模型预处理的ToTensor和Normalize

flyfish

py 复制代码

import torch
import numpy as np
from torchvision import transforms

mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)


# data0 =np.random.randint(0,255,size = [4,5,3],dtype='uint8')
# data0 = data0.astype(np.float64)
data0 = np.random.random((4, 5, 3))  # H x W x C
data0 = np.round(data0,4)
print(data0.shape)
print(data0)

data1 = transforms.ToTensor()(data0)
print(data1.shape)  # C x H x W
print(data1)
data2 = transforms.Normalize(mean, std)(data1)
print(data2)

ToTensor 是数据维度发生变化H x W x C 变为 C x H x W，数值没有变化
Normalize是 (data - mean) / std

使用numpy实现验证

py 复制代码

data1 = np.transpose(data0, (2, 0, 1))
print(data1.shape)
_std = np.array(std).reshape((3, 1, 1))
_mean = np.array(mean).reshape((3, 1, 1))

data2 = (data1 - _mean) / _std

print(data2)

原始数据的形状和内容可以是图像的高度，宽度，通道

(4, 5, 3)

复制代码

[[[0.8284 0.3419 0.6621]
  [0.59   0.2306 0.4112]
  [0.0636 0.406  0.2778]
  [0.9551 0.2097 0.7681]
  [0.3097 0.642  0.1968]]

 [[0.722  0.9844 0.4942]
  [0.1847 0.2435 0.3691]
  [0.658  0.5643 0.9468]
  [0.4002 0.7807 0.4393]
  [0.2461 0.9049 0.0585]]

 [[0.2606 0.067  0.6186]
  [0.284  0.8524 0.2102]
  [0.0447 0.0209 0.1313]
  [0.0587 0.594  0.1016]
  [0.6942 0.4514 0.7125]]

 [[0.8787 0.7917 0.1181]
  [0.9044 0.7948 0.3599]
  [0.1706 0.7463 0.899 ]
  [0.0758 0.2224 0.5447]
  [0.3336 0.6096 0.3065]]]

ToTensor 后的形状和内容

torch.Size([3, 4, 5])

复制代码

tensor([[[0.8284, 0.5900, 0.0636, 0.9551, 0.3097],
         [0.7220, 0.1847, 0.6580, 0.4002, 0.2461],
         [0.2606, 0.2840, 0.0447, 0.0587, 0.6942],
         [0.8787, 0.9044, 0.1706, 0.0758, 0.3336]],

        [[0.3419, 0.2306, 0.4060, 0.2097, 0.6420],
         [0.9844, 0.2435, 0.5643, 0.7807, 0.9049],
         [0.0670, 0.8524, 0.0209, 0.5940, 0.4514],
         [0.7917, 0.7948, 0.7463, 0.2224, 0.6096]],

        [[0.6621, 0.4112, 0.2778, 0.7681, 0.1968],
         [0.4942, 0.3691, 0.9468, 0.4393, 0.0585],
         [0.6186, 0.2102, 0.1313, 0.1016, 0.7125],
         [0.1181, 0.3599, 0.8990, 0.5447, 0.3065]]], dtype=torch.float64)

Normalize 后的形状和内容

复制代码

tensor([[[ 1.4996,  0.4585, -1.8402,  2.0528, -0.7655],
         [ 1.0349, -1.3114,  0.7555, -0.3703, -1.0432],
         [-0.9799, -0.8777, -1.9227, -1.8616,  0.9135],
         [ 1.7192,  1.8314, -1.3729, -1.7869, -0.6611]],

        [[-0.5094, -1.0063, -0.2232, -1.0996,  0.8304],
         [ 2.3589, -0.9487,  0.4835,  1.4496,  2.0040],
         [-1.7366,  1.7696, -1.9424,  0.6161, -0.0205],
         [ 1.4987,  1.5125,  1.2960, -1.0429,  0.6857]],

        [[ 1.1382,  0.0231, -0.5698,  1.6093, -0.9298],
         [ 0.3920, -0.1640,  2.4036,  0.1480, -1.5444],
         [ 0.9449, -0.8702, -1.2209, -1.3529,  1.3622],
         [-1.2796, -0.2049,  2.1911,  0.6164, -0.4422]]], dtype=torch.float64)

使用numpy实现验证的结果

复制代码

(3, 4, 5)
[[[ 1.49956332  0.45851528 -1.84017467  2.05283843 -0.76550218]
  [ 1.0349345  -1.31135371  0.75545852 -0.37030568 -1.04323144]
  [-0.97991266 -0.87772926 -1.92270742 -1.86157205  0.91353712]
  [ 1.71921397  1.83144105 -1.37292576 -1.78689956 -0.66113537]]

 [[-0.509375   -1.00625    -0.22321429 -1.09955357  0.83035714]
  [ 2.35892857 -0.94866071  0.48348214  1.44955357  2.00401786]
  [-1.73660714  1.76964286 -1.94241071  0.61607143 -0.02053571]
  [ 1.49866071  1.5125      1.29598214 -1.04285714  0.68571429]]

 [[ 1.13822222  0.02311111 -0.56977778  1.60933333 -0.92977778]
  [ 0.392      -0.164       2.40355556  0.148      -1.54444444]
  [ 0.94488889 -0.87022222 -1.22088889 -1.35288889  1.36222222]
  [-1.27955556 -0.20488889  2.19111111  0.61644444 -0.44222222]]]

两者除了保留小数位数不同外，其他一致