
    else:
        try:
            return getattr(F, name)
        except AttributeError:
            raise ValueError(f"Unknown activation function: {name}")
        
class MLP(nn.Module):
    def __init__(
        self,
        dim_in: int,
        dim_out: int,
        n_neurons: int,
        n_hidden_layers: int,
        activation: str = "relu",
        output_activation: Optional[str] = None,
        bias: bool = True,
    ):
        super().__init__()
        layers = [
            self.make_linear(
                dim_in, n_neurons, is_first=True, is_last=False, bias=bias
            ),
            self.make_activation(activation),
        ]
        for i in range(n_hidden_layers - 1):
            layers += [
                self.make_linear(
                    n_neurons, n_neurons, is_first=False, is_last=False, bias=bias
                ),
                self.make_activation(activation),
            ]
        layers += [
            self.make_linear(
                n_neurons, dim_out, is_first=False, is_last=True, bias=bias
            )
        ]
        self.layers = nn.Sequential(*layers)
        self.output_activation = get_activation(output_activation)

    def forward(self, x):
        x = self.layers(x)
        x = self.output_activation(x)
        return x

    def make_linear(self, dim_in, dim_out, is_first, is_last, bias=True):
        layer = nn.Linear(dim_in, dim_out, bias=bias)
        return layer

    def make_activation(self, activation):
        if activation == "relu":
            return nn.ReLU(inplace=True)
        elif activation == "silu":
            return nn.SiLU(inplace=True)
        else:
            raise NotImplementedError


class _TruncExp(Function):  # pylint: disable=abstract-method
    # Implementation from torch-ngp:
    # https://github.com/ashawkey/torch-ngp/blob/93b08a0d4ec1cc6e69d85df7f0acdfb99603b628/activation.py
    @staticmethod
    @custom_fwd(cast_inputs=torch.float32)
    def forward(ctx, x):  # pylint: disable=arguments-differ
        ctx.save_for_backward(x)
        return torch.exp(x)

    @staticmethod
    @custom_bwd
    def backward(ctx, g):  # pylint: disable=arguments-differ
        x = ctx.saved_tensors[0]
        return g * torch.exp(torch.clamp(x, max=15))


trunc_exp = _TruncExp.apply