元学习中由于需要二次求导,因此使用tensorflow的形式实现是最方便的
from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from collections import OrderedDict
from model_meta import common
class g(nn.Module):
def __init__(self):
super(g, self).__init__()
self.k1 = nn.Conv2d(in_channels=2, out_channels=2, kernel_size=3, padding=1, bias=True)
self.bn = nn.BatchNorm2d(2)
self.act = nn.LeakyReLU(0.1)
self.k = nn.Conv2d(in_channels=2, out_channels=2, kernel_size=3, padding=1, bias=True)
self.ad = nn.AdaptiveAvgPool2d(1)
self.bn1 = nn.BatchNorm2d(2)
self.ln =nn.Linear(2,2)
def forward(self, z, weights,c):
# a, b = torch.topk(z, 2, dim=-1, largest=True, sorted=True)
# return a
# print(weights)
# print(weights["k1.weight"],weights["k1.weight"].shape)
# print("Sadkasjhd")
# print(self.bn.running_mean,self.bn.training , self.bn.track_running_stats)
if c:
z = common.conv2d(z, weights["k1.weight"], bias=weights["k1.bias"])
# z = F.conv2d(z,weights["k1.weight"],stride=1, padding=1)
# z = common.batchnorm(running_mean=None, running_var=None, training)
# print(self.bn.running_mean)
z = common.batchnorm(z, weight=weights["bn.weight"], bias=weights["bn.bias"], running_mean=self.bn.running_mean, running_var=self.bn.running_var, training=self.training)
z = F.leaky_relu(z, self.act.negative_slope)
# print("negative_slope",self.act.negative_slope)
z = common.conv2d(z, weights["k.weight"], bias=weights["k.bias"])
z = common.batchnorm(z, weight=weights["bn1.weight"], bias=weights["bn1.bias"],
running_mean=self.bn1.running_mean, running_var=self.bn1.running_var,
training=self.training)
z = F.leaky_relu(z, self.act.negative_slope)
z = self.ad(z).squeeze(-1).squeeze(-1)
z = common.linear(z,weights["ln.weight"],weights["ln.bias"])
else:
z = self.k1(z)
z = self.bn(z)
z = self.act(z)
z = self.k(z)
z = self.bn1(z)
z = self.act(z)
z = self.ad(z).squeeze(-1).squeeze(-1)
z = self.ln(z)
return z
net =g().eval()
c = 2
h = 5
w = 5
num=255.
weights = OrderedDict(
(name, param ) for (name, param) in net.named_parameters())
print(weights)
z = torch.rand(1, c , h , w).float().view(1, c, h, w)*num
k=net(z,weights,1)
print("***********V1************")
print(k)
print("************V2***********")
k=net(z,weights,0)
print(k)
结果:
OrderedDict([('k1.weight', Parameter containing:
tensor([[[[-0.2247, -0.1581, -0.0898],
[ 0.0360, 0.0034, -0.0012],
[ 0.1881, -0.2175, -0.1558]],
[[ 0.2345, -0.2052, 0.2291],
[ 0.1458, -0.0778, -0.0761],
[ 0.1458, 0.1497, 0.1909]]],
[[[-0.1647, 0.0314, -0.2093],
[-0.0598, 0.0189, -0.2058],
[-0.2004, 0.0625, 0.1661]],
[[-0.1550, 0.2228, 0.2277],
[-0.1925, 0.1914, -0.1848],
[-0.0585, 0.2001, 0.1779]]]], requires_grad=True)), ('k1.bias', Parameter containing:
tensor([ 0.1321, -0.2026], requires_grad=True)), ('bn.weight', Parameter containing:
tensor([0.6140, 0.3376], requires_grad=True)), ('bn.bias', Parameter containing:
tensor([0., 0.], requires_grad=True)), ('k.weight', Parameter containing:
tensor([[[[ 0.1454, -0.1201, -0.0085],
[ 0.0584, 0.1009, 0.1226],
[-0.1576, 0.1127, -0.0389]],
[[ 0.0483, 0.0248, 0.0990],
[-0.2266, -0.1486, -0.0324],
[-0.0946, 0.0063, 0.1903]]],
[[[ 0.0238, 0.0458, -0.1987],
[-0.1096, -0.1962, -0.1864],
[-0.1547, -0.0741, 0.1740]],
[[-0.0820, 0.2186, 0.0900],
[-0.0165, 0.0776, 0.0946],
[ 0.0113, -0.2241, 0.2184]]]], requires_grad=True)), ('k.bias', Parameter containing:
tensor([-0.1629, -0.0589], requires_grad=True)), ('bn1.weight', Parameter containing:
tensor([0.5731, 0.4600], requires_grad=True)), ('bn1.bias', Parameter containing:
tensor([0., 0.], requires_grad=True)), ('ln.weight', Parameter containing:
tensor([[ 0.3224, -0.5423],
[ 0.3757, 0.0196]], requires_grad=True)), ('ln.bias', Parameter containing:
tensor([-0.0116, -0.4640], requires_grad=True))])
***********************
tensor([[0.1756, 0.0552]], grad_fn=<AddmmBackward>)
***********************
tensor([[0.1756, 0.0552]], grad_fn=<AddmmBackward>)