hypergan.samplers.grid_sampler module
from hypergan.samplers.base_sampler import BaseSampler
import numpy as np
class GridSampler(BaseSampler):
def __init__(self, gan, samples_per_row=8):
BaseSampler.__init__(self, gan, samples_per_row)
def _sample(self):
gan = self.gan
z_t = gan.encoder.z
#This isn't doing any gridlike stuff. Need to feed this into feed dict(also check size)
y = np.linspace(0,1, 6)
z = np.mgrid[-0.999:0.999:0.6, -0.999:0.999:0.26].reshape(2,-1).T
return {
'generator': gan.session.run(gan.generator.sample, feed_dict={z_t: z})
}
Classes
class GridSampler
class GridSampler(BaseSampler):
def __init__(self, gan, samples_per_row=8):
BaseSampler.__init__(self, gan, samples_per_row)
def _sample(self):
gan = self.gan
z_t = gan.encoder.z
#This isn't doing any gridlike stuff. Need to feed this into feed dict(also check size)
y = np.linspace(0,1, 6)
z = np.mgrid[-0.999:0.999:0.6, -0.999:0.999:0.26].reshape(2,-1).T
return {
'generator': gan.session.run(gan.generator.sample, feed_dict={z_t: z})
}
Ancestors (in MRO)
- GridSampler
- hypergan.samplers.base_sampler.BaseSampler
- builtins.object
Static methods
def __init__(
self, gan, samples_per_row=8)
Initialize self. See help(type(self)) for accurate signature.
def __init__(self, gan, samples_per_row=8):
BaseSampler.__init__(self, gan, samples_per_row)
def plot(
self, image, filename, save_sample)
Plot an image.
def plot(self, image, filename, save_sample):
""" Plot an image."""
image = np.minimum(image, 1)
image = np.maximum(image, -1)
image = np.squeeze(image)
# Scale to 0..255.
imin, imax = image.min(), image.max()
image = (image - imin) * 255. / (imax - imin) + .5
image = image.astype(np.uint8)
if save_sample:
try:
Image.fromarray(image).save(filename)
except Exception as e:
print("Warning: could not sample to ", filename, ". Please check permissions and make sure the path exists")
print(e)
GlobalViewer.update(image)
def sample(
self, path, save_samples)
def sample(self, path, save_samples):
gan = self.gan
with gan.session.as_default():
sample = self._sample()
data = sample['generator']
width = min(gan.batch_size(), self.samples_per_row)
stacks = [np.hstack(data[i*width:i*width+width]) for i in range(gan.batch_size()//width)]
sample_data = np.vstack(stacks)
self.plot(sample_data, path, save_samples)
sample_name = 'generator'
samples = [[sample_data, sample_name]]
return [{'image':path, 'label':'sample'} for sample_data, sample_filename in samples]