mirror of
https://github.com/deepfakes/faceswap.git
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torzdf
03d419c505
Add Optimizer Savings option to offload some optimizer calculations to the CPU Add output_shape to NNMeta class for referencing when building models
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
#!/usr/bin/env python3
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""" Optimizers for faceswap.py """
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# Naming convention inherited from Keras so ignore invalid names
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# pylint:disable=invalid-name
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import logging
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from keras import backend as K
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from keras.optimizers import Adam as KerasAdam
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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class Adam(KerasAdam):
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"""Adapted Keras Adam Optimizer to allow support of calculations
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on CPU for Tensorflow.
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Adapted from https://github.com/iperov/DeepFaceLab
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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, cpu_mode=0, **kwargs):
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super().__init__(lr, beta_1, beta_2, epsilon, decay, **kwargs)
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self.cpu_mode = self.set_cpu_mode(cpu_mode)
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@staticmethod
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def set_cpu_mode(cpu_mode):
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""" Set the CPU mode to 0 if not using tensorflow, else passed in arg """
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retval = False if K.backend() != "tensorflow" else cpu_mode
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logger.debug("Optimizer CPU Mode set to %s", retval)
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return retval
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def get_updates(self, loss, params):
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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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# Pass off to CPU if requested
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if self.cpu_mode:
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with K.tf.device("/cpu:0"):
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ms, vs, vhats = self.update_1(params)
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else:
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ms, vs, vhats = self.update_1(params)
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self.weights = [self.iterations] + ms + vs + vhats
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for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
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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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self.updates.append(K.update(m, m_t))
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self.updates.append(K.update(v, v_t))
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new_p = p_t
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# Apply constraints.
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if getattr(p, 'constraint', None) is not None:
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new_p = p.constraint(new_p)
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self.updates.append(K.update(p, new_p))
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return self.updates
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def update_1(self, params):
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""" First update on CPU or GPU """
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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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return ms, vs, vhats
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