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fix ModelBase, nnlib

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iperov committed Mar 13, 2019
1 parent da0fc2d commit 8da47fec13f077f4ef7866a970b533c3a1fe1cc6
Showing with 75 additions and 36 deletions.
  1. +1 −1 models/ModelBase.py
  2. +2 −2 models/Model_SAE/Model.py
  3. +72 −33 nnlib/nnlib.py
@@ -90,7 +90,7 @@ def __init__(self, model_path, training_data_src_path=None, training_data_dst_pa

if self.iter == 0 or ask_override:
default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size',0)
self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:0/default) : ", default_batch_size, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
self.options['batch_size'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % (default_batch_size), default_batch_size, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
else:
self.options['batch_size'] = self.options.get('batch_size', 0)

@@ -271,8 +271,8 @@ def onInitialize(self):
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))]

if self.is_training_mode:
self.src_dst_opt = AdamCPU(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
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)
self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)
self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1)

if self.options['archi'] == 'liae':
src_dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
@@ -71,8 +71,8 @@ class nnlib(object):
RandomNormal = keras.initializers.RandomNormal
Model = keras.models.Model
Adam = keras.optimizers.Adam
AdamCPU = nnlib.AdamCPU
#Adam = keras.optimizers.Adam
Adam = nnlib.Adam
modelify = nnlib.modelify
gaussian_blur = nnlib.gaussian_blur
@@ -194,7 +194,7 @@ def import_keras(device_config):
def __initialize_keras_functions():
keras = nnlib.keras
K = keras.backend

def modelify(model_functor):
def func(tensor):
return keras.models.Model (tensor, model_functor(tensor))
@@ -428,51 +428,89 @@ def get_config(self):
return dict(list(base_config.items()) + list(config.items()))
nnlib.Scale = Scale

class AdamCPU(keras.optimizers.Optimizer):
class Adam(keras.optimizers.Optimizer):
"""Adam optimizer.
Default parameters follow those provided in the original paper.
# Arguments
lr: float >= 0. Learning rate.
beta_1: float, 0 < beta < 1. Generally close to 1.
beta_2: float, 0 < beta < 1. Generally close to 1.
epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.
decay: float >= 0. Learning rate decay over each update.
amsgrad: boolean. Whether to apply the AMSGrad variant of this
algorithm from the paper "On the Convergence of Adam and
Beyond".
tf_cpu_mode: only for tensorflow backend
0 - default, no changes.
1 - allows to train x2 bigger network on same VRAM consuming RAM
2 - allows to train x3 bigger network on same VRAM consuming RAM*2 and CPU power.
# 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,
tf_cpu_mode=0, **kwargs):
super(AdamCPU, self).__init__(**kwargs)
epsilon=None, decay=0., amsgrad=False, tf_cpu_mode=0, **kwargs):
super(Adam, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.lr = K.variable(lr, name='lr')
self.beta_1 = K.variable(beta_1, name='beta_1')
self.beta_2 = K.variable(beta_2, name='beta_2')

self.epsilon = K.epsilon()
self.decay = K.variable(decay, name='decay')
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.initial_decay = decay
self.amsgrad = amsgrad
self.tf_cpu_mode = tf_cpu_mode

@keras.legacy.interfaces.legacy_get_updates_support
def get_updates(self, loss, params):
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)))

if self.tf_cpu_mode > 0:
with K.tf.device("/cpu:0"):
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]
(1. - K.pow(self.beta_1, t)))

e = K.tf.device("/cpu:0") if self.tf_cpu_mode > 0 else None
if e: e.__enter__()
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:
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]
vhats = [K.zeros(1) for _ in params]
if e: e.__exit__(None, None, None)

self.weights = [self.iterations] + ms + vs + vhats

self.weights = [self.iterations] + ms + vs

for p, g, m, v in zip(params, grads, ms, vs):
if self.tf_cpu_mode == 2:
with K.tf.device("/cpu:0"):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
e = K.tf.device("/cpu:0") if self.tf_cpu_mode == 2 else None
if e: e.__enter__()
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)
self.updates.append(K.update(vhat, vhat_t))
if e: e.__exit__(None, None, None)

if self.amsgrad:
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
else:
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)

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
@@ -487,12 +525,13 @@ def get_updates(self, loss, params):
def get_config(self):
config = {'lr': float(K.get_value(self.lr)),
'beta_1': float(K.get_value(self.beta_1)),
'beta_2': float(K.get_value(self.beta_2))
}
base_config = super(AdamCPU, self).get_config()
'beta_2': float(K.get_value(self.beta_2)),
'decay': float(K.get_value(self.decay)),
'epsilon': self.epsilon,
'amsgrad': self.amsgrad}
base_config = super(Adam, self).get_config()
return dict(list(base_config.items()) + list(config.items()))

nnlib.AdamCPU = AdamCPU
nnlib.Adam = Adam
'''
not implemented in plaidML
class ReflectionPadding2D(keras.layers.Layer):

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