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# -*- coding: utf-8 -*-
# file: ram.py
# author: songyouwei <youwei0314@gmail.com>
# Copyright (C) 2018. All Rights Reserved.
from layers.dynamic_rnn import DynamicLSTM
import torch
import torch.nn as nn
import torch.nn.functional as F
class RAM(nn.Module):
def locationed_memory(self, memory, memory_len, left_len, aspect_len):
batch_size = memory.shape[0]
seq_len = memory.shape[1]
memory_len = memory_len.cpu().numpy()
left_len = left_len.cpu().numpy()
aspect_len = aspect_len.cpu().numpy()
weight = [[] for i in range(batch_size)]
u = [[] for i in range(batch_size)]
for i in range(batch_size):
for idx in range(left_len[i]):
weight[i].append(1-(left_len[i]-idx)/memory_len[i])
u[i].append(idx - left_len[i])
for idx in range(left_len[i], left_len[i]+aspect_len[i]):
weight[i].append(1)
u[i].append(0)
for idx in range(left_len[i]+aspect_len[i], memory_len[i]):
weight[i].append(1-(idx-left_len[i]-aspect_len[i]+1)/memory_len[i])
u[i].append(idx-left_len[i]-aspect_len[i]+1)
for idx in range(memory_len[i], seq_len):
weight[i].append(1)
u[i].append(0)
u = torch.tensor(u, dtype=memory.dtype).to(self.opt.device).unsqueeze(2)
weight = torch.tensor(weight).to(self.opt.device).unsqueeze(2)
v = memory*weight
memory = torch.cat([v, u], dim=2)
return memory
def __init__(self, embedding_matrix, opt):
super(RAM, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.bi_lstm_context = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
self.att_linear = nn.Linear(opt.hidden_dim*2 + 1 + opt.embed_dim*2, 1)
self.gru_cell = nn.GRUCell(opt.hidden_dim*2 + 1, opt.embed_dim)
self.dense = nn.Linear(opt.embed_dim, opt.polarities_dim)
def forward(self, inputs):
text_raw_indices, aspect_indices, text_left_indices = inputs[0], inputs[1], inputs[2]
left_len = torch.sum(text_left_indices != 0, dim=-1)
memory_len = torch.sum(text_raw_indices != 0, dim=-1)
aspect_len = torch.sum(aspect_indices != 0, dim=-1)
nonzeros_aspect = aspect_len.float()
memory = self.embed(text_raw_indices)
memory, (_, _) = self.bi_lstm_context(memory, memory_len)
memory = self.locationed_memory(memory, memory_len, left_len, aspect_len)
aspect = self.embed(aspect_indices)
aspect = torch.sum(aspect, dim=1)
aspect = torch.div(aspect, nonzeros_aspect.unsqueeze(-1))
et = torch.zeros_like(aspect).to(self.opt.device)
batch_size = memory.size(0)
seq_len = memory.size(1)
for _ in range(self.opt.hops):
g = self.att_linear(torch.cat([memory,
torch.zeros(batch_size, seq_len, self.opt.embed_dim).to(self.opt.device) + et.unsqueeze(1),
torch.zeros(batch_size, seq_len, self.opt.embed_dim).to(self.opt.device) + aspect.unsqueeze(1)],
dim=-1))
alpha = F.softmax(g, dim=1)
i = torch.bmm(alpha.transpose(1, 2), memory).squeeze(1)
et = self.gru_cell(i, et)
out = self.dense(et)
return out