def test_get_mean_std():
train_data_loader = Dataloader(train_dataloader_params, shuffle=False)
train_data_loader.get_mean_std()
exit()
:param mean_std_path: 计算出来的均值和标准差存储的文件
:return:
"""
num_imgs = len(self.dataset[type])
for data in self.dataset[type]:
img = data[0]
for i in range(3):
# 一个通道的均值和标准差
self.means[i] += img[i, :, :].mean()
self.stdevs[i] += img[i, :, :].std()
self.means = np.asarray(self.means) / num_imgs
self.stdevs = np.asarray(self.stdevs) / num_imgs
print("{} : normMean = {}".format(type, self.means))
print("{} : normstdevs = {}".format(type, self.stdevs))
# 将得到的均值和标准差写到文件中，之后就能够从中读取
with open(mean_std_path, 'wb') as f:
pickle.dump(self.means, f)
pickle.dump(self.stdevs, f)
print('pickle done')
if __name__ == '__main__':
opt = options().parse()
dataloader = Dataloader(opt)
for x in dataloader.dirs:
mean_std_path = 'mean_std_value_' + x + '.pkl'
dataloader.get_mean_std(x, mean_std_path)