cedpsam/latentdiffusion

1import importlib2 3import torch4import numpy as np5 6from inspect import isfunction7from PIL import Image, ImageDraw, ImageFont8 9 10def log_txt_as_img(wh, xc, size=10):11    # wh a tuple of (width, height)12    # xc a list of captions to plot13    b = len(xc)14    txts = list()15    for bi in range(b):16        txt = Image.new("RGB", wh, color="white")17        draw = ImageDraw.Draw(txt)18        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)19        nc = int(40 * (wh[0] / 256))20        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))21 22        try:23            draw.text((0, 0), lines, fill="black", font=font)24        except UnicodeEncodeError:25            print("Cant encode string for logging. Skipping.")26 27        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.028        txts.append(txt)29    txts = np.stack(txts)30    txts = torch.tensor(txts)31    return txts32 33 34def ismap(x):35    if not isinstance(x, torch.Tensor):36        return False37    return (len(x.shape) == 4) and (x.shape[1] > 3)38 39 40def isimage(x):41    if not isinstance(x,torch.Tensor):42        return False43    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)44 45 46def exists(x):47    return x is not None48 49 50def default(val, d):51    if exists(val):52        return val53    return d() if isfunction(d) else d54 55 56def mean_flat(tensor):57    """58    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L8659    Take the mean over all non-batch dimensions.60    """61    return tensor.mean(dim=list(range(1, len(tensor.shape))))62 63 64def count_params(model, verbose=False):65    total_params = sum(p.numel() for p in model.parameters())66    if verbose:67        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")68    return total_params69 70 71def instantiate_from_config(config):72    if not "target" in config:73        if config == '__is_first_stage__':74            return None75        elif config == "__is_unconditional__":76            return None77        raise KeyError("Expected key `target` to instantiate.")78    return get_obj_from_str(config["target"])(**config.get("params", dict()))79 80 81def get_obj_from_str(string, reload=False):82    module, cls = string.rsplit(".", 1)83    if reload:84        module_imp = importlib.import_module(module)85        importlib.reload(module_imp)86    return getattr(importlib.import_module(module, package=None), cls)