def apply(self, x): # x: (batsize, seqlen)
emb = self.E[x] # (batsize, seqlen, inpembdim)
outs = T.tensordot(emb, self.W, 1) # (batsize, seqlen, outdim)
outsf = outs.reshape((outs.shape[0] * outs.shape[1], outs.shape[2])) # (batsize*seqlen, outdim)
outsfsm = Softmax()(outsf)
ret = outsfsm.reshape(outs.shape) # (batsize, seqlen, outdim)
return ret
def apply(self, x, mask=None): # (batsize, seqlen, dim)
mask = x.mask if mask is None else mask
if mask is not None:
assert (mask.ndim == x.ndim - 1) # mask must be (batsize, seqlen)
realm = T.cast(
T.tensordot(mask, T.ones((x.shape[-1], ), dtype="int32"), 0),
"float32")
x = x * realm
input = x.dimshuffle(0, 2, 1, 'x')
input_shape = None #input.shape
convout = T.nnet.conv2d(input,
self.filter,
input_shape,
self.filter_shape,
border_mode=self.border_mode,
subsample=(self.stride, 1),
filter_flip=self.filter_flip)
ret = convout[:, :, :, 0].dimshuffle(0, 2, 1)
if mask is not None: # compute new mask
print "conving the mask"
mask_shape = None
maskout = T.nnet.conv2d(T.cast(mask.dimshuffle(0, "x", 1, "x"),
"float32"),
self.maskfilter,
mask_shape,
self.maskfilter_shape,
border_mode=self.border_mode,
subsample=(self.stride, 1),
filter_flip=self.filter_flip)
mask = T.cast(maskout[:, 0, :, 0] > 0, "int32")
ret.mask = mask
return ret
def apply_mask(cls, xseq, maskseq=None):
if maskseq is None:
ret = xseq
else:
mask = T.tensordot(maskseq, T.ones((xseq.shape[2],)), 0) # f32^(batsize, seqlen, outdim) -- maskseq stacked
ret = mask * xseq
return ret
def apply(self, x): # x: (batsize, seqlen)
emb = self.E[x] # (batsize, seqlen, inpembdim)
outs = T.tensordot(emb, self.W, 1) # (batsize, seqlen, outdim)
outsf = outs.reshape((outs.shape[0] * outs.shape[1],
outs.shape[2])) # (batsize*seqlen, outdim)
outsfsm = Softmax()(outsf)
ret = outsfsm.reshape(outs.shape) # (batsize, seqlen, outdim)
return ret
def applymask(cls, xseq, maskseq=None):
if maskseq is None:
ret = xseq
else:
mask = T.tensordot(maskseq, T.ones((xseq.shape[2],)), 0) # f32^(batsize, seqlen, outdim) -- maskseq stacked
masker = T.concatenate([T.ones((xseq.shape[0], xseq.shape[1], 1)), T.zeros((xseq.shape[0], xseq.shape[1], xseq.shape[2] - 1))], axis=2) # f32^(batsize, seqlen, outdim) -- gives 100% prob to output 0
ret = xseq * mask + masker * (1.0 - mask)
return ret
def apply(self, x):
emb = self.recembed(x) if self.scanemb else self.E[
x] # (batsize, seqlen, inpembdim)
outs = self.recout(emb) if self.scanout else T.tensordot(
emb, self.W, 1)
ret = self.recret(outs) if self.scansm else Softmax()(outs.reshape(
(outs.shape[0] * outs.shape[1],
outs.shape[2]))).reshape(outs.shape) # (batsize*seqlen, outdim)
return ret
def applymask(cls, xseq, maskseq):
if maskseq is None:
return xseq
else:
mask = T.tensordot(maskseq, T.ones((xseq.shape[2],)), 0) # f32^(batsize, seqlen, outdim) -- maskseq stacked
masker = T.concatenate(
[T.ones((xseq.shape[0], xseq.shape[1], 1)),
T.zeros((xseq.shape[0], xseq.shape[1], xseq.shape[2] - 1))],
axis=2) # f32^(batsize, seqlen, outdim) -- gives 100% prob to output 0
ret = xseq * mask + masker * (1.0 - mask)
return ret
def apply(self, x, mask=None): # (batsize, seqlen, dim)
mask = x.mask if mask is None else mask
if mask is not None:
assert (mask.ndim == x.ndim - 1)
realm = T.tensordot(mask, T.ones((x.shape[-1], )), 0)
if self.mode == "max":
x = T.switch(realm, x, np.infty * (realm - 1))
else:
x = x * realm
if self.mode == "max":
ret = T.max(x, axis=-2)
elif self.mode == "sum":
ret = T.sum(x, axis=-2)
elif self.mode == "avg":
ret = T.sum(x, axis=-2) / x.shape[-2]
else:
raise Exception("unknown pooling mode: {:3s}".format(self.mode))
# ret: (batsize, dim)
if mask is not None:
mask = 1 * (T.sum(mask, axis=-1) > 0)
ret = T.switch(T.tensordot(mask, T.ones((x.shape[-1], )), 0), ret,
T.zeros_like(ret))
ret.mask = mask
return ret
def _get_apply_outputs(self, final, outputs, states, mask):
ret = []
if "enc" in self._return: # final states of topmost layer
ret.append(final)
if "all" in self._return: # states (over all time) of topmost layer
rete = outputs # (batsize, seqlen, dim) --> zero-fy according to mask
if self._maskconfig.maskset == MaskSetMode.ZERO and mask is not None:
fmask = T.tensordot(mask, T.ones((outputs.shape[2],)), 0)
rete = rete * fmask
ret.append(rete)
if "states" in self._return: # final states (over all layers)???
pass # TODO: do we need to support this?
if "mask" in self._return:
ret.append(mask)
if len(ret) == 1:
return ret[0]
else:
return ret
def apply(self, x):
emb = self.recembed(x) if self.scanemb else self.E[x] # (batsize, seqlen, inpembdim)
outs = self.recout(emb) if self.scanout else T.tensordot(emb, self.W, 1)
ret = self.recret(outs) if self.scansm else Softmax()(outs.reshape((outs.shape[0] * outs.shape[1], outs.shape[2]))).reshape(outs.shape) # (batsize*seqlen, outdim)
return ret
