def _thread_fun(self, thread_id):
# create a thread-specifc RNG
rng = randomgen.RandomGenerator(randomgen.Xoroshiro128(seed=20 + thread_id))
rnd_normal = None
rnd_perlin = None
t = threading.currentThread()
while getattr(t, 'do_run', True):
# Prepare one of each sampling patterns
if rnd_normal is None:
rnd_normal = rng.standard_normal(size=self.shape, dtype='float32')
rnd_normal /= np.linalg.norm(rnd_normal)
if rnd_perlin is None:
rnd_perlin = create_perlin_noise(px=self.shape[0], color=self.perlin_color, batch_size=1,
normalize=True, precalc_fade=self.perlin_fade)[0]
# Lock and put them into the queues.
with self.cv_not_full:
if len(self.queue_normal) >= self.queue_lengths and len(self.queue_perlin) >= self.queue_lengths:
self.cv_not_full.wait()
# Fill one or both queues.
if len(self.queue_normal) < self.queue_lengths:
self.queue_normal.append(rnd_normal)
rnd_normal = None
if len(self.queue_perlin) < self.queue_lengths:
self.queue_perlin.append(rnd_perlin)
rnd_perlin = None
self.cv_not_empty.notify_all()
def sample_std_normal(thread_id, shape, dtype):
# create a thread-specifc RNG
rng = randomgen.RandomGenerator(
randomgen.Xoroshiro128(seed=20 + thread_id))
t = threading.currentThread()
while getattr(t, 'do_run', True):
rnd_normal = rng.standard_normal(size=shape, dtype=dtype)
rnd_normal_queue.put(rnd_normal)
def _thread_fun(self, thread_id):
# create a thread-specifc RNG
rng = randomgen.RandomGenerator(
randomgen.Xoroshiro128(seed=20 + thread_id))
rnd_normal = None
rnd_lowfreq = None
t = threading.currentThread()
while getattr(t, 'do_run', True):
# Prepare one of each sampling patterns
if rnd_normal is None:
rnd_normal = rng.standard_normal(size=self.shape,
dtype='float32')
rnd_normal /= np.linalg.norm(rnd_normal)
if rnd_lowfreq is None:
# MODIFIED
# There are two concurring implementations of lowfreq noise, Perlin Noise by Brunner et al. and IDCT noise by Guo et al.
# We found them to be pretty much equivalent, but Guo's has a much simpler implementation :)
#
# Therefore we replaced Perlin noise with Guo's lowfreq generator.
# ALSO, the actual noise frequency is an important hyperparameter. Brunner et al. use a constant frequency for ImageNet.
# We found that using a random (low-ish) frequency works much better, as we can span a much larger spectrum, and also avoid the
# round "bubbles" found in the original "Guessing Smart" paper.
freq = rng.uniform(-5., 1.)
freq_ratio = 1. / (1. + np.exp(-freq))
rnd_lowfreq = create_idct_noise(size_px=299,
ratio=freq_ratio,
normalize=True,
rng_gen=rng)
rnd_lowfreq = (rnd_lowfreq, freq)
# Lock and put them into the queues.
with self.cv_not_full:
if len(self.queue_normal) >= self.queue_lengths and len(
self.queue_lowfreq) >= self.queue_lengths:
self.cv_not_full.wait()
# Fill one or both queues.
if len(self.queue_normal) < self.queue_lengths:
self.queue_normal.append(rnd_normal)
rnd_normal = None
if len(self.queue_lowfreq) < self.queue_lengths:
self.queue_lowfreq.append(rnd_lowfreq)
rnd_lowfreq = None
self.cv_not_empty.notify_all()
