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model_d3.py
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model_d3.py
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import numpy as np
import torch as T
from tqdm import tqdm
from torch import nn
from torch.nn import functional as F
from components.k_lstm import K_LSTM
from components.attention import Attention
class DiacritizerD3(nn.Module):
def __init__(self, config, device='cuda'):
super(DiacritizerD3, self).__init__()
self.max_word_len = config["train"]["max-word-len"]
self.max_sent_len = config["train"]["max-sent-len"]
self.char_embed_dim = config["train"]["char-embed-dim"]
self.sent_dropout_p = config["train"]["sent-dropout"]
self.diac_dropout_p = config["train"]["diac-dropout"]
self.vertical_dropout = config['train']['vertical-dropout']
self.recurrent_dropout = config['train']['recurrent-dropout']
self.recurrent_dropout_mode = config['train'].get('recurrent-dropout-mode', 'gal_tied')
self.recurrent_activation = config['train'].get('recurrent-activation', 'sigmoid')
self.sent_lstm_units = config["train"]["sent-lstm-units"]
self.word_lstm_units = config["train"]["word-lstm-units"]
self.decoder_units = config["train"]["decoder-units"]
self.sent_lstm_layers = config["train"]["sent-lstm-layers"]
self.word_lstm_layers = config["train"]["word-lstm-layers"]
self.cell = config['train'].get('rnn-cell', 'lstm')
self.num_layers = config["train"].get("num-layers", 2)
self.RNN_Layer = K_LSTM
self.batch_first = config['train'].get('batch-first', True)
self.baseline = config["train"].get("baseline", False)
self.device = device
def build(self, wembs: T.Tensor, abjad_size: int):
self.closs = F.cross_entropy
self.bloss = F.binary_cross_entropy_with_logits
rnn_kargs = dict(
recurrent_dropout_mode=self.recurrent_dropout_mode,
recurrent_activation=self.recurrent_activation,
)
self.sent_lstm = self.RNN_Layer(
input_size=300,
hidden_size=self.sent_lstm_units,
num_layers=self.sent_lstm_layers,
bidirectional=True,
vertical_dropout=self.vertical_dropout,
recurrent_dropout=self.recurrent_dropout,
batch_first=self.batch_first,
**rnn_kargs,
)
self.word_lstm = self.RNN_Layer(
input_size=self.sent_lstm_units * 2 + self.char_embed_dim,
hidden_size=self.word_lstm_units,
num_layers=self.word_lstm_layers,
bidirectional=True,
vertical_dropout=self.vertical_dropout,
recurrent_dropout=self.recurrent_dropout,
batch_first=self.batch_first,
return_states=True,
**rnn_kargs,
)
self.char_embs = nn.Embedding(
abjad_size,
self.char_embed_dim,
padding_idx=0,
)
self.attention = Attention(
kind="dot",
query_dim=self.word_lstm_units * 2,
input_dim=self.sent_lstm_units * 2,
)
self.lstm_decoder = self.RNN_Layer(
input_size=self.word_lstm_units * 2 + self.attention.Dout + 8,
hidden_size=self.word_lstm_units * 2,
num_layers=1,
bidirectional=False,
vertical_dropout=self.vertical_dropout,
recurrent_dropout=self.recurrent_dropout,
batch_first=self.batch_first,
return_states=True,
**rnn_kargs,
)
self.word_embs = T.tensor(wembs, dtype=T.float32)
self.classifier = nn.Linear(self.lstm_decoder.hidden_size, 15)
self.dropout = nn.Dropout(0.2)
def forward(self, sents, words, labels):
#^ sents : [b ts]
#^ words : [b ts tw]
#^ labels: [b ts tw]
word_mask = words.ne(0.).float()
#^ word_mask: [b ts tw 1]
if self.training:
q = 1.0 - self.sent_dropout_p
sdo = T.bernoulli(T.full(sents.shape, q))
sents_do = sents * sdo.long()
#^ sents_do : [b ts] ; DO(ts)
wembs = self.word_embs[sents_do]
#^ wembs : [b ts dw] ; DO(ts)
else:
wembs = self.word_embs[sents]
#^ wembs : [b ts dw]
sent_enc = self.sent_lstm(wembs.to(self.device))
#^ sent_enc : [b ts dwe]
sentword_do = sent_enc.unsqueeze(2)
#^ sentword_do : [b ts _ dwe]
sentword_do = self.dropout(sentword_do * word_mask.unsqueeze(-1))
#^ sentword_do : [b ts tw dwe]
word_index = words.view(-1, self.max_word_len)
#^ word_index: [b*ts tw]?
cembs = self.char_embs(word_index)
#^ cembs : [b*ts tw dc]
sentword_do = sentword_do.view(-1, self.max_word_len, self.sent_lstm_units * 2)
#^ sentword_do : [b*ts tw dwe]
char_embs = T.cat([cembs, sentword_do], dim=-1)
#^ char_embs : [b*ts tw dcw] ; dcw = dc + dwe
char_enc, _ = self.word_lstm(char_embs)
#^ char_enc: [b*ts tw dce]
char_enc_reshaped = char_enc.view(-1, self.max_sent_len, self.max_word_len, self.word_lstm_units * 2)
#^ char_enc: [b ts tw dce]
omit_self_mask = (1.0 - T.eye(self.max_sent_len)).unsqueeze(0).to(self.device)
attn_enc, attn_map = self.attention(char_enc_reshaped, sent_enc, word_mask.bool(), prejudice_mask=omit_self_mask)
#^ attn_enc: [b ts tw dae]
attn_enc = attn_enc.view(-1, self.max_sent_len*self.max_word_len, self.attention.Dout)
#^ attn_enc: [b*ts tw dae]
if self.training and self.diac_dropout_p > 0:
q = 1.0 - self.diac_dropout_p
ddo = T.bernoulli(T.full(labels.shape[:-1], q))
labels = labels * ddo.unsqueeze(-1).long().to(self.device)
#^ labels : [b ts tw] ; DO(ts)
labels = labels.view(-1, self.max_sent_len*self.max_word_len, 8).float()
#^ labels: [b*ts tw 8]
char_enc = char_enc.view(-1, self.max_sent_len*self.max_word_len, self.word_lstm_units * 2)
final_vec = T.cat([attn_enc, char_enc, labels], dim=-1)
#^ final_vec: [b ts*tw dae+8]
dec_out, _ = self.lstm_decoder(final_vec)
#^ dec_out: [b*ts tw du]
dec_out = dec_out.reshape(-1, self.max_word_len, self.lstm_decoder.hidden_size)
diac_out = self.classifier(self.dropout(dec_out))
#^ diac_out: [b*ts tw 7]
diac_out = diac_out.view(-1, self.max_sent_len, self.max_word_len, 15)
#^ diac_out: [b ts tw 7]
if not self.batch_first:
diac_out = diac_out.swapaxes(1, 0)
return diac_out, attn_map
def predict_step(self, sents, words, labels):
word_mask = words.ne(0.).float()
#^ mask: [b ts tw 1]
if self.training:
q = 1.0 - self.sent_dropout_p
sdo = T.bernoulli(T.full(sents.shape, q))
sents_do = sents * sdo.long()
#^ sents_do : [b ts] ; DO(ts)
wembs = self.word_embs[sents_do]
#^ wembs : [b ts dw] ; DO(ts)
else:
wembs = self.word_embs[sents]
#^ wembs : [b ts dw]
sent_enc = self.sent_lstm(wembs.to(self.device))
#^ sent_enc : [b ts dwe]
sentword_do = sent_enc.unsqueeze(2)
#^ sentword_do : [b ts _ dwe]
sentword_do = self.dropout(sentword_do * word_mask.unsqueeze(-1))
#^ sentword_do : [b ts tw dwe]
word_index = words.view(-1, self.max_word_len)
#^ word_index: [b*ts tw]?
cembs = self.char_embs(word_index)
#^ cembs : [b*ts tw dc]
sentword_do = sentword_do.view(-1, self.max_word_len, self.sent_lstm_units * 2)
#^ sentword_do : [b*ts tw dwe]
char_embs = T.cat([cembs, sentword_do], dim=-1)
#^ char_embs : [b*ts tw dcw] ; dcw = dc + dwe
char_enc, _ = self.word_lstm(char_embs)
#^ char_enc: [b*ts tw dce]
#^ word_states: ([b*ts dce], [b*ts dce])
char_enc = char_enc.view(-1, self.max_sent_len, self.max_word_len, self.word_lstm_units*2)
#^ char_enc: [b ts tw dce]
omit_self_mask = (1.0 - T.eye(self.max_sent_len)).unsqueeze(0).to(self.device)
attn_enc, _ = self.attention(char_enc, sent_enc, word_mask.bool(), prejudice_mask=omit_self_mask)
#^ attn_enc: [b ts tw dae]
all_out = T.zeros(*char_enc.size()[:-1], 15).to(self.device)
#^ all_out: [b ts tw 7]
batch_sz = char_enc.size()[0]
#^ batch_sz: b
zeros = T.zeros(1, batch_sz, self.lstm_decoder.hidden_size).to(self.device)
#^ zeros: [1 b du]
bos_tag = T.tensor([0,0,0,0,0,1,0,0]).unsqueeze(0)
#^ bos_tag: [1 8]
prev_label = T.cat([bos_tag]*batch_sz).to(self.device).float()
# bos_vec = T.cat([bos_tag]*batch_sz).to(self.device).float()
#^ prev_label: [b 8]
for ts in range(self.max_sent_len):
dec_hx = (zeros, zeros)
#^ dec_hx: [1 b du]
for tw in range(self.max_word_len):
final_vec = T.cat([attn_enc[:,ts,tw,:], char_enc[:,ts,tw,:], prev_label], dim=-1).unsqueeze(1)
#^ final_vec: [b 1 dce+8]
dec_out, dec_hx = self.lstm_decoder(final_vec, dec_hx)
#^ dec_out: [b 1 du]
dec_out = dec_out.squeeze(0)
dec_out = dec_out.transpose(0,1)
logits_raw = self.classifier(self.dropout(dec_out))
#^ logits_raw: [b 1 15]
out_idx = T.max(T.softmax(logits_raw.squeeze(), dim=-1), dim=-1)[1]
haraka, tanween, shadda = flat2_3head(out_idx.detach().cpu().numpy())
haraka_onehot = T.eye(6)[haraka].float().to(self.device)
#^ haraka_onehot+bos_tag: [b 6]
tanween = T.tensor(tanween).float().unsqueeze(-1).to(self.device)
shadda = T.tensor(shadda).float().unsqueeze(-1).to(self.device)
prev_label = T.cat([haraka_onehot, tanween, shadda], dim=-1)
all_out[:,ts,tw,:] = logits_raw.squeeze()
if not self.batch_first:
all_out = all_out.swapaxes(1, 0)
return all_out
def step(self, xt, yt, mask=None):
xt[1] = xt[1].to(self.device)
xt[2] = xt[2].to(self.device)
#^ yt: [b ts tw]
yt = yt.to(self.device)
if self.training:
diac, _ = self(*xt)
else:
diac = self.predict_step(*xt)
#^ diac[0] : [b ts tw 5]
loss = self.closs(diac.view(-1,15), yt.view(-1))
return loss
def predict(self, dataloader):
training = self.training
self.eval()
preds = {'haraka': [], 'shadda': [], 'tanween': []}
print("> Predicting...")
for inputs, _ in tqdm(dataloader, total=len(dataloader)):
inputs[1] = inputs[1].to(self.device)
inputs[2] = inputs[2].to(self.device)
diac = self.predict_step(*inputs)
output = np.argmax(T.softmax(diac.detach(), dim=-1).cpu().numpy(), axis=-1)
#^ [b ts tw]
haraka, tanween, shadda = flat_2_3head(output)
preds['haraka'].extend(haraka)
preds['tanween'].extend(tanween)
preds['shadda'].extend(shadda)
self.train(training)
return (
np.array(preds['haraka']),
np.array(preds["tanween"]),
np.array(preds["shadda"]),
)
def flat_2_3head(output):
haraka, tanween, shadda = [], [], []
# 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
# 0, F, FF, K, KK, D, DD, S, Sh, ShF, ShFF, ShK, ShKK, ShD, ShDD
convert = [
[0,0,0],
[1,0,0],
[1,1,0],
[2,0,0],
[2,1,0],
[3,0,0],
[3,1,0],
[4,0,0],
[0,0,1],
[1,0,1],
[1,1,1],
[2,0,1],
[2,1,1],
[3,0,1],
[3,1,1]
]
b, ts, tw = output.shape
for b_idx in range(b):
h_s, t_s, s_s = [], [], []
for w_idx in range(ts):
h_w, t_w, s_w = [], [], []
for c_idx in range(tw):
c = convert[int(output[b_idx, w_idx, c_idx])]
h_w += [c[0]]
t_w += [c[1]]
s_w += [c[2]]
h_s += [h_w]
t_s += [t_w]
s_s += [s_w]
haraka += [h_s]
tanween += [t_s]
shadda += [s_s]
return haraka, tanween, shadda
def flat2_3head(diac_idx):
haraka, tanween, shadda = [], [], []
# 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
# 0, F, FF, K, KK, D, DD, S, Sh, ShF, ShFF, ShK, ShKK, ShD, ShDD
convert = [
[0,0,0],
[1,0,0],
[1,1,0],
[2,0,0],
[2,1,0],
[3,0,0],
[3,1,0],
[4,0,0],
[0,0,1],
[1,0,1],
[1,1,1],
[2,0,1],
[2,1,1],
[3,0,1],
[3,1,1]
]
for diac in diac_idx:
c_out = convert[diac]
haraka += [c_out[0]]
tanween += [c_out[1]]
shadda += [c_out[2]]
return np.array(haraka), np.array(tanween), np.array(shadda)