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, 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