# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import os
import numpy as np
from . import extension
class PlotAttentionReport(extension.Extension):
"""Plot attention reporter.
Args:
att_vis_fn (espnet.nets.*_backend.e2e_asr.E2E.calculate_all_attentions):
Function of attention visualization.
data (list[tuple(str, dict[str, list[Any]])]): List json utt key items.
outdir (str): Directory to save figures.
converter (espnet.asr.*_backend.asr.CustomConverter):
Function to convert data.
device (int | torch.device): Device.
reverse (bool): If True, input and output length are reversed.
ikey (str): Key to access input
(for ASR/ST ikey="input", for MT ikey="output".)
iaxis (int): Dimension to access input
(for ASR/ST iaxis=0, for MT iaxis=1.)
okey (str): Key to access output
(for ASR/ST okey="input", MT okay="output".)
oaxis (int): Dimension to access output
(for ASR/ST oaxis=0, for MT oaxis=0.)
subsampling_factor (int): subsampling factor in encoder
"""
def __init__(
self,
att_vis_fn,
data,
outdir,
converter,
transform,
device,
reverse=False,
ikey="input",
iaxis=0,
okey="output",
oaxis=0,
subsampling_factor=1, ):
self.att_vis_fn = att_vis_fn
self.data = copy.deepcopy(data)
self.data_dict = {k: v for k, v in copy.deepcopy(data)}
# key is utterance ID
self.outdir = outdir
self.converter = converter
self.transform = transform
self.device = device
self.reverse = reverse
self.ikey = ikey
self.iaxis = iaxis
self.okey = okey
self.oaxis = oaxis
self.factor = subsampling_factor
if not os.path.exists(self.outdir):
os.makedirs(self.outdir)
def __call__(self, trainer):
"""Plot and save image file of att_ws matrix."""
att_ws, uttid_list = self.get_attention_weights()
if isinstance(att_ws, list): # multi-encoder case
num_encs = len(att_ws) - 1
# atts
for i in range(num_encs):
for idx, att_w in enumerate(att_ws[i]):
filename = "%s/%s.ep.{.updater.epoch}.att%d.png" % (
self.outdir, uttid_list[idx], i + 1, )
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
np_filename = "%s/%s.ep.{.updater.epoch}.att%d.npy" % (
self.outdir, uttid_list[idx], i + 1, )
np.save(np_filename.format(trainer), att_w)
self._plot_and_save_attention(att_w,
filename.format(trainer))
# han
for idx, att_w in enumerate(att_ws[num_encs]):
filename = "%s/%s.ep.{.updater.epoch}.han.png" % (
self.outdir, uttid_list[idx], )
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
np_filename = "%s/%s.ep.{.updater.epoch}.han.npy" % (
self.outdir, uttid_list[idx], )
np.save(np_filename.format(trainer), att_w)
self._plot_and_save_attention(
att_w, filename.format(trainer), han_mode=True)
else:
for idx, att_w in enumerate(att_ws):
filename = "%s/%s.ep.{.updater.epoch}.png" % (self.outdir,
uttid_list[idx], )
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
np_filename = "%s/%s.ep.{.updater.epoch}.npy" % (
self.outdir, uttid_list[idx], )
np.save(np_filename.format(trainer), att_w)
self._plot_and_save_attention(att_w, filename.format(trainer))
def log_attentions(self, logger, step):
"""Add image files of att_ws matrix to the tensorboard."""
att_ws, uttid_list = self.get_attention_weights()
if isinstance(att_ws, list): # multi-encoder case
num_encs = len(att_ws) - 1
# atts
for i in range(num_encs):
for idx, att_w in enumerate(att_ws[i]):
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
plot = self.draw_attention_plot(att_w)
logger.add_figure(
"%s_att%d" % (uttid_list[idx], i + 1),
plot.gcf(),
step, )
# han
for idx, att_w in enumerate(att_ws[num_encs]):
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
plot = self.draw_han_plot(att_w)
logger.add_figure(
"%s_han" % (uttid_list[idx]),
plot.gcf(),
step, )
else:
for idx, att_w in enumerate(att_ws):
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
plot = self.draw_attention_plot(att_w)
logger.add_figure("%s" % (uttid_list[idx]), plot.gcf(), step)
def get_attention_weights(self):
"""Return attention weights.
Returns:
numpy.ndarray: attention weights. float. Its shape would be
differ from backend.
* pytorch-> 1) multi-head case => (B, H, Lmax, Tmax), 2)
other case => (B, Lmax, Tmax).
* chainer-> (B, Lmax, Tmax)
"""
return_batch, uttid_list = self.transform(self.data, return_uttid=True)
batch = self.converter([return_batch], self.device)
if isinstance(batch, tuple):
att_ws = self.att_vis_fn(*batch)
else:
att_ws = self.att_vis_fn(**batch)
return att_ws, uttid_list
def trim_attention_weight(self, uttid, att_w):
"""Transform attention matrix with regard to self.reverse."""
if self.reverse:
enc_key, enc_axis = self.okey, self.oaxis
dec_key, dec_axis = self.ikey, self.iaxis
else:
enc_key, enc_axis = self.ikey, self.iaxis
dec_key, dec_axis = self.okey, self.oaxis
dec_len = int(self.data_dict[uttid][dec_key][dec_axis]["shape"][0])
enc_len = int(self.data_dict[uttid][enc_key][enc_axis]["shape"][0])
if self.factor > 1:
enc_len //= self.factor
if len(att_w.shape) == 3:
att_w = att_w[:, :dec_len, :enc_len]
else:
att_w = att_w[:dec_len, :enc_len]
return att_w
def draw_attention_plot(self, att_w):
"""Plot the att_w matrix.
Returns:
matplotlib.pyplot: pyplot object with attention matrix image.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.clf()
att_w = att_w.astype(np.float32)
if len(att_w.shape) == 3:
for h, aw in enumerate(att_w, 1):
plt.subplot(1, len(att_w), h)
plt.imshow(aw, aspect="auto")
plt.xlabel("Encoder Index")
plt.ylabel("Decoder Index")
else:
plt.imshow(att_w, aspect="auto")
plt.xlabel("Encoder Index")
plt.ylabel("Decoder Index")
plt.tight_layout()
return plt
def draw_han_plot(self, att_w):
"""Plot the att_w matrix for hierarchical attention.
Returns:
matplotlib.pyplot: pyplot object with attention matrix image.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.clf()
if len(att_w.shape) == 3:
for h, aw in enumerate(att_w, 1):
legends = []
plt.subplot(1, len(att_w), h)
for i in range(aw.shape[1]):
plt.plot(aw[:, i])
legends.append("Att{}".format(i))
plt.ylim([0, 1.0])
plt.xlim([0, aw.shape[0]])
plt.grid(True)
plt.ylabel("Attention Weight")
plt.xlabel("Decoder Index")
plt.legend(legends)
else:
legends = []
for i in range(att_w.shape[1]):
plt.plot(att_w[:, i])
legends.append("Att{}".format(i))
plt.ylim([0, 1.0])
plt.xlim([0, att_w.shape[0]])
plt.grid(True)
plt.ylabel("Attention Weight")
plt.xlabel("Decoder Index")
plt.legend(legends)
plt.tight_layout()
return plt
def _plot_and_save_attention(self, att_w, filename, han_mode=False):
if han_mode:
plt = self.draw_han_plot(att_w)
else:
plt = self.draw_attention_plot(att_w)
plt.savefig(filename)
plt.close()
class PlotCTCReport(extension.Extension):
"""Plot CTC reporter.
Args:
ctc_vis_fn (espnet.nets.*_backend.e2e_asr.E2E.calculate_all_ctc_probs):
Function of CTC visualization.
data (list[tuple(str, dict[str, list[Any]])]): List json utt key items.
outdir (str): Directory to save figures.
converter (espnet.asr.*_backend.asr.CustomConverter):
Function to convert data.
device (int | torch.device): Device.
reverse (bool): If True, input and output length are reversed.
ikey (str): Key to access input
(for ASR/ST ikey="input", for MT ikey="output".)
iaxis (int): Dimension to access input
(for ASR/ST iaxis=0, for MT iaxis=1.)
okey (str): Key to access output
(for ASR/ST okey="input", MT okay="output".)
oaxis (int): Dimension to access output
(for ASR/ST oaxis=0, for MT oaxis=0.)
subsampling_factor (int): subsampling factor in encoder
"""
def __init__(
self,
ctc_vis_fn,
data,
outdir,
converter,
transform,
device,
reverse=False,
ikey="input",
iaxis=0,
okey="output",
oaxis=0,
subsampling_factor=1, ):
self.ctc_vis_fn = ctc_vis_fn
self.data = copy.deepcopy(data)
self.data_dict = {k: v for k, v in copy.deepcopy(data)}
# key is utterance ID
self.outdir = outdir
self.converter = converter
self.transform = transform
self.device = device
self.reverse = reverse
self.ikey = ikey
self.iaxis = iaxis
self.okey = okey
self.oaxis = oaxis
self.factor = subsampling_factor
if not os.path.exists(self.outdir):
os.makedirs(self.outdir)
def __call__(self, trainer):
"""Plot and save image file of ctc prob."""
ctc_probs, uttid_list = self.get_ctc_probs()
if isinstance(ctc_probs, list): # multi-encoder case
num_encs = len(ctc_probs) - 1
for i in range(num_encs):
for idx, ctc_prob in enumerate(ctc_probs[i]):
filename = "%s/%s.ep.{.updater.epoch}.ctc%d.png" % (
self.outdir, uttid_list[idx], i + 1, )
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
np_filename = "%s/%s.ep.{.updater.epoch}.ctc%d.npy" % (
self.outdir, uttid_list[idx], i + 1, )
np.save(np_filename.format(trainer), ctc_prob)
self._plot_and_save_ctc(ctc_prob, filename.format(trainer))
else:
for idx, ctc_prob in enumerate(ctc_probs):
filename = "%s/%s.ep.{.updater.epoch}.png" % (self.outdir,
uttid_list[idx], )
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
np_filename = "%s/%s.ep.{.updater.epoch}.npy" % (
self.outdir, uttid_list[idx], )
np.save(np_filename.format(trainer), ctc_prob)
self._plot_and_save_ctc(ctc_prob, filename.format(trainer))
def log_ctc_probs(self, logger, step):
"""Add image files of ctc probs to the tensorboard."""
ctc_probs, uttid_list = self.get_ctc_probs()
if isinstance(ctc_probs, list): # multi-encoder case
num_encs = len(ctc_probs) - 1
for i in range(num_encs):
for idx, ctc_prob in enumerate(ctc_probs[i]):
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
plot = self.draw_ctc_plot(ctc_prob)
logger.add_figure(
"%s_ctc%d" % (uttid_list[idx], i + 1),
plot.gcf(),
step, )
else:
for idx, ctc_prob in enumerate(ctc_probs):
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
plot = self.draw_ctc_plot(ctc_prob)
logger.add_figure("%s" % (uttid_list[idx]), plot.gcf(), step)
def get_ctc_probs(self):
"""Return CTC probs.
Returns:
numpy.ndarray: CTC probs. float. Its shape would be
differ from backend. (B, Tmax, vocab).
"""
return_batch, uttid_list = self.transform(self.data, return_uttid=True)
batch = self.converter([return_batch], self.device)
if isinstance(batch, tuple):
probs = self.ctc_vis_fn(*batch)
else:
probs = self.ctc_vis_fn(**batch)
return probs, uttid_list
def trim_ctc_prob(self, uttid, prob):
"""Trim CTC posteriors accoding to input lengths."""
enc_len = int(self.data_dict[uttid][self.ikey][self.iaxis]["shape"][0])
if self.factor > 1:
enc_len //= self.factor
prob = prob[:enc_len]
return prob
def draw_ctc_plot(self, ctc_prob):
"""Plot the ctc_prob matrix.
Returns:
matplotlib.pyplot: pyplot object with CTC prob matrix image.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
ctc_prob = ctc_prob.astype(np.float32)
plt.clf()
topk_ids = np.argsort(ctc_prob, axis=1)
n_frames, vocab = ctc_prob.shape
times_probs = np.arange(n_frames)
plt.figure(figsize=(20, 8))
# NOTE: index 0 is reserved for blank
for idx in set(topk_ids.reshape(-1).tolist()):
if idx == 0:
plt.plot(
times_probs,
ctc_prob[:, 0],
":",
label="<blank>",
color="grey")
else:
plt.plot(times_probs, ctc_prob[:, idx])
plt.xlabel(u"Input [frame]", fontsize=12)
plt.ylabel("Posteriors", fontsize=12)
plt.xticks(list(range(0, int(n_frames) + 1, 10)))
plt.yticks(list(range(0, 2, 1)))
plt.tight_layout()
return plt
def _plot_and_save_ctc(self, ctc_prob, filename):
plt = self.draw_ctc_plot(ctc_prob)
plt.savefig(filename)
plt.close()