模型预处理的ToTensor和Normalize

模型预处理的ToTensor和Normalize

flyfish

import torch
import numpy as np
from torchvision import transformsmean = (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

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) / _stdprint(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)

[[[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])

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

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

(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