def mask_blending_gan(offset_generator, mask_generator, discriminator,
nb_fake=64, nb_real=32):
assert len(mask_generator.input_shape) == 2
assert len(offset_generator.input_shape) == 2
g = Graph()
mask_input_dim = mask_generator.input_shape[1]
z_shape = (nb_fake, offset_generator.input_shape[1] - mask_input_dim)
g.add_input(GAN.z_name, batch_input_shape=z_shape)
g.add_node(Dense(mask_input_dim, activation='relu'), 'dense_mask',
input=GAN.z_name)
g.add_node(mask_generator, 'mask_generator', input='dense_mask')
g.add_node(offset_generator, 'gen_offset',
inputs=[GAN.z_name, 'dense_mask'])
g.add_node(PyramidBlending(mask_generator, input_pyramid_layers=3,
mask_pyramid_layers=2),
GAN.generator_name,
input='gen_offset')
real_shape = (nb_real,) + g.nodes[GAN.generator_name].output_shape[1:]
g.add_input(GAN.real_name, batch_input_shape=real_shape)
g.add_node(discriminator, "discriminator",
inputs=[GAN.generator_name, "real"], concat_axis=0)
gan_outputs(g, fake_for_gen=(0, nb_fake),
fake_for_dis=(nb_fake//2, nb_fake),
real=(nb_fake, nb_fake+nb_real))
return g
def mask_blending_gan_new(offset_generator,
mask_generator,
discriminator,
nb_fake=64,
nb_real=32):
assert len(mask_generator.input_shape) == 2
assert len(offset_generator.input_shape) == 2
g = Graph()
mask_input_dim = mask_generator.input_shape[1]
z_shape = (nb_fake, offset_generator.input_shape[1] - mask_input_dim)
g.add_input(GAN.z_name, batch_input_shape=z_shape)
g.add_node(Dense(32), 'gen_driver_dense_1', input=GAN.z_name)
g.add_node(BatchNormalization(),
'gen_driver_bn_1',
input='gen_driver_dense_1')
g.add_node(Activation('relu'), 'gen_driver_act_1', input='gen_driver_bn_1')
g.add_node(Dense(mask_input_dim),
'gen_driver_dense_2',
input='gen_driver_act_1')
g.add_node(BatchNormalization(),
'gen_driver_bn_2',
input='gen_driver_dense_2')
g.add_node(Layer(), 'driver', input='gen_driver_bn_2')
# g.add_node(ZeroGradient(), 'gen_driver_zero_grad', input='driver')
g.add_node(mask_generator, 'mask_generator', input='driver')
g.add_node(offset_generator, 'gen_offset', input=GAN.z_name)
g.add_node(PyramidBlending(mask_generator,
input_pyramid_layers=3,
mask_pyramid_layers=2),
'blending',
input='gen_offset')
reg_layer = Layer()
act = ActivityInBoundsRegularizer(-1, 1)
act.set_layer(reg_layer)
reg_layer.regularizers = [act]
g.add_node(reg_layer, GAN.generator_name, input='blending')
real_shape = (nb_real, ) + g.nodes[GAN.generator_name].output_shape[1:]
g.add_input(GAN.real_name, batch_input_shape=real_shape)
g.add_node(discriminator,
"discriminator",
inputs=[GAN.generator_name, "real"],
concat_axis=0)
gan_outputs(g,
fake_for_gen=(0, nb_fake - nb_real),
fake_for_dis=(nb_fake - nb_real, nb_fake),
real=(nb_fake, nb_fake + nb_real))
return g
def test_gan_get_config(tmpdir):
z_shape = (1, 8, 8)
z = Input(z_shape, name='z')
g_out = Convolution2D(10, 2, 2, activation='relu', border_mode='same')(z)
generator = Container(z, g_out)
f, r = Input(z_shape, name='f'), Input(z_shape, name='r')
dis_input = merge([f, r], mode='concat', concat_axis=1)
dis_conv = Convolution2D(5, 2, 2, activation='relu')(dis_input)
dis_flatten = Flatten()(dis_conv)
dis = Dense(1, activation='sigmoid')(dis_flatten)
discriminator = Container([f, r], gan_outputs(dis))
gan = GAN(generator, discriminator, z_shape, z_shape)
weights_fname = str(tmpdir.mkdir("weights").join("{}.hdf5"))
gan.save_weights(weights_fname)
true_config = gan.get_config()
import json
with open(os.path.join(TEST_OUTPUT_DIR, "true_config.json"), 'w+') as f:
json.dump(true_config, f, indent=2)
gan_from_config = layer_from_config(true_config, custom_objects={
'GAN': GAN,
'Split': Split,
})
with open(os.path.join(TEST_OUTPUT_DIR, "loaded_config.json"), 'w+') as f:
json.dump(gan_from_config.get_config(), f, indent=2)
gan_from_config.load_weights(weights_fname)
def test_gan_custom_layer_graph():
z_shape = (1, 8, 8)
z = Input(shape=z_shape, name='z')
gen_cond = Input(shape=(1, 8, 8), name='gen_cond')
inputs = [z, gen_cond]
gen_input = merge(inputs, mode='concat', concat_axis=1)
gen_output = Convolution2D(1, 2, 2, activation='relu',
name='g1',
border_mode='same')(gen_input)
generator = Container(inputs, gen_output)
f, r = Input(z_shape, name='fake'), Input(z_shape, name='real')
inputs = [f, r]
dis_input = merge(inputs, mode='concat', concat_axis=0)
dis_conv = Convolution2D(5, 2, 2, name='d1', activation='relu')(dis_input)
dis_flatten = Flatten()(dis_conv)
dis = Dense(1, activation='sigmoid')(dis_flatten)
discriminator = Container(inputs, gan_outputs(dis))
gan = GAN(generator, discriminator, z_shape=z_shape, real_shape=z_shape)
gan.build('adam', 'adam', gan_binary_crossentropy)
fn = gan.compile_custom_layers(['g1', 'd1'])
z = np.random.uniform(-1, 1, (64,) + z_shape)
real = np.random.uniform(-1, 1, (64,) + z_shape)
cond = np.random.uniform(-1, 1, (64,) + z_shape)
print(z.shape)
print(real.shape)
print(cond.shape)
fn({'z': z, 'gen_cond': cond, 'real': real})
def get_discriminator():
f, r = Input(z_shape, name='f'), Input(z_shape, name='r')
inputs = [f, r]
dis_input = merge(inputs, mode='concat', concat_axis=1)
dis_conv = Convolution2D(5, 2, 2, activation='relu')(dis_input)
dis_flatten = Flatten()(dis_conv)
dis = Dense(1, activation='sigmoid')(dis_flatten)
return Container(inputs, gan_outputs(dis))
def discriminator(x):
return gan_outputs(sequential([
Flatten(),
Dense(1),
])(concat(x)),
fake_for_gen=(0, 10),
fake_for_dis=(0, 10),
real=(10, 20))
def mask_blending_gan_new(offset_generator, mask_generator, discriminator,
nb_fake=64, nb_real=32):
assert len(mask_generator.input_shape) == 2
assert len(offset_generator.input_shape) == 2
g = Graph()
mask_input_dim = mask_generator.input_shape[1]
z_shape = (nb_fake, offset_generator.input_shape[1] - mask_input_dim)
g.add_input(GAN.z_name, batch_input_shape=z_shape)
g.add_node(Dense(32), 'gen_driver_dense_1', input=GAN.z_name)
g.add_node(BatchNormalization(), 'gen_driver_bn_1',
input='gen_driver_dense_1')
g.add_node(Activation('relu'), 'gen_driver_act_1',
input='gen_driver_bn_1')
g.add_node(Dense(mask_input_dim), 'gen_driver_dense_2',
input='gen_driver_act_1')
g.add_node(BatchNormalization(), 'gen_driver_bn_2',
input='gen_driver_dense_2')
g.add_node(Layer(), 'driver', input='gen_driver_bn_2')
# g.add_node(ZeroGradient(), 'gen_driver_zero_grad', input='driver')
g.add_node(mask_generator, 'mask_generator', input='driver')
g.add_node(offset_generator, 'gen_offset',
input=GAN.z_name)
g.add_node(PyramidBlending(mask_generator, input_pyramid_layers=3,
mask_pyramid_layers=2),
'blending', input='gen_offset')
reg_layer = Layer()
act = ActivityInBoundsRegularizer(-1, 1)
act.set_layer(reg_layer)
reg_layer.regularizers = [act]
g.add_node(reg_layer, GAN.generator_name, input='blending')
real_shape = (nb_real,) + g.nodes[GAN.generator_name].output_shape[1:]
g.add_input(GAN.real_name, batch_input_shape=real_shape)
g.add_node(discriminator, "discriminator",
inputs=[GAN.generator_name, "real"], concat_axis=0)
gan_outputs(g, fake_for_gen=(0, nb_fake - nb_real),
fake_for_dis=(nb_fake - nb_real, nb_fake),
real=(nb_fake, nb_fake+nb_real))
return g
def mask_blending_gan(offset_generator,
mask_generator,
discriminator,
nb_fake=64,
nb_real=32):
assert len(mask_generator.input_shape) == 2
assert len(offset_generator.input_shape) == 2
g = Graph()
mask_input_dim = mask_generator.input_shape[1]
z_shape = (nb_fake, offset_generator.input_shape[1] - mask_input_dim)
g.add_input(GAN.z_name, batch_input_shape=z_shape)
g.add_node(Dense(mask_input_dim, activation='relu'),
'dense_mask',
input=GAN.z_name)
g.add_node(mask_generator, 'mask_generator', input='dense_mask')
g.add_node(offset_generator,
'gen_offset',
inputs=[GAN.z_name, 'dense_mask'])
g.add_node(PyramidBlending(mask_generator,
input_pyramid_layers=3,
mask_pyramid_layers=2),
GAN.generator_name,
input='gen_offset')
real_shape = (nb_real, ) + g.nodes[GAN.generator_name].output_shape[1:]
g.add_input(GAN.real_name, batch_input_shape=real_shape)
g.add_node(discriminator,
"discriminator",
inputs=[GAN.generator_name, "real"],
concat_axis=0)
gan_outputs(g,
fake_for_gen=(0, nb_fake),
fake_for_dis=(nb_fake // 2, nb_fake),
real=(nb_fake, nb_fake + nb_real))
return g
def simple_gan():
z = Input(batch_shape=simple_gan_z_shape, name='z')
generator = sequential([
Dense(simple_gan_nb_z, activation='relu', name='g1'),
Dense(simple_gan_nb_z, activation='relu', name='g2'),
Dense(simple_gan_nb_out, activation='sigmoid', name='g3'),
])(z)
fake = Input(batch_shape=simple_gan_real_shape, name='fake')
real = Input(batch_shape=simple_gan_real_shape, name='real')
discriminator = sequential([
Dense(20, activation='relu', input_dim=2, name='d1'),
Dense(1, activation='sigmoid', name='d2')
])(concat([fake, real], axis=0))
return GAN(Container(z, generator),
Container([fake, real], gan_outputs(discriminator)),
simple_gan_z_shape[1:], simple_gan_real_shape[1:])
def test_gan_graph():
z_shape = (1, 8, 8)
z = Input(shape=z_shape, name='z')
gen_cond = Input(shape=(1, 8, 8), name='gen_cond')
inputs = [z, gen_cond]
gen_input = merge(inputs, mode='concat', concat_axis=1)
gen_output = Convolution2D(10, 2, 2, activation='relu',
border_mode='same')(gen_input)
generator = Container(inputs, gen_output)
f, r = Input(z_shape, name='f'), Input(z_shape, name='r')
inputs = [f, r]
dis_input = merge(inputs, mode='concat', concat_axis=1)
dis_conv = Convolution2D(5, 2, 2, activation='relu')(dis_input)
dis_flatten = Flatten()(dis_conv)
dis = Dense(1, activation='sigmoid')(dis_flatten)
discriminator = Container(inputs, gan_outputs(dis))
gan = GAN(generator, discriminator, z_shape=z_shape, real_shape=z_shape)
gan.build('adam', 'adam', gan_binary_crossentropy)
gan.compile()
gan.generate({'gen_cond': np.zeros((64,) + z_shape)}, nb_samples=64)
def discriminator(x):
return gan_outputs(sequential([
Flatten(),
Dense(1),
])(concat(x)), fake_for_gen=(0, 10), fake_for_dis=(0, 10),
real=(10, 20))