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| import torch | |
| import random | |
| from torch.nn import functional as F | |
| from PIL import Image | |
| class AlignerCantFindFaceError(Exception): | |
| pass | |
| class MaskerCantFindFaceError(Exception): | |
| pass | |
| def tensor2im(var): | |
| var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy() | |
| var = (var + 1) / 2 | |
| var[var < 0] = 0 | |
| var[var > 1] = 1 | |
| var = var * 255 | |
| return Image.fromarray(var.astype("uint8")) | |
| def tensor2im_no_tfm(var): | |
| var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy() | |
| var = var * 255 | |
| return Image.fromarray(var.astype("uint8")) | |
| def printer(obj, tabs=0): | |
| for (key, value) in obj.items(): | |
| try: | |
| _ = value.items() | |
| print(" " * tabs + str(key) + ":") | |
| printer(value, tabs + 4) | |
| except: | |
| print(f" " * tabs + str(key) + " : " + str(value)) | |
| def get_keys(d, name, key="state_dict"): | |
| if key in d: | |
| d = d[key] | |
| d_filt = {k[len(name) + 1 :]: v for k, v in d.items() if k[: len(name) + 1] == name + '.'} | |
| return d_filt | |
| def setup_seed(seed): | |
| random.seed(seed) | |
| torch.random.manual_seed(seed) | |