| """ |
| Adapted from: https://github.com/openai/glide-text2im/blob/69b530740eb6cef69442d6180579ef5ba9ef063e/glide_text2im/download.py |
| """ |
|
|
| import os |
| from functools import lru_cache |
| from typing import Dict, Optional |
|
|
| import requests |
| import torch |
| from filelock import FileLock |
| from tqdm.auto import tqdm |
|
|
| MODEL_PATHS = { |
| "base40M-imagevec": "https://openaipublic.azureedge.net/main/point-e/base_40m_imagevec.pt", |
| "base40M-textvec": "https://openaipublic.azureedge.net/main/point-e/base_40m_textvec.pt", |
| "base40M-uncond": "https://openaipublic.azureedge.net/main/point-e/base_40m_uncond.pt", |
| "base40M": "https://openaipublic.azureedge.net/main/point-e/base_40m.pt", |
| "base300M": "https://openaipublic.azureedge.net/main/point-e/base_300m.pt", |
| "base1B": "https://openaipublic.azureedge.net/main/point-e/base_1b.pt", |
| "upsample": "https://openaipublic.azureedge.net/main/point-e/upsample_40m.pt", |
| "sdf": "https://openaipublic.azureedge.net/main/point-e/sdf.pt", |
| "pointnet": "https://openaipublic.azureedge.net/main/point-e/pointnet.pt", |
| } |
|
|
|
|
| @lru_cache() |
| def default_cache_dir() -> str: |
| return os.path.join(os.path.abspath(os.getcwd()), "point_e_model_cache") |
|
|
|
|
| def fetch_file_cached( |
| url: str, progress: bool = True, cache_dir: Optional[str] = None, chunk_size: int = 4096 |
| ) -> str: |
| """ |
| Download the file at the given URL into a local file and return the path. |
| If cache_dir is specified, it will be used to download the files. |
| Otherwise, default_cache_dir() is used. |
| """ |
| if cache_dir is None: |
| cache_dir = default_cache_dir() |
| os.makedirs(cache_dir, exist_ok=True) |
| local_path = os.path.join(cache_dir, url.split("/")[-1]) |
| if os.path.exists(local_path): |
| return local_path |
|
|
| response = requests.get(url, stream=True) |
| size = int(response.headers.get("content-length", "0")) |
| with FileLock(local_path + ".lock"): |
| if progress: |
| pbar = tqdm(total=size, unit="iB", unit_scale=True) |
| tmp_path = local_path + ".tmp" |
| with open(tmp_path, "wb") as f: |
| for chunk in response.iter_content(chunk_size): |
| if progress: |
| pbar.update(len(chunk)) |
| f.write(chunk) |
| os.rename(tmp_path, local_path) |
| if progress: |
| pbar.close() |
| return local_path |
|
|
|
|
| def load_checkpoint( |
| checkpoint_name: str, |
| device: torch.device, |
| progress: bool = True, |
| cache_dir: Optional[str] = None, |
| chunk_size: int = 4096, |
| ) -> Dict[str, torch.Tensor]: |
| if checkpoint_name not in MODEL_PATHS: |
| raise ValueError( |
| f"Unknown checkpoint name {checkpoint_name}. Known names are: {MODEL_PATHS.keys()}." |
| ) |
| path = fetch_file_cached( |
| MODEL_PATHS[checkpoint_name], progress=progress, cache_dir=cache_dir, chunk_size=chunk_size |
| ) |
| return torch.load(path, map_location=device) |
|
|
