| from typing import Optional, Dict, cast |
|
|
| import torch |
|
|
| from . import RearrangeMixin, ReduceMixin |
| from ._einmix import _EinmixMixin |
| from .._torch_specific import apply_for_scriptable_torch |
|
|
| __author__ = 'Alex Rogozhnikov' |
|
|
|
|
| class Rearrange(RearrangeMixin, torch.nn.Module): |
| def forward(self, input): |
| return apply_for_scriptable_torch(self._recipe, input, reduction_type='rearrange') |
|
|
| def _apply_recipe(self, x): |
| |
| pass |
|
|
|
|
| class Reduce(ReduceMixin, torch.nn.Module): |
| def forward(self, input): |
| return apply_for_scriptable_torch(self._recipe, input, reduction_type=self.reduction) |
|
|
| def _apply_recipe(self, x): |
| |
| pass |
|
|
|
|
| class EinMix(_EinmixMixin, torch.nn.Module): |
| def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound): |
| self.weight = torch.nn.Parameter(torch.zeros(weight_shape).uniform_(-weight_bound, weight_bound), |
| requires_grad=True) |
| if bias_shape is not None: |
| self.bias = torch.nn.Parameter(torch.zeros(bias_shape).uniform_(-bias_bound, bias_bound), |
| requires_grad=True) |
| 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 forward(self, input): |
| if self.pre_rearrange is not None: |
| input = self.pre_rearrange(input) |
| result = torch.einsum(self.einsum_pattern, input, self.weight) |
| if self.bias is not None: |
| result += self.bias |
| if self.post_rearrange is not None: |
| result = self.post_rearrange(result) |
| return result |
|
|
