model.py

from __future__ import annotations

import pathlib
import pickle
import sys

import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

current_dir = pathlib.Path(__file__).parent
submodule_dir = current_dir / "projected_gan"
sys.path.insert(0, submodule_dir.as_posix())


class Model:

    MODEL_NAMES = [
        "art_painting",
        "church",
        "bedroom",
        "cityscapes",
        "clevr",
        "ffhq",
        "flowers",
        "landscape",
        "pokemon",
    ]

    def __init__(self):
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        self._download_all_models()
        self.model_name = self.MODEL_NAMES[3]
        self.model = self._load_model(self.model_name)

    def _load_model(self, model_name: str) -> nn.Module:
        path = hf_hub_download("public-data/projected_gan", f"models/{model_name}.pkl")
        with open(path, "rb") as f:
            model = pickle.load(f)["G_ema"]
        model.eval()
        model.to(self.device)
        return model

    def set_model(self, model_name: str) -> None:
        if model_name == self.model_name:
            return
        self.model_name = model_name
        self.model = self._load_model(model_name)

    def _download_all_models(self):
        for name in self.MODEL_NAMES:
            self._load_model(name)

    def generate_z(self, seed: int) -> torch.Tensor:
        seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
        z = np.random.RandomState(seed).randn(1, self.model.z_dim)
        return torch.from_numpy(z).float().to(self.device)

    def postprocess(self, tensor: torch.Tensor) -> np.ndarray:
        tensor = (tensor.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
        return tensor.cpu().numpy()

    @torch.inference_mode()
    def generate(self, z: torch.Tensor, label: torch.Tensor, truncation_psi: float) -> torch.Tensor:
        return self.model(z, label, truncation_psi=truncation_psi)

    def generate_image(self, seed: int, truncation_psi: float) -> np.ndarray:
        z = self.generate_z(seed)
        label = torch.zeros([1, self.model.c_dim], device=self.device)

        out = self.generate(z, label, truncation_psi)
        out = self.postprocess(out)
        return out[0]

    def set_model_and_generate_image(self, model_name: str, seed: int, truncation_psi: float) -> np.ndarray:
        self.set_model(model_name)
        return self.generate_image(seed, truncation_psi)