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0161e74 | 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 | import torch.nn as nn
from typing import Optional
from torch import nn, Tensor
import pdb
class CategoryValueEncoder(nn.Module):
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
):
super().__init__()
self.embedding = nn.Embedding(
num_embeddings, embedding_dim, padding_idx=padding_idx
)
self.enc_norm = nn.LayerNorm(embedding_dim)
def forward(self, x: Tensor) -> Tensor:
x = x.long()
x = self.embedding(x) # (batch, seq_len, embsize)
x = self.enc_norm(x)
return x
class GeneEncoder(nn.Module):
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
):
super().__init__()
self.embedding = nn.Embedding(
num_embeddings, embedding_dim, padding_idx=padding_idx
)
self.enc_norm = nn.LayerNorm(embedding_dim)
def forward(self, x: Tensor) -> Tensor:
x = self.embedding(x) # (batch, seq_len, embsize)
x = self.enc_norm(x)
return x
class PerturbationEmbedding(nn.Module):
def __init__(self, num_perturbations, emb_dim, max_comb_len=2, fusion_method='mlp', output_matrix=False):
super().__init__()
self.embedding = nn.Embedding(num_perturbations, emb_dim)
self.fusion_method = fusion_method
self.max_comb_len = max_comb_len
self.output_matrix = output_matrix
self.output_dim = emb_dim if not output_matrix else emb_dim * emb_dim
if fusion_method == 'mlp':
self.fusion = nn.Sequential(
nn.Linear(emb_dim * max_comb_len, emb_dim * 2),
nn.ReLU(),
nn.Linear(emb_dim * 2, self.output_dim)
)
elif fusion_method == 'sum':
self.fusion = None
else:
raise ValueError(f"Unsupported fusion method: {fusion_method}")
def forward(self, ids):
emb = self.embedding(ids) # [B, C, D]
if self.fusion_method == 'mlp':
emb = emb.view(emb.size(0), -1) # [B, C*D]
fused = self.fusion(emb) # [B, D] or [B, D*D]
if self.output_matrix:
B = fused.size(0)
D = int(self.output_dim ** 0.5)
return fused.view(B, D, D) # [B, D, D]
else:
return fused
elif self.fusion_method == 'sum':
out = emb.sum(dim=1) # [B, D]
if self.output_matrix:
B = out.size(0)
D = out.size(1)
return out.view(B, D, 1).expand(B, D, D) # dummy expansion
return out
# class PerturbationEmbedding(nn.Module):
# def __init__(self, num_perturbations, emb_dim, max_comb_len=2, fusion_method='mlp'):
# """
# Args:
# num_perturbations: 词表大小
# emb_dim: 嵌入维度
# max_comb_len: 每个 condition 最多包含的 token 数量（如 drug1, drug2）
# fusion_method: 'mlp' 或 'sum'
# """
# super().__init__()
# self.embedding = nn.Embedding(num_perturbations, emb_dim)
# self.fusion_method = fusion_method
# self.max_comb_len = max_comb_len
# if fusion_method == 'mlp':
# self.fusion = nn.Sequential(
# nn.Linear(emb_dim * max_comb_len, emb_dim),
# nn.ReLU(),
# nn.Linear(emb_dim, emb_dim)
# )
# elif fusion_method == 'sum':
# self.fusion = None
# else:
# raise ValueError(f"Unsupported fusion method: {fusion_method}")
# def init_weights(self, m):
# if isinstance(m, nn.Linear):
# nn.init.xavier_uniform_(m.weight)
# nn.init.zeros_(m.bias)
# def initialize_weights(self):
# self.apply(self.init_weights)
# def forward(self, ids):
# """
# Args:
# ids: LongTensor of shape [B, max_comb_len]
# Returns:
# fused: Tensor of shape [B, emb_dim]
# """
# emb = self.embedding(ids) # [B, C, D]
# if self.fusion_method == 'mlp':
# emb = emb.view(emb.size(0), -1) # [B, C*D]
# return self.fusion(emb) # [B, D]
# elif self.fusion_method == 'sum':
# if emb.dim() == 2:
# return emb.sum(dim=0) # [B, D]
# elif emb.dim() == 3:
# return emb.sum(dim=1) # [B, C, D]
# else:
# raise ValueError(f"Unsupported dimension: {ids.dim()}") |