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import importlib |
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import torch |
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import numpy as np |
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from inspect import isfunction |
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from PIL import Image, ImageDraw, ImageFont |
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def log_txt_as_img(wh, xc, size=10): |
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b = len(xc) |
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txts = list() |
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for bi in range(b): |
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txt = Image.new("RGB", wh, color="white") |
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draw = ImageDraw.Draw(txt) |
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font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) |
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nc = int(40 * (wh[0] / 256)) |
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lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) |
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try: |
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draw.text((0, 0), lines, fill="black", font=font) |
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except UnicodeEncodeError: |
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print("Cant encode string for logging. Skipping.") |
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txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 |
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txts.append(txt) |
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txts = np.stack(txts) |
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txts = torch.tensor(txts) |
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return txts |
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def ismap(x): |
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if not isinstance(x, torch.Tensor): |
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return False |
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return (len(x.shape) == 4) and (x.shape[1] > 3) |
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def isimage(x): |
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if not isinstance(x,torch.Tensor): |
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return False |
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return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) |
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def exists(x): |
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return x is not None |
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def default(val, d): |
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if exists(val): |
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return val |
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return d() if isfunction(d) else d |
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def mean_flat(tensor): |
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""" |
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https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 |
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Take the mean over all non-batch dimensions. |
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""" |
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return tensor.mean(dim=list(range(1, len(tensor.shape)))) |
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def count_params(model, verbose=False): |
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total_params = sum(p.numel() for p in model.parameters()) |
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if verbose: |
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print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") |
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return total_params |
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def instantiate_from_config(config): |
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if not "target" in config: |
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if config == '__is_first_stage__': |
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return None |
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elif config == "__is_unconditional__": |
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return None |
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raise KeyError("Expected key `target` to instantiate.") |
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return get_obj_from_str(config["target"])(**config.get("params", dict())) |
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def get_obj_from_str(string, reload=False): |
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module, cls = string.rsplit(".", 1) |
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if reload: |
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module_imp = importlib.import_module(module) |
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importlib.reload(module_imp) |
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return getattr(importlib.import_module(module, package=None), cls) |
