55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
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from typing import Optional, Dict, cast
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import chainer
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from . import RearrangeMixin, ReduceMixin
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from ._einmix import _EinmixMixin
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__author__ = "Alex Rogozhnikov"
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class Rearrange(RearrangeMixin, chainer.Link):
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def __call__(self, x):
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return self._apply_recipe(x)
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class Reduce(ReduceMixin, chainer.Link):
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def __call__(self, x):
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return self._apply_recipe(x)
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class EinMix(_EinmixMixin, chainer.Link):
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def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
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uniform = chainer.variable.initializers.Uniform
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with self.init_scope():
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self.weight = chainer.variable.Parameter(uniform(weight_bound), weight_shape)
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if bias_shape is not None:
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self.bias = chainer.variable.Parameter(uniform(bias_bound), bias_shape)
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else:
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self.bias = None
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def _create_rearrange_layers(
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self,
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pre_reshape_pattern: Optional[str],
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pre_reshape_lengths: Optional[Dict],
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post_reshape_pattern: Optional[str],
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post_reshape_lengths: Optional[Dict],
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):
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self.pre_rearrange = None
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if pre_reshape_pattern is not None:
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self.pre_rearrange = Rearrange(pre_reshape_pattern, **cast(dict, pre_reshape_lengths))
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self.post_rearrange = None
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if post_reshape_pattern is not None:
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self.post_rearrange = Rearrange(post_reshape_pattern, **cast(dict, post_reshape_lengths))
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def __call__(self, input):
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if self.pre_rearrange is not None:
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input = self.pre_rearrange(input)
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result = chainer.functions.einsum(self.einsum_pattern, input, self.weight)
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if self.bias is not None:
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result = result + self.bias
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if self.post_rearrange is not None:
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result = self.post_rearrange(result)
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return result
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