| from torchvision.transforms.functional import normalize |
| import torch.nn as nn |
| import numpy as np |
| import os |
|
|
| def denormalize(tensor, mean, std): |
| mean = np.array(mean) |
| std = np.array(std) |
|
|
| _mean = -mean/std |
| _std = 1/std |
| return normalize(tensor, _mean, _std) |
|
|
| class Denormalize(object): |
| def __init__(self, mean, std): |
| mean = np.array(mean) |
| std = np.array(std) |
| self._mean = -mean/std |
| self._std = 1/std |
|
|
| def __call__(self, tensor): |
| if isinstance(tensor, np.ndarray): |
| return (tensor - self._mean.reshape(-1,1,1)) / self._std.reshape(-1,1,1) |
| return normalize(tensor, self._mean, self._std) |
|
|
| def set_bn_momentum(model, momentum=0.1): |
| for m in model.modules(): |
| if isinstance(m, nn.BatchNorm2d): |
| m.momentum = momentum |
|
|
| def fix_bn(model): |
| for m in model.modules(): |
| if isinstance(m, nn.BatchNorm2d): |
| m.eval() |
|
|
| def mkdir(path): |
| if not os.path.exists(path): |
| os.mkdir(path) |
|
|
