src/model.py

import torch as t, torch.nn as nn, torch.nn.functional as F

def cv(n_i, n_o, **kw): return nn.Conv2d(n_i, n_o, 3, padding=1, **kw)

class C(nn.Module):
    def forward(self, x): return t.tanh(x / 3) * 3

class B(nn.Module):
    def __init__(s, n_i, n_o):
        super().__init__()
        s.c = nn.Sequential(cv(n_i, n_o), nn.ReLU(), cv(n_o, n_o), nn.ReLU(), cv(n_o, n_o))
        s.s = nn.Conv2d(n_i, n_o, 1, bias=False) if n_i != n_o else nn.Identity()
        s.f = nn.ReLU()
    def forward(s, x): return s.f(s.c(x) + s.s(x))

def E(lc=4):
    return nn.Sequential(
        cv(3, 64), B(64, 64), cv(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64),
        cv(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64),
        cv(64, 64, stride=2, bias=False), B(64, 64), B(64, 64), B(64, 64),
        cv(64, lc),
    )

def D(lc=16):
    return nn.Sequential(
        C(), cv(lc, 48), nn.ReLU(), B(48, 48), B(48, 48), nn.Upsample(scale_factor=2),
        cv(48, 48, bias=False), B(48, 48), B(48, 48), nn.Upsample(scale_factor=2),
        cv(48, 48, bias=False), B(48, 48), nn.Upsample(scale_factor=2),
        cv(48, 48, bias=False), B(48, 48), cv(48, 3),
    )

class M(nn.Module):
    lm, ls = 3, 0.5
    def __init__(s, ep="encoder.pth", dp="decoder.pth", lc=None):
        super().__init__()
        if lc is None: lc = s.glc(str(ep))
        s.e, s.d = E(lc), D(lc)

        def f(sd, mod, pfx):
            f_sd = {k.strip(pfx): v for k, v in sd.items() if k.strip(pfx) in mod.state_dict() and v.size() == mod.state_dict()[k.strip(pfx)].size()}
            print(f"num keys: {len(f_sd)} of {len(mod.state_dict())}")
            mod.load_state_dict(f_sd, strict=False)

        if ep: f(t.load(ep, map_location="cpu", weights_only=True), s.e, "encoder.")
        if dp: f(t.load(dp, map_location="cpu", weights_only=True), s.d, "decoder.")

        s.e.requires_grad_(False)
        s.d.requires_grad_(False)

    def glc(s, ep): return 16 if "taef1" in ep or "taesd3" in ep else 4

    @staticmethod
    def sl(x): return x.div(2 * M.lm).add(M.ls).clamp(0, 1)

    @staticmethod
    def ul(x): return x.sub(M.ls).mul(2 * M.lm)

    def forward(s, x, rl=False):
        l, o = s.e(x), s.d(s.e(x))
        return (o.clamp(0, 1), l) if rl else o.clamp(0, 1)