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| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| import math | |
| class MLP(nn.Module): | |
| def __init__(self, in_feat, hid_feat=None, out_feat=None, dropout=0.): | |
| super().__init__() | |
| if not hid_feat: | |
| hid_feat = in_feat | |
| if not out_feat: | |
| out_feat = in_feat | |
| self.fc1 = nn.Linear(in_feat, hid_feat) | |
| self.act = torch.nn.ReLU() | |
| self.fc2 = nn.Linear(hid_feat,out_feat) | |
| self.droprateout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| x = self.fc1(x) | |
| x = self.act(x) | |
| x = self.fc2(x) | |
| return self.droprateout(x) | |
| class Attention_new(nn.Module): | |
| def __init__(self, dim, heads, attention_dropout=0.): | |
| super().__init__() | |
| assert dim % heads == 0 | |
| self.heads = heads | |
| self.scale = 1./dim**0.5 | |
| self.q = nn.Linear(dim, dim) | |
| self.k = nn.Linear(dim, dim) | |
| self.v = nn.Linear(dim, dim) | |
| self.e = nn.Linear(dim, dim) | |
| self.d_k = dim // heads | |
| self.heads = heads | |
| self.out_e = nn.Linear(dim,dim) | |
| self.out_n = nn.Linear(dim, dim) | |
| def forward(self, node, edge): | |
| b, n, c = node.shape | |
| q_embed = self.q(node).view(-1, n, self.heads, c//self.heads) | |
| k_embed = self.k(node).view(-1, n, self.heads, c//self.heads) | |
| v_embed = self.v(node).view(-1, n, self.heads, c//self.heads) | |
| e_embed = self.e(edge).view(-1, n, n, self.heads, c//self.heads) | |
| q_embed = q_embed.unsqueeze(2) | |
| k_embed = k_embed.unsqueeze(1) | |
| attn = q_embed * k_embed | |
| attn = attn/ math.sqrt(self.d_k) | |
| attn = attn * (e_embed + 1) * e_embed | |
| edge = self.out_e(attn.flatten(3)) | |
| attn = F.softmax(attn, dim=2) | |
| v_embed = v_embed.unsqueeze(1) | |
| v_embed = attn * v_embed | |
| v_embed = v_embed.sum(dim=2).flatten(2) | |
| node = self.out_n(v_embed) | |
| return node, edge | |
| class Encoder_Block(nn.Module): | |
| def __init__(self, dim, heads, act, mlp_ratio=4, drop_rate=0.): | |
| super().__init__() | |
| self.ln1 = nn.LayerNorm(dim) | |
| self.attn = Attention_new(dim, heads, drop_rate) | |
| self.ln3 = nn.LayerNorm(dim) | |
| self.ln4 = nn.LayerNorm(dim) | |
| self.mlp = MLP(dim, dim*mlp_ratio, dim, dropout=drop_rate) | |
| self.mlp2 = MLP(dim, dim*mlp_ratio, dim, dropout=drop_rate) | |
| self.ln5 = nn.LayerNorm(dim) | |
| self.ln6 = nn.LayerNorm(dim) | |
| def forward(self, x, y): | |
| x1 = self.ln1(x) | |
| x2, y1 = self.attn(x1, y) | |
| x2 = x1 + x2 | |
| y2 = y1 + y | |
| x2 = self.ln3(x2) | |
| y2 = self.ln4(y2) | |
| x = self.ln5(x2 + self.mlp(x2)) | |
| y = self.ln6(y2 + self.mlp2(y2)) | |
| return x, y | |
| class TransformerEncoder(nn.Module): | |
| def __init__(self, dim, depth, heads, act, mlp_ratio=4, drop_rate=0.1): | |
| super().__init__() | |
| self.Encoder_Blocks = nn.ModuleList([ | |
| Encoder_Block(dim, heads, act, mlp_ratio, drop_rate) | |
| for i in range(depth)]) | |
| def forward(self, x, y): | |
| for Encoder_Block in self.Encoder_Blocks: | |
| x, y = Encoder_Block(x, y) | |
| return x, y | |