| import importlib |
|
|
| import torch |
| from torch import optim |
| import numpy as np |
|
|
| from inspect import isfunction |
| from PIL import Image, ImageDraw, ImageFont |
|
|
|
|
| def log_txt_as_img(wh, xc, size=10): |
| |
| |
| b = len(xc) |
| txts = list() |
| for bi in range(b): |
| txt = Image.new("RGB", wh, color="white") |
| draw = ImageDraw.Draw(txt) |
| font = ImageFont.truetype('font/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): |
| 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"])(**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) |
|
|
