File size: 7,661 Bytes
8cd7b86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | """
(c) Adaptation of the code from https://github.com/THUDM/GraphMAE
"""
from typing import Optional
from itertools import chain
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.utils import dropout_edge
from torch_geometric.utils import add_self_loops
from .acm_gin import ACM_GIN_model
def sce_loss(x, y, alpha=3):
x = F.normalize(x, p=2, dim=-1)
y = F.normalize(y, p=2, dim=-1)
loss = (1 - (x * y).sum(dim=-1)).pow_(alpha)
loss = loss.mean()
return loss
def setup_module(
m_type,
in_dim,
out_dim,
num_hidden,
num_layers,
activation,
batchnorm,
) -> nn.Module:
if m_type == "acm_gin":
mod = ACM_GIN_model(
int(in_dim),
int(out_dim),
num_layers,
int(num_hidden),
batchnorm,
activation=activation,
)
else:
raise NotImplementedError
return mod
class PreModel(nn.Module):
def __init__(
self,
in_dim: int,
edge_in_dim: int,
num_hidden: int,
num_layers: int,
nhead: int,
nhead_out: int,
activation: str,
feat_drop: float,
attn_drop: float,
negative_slope: float,
residual: bool,
norm: Optional[str],
mask_rate: float = 0.3,
encoder_type: str = "gat",
decoder_type: str = "gat",
loss_fn: str = "sce",
drop_edge_rate: float = 0.0,
replace_rate: float = 0.1,
alpha_l: float = 2,
concat_hidden: bool = False,
batchnorm=False,
):
super(PreModel, self).__init__()
self._mask_rate = mask_rate
self._encoder_type = encoder_type
self._decoder_type = decoder_type
self._drop_edge_rate = drop_edge_rate
self._output_hidden_size = num_hidden
self._concat_hidden = concat_hidden
self._replace_rate = replace_rate
self._mask_token_rate = 1 - self._replace_rate
assert num_hidden % nhead == 0
assert num_hidden % nhead_out == 0
enc_num_hidden = num_hidden
enc_nhead = 1
dec_in_dim = num_hidden
dec_num_hidden = num_hidden
# Build encoder
self.encoder = setup_module(
m_type=encoder_type,
in_dim=in_dim,
out_dim=enc_num_hidden,
num_hidden=enc_num_hidden,
num_layers=num_layers,
activation=activation,
batchnorm=batchnorm,
)
# Build decoder for attribute prediction
self.decoder = setup_module(
m_type=decoder_type,
in_dim=dec_in_dim,
out_dim=in_dim,
num_hidden=dec_num_hidden,
num_layers=1,
activation=activation,
batchnorm=batchnorm,
)
self.enc_mask_token = nn.Parameter(torch.zeros(1, in_dim))
if concat_hidden:
self.encoder_to_decoder = nn.Linear(
dec_in_dim * num_layers, dec_in_dim, bias=False
)
else:
self.encoder_to_decoder = nn.Linear(dec_in_dim, dec_in_dim, bias=False)
# Setup loss function
self.criterion = self.setup_loss_fn(loss_fn, alpha_l)
@property
def output_hidden_dim(self):
return self._output_hidden_size
def setup_loss_fn(self, loss_fn, alpha_l):
if loss_fn == "mse":
criterion = nn.MSELoss()
elif loss_fn == "sce":
criterion = partial(sce_loss, alpha=alpha_l)
else:
raise NotImplementedError
return criterion
def encoding_mask_noise(self, x, mask_rate=0.3, virtual_node_index=None):
num_nodes = x.shape[0]
all_indices = torch.arange(num_nodes, device=x.device)
# Remove virtual node index from masking candidates
if virtual_node_index is not None:
all_indices = all_indices[~torch.isin(all_indices, virtual_node_index)]
perm = all_indices[torch.randperm(len(all_indices), device=x.device)]
# random masking
num_mask_nodes = int(mask_rate * len(perm))
mask_nodes = perm[:num_mask_nodes]
keep_nodes = perm[num_mask_nodes:]
out_x = x.clone()
if self._replace_rate > 0:
num_noise_nodes = int(self._replace_rate * num_mask_nodes)
perm_mask = torch.randperm(num_mask_nodes, device=x.device)
token_nodes = mask_nodes[
perm_mask[: int(self._mask_token_rate * num_mask_nodes)]
]
noise_nodes = mask_nodes[
perm_mask[-int(self._replace_rate * num_mask_nodes) :]
]
noise_to_be_chosen = torch.randperm(len(perm), device=x.device)[
:num_noise_nodes
]
noise_to_be_chosen = all_indices[noise_to_be_chosen]
out_x[token_nodes] = 0.0
out_x[noise_nodes] = x[noise_to_be_chosen]
else:
token_nodes = mask_nodes
out_x[mask_nodes] = 0.0
out_x[token_nodes] += self.enc_mask_token
return out_x, (mask_nodes, keep_nodes)
def forward(self, batch):
# ---- attribute reconstruction ----
x, edge_index, edge_attr, virtual_node_index, batch = (
batch.x,
batch.edge_index,
batch.edge_attr,
getattr(batch, "virtual_node_index", None),
batch.batch,
)
loss = self.mask_attr_prediction(
x, edge_index, edge_attr, batch, virtual_node_index
)
return loss
def mask_attr_prediction(self, x, edge_index, edge_attr, batch, virtual_node_index):
use_x, (mask_nodes, keep_nodes) = self.encoding_mask_noise(
x,
self._mask_rate,
virtual_node_index,
)
if self._drop_edge_rate > 0:
use_edge_index, masked_edges = dropout_edge(
edge_index, self._drop_edge_rate
)
use_edge_attr = edge_attr[masked_edges]
use_edge_index, use_edge_attr = add_self_loops(
use_edge_index, use_edge_attr, fill_value="min"
)
else:
use_edge_index = edge_index
use_edge_attr = edge_attr
enc_rep, all_hidden = self.encoder(
use_x, use_edge_index, use_edge_attr, return_hidden=True
)
if self._concat_hidden:
enc_rep = torch.cat(all_hidden, dim=1)
# ---- attribute reconstruction ----
rep = self.encoder_to_decoder(enc_rep)
if self._decoder_type not in ("mlp", "linear"):
# * remask, re-mask
rep[mask_nodes] = 0
if self._decoder_type in ("mlp", "linear"):
recon = self.decoder(rep)
else:
recon = self.decoder(rep, use_edge_index, use_edge_attr)
x_init = x[mask_nodes]
x_rec = recon[mask_nodes]
loss = self.criterion(x_rec, x_init)
return loss
def embed(self, x, edge_index, edge_attr, batch):
if self._concat_hidden:
enc_rep, all_hidden = self.encoder(
x, edge_index, edge_attr, return_hidden=True
)
enc_rep = torch.cat(all_hidden, dim=1)
else:
enc_rep = self.encoder(x, edge_index, edge_attr)
rep = self.encoder_to_decoder(enc_rep)
return rep
@property
def enc_params(self):
return self.encoder.parameters()
@property
def dec_params(self):
return chain(*[self.encoder_to_decoder.parameters(), self.decoder.parameters()])
|