class TensorBoardReport(chainer.training.Extension):
def __init__(self, out_dir):
self.writer = SummaryWriter(out_dir)
def __call__(self, trainer):
observations = trainer.observation
n_iter = trainer.updater.iteration
for n, v in observations.items():
if isinstance(v, chainer.Variable):
value = v.data
# elif isinstance(v, chainer.cuda.cupy.ndarray):
elif isinstance(v, chainer.backends.cuda.ndarray):
value = chainer.cuda.to_cpu(v)
else:
value = v
self.writer.add_scalar(n, value, n_iter)
# Optimizer
link = trainer.updater.get_optimizer('main').target
for name, param in link.namedparams():
self.writer.add_histogram(name, chainer.cuda.to_cpu(param.data),
n_iter)
class TensorBoardReport(chainer.training.Extension):
def __init__(self, writer=None):
self.writer = writer
def __call__(self, trainer: chainer.training.Trainer):
if self.writer is None:
self.writer = SummaryWriter(Path(trainer.out))
observations = trainer.observation
n_iter = trainer.updater.iteration
for n, v in observations.items():
if isinstance(v, chainer.Variable):
v = v.data
if isinstance(v, chainer.cuda.cupy.ndarray):
v = chainer.cuda.to_cpu(v)
self.writer.add_scalar(n, v, n_iter)
link = trainer.updater.get_optimizer('main').target
for name, param in link.namedparams():
self.writer.add_histogram(name,
chainer.cuda.to_cpu(param.data),
n_iter,
bins=100)
import math
import chainer
import numpy as np
from datetime import datetime
from tb_chainer import utils, SummaryWriter
vgg = chainer.links.VGG16Layers()
writer = SummaryWriter('runs/'+datetime.now().strftime('%B%d %H:%M:%S'))
sample_rate = 44100
freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
for n_iter in range(100):
M_global = np.random.rand(1) # value to keep
writer.add_scalar('M_global', M_global[0], n_iter)
x = np.random.rand(32, 3, 64, 64) # output from network
if n_iter % 10 == 0:
x = utils.make_grid(x)
writer.add_image('Image', x, n_iter)
x = np.zeros(sample_rate*2)
for i in range(x.shape[0]):
x[i] = np.cos(freqs[n_iter//10] * np.pi * float(i) / float(sample_rate)) # sound amplitude should in [-1, 1]
writer.add_audio('Audio', x, n_iter)
for name, param in vgg.namedparams():
writer.add_histogram(name, chainer.cuda.to_cpu(param.data), n_iter)
writer.add_text('Text', 'text logged at step:'+str(n_iter), n_iter)
writer.add_text('another Text', 'another text logged at step:'+str(n_iter), n_iter)
writer.close()