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SAE: added option "Use CA weights":

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Initialize network with 'Convolution Aware' weights. This may help to achieve a higher accuracy model, but consumes time at first run.
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iperov 2019-03-16 12:54:36 +04:00
parent 71ff0ce1a7
commit d6a45763a2
4 changed files with 346 additions and 18 deletions
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196
nnlib/nnlib.py
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@ -3,8 +3,13 @@ import sys
import contextlib
import numpy as np
from .CAInitializer import CAGenerateWeights
import multiprocessing
from joblib import Subprocessor
from utils import std_utils
from .device import device
from interact import interact as io
class nnlib(object):
device = device #forwards nnlib.devicelib to device in order to use nnlib as standalone lib
@ -73,6 +78,7 @@ Model = keras.models.Model
#Adam = keras.optimizers.Adam
Adam = nnlib.Adam
Padam = nnlib.Padam
modelify = nnlib.modelify
gaussian_blur = nnlib.gaussian_blur
@ -82,6 +88,7 @@ dssim = nnlib.dssim
PixelShuffler = nnlib.PixelShuffler
SubpixelUpscaler = nnlib.SubpixelUpscaler
Scale = nnlib.Scale
CAInitializerMP = nnlib.CAInitializerMP
#ReflectionPadding2D = nnlib.ReflectionPadding2D
#AddUniformNoise = nnlib.AddUniformNoise
@ -91,7 +98,6 @@ Scale = nnlib.Scale
keras_contrib = nnlib.keras_contrib
GroupNormalization = keras_contrib.layers.GroupNormalization
InstanceNormalization = keras_contrib.layers.InstanceNormalization
Padam = keras_contrib.optimizers.Padam
"""
code_import_dlib_string = \
"""
@ -497,7 +503,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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__()
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)
@ -532,6 +538,126 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
base_config = super(Adam, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
nnlib.Adam = Adam
class Padam(keras.optimizers.Optimizer):
"""Partially adaptive momentum estimation optimizer.
# 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".
partial: float, 0 <= partial <= 0.5 . Parameter controlling partial
momentum adaption. For `partial=0`, this optimizer behaves like SGD,
for `partial=0.5` it behaves like AMSGrad.
# References
- [Closing the Generalization Gap of Adaptive Gradient Methods
in Training Deep Neural Networks](https://arxiv.org/pdf/1806.06763.pdf)
"""
def __init__(self, lr=1e-1, beta_1=0.9, beta_2=0.999,
epsilon=1e-8, decay=0., amsgrad=False, partial=1. / 8., tf_cpu_mode=0, **kwargs):
if partial < 0 or partial > 0.5:
raise ValueError(
"Padam: 'partial' must be a positive float with a maximum "
"value of `0.5`, since higher values will cause divergence "
"during training."
)
super(Padam, 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.decay = K.variable(decay, name='decay')
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.partial = partial
self.initial_decay = decay
self.amsgrad = amsgrad
self.tf_cpu_mode = tf_cpu_mode
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)))
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:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
if e: e.__exit__(None, None, None)
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:
denom = (K.sqrt(vhat_t) + self.epsilon)
else:
denom = (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
# Partial momentum adaption.
new_p = p - (lr_t * (m_t / (denom ** (self.partial * 2))))
# 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 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)),
'decay': float(K.get_value(self.decay)),
'epsilon': self.epsilon,
'amsgrad': self.amsgrad,
'partial': self.partial}
base_config = super(Padam, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
nnlib.Padam = Padam
def CAInitializerMP( conv_weights_list ):
result = CAInitializerMPSubprocessor ( [ (i, K.int_shape(conv_weights)) for i, conv_weights in enumerate(conv_weights_list) ], K.floatx(), K.image_data_format() ).run()
for idx, weights in result:
K.set_value ( conv_weights_list[idx], weights )
nnlib.CAInitializerMP = CAInitializerMP
'''
not implemented in plaidML
class ReflectionPadding2D(keras.layers.Layer):
@ -812,5 +938,71 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
if nnlib.tf is not None:
nnlib.tf_sess = None
nnlib.tf = None
class CAInitializerMPSubprocessor(Subprocessor):
class Cli(Subprocessor.Cli):
#override
def on_initialize(self, client_dict):
self.floatx = client_dict['floatx']
self.data_format = client_dict['data_format']
#override
def process_data(self, data):
idx, shape = data
weights = CAGenerateWeights (shape, self.floatx, self.data_format)
return idx, weights
#override
def get_data_name (self, data):
#return string identificator of your data
return "undefined"
#override
def __init__(self, idx_shapes_list, floatx, data_format ):
self.idx_shapes_list = idx_shapes_list
self.floatx = floatx
self.data_format = data_format
self.result = []
super().__init__('CAInitializerMP', CAInitializerMPSubprocessor.Cli)
#override
def on_clients_initialized(self):
io.progress_bar ("Processing", len (self.idx_shapes_list))
#override
def on_clients_finalized(self):
io.progress_bar_close()
#override
def process_info_generator(self):
for i in range(multiprocessing.cpu_count()):
yield 'CPU%d' % (i), {}, {'device_idx': i,
'device_name': 'CPU%d' % (i),
'floatx' : self.floatx,
'data_format' : self.data_format
}
#override
def get_data(self, host_dict):
if len (self.idx_shapes_list) > 0:
return self.idx_shapes_list.pop(0)
return None
#override
def on_data_return (self, host_dict, data):
self.idx_shapes_list.insert(0, data)
#override
def on_result (self, host_dict, data, result):
self.result.append ( result )
io.progress_bar_inc(1)
#override
def get_result(self):
return self.result