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| import importlib | |
| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| from inspect import isfunction | |
| from PIL import Image, ImageDraw, ImageFont | |
| import hashlib | |
| import requests | |
| import os | |
| URL_MAP = { | |
| 'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt', | |
| 'vggishish_mean_std_melspec_10s_22050hz': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/train_means_stds_melspec_10s_22050hz.txt', | |
| 'melception': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/melception-21-05-10T09-28-40.pt', | |
| } | |
| CKPT_MAP = { | |
| 'vggishish_lpaps': 'vggishish16.pt', | |
| 'vggishish_mean_std_melspec_10s_22050hz': 'train_means_stds_melspec_10s_22050hz.txt', | |
| 'melception': 'melception-21-05-10T09-28-40.pt', | |
| } | |
| MD5_MAP = { | |
| 'vggishish_lpaps': '197040c524a07ccacf7715d7080a80bd', | |
| 'vggishish_mean_std_melspec_10s_22050hz': 'f449c6fd0e248936c16f6d22492bb625', | |
| 'melception': 'a71a41041e945b457c7d3d814bbcf72d', | |
| } | |
| def download(url, local_path, chunk_size=1024): | |
| os.makedirs(os.path.split(local_path)[0], exist_ok=True) | |
| with requests.get(url, stream=True) as r: | |
| total_size = int(r.headers.get("content-length", 0)) | |
| with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: | |
| with open(local_path, "wb") as f: | |
| for data in r.iter_content(chunk_size=chunk_size): | |
| if data: | |
| f.write(data) | |
| pbar.update(chunk_size) | |
| def md5_hash(path): | |
| with open(path, "rb") as f: | |
| content = f.read() | |
| return hashlib.md5(content).hexdigest() | |
| def log_txt_as_img(wh, xc, size=10): | |
| # wh a tuple of (width, height) | |
| # xc a list of captions to plot | |
| b = len(xc) | |
| txts = list() | |
| for bi in range(b): | |
| txt = Image.new("RGB", wh, color="white") | |
| draw = ImageDraw.Draw(txt) | |
| font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) | |
| nc = int(40 * (wh[0] / 256)) | |
| lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) | |
| try: | |
| draw.text((0, 0), lines, fill="black", font=font) | |
| except UnicodeEncodeError: | |
| print("Cant encode string for logging. Skipping.") | |
| txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 | |
| txts.append(txt) | |
| txts = np.stack(txts) | |
| txts = torch.tensor(txts) | |
| return txts | |
| def ismap(x): | |
| if not isinstance(x, torch.Tensor): | |
| return False | |
| return (len(x.shape) == 4) and (x.shape[1] > 3) | |
| def isimage(x): | |
| if not isinstance(x,torch.Tensor): | |
| return False | |
| return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) | |
| def exists(x): | |
| return x is not None | |
| def default(val, d): | |
| if exists(val): | |
| return val | |
| return d() if isfunction(d) else d | |
| def mean_flat(tensor): | |
| """ | |
| https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 | |
| Take the mean over all non-batch dimensions. | |
| """ | |
| return tensor.mean(dim=list(range(1, len(tensor.shape)))) | |
| def count_params(model, verbose=False): | |
| total_params = sum(p.numel() for p in model.parameters()) | |
| if verbose: | |
| print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") | |
| return total_params | |
| def instantiate_from_config(config,reload=False): | |
| if not "target" in config: | |
| if config == '__is_first_stage__': | |
| return None | |
| elif config == "__is_unconditional__": | |
| return None | |
| raise KeyError("Expected key `target` to instantiate.") | |
| return get_obj_from_str(config["target"],reload=reload)(**config.get("params", dict())) | |
| def get_obj_from_str(string, reload=False): | |
| module, cls = string.rsplit(".", 1) | |
| if reload: | |
| module_imp = importlib.import_module(module) | |
| importlib.reload(module_imp) | |
| return getattr(importlib.import_module(module, package=None), cls) | |
| def get_ckpt_path(name, root, check=False): | |
| assert name in URL_MAP | |
| path = os.path.join(root, CKPT_MAP[name]) | |
| if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): | |
| print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) | |
| download(URL_MAP[name], path) | |
| md5 = md5_hash(path) | |
| assert md5 == MD5_MAP[name], md5 | |
| return path | |