Coverage for hypergan/samplers/aligned_sampler.py : 15%

from hypergan.samplers.base_sampler import BaseSampler 

import tensorflow as tf 

import numpy as np 

class AlignedSampler(BaseSampler): 

def __init__(self, gan): 

BaseSampler.__init__(self, gan) 

self.xa_v = None 

self.xb_v = None 

self.created = False 

def sample(self, path, sample_to_file): 

gan = self.gan 

cyca = gan.cyca 

cycb = gan.cycb 

xa_t = gan.inputs.xa 

xba_t = gan.xba 

xab_t = gan.xab 

xb_t = gan.inputs.xb 

sess = gan.session 

config = gan.config 

if(not self.created): 

self.xa_v, self.xb_v = sess.run([xa_t, xb_t]) 

self.created = True 

xab_v, xba_v, samplea, sampleb = sess.run([xab_t, xba_t, cyca, cycb], {xa_t: self.xa_v, xb_t: self.xb_v}) 

stacks = [] 

bs = gan.batch_size() // 2 

width = 5 

for i in range(1): 

stacks.append([self.xa_v[i*width+width+j] for j in range(width)]) 

for i in range(1): 

stacks.append([xab_v[i*width+width+j] for j in range(width)]) 

for i in range(1): 

stacks.append([samplea[i*width+width+j] for j in range(width)]) 

for i in range(1): 

stacks.append([self.xb_v[i*width+width+j] for j in range(width)]) 

for i in range(1): 

stacks.append([xba_v[i*width+width+j] for j in range(width)]) 

for i in range(1): 

stacks.append([sampleb[i*width+width+j] for j in range(width)]) 

#[print(np.shape(s)) for s in stacks] 

images = np.vstack([np.hstack(s) for s in stacks]) 

self.plot(images, path, sample_to_file) 

return [{'image': path, 'label': 'tiled x sample'}]