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fix ModelBase, nnlib
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iperov
parent
90a7d4b1e7
commit
a9026ccb67
2 changed files with 27 additions and 67 deletions
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@ -148,18 +148,12 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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suppressor.__exit__()
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@staticmethod
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def import_keras(device_config = None):
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def import_keras(device_config):
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if nnlib.keras is not None:
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return nnlib.code_import_keras
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if device_config is None:
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device_config = nnlib.active_DeviceConfig
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nnlib.active_DeviceConfig = device_config
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if "tensorflow" in device_config.backend:
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nnlib._import_tf(device_config)
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device_config = nnlib.active_DeviceConfig
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elif device_config.backend == "plaidML":
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os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
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os.environ["PLAIDML_DEVICE_IDS"] = ",".join ( [ nnlib.device.getDeviceID(idx) for idx in device_config.gpu_idxs] )
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@ -435,38 +429,16 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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nnlib.Scale = Scale
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class AdamCPU(keras.optimizers.Optimizer):
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"""Adam optimizer.
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Default parameters follow those provided in the original paper.
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# Arguments
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lr: float >= 0. Learning rate.
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beta_1: float, 0 < beta < 1. Generally close to 1.
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beta_2: float, 0 < beta < 1. Generally close to 1.
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epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.
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decay: float >= 0. Learning rate decay over each update.
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amsgrad: boolean. Whether to apply the AMSGrad variant of this
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algorithm from the paper "On the Convergence of Adam and
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Beyond".
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# References
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- [Adam - A Method for Stochastic Optimization](
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https://arxiv.org/abs/1412.6980v8)
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- [On the Convergence of Adam and Beyond](
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https://openreview.net/forum?id=ryQu7f-RZ)
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"""
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def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,
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epsilon=None, decay=0., amsgrad=False, tf_cpu_mode=0, **kwargs):
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tf_cpu_mode=0, **kwargs):
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super(AdamCPU, self).__init__(**kwargs)
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with K.name_scope(self.__class__.__name__):
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self.iterations = K.variable(0, dtype='int64', name='iterations')
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self.lr = K.variable(lr, name='lr')
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self.beta_1 = K.variable(beta_1, name='beta_1')
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self.beta_2 = K.variable(beta_2, name='beta_2')
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self.decay = K.variable(decay, name='decay')
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if epsilon is None:
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epsilon = K.epsilon()
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self.epsilon = epsilon
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self.initial_decay = decay
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self.amsgrad = amsgrad
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self.epsilon = K.epsilon()
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self.tf_cpu_mode = tf_cpu_mode
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@keras.legacy.interfaces.legacy_get_updates_support
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@ -474,12 +446,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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grads = self.get_gradients(loss, params)
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self.updates = [K.update_add(self.iterations, 1)]
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lr = self.lr
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if self.initial_decay > 0:
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lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
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K.dtype(self.decay))))
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t = K.cast(self.iterations, K.floatx()) + 1
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lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
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(1. - K.pow(self.beta_1, t)))
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@ -488,21 +456,13 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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with K.tf.device("/cpu:0"):
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ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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if self.amsgrad:
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vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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else:
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vhats = [K.zeros(1) for _ in params]
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else:
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ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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if self.amsgrad:
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vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
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else:
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vhats = [K.zeros(1) for _ in params]
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self.weights = [self.iterations] + ms + vs + vhats
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self.weights = [self.iterations] + ms + vs
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for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
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for p, g, m, v in zip(params, grads, ms, vs):
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if self.tf_cpu_mode == 2:
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with K.tf.device("/cpu:0"):
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m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
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@ -511,12 +471,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
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v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
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if self.amsgrad:
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vhat_t = K.maximum(vhat, v_t)
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p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
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self.updates.append(K.update(vhat, vhat_t))
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else:
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p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
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p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
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self.updates.append(K.update(m, m_t))
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self.updates.append(K.update(v, v_t))
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@ -532,10 +487,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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def get_config(self):
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config = {'lr': float(K.get_value(self.lr)),
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'beta_1': float(K.get_value(self.beta_1)),
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'beta_2': float(K.get_value(self.beta_2)),
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'decay': float(K.get_value(self.decay)),
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'epsilon': self.epsilon,
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'amsgrad': self.amsgrad}
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'beta_2': float(K.get_value(self.beta_2))
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}
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base_config = super(AdamCPU, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@ -561,7 +514,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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@staticmethod
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def import_keras_contrib(device_config = None):
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def import_keras_contrib(device_config):
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if nnlib.keras_contrib is not None:
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return nnlib.code_import_keras_contrib
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@ -588,7 +541,12 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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@staticmethod
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def import_all(device_config = None):
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if nnlib.code_import_all is None:
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if nnlib.code_import_all is None:
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if device_config is None:
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device_config = nnlib.active_DeviceConfig
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else:
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nnlib.active_DeviceConfig = device_config
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nnlib.import_keras(device_config)
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nnlib.import_keras_contrib(device_config)
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nnlib.code_import_all = compile (nnlib.code_import_keras_string + '\n'
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@ -600,8 +558,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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@staticmethod
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def __initialize_all_functions():
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exec (nnlib.import_keras(), locals(), globals())
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exec (nnlib.import_keras_contrib(), locals(), globals())
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exec (nnlib.import_keras(nnlib.active_DeviceConfig), locals(), globals())
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exec (nnlib.import_keras_contrib(nnlib.active_DeviceConfig), locals(), globals())
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class DSSIMMSEMaskLoss(object):
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def __init__(self, mask, is_mse=False):
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