def test_proxy_methods(self):
with self.test_session():
gan = mock_gan()
mock_sample = tf.constant(1., shape=[1, 1])
multi = MultiComponent(combine='concat',
components=[
MockLoss(gan, sample=mock_sample),
MockLoss(gan, sample=mock_sample)
])
multi.proxy()
self.assertEqual(multi.proxy_called, [True, True])
def test_proxy_methods(self):
with self.test_session():
gan = mock_gan()
mock_sample = tf.constant(1., shape=[1,1])
multi = MultiComponent(combine='concat',
components=[
MockLoss(gan, sample=mock_sample),
MockLoss(gan, sample=mock_sample)
])
multi.proxy()
self.assertEqual(multi.proxy_called, [True, True])
def build(self, net):
gan = self.gan
config = self.config
self.d_variables = []
discs = []
self.kwargs["input"] = net
self.kwargs["reuse"] = self.ops._reuse
for i in range(config.discriminator_count or 0):
name = self.ops.description + "_d_" + str(i)
self.kwargs["name"] = name
print(">>CREATING ", i)
disc = config['discriminator_class'](gan, config, **self.kwargs)
self.ops.add_weights(disc.variables())
self.d_variables += [disc.variables()]
discs.append(disc)
for i, dconfig in enumerate(config.discriminators):
name = self.ops.description + "_d_" + str(i)
self.kwargs["name"] = name
disc = dconfig['class'](gan, dconfig, **self.kwargs)
self.ops.add_weights(disc.variables())
self.d_variables += [disc.variables()]
discs.append(disc)
combine = MultiComponent(combine=self.config.combine or "concat",
components=discs)
self.sample = combine.sample
self.children = discs
return self.sample
def add_supervised_loss(self):
if self.args.classloss:
print("[discriminator] Class loss is on. Semi-supervised learning mode activated.")
supervised_loss = SupervisedLoss(self.gan, self.gan.config.loss)
self.gan.loss = MultiComponent(components=[supervised_loss, self.gan.loss], combine='add')
#EWW
else:
print("[discriminator] Class loss is off. Unsupervised learning mode activated.")
def test_sample(self):
with self.test_session():
gan = mock_gan()
mock_sample = tf.constant(1., shape=[1, 1])
multi = MultiComponent(combine='concat',
components=[
MockLoss(gan, sample=mock_sample),
MockLoss(gan, sample=mock_sample)
])
gan.encoder = multi
self.assertEqual(gan.ops.shape(multi.sample), [1, 2])
def test_combine_dict(self):
with self.test_session():
gan = mock_gan()
ops = gan.ops
mock_sample = tf.constant(1., shape=[1])
multi = MultiComponent(combine='add',
components=[
MockLoss(gan, sample={"a": "b"}),
MockLoss(gan, sample={"b": "c"})
])
self.assertEqual(len(multi.sample), 2)
self.assertEqual(multi.sample['a'], 'b')
self.assertEqual(multi.sample['b'], 'c')
def test_sample_loss(self):
with self.test_session():
gan = mock_gan()
ops = gan.ops
mock_sample = tf.constant(1., shape=[1])
multi = MultiComponent(combine='add',
components=[
MockLoss(gan,
sample=[mock_sample, None]),
MockLoss(
gan,
sample=[mock_sample, mock_sample])
])
self.assertEqual(len(multi.sample), 2)
self.assertEqual(ops.shape(multi.sample[0]), [1])
self.assertEqual(ops.shape(multi.sample[1]), [1])