# encoding: utf-8
"""Class Declaration of Transformer's CTC."""
import logging
import chainer
import chainer.functions as F
import chainer.links as L
import numpy as np
# TODO(nelson): Merge chainer_backend/transformer/ctc.py in chainer_backend/ctc.py
[docs]class CTC(chainer.Chain):
"""Chainer implementation of ctc layer.
Args:
odim (int): The output dimension.
eprojs (int | None): Dimension of input vectors from encoder.
dropout_rate (float): Dropout rate.
"""
def __init__(self, odim, eprojs, dropout_rate):
"""Initialize CTC."""
super(CTC, self).__init__()
self.dropout_rate = dropout_rate
self.loss = None
with self.init_scope():
self.ctc_lo = L.Linear(eprojs, odim)
def __call__(self, hs, ys):
"""CTC forward.
Args:
hs (list of chainer.Variable | N-dimension array):
Input variable from encoder.
ys (list of chainer.Variable | N-dimension array):
Input variable of decoder.
Returns:
chainer.Variable: A variable holding a scalar value of the CTC loss.
"""
self.loss = None
ilens = [x.shape[0] for x in hs]
olens = [x.shape[0] for x in ys]
# zero padding for hs
y_hat = self.ctc_lo(
F.dropout(F.pad_sequence(hs), ratio=self.dropout_rate), n_batch_axes=2
)
y_hat = F.separate(y_hat, axis=1) # ilen list of batch x hdim
# zero padding for ys
y_true = F.pad_sequence(ys, padding=-1) # batch x olen
# get length info
input_length = chainer.Variable(self.xp.array(ilens, dtype=np.int32))
label_length = chainer.Variable(self.xp.array(olens, dtype=np.int32))
logging.info(
self.__class__.__name__ + " input lengths: " + str(input_length.data)
)
logging.info(
self.__class__.__name__ + " output lengths: " + str(label_length.data)
)
# get ctc loss
self.loss = F.connectionist_temporal_classification(
y_hat, y_true, 0, input_length, label_length
)
logging.info("ctc loss:" + str(self.loss.data))
return self.loss
[docs] def log_softmax(self, hs):
"""Log_softmax of frame activations.
Args:
hs (list of chainer.Variable | N-dimension array):
Input variable from encoder.
Returns:
chainer.Variable: A n-dimension float array.
"""
y_hat = self.ctc_lo(F.pad_sequence(hs), n_batch_axes=2)
return F.log_softmax(y_hat.reshape(-1, y_hat.shape[-1])).reshape(y_hat.shape)
[docs]class WarpCTC(chainer.Chain):
"""Chainer implementation of warp-ctc layer.
Args:
odim (int): The output dimension.
eproj (int | None): Dimension of input vector from encoder.
dropout_rate (float): Dropout rate.
"""
def __init__(self, odim, eprojs, dropout_rate):
"""Initialize WarpCTC."""
super(WarpCTC, self).__init__()
# The main difference between the ctc for transformer and
# the rnn is because the target (ys) is already a list of
# arrays located in the cpu, while in rnn routine the target is
# a list of variables located in cpu/gpu. If the target of rnn becomes
# a list of cpu arrays then this file would be no longer required.
from chainer_ctc.warpctc import ctc as warp_ctc
self.ctc = warp_ctc
self.dropout_rate = dropout_rate
self.loss = None
with self.init_scope():
self.ctc_lo = L.Linear(eprojs, odim)
[docs] def forward(self, hs, ys):
"""Core function of the Warp-CTC layer.
Args:
hs (iterable of chainer.Variable | N-dimension array):
Input variable from encoder.
ys (iterable of N-dimension array): Input variable of decoder.
Returns:
chainer.Variable: A variable holding a scalar value of the CTC loss.
"""
self.loss = None
ilens = [hs.shape[1]] * hs.shape[0]
olens = [x.shape[0] for x in ys]
# zero padding for hs
# output batch x frames x hdim > frames x batch x hdim
y_hat = self.ctc_lo(
F.dropout(hs, ratio=self.dropout_rate), n_batch_axes=2
).transpose(1, 0, 2)
# get length info
logging.info(self.__class__.__name__ + " input lengths: " + str(ilens))
logging.info(self.__class__.__name__ + " output lengths: " + str(olens))
# get ctc loss
self.loss = self.ctc(y_hat, ilens, ys)[0]
logging.info("ctc loss:" + str(self.loss.data))
return self.loss
[docs] def log_softmax(self, hs):
"""Log_softmax of frame activations.
Args:
hs (list of chainer.Variable | N-dimension array):
Input variable from encoder.
Returns:
chainer.Variable: A n-dimension float array.
"""
y_hat = self.ctc_lo(F.pad_sequence(hs), n_batch_axes=2)
return F.log_softmax(y_hat.reshape(-1, y_hat.shape[-1])).reshape(y_hat.shape)
[docs] def argmax(self, hs_pad):
"""Argmax of frame activations.
:param chainer variable hs_pad: 3d tensor (B, Tmax, eprojs)
:return: argmax applied 2d tensor (B, Tmax)
:rtype: chainer.Variable.
"""
return F.argmax(self.ctc_lo(F.pad_sequence(hs_pad), n_batch_axes=2), axis=-1)