def generate_sample(self, counter):
n = int(np.random.random() *
(len(self.x_data) - self.sampler_batch_size))
x_batch = self.x_data[n:n + self.sampler_batch_size, :, :, :]
z_batch = self.sess.run(self.sample_z_batch_op)
sampler = [
ops.inverse_transform(self.G),
ops.inverse_transform(self.X_recons)
]
g, x_recons = self.sess.run(sampler,
feed_dict={
self.X: x_batch,
self.Z: z_batch
})
filename = '{}.png'.format(counter)
recons_path = os.path.join(self.samples_path, 'recons')
utils.make_sure_path_exits(recons_path)
utils.save_images_h(os.path.join(recons_path, filename),
np.concatenate([x_batch, x_recons], 0),
self.sampler_batch_size)
gens_path = os.path.join(self.samples_path, 'gens')
utils.make_sure_path_exits(gens_path)
utils.save_images_h(os.path.join(gens_path, filename), g,
int(np.sqrt(self.sampler_batch_size)))
self.inc_scores.append(self.get_inception_score())
self.diff_scores.append(self.get_inception_score_diff())
self._save_score()
return [gens_path, recons_path]
def _post_train(self):
imgs = []
for path in self.sample_imgs:
img = cv2.imread(path)
imgs.append(cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
imgs = np.array(imgs)
samplefile_path = os.path.join(self.samples_path, 'combined.png')
utils.save_images_h(samplefile_path, imgs, 10)
def generate_sample(self, counter):
z = self.sess.run(self.sample_z_batch_op)
samples = self.sess.run(ops.inverse_transform(self.G),
feed_dict={self.Z: z})
utils.make_sure_path_exits(self.samples_path)
samplefile_path = os.path.join(self.samples_path,
'{}.png'.format(counter))
utils.save_images_h(path=samplefile_path,
images=samples,
imagesPerRow=self.sampler_interpolations)
return [samplefile_path]
def generate_sample(self, counter):
z_batch = self.sess.run(self.sample_z_batch_op)
g = self.sess.run(ops.inverse_transform(self.G),
feed_dict={self.Z: z_batch})
# img_array = tf.transpose(samples, [1, 0, 2, 3, 4])
utils.make_sure_path_exits(self.samples_path)
samplefile_path = os.path.join(self.samples_path,
'{}.png'.format(counter))
utils.save_images_h(samplefile_path, g,
int(np.sqrt(self.sampler_batch_size)))
self.inc_scores.append(self.get_inception_score())
self._save_score()
return [samplefile_path]
def generate_sample(self, counter):
x_batch, z_batch = self.sess.run(
[self.sample_x_batch_op, self.sample_z_batch_op])
sampler = [
ops.inverse_transform(self.G),
ops.inverse_transform(self.X_recons)
]
g, x_recons = self.sess.run(sampler,
feed_dict={
self.X: x_batch,
self.Z: z_batch
})
filename = '{}.png'.format(counter)
recons_path = os.path.join(self.samples_path, 'train/recons')
utils.make_sure_path_exits(recons_path)
utils.save_images_h(os.path.join(recons_path, filename),
np.concatenate([x_batch, x_recons], 0),
self.sampler_batch_size)
gens_path = os.path.join(self.samples_path, 'train/gens')
utils.make_sure_path_exits(gens_path)
utils.save_images_h(os.path.join(gens_path, filename), g,
int(np.sqrt(self.sampler_batch_size)))
# Test set
x_batch = self.sess.run(self.test_x_batch_op)
g, x_recons = self.sess.run(sampler,
feed_dict={
self.X: x_batch,
self.Z: self.test_z_batch
})
filename = '{}.png'.format(counter)
recons_path = os.path.join(self.samples_path, 'test/recons')
utils.make_sure_path_exits(recons_path)
utils.save_images_h(os.path.join(recons_path, filename),
np.concatenate([x_batch, x_recons], 0),
self.sampler_batch_size)
gens_path = os.path.join(self.samples_path, 'test/gens')
utils.make_sure_path_exits(gens_path)
utils.save_images_h(os.path.join(gens_path, filename), g,
int(np.sqrt(self.sampler_batch_size)))
return [gens_path, recons_path]