| 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) |
