A python package about science compute algorithm, include natural language, image, neural network, optimize algorithm, machine learning, graphic algorithm, etc.

Project description

scient

scient一个用python实现科学计算相关算法的包，包括自然语言、图像、神经网络、优化算法、机器学习、图计算等模块。

scient源码和编译安装包可以在Python package index获取。

The source code and binary installers for the latest released version are available at the [Python package index].

https://pypi.org/project/scient

可以用pip安装scient。

You can install scient like this:

pip install scient

也可以用setup.py安装。

Or in the scient directory, execute:

python setup.py install

scient.image

图像相关算法模块，包括边缘检测、图像相似度计算、图像质量评价、图像特征提取等。

scient.image.friqa

全参考图像质量评价模块，包括峰值信噪比（PSNR），结构相似度（SSIM），直方图相似度（HistSim）。

scient.image.friqa.psnr(image1,image2,max_pix=255)

Parameters

image1 : numpy.array 2D or 3D，参考图像

image2 : numpy.array 2D or 3D，待评价图像

max_pix : int, optional default is 255, 像素值的最大值，默认值是255.

Returns

float

Algorithms

PSNR(Peak Signal to Noise Ratio)，峰值信噪比，是一种评价图像的客观标准，单位dB。图像在经过压缩之后，会在某种程度与原始图像不同，PSNR值用来衡量经过处理后的图像品质是否令人满意。

$$ PSNR=10 \cdot \log _ {10} ( \frac { MAX _ I ^ 2 } { MSE }) = 20 \cdot \log _ {10} ( \frac { MAX _ I } { MSE }) $$

其中，$MAX _ I$是图像像素值的最大值，一般每个采样点用8位表示，那么$MAX _ I$就是255。

$MSE$是待评价图像与参考图像的均方误差，$MSE$越小，PSNR越大；PSNR越大，待评价图像质量越好。

PSNR高于40dB说明待评价图像质量极好,非常接近原始图像；
PSNR在30—40dB说明待评价图像质量是较好，虽然有明显失真但可以接受；
PSNR在20—30dB说明待评价图像质量差；
PSNR低于20dB说明待评价图像质量不可接受。

PSNR缺点：基于对应像素点间的误差，即基于误差敏感的图像质量评价。由于并未考虑到人眼的视觉特性（人眼对空间频率较低的对比差异敏感度较高，人眼对亮度对比差异的敏感度较色度高，人眼对一个区域的感知结果会受到其周围邻近区域的影响等），因而经常出现评价结果与人的主观感觉不一致的情况。

Examples

import os
from scient.image import friqa
import numpy
from PIL import Image

ref_image='test/data/I10.BMP'
images=['test/data/I10.BMP','test/data/i10_23_3.bmp','test/data/i10_23_4.bmp','test/data/i10_23_5.bmp','test/data/i10_24_5.bmp']

#读取图像文件
ref_image=Image.open(os.path.join(os.path.dirname(friqa.__file__),'..',ref_image))
images=[Image.open(os.path.join(os.path.dirname(friqa.__file__),'..',i)) for i in images]

#计算psnr
for i in images:
    print(friqa.psnr(numpy.array(ref_image),numpy.array(i)))

运行结果

100
32.436263852012544
31.184291262813648
30.272831107297733
29.3584810257951

scient.image.friqa.ssim(image1,image2,k1=0.01,k2=0.03,block_size=(8, 8),max_pix=255)

Parameters

image1 : numpy.array 2D

image2 : numpy.array 2D

k1 : float, optional，k1<<1,避免分母为0造成不稳定. The default is 0.01.

k2 : float, optional，k2<<1,避免分母为0造成不稳定. The default is 0.03.

block_size : tuple, optional，将图像分成多个block,采用gaussian加权计算所有block的均值、方差、协方差,进而计算所有block的ssim,最后的ssim取所有block的平均值. The default is (8,8).

max_pix : int, optional default is 255, 像素值的最大值，默认值是255.

Returns

float

Algorithms

SSIM(Structural Similarity)，结构相似度，用于衡量两个图像相似程度，或检测图像的失真程度。 SSIM基于样本之间的亮度(luminance,像素平均值)、对比度(contrast,像素标准差)和结构(structure,像素减均值除以标准差)计算。

$$ SSIM(x,y)=f(l(x,y),c(x,y),s(x,y)) $$

$l(x,y)$为亮度对比函数，是关于图像的平均灰度$μ_x,μ_y$的函数；

$$ l(x,y)=\frac { 2μ_x μ_y + C1 } { μ_x^2 μ_y^2 + C1 } $$

$$ μ_x=\frac { 1 } { N } \sum^{N}_{i=1}{x_i} $$

$$ C1=(K_1 L)^2 $$

像素值的最大值，默认值是255. K1<<1。

$c(x,y)$为对比度对比函数，是关于图像的标准差$σ_x,σ_y$的函数；

$$ c(x,y)=\frac { 2σ_x σ_y + C2 } { σ_x^2 σ_y^2 + C2 } $$

$$ σ_x=(\frac { 1 } { N-1 } \sum^{N}_{i=1}{(x_i-μ_x)^2})^{\frac { 1 } { 2 }} $$

$$ C2=(K_2 L)^2 $$

K2<<1

$s(x,y)$为结构对比函数，是关于图像的标准化$\frac { x-μ_x } { σ_x },\frac { y-μ_y } { σ_y }$的函数；

$$ s(x,y)=\frac { σ_{xy} + C3 } { σ_x σ_y + C3 } $$

$$ σ_{xy}=\frac { 1 } { N-1 } (\sum^{N}_{i=1}{(x_i-μ_x)(y_i-μ_y)}) $$

$$ SSIM(x,y)=[l(x,y)]^α[c(x,y)]^β[s(x,y)]^γ $$

α,β,γ取1，令$C_3=\frac { C_2 } { 2 }$，可将SSIM简化为：

$$ SSIM(x,y)=\frac { (2μ_x μ_y + C1)(2σ_{xy} + C2) } { (μ_x^2 μ_y^2 + C1)(σ_x^2 σ_y^2 + C2) } $$

SSIM取值范围为[0,1]，值越大表示图像质量越好。 SSIM具有：对称性，ssim(x,y)==ssim(y,x); 有界性,ssim(x,y)<=1; 最大值唯一性，当且仅当x==y时，ssim(x,y)==1。 SSIM缺点：对于图像出现位移、缩放、旋转（皆属于非结构性的失真）的情况无法有效的判断。

Examples

import os
from scient.image import friqa
import numpy
from PIL import Image

ref_image='test/data/I10.BMP'
images=['test/data/I10.BMP','test/data/i10_23_3.bmp','test/data/i10_23_4.bmp','test/data/i10_23_5.bmp','test/data/i10_24_5.bmp']

#读取图像文件
ref_image=Image.open(os.path.join(os.path.dirname(friqa.__file__),'..',ref_image))
images=[Image.open(os.path.join(os.path.dirname(friqa.__file__),'..',i)) for i in images]

#计算ssim
for i in images:
    print(friqa.ssim(numpy.array(ref_image.convert("L")),numpy.array(i.convert("L"))))

运行结果

1.0
0.8568124416229375
0.6810351495300123
0.5575398637742431
0.5072153083460104

scient.image.feature

图像特征提取模块，包括BRISQUE，基于累积概率的锐化因子（CPB），曝光度。

scient.image.feature.brisque(image)

Parameters

image : numpy.array 2D

Returns

tuple ('gdd_α','gdd_σ', 'aggd_α1','aggd_η1','aggd_σl1','aggd_σr1', 'aggd_α2','aggd_η2','aggd_σl2','aggd_σr2', 'aggd_α3','aggd_η3','aggd_σl3','aggd_σr3', 'aggd_α4','aggd_η4','aggd_σl4','aggd_σr4')

Algorithms

BRISQUE（Blind/Referenceless Image Spatial QUality Evaluator），是一种无参考的空间域图像质量评估算法。先计算Mean Subtracted Contrast Normalized Coefficients（MSCN系数），MSCN系数反映了由于失真的存在而改变的特征统计，可以用来作为图像失真的统计特征。再用MSCN系数估计Generalized Gaussian Distribution（GDD）的参数α、σ，以及Asymmetric Generalized Gaussian Distribution（AGGD）在Horizontal Neighbour, Vertical Neighbour, On Diagonal Neighbour, Off Diagonal Neighbour上的参数α、η、σl、σr，将GDD的两个参数和AGGD的16个参数作为输出的特征。

MSCN系数：

$$ MSCN(i,j)=\frac { I(i,j)-μ(i,j) } { σ(i,j)+C } $$

$$ μ(i,j)=\sum^{K}{k=-K}{\sum^{L}{l=-L}{w_{k,l}I_{k,l}(i,j)}} $$

$$ σ(i,j)=\sqrt{\sum^{K}{k=-K}{\sum^{L}{l=-L}{w_{k,l}(I_{k,l}(i,j)-μ(i,j))^2}}} $$

其中$I(i,j)$表示原始图像i行j列元素的值。

Generalized Gaussian Distribution：

$$ f(x;α,σ^2)=\frac {α} {2βΓ(1/α)} e^{-(\frac {|x|}{β})^α} $$

$$ β=σ\sqrt{\frac{Γ(1/α)}{Γ(3/α)}} $$

$$ Γ(α)=\int^{\infty}_{0}{t^{α-1}e^{-t}dt} α>0 $$

Neighbours:

$$ HorizontalNeighbour(i,j)=MSCN(i,j)MSCN(i,j+1) $$

$$ VerticalNeighbour(i,j)=MSCN(i,j)MSCN(i+1,j) $$

$$ OnDiagonalNeighbour(i,j)=MSCN(i,j)MSCN(i+1,j+1) $$

$$ OffDiagonalNeighbour(i,j)=MSCN(i,j)MSCN(i+1,j-1) $$

Asymmetric Generalized Gaussian Distribution:

$$ f(x;α,σ_l^2,σ_r^2)= \frac {α}{(β_l+β_r)Γ(1/α)}e^{-(\frac {-x}{β_l})^α} x<0 \frac {α}{(β_l+β_r)Γ(1/α)}e^{-(\frac {x}{β_r})^α} x>=0 β_l=σ_l\sqrt{\frac{Γ(1/α)}{Γ(3/α)}} \ β_r=σ_r\sqrt{\frac{Γ(1/α)}{Γ(3/α)}} $$

Examples

import os
from scient.image import feature
import numpy
from PIL import Image

images=['test/data/I10.BMP','test/data/i10_23_3.bmp','test/data/i10_23_4.bmp','test/data/i10_23_5.bmp','test/data/i10_24_5.bmp']

#读取图像文件
images=[Image.open(os.path.join(os.path.dirname(feature.__file__),'..',i)) for i in images]

#计算brisque
brisques=[]
for i in images:
    brisques.append(feature.brisque(numpy.array(i.convert('L'))))
print(brisques)

运行结果

[(2.8390000000000026, 0.5387382509471336, 0.8180000000000005, 0.1597336483186561, 0.19928197982139934, 0.4696747920784309, 0.8640000000000005, 0.17081167501931036, 0.1703080506100513, 0.440894038756712, 0.8610000000000007, -0.002437981115828319, 0.2983089768677447, 0.2943996123553127, 0.8670000000000007, 0.03657370089459203, 0.2641503963750437, 0.32229688865209727), (2.179000000000002, 0.3755805588864052, 0.6610000000000005, 0.2105638785869636, 0.06573065885425396, 0.3546433105372317, 0.7250000000000005, 0.2035633011201771, 0.04895566298941261, 0.2895746994148656, 0.7110000000000005, 0.09196294223642214, 0.10660221933416321, 0.22150476223116147, 0.7220000000000004, 0.10061626044729756, 0.09951649928883519, 0.22307536755643081), (1.489000000000001, 0.19567592119387475, 0.4370000000000002, 0.16656579278574843, 0.005144811587270607, 0.1595102390164801, 0.4400000000000002, 0.14819323960693676, 0.007946536338563829, 0.14400949152877282, 0.46900000000000025, 0.1304195444573072, 0.010840852166168865, 0.12285748598680354, 0.47300000000000025, 0.12785146234621667, 0.011051488263507676, 0.11939877242752284), (1.2570000000000008, 0.1189807661854071, 0.2940000000000001, 0.09858069094224381, 0.0033503171775502846, 0.1003980673321924, 0.2960000000000001, 0.09662228540309649, 0.0037953392707882772, 0.09854664422093222, 0.3160000000000001, 0.08840261656054116, 0.004225987220008733, 0.08029184471742051, 0.3180000000000001, 0.08631426420092875, 0.004399447310061135, 0.07751730107145516), (1.203000000000001, 0.14103130545847511, 0.3270000000000001, 0.10623288442963101, 0.008919473174326557, 0.12226537626029133, 0.3280000000000001, 0.06853644417080812, 0.02378947796849877, 0.10143999168472712, 0.33900000000000013, 0.05689116726400874, 0.02385946076111514, 0.08256978072093775, 0.33900000000000013, 0.05450324427873719, 0.02492368706293601, 0.0813272014967197)]

scient.image.hash

图像hash模块，包括均值hash(mean hash),差值hash(diff hash),感知hash(percept hash)。

scient.image.hash.percept(image,hash_size=64)

Parameters

image : numpy.array 2D

hash_size : 输出hash值的长度

Returns

list

Algorithms

先将图片缩放成hash_sizehash_size大小，然后对图像进行离散余弦变换DCT，并输出左上角hw=hash_size的mean hash。 DCT是一种特殊的傅立叶变换，将图片从像素域变换为频率域，并且DCT矩阵从左上角到右下角代表越来越高频率的系数，图片的主要信息保留左上角的低频区域。

一维DCT变换：

$$ F(x)=c(x)\sum^{N-1}_{i=0}{f(i)cos(\frac {(i+0.5)π}{N}x) } $$

$$ c(x)=\left{\begin{matrix}\sqrt{\frac{1}{N}} ,x=0\\sqrt{\frac{2}{N}} ,x!=0 \end{matrix}\right. $$

f(i)为原始的信号，F(x)是DCT变换后的系数，N为原始信号的点数，c(x)是补偿系数。

二维DCT变换：

$$ F(x,y)=c(x)c(y)sum^{N-1}{i=0}{sum^{N-1}{j=0}{f(i,j)cos(\frac {(i+0.5)π}{N}x)cos(\frac {(j+0.5)π}{N}y)}} $$

$$ c(x)=\left{\begin{matrix}\sqrt{\frac{1}{N}} ,x=0\\sqrt{\frac{2}{N}} ,x!=0 \end{matrix}\right. $$

二维DCT变换也可表示为：

$$ F=AfA^T $$

$$ A(i,j)=c(i)cos(\frac {(j+0.5)π}{N}i) $$

此形式更方便计算。DCT变换是对称的，因此可以对经过DCT变换的图片进行还原操作。

Examples

计算图像感知相似度时，首先计算图像的PHASH值，再采用海明（hamming）距离相似度计算图片PHASH值的相似度。

#采用感知hash计算图片的感知相似度
import os
from scient.image import hash
from scient.algorithms import similar
import numpy
from PIL import Image

ref_image='test/data/I10.BMP'
images=['test/data/I10.BMP','test/data/i10_23_3.bmp','test/data/i10_23_4.bmp','test/data/i10_23_5.bmp','test/data/i10_24_5.bmp']

#读取图像文件
ref_image=Image.open(os.path.join(os.path.dirname(hash.__file__),'..',ref_image))
images=[Image.open(os.path.join(os.path.dirname(hash.__file__),'..',i)) for i in images]

#计算感知hash
phash=hash.percept(numpy.array(ref_image.convert("L")))
phashs=[hash.percept(numpy.array(i.convert("L"))) for i in images]

#计算感知相似度
for i in phashs:
    print(similar.hamming(i,phash))

运行结果

1.0
0.9384615384615385
0.8615384615384616
0.8153846153846154
0.6

scient.neuralnet

神经网络相关算法模块，包括attention、transformer、bert、lstm、resnet、crf、dataset、fit等。

scient.neuralnet.fit

神经网络训练模块，将torch构建的神经网络模型的训练方式简化为model.fit()，使torch神经网络模型训练更简捷，更优雅。

使用步骤：

（1）基于torch构建模型model，采用torch.utils.data.DataLoader加载训练数据集train_loader、验证数据集eval_loader(可选)；

（2）采用fit.set()设置模型训练参数，参数详情：

optimizer=None: 优化器，可以用类似torch.optim模块内的优化器来定义；
scheduler=None: 优化器的调度器，可以用类似torch.optim.lr_scheduler模块内的调度器来定义；
loss_func=None: 损失函数，可以用类似torch.nn.CrossEntropyLoss()来定义；
grad_func=None: 梯度操作函数，可进行如梯度裁剪的操作；
perform_func=None: 模型性能函数，模型传入预测值和实际值，用以评估模型性能；
n_iter=10: 模型在数据集上迭代训练的次数；
- 如果n_iter为int,表示模型在数据集上迭代训练n_iter后停止；
- 如果n_iter为(int,int),表示模型在数据集上迭代训练的最小min_iter和最大max_iter次数, 如果迭代次数超过min_iter且eval的perform_func比上一个iter大，结束训练。n_iter为(int,int)时，必须提供eval_loader，且perform_func必须是一个数值，且值越大模型性能越好；
device=None: 模型训练的设备，如device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')；
n_batch_step: 每n个batch更新一次optimizer的梯度，以节省显存及计算量；
n_batch_plot: 每n个batch更新一下损失曲线，训练过程中会实时绘制损失曲线；
save_path: 每个iter完成后模型保存路径，模型名为“模型类名_iter_i.checkpoint”,保存的内容为{'model_state_dict':model.state_dict(),'optimizer_state_dict':optimizer.state_dict(),'batch_loss':batch_loss}，如果训练时未提供eval_loader，batch_loss=train_batch_loss, 否则batch_loss=[train_batch_loss,eval_batch_loss]

（3）采用model.fit(train_loader,eval_loader,mode=('input','target'))训练模型：

train_loader: 训练数据集
eval_loader: 验证数据集
mode: 数据集包含的内容，分四种情况：
- mode=('input','target'), loader data item is one input and one target;
- mode='input', loader data item is only one input;
- mode=('inputs','target'), loader data item is a list of input and one target;
- mode='inputs', loader data item is a list of input.
- mode中不包含target时，不能使用perform_func

Examples

首先构建模型model、训练数据加载器train_loader、验证数据加载器eval_loader：

import os
import torch
from scient.neuralnet import resnet, fit
import torchvision.transforms as tt
from torchvision.datasets import ImageFolder

# 数据转换（归一化和数据增强）
stats = ((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
train_tfms = tt.Compose([tt.RandomCrop(160, padding=4, padding_mode='reflect'), 
                         tt.RandomHorizontalFlip(), 
                         tt.ToTensor(), 
                         tt.Normalize(*stats,inplace=True)])
valid_tfms = tt.Compose([tt.Resize([160,160]),tt.ToTensor(), tt.Normalize(*stats)])

# 创建ImageFolder对象
data_train = ImageFolder(os.path.join(os.path.dirname(fit.__file__),'..','test/data/imagewoof/train'), train_tfms)
data_eval = ImageFolder(os.path.join(os.path.dirname(fit.__file__),'..','test/data/imagewoof/val'), valid_tfms)

# 设置批量大小
batch_size = 2

# 创建训练集和验证集的数据加载器
train_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True)
eval_loader = torch.utils.data.DataLoader(data_eval, batch_size=batch_size, shuffle=False)

#resnet50模型
model=resnet.ResNet50(n_class=3)

然后设置模型训练参数、训练模型：

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
loss_func=torch.nn.CrossEntropyLoss()
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,n_iter=5,device=device)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

运行结果：

train iter 0: avg_batch_loss=1.27345: 100%|██████████| 60/60 [00:04<00:00, 12.92it/s]                   
eval iter 0: avg_batch_loss=1.33363: 100%|██████████| 8/8 [00:00<00:00, 59.44it/s]                   
train iter 1: avg_batch_loss=1.24023: 100%|██████████| 60/60 [00:04<00:00, 13.39it/s]                   
eval iter 1: avg_batch_loss=1.08319: 100%|██████████| 8/8 [00:00<00:00, 58.83it/s]                   
train iter 2: batch_loss=1.42699 avg_batch_loss=1.16666:  63%|██████▎   | 38/60 [00:02<00:01, 13.37it/s]

Examples: 训练时不使用eval_loader

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
loss_func=torch.nn.CrossEntropyLoss()
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,n_iter=5,device=device)

#训练
model.fit(train_loader=train_loader,mode=('input','target'))

运行结果：

train iter 0: avg_batch_loss=1.07998: 100%|██████████| 60/60 [00:04<00:00, 12.27it/s]                   
train iter 1: avg_batch_loss=1.16323: 100%|██████████| 60/60 [00:04<00:00, 12.95it/s]                   
train iter 2: batch_loss=0.61398 avg_batch_loss=1.00838:  67%|██████▋   | 40/60 [00:03<00:01, 13.06it/s]

Examples: 使用scheduler在训练过程中改变学习率等optimizer参数

#设置训练参数
n_iter=5
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.0001, epochs=n_iter,steps_per_epoch=len(train_loader))
loss_func=torch.nn.CrossEntropyLoss()
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,n_iter=n_iter,device=device)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

Examples: 使用perform_func在训练过程中评估模型性能

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
loss_func=torch.nn.CrossEntropyLoss()
def perform_func(y_hat,y):#perform_func的输入是预测值y_hat和实际值y
    y_hat,y=torch.concat(y_hat),torch.concat(y)#先将y_hat和y分别concat，由于y_hat和y是按loader分批计算和收集的，所以y_hat和y是batch_size大小的多个对象组成的list
    _,y_hat=y_hat.max(axis=1)#该模型输出值y_hat最大值对应的索引是预测的类别
    return round((y_hat==y).sum().item()/len(y),4)#输出准确率，并保留4位小数
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,perform_func=perform_func,n_iter=5,device=device)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

运行结果，可以在每个iter结束后得到perform的值：

train iter 0: avg_batch_loss=1.27428 perform=0.3417: 100%|██████████| 60/60 [00:04<00:00, 12.34it/s]    
eval iter 0: avg_batch_loss=1.09305 perform=0.4: 100%|██████████| 8/8 [00:00<00:00, 55.59it/s]       
train iter 1: avg_batch_loss=1.09102 perform=0.4417: 100%|██████████| 60/60 [00:04<00:00, 13.30it/s]    
eval iter 1: avg_batch_loss=1.18128 perform=0.4: 100%|██████████| 8/8 [00:00<00:00, 60.46it/s]       
train iter 2: avg_batch_loss=1.24860 perform=0.3583: 100%|██████████| 60/60 [00:04<00:00, 13.19it/s]    
eval iter 2: avg_batch_loss=1.23469 perform=0.4: 100%|██████████| 8/8 [00:00<00:00, 60.57it/s]

Examples: 使用grad_func在训练过程对梯度进行裁剪

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
loss_func=torch.nn.CrossEntropyLoss()
def grad_func(x):#grad_func的输入是model.parameters(),该操作在loss.backward()后起作用
    torch.nn.utils.clip_grad_value_(x, 0.1)
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,grad_func=grad_func,n_iter=5,device=device)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

Examples: 使用n_batch_step在小显存上模拟大batch_size的训练

该功能实现了多次反向误差传播并累积梯度后，再让optimizer进行梯度下降优化。

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
loss_func=torch.nn.CrossEntropyLoss()
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,n_iter=5,device=device,n_batch_step=5)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

Examples: 当训练到一定迭代次数后，如果模型在验证集上性能下降，提前停止训练

用n_iter=(min_iter,max_iter)设置模型的最小和最大训练迭代次数，当模型训练迭次数超过min_iter时，判断本次迭代训练模型性能是否优于上次迭代训练模型性能，如果不优于上次，则停止训练。过功能可防止过多的训练导致过拟合。该功能需要在eval_loader上计算perform，因此eval_loader不能为空，且perform_func输出必须为一个数值，该数值越大表示模型越优。

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
loss_func=torch.nn.CrossEntropyLoss()
def perform_func(y_hat,y):#perform_func的输入是预测值y_hat和实际值y
    y_hat,y=torch.concat(y_hat),torch.concat(y)#先将y_hat和y分别concat，由于y_hat和y是按loader分批计算和收集的，所以y_hat和y是batch_size大小的多个对象组成的list
    _,y_hat=y_hat.max(axis=1)#该模型输出值y_hat最大值对应的索引是预测的类别
    return round((y_hat==y).sum().item()/len(y),4)#输出准确率，并保留4位小数
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,perform_func=perform_func,n_iter=(5,20),device=device)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

运行结果，可以看到模型运行到第iter 6停止，并提示性能最佳的模型是iter 4：

train iter 0: avg_batch_loss=1.17016 perform=0.375: 100%|██████████| 60/60 [00:04<00:00, 12.60it/s]     
eval iter 0: avg_batch_loss=1.48805 perform=0.3333: 100%|██████████| 8/8 [00:00<00:00, 60.23it/s]    
train iter 1: avg_batch_loss=1.17200 perform=0.3833: 100%|██████████| 60/60 [00:04<00:00, 13.08it/s]    
eval iter 1: avg_batch_loss=1.18933 perform=0.2667: 100%|██████████| 8/8 [00:00<00:00, 59.39it/s]    
train iter 2: avg_batch_loss=1.09923 perform=0.4333: 100%|██████████| 60/60 [00:04<00:00, 13.14it/s]    
eval iter 2: avg_batch_loss=1.32449 perform=0.3333: 100%|██████████| 8/8 [00:00<00:00, 60.92it/s]    
train iter 3: avg_batch_loss=1.20507 perform=0.4083: 100%|██████████| 60/60 [00:05<00:00, 11.66it/s]    
eval iter 3: avg_batch_loss=1.23331 perform=0.2667: 100%|██████████| 8/8 [00:00<00:00, 57.59it/s]    
train iter 4: avg_batch_loss=1.09205 perform=0.4167: 100%|██████████| 60/60 [00:04<00:00, 12.87it/s]    
eval iter 4: avg_batch_loss=1.11206 perform=0.4: 100%|██████████| 8/8 [00:00<00:00, 59.80it/s]       
train iter 5: avg_batch_loss=1.10706 perform=0.4583: 100%|██████████| 60/60 [00:04<00:00, 12.94it/s]    
eval iter 5: avg_batch_loss=1.07162 perform=0.4: 100%|██████████| 8/8 [00:00<00:00, 39.96it/s]       
train iter 6: avg_batch_loss=1.15846 perform=0.4333: 100%|██████████| 60/60 [00:04<00:00, 12.34it/s]    
eval iter 6: avg_batch_loss=1.16467 perform=0.4: 100%|██████████| 8/8 [00:00<00:00, 58.85it/s]early stop and the best model is iter 4, the perform is 0.4

Examples: 训练过程中实时显示loss曲线，并在每一个iter完成后保存模型

设置n_batch_plot和save_path，保存的模型以checkpoint为后缀名，可以用torch.load打开保存的模型，模型里保存了3项内容：model_state_dict、optimizer_state_dict、batch_loss

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
loss_func=torch.nn.CrossEntropyLoss()
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,n_iter=5,device=device,n_batch_plot=5,save_path='d:/')

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

#打开并查看保存的模型
checkpoint=torch.load('D:/ResNet_iter_2.checkpoint')
checkpoint.keys()
checkpoint['batch_loss']
checkpoint['model_state_dict']

Examples: 如果型输出结果本身就是损失，可以省略loss_func

先定义一个输出为loss的模型

#模型
class output_loss(torch.nn.Module):
    def __init__(self):
        super(output_loss,self).__init__()

        self.model=resnet.ResNet50(n_class=3)
        self.loss_func=torch.nn.CrossEntropyLoss()

    def forward(self,x,y):
        y_hat=self.model(x)
        return self.loss_func(y_hat,y)#输出为loss无需在训练过程中计算loss
    
model=output_loss()

然后设置模型训练参数时，省略loss_func，因为此时loader的input和target都要输入到模型的forward中，因此可以将其看成inputs=[input,target]，在训练时mode='inputs'

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
model=fit.set(model,optimizer=optimizer,n_iter=10,device=device)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode='inputs')

Examples: 如果型输出结果有多个值，只用其中的1个值计算损失，需要自定义loss_func

先定义一个输出为多个值的模型

#模型
class output_multi(torch.nn.Module):
    def __init__(self):
        super(output_multi,self).__init__()

        self.model=resnet.ResNet50(n_class=3)

    def forward(self,x):
        y_hat=self.model(x)
        return y_hat,x#输出为两个值，只使用y_hat计算损失

model=output_multi()

然后设置模型训练参数时，对loss_func进行修改，用其中需要参与loss计算的部分计算loss

loss_func_=torch.nn.CrossEntropyLoss()
def loss_func(y_hat,y):
    return loss_func_(y_hat[0],y)#指定用输型输出的第0个值计算loss

#设置训练参数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)
model=fit.set(model,optimizer=optimizer,loss_func=loss_func,n_iter=10,device=device)

#训练
model.fit(train_loader=train_loader,eval_loader=eval_loader,mode=('input','target'))

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Examples: 训练时不使用eval_loader

Examples: 使用scheduler在训练过程中改变学习率等optimizer参数

Examples: 使用perform_func在训练过程中评估模型性能

Examples: 使用grad_func在训练过程对梯度进行裁剪

Examples: 使用n_batch_step在小显存上模拟大batch_size的训练

Examples: 当训练到一定迭代次数后，如果模型在验证集上性能下降，提前停止训练

Examples: 训练过程中实时显示loss曲线，并在每一个iter完成后保存模型

Examples: 如果型输出结果本身就是损失，可以省略loss_func

Examples: 如果型输出结果有多个值，只用其中的1个值计算损失，需要自定义loss_func

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