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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import repeat
from ...converter import AttnLabelConverter as ATTN
from .addon_module import *
class Attention(nn.Module):
def __init__(self,
kernel_size,
kernel_dim,
input_size,
hidden_size,
num_classes,
embed_dim=None,
attn_type='coverage',
embed_target=False,
enc_init=False, #init hidden state of decoder with enc output
teacher_forcing=1.0,
droprate=0.1,
method='concat',
seqmodel='ViT',
viz_attn: bool = False,
device='cuda'
):
super(Attention, self).__init__()
if embed_dim is None: embed_dim = input_size
if embed_target:
self.embedding = nn.Embedding(num_classes, embed_dim, padding_idx=ATTN.START())
common = {
'input_size': input_size,
'hidden_size': hidden_size,
'num_embeddings': embed_dim if embed_target else num_classes,
'num_classes': num_classes
}
if attn_type == 'luong':
common['method'] = method
self.attention_cell = LuongAttention(**common)
elif attn_type == 'loc_aware':
self.attention_cell = LocationAwareAttention(kernel_size=kernel_size, kernel_dim=kernel_dim, **common)
elif attn_type == 'coverage':
self.attention_cell = LocationAwareAttention(kernel_size=kernel_size, kernel_dim=kernel_dim, **common)
else:
self.attention_cell = BahdanauAttention(**common)
self.dropout = nn.Dropout(droprate)
self.embed_target = embed_target
self.hidden_size = hidden_size
self.input_size = input_size
self.num_classes = num_classes
self.teacher_forcing = teacher_forcing
self.device = device
self.attn_type = attn_type
self.enc_init = enc_init
self.viz_attn = viz_attn
self.seqmodel = seqmodel
if enc_init: self.init_hidden()
def _embed_text(self, input_char):
return self.embedding(input_char)
def _char_to_onehot(self, input_char, onehot_dim=38):
input_char = input_char.unsqueeze(1)
batch_size = input_char.size(0)
one_hot = torch.FloatTensor(batch_size, onehot_dim).zero_().to(self.device)
one_hot = one_hot.scatter_(1, input_char, 1)
return one_hot
def init_hidden(self):
self.proj_init_h = nn.Linear(self.input_size, self.hidden_size, bias=True)
self.proj_init_c = nn.Linear(self.input_size, self.hidden_size, bias=True)
def forward_beam(
self,
batch_H: torch.Tensor,
batch_max_length=25,
beam_size=4,
):
batch_size = batch_H.size(0)
assert batch_size == 1
num_steps = batch_max_length + 1
batch_H = batch_H.squeeze(dim=0)
batch_H = repeat(batch_H, "s e -> b s e", b = beam_size)
if self.enc_init:
if self.seqmodel == 'BiLSTM':
init_embedding = batch_H.mean(dim=1)
else:
init_embedding = batch_H[:, 0, :]
h_0 = self.proj_init_h(init_embedding)
c_0 = self.proj_init_c(init_embedding)
hidden = (h_0, c_0)
else:
hidden = (torch.zeros(beam_size, self.hidden_size, dtype=torch.float32, device=self.device),
torch.zeros(beam_size, self.hidden_size, dtype=torch.float32, device=self.device))
if self.attn_type == 'coverage':
alpha_cum = torch.zeros(beam_size, batch_H.shape[1], 1, dtype=torch.float32, device=self.device)
self.attention_cell.reset_mem()
k_prev_words = torch.LongTensor([[ATTN.START()]] * beam_size).to(self.device)
seqs = k_prev_words
targets = k_prev_words.squeeze(dim=-1)
top_k_scores = torch.zeros(beam_size, 1).to(self.device)
if self.viz_attn:
seqs_alpha = torch.ones(beam_size, 1, batch_H.shape[1]).to(self.device)
complete_seqs = list()
if self.viz_attn:
complete_seqs_alpha = list()
complete_seqs_scores = list()
for step in range(num_steps):
embed_text = self._char_to_onehot(targets, onehot_dim=self.num_classes) if not self.embed_target else self._embed_text(targets)
output, hidden, alpha = self.attention_cell(hidden, batch_H, embed_text)
output = self.dropout(output)
vocab_size = output.shape[1]
scores = F.log_softmax(output, dim=-1)
scores = top_k_scores.expand_as(scores) + scores
if step == 0:
top_k_scores, top_k_words = scores[0].topk(beam_size, 0, True, True)
else:
top_k_scores, top_k_words = scores.view(-1).topk(beam_size, 0, True, True)
prev_word_inds = top_k_words // vocab_size
next_word_inds = top_k_words % vocab_size
seqs = torch.cat([seqs[prev_word_inds], next_word_inds.unsqueeze(1)], dim=1)
if self.viz_attn:
seqs_alpha = torch.cat([seqs_alpha[prev_word_inds], alpha[prev_word_inds].permute(0, 2, 1)],
dim=1)
incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if
next_word != ATTN.END()]
complete_inds = list(set(range(len(next_word_inds))) - set(incomplete_inds))
if len(complete_inds) > 0:
complete_seqs.extend(seqs[complete_inds].tolist())
if self.viz_attn:
complete_seqs_alpha.extend(seqs_alpha[complete_inds])
complete_seqs_scores.extend(top_k_scores[complete_inds])
beam_size = beam_size - len(complete_inds)
if beam_size == 0:
break
seqs = seqs[incomplete_inds]
if self.viz_attn:
seqs_alpha = seqs_alpha[incomplete_inds]
hidden = hidden[0][prev_word_inds[incomplete_inds]], \
hidden[1][prev_word_inds[incomplete_inds]]
batch_H = batch_H[prev_word_inds[incomplete_inds]]
top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1)
targets = next_word_inds[incomplete_inds]
if self.attn_type == 'coverage':
alpha_cum = alpha_cum + alpha
alpha_cum = alpha_cum[incomplete_inds]
self.attention_cell.set_mem(alpha_cum)
elif self.attn_type == 'loc_aware':
self.attention_cell.set_mem(alpha)
if len(complete_inds) == 0:
seq = seqs[0][1:].tolist()
seq = torch.LongTensor(seq).unsqueeze(0)
score = top_k_scores[0]
if self.viz_attn:
alphas = seqs_alpha[0][1:, ...]
return seq, score, alphas
else:
return seq, score, None
else:
combine_lst = tuple(zip(complete_seqs, complete_seqs_scores))
best_ind = combine_lst.index(max(combine_lst, key=lambda x: x[1] / len(x[0]))) #https://youtu.be/XXtpJxZBa2c?t=2407
seq = complete_seqs[best_ind][1:] #not include [GO] token
seq = torch.LongTensor(seq).unsqueeze(0)
score = max(complete_seqs_scores)
if self.viz_attn:
alphas = complete_seqs_alpha[best_ind][1:, ...]
return seq, score, alphas
else:
return seq, score, None
def forward_greedy(self, batch_H, text, is_train=True, is_test=False, batch_max_length=25):
batch_size = batch_H.size(0)
num_steps = batch_max_length + 1
if self.enc_init:
if self.seqmodel == 'BiLSTM':
init_embedding = batch_H.mean(dim=1)
encoder_hidden = batch_H
else:
encoder_hidden = batch_H
init_embedding = batch_H[:, 0, :]
h_0 = self.proj_init_h(init_embedding)
c_0 = self.proj_init_c(init_embedding)
hidden = (h_0, c_0)
else:
encoder_hidden = batch_H
hidden = (torch.zeros(batch_size, self.hidden_size, dtype=torch.float32, device=self.device),
torch.zeros(batch_size, self.hidden_size, dtype=torch.float32, device=self.device))
targets = torch.zeros(batch_size, dtype=torch.long, device=self.device) # [GO] token
probs = torch.zeros(batch_size, num_steps, self.num_classes, dtype=torch.float32, device=self.device)
if self.viz_attn:
self.alpha_stores = torch.zeros(batch_size, num_steps, encoder_hidden.shape[1], 1, dtype=torch.float32, device=self.device)
if self.attn_type == 'coverage':
alpha_cum = torch.zeros(batch_size, encoder_hidden.shape[1], 1, dtype=torch.float32, device=self.device)
self.attention_cell.reset_mem()
if is_test:
end_flag = torch.zeros(batch_size, dtype=torch.bool, device=self.device)
for i in range(num_steps):
embed_text = self._char_to_onehot(targets, onehot_dim=self.num_classes) if not self.embed_target else self._embed_text(targets)
output, hidden, alpha = self.attention_cell(hidden, encoder_hidden, embed_text)
output = self.dropout(output)
if self.viz_attn:
self.alpha_stores[:, i] = alpha
if self.attn_type == 'coverage':
alpha_cum = alpha_cum + alpha
self.attention_cell.set_mem(alpha_cum)
elif self.attn_type == 'loc_aware':
self.attention_cell.set_mem(alpha)
probs_step = output
probs[:, i, :] = probs_step
if i == num_steps - 1:
break
if is_train:
if self.teacher_forcing < random.random():
_, next_input = probs_step.max(1)
targets = next_input
else:
targets = text[:, i+1]
else:
_, next_input = probs_step.max(1)
targets = next_input
if is_test:
end_flag = end_flag | (next_input == ATTN.END())
if end_flag.all():
break
_, preds_index = probs.max(2)
return preds_index, probs, None # batch_size x num_steps x num_classes
def forward(self, beam_size, batch_H, text, batch_max_length, is_train=True, is_test=False):
if is_train:
return self.forward_greedy(batch_H, text, is_train, is_test, batch_max_length)
else:
if beam_size > 1:
return self.forward_beam(batch_H, batch_max_length, beam_size)
else:
return self.forward_greedy(batch_H, text, is_train, is_test, batch_max_length)
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