@
目录- 前言
- 1.tensor基础操作
- 2.线性回归模型
- 3.数据集和数据加载器 (from torch.utils.data import Dataset,DataLoader)
- 4.图像处理:手写数字识别
- 5.制作图片数据集(以flower102为例)
- 6.迁移学习
前言
本文只是对于pytorch深度学习框架的使用方法的介绍,如果涉及算法中复杂的数学原理,本文将不予阐述,敬请读者自行阅读相关论文或者文献。
1.tensor基础操作
1.1 tensor的dtype类型
代码 | 含义 |
---|---|
float32 | 32位float |
float | floa |
float64 | 64位float |
double | double |
float16 | 16位float |
bfloat16 | 比float范围大但精度低 |
int8 | 8位int |
int16 | 16位int |
short | short |
int32 | 32位int |
int | int |
int64 | 64位int |
long | long |
complex32 | 32位complex |
complex64 | 64位complex |
cfloat | complex float |
complex128 | 128位complex float |
cdouble | complex double |
1.2 创建tensor(建议写出参数名字)
创建tensor时,有很多参数可以选择,为节省篇幅,本文在列举API时只列举一次,不列举重载的API。
1.2.1 空tensor(无用数据填充)
API
@overload
def empty(size: Sequence[Union[_int, SymInt]], *, memory_format: Optional[memory_format]=None, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...
size:[行数,列数]
dtype(deepth type):数据类型
device:选择运算设备
requires_grad:是否进行自动求导,默认为False
示例
gpu=torch.device("cuda")
empty_tensor=torch.empty(size=[3,4],device=gpu,requires_grad=True)
print(empty_tensor)
输出
tensor([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]], device='cuda:0', requires_grad=True)
1.2.2 全一tensor
@overload
def ones(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...
size:[行数,列数]
dtype(deepth type):数据类型
device:选择运算设备
requires_grad:是否进行自动求导,默认为False
1.2.3 全零tensor
@overload
def zeros(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...
1.2.4 随机值[0,1)的tensor
@overload
def rand(size: _size, *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...
1.2.5 随机值为整数且规定上下限的tensor
API
@overload
def randint(low: _int, high: _int, size: _size, *, generator: Optional[Generator]=None, dtype: Optional[_dtype]=None, device: Device=None, requires_grad: _bool=False) -> Tensor: ...
示例
int_tensor=torch.randint(low=0,high=20,size=[5,6],device=gpu)
print(int_tensor)
输出
tensor([[18, 0, 14, 7, 18, 14],
[17, 0, 2, 0, 0, 3],
[16, 17, 5, 15, 1, 14],
[ 7, 12, 8, 6, 4, 11],
[12, 4, 7, 5, 3, 3]], device='cuda:0')
1.2.6 随机值均值0方差1的tensor
@overload
def randn(size: _size, *, generator: Optional[Generator], names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Tensor: ...
1.2.7 从列表或numpy数组创建tensor
def tensor(data: Any, dtype: Optional[_dtype]=None, device: Device=None, requires_grad: _bool=False) -> Tensor: ...
- 如果使用
torch.from_numpy()
,返回的tensor与ndarray共享内存。
1.3 tensor常用成员函数和成员变量
1.3.1 转为numpy数组
def numpy(self,*args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__
pass
- 只有在CPU上运算的tensor才可以转为numpy数组
- tensor.requires_grad属性为True的tensor不能转为numpy数组
1.3.2 获得单元素tensor的值item
def item(self): # real signature unknown; restored from __doc__
...
- 如果tensor只有一个元素,就返回它的值
- 如果tensor有多个元素,抛出ValueError
1.3.3 获取维度个数
def dim(self): #real signature unknown; restored from __doc__
return 0
- 返回一个int表示维度个数
1.3.4 获取数据类型
dtype = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
1.3.5 获取形状
def size(self,dim=None): # real signature unknown; restored from __doc__
pass
- 使用
.shape
效果相同
1.3.6 浅拷贝与深拷贝
detach函数浅拷贝
假设有模型A和模型B,我们需要将A的输出作为B的输入,但训练时我们只训练模型B. 那么可以这样做:
input_B = output_A.detach()
它可以使两个计算图的梯度传递断开,从而实现我们所需的功能。
返回一个新的tensor,新的tensor和原来的tensor共享数据内存,但不涉及梯度计算,即requires_grad=False。修改其中一个tensor的值,另一个也会改变,因为是共享同一块内存。
sequence_tensor=torch.tensor(np.array([[[1,2,3],
[4,5,6]],
[[9,8,7],
[6,5,4]]]),
dtype=torch.float,device=gpu,)
sequence_tensor_shallowCp=sequence_tensor.detach()
sequence_tensor_shallowCp+=1
print(sequence_tensor)
print(sequence_tensor_shallowCp.requires_grad)
输出
tensor([[[ 2., 3., 4.],
[ 5., 6., 7.]],
[[10., 9., 8.],
[ 7., 6., 5.]]], device='cuda:0')
False
深拷贝
- 法一:
.clone().detach()
- 法二:
.new_tensor()
1.3.7 形状变换
转置
向量或矩阵转置
def t(self): # real signature unknown; restored from __doc__
"""
t() -> Tensor
See :func:`torch.t`
"""
return _te.Tensor(*(), **{})
- 返回值与原tensor共享内存!
指定两个维度进行转置:
def permute(self, dims: _size) -> Tensor:
r"""
permute(*dims) -> Tensor
See :func:`torch.permute`
"""
...
- 返回值与原tensor共享内存!
- 对矩阵来说,
.t()
等价于.permute(0, 1)
多维度同时转置
def permute(self, *dims): # real signature unknown; restored from __doc__
"""
permute(*dims) -> Tensor
See :func:`torch.permute`
"""
return _te.Tensor(*(), **{})
- 把要转置的维度放到对应位置上,比如对于三维tensor,x、y、z分别对应0、1、2,如果想要转置x轴和z轴,则输入2、1、0即可
- 返回值与原tensor共享内存!
cat
堆叠
cat
可以把两个或多个tensor沿着指定的维度进行连接,连接后的tensor维度个数不变,指定维度上的大小改变,非指定维度上的大小不变。譬如,两个shape=(3,)
行向量按dim=0
连接,变成1个shape=(6,)
的行向量;2个3阶方阵按dim=0
连接,就变成1个(6, 3)
的矩阵。
cat
在使用时对输入的这些tensor有要求:除了指定维度,其他维度的大小必须相同。譬如,1个shape=(1, 6)
的矩阵可以和1个shape=(2, 6)
的矩阵在dim=0
连接。
例子可以参考下面的定义和注释。
def cat(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor:
r"""
cat(tensors, dim=0, *, out=None) -> Tensor
Concatenates the given sequence of :attr:`seq` tensors in the given dimension.
All tensors must either have the same shape (except in the concatenating
dimension) or be a 1-D empty tensor with size ``(0,)``.
:func:`torch.cat` can be seen as an inverse operation for :func:`torch.split`
and :func:`torch.chunk`.
:func:`torch.cat` can be best understood via examples.
.. seealso::
:func:`torch.stack` concatenates the given sequence along a new dimension.
Args:
tensors (sequence of Tensors): any python sequence of tensors of the same type.
Non-empty tensors provided must have the same shape, except in the
cat dimension.
dim (int, optional): the dimension over which the tensors are concatenated
Keyword args:
out (Tensor, optional): the output tensor.
Example::
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497]])
>>> torch.cat((x, x, x), 0)
tensor([[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497],
[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497],
[ 0.6580, -1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497]])
>>> torch.cat((x, x, x), 1)
tensor([[ 0.6580, -1.0969, -0.4614, 0.6580, -1.0969, -0.4614, 0.6580,
-1.0969, -0.4614],
[-0.1034, -0.5790, 0.1497, -0.1034, -0.5790, 0.1497, -0.1034,
-0.5790, 0.1497]])
"""
...
- 返回值与原tensor不共享内存!
stack
堆叠
stack
与cat
有很大的区别,stack
把两个或多个tensor在dim
上创建一个全新的维度进行连接,非指定维度个数不变,创建的维度的大小取决于这次连接使用了多少个tensor。譬如,3个shape=(3,)
行向量按dim=0
连接,会变成一个shape=(3, 3)
的矩阵;两个3阶方阵按dim=-1
连接,就变成一个(3, 3, 2)
的tensor。
def stack(tensors: Union[Tuple[Tensor, ...], List[Tensor]], dim: _int = 0, *, out: Optional[Tensor] = None) -> Tensor:
r"""
stack(tensors, dim=0, *, out=None) -> Tensor
Concatenates a sequence of tensors along a new dimension.
All tensors need to be of the same size.
.. seealso::
:func:`torch.cat` concatenates the given sequence along an existing dimension.
Arguments:
tensors (sequence of Tensors): sequence of tensors to concatenate
dim (int, optional): dimension to insert. Has to be between 0 and the number
of dimensions of concatenated tensors (inclusive). Default: 0
Keyword args:
out (Tensor, optional): the output tensor.
Example::
>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.3367, 0.1288, 0.2345],
[ 0.2303, -1.1229, -0.1863]])
>>> x = torch.stack((x, x)) # same as torch.stack((x, x), dim=0)
>>> x
tensor([[[ 0.3367, 0.1288, 0.2345],
[ 0.2303, -1.1229, -0.1863]],
[[ 0.3367, 0.1288, 0.2345],
[ 0.2303, -1.1229, -0.1863]]])
>>> x.size()
torch.Size([2, 2, 3])
>>> x = torch.stack((x, x), dim=1)
tensor([[[ 0.3367, 0.1288, 0.2345],
[ 0.3367, 0.1288, 0.2345]],
[[ 0.2303, -1.1229, -0.1863],
[ 0.2303, -1.1229, -0.1863]]])
>>> x = torch.stack((x, x), dim=2)
tensor([[[ 0.3367, 0.3367],
[ 0.1288, 0.1288],
[ 0.2345, 0.2345]],
[[ 0.2303, 0.2303],
[-1.1229, -1.1229],
[-0.1863, -0.1863]]])
>>> x = torch.stack((x, x), dim=-1)
tensor([[[ 0.3367, 0.3367],
[ 0.1288, 0.1288],
[ 0.2345, 0.2345]],
[[ 0.2303, 0.2303],
[-1.1229, -1.1229],
[-0.1863, -0.1863]]])
"""
...
- 返回值与原tensor不共享内存!
view
改变形状
view
先把数据变成一维数组,然后再转换成指定形状。变换前后的元素个数并不会改变,所以变换前后的shape的乘积必须相等。详细例子如下:
def view(self, *shape): # real signature unknown; restored from __doc__
"""
Example::
>>> x = torch.randn(4, 4)
>>> x.size()
torch.Size([4, 4])
>>> y = x.view(16)
>>> y.size()
torch.Size([16])
>>> z = x.view(-1, 8) # the size -1 is inferred from other dimensions
>>> z.size()
torch.Size([2, 8])
>>> a = torch.randn(1, 2, 3, 4)
>>> a.size()
torch.Size([1, 2, 3, 4])
>>> b = a.transpose(1, 2) # Swaps 2nd and 3rd dimension
>>> b.size()
torch.Size([1, 3, 2, 4])
>>> c = a.view(1, 3, 2, 4) # Does not change tensor layout in memory
>>> c.size()
torch.Size([1, 3, 2, 4])
>>> torch.equal(b, c)
False
"""
return _te.Tensor(*(), **{})
- 返回值与原tensor共享内存
reshape
改变形状
reshape
与view
的区别如下:
view
只能改变连续(.contiguous())的tensor,如果已经对tensor进行了permute、transpose等操作,tensor在内存中会变得不连续,此时调用view
会报错。且view
方法与原来的tensor共享内存。reshape
再调用时自动检测原tensor是否连续,如果是,则等价于view
;如果不是,先调用.contiguous()
,再调用view
,此时返回值与原来tensor不共享内存。
def reshape(self, shape: Sequence[Union[_int, SymInt]]) -> Tensor:
...
1.3.8 数学运算
def mean(self, dim=None, keepdim=False, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__
...
def sum(self, dim=None, keepdim=False, dtype=None): # real signature unknown; restored from __doc__
...
def median(self, dim=None, keepdim=False): # real signature unknown; restored from __doc__
...
def mode(self, dim=None, keepdim=False): # real signature unknown; restored from __doc__
...
def dist(self, other, p=2): # real signature unknown; restored from __doc__
...
def std(self, dim, unbiased=True, keepdim=False): # real signature unknown; restored from __doc__
...
def var(self, dim, unbiased=True, keepdim=False): # real signature unknown; restored from __doc__
...
def cumsum(self, dim, dtype=None): # real signature unknown; restored from __doc__
...
def cumprod(self, dim, dtype=None): # real signature unknown; restored from __doc__
...
1.3.9 使用指定设备计算tensor
to
可以把tensor转移到指定设备上。
def to(self, *args, **kwargs): # real signature unknown; restored from __doc__
"""
Example::
>>> tensor = torch.randn(2, 2) # Initially dtype=float32, device=cpu
>>> tensor.to(torch.float64)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64)
>>> cuda0 = torch.device('cuda:0')
>>> tensor.to(cuda0)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], device='cuda:0')
>>> tensor.to(cuda0, dtype=torch.float64)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
>>> other = torch.randn((), dtype=torch.float64, device=cuda0)
>>> tensor.to(other, non_blocking=True)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
"""
return _te.Tensor(*(), **{})
2.线性回归模型
2.1 自动求导机制
- 在pytorch中,如果设置一个 tensor 的属性 requires_grad 为 True,那么它将会追踪对于该张量的所有操作。当完成计算后可以通过调用 tensor.backward 函数,来自动计算所有的梯度。这个张量的所有梯度将会自动累加到 grad 属性。
- 由于是累加,因此在进行线性回归模型的计算时,每轮都要用 tensor.zero_ 函数清空一次 grad 属性
示例
sequence_tensor=torch.tensor(np.array([[[1,2,3],
[4,5,6]],
[[9,8,7],
[6,5,4]]]),
dtype=torch.float,device=gpu,requires_grad=True)
multi_tensor=sequence_tensor*3+1
multi_tensor_mean=multi_tensor.mean()
multi_tensor_mean.backward()
print(sequence_tensor.grad)
输出
tensor([[[0.2500, 0.2500, 0.2500],
[0.2500, 0.2500, 0.2500]],
[[0.2500, 0.2500, 0.2500],
[0.2500, 0.2500, 0.2500]]], device='cuda:0')
2.2 nn.Module的继承(from torch import nn)
2.2.1 概述
nn.Module是torch.nn提供的一个类,是pytorch中定义网络的必要的一个父类,在这个类中定义了很多有用的方法,使我们非常方便地计算。在我们进行网络的定义时,有两个地方需要特别注意:
- 在定义成员变量时必须调用super函数,继承父类__init__参数,即,在__init__中必须调用super(<the name of the variable>,self)函数
- 通常还会在__init__中定义网络的结构
- 必须定义forward函数,表示网络中前向传播的过程
2.2.2 实例
class lr(nn.Module):
def __init__(self):
super(lr,self).__init__()
self.linear=nn.Linear(1,1)
def forward(self,x):
y_predict=self.linear(x)
return y_predict
其中,nn.Linear函数的参数为:输入的特征量,输出的特征量。
2.3 优化器类(from torch import optim)
2.3.1 概述
优化器(optimizer),用来操纵参数的梯度以更新参数,常见的方法有随机梯度下降(stochastic gradient descent)(SGD)等。
- torch.optim.SGD(参数,float 学习率)
- torch.optim.Adam(参数,float 学习率)
2.3.2 流程
- 调用Module.parameters函数获取模型参数,并定义学习率,进行实例化
- 用实例化对象调同 .zero_grad 函数,将参数重置为0
- 调用tensor.backward函数反向传播,获得梯度
- 用实例化对象调用 .step 函数更新参数
2.3.3 动态学习率(import torch.optim.lr_scheduler)
lr_scheduler允许模型在训练的过程中动态更新学习率,且提供了许多种策略可供选择,以下列举一些常用的:
指数衰减:在训练的过程中,学习率以设定的gamma参数进行指数的衰减。
class ExponentialLR(LRScheduler):
"""Decays the learning rate of each parameter group by gamma every epoch.
When last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
gamma (float): Multiplicative factor of learning rate decay.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool): If ``True``, prints a message to stdout for
each update. Default: ``False``.
"""
def __init__(self, optimizer, gamma, last_epoch=-1, verbose=False):
self.gamma = gamma
super().__init__(optimizer, last_epoch, verbose)
固定步长衰减:在固定的训练周期后,以指定的频率进行衰减。
class StepLR(LRScheduler):
"""Decays the learning rate of each parameter group by gamma every
step_size epochs. Notice that such decay can happen simultaneously with
other changes to the learning rate from outside this scheduler. When
last_epoch=-1, sets initial lr as lr.
Args:
optimizer (Optimizer): Wrapped optimizer.
step_size (int): Period of learning rate decay.
gamma (float): Multiplicative factor of learning rate decay.
Default: 0.1.
last_epoch (int): The index of last epoch. Default: -1.
verbose (bool): If ``True``, prints a message to stdout for
each update. Default: ``False``.
Example:
>>> # xdoctest: +SKIP
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05 if epoch < 30
>>> # lr = 0.005 if 30 <= epoch < 60
>>> # lr = 0.0005 if 60 <= epoch < 90
>>> # ...
>>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
>>> for epoch in range(100):
>>> train(...)
>>> validate(...)
>>> scheduler.step()
"""
def __init__(self, optimizer, step_size, gamma=0.1, last_epoch=-1, verbose=False):
self.step_size = step_size
self.gamma = gamma
super().__init__(optimizer, last_epoch, verbose)
- 用法:创建scheduler的时候绑定optimizer对象,然后在调用
optimizer.step()
后面跟着scheduler.step()
即可。
2.4 代价函数(from torch import nn)
在torch.nn中已经定义好了很多代价函数,只需要调用它们并且传入真实值、预测值,就可以返回结果,例如:
- 均方误差:nn.MSELoss()
- 交叉熵误差:nn.CrossEntropyLoss()
当然,也可以自己定义loss的计算过程。
2.5 评估模型
- Module.eval()表示设置模型为评估模式,即预测模式
- Module.train(mdoe=True)表示设置模型为训练模式
2.6 线性回归模型的建立
2.6.1 流程
- 定义网络,注意:实现super函数和forward函数
- 准备数据
- 实例化网络、代价函数、优化器
- 进行循环,调用Module.forward函数前向传播,调用代价函数进行计算,调用优化器类进行参数更新
- 使用pyplot进行模型评估
2.6.2 示例
if __name__=="__main__":
import torch
import numpy as np
from torch import nn
from torch import optim
from matplotlib import pyplot
gpu=torch.device("cuda")
cpu="cpu"
#定义网络
class lr(nn.Module):
def __init__(self):
#继承成员变量
super(lr,self).__init__()
self.linear=nn.Linear(1,1)
#定义前向传播函数
def forward(self,x):
y_predict=self.linear(x)
return y_predict
#准备数据
x_train=torch.rand([200,1],device=gpu)
y_train=torch.matmul(x_train,torch.tensor([[3]],dtype=torch.float32,requires_grad=True,device=gpu))+8
#实例化
model_lr=lr().to(gpu)
optimizer=optim.SGD(model_lr.parameters(),0.02)
cost_fn=nn.MSELoss()
#开始计算
for i in range(1000):
y_predict=model_lr.forward(x_train)
cost=cost_fn(y_predict,y_train)
optimizer.zero_grad()
cost.backward(retain_graph=True)
optimizer.step()
if i%20==0:
print(cost.item())
print(list(model_lr.parameters()))
#进行预测与评估
model_lr.eval()
y_predict_numpy=model_lr.forward(x_train).to(cpu).detach().numpy()
x_train_numpy=x_train.to(cpu).detach().numpy()
y_train_numpy=y_train.to(cpu).detach().numpy()
pyplot.scatter(x_train_numpy,y_predict_numpy,c="r")
pyplot.plot(x_train_numpy,y_train_numpy)
pyplot.show()
输出
4.7310328227467835e-05
[Parameter containing:
tensor([[3.0237]], device='cuda:0', requires_grad=True), Parameter containing:
tensor([7.9876], device='cuda:0', requires_grad=True)]
绘制图
3.数据集和数据加载器 (from torch.utils.data import Dataset,DataLoader)
3.1 Dataset类的继承(from torch.utils.data import Dataset)
3.1.1 概述
在pytorch中提供了数据集的父类torch.utils.data.Dataset,继承这个父类,我们可以非常快速地实现对数据的加载,与继承nn.Module类一样,我们同样必须定义一些必要的成员函数
- __getitem__(self,index),用来进行索引,可以用 [ ]
- __len__(self),用来获取元素个数
3.1.2 实例
SMSData_path="D:\Desktop\PycharmProjects\exercise\SMSSpamCollection"
#数据来源:http://archive.ics.uci.edu/ml/machine-learning-databases/00228/
class SMSData(Dataset):
def __init__(self):
self.data=open(SMSData_path,"r",encoding="utf-8").readlines()
def __getitem__(self, index):
current_line=self.data[index].strip()
label=current_line[:4].strip()
content=current_line[4:].strip()
return [label,content]
def __len__(self):
return len(self.data)
SMSex=SMSData()
print(SMSex.__getitem__(5))
print(SMSex.__len__())
输出
['spam', "FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv"]
5574
3.2 DataLoader类
3.2.1 API
class DataLoader(Generic[T_co]):
def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1,
shuffle: Optional[bool] = None, sampler: Union[Sampler, Iterable, None] = None,
batch_sampler: Union[Sampler[Sequence], Iterable[Sequence], None] = None,
num_workers: int = 0, collate_fn: Optional[_collate_fn_t] = None,
pin_memory: bool = False, drop_last: bool = False,
timeout: float = 0, worker_init_fn: Optional[_worker_init_fn_t] = None,
multiprocessing_context=None, generator=None,
*, prefetch_factor: int = 2,
persistent_workers: bool = False,
pin_memory_device: str = ""):
#只列出参数表,以下详细内容不再列出
dataset:以Dataset类为父类的自定义类的实例化对象
batch_size:批处理的个数
shuffle:bool类型,若为True则表示提前打乱数据
num_workers:加载数据时用到的线程数
drop_last :bool类型,若为True:这个是对最后的未完成的batch来说的,比如你的batch_size设置为64,而一个训练集只有100个样本,那么训练的时候后面的36个就被扔掉了。如果为False(默认),那么会继续正常执行,只是最后的batch_size会小一点。
timeout:如果是正数,表明等待从worker进程中收集一个batch等待的时间,若超出设定的时间还没有收集到,那就不收集这个内容了。这个numeric应总是大于等于0,默认为0
3.2.2 示例
import torch
from torch.utils.data import Dataset,DataLoader
import chardet
gpu = torch.device("cuda")
cpu="cpu"
try:
SMSData_path="SMSSpamCollection"
#获取文件编码方式
with open(SMSData_path,"rb") as file:
file_format=chardet.detect(file.read())["encoding"]
class SMSData(Dataset):
def __init__(self):
self.data=open(SMSData_path,"r",encoding=file_format).readlines()
def __getitem__(self, index):
current_line=self.data[index].strip()
origin=current_line[:4].strip()
content=current_line[4:].strip()
return [origin,content]
def __len__(self):
return len(self.data)
SMSex=SMSData()
SMSData_loader=DataLoader(dataset=SMSex,batch_size=2,shuffle=False,num_workers=2)
if __name__=='__main__':#如果设置多线程,一定要加这句话,否则会报错
for i in SMSData_loader:
print("遍历一:",i)
break
for i in enumerate(SMSData_loader):
print("遍历二:",i)
break
for batch_index,(label,content) in enumerate(SMSData_loader):
print("遍历三:",batch_index,label,content)
break
except BaseException as error:
print(error)
输出
遍历一: [('ham', 'ham'), ('Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'Ok lar... Joking wif u oni...')]
遍历二: (0, [('ham', 'ham'), ('Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'Ok lar... Joking wif u oni...')])
遍历三: 0 ('ham', 'ham') ('Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'Ok lar... Joking wif u oni...')
- 可见,DataLoader是一个可遍历对象,每轮中返回的数据以列表的方式存储,且列表中每个元素都是一个元组,列表的长度等于Dataset.__getitem__返回的列表长度,元组的长度等于batch_size参数的大小
4.图像处理:手写数字识别
4.1 torchvision模块
4.1.1 transforms.ToTensor类(仿函数)
class ToTensor:
def __init__(self) -> None:
_log_api_usage_once(self)
- 将原始的PILImage数据类型或者numpy.array数据类型化为tensor数据类型。
- 如果 PIL Image 属于 (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)中的一种图像类型,或者 numpy.ndarray 格式数据类型是 np.uint8 ,则将 [0, 255] 的数据转为 [0.0, 1.0] ,也就是说将所有数据除以 255 进行归一化。
4.1.2 transforms.Normalize类(仿函数)
class Normalize(torch.nn.Module):
def __init__(self, mean, std, inplace=False):
super().__init__()
_log_api_usage_once(self)
self.mean = mean
self.std = std
self.inplace = inplace
mean:数据类型为元组,元组的长度取决于通道数
std:数据类型为元组,元组的长度取决于通道数
- 此函数可以将tensor进行标准化,使其在每个通道上都转化为均值为mean,标准差为std的高斯分布。
4.1.3 transforms.Compose类(仿函数)
class Compose:
def __init__(self, transforms):
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(self)
self.transforms = transforms
transforms:数据类型为列表,列表中每个元素都是transforms模块中的一个类,如ToTensor和Normalize(隐式构造)。
- 此函数可以将许多transforms类结合起来同时使用。
4.1.4 示例
import torchvision
if __name__ == '__main__':
MNIST=torchvision.datasets.MNIST(root="./data",train=True,download=False,transform=None)
MNIST_normalize=torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0),(1))])(MNIST[0][0])
print(MNIST_normalize)
4.2 网络构建
4.2.1 激活函数大全
- 在pytorch中已经实现了上述很多的激活函数,下面我们将使用ReLU激活函数进行网络构建。
4.2.2 演示代码(在gpu上)
import torchvision
import torch
from torch.utils.data import DataLoader
from torch import nn
from torch import optim
from torch.nn import functional as Activate
from matplotlib import pyplot
# 定义所用网络
class ExNet(nn.Module):
def __init__(self):
# super函数调用
super(ExNet, self).__init__()
# 卷积层1
self.conv1 = nn.Conv2d(1, 15, 5)
'''
输入通道数1,输出通道数15,核的大小5,输入必须为1,输出可以自定义
'''
# 卷积层2
self.conv2 = nn.Conv2d(15, 30, 3)
'''
输入通道数15,输出通道数30,核的大小3,输入必须与上层的输出一致,输出可以自定义
'''
# 全连接层1
self.fully_connected_1 = nn.Linear(30 * 10 * 10, 40)
'''
MNIST原始图像是1*28*28,输入为batch_size*1*28*28,经过卷积层1后,变为batch_size*15*24*24
经过池化层后,变为batch_size*15*12*12
经过卷积层2后,变为batch_size*30*10*10
这个全连接层的第一层输入个数就是这么来的
'''
# 全连接层2
self.fully_connected_2 = nn.Linear(40, 10)
'''
输入与上层保持一致
由于要鉴别十个数字,因此输出层的神经元个数必须是10
'''
# 定义前向传播
def forward(self, x):
in_size = x.size(0) # 在本例中in_size,也就是BATCH_SIZE的值。输入的x可以看成是batch_size*1*28*28的张量。
# 卷积层1
out = self.conv1(x) # batch*1*28*28 -> batch*15*24*24
out = Activate.relu(out) # 调用ReLU激活函数
# 池化层
out = Activate.max_pool2d(out, 2, 2) # batch*15*24*24 -> batch*15*12*12(2*2的池化层会减半)
# 卷积层2
out = self.conv2(out) # batch*15*12*12 -> batch*30*10*10
out = Activate.relu(out) # 调用ReLU激活函数
# flatten处理
out = out.view(in_size, -1)
# 全连接层1
out = self.fully_connected_1(out)
out = Activate.relu(out)
# 全连接层2
out = self.fully_connected_2(out)
# 归一化处理,以便进行交叉熵代价函数的运算
out = Activate.log_softmax(out, dim=1)
return out
# 开始训练
def train(the_model, the_device, train_loader, the_optimizer, the_epoch):
# 模型相关设置
the_model=the_model.to(device=the_device)
the_model.train(mode=True)
# 用来绘制图像的变量
list_times = []
list_cost = []
# 每轮循环
for batch_idx, (data, target) in enumerate(train_loader):
# 转移到指定设备上计算
data = data.to(the_device);target = target.to(the_device)
# 优化器参数重置
the_optimizer.zero_grad()
# 向前计算
output = the_model.forward(data)
# 计算误差
cost = Activate.nll_loss(output, target)
# 反向传播
cost.backward()
# 参数更新
the_optimizer.step()
# 打印信息
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
the_epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), cost.item()))
print(batch_idx, cost.item())
list_times.append(batch_idx)
list_cost.append(cost.item())
# 绘制图像
pyplot.scatter(list_times, list_cost)
pyplot.savefig("costImage.jpg")
pyplot.show()
return
def test(the_model, the_device, the_test_loader):
# 设置训练模式
the_model=the_model.to(device=the_device)
the_model.eval()
# 测试的结果集
acc_vector = []
cost_vector = []
#开始测试
with torch.no_grad():
for index, (data, target) in enumerate(the_test_loader):
# 转移到指定设备上计算
data = data.to(the_device);target = target.to(the_device)
# 向前计算
output = the_model.forward(data)
# 计算误差
cost = Activate.nll_loss(output, target)
cost_vector.append(cost)
pred = output.max(dim=1)[-1] # output的尺寸是[batch_size,10],对每行取最大值,返回索引编号,即代表模型预测手写数字的结果
cur_acc = pred.eq(target).float().mean() # 均值代表每组batch_size中查准率
acc_vector.append(cur_acc)
# 打印结果
print("平均查准率:{}".format(sum(acc_vector)/len(acc_vector)))
print("average cost:{}".format(sum(cost_vector)/len(cost_vector)))
return
if __name__ == '__main__':
gpu = torch.device("cuda")
cpu = "cpu"
# 准备数据
transAndNorm = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0), (1))])
MNISTData = torchvision.datasets.MNIST(root="./data", train=True, download=False, transform=transAndNorm)
MNISTtest = torchvision.datasets.MNIST(root="./data", train=False, download=False, transform=transAndNorm)
MNISTData_loader = DataLoader(dataset=MNISTData, batch_size=10, shuffle=True)
MNISTtest_loader = DataLoader(dataset=MNISTtest, batch_size=10, shuffle=True)
# 实例化网络和优化器
MNISTnet_Ex = ExNet()
MNIST_optimizer = optim.Adam(MNISTnet_Ex.parameters(), lr=0.001) # lr(learning rate)是学习率
for i in range(1,2):
train(the_model=MNISTnet_Ex, the_device=gpu, train_loader=MNISTData_loader, the_optimizer=MNIST_optimizer, the_epoch=i)
test(the_model=MNISTnet_Ex, the_device=gpu, the_test_loader=MNISTtest_loader)
输出
平均查准率:0.9804015159606934
average cost:0.061943911015987396
散点图
5.制作图片数据集(以flower102为例)
在刚刚的MNIST手写数字识别分类任务中,我们使用的数据集是pytorch官方内置的图片数据集。现在,我们要从零开始,尝试制作我们自己的数据集。
Oxford 102 Flower 是一个图像分类数据集,由 102 个花卉类别组成。被选为英国常见花卉的花卉。每个类别由 40 到 258 张图像组成。图像具有大尺度、姿势和光线变化。此外,还有一些类别在类别内有很大的变化,还有几个非常相似的类别。这里是flower102数据集的下载地址。解压后的文件目录如下:
5.1 建立数据集骨架
如第三章一样建立即可,如下:
import torch
from torch.utils.data import Dataset
import os
gpu = torch.device("cuda")
cpu = "cpu"
class flower102(Dataset):
def __init__(self,root,resize,mode):
super(flower102,self).__init__()
pass
def __len__(self):
pass
def __getitem__(self, item):
pass
5.2 建立从名称到数字标签的映射
在训练集中,这102种花的类别名称如上图所示(我这里是经过重命名的),我们定义名称flower1
为数字标签1
,这样我们就建立了一个映射。接下来,稍微修改一下构造函数,就可以实现全部的映射。如下:
import csv
import glob
import random
import os
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
gpu = torch.device("cuda")
cpu = "cpu"
class flower102(Dataset):
def __init__(self, root, resize, mode):
super(flower102, self).__init__()
self.root = root
self.train_root = os.path.join(self.root, "train")
self.val_root = os.path.join(self.root, "valid")
self.test_root = os.path.join(self.root, "test")
self.resize = resize
self.mode = mode
self.mean = [0.485, 0.456, 0.406]
self.std = [0.229, 0.224, 0.225]
self.cat2label = {} # 创建一个空字典,用于存储映射关系。
for name in sorted(os.listdir(os.path.join(self.train_root))): # 遍历训练集目录下的文件和文件夹,并按照名称排序。
if not os.path.isdir(os.path.join(self.train_root, name)): # 如果遍历到的是文件而不是文件夹,则跳过该项继续遍历下一项。
continue
elif not (name in self.cat2label):
self.cat2label[name] = len(self.cat2label.keys()) # 将文件夹名称与类别标签对应,类别标签为字典长度(每次循环增加1)。
print(self.cat2label) # 打印映射关系字典。
def __len__(self):
pass
def __getitem__(self, idx):
pass
# 创建数据集实例
db = flower102(r"D:\Desktop\Datasets\flower102\dataset", resize=224, mode="train")
结果如下:
{'flower1': 0, 'flower10': 1, 'flower100': 2, 'flower101': 3, 'flower102': 4, 'flower11': 5, 'flower12': 6, 'flower13': 7, 'flower14': 8, 'flower15': 9, 'flower16': 10, 'flower17': 11, 'flower18': 12, 'flower19': 13, 'flower2': 14, 'flower20': 15, 'flower21': 16, 'flower22': 17, 'flower23': 18, 'flower24': 19, 'flower25': 20, 'flower26': 21, 'flower27': 22, 'flower28': 23, 'flower29': 24, 'flower3': 25, 'flower30': 26, 'flower31': 27, 'flower32': 28, 'flower33': 29, 'flower34': 30, 'flower35': 31, 'flower36': 32, 'flower37': 33, 'flower38': 34, 'flower39': 35, 'flower4': 36, 'flower40': 37, 'flower41': 38, 'flower42': 39, 'flower43': 40, 'flower44': 41, 'flower45': 42, 'flower46': 43, 'flower47': 44, 'flower48': 45, 'flower49': 46, 'flower5': 47, 'flower50': 48, 'flower51': 49, 'flower52': 50, 'flower53': 51, 'flower54': 52, 'flower55': 53, 'flower56': 54, 'flower57': 55, 'flower58': 56, 'flower59': 57, 'flower6': 58, 'flower60': 59, 'flower61': 60, 'flower62': 61, 'flower63': 62, 'flower64': 63, 'flower65': 64, 'flower66': 65, 'flower67': 66, 'flower68': 67, 'flower69': 68, 'flower7': 69, 'flower70': 70, 'flower71': 71, 'flower72': 72, 'flower73': 73, 'flower74': 74, 'flower75': 75, 'flower76': 76, 'flower77': 77, 'flower78': 78, 'flower79': 79, 'flower8': 80, 'flower80': 81, 'flower81': 82, 'flower82': 83, 'flower83': 84, 'flower84': 85, 'flower85': 86, 'flower86': 87, 'flower87': 88, 'flower88': 89, 'flower89': 90, 'flower9': 91, 'flower90': 92, 'flower91': 93, 'flower92': 94, 'flower93': 95, 'flower94': 96, 'flower95': 97, 'flower96': 98, 'flower97': 99, 'flower98': 100, 'flower99': 101}
5.3 建立csv数据
在建立了从名称到数字标签的映射后,我们希望有一个csv文件,里面存储了所有的图片路径及其数字标签,接下来,我们将定义一个load_csv函数去完成这件事,如下:
import csv
import glob
import random
import os
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
gpu = torch.device("cuda")
cpu = "cpu"
class flower102(Dataset):
def __init__(self, root, resize, mode):
super(flower102, self).__init__()
self.root = root
self.train_root = os.path.join(self.root, "train")
self.val_root = os.path.join(self.root, "valid")
self.test_root = os.path.join(self.root, "test")
self.resize = resize
self.mode = mode
self.mean = [0.485, 0.456, 0.406]
self.std = [0.229, 0.224, 0.225]
self.cat2label = {} # 创建一个空字典,用于存储映射关系。
for name in sorted(os.listdir(os.path.join(self.train_root))): # 遍历训练集目录下的文件和文件夹,并按照名称排序。
if not os.path.isdir(os.path.join(self.train_root, name)): # 如果遍历到的是文件而不是文件夹,则跳过该项继续遍历下一项。
continue
elif not (name in self.cat2label):
self.cat2label[name] = len(self.cat2label.keys()) # 将文件夹名称与类别标签对应,类别标签为字典长度(每次循环增加1)。
print(self.cat2label) # 打印映射关系字典。
if mode == "train":
self.images, self.labels = self.load_csv("images_train.csv")
elif mode == "valid":
self.images, self.labels = self.load_csv("images_valid.csv")
else:
raise Exception("invalid mode!", self.mode)
# 加载CSV文件并返回图像路径和标签列表
def load_csv(self, filename):
# 如果CSV文件不存在,则根据训练集目录和映射关系生成CSV文件
if not os.path.exists(os.path.join(self.root, filename)):
images = []
for name in self.cat2label.keys():
images += glob.glob(os.path.join(self.root, self.mode, name, "*.png"))
images += glob.glob(os.path.join(self.root, self.mode, name, "*.jpg"))
images += glob.glob(os.path.join(self.root, self.mode, name, "*.jpeg"))
random.shuffle(images)
with open(os.path.join(self.root, filename), mode="w", newline="") as f:
writer = csv.writer(f)
for img in images:
label = self.cat2label[img.split(os.sep)[-2]]
writer.writerow([img, label])
print("written into csv file:", filename)
# 从CSV文件中读取图像路径和标签
images = []
labels = []
with open(os.path.join(self.root, filename)) as f:
reader = csv.reader(f)
for row in reader:
img, label = row
label = int(label)
images.append(img)
labels.append(label)
assert len(images) == len(labels)
return images, labels
# 反归一化
def denormalize(self, x_hat):
pass
def __len__(self):
pass
def __getitem__(self, idx):
pass
# 创建数据集实例
db = flower102(r"D:\Desktop\Datasets\flower102\dataset", resize=224, mode="train")
然后,我们获得了一个如下的csv文件:
5.4 完善成员函数和transform过程
在完成了load_csv函数后,这个数据集基本制作完成,接下来只需要完善__len__函数和__getitem__函数,并定义transform过程即可。
import csv
import glob
import random
import os
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
gpu = torch.device("cuda")
cpu = "cpu"
class flower102(Dataset):
def __init__(self, root, resize, mode):
super(flower102, self).__init__()
self.root = root
self.train_root = os.path.join(self.root, "train")
self.val_root = os.path.join(self.root, "valid")
self.test_root = os.path.join(self.root, "test")
self.resize = resize
self.mode = mode
self.mean = [0.485, 0.456, 0.406]
self.std = [0.229, 0.224, 0.225]
self.cat2label = {} # 创建一个空字典,用于存储映射关系。
for name in sorted(os.listdir(os.path.join(self.train_root))): # 遍历训练集目录下的文件和文件夹,并按照名称排序。
if not os.path.isdir(os.path.join(self.train_root, name)): # 如果遍历到的是文件而不是文件夹,则跳过该项继续遍历下一项。
continue
elif not (name in self.cat2label):
self.cat2label[name] = len(self.cat2label.keys()) # 将文件夹名称与类别标签对应,类别标签为字典长度(每次循环增加1)。
print(self.cat2label) # 打印映射关系字典。
if mode == "train":
self.images, self.labels = self.load_csv("images_train.csv")
elif mode == "valid":
self.images, self.labels = self.load_csv("images_valid.csv")
else:
raise Exception("invalid mode!", self.mode)
# 加载CSV文件并返回图像路径和标签列表
def load_csv(self, filename):
# 如果CSV文件不存在,则根据训练集目录和映射关系生成CSV文件
if not os.path.exists(os.path.join(self.root, filename)):
images = []
for name in self.cat2label.keys():
images += glob.glob(os.path.join(self.root, self.mode, name, "*.png"))
images += glob.glob(os.path.join(self.root, self.mode, name, "*.jpg"))
images += glob.glob(os.path.join(self.root, self.mode, name, "*.jpeg"))
random.shuffle(images)
with open(os.path.join(self.root, filename), mode="w", newline="") as f:
writer = csv.writer(f)
for img in images:
label = self.cat2label[img.split(os.sep)[-2]]
writer.writerow([img, label])
print("written into csv file:", filename)
# 从CSV文件中读取图像路径和标签
images = []
labels = []
with open(os.path.join(self.root, filename)) as f:
reader = csv.reader(f)
for row in reader:
img, label = row
label = int(label)
images.append(img)
labels.append(label)
assert len(images) == len(labels)
return images, labels
# 反归一化
def denormalize(self, x_hat):
# x_hat = (x - mean) / std
# x = x_hat * std + mean
# x.size(): [c, h, w]
# mean.size(): [3] => [3, 1, 1]
mean = torch.tensor(self.mean).unsqueeze(1).unsqueeze(1)
std = torch.tensor(self.std).unsqueeze(1).unsqueeze(1)
x = x_hat * std + mean
return x
def __len__(self):
# 返回数据集中样本的数量
return len(self.images)
def __getitem__(self, idx):
# 根据索引获取图像和标签
img, label = self.images[idx], self.labels[idx]
# 定义数据的预处理操作
tf = transforms.Compose([
lambda x: Image.open(x).convert("RGB"), # 以RGB格式打开图像
transforms.Resize((int(self.resize * 1.25), int(self.resize * 1.25))), # 调整图像大小为resize的1.25倍
transforms.RandomRotation(15), # 随机旋转图像(最大旋转角度为15度)
transforms.CenterCrop(self.resize), # 将图像中心裁剪为resize大小
transforms.ToTensor(), # 将图像转换为Tensor类型
transforms.Normalize(mean=self.mean, std=self.std), # 归一化图像
])
# 对图像进行预处理操作
img = tf(img)
label = torch.tensor(label)
return img, label
# 创建数据集实例
db = flower102(r"D:\Desktop\Datasets\flower102\dataset", resize=224, mode="train")
5.5 DataLoader检验
if __name__=='__main__' :
loader = DataLoader(dataset=db, shuffle=True,num_workers=1,batch_size=8)
import matplotlib.pyplot as plt
data,target=next(iter(db))
print(data.shape)
plt.imshow(transforms.ToPILImage()(db.denormalize(data)))
plt.show()
成功显示:
6.迁移学习
6.1 现有模型的保存和加载
6.1.1 保存(torch.save函数)
我们要保存的是:
- 实例化的网络的数据
- 实例化的优化器的数据
def save(
obj: object,
f: FILE_LIKE,
pickle_module: Any = pickle,
pickle_protocol: int = DEFAULT_PROTOCOL,
_use_new_zipfile_serialization: bool = True
) -> None:...
- 我们只需要把string类型的文件名作为参数输入即可
把数据加载进网络
- Module.load_state_dict函数,我们只需要用torch.load函数的返回值作为参数即可
把数据加载进优化器
- optim.load_state_dict函数,我们只需要用torch.load函数的返回值作为参数即可
6.1.3 示例
torch.save(MNISTnet_Ex.state_dict(),"MNIST.pt")
torch.save(optimzer.state_dict(),"optimizer.pt")
MNISTnet_Ex.load_state_dict(torch.load("MNIST.pt"))
optimzer.load_state_dict(torch.load("optimizer.pt"))
6.2 使用预训练的模型(以resnet50为例)
pytoch官方提供了不少与训练的模型可供使用,如下:
关于这些模型的详细用途,可以自行前往pytorch官网查阅相关资料,具体原理本文不再涉及。
6.2.1 确定初始化参数
在使用预训练模型的过程中,最重要的一步是,确定这个预训练模型中哪些参数是需要训练的,哪些参数是不需要训练的,哪些参数是要修改的。
首先,查看一下resnet50的网络结构:
import torchvision.models as models
print(models.resnet50(pretrained=True))
Resnet(
...
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=2048, out_features=1000, bias=True)
)
看到最后一层是一个1000分类的全连接层,而我们第五章制作的数据集里,只需要102分类,因此,我们选择只修改最后一层的参数并训练。如下所示:
import torchvision.models as models
import torch.nn as nn
def set_parameter_requires_grad(model,need_train):
if not need_train:
for para in model.parameters():
para.requires_grad = False
return
def initalize_resnet50(num_classes,need_train=False,pretrained=True):
trained_model=models.resnet50(pretrained=pretrained)
input_size=224
set_parameter_requires_grad(trained_model, need_train)
trained_model.fc = nn.Sequential(
nn.Linear(trained_model.fc.in_features, num_classes),
nn.LogSoftmax(dim=1),
)
# trained_model.fc = nn.Sequential(
# nn.Linear(trained_model.fc.in_features, num_classes),
# nn.Flatten(),
# )
return trained_model,input_size
resnet50,input_size=initalize_resnet50(num_classes=102,need_train=False,pretrained=True)
6.3 开始训练
训练的流程和记录如第四章所示即可,如下:
import copy # 导入copy模块,用于深拷贝对象
import os.path # 导入os.path模块,用于操作文件路径
import time # 导入time模块,用于计时
def train(model, dataLoader, criterion, optimzer, num_epoch, device, filename):
"""
训练函数
Args:
model: 模型对象
dataLoader: 数据加载器
criterion: 损失函数
optimzer: 优化器
num_epoch: 迭代次数
device: 计算设备
filename: 保存模型的文件名
Returns:
model: 训练后的模型
train_acc_history: 训练集准确率历史
train_losses: 训练集损失历史
l_rs: 优化器学习率历史
"""
since = time.time() # 获取当前时间
best_epoch = {"epoch": -1,
"acc": 0
} # 存储最佳模型的epoch和准确率
model.to(device) # 将模型移动到计算设备上
train_acc_history = [] # 存储训练集准确率历史
train_losses = [] # 存储训练集损失历史
l_rs = [optimzer.param_groups[0]['lr']] # 存储优化器学习率历史
best_model_wts = copy.deepcopy(model.state_dict()) # 深拷贝当前模型的权重作为最佳模型权重
for epoch in range(num_epoch): # 迭代训练
print("Epoch {}/{}".format(epoch, num_epoch - 1))
print('*' * 10)
running_loss = 0.0 # 初始化损失总和
running_correct = 0.0 # 初始化正确预测的样本数总和
for data, target in dataLoader: # 遍历数据加载器中的每个批次
data = data.to(device) # 将输入数据移动到计算设备上
target = target.to(device) # 将目标数据移动到计算设备上
optimzer.zero_grad() # 清零梯度
output = model.forward(data) # 前向传播
loss = criterion(output, target) # 计算损失
pred = output.argmax(dim=1) # 获取预测结果
loss.backward() # 反向传播
optimzer.step() # 更新参数
running_loss += loss.item() * data.size(0) # 累加损失
running_correct += torch.eq(pred, target).sum().float().item() # 累加正确预测的样本数
epoch_loss = running_loss / len(dataLoader.dataset) # 计算平均损失
epoch_acc = running_correct / len(dataLoader.dataset) # 计算准确率
time_elapsed = time.time() - since # 计算训练时间
print("Time elapsed {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))
print("Loss: {:4f} Acc:{:.4f}".format(epoch_loss, epoch_acc))
train_acc_history.append(epoch_acc) # 将准确率添加到历史列表中
train_losses.append(epoch_loss) # 将损失添加到历史列表中
if (epoch_acc > best_epoch["acc"]): # 更新最佳模型信息
best_epoch = {
"epoch": epoch,
"acc": epoch_acc
}
best_model_wts = copy.deepcopy(model.state_dict()) # 深拷贝当前模型权重作为最佳模型权重
state = {
"state_dict": model.state_dict(),
"best_acc": best_epoch["acc"],
"optimzer": optimzer.state_dict(),
}
torch.save(state, filename) # 保存最佳模型的状态字典到文件
print("Optimzer learning rate : {:.7f}".format(optimzer.param_groups[0]['lr'])) # 打印当前优化器学习率
l_rs.append(optimzer.param_groups[0]['lr']) # 将当前优化器学习率添加到历史列表中
print()
time_elapsed = time.time() - since # 计算总训练时间
print("Training complete in {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))
print("Best epoch:", best_epoch)
model.load_state_dict(best_model_wts) # 加载最佳模型权重
return model, train_acc_history, train_losses, l_rs
if __name__ == "__main__":
import torch
import Net
import torch.nn as nn
import torch.optim as optim
optimzer = optim.Adam(params=Net.resnet50.parameters(), lr=1e-2) # 创建Adam优化器
sche = optim.lr_scheduler.StepLR(optimizer=optimzer, step_size=10, gamma=0.5) # 创建学习率调度器
criterion = nn.NLLLoss() # 创建负对数似然损失函数
#criterion=nn.CrossEntropyLoss()
import flower102
from torch.utils.data import DataLoader
db = flower102.flower102(r"D:\Desktop\Datasets\flower102\dataset", resize=Net.input_size, mode="train") # 创建数据集对象
loader = DataLoader(dataset=db, shuffle=True, num_workers=1, batch_size=5) # 创建数据加载器
model = Net.resnet50 # 创建模型对象
filename = "checkpoint.pth" # 模型保存文件名
if os.path.exists(filename): # 如果存在模型文件
checkpoint = torch.load(filename) # 加载模型状态字典
model.load_state_dict(checkpoint["state_dict"]) # 加载模型权重
model, train_acc_history, train_loss, LRS = train(model=model, dataLoader=loader, criterion=criterion,
optimzer=optimzer, num_epoch=5,
device=torch.device("cuda"), filename=filename)
下面是我训练5轮的结果:
Epoch0/4
**********
Time elapsed 0m 37s
Loss: 11.229704 Acc:0.3515
Optimzer learning rate : 0.0100000
Epoch1/4
**********
Time elapsed 1m 12s
Loss: 8.165128 Acc:0.5697
Optimzer learning rate : 0.0100000
Epoch2/4
**********
Time elapsed 2m 4s
Loss: 7.410833 Acc:0.6363
Optimzer learning rate : 0.0100000
Epoch3/4
**********
Time elapsed 2m 60s
Loss: 6.991850 Acc:0.6822
Optimzer learning rate : 0.0100000
Epoch4/4
**********
Time elapsed 3m 44s
Loss: 6.482804 Acc:0.7128
Optimzer learning rate : 0.0100000
Training complete in 3m 44s
Best epoch: {'epoch': 4, 'acc': 0.7127594627594628}
标签:__,教程,入门,self,torch,Pytorch,import,def,tensor From: https://www.cnblogs.com/UnderTurrets/p/18380855本文由博客一文多发平台 OpenWrite 发布!