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https://github.com/iperov/DeepFaceLab.git
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SAE: removed simple_optimizer . Added optimizer mode for tensorflow only (NVIDIA cards), allows to train x2-x3 bigger networks with normal Adam optimizer, consuming VRAM and CPU power.
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iperov
parent
7d6ca32250
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58763756f5
3 changed files with 100 additions and 37 deletions
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@ -34,6 +34,11 @@ class ModelBase(object):
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device_args['force_gpu_idx'] = io.input_int("Which GPU idx to choose? ( skip: best GPU ) : ", -1, [ x[0] for x in idxs_names_list] )
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self.device_args = device_args
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nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, **self.device_args) )
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self.device_config = nnlib.active_DeviceConfig
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self.keras = nnlib.keras
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self.K = nnlib.keras.backend
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io.log_info ("Loading model...")
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self.model_path = model_path
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@ -122,13 +127,8 @@ class ModelBase(object):
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if self.src_scale_mod == 0:
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self.options.pop('src_scale_mod')
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self.onInitializeOptions(self.iter == 0, ask_override)
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nnlib.import_all ( nnlib.DeviceConfig(allow_growth=False, **self.device_args) )
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self.device_config = nnlib.active_DeviceConfig
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self.keras = nnlib.keras
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self.K = nnlib.keras.backend
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self.onInitialize()
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self.options['batch_size'] = self.batch_size
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@ -42,11 +42,12 @@ class SAEModel(ModelBase):
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self.options['face_type'] = self.options.get('face_type', default_face_type)
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self.options['learn_mask'] = self.options.get('learn_mask', True)
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if is_first_run or ask_override:
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def_simple_optimizer = self.options.get('simple_optimizer', False)
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self.options['simple_optimizer'] = io.input_bool ("Use simple optimizer? (y/n, ?:help skip:%s) : " % ( yn_str[def_simple_optimizer] ), def_simple_optimizer, help_message="Simple optimizer allows you to train bigger network or more batch size, sacrificing training accuracy.")
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if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend:
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def_optimizer_mode = self.options.get('optimizer_mode', 1)
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self.options['optimizer_mode'] = io.input_int ("Optimizer mode? ( 1,2,3 ?:help skip:%d) : " % (def_optimizer_mode), def_optimizer_mode, help_message="1 - no changes. 2 - allows you to train x2 bigger network consuming RAM. 3 - allows you to train x3 bigger network consuming huge amount of RAM and slower, depends on CPU power.")
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else:
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self.options['simple_optimizer'] = self.options.get('simple_optimizer', False)
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self.options['optimizer_mode'] = self.options.get('optimizer_mode', 1)
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if is_first_run:
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self.options['archi'] = io.input_str ("AE architecture (df, liae, vg ?:help skip:%s) : " % (default_archi) , default_archi, ['df','liae','vg'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes. 'vg' - currently testing.").lower()
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@ -270,12 +271,8 @@ class SAEModel(ModelBase):
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psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
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if self.is_training_mode:
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if self.options['simple_optimizer']:
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self.src_dst_opt = DFLOptimizer(lr=5e-5)
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self.src_dst_mask_opt = DFLOptimizer(lr=5e-5)
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else:
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self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
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self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
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self.src_dst_opt = AdamCPU(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
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self.src_dst_mask_opt = AdamCPU(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
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if self.options['archi'] == 'liae':
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src_dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
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100
nnlib/nnlib.py
100
nnlib/nnlib.py
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@ -72,7 +72,7 @@ RandomNormal = keras.initializers.RandomNormal
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Model = keras.models.Model
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Adam = keras.optimizers.Adam
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DFLOptimizer = nnlib.DFLOptimizer
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AdamCPU = nnlib.AdamCPU
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modelify = nnlib.modelify
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gaussian_blur = nnlib.gaussian_blur
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@ -434,28 +434,93 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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return dict(list(base_config.items()) + list(config.items()))
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nnlib.Scale = Scale
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class DFLOptimizer(keras.optimizers.Optimizer):
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def __init__(self, lr=0.001, **kwargs):
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super(DFLOptimizer, self).__init__(**kwargs)
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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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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(0.9, name='beta_1')
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self.beta_2 = K.variable(0.998, name='beta_2')
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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.tf_cpu_mode = tf_cpu_mode
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@keras.legacy.interfaces.legacy_get_updates_support
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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_t = self.lr * ( ( K.cast(self.iterations, K.floatx()) ) % 100 + 1 ) / 100.0
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self.weights = []
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for p, g in zip(params, grads):
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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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m_t = (1. - self.beta_1) * g
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v_t = (1. - self.beta_2) * K.square(g)
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new_p = p - lr_t * m_t / (K.sqrt(v_t) + K.epsilon() )
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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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if self.tf_cpu_mode > 0:
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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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for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
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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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v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
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else:
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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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@ -467,13 +532,14 @@ 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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}
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base_config = super(DFLOptimizer, self).get_config()
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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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base_config = super(AdamCPU, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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nnlib.DFLOptimizer = DFLOptimizer
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nnlib.AdamCPU = AdamCPU
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'''
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not implemented in plaidML
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class ReflectionPadding2D(keras.layers.Layer):
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