File size: 1,981 Bytes
e1a9145 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | from typing import Optional, Dict, cast
import chainer
from . import RearrangeMixin, ReduceMixin
from ._einmix import _EinmixMixin
__author__ = 'Alex Rogozhnikov'
class Rearrange(RearrangeMixin, chainer.Link):
def __call__(self, x):
return self._apply_recipe(x)
class Reduce(ReduceMixin, chainer.Link):
def __call__(self, x):
return self._apply_recipe(x)
class EinMix(_EinmixMixin, chainer.Link):
def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
uniform = chainer.variable.initializers.Uniform
with self.init_scope():
self.weight = chainer.variable.Parameter(uniform(weight_bound), weight_shape)
if bias_shape is not None:
self.bias = chainer.variable.Parameter(uniform(bias_bound), bias_shape)
else:
self.bias = None
def _create_rearrange_layers(self,
pre_reshape_pattern: Optional[str],
pre_reshape_lengths: Optional[Dict],
post_reshape_pattern: Optional[str],
post_reshape_lengths: Optional[Dict],
):
self.pre_rearrange = None
if pre_reshape_pattern is not None:
self.pre_rearrange = Rearrange(pre_reshape_pattern, **cast(dict, pre_reshape_lengths))
self.post_rearrange = None
if post_reshape_pattern is not None:
self.post_rearrange = Rearrange(post_reshape_pattern, **cast(dict, post_reshape_lengths))
def __call__(self, input):
if self.pre_rearrange is not None:
input = self.pre_rearrange(input)
result = chainer.functions.einsum(self.einsum_pattern, input, self.weight)
if self.bias is not None:
result = result + self.bias
if self.post_rearrange is not None:
result = self.post_rearrange(result)
return result
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