def __init__(self, generator: Model, discriminator: Model):
self.g = generator
assert hasattr(self.g, 'optimizer'), "Did you forgot to call model.compile(...)?"
self.g_optimizer = keras_copy(self.g.optimizer)
self.d = discriminator
assert hasattr(self.d, 'optimizer'), "Did you forgot to call model.compile(...)?"
self.d_optimizer = keras_copy(self.d.optimizer)
self.z = self.g.inputs[0]
self.z_input_layer = get_layer(self.z)
self.z_shape = self.z_input_layer.get_output_shape_at(0)
self._build()
def test_simple_gan_generator():
nb_units = 2
bs = 8
z = Input(batch_shape=(bs, 50))
labels = Input(batch_shape=(bs, 22))
depth_map = Input(batch_shape=(bs, 1, 16, 16))
tag3d = Input(batch_shape=(bs, 1, 64, 64))
blur, (light_sb, light_sw, light_t), background, details = \
simple_gan_generator(nb_units, z, labels, depth_map, tag3d, depth=2)
inputs = [z, labels, depth_map, tag3d]
fn = K.function([K.learning_phase(), z, labels, depth_map, tag3d],
[blur, light_sb, light_sw, light_t, background, details])
out = fn([1] + [np.random.sample(get_layer(x).output_shape) for x in inputs])
assert out[0].shape == (bs, 1)
for i, out_arr in enumerate(out[1:]):
assert out_arr.shape == (bs, 1, 64, 64), i + 1
def test_simple_gan_generator():
nb_units = 2
bs = 8
z = Input(batch_shape=(bs, 50))
labels = Input(batch_shape=(bs, 22))
depth_map = Input(batch_shape=(bs, 1, 16, 16))
tag3d = Input(batch_shape=(bs, 1, 64, 64))
blur, (light_sb, light_sw, light_t), background, details = \
simple_gan_generator(nb_units, z, labels, depth_map, tag3d, depth=2)
inputs = [z, labels, depth_map, tag3d]
fn = K.function([K.learning_phase(), z, labels, depth_map, tag3d],
[blur, light_sb, light_sw, light_t, background, details])
out = fn([1] +
[np.random.sample(get_layer(x).output_shape) for x in inputs])
assert out[0].shape == (bs, 1)
for i, out_arr in enumerate(out[1:]):
assert out_arr.shape == (bs, 1, 64, 64), i + 1
def sample_generator():
z_shape = get_layer(generator.inputs[0]).batch_input_shape
while True:
z = np.random.uniform(-1, 1, (batch_size, ) + z_shape[1:])
outs = generator_predict(z)
raw_labels = outs.pop('labels')
pos = 0
labels = np.zeros(len(raw_labels), dtype=dist.norm_dtype)
for name, size in dist.norm_nb_elems.items():
labels[name] = raw_labels[:, pos:pos+size]
pos += size
deleted_keys = []
if selected_outputs != 'all':
for name in list(outs.keys()):
if name not in selected_outputs:
del outs[name]
deleted_keys.append(name)
if not outs:
raise Exception("Got no outputs. Removed {}. Selected outputs {}"
.format(deleted_keys, selected_outputs))
outs['labels'] = labels
outs['discriminator'] = discriminator.predict(outs['fake'])
yield outs
def sample_generator():
z_shape = get_layer(generator.inputs[0]).batch_input_shape
while True:
z = np.random.uniform(-1, 1, (batch_size, ) + z_shape[1:])
outs = generator_predict(z)
raw_labels = outs.pop('labels')
pos = 0
labels = np.zeros(len(raw_labels), dtype=dist.norm_dtype)
for name, size in dist.norm_nb_elems.items():
labels[name] = raw_labels[:, pos:pos + size]
pos += size
deleted_keys = []
if selected_outputs != 'all':
for name in list(outs.keys()):
if name not in selected_outputs:
del outs[name]
deleted_keys.append(name)
if not outs:
raise Exception(
"Got no outputs. Removed {}. Selected outputs {}".format(
deleted_keys, selected_outputs))
outs['labels'] = labels
outs['discriminator'] = discriminator.predict(outs['fake'])
yield outs
