mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
815c843f63
* Add simple backend tests for lib.model * Document lib.model * Fix GMSD Loss for AMD * Remove obsolete code from lib.model
146 lines
5 KiB
Python
146 lines
5 KiB
Python
#!/usr/bin/env python3
|
|
""" Optimizers for faceswap.py """
|
|
# Naming convention inherited from Keras so ignore invalid names
|
|
# pylint:disable=invalid-name
|
|
|
|
import logging
|
|
|
|
from keras import backend as K
|
|
from keras.optimizers import Adam as KerasAdam
|
|
|
|
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
|
|
|
|
|
|
class Adam(KerasAdam):
|
|
"""Adapted Keras Adam Optimizer to allow support of calculations on CPU for Tensorflow.
|
|
|
|
Default parameters follow those provided in the original paper. Adapted from
|
|
https://github.com/iperov/DeepFaceLab
|
|
|
|
Parameters
|
|
----------
|
|
lr: float, optional
|
|
>= `0`. Learning rate. Default: `0.001`
|
|
beta_1: float, optional
|
|
`0` < beta < `1` Generally close to `1`. Default: `0.9`
|
|
beta_2: float, optional
|
|
`0` < beta < `1`. Generally close to `1`. Default: `0.999`
|
|
epsilon: float, optional
|
|
>= `0`. Fuzz factor. If ``None``, defaults to `K.epsilon()`. Default: ``None``
|
|
decay: float, optional
|
|
>= 0. Learning rate decay over each update. Default: `0`
|
|
amsgrad: bool, optional
|
|
``True`` to apply the AMSGrad variant of this algorithm from the paper "On the Convergence
|
|
of Adam and Beyond" otherwise ``False``. Default: ``False``
|
|
cpu_mode: bool, optional
|
|
Set to ``True`` to perform some of the calculations on CPU for Nvidia backends, otherwise
|
|
``False``. Default: ``False``
|
|
kwargs: dict
|
|
Any additional standard Keras optimizer keyword arguments
|
|
|
|
References
|
|
----------
|
|
- Adam - A Method for Stochastic Optimization - https://arxiv.org/abs/1412.6980v8
|
|
|
|
- On the Convergence of Adam and Beyond - https://openreview.net/forum?id=ryQu7f-RZ
|
|
"""
|
|
|
|
def __init__(self,
|
|
lr=0.001,
|
|
beta_1=0.9,
|
|
beta_2=0.999,
|
|
epsilon=None,
|
|
decay=0.,
|
|
amsgrad=False,
|
|
cpu_mode=False,
|
|
**kwargs):
|
|
super().__init__(lr, beta_1, beta_2, epsilon, decay, **kwargs)
|
|
self.cpu_mode = self._set_cpu_mode(cpu_mode)
|
|
|
|
@staticmethod
|
|
def _set_cpu_mode(cpu_mode):
|
|
""" Sets the CPU mode to False if not using Tensorflow, otherwise the given value.
|
|
|
|
Parameters
|
|
----------
|
|
cpu_mode: bool
|
|
Set to ``True`` to perform some of the calculations on CPU for Nvidia backends,
|
|
otherwise ``False``.
|
|
|
|
Returns
|
|
-------
|
|
bool
|
|
``True`` if some calculations should be performed on CPU otherwise ``False``
|
|
"""
|
|
retval = False if K.backend() != "tensorflow" else cpu_mode
|
|
logger.debug("Optimizer CPU Mode set to %s", retval)
|
|
return retval
|
|
|
|
def get_updates(self, loss, params):
|
|
""" Obtain the optimizer loss updates.
|
|
|
|
Parameters
|
|
----------
|
|
loss: list
|
|
List of tensors
|
|
|
|
params: list
|
|
List of tensors
|
|
|
|
Returns
|
|
-------
|
|
list
|
|
List of tensors
|
|
"""
|
|
grads = self.get_gradients(loss, params)
|
|
self.updates = [K.update_add(self.iterations, 1)]
|
|
|
|
lr = self.lr
|
|
if self.initial_decay > 0:
|
|
lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
|
|
K.dtype(self.decay))))
|
|
|
|
t = K.cast(self.iterations, K.floatx()) + 1
|
|
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
|
|
(1. - K.pow(self.beta_1, t)))
|
|
|
|
# Pass off to CPU if requested
|
|
if self.cpu_mode:
|
|
with K.tf.device("/cpu:0"):
|
|
ms, vs, vhats = self._update_1(params)
|
|
else:
|
|
ms, vs, vhats = self._update_1(params)
|
|
|
|
self.weights = [self.iterations] + ms + vs + vhats
|
|
|
|
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
|
|
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
|
|
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
|
|
if self.amsgrad:
|
|
vhat_t = K.maximum(vhat, v_t)
|
|
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
|
|
self.updates.append(K.update(vhat, vhat_t))
|
|
else:
|
|
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
|
|
|
|
self.updates.append(K.update(m, m_t))
|
|
self.updates.append(K.update(v, v_t))
|
|
new_p = p_t
|
|
|
|
# Apply constraints.
|
|
if getattr(p, 'constraint', None) is not None:
|
|
new_p = p.constraint(new_p)
|
|
|
|
self.updates.append(K.update(p, new_p))
|
|
return self.updates
|
|
|
|
def _update_1(self, params):
|
|
""" Perform the first update. Run under CPU context if running on Tensorflow and CPU mode
|
|
is enabled, otherwise run on the default device. """
|
|
ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
|
|
vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
|
|
if self.amsgrad:
|
|
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
|
|
else:
|
|
vhats = [K.zeros(1) for _ in params]
|
|
return ms, vs, vhats
|