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torchvision.transforms.Normalize()中的mean和std参数做什么用呢？
疑问1：
按照我的理解，归一化就是要把图片3个通道中的数据整理到[-1, 1]区间。
x = (x - mean(x))/stddev(x)
只要输入数据集x确定了，mean(x)和stddev(x)也就是确定的数值了，为什么Normalize()函数还需要输入mean和std的数值呢？
疑问2：
RGB单个通道的值不是[0, 255]吗？所以一个通道的均值应该在127附近才对。
如果Normalize()函数按照下面的版式去计算 x = (x - mean)/std 因为RGB是[0, 255]，算出来的x就不可能落在[-1,
1]区间了。
疑问3：
可我看很多代码里面是这样的：
torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229,
0.224, 0.225])
这一组值是怎么来的？ 为什么这三个通道的均值都是小于1的值呢？
1、
mean 和 std 肯定要在normalize之前自己先算好再传进去的，不然每次normalize程序就得把所有的图片都读取一遍算这两个数…
2、有两种情况：
a)如果是imagenet数据集，那么ImageNet的数据在加载的时候就已经转换成了[0, 1].
b)应用了torchvision.transforms.ToTensor，其作用是
（ Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to
a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] ）
3、[0.485, 0.456, 0.406]这一组平均值是从imagenet训练集中抽样算出来的。
