def __init__(self, gan, config, d_vars=None, g_vars=None, name="BaseTrainer"):
self.current_step = 0
self.g_vars = g_vars
self.d_vars = d_vars
self.train_hooks = []
GANComponent.__init__(self, gan, config, name=name)
def __init__(self,
config=None,
inputs=None,
device='/gpu:0',
ops_config=None,
ops_backend=TensorflowOps,
batch_size=None,
width=None,
height=None,
channels=None):
""" Initialized a new GAN."""
self.inputs = inputs
self.device = device
self.ops_backend = ops_backend
self.ops_config = ops_config
self.created = False
self.components = []
self._batch_size = batch_size
self._width = width
self._height = height
self._channels = channels
if config == None:
config = hg.Configuration.default()
# A GAN as a component has a parent of itself
# gan.gan.gan.gan.gan.gan
GANComponent.__init__(self, self, config)
def __init__(self, gan, config, d_vars=None, g_vars=None, loss=None):
GANComponent.__init__(self, gan, config)
self.create_called = False
self.current_step = 0
self.g_vars = g_vars
self.d_vars = d_vars
self.loss = loss
def __init__(self,
gan,
config,
d_vars=None,
g_vars=None,
loss=None,
name="BaseTrainer"):
self.current_step = 0
self.g_vars = g_vars
self.d_vars = d_vars
self.loss = loss
self.d_shake = None
self.g_shake = None
self.train_hooks = []
for hook_config in (config.hooks or []):
hook_config = hc.lookup_functions(hook_config.copy())
defn = {
k: v
for k, v in hook_config.items()
if k in inspect.getargspec(hook_config['class']).args
}
defn['gan'] = gan
defn['config'] = hook_config
defn['trainer'] = self
hook = hook_config["class"](**defn)
losses = hook.losses()
if losses[0] is not None:
self.loss.sample[0] += losses[0]
if losses[1] is not None:
self.loss.sample[1] += losses[1]
self.train_hooks.append(hook)
GANComponent.__init__(self, gan, config, name=name)
def __init__(self, gan, config, discriminator=None, generator=None):
GANComponent.__init__(self, gan, config)
self.metrics = {}
self.sample = None
self.ops = None
self.discriminator = discriminator
self.generator = generator
def __init__(self,
config=None,
inputs=None,
device='/gpu:0',
ops_config=None,
ops_backend=TensorflowOps,
graph=None,
batch_size=None,
width=None,
height=None,
channels=None,
debug=None,
session=None,
name="hypergan"):
""" Initialized a new GAN."""
self.inputs = inputs
self.device = device
self.ops_backend = ops_backend
self.ops_config = ops_config
self.components = []
self._batch_size = batch_size
self._width = width
self._height = height
self._channels = channels
self.debug = debug
self.name = name
self.session = session
self.skip_connections = SkipConnections()
self.destroy = False
if graph is None:
graph = tf.get_default_graph()
self.graph = graph
if config == None:
config = hg.Configuration.default()
if debug and not isinstance(self.session,
tf_debug.LocalCLIDebugWrapperSession):
self.session = tf_debug.LocalCLIDebugWrapperSession(self.session)
self.session.add_tensor_filter("has_inf_or_nan",
tf_debug.has_inf_or_nan)
else:
tfconfig = tf.ConfigProto(allow_soft_placement=True)
#tfconfig = tf.ConfigProto(log_device_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.device(self.device):
self.session = self.session or tf.Session(config=tfconfig,
graph=graph)
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.steps = tf.Variable(0, trainable=False, name='global_step')
self.increment_step = tf.assign(self.steps, self.steps + 1)
# A GAN as a component has a parent of itself
# gan.gan.gan.gan.gan.gan
GANComponent.__init__(self, self, config, name=self.name)
self.ops.debug = debug
def __init__(self,
gan,
config,
name="BaseGenerator",
input=None,
reuse=False):
self.input = input
self.name = name
GANComponent.__init__(self, gan, config, name=name, reuse=reuse)
def __init__(self,
gan,
config,
name=None,
input=None,
reuse=None,
features=None):
self.input = input
self.name = name
self.features = features
GANComponent.__init__(self, gan, config, name=name, reuse=reuse)
def __init__(self, gan, config, discriminator=None, generator=None, x=None, split=2, d_fake=None, d_real=None, reuse=False, name="BaseLoss"):
self.sample = None
self.ops = None
self.reuse=reuse
self.x = x
self.d_fake = d_fake
self.d_real = d_real
self.discriminator = discriminator or gan.discriminator
self.generator = generator
self.split = split
GANComponent.__init__(self, gan, config, name=name)
def __init__(self,
gan,
config,
d_vars=None,
g_vars=None,
name="BaseTrainer"):
self.current_step = 0
self.g_vars = g_vars
self.d_vars = d_vars
self.train_hooks = []
GANComponent.__init__(self, gan, config, name=name)
def __init__(self,
gan,
config,
name=None,
input=None,
reuse=None,
features=None,
weights=None,
biases=None):
GANComponent.__init__(self, gan, config)
self.input = input
self.name = name
self.features = features
def __init__(self,
gan,
config,
name=None,
input=None,
reuse=None,
x=None,
g=None):
self.input = input
self.name = name
self.x = x
self.g = g
GANComponent.__init__(self, gan, config, name=name, reuse=reuse)
def __init__(self, config=None, inputs=None, device='/gpu:0', ops_config=None, ops_backend=TensorflowOps, graph=None,
batch_size=None, width=None, height=None, channels=None, debug=None, session=None, name="hypergan"):
""" Initialized a new GAN."""
self.inputs = inputs
self.device = device
self.ops_backend = ops_backend
self.ops_config = ops_config
self.components = []
self._batch_size = batch_size
self._width = width
self._height = height
self._channels = channels
self.debug = debug
self.name = name
self.session = session
self.skip_connections = SkipConnections()
self.destroy = False
if graph is None:
graph = tf.get_default_graph()
self.graph = graph
if config == None:
config = hg.Configuration.default()
if debug and not isinstance(self.session, tf_debug.LocalCLIDebugWrapperSession):
self.session = tf_debug.LocalCLIDebugWrapperSession(self.session)
self.session.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
else:
tfconfig = tf.ConfigProto(allow_soft_placement=True)
#tfconfig = tf.ConfigProto(log_device_placement=True)
tfconfig.gpu_options.allow_growth=True
with tf.device(self.device):
self.session = self.session or tf.Session(config=tfconfig, graph=graph)
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.steps = tf.Variable(0, trainable=False, name='global_step')
self.increment_step = tf.assign(self.steps, self.steps+1)
# A GAN as a component has a parent of itself
# gan.gan.gan.gan.gan.gan
GANComponent.__init__(self, self, config, name=self.name)
self.ops.debug = debug
def __init__(self,
gan,
config,
discriminator=None,
generator=None,
x=None,
split=2,
d_fake=None,
d_real=None,
reuse=False,
name="BaseLoss"):
self.sample = None
self.ops = None
self.reuse = reuse
self.x = x
self.d_fake = d_fake
self.d_real = d_real
self.discriminator = discriminator
self.generator = generator
self.split = split
GANComponent.__init__(self, gan, config, name=name)
def __init__(self, gan, config, g=None, x=None, name=None, input=None, reuse=None, features=[], skip_connections=[]):
self.x = x
self.g = g
GANComponent.__init__(self, gan, config, name=name, reuse=reuse)
def __init__(self, gan, config, trainable_gan):
self.current_step = 0
self.train_hooks = gan.hooks
self.trainable_gan = trainable_gan
GANComponent.__init__(self, gan, config)
def __init__(self, gan, config, input=None):
GANComponent.__init__(self, gan, config)
self.input = input
def __init__(self, gan, config, name=None, input=None, reuse=None, features=None):
self.input = input
self.name = name
self.features = features
GANComponent.__init__(self, gan, config, name=name, reuse=reuse)
def __init__(self, gan, config, name="BaseGenerator", input=None, reuse=False):
self.input = input
self.name = name
GANComponent.__init__(self, gan, config, name=name, reuse=reuse)
