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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GATConv, GINConv
from torch.autograd import Variable
import numpy as np
from base_model import *
from inits import glorot, zeros
import pdb
# adopted from: https://github.com/xjtushujun/meta-weight-net
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted from: Adrien Ecoffet https://github.com/AdrienLE
def params(self):
for name, param in self.named_params(self):
yield param
def named_leaves(self):
return []
def named_submodules(self):
return []
def named_params(self, curr_module=None, memo=None, prefix=''):
if memo is None:
memo = set()
if hasattr(curr_module, 'named_leaves'):
for name, p in curr_module.named_leaves():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
else:
for name, p in curr_module._parameters.items():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
for mname, module in curr_module.named_children():
submodule_prefix = prefix + ('.' if prefix else '') + mname
for name, p in self.named_params(module, memo, submodule_prefix):
yield name, p
def update_params(self, lr_inner, first_order=False, source_params=None, detach=False):
if source_params is not None:
for tgt, src in zip(self.named_params(self), source_params):
name_t, param_t = tgt
grad = src
if first_order:
grad = to_var(grad.detach().data)
tmp = param_t - lr_inner * grad
self.set_param(self, name_t, tmp)
else:
for name, param in self.named_params(self):
if not detach:
grad = param.grad
if first_order:
grad = to_var(grad.detach().data)
tmp = param - lr_inner * grad
self.set_param(self, name, tmp)
else:
param = param.detach_() # https://blog.csdn.net/qq_39709535/article/details/81866686
self.set_param(self, name, param)
def set_param(self, curr_mod, name, param):
if '.' in name:
n = name.split('.')
module_name = n[0]
rest = '.'.join(n[1:])
for name, mod in curr_mod.named_children():
if module_name == name:
self.set_param(mod, rest, param)
break
else:
setattr(curr_mod, name, param)
def detach_params(self):
for name, param in self.named_params(self):
self.set_param(self, name, param.detach())
def copy(self, other, same_var=False):
for name, param in other.named_params():
if not same_var:
param = to_var(param.data.clone(), requires_grad=True)
self.set_param(name, param)
class MetaEmb(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Embedding(*args, **kwargs)
nn.init.xavier_normal_(ignore.weight)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
def forward(self, x):
return F.embedding(x, self.weight)
def named_leaves(self):
return [('weight', self.weight)]
class MetaGCN(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = GCNConv(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
self.gcn = [GCN(self.weight.shape[0], self.weight.shape[1], self.weight, self.bias)]
def forward(self, n_id, edge_index):
return self.gcn[0](n_id, edge_index)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class MetaGAT(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = GATConv(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
self.register_buffer('att', to_var(ignore.att.data, requires_grad=True))
self.gat= [GAT(ignore.weight.shape[0], ignore.weight.shape[1], self.weight, self.att, self.bias)]
def forward(self, n_id, edge_index):
return self.gat[0](n_id, edge_index)
def named_leaves(self):
return [('weight', self.weight), ('att', self.att), ('bias', self.bias)]
class MetaGIN(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Linear(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
ignore2 = nn.Linear(*args, **kwargs)
self.register_buffer('weight2', to_var(ignore2.weight.data, requires_grad=True))
self.register_buffer('bias2', to_var(ignore2.bias.data, requires_grad=True))
self.gin = [GIN(self.weight, self.bias, self.weight2, self.bias2)]
def forward(self, n_id, edge_index):
return self.gin[0](n_id, edge_index)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias), ('weight2', self.weight2), ('bias2', self.bias2)]
class MetaSGConv(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Linear(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
self.sgc = [SGC(self.weight, self.bias, K=2)]
def forward(self, n_id, edge_index):
return self.sgc[0](n_id, edge_index)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class MetaNetworkGCN(MetaModule):
def __init__(self, args, num_node):
super(MetaNetworkGCN, self).__init__()
self.node_emb = MetaEmb(num_node, args.emb_dim)
conv_layers = []
for _ in range(args.num_layers):
conv_layers.append(MetaGCN(args.emb_dim, args.emb_dim))
self.conv_layers = nn.ModuleList(conv_layers)
def forward(self, n_id, edge_index):
for i, conv in enumerate(self.conv_layers):
if i == 0:
x = conv(self.node_emb(n_id), edge_index.detach())
else:
x = F.relu(x)
x = conv(x, edge_index.detach())
return x
class MetaNetworkGAT(MetaModule):
def __init__(self, args, num_node):
super(MetaNetworkGAT, self).__init__()
self.node_emb = MetaEmb(num_node, args.emb_dim)
conv_layers = []
for _ in range(args.num_layers):
conv_layers.append(MetaGAT(args.emb_dim, args.emb_dim))
self.conv_layers = nn.ModuleList(conv_layers)
def forward(self, n_id, edge_index):
for i, conv in enumerate(self.conv_layers):
if i == 0:
x = conv(self.node_emb(n_id), edge_index)
else:
x = F.relu(x)
x = conv(x, edge_index)
return x
class MetaNetworkGIN(MetaModule):
def __init__(self, args, num_node):
super(MetaNetworkGIN, self).__init__()
self.node_emb = MetaEmb(num_node, args.emb_dim)
conv_layers = []
for _ in range(args.num_layers):
conv_layers.append(MetaGIN(args.emb_dim, args.emb_dim))
self.conv_layers = nn.ModuleList(conv_layers)
def forward(self, n_id, edge_index):
for i, conv in enumerate(self.conv_layers):
if i == 0:
x = conv(self.node_emb(n_id), edge_index)
else:
x = F.relu(x)
x = conv(x, edge_index)
return x
class MetaNetworkSGC(MetaModule):
def __init__(self, args, num_node):
super(MetaNetworkSGC, self).__init__()
self.node_emb = MetaEmb(num_node, args.emb_dim)
conv_layers = []
for _ in range(args.num_layers):
conv_layers.append(MetaSGConv(args.emb_dim, args.emb_dim))
self.conv_layers = nn.ModuleList(conv_layers)
def forward(self, n_id, edge_index):
for i, conv in enumerate(self.conv_layers):
if i == 0:
x = conv(self.node_emb(n_id), edge_index)
else:
x = F.relu(x)
x = conv(x, edge_index)
return x
class MetaLinear(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Linear(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
def forward(self, x):
return F.linear(x, self.weight, self.bias)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class Weight(MetaModule):
def __init__(self, in_channel, hidden, out_channel):
super(Weight, self).__init__()
self.linear1 = MetaLinear(in_channel, hidden)
self.linear2 = MetaLinear(hidden, out_channel)
def forward(self, x):
x = self.linear1(x)
x = F.relu(x)
x = self.linear2(x)
x = F.sigmoid(x)
return x
class Phi(MetaModule):
def __init__(self, in_channel, hidden, out_channel):
super(Phi, self).__init__()
self.linear1 = MetaLinear(in_channel, hidden)
self.linear2 = MetaLinear(hidden, out_channel)
def forward(self, x):
x = self.linear1(x)
x = F.relu(x)
x = self.linear2(x)
return x
class MetaNetworkHUB(MetaModule):
def __init__(self, args):
super(MetaNetworkHUB, self).__init__()
conv_layers = []
for _ in range(args.num_layers):
conv_layers.append(MetaGAT(args.emb_dim, args.emb_dim))
self.conv_layers = nn.ModuleList(conv_layers)
def forward(self, x, edge_index):
for i, conv in enumerate(self.conv_layers):
if i == 0:
x = conv(x, edge_index)
else:
x = F.relu(x)
x = conv(x, edge_index)
return x