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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)
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