def shpcmp(shape1, shape2):
# Lambda to compare lists except for None's.
cmplst = lambda l1, l2: all([
elem1 == elem2 if None not in [elem1, elem2] else True
for elem1, elem2 in zip(l1, l2)
]) and (len(l1) == len(l2))
# First test: Shape1 and shape2 must both (not) be a list of list
shpeq = pyk.islistoflists(shape1) == pyk.islistoflists(shape2)
# Second test: number of inputs.
shpeq = shpeq and (len(pyk.list2listoflists(shape1)) == len(
pyk.list2listoflists(shape2)))
# Third test: list comparisons
shpeq = shpeq and all([
cmplst(l1, l2) for l1, l2 in zip(pyk.list2listoflists(shape1),
pyk.list2listoflists(shape2))
])
# Done
return shpeq
def inferoutshape(self, inpshape=None, checkinput=True):
if inpshape is None:
inpshape = self.inpshape
if checkinput:
# Compare shape with Antipasti inpshape
assert netutils.shpcmp(self.lasinpshape, inpshape), "Lasagne input shape is not consistent with the " \
"inferred Antipasti input shape."
# Get output shape from Lasagne
outshape = las.layers.get_output_shape(
self.outputlayers, {
inplayer: ishp
for inplayer, ishp in zip(pyk.obj2list(self.inputlayers),
pyk.list2listoflists(inpshape))
})
outshape = pyk.listoftuples2listoflists(outshape) if pyk.islistoflists(
outshape) else list(outshape)
return outshape
def parselayerinfo(dim=None,
inpdim=None,
issequence=None,
allowsequences=None,
numinp=None,
inpshape=None,
verbose=True):
parsey = {
'dim': dim,
'inpdim': inpdim,
'issequence': issequence,
'allowsequences': allowsequences,
'numinp': numinp,
'inpshape': inpshape
}
# Parse from inpshape
if parsey['inpshape'] is not None:
# Make sure inpshape is a list
assert isinstance(
parsey['inpshape'],
list), "inpshape must be a list, e.g. [None, 3, None, None]."
# Fetch number of inputs
if pyk.islistoflists(parsey['inpshape']):
_numinp = len(parsey['inpshape'])
_inpdim = [len(ishp) for ishp in parsey['inpshape']]
else:
_numinp = 1
_inpdim = len(parsey['inpshape'])
# Write to parsed (if not written already)
# numinp
parsey['numinp'] = _numinp if parsey['numinp'] is None else parsey[
'numinp']
# Consistency check
assert parsey['numinp'] == _numinp, "The provided inpshape requires numinp = {}, " \
"but the value given was {}".format(_numinp, parsey['numinp'])
# inpdim
parsey['inpdim'] = _inpdim if parsey['inpdim'] is None else parsey[
'inpdim']
assert parsey['inpdim'] == _inpdim, "The provided inpshape requires inpdim = {}, " \
"but the value given was {}".format(_inpdim, parsey['inpdim'])
# Check if dim, inpdim, issequence or allowsequences is a list of multiple elements and numinp is not given.
if parsey['numinp'] is None:
for argname in ['dim', 'inpdim', 'issequence', 'allowsequences']:
if isinstance(parsey[argname], list):
parsey['numinp'] = len(parsey[argname])
# Parse from numinp
if parsey['numinp'] is not None:
for argname, argtype in zip(
['dim', 'inpdim', 'issequence', 'allowsequences'],
[int, int, bool, bool]):
if isinstance(parsey[argname], argtype) and parsey['numinp'] > 1:
# If numinp is > 1 and allowseqences or issequence or inpdim or dim is bool or bool or int or int,
# assume that the user (or the author) is too lazy to type in a list.
parsey[argname] = [
parsey[argname],
] * parsey['numinp']
elif isinstance(parsey[argname], list) and parsey['numinp'] > 1:
# If the user was not lazy, make sure the given list sizes check out
assert len(parsey[argname]) == parsey['numinp'], \
"{} must be a {} or a list of length {} (= numinp).".format(argname, argtype, parsey['numinp'])
# Check if inpshape is consistent
if parsey['inpshape'] is not None and parsey['numinp'] > 1:
assert pyk.islistoflists(parsey['inpshape']) and len(
parsey['inpshape']) == parsey['numinp']
else:
if verbose:
warn("Guessing that numinp = 1.")
# Guess numinp = 1.
parsey['numinp'] = 1
# Parse allowsequences
# At this point, allowsequences must be known (or no conclusions can be drawn on issequence and dim)
if parsey['allowsequences'] is None:
if verbose:
warn("Guessing that sequences are allowed.")
parsey['allowsequences'] = pyk.delist([
True,
] * parsey['numinp'])
else:
# Okay, so it's known if sequences are allowed. Check if issequence is consistent.
if pyk.obj2list(parsey['allowsequences']) == [
False,
] * parsey['numinp'] and parsey['issequence'] is not None:
# If sequences are not allowed, make sure issequence is False
assert pyk.obj2list(parsey['issequence']) == [False,] * parsey['numinp'], \
"Input(s) are not allowed to be sequential, yet they are."
# Parse issequence
if parsey['issequence'] is not None:
# Delist issequence
parsey['issequence'] = pyk.delist(parsey['issequence']) \
if isinstance(parsey['issequence'], list) else parsey['issequence']
# Check if issequence is consistent with everything
if isinstance(parsey['issequence'], list):
assert len(parsey['issequence']) == parsey['numinp'], "issequence must be a list of the same lenght as " \
"numinp = {} if numinp > 1.".format(parsey['numinp'])
# Check if consistent with allowsequences. At this point, issequence may have None's.
assert all([(bool(isseq) and allowseq) or not isseq
for isseq, allowseq in zip(pyk.obj2list(parsey['issequence']),
pyk.obj2list(parsey['allowsequences']))]), \
"Input is a sequence although it's not allowed to. " \
"issequence = {}, allowsequences = {}.".format(parsey['issequence'], parsey['allowsequences'])
else:
if verbose:
warn("Guessing that input(s) is(are) not sequential.")
parsey['issequence'] = pyk.delist([
False,
] * parsey['numinp'])
# Parse inpdim
# Compute expected inpdim from what's known
# Check in from issequence
_inpdim = pyk.delist(
[5 if isseq else None for isseq in pyk.obj2list(parsey['issequence'])])
# Check in from dim
if parsey['dim'] is not None:
_inpdim = pyk.delist([
5 if d == 3 else indim for d, indim in zip(
pyk.obj2list(parsey['dim']), pyk.obj2list(_inpdim))
])
_inpdim = pyk.delist([
4 if (d == 2 and not isseq) else indim for d, indim, isseq in zip(
pyk.obj2list(parsey['dim']), pyk.obj2list(_inpdim),
pyk.obj2list(parsey['issequence']))
])
if parsey['inpdim'] is None:
# Make sure there are no None's remaining in _inpdim
assert None not in pyk.obj2list(
_inpdim
), "Input dimensionality could not be parsed due to missing information."
parsey['inpdim'] = _inpdim
else:
assert pyk.smartlen(parsey['inpdim']) == pyk.smartlen(_inpdim), \
"Expected {} elements in inpdim, got {}.".format(pyk.smartlen(_inpdim), pyk.smartlen(parsey['inpdim']))
# Check consistency with the expected _inpdim
assert all([_indim == indim for _indim, indim in zip(pyk.obj2list(_inpdim), pyk.obj2list(parsey['inpdim']))
if _indim is not None]), \
"Provided inpdim is inconsistent with either dim or issequence."
# Parse dim
# Compute expected _inpdim from what's known
_dim = pyk.delist([
2 if (indim == 4 or isseq) else 3 for indim, isseq in zip(
pyk.obj2list(parsey['inpdim']), pyk.obj2list(parsey['issequence']))
])
# Check in from dim
if parsey['dim'] is None:
parsey['dim'] = _dim
else:
assert parsey[
'dim'] == _dim, "Given dim ({}) is not consistent with expectation ({})".format(
parsey['dim'], _dim)
# Reparse inpshape
if parsey['inpshape'] is None:
parsey['inpshape'] = pyk.delist([[
None,
] * indim for indim in pyk.obj2list(parsey['inpdim'])])
# Return parsey :(
return parsey
def __theano__pool(self,
inp,
ds,
stride=None,
padding=None,
poolmode='max',
dim=None,
ignoreborder=True,
issequence=False):
# Do imports locally to prevent circular dependencies
import netutils as nu
import pykit as pyk
from theano.tensor.signal import pool as downsample
# Determine the dimensionality of convolution (2 or 3?)
if dim is None:
dim = 3 if not issequence and len(ds) == 3 and inp.ndim == 5 else 2
# Defaults
poolmode = 'average_exc_pad' if poolmode in [
'mean', 'average', 'average_exc_pad'
] else poolmode
padding = [[0, 0]] * dim if padding is None else padding
stride = ds if stride is None else stride
# Autofix inputs
if isinstance(padding, int):
padding = [padding] * dim
if not pyk.islistoflists(pyk.obj2list(padding)):
padding = [[padval] * dim for padval in pyk.obj2list(padding)]
if isinstance(stride, int):
stride = [stride] * dim
# Check if theano can pad input as required
autopaddable = all(
[all([dimpad == pad[0] for dimpad in pad]) for pad in padding])
# Reshape 2D sequential data if required
# Log input shape
inpshape = inp.shape
reallyissequential = issequence and inp.ndim == 5
if issequence:
if reallyissequential:
# Sequential input must be paddable by theano. This is required to reshape the sequential input back to
# its original shape after pooling.
assert autopaddable, "Sequential inputs must be paddable by theano. Provided padding {} cannot be " \
"handled at present.".format(padding)
inp = inp.reshape((inpshape[0] * inpshape[1], inpshape[2],
inpshape[3], inpshape[4]),
ndim=4)
ds = ds[0:2]
stride = stride[0:2]
padding = padding[0:2]
else:
warn(
"Expected 5D sequential output, but got 4D non-sequential instead."
)
# Determine what theano needs to be told about how to pad the input
if autopaddable:
autopadding = tuple([pad[0] for pad in padding])
else:
autopadding = (0, ) * dim
if not autopaddable and not all(
[padval == 0 for padval in pyk.flatten(padding)]):
if not th.config.device == 'cpu' and not self.cpupadwarned:
warn(
"Padding might occur on the CPU, which tends to slow things down."
)
self.cpupadwarned = True
inp = nu.pad(inp, padding)
if dim == 2:
y = downsample.pool_2d(input=inp,
ds=ds,
st=stride,
padding=autopadding,
ignore_border=ignoreborder,
mode=poolmode)
elif dim == 3:
# parse downsampling ratio, stride and padding
dsyx = ds[0:2]
styx = stride[0:2]
padyx = autopadding[0:2]
ds0z = (1, ds[2])
st0z = (1, stride[2])
pad0z = (0, autopadding[2])
# Dowsnample yx
H = downsample.pool_2d(input=inp,
ds=dsyx,
st=styx,
padding=padyx,
mode=poolmode)
# Rotate tensor
H = H.dimshuffle(0, 2, 3, 4, 1)
# Downsample 0z
H = downsample.pool_2d(input=H,
ds=ds0z,
st=st0z,
padding=pad0z,
mode=poolmode)
# Undo rotate tensor
y = H.dimshuffle(0, 4, 1, 2, 3)
else:
raise NotImplementedError("Pooling is implemented in 2D and 3D.")
if issequence and reallyissequential:
# Compute symbolic pool output length
if ignoreborder:
pooleny, poolenx = \
[T.floor((inpshape[tensorindex] + 2 * autopadding[index] - ds[index] + stride[index])/stride[index])
for index, tensorindex in enumerate([3, 4])]
else:
poolen = [None, None]
for index, tensorindex in enumerate([3, 4]):
if stride[index] >= ds[index]:
poolen[index] = T.floor(
(inpshape[tensorindex] + stride[index] - 1) /
stride[index])
else:
plen = T.floor((inpshape[tensorindex] - ds[index] +
stride[index] - 1) / stride[index])
poolen[index] = T.switch(plen > 0, plen, 0)
pooleny, poolenx = poolen
y = y.reshape(
(inpshape[0], inpshape[1], inpshape[2], pooleny, poolenx),
ndim=5)
return y
def __theano__conv(self,
inp,
filters,
stride=None,
dilation=None,
padding=None,
bias=None,
filtergradmask=None,
biasgradmask=None,
filtermask=None,
biasmask=None,
filtergradclips=None,
biasgradclips=None,
dim=None,
convmode='same',
issequence=False,
implementation='auto'):
# Do imports locally to prevent circular dependencies
import netutils as nu
import theanops as tho
import pykit as pyk
# Determine the dimensionality of convolution (2 or 3?)
if dim is None:
dim = 3 if not issequence and len(
filters.get_value().shape) == 5 and inp.ndim == 5 else 2
# Smart fix: if convmode is 'same', stride != 1 and padding is None: set automagically set padding.
# Defaults
padding = [[0, 0]] * dim if padding is None else padding
stride = [1] * dim if stride is None else stride
dilation = [1] * dim if dilation is None else dilation
filtergradclips = [
-np.inf, np.inf
] if filtergradclips is None else list(filtergradclips)
biasgradclips = [-np.inf, np.inf
] if biasgradclips is None else list(biasgradclips)
# Autofix inputs
if isinstance(padding, int):
padding = [padding] * dim
if not pyk.islistoflists(pyk.obj2list(padding)):
padding = [[padval] * dim for padval in pyk.obj2list(padding)]
if isinstance(stride, int):
stride = [stride] * dim
if isinstance(dilation, int):
dilation = [dilation] * dim
# TODO: Tests
pass
# Reshape 2D sequential data if required
# Log input shape
inpshape = inp.shape
reallyissequential = issequence and inp.ndim == 5
if issequence:
if reallyissequential:
inp = inp.reshape((inpshape[0] * inpshape[1], inpshape[2],
inpshape[3], inpshape[4]),
ndim=4)
stride = stride[0:2]
padding = padding[0:2]
# TODO: Get rid of these restrictions
assert stride == [
1, 1
], "Strided convolution is not implemented for sequential data."
assert convmode == 'same', "Convmode must be 'same' for sequential data."
else:
warn(
"Expected 5D sequential output, but got 4D non-sequential instead."
)
# Apply gradient masks if required
if filtergradmask is not None:
filters = tho.maskgradient(filters, filtergradmask)
if biasgradmask is not None and bias is not None:
bias = tho.maskgradient(bias, biasgradmask)
# Apply masks if required
if filtermask is not None:
filters = filtermask * filters
if biasmask is not None:
bias = biasmask * bias
# Determine border_mode for CuDNN/3D conv
autopaddable, bordermode, trim = self.__theano__bordermode(
convmode, padding,
filters.get_value().shape)
# Pad input if required (warn that it's ridiculously slow)
if not autopaddable and not all(
[padval == 0 for padval in pyk.flatten(padding)]):
if not isinstance(bordermode,
str) and pyk.islistoflists(bordermode):
# Override padding for 3D convolutions
inp = nu.pad(inp, bordermode)
bordermode = 'valid'
else:
inp = nu.pad(inp, padding)
# Switch implementation
if implementation == 'auto':
# Fall back implementation: 'vanilla'
implementation = 'vanilla'
if dilation != [1, 1]:
implementation = 'dilated'
# Convolve 2D (with gradmask + bias), reshape sequential data
if dim == 2:
if implementation == 'vanilla':
if list(dilation) != [1, 1]:
warn(
"Filter dilation is not possible with this implementation."
)
# Convolve
y = T.nnet.conv2d(input=inp,
filters=th.gradient.grad_clip(
filters, *filtergradclips),
border_mode=tuple(bordermode) if isinstance(
bordermode, list) else bordermode,
filter_shape=filters.get_value().shape,
subsample=tuple(stride))
elif implementation == 'dilated':
# Make sure stride is 1
assert list(stride) == [
1, 1
], "Stride should equal [1, 1] for dilated convolutions."
assert not issequence, "Dilated convolution is not supported for sequential data."
# Dilated conv can't handle padding at the moment, do this manually
if isinstance(bordermode, tuple):
padding = [[bm, bm] for bm in bordermode]
inp = nu.pad(inp, padding)
elif bordermode == 'full':
raise NotImplementedError(
"Convolution mode 'full' is not implemented for dilated convolutions."
)
elif bordermode == 'valid':
pass
elif bordermode == 'half':
assert all([d % 2 == 0 for d in dilation]), "Dilation amount must be divisible by 2 for dilated " \
"convolution with 'same' border handling."
padding = [[
(filters.get_value().shape[n] - 1) * d / 2,
] * 2 for n, d in zip([2, 3], dilation)]
inp = nu.pad(inp, padding)
else:
raise NotImplementedError(
"Unknown bordermode: {}.".format(bordermode))
# Get output image shape
oishp = [
inp.shape[n] - (filters.shape[n] - 1) * d
for n, d in zip([2, 3], dilation)
]
# Get computin'
op = T.nnet.abstract_conv.AbstractConv2d_gradWeights(
subsample=tuple(dilation),
border_mode='valid',
filter_flip=False)
y = op(inp.transpose(1, 0, 2, 3),
filters.transpose(1, 0, 2, 3), tuple(oishp))
y = y.transpose(1, 0, 2, 3)
else:
raise NotImplementedError(
"Implementation {} is not implemented.".format(
implementation))
# Trim if required
if trim:
y = self.__theano__convtrim(
inp=y, filtershape=filters.get_value().shape)
# Add bias if required
if bias is not None:
y = y + th.gradient.grad_clip(bias, *biasgradclips).dimshuffle(
'x', 0, 'x', 'x')
elif dim == 3:
# Convolve 3D (with bias)
if implementation == 'auto' or implementation == 'conv2d':
assert stride == [
1, 1, 1
], "Implementation 'conv2d' does not support strided convolution in 3D."
assert convmode == 'valid', "Implementation 'conv2d' only supports 'valid' convolutions."
y = T.nnet.conv3d2d.conv3d(
signals=inp,
filters=th.gradient.grad_clip(filters, *filtergradclips),
border_mode=bordermode,
filters_shape=filters.get_value().shape)
else:
raise NotImplementedError(
"Implementation {} is not implemented.".format(
implementation))
# Trim if required
if trim:
y = self.__theano__convtrim(
inp=y, filtershape=filters.get_value().shape)
# Add bias if required
if bias is not None:
y = y + th.gradient.grad_clip(bias, *biasgradclips).dimshuffle(
'x', 'x', 0, 'x', 'x')
else:
raise NotImplementedError(
"Convolution is implemented in 2D and 3D.")
# Reshape sequential data
if issequence and reallyissequential:
y = y.reshape(
(inpshape[0], inpshape[1], filters.get_value().shape[0],
inpshape[3], inpshape[4]),
ndim=5)
# Return
return y
def __theano__conv(self,
inp,
filters,
stride=None,
padding=None,
bias=None,
filtergradmask=None,
biasgradmask=None,
filtermask=None,
biasmask=None,
filtergradclips=None,
biasgradclips=None,
dim=None,
convmode='same',
issequence=False,
implementation='auto'):
# Do imports locally to prevent circular dependencies
import netutils as nu
import theanops as tho
import pykit as pyk
# Determine the dimensionality of convolution (2 or 3?)
if dim is None:
dim = 3 if not issequence and len(
filters.get_value().shape) == 5 and inp.ndim == 5 else 2
# Smart fix: if convmode is 'same', stride != 1 and padding is None: set automagically set padding.
# Defaults
padding = [[0, 0]] * dim if padding is None else padding
stride = [1] * dim if stride is None else stride
filtergradclips = [
-np.inf, np.inf
] if filtergradclips is None else list(filtergradclips)
biasgradclips = [-np.inf, np.inf
] if biasgradclips is None else list(biasgradclips)
# Autofix inputs
if isinstance(padding, int):
padding = [padding] * dim
if not pyk.islistoflists(pyk.obj2list(padding)):
padding = [[padval] * dim for padval in pyk.obj2list(padding)]
if isinstance(stride, int):
stride = [stride] * dim
# TODO: Tests
pass
# Reshape 2D sequential data if required
# Log input shape
inpshape = inp.shape
reallyissequential = issequence and inp.ndim == 5
if issequence:
if reallyissequential:
inp = inp.reshape((inpshape[0] * inpshape[1], inpshape[2],
inpshape[3], inpshape[4]),
ndim=4)
stride = stride[0:2]
padding = padding[0:2]
# TODO: Get rid of these restrictions
assert stride == [
1, 1
], "Strided convolution is not implemented for sequential data."
assert convmode == 'same', "Convmode must be 'same' for sequential data."
else:
warn(
"Expected 5D sequential output, but got 4D non-sequential instead."
)
# Apply gradient masks if required
if filtergradmask is not None:
filters = tho.maskgradient(filters, filtergradmask)
if biasgradmask is not None and bias is not None:
bias = tho.maskgradient(bias, biasgradmask)
# Apply masks if required
if filtermask is not None:
filters = filtermask * filters
if biasmask is not None:
bias = biasmask * bias
# Determine border_mode for CuDNN/3D conv
autopaddable, bordermode, trim = self.__theano__bordermode(
convmode, padding,
filters.get_value().shape)
# Pad input if required (warn that it's ridiculously slow)
if not autopaddable and not all(
[padval == 0 for padval in pyk.flatten(padding)]):
if not th.config.device == 'cpu' and not self.cpupadwarned:
warn(
"Padding might occur on the CPU, which tends to slow things down."
)
self.cpupadwarned = True
if not isinstance(bordermode,
str) and pyk.islistoflists(bordermode):
# Override padding for 3D convolutions
inp = nu.pad(inp, bordermode)
bordermode = 'valid'
else:
inp = nu.pad(inp, padding)
# Convolve 2D (with gradmask + bias), reshape sequential data
if dim == 2:
if implementation == 'auto':
# Convolve
y = T.nnet.conv2d(input=inp,
filters=th.gradient.grad_clip(
filters, *filtergradclips),
border_mode=tuple(bordermode) if isinstance(
bordermode, list) else bordermode,
filter_shape=filters.get_value().shape,
subsample=tuple(stride))
else:
raise NotImplementedError(
"Implementation {} is not implemented.".format(
implementation))
# Trim if required
if trim:
y = self.__theano__convtrim(
inp=y, filtershape=filters.get_value().shape)
# Add bias if required
if bias is not None:
y = y + th.gradient.grad_clip(bias, *biasgradclips).dimshuffle(
'x', 0, 'x', 'x')
elif dim == 3:
# Convolve 3D (with bias)
if implementation == 'auto' or implementation == 'conv2d':
assert stride == [
1, 1, 1
], "Implementation 'conv2d' does not support strided convolution in 3D."
assert convmode == 'valid', "Implementation 'conv2d' only supports 'valid' convolutions."
y = T.nnet.conv3d2d.conv3d(
signals=inp,
filters=th.gradient.grad_clip(filters, *filtergradclips),
border_mode=bordermode,
filters_shape=filters.get_value().shape)
else:
raise NotImplementedError(
"Implementation {} is not implemented.".format(
implementation))
# Trim if required
if trim:
y = self.__theano__convtrim(
inp=y, filtershape=filters.get_value().shape)
# Add bias if required
if bias is not None:
y = y + th.gradient.grad_clip(bias, *biasgradclips).dimshuffle(
'x', 'x', 0, 'x', 'x')
else:
raise NotImplementedError(
"Convolution is implemented in 2D and 3D.")
# Reshape sequential data
if issequence and reallyissequential:
y = y.reshape(
(inpshape[0], inpshape[1], filters.get_value().shape[0],
inpshape[3], inpshape[4]),
ndim=5)
# Return
return y
def __theano__conv(self, inp, filters, stride=None, padding=None, bias=None, filtergradmask=None,
biasgradmask=None, filtermask=None, biasmask=None, filtergradclips=None, biasgradclips=None,
dim=None, convmode='same', issequence=False, implementation='auto'):
# Do imports locally to prevent circular dependencies
import netutils as nu
import theanops as tho
import pykit as pyk
# Determine the dimensionality of convolution (2 or 3?)
if dim is None:
dim = 3 if not issequence and len(filters.get_value().shape) == 5 and inp.ndim == 5 else 2
# Smart fix: if convmode is 'same', stride != 1 and padding is None: set automagically set padding.
# Defaults
padding = [[0, 0]] * dim if padding is None else padding
stride = [1] * dim if stride is None else stride
filtergradclips = [-np.inf, np.inf] if filtergradclips is None else list(filtergradclips)
biasgradclips = [-np.inf, np.inf] if biasgradclips is None else list(biasgradclips)
# Autofix inputs
if isinstance(padding, int):
padding = [padding] * dim
if not pyk.islistoflists(pyk.obj2list(padding)):
padding = [[padval] * dim for padval in pyk.obj2list(padding)]
if isinstance(stride, int):
stride = [stride] * dim
# TODO: Tests
pass
# Reshape 2D sequential data if required
# Log input shape
inpshape = inp.shape
reallyissequential = issequence and inp.ndim == 5
if issequence:
if reallyissequential:
inp = inp.reshape((inpshape[0] * inpshape[1], inpshape[2], inpshape[3], inpshape[4]), ndim=4)
stride = stride[0:2]
padding = padding[0:2]
# TODO: Get rid of these restrictions
assert stride == [1, 1], "Strided convolution is not implemented for sequential data."
assert convmode == 'same', "Convmode must be 'same' for sequential data."
else:
warn("Expected 5D sequential output, but got 4D non-sequential instead.")
# Apply gradient masks if required
if filtergradmask is not None:
filters = tho.maskgradient(filters, filtergradmask)
if biasgradmask is not None and bias is not None:
bias = tho.maskgradient(bias, biasgradmask)
# Apply masks if required
if filtermask is not None:
filters = filtermask * filters
if biasmask is not None:
bias = biasmask * bias
# Determine border_mode for CuDNN/3D conv
autopaddable, bordermode, trim = self.__theano__bordermode(convmode, padding, filters.get_value().shape)
# Pad input if required (warn that it's ridiculously slow)
if not autopaddable and not all([padval == 0 for padval in pyk.flatten(padding)]):
if not th.config.device == 'cpu' and not self.cpupadwarned:
warn("Padding might occur on the CPU, which tends to slow things down.")
self.cpupadwarned = True
if not isinstance(bordermode, str) and pyk.islistoflists(bordermode):
# Override padding for 3D convolutions
inp = nu.pad(inp, bordermode)
bordermode = 'valid'
else:
inp = nu.pad(inp, padding)
# Convolve 2D (with gradmask + bias), reshape sequential data
if dim == 2:
if implementation == 'auto':
# Convolve
y = T.nnet.conv2d(input=inp, filters=th.gradient.grad_clip(filters, *filtergradclips),
border_mode=tuple(bordermode) if isinstance(bordermode, list) else bordermode,
filter_shape=filters.get_value().shape, subsample=tuple(stride))
else:
raise NotImplementedError("Implementation {} is not implemented.".format(implementation))
# Trim if required
if trim:
y = self.__theano__convtrim(inp=y, filtershape=filters.get_value().shape)
# Add bias if required
if bias is not None:
y = y + th.gradient.grad_clip(bias, *biasgradclips).dimshuffle('x', 0, 'x', 'x')
elif dim == 3:
# Convolve 3D (with bias)
if implementation == 'auto' or implementation == 'conv2d':
assert stride == [1, 1, 1], "Implementation 'conv2d' does not support strided convolution in 3D."
assert convmode == 'valid', "Implementation 'conv2d' only supports 'valid' convolutions."
y = T.nnet.conv3d2d.conv3d(signals=inp, filters=th.gradient.grad_clip(filters, *filtergradclips),
border_mode=bordermode,
filters_shape=filters.get_value().shape)
else:
raise NotImplementedError("Implementation {} is not implemented.".format(implementation))
# Trim if required
if trim:
y = self.__theano__convtrim(inp=y, filtershape=filters.get_value().shape)
# Add bias if required
if bias is not None:
y = y + th.gradient.grad_clip(bias, *biasgradclips).dimshuffle('x', 'x', 0, 'x', 'x')
else:
raise NotImplementedError("Convolution is implemented in 2D and 3D.")
# Reshape sequential data
if issequence and reallyissequential:
y = y.reshape((inpshape[0], inpshape[1], filters.get_value().shape[0], inpshape[3], inpshape[4]), ndim=5)
# Return
return y
def __theano__pool(self, inp, ds, stride=None, padding=None, poolmode='max', dim=None,
ignoreborder=True, issequence=False):
# Do imports locally to prevent circular dependencies
import netutils as nu
import pykit as pyk
from theano.tensor.signal import downsample
# Determine the dimensionality of convolution (2 or 3?)
if dim is None:
dim = 3 if not issequence and len(ds) == 3 and inp.ndim == 5 else 2
# Defaults
poolmode = 'average_exc_pad' if poolmode in ['mean', 'average', 'average_exc_pad'] else poolmode
padding = [[0, 0]] * dim if padding is None else padding
stride = ds if stride is None else stride
# Autofix inputs
if isinstance(padding, int):
padding = [padding] * dim
if not pyk.islistoflists(pyk.obj2list(padding)):
padding = [[padval] * dim for padval in pyk.obj2list(padding)]
if isinstance(stride, int):
stride = [stride] * dim
# Check if theano can pad input as required
autopaddable = all([all([dimpad == pad[0] for dimpad in pad]) for pad in padding])
# Reshape 2D sequential data if required
# Log input shape
inpshape = inp.shape
reallyissequential = issequence and inp.ndim == 5
if issequence:
if reallyissequential:
# Sequential input must be paddable by theano. This is required to reshape the sequential input back to
# its original shape after pooling.
assert autopaddable, "Sequential inputs must be paddable by theano. Provided padding {} cannot be " \
"handled at present.".format(padding)
inp = inp.reshape((inpshape[0] * inpshape[1], inpshape[2], inpshape[3], inpshape[4]), ndim=4)
ds = ds[0:2]
stride = stride[0:2]
padding = padding[0:2]
else:
warn("Expected 5D sequential output, but got 4D non-sequential instead.")
# Determine what theano needs to be told about how to pad the input
if autopaddable:
autopadding = tuple([pad[0] for pad in padding])
else:
autopadding = (0,) * dim
if not autopaddable and not all([padval == 0 for padval in pyk.flatten(padding)]):
if not th.config.device == 'cpu' and not self.cpupadwarned:
warn("Padding might occur on the CPU, which tends to slow things down.")
self.cpupadwarned = True
inp = nu.pad(inp, padding)
if dim == 2:
y = downsample.max_pool_2d(input=inp, ds=ds, st=stride, padding=autopadding, ignore_border=ignoreborder,
mode=poolmode)
elif dim == 3:
# parse downsampling ratio, stride and padding
dsyx = ds[0:2]
styx = stride[0:2]
padyx = autopadding[0:2]
ds0z = (1, ds[2])
st0z = (1, stride[2])
pad0z = (0, autopadding[2])
# Dowsnample yx
H = downsample.max_pool_2d(input=inp, ds=dsyx, st=styx, padding=padyx, mode=poolmode)
# Rotate tensor
H = H.dimshuffle(0, 2, 3, 4, 1)
# Downsample 0z
H = downsample.max_pool_2d(input=H, ds=ds0z, st=st0z, padding=pad0z, mode=poolmode)
# Undo rotate tensor
y = H.dimshuffle(0, 4, 1, 2, 3)
else:
raise NotImplementedError("Pooling is implemented in 2D and 3D.")
if issequence and reallyissequential:
# Compute symbolic pool output length
if ignoreborder:
pooleny, poolenx = \
[T.floor((inpshape[tensorindex] + 2 * autopadding[index] - ds[index] + stride[index])/stride[index])
for index, tensorindex in enumerate([3, 4])]
else:
poolen = [None, None]
for index, tensorindex in enumerate([3, 4]):
if stride[index] >= ds[index]:
poolen[index] = T.floor((inpshape[tensorindex] + stride[index] - 1)/stride[index])
else:
plen = T.floor((inpshape[tensorindex] - ds[index] + stride[index] - 1)/stride[index])
poolen[index] = T.switch(plen > 0, plen, 0)
pooleny, poolenx = poolen
y = y.reshape((inpshape[0], inpshape[1], inpshape[2], pooleny, poolenx), ndim=5)
return y