def __init__(self, **kwargs):
outnum = kwargs.pop('outnum', 1)
outact = kwargs.pop('outact', None)
numhid = kwargs.pop('numhid', 64)
clip = kwargs.pop('clip', [-1., 1.])
use_bias = kwargs.pop('use_bias', False)
# patest = dict(relu='relu', out='clip', pixel='relu', gauss='relu')
patest = dict(relu='linear',
out='linear',
pixel='linear',
gauss='linear')
patest.update(kwargs.pop('patest', {}))
explain = dict(relu='zplus', out='zclip', pixel='zb', gauss='wsquare')
explain.update(kwargs.pop('explain', {}))
super().__init__(**kwargs)
with self.name_scope():
self.add(Concat())
# Same as Dense + reshape since we're coming from 1x1
self.add(
Conv2DTranspose(numhid * 8,
4,
strides=1,
padding=0,
use_bias=use_bias,
explain=explain['gauss'],
regimes=estimators[patest['gauss']]()))
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 4 x 4
self.add(
Conv2DTranspose(numhid * 4,
5,
strides=1,
padding=0,
output_padding=0,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 8 x 8
self.add(
Conv2DTranspose(numhid * 2,
5,
strides=1,
padding=0,
output_padding=0,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 12 x 12
self.add(
Conv2DTranspose(numhid * 2,
5,
strides=1,
padding=0,
output_padding=0,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 16 x 16
self.add(
Conv2DTranspose(numhid,
5,
strides=1,
padding=0,
output_padding=0,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 20 x 20
self.add(
Conv2DTranspose(numhid,
5,
strides=1,
padding=0,
output_padding=0,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 24 x 24
self.add(
Conv2DTranspose(numhid,
5,
strides=1,
padding=0,
output_padding=0,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 28 x 28
self.add(
Conv2DTranspose(outnum,
5,
strides=1,
padding=0,
output_padding=0,
use_bias=use_bias,
explain=explain['out'],
regimes=estimators[patest['out']](),
noregconst=-1.))
if outact == 'relu':
self.add(ReLU())
elif outact == 'clip':
self.add(Clip(low=clip[0], high=clip[1]))
elif outact == 'tanh':
self.add(Tanh())
elif outact == 'batchnorm':
self.add(BatchNorm(scale=False, center=False))
self.add(Identity())
else:
self.add(Identity())
def __init__(self, **kwargs):
outnum = kwargs.pop('outnum', 1)
outact = kwargs.pop('outact', None)
numhid = kwargs.pop('numhid', 64)
clip = kwargs.pop('clip', [-1., 1.])
use_bias = kwargs.pop('use_bias', False)
use_bnrm = kwargs.pop('use_bnorm', True)
# patest = dict(relu='relu', out='clip', pixel='relu', gauss='relu')
patest = dict(relu='linear',
out='linear',
pixel='linear',
gauss='linear')
patest.update(kwargs.pop('patest', {}))
explain = dict(relu='zplus', out='zclip', pixel='zb', gauss='wsquare')
explain.update(kwargs.pop('explain', {}))
super().__init__(**kwargs)
with self.name_scope():
self.add(Concat())
self.add(
Conv2DTranspose(numhid * 8,
4,
strides=1,
padding=0,
use_bias=use_bias,
explain=explain['gauss'],
regimes=estimators[patest['gauss']]()))
if use_bnrm:
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 3 x 3
self.add(
Conv2DTranspose(numhid * 4,
4,
strides=1,
padding=0,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
if use_bnrm:
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 7 x 7
self.add(
Conv2DTranspose(numhid * 2,
4,
strides=2,
padding=1,
use_bias=use_bias,
explain=explain['relu'],
regimes=estimators[patest['relu']]()))
if use_bnrm:
self.add(BatchNorm())
self.add(ReLU())
# _numhid x 14 x 14
self.add(
Conv2DTranspose(outnum,
4,
strides=2,
padding=1,
use_bias=use_bias,
explain=explain['out'],
regimes=estimators[patest['out']](),
noregconst=-1.))
if outact == 'relu':
self.add(ReLU())
elif outact == 'clip':
self.add(Clip(low=clip[0], high=clip[1]))
elif outact == 'tanh':
self.add(Tanh())
elif outact == 'batchnorm':
self.add(BatchNorm(scale=False, center=False))
self.add(Identity())
else:
self.add(Identity())