File size: 3,036 Bytes
4943752 d65ddc0 4943752 d65ddc0 4943752 d65ddc0 4943752 d65ddc0 4943752 d65ddc0 4943752 d65ddc0 4943752 d65ddc0 4943752 d65ddc0 4943752 |
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 |
import torch
from anonymous_demo.network.sa_encoder import Encoder
from torch import nn
class LSA(nn.Module):
def __init__(self, bert, opt):
super(LSA, self).__init__()
self.opt = opt
self.encoder = Encoder(bert.config, opt)
self.encoder_left = Encoder(bert.config, opt)
self.encoder_right = Encoder(bert.config, opt)
self.linear_window_3h = nn.Linear(opt.embed_dim * 3, opt.embed_dim)
self.linear_window_2h = nn.Linear(opt.embed_dim * 2, opt.embed_dim)
self.eta1 = nn.Parameter(torch.tensor(self.opt.eta, dtype=torch.float))
self.eta2 = nn.Parameter(torch.tensor(self.opt.eta, dtype=torch.float))
def forward(
self,
global_context_features,
spc_mask_vec,
lcf_matrix,
left_lcf_matrix,
right_lcf_matrix,
):
masked_global_context_features = torch.mul(
spc_mask_vec, global_context_features
)
# # --------------------------------------------------- #
lcf_features = torch.mul(global_context_features, lcf_matrix)
lcf_features = self.encoder(lcf_features)
# # --------------------------------------------------- #
left_lcf_features = torch.mul(masked_global_context_features, left_lcf_matrix)
left_lcf_features = self.encoder_left(left_lcf_features)
# # --------------------------------------------------- #
right_lcf_features = torch.mul(masked_global_context_features, right_lcf_matrix)
right_lcf_features = self.encoder_right(right_lcf_features)
# # --------------------------------------------------- #
if "lr" == self.opt.window or "rl" == self.opt.window:
if self.eta1 <= 0 and self.opt.eta != -1:
torch.nn.init.uniform_(self.eta1)
print("reset eta1 to: {}".format(self.eta1.item()))
if self.eta2 <= 0 and self.opt.eta != -1:
torch.nn.init.uniform_(self.eta2)
print("reset eta2 to: {}".format(self.eta2.item()))
if self.opt.eta >= 0:
cat_features = torch.cat(
(
lcf_features,
self.eta1 * left_lcf_features,
self.eta2 * right_lcf_features,
),
-1,
)
else:
cat_features = torch.cat(
(lcf_features, left_lcf_features, right_lcf_features), -1
)
sent_out = self.linear_window_3h(cat_features)
elif "l" == self.opt.window:
sent_out = self.linear_window_2h(
torch.cat((lcf_features, self.eta1 * left_lcf_features), -1)
)
elif "r" == self.opt.window:
sent_out = self.linear_window_2h(
torch.cat((lcf_features, self.eta2 * right_lcf_features), -1)
)
else:
raise KeyError("Invalid parameter:", self.opt.window)
return sent_out
|