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write_on_tensorboard.py
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write_on_tensorboard.py
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"""
For observing the results using tensorboard
1. wav
2. spectrogram
3. loss
"""
from tensorboardX import SummaryWriter
import matplotlib
import config as cfg
class Writer(SummaryWriter):
def __init__(self, logdir):
super(Writer, self).__init__(logdir)
# mask real/ imag
cmap_custom = {
'red': ((0.0, 0.0, 0.0),
(1 / 63, 0.0, 0.0),
(2 / 63, 0.0, 0.0),
(3 / 63, 0.0, 0.0),
(4 / 63, 0.0, 0.0),
(5 / 63, 0.0, 0.0),
(6 / 63, 0.0, 0.0),
(7 / 63, 0.0, 0.0),
(8 / 63, 0.0, 0.0),
(9 / 63, 0.0, 0.0),
(10 / 63, 0.0, 0.0),
(11 / 63, 0.0, 0.0),
(12 / 63, 0.0, 0.0),
(13 / 63, 0.0, 0.0),
(14 / 63, 0.0, 0.0),
(15 / 63, 0.0, 0.0),
(16 / 63, 0.0, 0.0),
(17 / 63, 0.0, 0.0),
(18 / 63, 0.0, 0.0),
(19 / 63, 0.0, 0.0),
(20 / 63, 0.0, 0.0),
(21 / 63, 0.0, 0.0),
(22 / 63, 0.0, 0.0),
(23 / 63, 0.0, 0.0),
(24 / 63, 0.5625, 0.5625),
(25 / 63, 0.6250, 0.6250),
(26 / 63, 0.6875, 0.6875),
(27 / 63, 0.7500, 0.7500),
(28 / 63, 0.8125, 0.8125),
(29 / 63, 0.8750, 0.8750),
(30 / 63, 0.9375, 0.9375),
(31 / 63, 1.0, 1.0),
(32 / 63, 1.0, 1.0),
(33 / 63, 1.0, 1.0),
(34 / 63, 1.0, 1.0),
(35 / 63, 1.0, 1.0),
(36 / 63, 1.0, 1.0),
(37 / 63, 1.0, 1.0),
(38 / 63, 1.0, 1.0),
(39 / 63, 1.0, 1.0),
(40 / 63, 1.0, 1.0),
(41 / 63, 1.0, 1.0),
(42 / 63, 1.0, 1.0),
(43 / 63, 1.0, 1.0),
(44 / 63, 1.0, 1.0),
(45 / 63, 1.0, 1.0),
(46 / 63, 1.0, 1.0),
(47 / 63, 1.0, 1.0),
(48 / 63, 1.0, 1.0),
(49 / 63, 1.0, 1.0),
(50 / 63, 1.0, 1.0),
(51 / 63, 1.0, 1.0),
(52 / 63, 1.0, 1.0),
(53 / 63, 1.0, 1.0),
(54 / 63, 1.0, 1.0),
(55 / 63, 1.0, 1.0),
(56 / 63, 0.9375, 0.9375),
(57 / 63, 0.8750, 0.8750),
(58 / 63, 0.8125, 0.8125),
(59 / 63, 0.7500, 0.7500),
(60 / 63, 0.6875, 0.6875),
(61 / 63, 0.6250, 0.6250),
(62 / 63, 0.5625, 0.5625),
(63 / 63, 0.5000, 0.5000)),
'green': ((0.0, 0.0, 0.0),
(1 / 63, 0.0, 0.0),
(2 / 63, 0.0, 0.0),
(3 / 63, 0.0, 0.0),
(4 / 63, 0.0, 0.0),
(5 / 63, 0.0, 0.0),
(6 / 63, 0.0, 0.0),
(7 / 63, 0.0, 0.0),
(8 / 63, 0.0625, 0.0625),
(9 / 63, 0.1250, 0.1250),
(10 / 63, 0.1875, 0.1875),
(11 / 63, 0.2500, 0.2500),
(12 / 63, 0.3125, 0.3125),
(13 / 63, 0.3750, 0.3750),
(14 / 63, 0.4375, 0.4375),
(15 / 63, 0.5000, 0.5000),
(16 / 63, 0.5625, 0.5625),
(17 / 63, 0.6250, 0.6250),
(18 / 63, 0.6875, 0.6875),
(19 / 63, 0.7500, 0.7500),
(20 / 63, 0.8125, 0.8125),
(21 / 63, 0.8750, 0.8750),
(22 / 63, 0.9375, 0.9375),
(23 / 63, 1.0, 1.0),
(24 / 63, 1.0, 1.0),
(25 / 63, 1.0, 1.0),
(26 / 63, 1.0, 1.0),
(27 / 63, 1.0, 1.0),
(28 / 63, 1.0, 1.0),
(29 / 63, 1.0, 1.0),
(30 / 63, 1.0, 1.0),
(31 / 63, 1.0, 1.0),
(32 / 63, 1.0, 1.0),
(33 / 63, 1.0, 1.0),
(34 / 63, 1.0, 1.0),
(35 / 63, 1.0, 1.0),
(36 / 63, 1.0, 1.0),
(37 / 63, 1.0, 1.0),
(38 / 63, 1.0, 1.0),
(39 / 63, 1.0, 1.0),
(40 / 63, 0.9375, 0.9375),
(41 / 63, 0.8750, 0.8750),
(42 / 63, 0.8125, 0.8125),
(43 / 63, 0.7500, 0.7500),
(44 / 63, 0.6875, 0.6875),
(45 / 63, 0.6250, 0.6250),
(46 / 63, 0.5625, 0.5625),
(47 / 63, 0.5000, 0.5000),
(48 / 63, 0.4375, 0.4375),
(49 / 63, 0.3750, 0.3750),
(50 / 63, 0.3125, 0.3125),
(51 / 63, 0.2500, 0.2500),
(52 / 63, 0.1875, 0.1875),
(53 / 63, 0.1250, 0.1250),
(54 / 63, 0.0625, 0.0625),
(55 / 63, 0.0, 0.0),
(56 / 63, 0.0, 0.0),
(57 / 63, 0.0, 0.0),
(58 / 63, 0.0, 0.0),
(59 / 63, 0.0, 0.0),
(60 / 63, 0.0, 0.0),
(61 / 63, 0.0, 0.0),
(62 / 63, 0.0, 0.0),
(63 / 63, 0.0, 0.0)),
'blue': ((0.0, 0.5625, 0.5625),
(1 / 63, 0.6250, 0.6250),
(2 / 63, 0.6875, 0.6875),
(3 / 63, 0.7500, 0.7500),
(4 / 63, 0.8125, 0.8125),
(5 / 63, 0.8750, 0.8750),
(6 / 63, 0.9375, 0.9375),
(7 / 63, 1.0, 1.0),
(8 / 63, 1.0, 1.0),
(9 / 63, 1.0, 1.0),
(10 / 63, 1.0, 1.0),
(11 / 63, 1.0, 1.0),
(12 / 63, 1.0, 1.0),
(13 / 63, 1.0, 1.0),
(14 / 63, 1.0, 1.0),
(15 / 63, 1.0, 1.0),
(16 / 63, 1.0, 1.0),
(17 / 63, 1.0, 1.0),
(18 / 63, 1.0, 1.0),
(19 / 63, 1.0, 1.0),
(20 / 63, 1.0, 1.0),
(21 / 63, 1.0, 1.0),
(22 / 63, 1.0, 1.0),
(23 / 63, 1.0, 1.0),
(24 / 63, 1.0, 1.0),
(25 / 63, 1.0, 1.0),
(26 / 63, 1.0, 1.0),
(27 / 63, 1.0, 1.0),
(28 / 63, 1.0, 1.0),
(29 / 63, 1.0, 1.0),
(30 / 63, 1.0, 1.0),
(31 / 63, 1.0, 1.0),
(32 / 63, 0.9375, 0.9375),
(33 / 63, 0.8750, 0.8750),
(34 / 63, 0.8125, 0.8125),
(35 / 63, 0.7500, 0.7500),
(36 / 63, 0.6875, 0.6875),
(37 / 63, 0.6250, 0.6250),
(38 / 63, 0.5625, 0.5625),
(39 / 63, 0.0, 0.0),
(40 / 63, 0.0, 0.0),
(41 / 63, 0.0, 0.0),
(42 / 63, 0.0, 0.0),
(43 / 63, 0.0, 0.0),
(44 / 63, 0.0, 0.0),
(45 / 63, 0.0, 0.0),
(46 / 63, 0.0, 0.0),
(47 / 63, 0.0, 0.0),
(48 / 63, 0.0, 0.0),
(49 / 63, 0.0, 0.0),
(50 / 63, 0.0, 0.0),
(51 / 63, 0.0, 0.0),
(52 / 63, 0.0, 0.0),
(53 / 63, 0.0, 0.0),
(54 / 63, 0.0, 0.0),
(55 / 63, 0.0, 0.0),
(56 / 63, 0.0, 0.0),
(57 / 63, 0.0, 0.0),
(58 / 63, 0.0, 0.0),
(59 / 63, 0.0, 0.0),
(60 / 63, 0.0, 0.0),
(61 / 63, 0.0, 0.0),
(62 / 63, 0.0, 0.0),
(63 / 63, 0.0, 0.0))
}
# mask magnitude
cmap_custom2 = {
'red': ((0.0, 1.0, 1.0),
(1 / 32, 1.0, 1.0),
(2 / 32, 1.0, 1.0),
(3 / 32, 1.0, 1.0),
(4 / 32, 1.0, 1.0),
(5 / 32, 1.0, 1.0),
(6 / 32, 1.0, 1.0),
(7 / 32, 1.0, 1.0),
(8 / 32, 1.0, 1.0),
(9 / 32, 1.0, 1.0),
(10 / 32, 1.0, 1.0),
(11 / 32, 1.0, 1.0),
(12 / 32, 1.0, 1.0),
(13 / 32, 1.0, 1.0),
(14 / 32, 1.0, 1.0),
(15 / 32, 1.0, 1.0),
(16 / 32, 1.0, 1.0),
(17 / 32, 1.0, 1.0),
(18 / 32, 1.0, 1.0),
(19 / 32, 1.0, 1.0),
(20 / 32, 1.0, 1.0),
(21 / 32, 1.0, 1.0),
(22 / 32, 1.0, 1.0),
(23 / 32, 1.0, 1.0),
(24 / 32, 1.0, 1.0),
(25 / 32, 0.9375, 0.9375),
(26 / 32, 0.8750, 0.8750),
(27 / 32, 0.8125, 0.8125),
(28 / 32, 0.7500, 0.7500),
(29 / 32, 0.6875, 0.6875),
(30 / 32, 0.6250, 0.6250),
(31 / 32, 0.5625, 0.5625),
(32 / 32, 0.5000, 0.5000)),
'green': ((0.0, 1.0, 1.0),
(1 / 32, 1.0, 1.0),
(2 / 32, 1.0, 1.0),
(3 / 32, 1.0, 1.0),
(4 / 32, 1.0, 1.0),
(5 / 32, 1.0, 1.0),
(6 / 32, 1.0, 1.0),
(7 / 32, 1.0, 1.0),
(8 / 32, 1.0, 1.0),
(9 / 32, 0.9375, 0.9375),
(10 / 32, 0.8750, 0.8750),
(11 / 32, 0.8125, 0.8125),
(12 / 32, 0.7500, 0.7500),
(13 / 32, 0.6875, 0.6875),
(14 / 32, 0.6250, 0.6250),
(15 / 32, 0.5625, 0.5625),
(16 / 32, 0.5000, 0.5000),
(17 / 32, 0.4375, 0.4375),
(18 / 32, 0.3750, 0.3750),
(19 / 32, 0.3125, 0.3125),
(20 / 32, 0.2500, 0.2500),
(21 / 32, 0.1875, 0.1875),
(22 / 32, 0.1250, 0.1250),
(23 / 32, 0.0625, 0.0625),
(24 / 32, 0.0, 0.0),
(25 / 32, 0.0, 0.0),
(26 / 32, 0.0, 0.0),
(27 / 32, 0.0, 0.0),
(28 / 32, 0.0, 0.0),
(29 / 32, 0.0, 0.0),
(30 / 32, 0.0, 0.0),
(31 / 32, 0.0, 0.0),
(32 / 32, 0.0, 0.0)),
'blue': ((0.0, 1.0, 1.0),
(1 / 32, 0.9375, 0.9375),
(2 / 32, 0.8750, 0.8750),
(3 / 32, 0.8125, 0.8125),
(4 / 32, 0.7500, 0.7500),
(5 / 32, 0.6875, 0.6875),
(6 / 32, 0.6250, 0.6250),
(7 / 32, 0.5625, 0.5625),
(8 / 32, 0.0, 0.0),
(9 / 32, 0.0, 0.0),
(10 / 32, 0.0, 0.0),
(11 / 32, 0.0, 0.0),
(12 / 32, 0.0, 0.0),
(13 / 32, 0.0, 0.0),
(14 / 32, 0.0, 0.0),
(15 / 32, 0.0, 0.0),
(16 / 32, 0.0, 0.0),
(17 / 32, 0.0, 0.0),
(18 / 32, 0.0, 0.0),
(19 / 32, 0.0, 0.0),
(20 / 32, 0.0, 0.0),
(21 / 32, 0.0, 0.0),
(22 / 32, 0.0, 0.0),
(23 / 32, 0.0, 0.0),
(24 / 32, 0.0, 0.0),
(25 / 32, 0.0, 0.0),
(26 / 32, 0.0, 0.0),
(27 / 32, 0.0, 0.0),
(28 / 32, 0.0, 0.0),
(29 / 32, 0.0, 0.0),
(30 / 32, 0.0, 0.0),
(31 / 32, 0.0, 0.0),
(32 / 32, 0.0, 0.0))
}
self.cmap_custom = matplotlib.colors.LinearSegmentedColormap('testCmap', segmentdata=cmap_custom, N=256)
self.cmap_custom2 = matplotlib.colors.LinearSegmentedColormap('testCmap2', segmentdata=cmap_custom2, N=256)
def log_loss(self, train_loss, vali_loss, step):
self.add_scalar('train_loss', train_loss, step)
self.add_scalar('vali_loss', vali_loss, step)
def log_sub_loss(self, train_main_loss, train_sub_loss, vali_main_loss, vali_sub_loss, step):
self.add_scalar('train_main_loss', train_main_loss, step)
self.add_scalar('train_sub_loss', train_sub_loss, step)
self.add_scalar('vali_main_loss', vali_main_loss, step)
self.add_scalar('vali_sub_loss', vali_sub_loss, step)
def log_score(self, vali_pesq, vali_stoi, step):
self.add_scalar('vali_pesq', vali_pesq, step)
self.add_scalar('vali_stoi', vali_stoi, step)
def log_wav(self, mixed_wav, clean_wav, est_wav, step):
# <Audio>
self.add_audio('mixed_wav', mixed_wav, step, cfg.fs)
self.add_audio('clean_target_wav', clean_wav, step, cfg.fs)
self.add_audio('estimated_wav', est_wav, step, cfg.fs)
def log_spectrogram(self, mixed_wav, clean_wav, noise_wav, est_wav, step):
# <Data>
self.add_image('data/mixed_spectrogram',
plot_spectrogram_to_numpy(mixed_wav, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
None, [-150, -40], 'dB'), step,
dataformats='HWC')
self.add_image('data/clean_spectrogram',
plot_spectrogram_to_numpy(clean_wav, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
None, [-150, -40], 'dB'), step,
dataformats='HWC')
self.add_image('data/noise_spectrogram',
plot_spectrogram_to_numpy(noise_wav, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
None, [-150, -40], 'dB'), step,
dataformats='HWC')
self.add_image('data/clean_unwrap_phase',
plot_spectrogram_to_numpy(clean_wav, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
'phase', [-500, 500], None), step,
dataformats='HWC')
# <Results>
self.add_image('result/estimated_spectrogram',
plot_spectrogram_to_numpy(est_wav, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
None, [-150, -40], 'dB'), step,
dataformats='HWC')
self.add_image('result/estimated_unwrap_phase',
plot_spectrogram_to_numpy(est_wav, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
'phase', [-500, 500], None), step,
dataformats='HWC')
self.add_image('result/estimated_magnitude-clean_magnitude',
plot_spectrogram_to_numpy(est_wav - clean_wav, cfg.fs, cfg.win_len,
int(cfg.ola_ratio), None,
[-80, 80], 'dB'), step, dataformats='HWC')
self.add_image('result/estimated_unwrap_phase-clean_unwrap_phase',
plot_spectrogram_to_numpy(est_wav - clean_wav, cfg.fs, cfg.win_len,
int(cfg.ola_ratio), 'phase',
[-500, 500], None), step, dataformats='HWC')
def log_mask_spectrogram(self, est_mask_real, est_mask_imag, step):
# <Data>
self.add_image('result/estimated_mask_magnitude',
plot_mask_to_numpy(np.sqrt(est_mask_real ** 2 + est_mask_imag ** 2), cfg.fs, cfg.win_len,
int(cfg.ola_ratio), 0, 2,
cmap=self.cmap_custom2), step, dataformats='HWC')
self.add_image('result/estimated_mask_real',
plot_mask_to_numpy(est_mask_real, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
-2, 2, cmap=self.cmap_custom), step, dataformats='HWC')
self.add_image('result/estimated_mask_imag',
plot_mask_to_numpy(est_mask_imag, cfg.fs, cfg.win_len, int(cfg.ola_ratio),
-2, 2, cmap=self.cmap_custom), step, dataformats='HWC')