PyTorch的参数固定以及detach clone

detach

detach()后的tensor与原tensor共享数据内存，当原始tensor在计算图中数值发生反向传播等更新之后，detach()的tensor值也发生了改变。

import torch
from torch import optim
from torch.nn import Parameter

x = torch.tensor(1.)
a = torch.tensor(1., requires_grad=True)
b = Parameter(torch.tensor(2.))

y = a**2 * x + b * x

z = y**2 + 2*y

optimizer = optim.SGD([a, b], lr=0.01)

ta = a.detach()
tb = b.detach()

print('before:', a, b, ta, tb)
print()

optimizer.zero_grad()
z.backward()
optimizer.step()

print('before:', a, b, ta, tb)

# before: tensor(1., requires_grad=True) Parameter containing:
# tensor(2., requires_grad=True) tensor(1.) tensor(2.)
#
# before: tensor(0.8400, requires_grad=True) Parameter containing:
# tensor(1.9200, requires_grad=True) tensor(0.8400) tensor(1.9200)

clone

clone使用了新的内存，当原始tensor在计算图中数值发生反向传播等更新之后，clone()的tensor值不会发生变化。

如果新的tensor从 tensor(requires_grad=True)或者Parameter 克隆而来，则其grad_fn=，即表示其仍可以作为中间节点传播梯度，相当于一次恒等映射。

import torch
from torch import optim
from torch.nn import Parameter

x = torch.tensor(1.)
a = torch.tensor(1., requires_grad=True)
b = Parameter(torch.tensor(2.))

y = a**2 * x + b * x

z = y**2 + 2*y

optimizer = optim.SGD([a, b], lr=0.01)

ta = a.clone()
tb = b.clone()

print('before:', a, b, ta, tb)
print()

optimizer.zero_grad()
z.backward()
optimizer.step()

print('before:', a, b, ta, tb)

# before: tensor(1., requires_grad=True) Parameter containing:
# tensor(2., requires_grad=True) tensor(1., grad_fn=<CloneBackward>) tensor(2., grad_fn=<CloneBackward>)
# 
# before: tensor(0.8400, requires_grad=True) Parameter containing:
# tensor(1.9200, requires_grad=True) tensor(1., grad_fn=<CloneBackward>) tensor(2., grad_fn=<CloneBackward>)

import torch
from torch import optim
from torch.nn import Parameter

x = torch.tensor(1.)
a = torch.tensor(1., requires_grad=True)
b = Parameter(torch.tensor(2.))

y = a**2 * x + b * x

z = y**2 + 2*y

# 从z -> z2相当于一个恒等映射，梯度还是能够传播的
z2 = z.clone()

optimizer = optim.SGD([a, b], lr=0.01)
print('before:', a, b)
print()

optimizer.zero_grad()
z2.backward()
optimizer.step()

print('before:', a, b)

# before: tensor(1., requires_grad=True) Parameter containing:
# tensor(2., requires_grad=True)
# 
# before: tensor(0.8400, requires_grad=True) Parameter containing:
# tensor(1.9200, requires_grad=True)

lambda

from functools import reduce

foo = [2, 18, 9, 22, 17, 24, 8, 12, 27]

# lambda语句中，冒号前是参数，可以有多个，用逗号隔开，冒号右边的返回值。
# 搭配filter
print(filter(lambda x: x % 3 == 0, foo))
print(list(filter(lambda x: x % 3 == 0, foo)))
print()

# 搭配map
print(map(lambda x: x * 2 + 10, foo))
print(list(map(lambda x: x * 2 + 10, foo)))
print()

# 搭配reduce
# reduce累加，lambda先计算x2 + y18 = 20，然后x20 + y9 = 29
print(reduce(lambda x, y: x + y, foo))

# <filter object at 0x000002206C252A88>
# [18, 9, 24, 12, 27]
# 
# <map object at 0x000002206C1FF608>
# [14, 46, 28, 54, 44, 58, 26, 34, 64]
# 
# 139

detach固定部分参数

缺点：只能固定detach之前的所有参数

# 在Net.forward里删除out = out.detach()，结果为：
# layer1.weight False
# layer1.bias False
# layer2.weight False
# layer2.bias False
# 即所有参数发生了变化（优化）

# 在Net.forward里添加out = out.detach()，结果为：
# layer1.weight True
# layer1.bias True
# layer2.weight False
# layer2.bias False
# 即self.layer1里的参数没有反生变化，这是因为out = out.detach()返回的tensor不能传播梯度，
# 所以在反向传播至该tensor时，不能再向前传播，所以其之前的参数将被锁定

import torch
from torch import nn
from torch.nn import functional as F
from torch import optim


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()

        self.layer1 = nn.Linear(10, 5)
        self.layer2 = nn.Linear(5, 3)

    def forward(self, x):
        out = self.layer1(x)
        out = out.detach()
        out = F.relu(self.layer2(out))
        return out


net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.01)

input = torch.randn(8, 10)

# 把训练前各个参数的值存储起来
storeParam = {
    }
for name, param in net.named_parameters():
    storeParam[name] = param.detach().clone()

for i in range(100):
    out = net(input)
    loss = F.mse_loss(out, torch.zeros(8, 3))
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# 比较训练前后各个参数的值是否相同
for name, param in net.named_parameters():
    print(f"{
      name} {
      torch.equal(param, storeParam[name])}")

requires_grad = False固定部分参数

# 通过self.layer1.weight.requires_grad = False，只固定该参数

# loss的requires_grad=False则其不能调用backward；某non-leaf Tensor的requires_grad=False则其之前的参数由于梯度反向传播时被截断，
# 所以不会得到更新；leaf Tensor的requires_grad=False，表示其不需要梯度，所以其也不能被更新。

import torch
from torch import nn
from torch.nn import functional as F
from torch import optim


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()

        self.layer1 = nn.Linear(10, 5)
        self.layer1.weight.requires_grad = False
        self.layer2 = nn.Linear(5, 3)

    def forward(self, x):
        out = self.layer1(x)
        out = F.relu(self.layer2(out))
        return out


net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.01)

input = torch.randn(8, 10)

# 把训练前各个参数的值存储起来
storeParam = {
    }
for name, param in net.named_parameters():
    storeParam[name] = param.detach().clone()

for i in range(100):
    out = net(input)
    loss = F.mse_loss(out, torch.zeros(8, 3))
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# 比较训练前后各个参数的值是否相同
for name, param in net.named_parameters():
    print(f"{
      name} {
      torch.equal(param, storeParam[name])}")
    
# layer1.weight True
# layer1.bias False
# layer2.weight False
# layer2.bias False