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Runtime error
| import importlib | |
| import torch | |
| import numpy as np | |
| from inspect import isfunction | |
| from PIL import Image, ImageDraw, ImageFont | |
| def str2bool(v): | |
| if isinstance(v, bool): | |
| return v | |
| if v.lower() in ('yes', 'true', 't', 'y', '1'): | |
| return True | |
| elif v.lower() in ('no', 'false', 'f', 'n', '0'): | |
| return False | |
| else: | |
| raise ValueError('Boolean value expected.') | |
| 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) | |
| 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): | |
| 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.") | |
| if "instantiate_with_dict" in config and config["instantiate_with_dict"]: | |
| # input parameter is one dict | |
| return get_obj_from_str(config["target"])(config.get("params", dict()), **kwargs) | |
| else: | |
| 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) | |
| def check_istarget(name, para_list): | |
| """ | |
| name: full name of source para | |
| para_list: partial name of target para | |
| """ | |
| istarget=False | |
| for para in para_list: | |
| if para in name: | |
| return True | |
| return istarget |